A model for simulating adaptive, dynamic flows on networks: Application to petroleum infrastructure

DOE PAGES

Corbet, Thomas F.; Beyeler, Walt; Wilson, Michael L.; ...

2017-10-03

Simulation models can greatly improve decisions meant to control the consequences of disruptions to critical infrastructures. We describe a dynamic flow model on networks purposed to inform analyses by those concerned about consequences of disruptions to infrastructures and to help policy makers design robust mitigations. We conceptualize the adaptive responses of infrastructure networks to perturbations as market transactions and business decisions of operators. We approximate commodity flows in these networks by a diffusion equation, with nonlinearities introduced to model capacity limits. To illustrate the behavior and scalability of the model, we show its application first on two simple networks, thenmore » on petroleum infrastructure in the United States, where we analyze the effects of a hypothesized earthquake.« less

A model for simulating adaptive, dynamic flows on networks: Application to petroleum infrastructure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Corbet, Thomas F.; Beyeler, Walt; Wilson, Michael L.

Simulation models can greatly improve decisions meant to control the consequences of disruptions to critical infrastructures. We describe a dynamic flow model on networks purposed to inform analyses by those concerned about consequences of disruptions to infrastructures and to help policy makers design robust mitigations. We conceptualize the adaptive responses of infrastructure networks to perturbations as market transactions and business decisions of operators. We approximate commodity flows in these networks by a diffusion equation, with nonlinearities introduced to model capacity limits. To illustrate the behavior and scalability of the model, we show its application first on two simple networks, thenmore » on petroleum infrastructure in the United States, where we analyze the effects of a hypothesized earthquake.« less

An integrated workflow for stress and flow modelling using outcrop-derived discrete fracture networks

NASA Astrophysics Data System (ADS)

Bisdom, K.; Nick, H. M.; Bertotti, G.

2017-06-01

Fluid flow in naturally fractured reservoirs is often controlled by subseismic-scale fracture networks. Although the fracture network can be partly sampled in the direct vicinity of wells, the inter-well scale network is poorly constrained in fractured reservoir models. Outcrop analogues can provide data for populating domains of the reservoir model where no direct measurements are available. However, extracting relevant statistics from large outcrops representative of inter-well scale fracture networks remains challenging. Recent advances in outcrop imaging provide high-resolution datasets that can cover areas of several hundred by several hundred meters, i.e. the domain between adjacent wells, but even then, data from the high-resolution models is often upscaled to reservoir flow grids, resulting in loss of accuracy. We present a workflow that uses photorealistic georeferenced outcrop models to construct geomechanical and fluid flow models containing thousands of discrete fractures covering sufficiently large areas, that does not require upscaling to model permeability. This workflow seamlessly integrates geomechanical Finite Element models with flow models that take into account stress-sensitive fracture permeability and matrix flow to determine the full permeability tensor. The applicability of this workflow is illustrated using an outcropping carbonate pavement in the Potiguar basin in Brazil, from which 1082 fractures are digitised. The permeability tensor for a range of matrix permeabilities shows that conventional upscaling to effective grid properties leads to potential underestimation of the true permeability and the orientation of principal permeabilities. The presented workflow yields the full permeability tensor model of discrete fracture networks with stress-induced apertures, instead of relying on effective properties as most conventional flow models do.

Numerical Simulation of Sickle Cell Blood Flow in the Microcirculation

NASA Astrophysics Data System (ADS)

Berger, Stanley A.; Carlson, Brian E.

2001-11-01

A numerical simulation of normal and sickle cell blood flow through the transverse arteriole-capillary microcirculation is carried out to model the dominant mechanisms involved in the onset of vascular stasis in sickle cell disease. The transverse arteriole-capillary network is described by Strahler's network branching method, and the oxygen and blood transport in the capillaries is modeled by a Krogh cylinder analysis utilizing Lighthill's lubrication theory, as developed by Berger and King. Poiseuille's law is used to represent blood flow in the arterioles. Applying this flow and transport model and utilizing volumetric flow continuity at each network bifurcation, a nonlinear system of equations is obtained, which is solved iteratively using a steepest descent algorithm coupled with a Newton solver. Ten different networks are generated and flow results are calculated for normal blood and sickle cell blood without and with precapillary oxygen loss. We find that total volumetric blood flow through the network is greater in the two sickle cell blood simulations than for normal blood owing to the anemia associated with sickle cell disease. The percentage of capillary blockage in the network increases dramatically with decreasing pressure drop across the network in the sickle cell cases while there is no blockage when normal blood flows through simulated networks. It is concluded that, in sickle cell disease, without any vasomotor dilation response to decreasing oxygen concentrations in the blood, capillary blockage will occur in the microvasculature even at average pressure drops across the transverse arteriole-capillary networks.

Dynamics of pulsatile flow in fractal models of vascular branching networks.

PubMed

Bui, Anh; Sutalo, Ilija D; Manasseh, Richard; Liffman, Kurt

2009-07-01

Efficient regulation of blood flow is critically important to the normal function of many organs, especially the brain. To investigate the circulation of blood in complex, multi-branching vascular networks, a computer model consisting of a virtual fractal model of the vasculature and a mathematical model describing the transport of blood has been developed. Although limited by some constraints, in particular, the use of simplistic, uniformly distributed model for cerebral vasculature and the omission of anastomosis, the proposed computer model was found to provide insights into blood circulation in the cerebral vascular branching network plus the physiological and pathological factors which may affect its functionality. The numerical study conducted on a model of the middle cerebral artery region signified the important effects of vessel compliance, blood viscosity variation as a function of the blood hematocrit, and flow velocity profile on the distributions of flow and pressure in the vascular network.

Generation of Complex Karstic Conduit Networks with a Hydro-chemical Model

NASA Astrophysics Data System (ADS)

De Rooij, R.; Graham, W. D.

2016-12-01

The discrete-continuum approach is very well suited to simulate flow and solute transport within karst aquifers. Using this approach, discrete one-dimensional conduits are embedded within a three-dimensional continuum representative of the porous limestone matrix. Typically, however, little is known about the geometry of the karstic conduit network. As such the discrete-continuum approach is rarely used for practical applications. It may be argued, however, that the uncertainty associated with the geometry of the network could be handled by modeling an ensemble of possible karst conduit networks within a stochastic framework. We propose to generate stochastically realistic karst conduit networks by simulating the widening of conduits as caused by the dissolution of limestone over geological relevant timescales. We illustrate that advanced numerical techniques permit to solve the non-linear and coupled hydro-chemical processes efficiently, such that relatively large and complex networks can be generated in acceptable time frames. Instead of specifying flow boundary conditions on conduit cells to recharge the network as is typically done in classical speleogenesis models, we specify an effective rainfall rate over the land surface and let model physics determine the amount of water entering the network. This is advantageous since the amount of water entering the network is extremely difficult to reconstruct, whereas the effective rainfall rate may be quantified using paleoclimatic data. Furthermore, we show that poorly known flow conditions may be constrained by requiring a realistic flow field. Using our speleogenesis model we have investigated factors that influence the geometry of simulated conduit networks. We illustrate that our model generates typical branchwork, network and anastomotic conduit systems. Flow, solute transport and water ages in karst aquifers are simulated using a few illustrative networks.

Power laws and fragility in flow networks.

PubMed

Shore, Jesse; Chu, Catherine J; Bianchi, Matt T

2013-01-01

What makes economic and ecological networks so unlike other highly skewed networks in their tendency toward turbulence and collapse? Here, we explore the consequences of a defining feature of these networks: their nodes are tied together by flow. We show that flow networks tend to the power law degree distribution (PLDD) due to a self-reinforcing process involving position within the global network structure, and thus present the first random graph model for PLDDs that does not depend on a rich-get-richer function of nodal degree. We also show that in contrast to non-flow networks, PLDD flow networks are dramatically more vulnerable to catastrophic failure than non-PLDD flow networks, a finding with potential explanatory power in our age of resource- and financial-interdependence and turbulence.

Visualization and Hierarchical Analysis of Flow in Discrete Fracture Network Models

NASA Astrophysics Data System (ADS)

Aldrich, G. A.; Gable, C. W.; Painter, S. L.; Makedonska, N.; Hamann, B.; Woodring, J.

2013-12-01

Flow and transport in low permeability fractured rock is primary in interconnected fracture networks. Prediction and characterization of flow and transport in fractured rock has important implications in underground repositories for hazardous materials (eg. nuclear and chemical waste), contaminant migration and remediation, groundwater resource management, and hydrocarbon extraction. We have developed methods to explicitly model flow in discrete fracture networks and track flow paths using passive particle tracking algorithms. Visualization and analysis of particle trajectory through the fracture network is important to understanding fracture connectivity, flow patterns, potential contaminant pathways and fast paths through the network. However, occlusion due to the large number of highly tessellated and intersecting fracture polygons preclude the effective use of traditional visualization methods. We would also like quantitative analysis methods to characterize the trajectory of a large number of particle paths. We have solved these problems by defining a hierarchal flow network representing the topology of particle flow through the fracture network. This approach allows us to analyses the flow and the dynamics of the system as a whole. We are able to easily query the flow network, and use paint-and-link style framework to filter the fracture geometry and particle traces based on the flow analytics. This allows us to greatly reduce occlusion while emphasizing salient features such as the principal transport pathways. Examples are shown that demonstrate the methodology and highlight how use of this new method allows quantitative analysis and characterization of flow and transport in a number of representative fracture networks.

Flow interaction based propagation model and bursty influence behavior analysis of Internet flows

NASA Astrophysics Data System (ADS)

Wu, Xiao-Yu; Gu, Ren-Tao; Ji, Yue-Feng

2016-11-01

QoS (quality of service) fluctuations caused by Internet bursty flows influence the user experience in the Internet, such as the increment of packet loss and transmission time. In this paper, we establish a mathematical model to study the influence propagation behavior of the bursty flow, which is helpful for developing a deep understanding of the network dynamics in the Internet complex system. To intuitively reflect the propagation process, a data flow interaction network with a hierarchical structure is constructed, where the neighbor order is proposed to indicate the neighborhood relationship between the bursty flow and other flows. The influence spreads from the bursty flow to each order of neighbors through flow interactions. As the influence spreads, the bursty flow has negative effects on the odd order neighbors and positive effects on the even order neighbors. The influence intensity of bursty flow decreases sharply between two adjacent orders and the decreasing degree can reach up to dozens of times in the experimental simulation. Moreover, the influence intensity increases significantly when network congestion situation becomes serious, especially for the 1st order neighbors. Network structural factors are considered to make a further study. Simulation results show that the physical network scale expansion can reduce the influence intensity of bursty flow by decreasing the flow distribution density. Furthermore, with the same network scale, the influence intensity in WS small-world networks is 38.18% and 18.40% lower than that in ER random networks and BA scale-free networks, respectively, due to a lower interaction probability between flows. These results indicate that the macro-structural changes such as network scales and styles will affect the inner propagation behaviors of the bursty flow.

Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata

PubMed Central

Chen, Yangzhou; Guo, Yuqi; Wang, Ying

2017-01-01

In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research. PMID:28353664

Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata.

PubMed

Chen, Yangzhou; Guo, Yuqi; Wang, Ying

2017-03-29

In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research.

Experimental and modeling study of Newtonian and non-Newtonian fluid flow in pore network micromodels.

PubMed

Perrin, Christian L; Tardy, Philippe M J; Sorbie, Ken S; Crawshaw, John C

2006-03-15

The in situ rheology of polymeric solutions has been studied experimentally in etched silicon micromodels which are idealizations of porous media. The rectangular channels in these etched networks have dimensions typical of pore sizes in sandstone rocks. Pressure drop/flow rate relations have been measured for water and non-Newtonian hydrolyzed-polyacrylamide (HPAM) solutions in both individual straight rectangular capillaries and in networks of such capillaries. Results from these experiments have been analyzed using pore-scale network modeling incorporating the non-Newtonian fluid mechanics of a Carreau fluid. Quantitative agreement is seen between the experiments and the network calculations in the Newtonian and shear-thinning flow regions demonstrating that the 'shift factor,'alpha, can be calculated a priori. Shear-thickening behavior was observed at higher flow rates in the micromodel experiments as a result of elastic effects becoming important and this remains to be incorporated in the network model.

Experimental study of overland flow resistance coefficient model of grassland based on BP neural network

NASA Astrophysics Data System (ADS)

Jiao, Peng; Yang, Er; Ni, Yong Xin

2018-06-01

The overland flow resistance on grassland slope of 20° was studied by using simulated rainfall experiments. Model of overland flow resistance coefficient was established based on BP neural network. The input variations of model were rainfall intensity, flow velocity, water depth, and roughness of slope surface, and the output variations was overland flow resistance coefficient. Model was optimized by Genetic Algorithm. The results show that the model can be used to calculate overland flow resistance coefficient, and has high simulation accuracy. The average prediction error of the optimized model of test set is 8.02%, and the maximum prediction error was 18.34%.

Incorporation of Condensation Heat Transfer in a Flow Network Code

NASA Technical Reports Server (NTRS)

Anthony, Miranda; Majumdar, Alok; McConnaughey, Paul K. (Technical Monitor)

2001-01-01

In this paper we have investigated the condensation of water vapor in a short tube. A numerical model of condensation heat transfer was incorporated in a flow network code. The flow network code that we have used in this paper is Generalized Fluid System Simulation Program (GFSSP). GFSSP is a finite volume based flow network code. Four different condensation models were presented in the paper. Soliman's correlation has been found to be the most stable in low flow rates which is of particular interest in this application. Another highlight of this investigation is conjugate or coupled heat transfer between solid or fluid. This work was done in support of NASA's International Space Station program.

An Open-Access Modeled Passenger Flow Matrix for the Global Air Network in 2010

PubMed Central

Huang, Zhuojie; Wu, Xiao; Garcia, Andres J.; Fik, Timothy J.; Tatem, Andrew J.

2013-01-01

The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers to rely on scheduled flight seat capacity data or simple models of flow. This study describes the construction of an open-access modeled passenger flow matrix for all airports with a host city-population of more than 100,000 and within two transfers of air travel from various publicly available air travel datasets. Data on network characteristics, city population, and local area GDP amongst others are utilized as covariates in a spatial interaction framework to predict the air transportation flows between airports. Training datasets based on information from various transportation organizations in the United States, Canada and the European Union were assembled. A log-linear model controlling the random effects on origin, destination and the airport hierarchy was then built to predict passenger flows on the network, and compared to the results produced using previously published models. Validation analyses showed that the model presented here produced improved predictive power and accuracy compared to previously published models, yielding the highest successful prediction rate at the global scale. Based on this model, passenger flows between 1,491 airports on 644,406 unique routes were estimated in the prediction dataset. The airport node characteristics and estimated passenger flows are freely available as part of the Vector-Borne Disease Airline Importation Risk (VBD-Air) project at: www.vbd-air.com/data. PMID:23691194

An open-access modeled passenger flow matrix for the global air network in 2010.

PubMed

Huang, Zhuojie; Wu, Xiao; Garcia, Andres J; Fik, Timothy J; Tatem, Andrew J

2013-01-01

The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers to rely on scheduled flight seat capacity data or simple models of flow. This study describes the construction of an open-access modeled passenger flow matrix for all airports with a host city-population of more than 100,000 and within two transfers of air travel from various publicly available air travel datasets. Data on network characteristics, city population, and local area GDP amongst others are utilized as covariates in a spatial interaction framework to predict the air transportation flows between airports. Training datasets based on information from various transportation organizations in the United States, Canada and the European Union were assembled. A log-linear model controlling the random effects on origin, destination and the airport hierarchy was then built to predict passenger flows on the network, and compared to the results produced using previously published models. Validation analyses showed that the model presented here produced improved predictive power and accuracy compared to previously published models, yielding the highest successful prediction rate at the global scale. Based on this model, passenger flows between 1,491 airports on 644,406 unique routes were estimated in the prediction dataset. The airport node characteristics and estimated passenger flows are freely available as part of the Vector-Borne Disease Airline Importation Risk (VBD-Air) project at: www.vbd-air.com/data.

A generalized network flow model for the multi-mode resource-constrained project scheduling problem with discounted cash flows

NASA Astrophysics Data System (ADS)

Chen, Miawjane; Yan, Shangyao; Wang, Sin-Siang; Liu, Chiu-Lan

2015-02-01

An effective project schedule is essential for enterprises to increase their efficiency of project execution, to maximize profit, and to minimize wastage of resources. Heuristic algorithms have been developed to efficiently solve the complicated multi-mode resource-constrained project scheduling problem with discounted cash flows (MRCPSPDCF) that characterize real problems. However, the solutions obtained in past studies have been approximate and are difficult to evaluate in terms of optimality. In this study, a generalized network flow model, embedded in a time-precedence network, is proposed to formulate the MRCPSPDCF with the payment at activity completion times. Mathematically, the model is formulated as an integer network flow problem with side constraints, which can be efficiently solved for optimality, using existing mathematical programming software. To evaluate the model performance, numerical tests are performed. The test results indicate that the model could be a useful planning tool for project scheduling in the real world.

Micro/Nano-pore Network Analysis of Gas Flow in Shale Matrix

PubMed Central

Zhang, Pengwei; Hu, Liming; Meegoda, Jay N.; Gao, Shengyan

2015-01-01

The gas flow in shale matrix is of great research interests for optimized shale gas extraction. The gas flow in the nano-scale pore may fall in flow regimes such as viscous flow, slip flow and Knudsen diffusion. A 3-dimensional nano-scale pore network model was developed to simulate dynamic gas flow, and to describe the transient properties of flow regimes. The proposed pore network model accounts for the various size distributions and low connectivity of shale pores. The pore size, pore throat size and coordination number obey normal distribution, and the average values can be obtained from shale reservoir data. The gas flow regimes were simulated using an extracted pore network backbone. The numerical results show that apparent permeability is strongly dependent on pore pressure in the reservoir and pore throat size, which is overestimated by low-pressure laboratory tests. With the decrease of reservoir pressure, viscous flow is weakening, then slip flow and Knudsen diffusion are gradually becoming dominant flow regimes. The fingering phenomenon can be predicted by micro/nano-pore network for gas flow, which provides an effective way to capture heterogeneity of shale gas reservoir. PMID:26310236

Micro/Nano-pore Network Analysis of Gas Flow in Shale Matrix.

PubMed

Zhang, Pengwei; Hu, Liming; Meegoda, Jay N; Gao, Shengyan

2015-08-27

The gas flow in shale matrix is of great research interests for optimized shale gas extraction. The gas flow in the nano-scale pore may fall in flow regimes such as viscous flow, slip flow and Knudsen diffusion. A 3-dimensional nano-scale pore network model was developed to simulate dynamic gas flow, and to describe the transient properties of flow regimes. The proposed pore network model accounts for the various size distributions and low connectivity of shale pores. The pore size, pore throat size and coordination number obey normal distribution, and the average values can be obtained from shale reservoir data. The gas flow regimes were simulated using an extracted pore network backbone. The numerical results show that apparent permeability is strongly dependent on pore pressure in the reservoir and pore throat size, which is overestimated by low-pressure laboratory tests. With the decrease of reservoir pressure, viscous flow is weakening, then slip flow and Knudsen diffusion are gradually becoming dominant flow regimes. The fingering phenomenon can be predicted by micro/nano-pore network for gas flow, which provides an effective way to capture heterogeneity of shale gas reservoir.

An efective fractal-tree closure model for simulating blood flow in large arterial networks

PubMed Central

Perdikaris, Paris; Grinberg, Leopold; Karniadakis, George Em.

2014-01-01

The aim of the present work is to address the closure problem for hemodynamic simulations by developing a exible and effective model that accurately distributes flow in the downstream vasculature and can stably provide a physiological pressure out flow boundary condition. To achieve this goal, we model blood flow in the sub-pixel vasculature by using a non-linear 1D model in self-similar networks of compliant arteries that mimic the structure and hierarchy of vessels in the meso-vascular regime (radii 500 μm – 10 μm). We introduce a variable vessel length-to-radius ratio for small arteries and arterioles, while also addressing non-Newtonian blood rheology and arterial wall viscoelasticity effects in small arteries and arterioles. This methodology aims to overcome substantial cut-off radius sensitivities, typically arising in structured tree and linearized impedance models. The proposed model is not sensitive to out flow boundary conditions applied at the end points of the fractal network, and thus does not require calibration of resistance/capacitance parameters typically required for out flow conditions. The proposed model convergences to a periodic state in two cardiac cycles even when started from zero-flow initial conditions. The resulting fractal-trees typically consist of thousands to millions of arteries, posing the need for efficient parallel algorithms. To this end, we have scaled up a Discontinuous Galerkin solver that utilizes the MPI/OpenMP hybrid programming paradigm to thousands of computer cores, and can simulate blood flow in networks of millions of arterial segments at the rate of one cycle per 5 minutes. The proposed model has been extensively tested on a large and complex cranial network with 50 parent, patient-specific arteries and 21 outlets to which fractal trees where attached, resulting to a network of up to 4,392,484 vessels in total, and a detailed network of the arm with 276 parent arteries and 103 outlets (a total of 702,188 vessels after attaching the fractal trees), returning physiological flow and pressure wave predictions without requiring any parameter estimation or calibration procedures. We present a novel methodology to overcome substantial cut-off radius sensitivities PMID:25510364

Single-phase power distribution system power flow and fault analysis

NASA Technical Reports Server (NTRS)

Halpin, S. M.; Grigsby, L. L.

1992-01-01

Alternative methods for power flow and fault analysis of single-phase distribution systems are presented. The algorithms for both power flow and fault analysis utilize a generalized approach to network modeling. The generalized admittance matrix, formed using elements of linear graph theory, is an accurate network model for all possible single-phase network configurations. Unlike the standard nodal admittance matrix formulation algorithms, the generalized approach uses generalized component models for the transmission line and transformer. The standard assumption of a common node voltage reference point is not required to construct the generalized admittance matrix. Therefore, truly accurate simulation results can be obtained for networks that cannot be modeled using traditional techniques.

Current-flow efficiency of networks

NASA Astrophysics Data System (ADS)

Liu, Kai; Yan, Xiaoyong

2018-02-01

Many real-world networks, from infrastructure networks to social and communication networks, can be formulated as flow networks. How to realistically measure the transport efficiency of these networks is of fundamental importance. The shortest-path-based efficiency measurement has limitations, as it assumes that flow travels only along those shortest paths. Here, we propose a new metric named current-flow efficiency, in which we calculate the average reciprocal effective resistance between all pairs of nodes in the network. This metric takes the multipath effect into consideration and is more suitable for measuring the efficiency of many real-world flow equilibrium networks. Moreover, this metric can handle a disconnected graph and can thus be used to identify critical nodes and edges from the efficiency-loss perspective. We further analyze how the topological structure affects the current-flow efficiency of networks based on some model and real-world networks. Our results enable a better understanding of flow networks and shed light on the design and improvement of such networks with higher transport efficiency.

A two-stage flow-based intrusion detection model for next-generation networks.

PubMed

Umer, Muhammad Fahad; Sher, Muhammad; Bi, Yaxin

2018-01-01

The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results.

A two-stage flow-based intrusion detection model for next-generation networks

PubMed Central

2018-01-01

The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results. PMID:29329294

Network modeling for reverse flows of end-of-life vehicles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ene, Seval; Öztürk, Nursel

2015-04-15

Highlights: • We developed a network model for reverse flows of end-of-life vehicles. • The model considers all recovery operations for end-of-life vehicles. • A scenario-based model is used for uncertainty to improve real case applications. • The model is adequate to real case applications for end-of-life vehicles recovery. • Considerable insights are gained from the model by sensitivity analyses. - Abstract: Product recovery operations are of critical importance for the automotive industry in complying with environmental regulations concerning end-of-life products management. Manufacturers must take responsibility for their products over the entire life cycle. In this context, there is amore » need for network design methods for effectively managing recovery operations and waste. The purpose of this study is to develop a mathematical programming model for managing reverse flows in end-of-life vehicles’ recovery network. A reverse flow is the collection of used products from consumers and the transportation of these products for the purpose of recycling, reuse or disposal. The proposed model includes all operations in a product recovery and waste management network for used vehicles and reuse for vehicle parts such as collection, disassembly, refurbishing, processing (shredding), recycling, disposal and reuse of vehicle parts. The scope of the network model is to determine the numbers and locations of facilities in the network and the material flows between these facilities. The results show the performance of the model and its applicability for use in the planning of recovery operations in the automotive industry. The main objective of recovery and waste management is to maximize revenue and minimize pollution in end-of-life product operations. This study shows that with an accurate model, these activities may provide economic benefits and incentives in addition to protecting the environment.« less

The effect of inclusion of inlets in dual drainage modelling

NASA Astrophysics Data System (ADS)

Chang, Tsang-Jung; Wang, Chia-Ho; Chen, Albert S.; Djordjević, Slobodan

2018-04-01

In coupled sewer and surface flood modelling approaches, the flow process in gullies is often ignored although the overland flow is drained to sewer network via inlets and gullies. Therefore, the flow entering inlets is transferred to the sewer network immediately, which may lead to a different flood estimation than the reality. In this paper, we compared two modelling approach with and without considering the flow processes in gullies in the coupled sewer and surface modelling. Three historical flood events were adopted for model calibration and validation. The results showed that the inclusion of flow process in gullies can further improve the accuracy of urban flood modelling.

Gas network model allows full reservoir coupling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Methnani, M.M.

The gas-network flow model (Gasnet) developed for and added to an existing Qatar General Petroleum Corp. (OGPC) in-house reservoir simulator, allows improved modeling of the interaction among the reservoir, wells, and pipeline networks. Gasnet is a three-phase model that is modified to handle gas-condensate systems. The numerical solution is based on a control volume scheme that uses the concept of cells and junctions, whereby pressure and phase densities are defined in cells, while phase flows are defined at junction links. The model features common numerical equations for the reservoir, the well, and the pipeline components and an efficient state-variable solutionmore » method in which all primary variables including phase flows are solved directly. Both steady-state and transient flow events can be simulated with the same tool. Three test cases show how the model runs. One case simulates flow redistribution in a simple two-branch gas network. The second simulates a horizontal gas well in a waterflooded gas reservoir. The third involves an export gas pipeline coupled to a producing reservoir.« less

Prediction of Flow Stress in Cadmium Using Constitutive Equation and Artificial Neural Network Approach

NASA Astrophysics Data System (ADS)

Sarkar, A.; Chakravartty, J. K.

2013-10-01

A model is developed to predict the constitutive flow behavior of cadmium during compression test using artificial neural network (ANN). The inputs of the neural network are strain, strain rate, and temperature, whereas flow stress is the output. Experimental data obtained from compression tests in the temperature range -30 to 70 °C, strain range 0.1 to 0.6, and strain rate range 10-3 to 1 s-1 are employed to develop the model. A three-layer feed-forward ANN is trained with Levenberg-Marquardt training algorithm. It has been shown that the developed ANN model can efficiently and accurately predict the deformation behavior of cadmium. This trained network could predict the flow stress better than a constitutive equation of the type.

Two-phase flow characterization based on advanced instrumentation, neural networks, and mathematical modeling

NASA Astrophysics Data System (ADS)

Mi, Ye

1998-12-01

The major objective of this thesis is focused on theoretical and experimental investigations of identifying and characterizing vertical and horizontal flow regimes in two-phase flows. A methodology of flow regime identification with impedance-based neural network systems and a comprehensive model of vertical slug flow have been developed. Vertical slug flow has been extensively investigated and characterized with geometric, kinematic and hydrodynamic parameters. A multi-sensor impedance void-meter and a multi-sensor magnetic flowmeter were developed. The impedance void-meter was cross-calibrated with other reliable techniques for void fraction measurements. The performance of the impedance void-meter to measure the void propagation velocity was evaluated by the drift flux model. It was proved that the magnetic flowmeter was applicable to vertical slug flow measurements. Separable signals from these instruments allow us to unearth most characteristics of vertical slug flow. A methodology of vertical flow regime identification was developed. Supervised neural network and self-organizing neural network systems were employed. First, they were trained with results from an idealized simulation of impedance in a two-phase mixture. The simulation was mainly based on Mishima and Ishii's flow regime map, the drift flux model, and the newly developed model of slug flow. Then, these trained systems were tested with impedance signals. The results showed that the neural network systems were appropriate classifiers of vertical flow regimes. The theoretical models and experimental databases used in the simulation were reliable. Furthermore, this approach was applied successfully to horizontal flow identification. A comprehensive model was developed to predict important characteristics of vertical slug flow. It was realized that the void fraction of the liquid slug is determined by the relative liquid motion between the Taylor bubble tail and the Taylor bubble wake. Relying on this understanding and experimental results, a special relationship was built for the void fraction of the liquid slug. The prediction of the void fraction of the liquid slug was considerably improved. Experimental characterization of vertical slug flows was performed extensively with the impedance void-meter and the magnetic flowmeter. The theoretical predictions were compared with the experimental results. The agreements between them are very satisfactory.

Network-Theoretic Modeling of Fluid Flow

DTIC Science & Technology

2015-07-29

Final Report STIR: Network-Theoretic Modeling of Fluid Flow ARO Grant W911NF-14-1-0386 Program manager: Dr. Samuel Stanton ( August 1, 2014–April 30...Morzyński, M., and Comte , P., “A finite-time thermodynamics of unsteady fluid flows,” Journal of Non-Equilibrium Thermody- namics, Vol. 33, No. 2

Modeling the future evolution of the virtual water trade network: A combination of network and gravity models

NASA Astrophysics Data System (ADS)

Sartori, Martina; Schiavo, Stefano; Fracasso, Andrea; Riccaboni, Massimo

2017-12-01

The paper investigates how the topological features of the virtual water (VW) network and the size of the associated VW flows are likely to change over time, under different socio-economic and climate scenarios. We combine two alternative models of network formation -a stochastic and a fitness model, used to describe the structure of VW flows- with a gravity model of trade to predict the intensity of each bilateral flow. This combined approach is superior to existing methodologies in its ability to replicate the observed features of VW trade. The insights from the models are used to forecast future VW flows in 2020 and 2050, under different climatic scenarios, and compare them with future water availability. Results suggest that the current trend of VW exports is not sustainable for all countries. Moreover, our approach highlights that some VW importers might be exposed to "imported water stress" as they rely heavily on imports from countries whose water use is unsustainable.

A multiclass vehicular dynamic traffic flow model for main roads and dedicated lanes/roads of multimodal transport network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sossoe, K.S., E-mail: kwami.sossoe@irt-systemx.fr; Lebacque, J-P., E-mail: jean-patrick.lebacque@ifsttar.fr

2015-03-10

We present in this paper a model of vehicular traffic flow for a multimodal transportation road network. We introduce the notion of class of vehicles to refer to vehicles of different transport modes. Our model describes the traffic on highways (which may contain several lanes) and network transit for pubic transportation. The model is drafted with Eulerian and Lagrangian coordinates and uses a Logit model to describe the traffic assignment of our multiclass vehicular flow description on shared roads. The paper also discusses traffic streams on dedicated lanes for specific class of vehicles with event-based traffic laws. An Euler-Lagrangian-remap schememore » is introduced to numerically approximate the model’s flow equations.« less

Simulation Model for Scenario Optimization of the Ready-Mix Concrete Delivery Problem

NASA Astrophysics Data System (ADS)

Galić, Mario; Kraus, Ivan

2016-12-01

This paper introduces a discrete simulation model for solving routing and network material flow problems in construction projects. Before the description of the model a detailed literature review is provided. The model is verified using a case study of solving the ready-mix concrete network flow and routing problem in metropolitan area in Croatia. Within this study real-time input parameters were taken into account. Simulation model is structured in Enterprise Dynamics simulation software and Microsoft Excel linked with Google Maps. The model is dynamic, easily managed and adjustable, but also provides good estimation for minimization of costs and realization time in solving discrete routing and material network flow problems.

A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem.

PubMed

Cai, Kaiquan; Jia, Yaoguang; Zhu, Yanbo; Xiao, Mingming

2015-01-01

Network-wide air traffic flow management (ATFM) is an effective way to alleviate demand-capacity imbalances globally and thereafter reduce airspace congestion and flight delays. The conventional ATFM models assume the capacities of airports or airspace sectors are all predetermined. However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical. This paper investigates the stochastic air traffic network flow optimization (SATNFO) problem, which is formulated as a weighted biobjective 0-1 integer programming model. In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem. Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity.

Internal fracture heterogeneity in discrete fracture network modelling: Effect of correlation length and textures with connected and disconnected permeability field

NASA Astrophysics Data System (ADS)

Frampton, A.; Hyman, J.; Zou, L.

2017-12-01

Analysing flow and transport in sparsely fractured media is important for understanding how crystalline bedrock environments function as barriers to transport of contaminants, with important applications towards subsurface repositories for storage of spent nuclear fuel. Crystalline bedrocks are particularly favourable due to their geological stability, low advective flow and strong hydrogeochemical retention properties, which can delay transport of radionuclides, allowing decay to limit release to the biosphere. There are however many challenges involved in quantifying and modelling subsurface flow and transport in fractured media, largely due to geological complexity and heterogeneity, where the interplay between advective and dispersive flow strongly impacts both inert and reactive transport. A key to modelling transport in a Lagrangian framework involves quantifying pathway travel times and the hydrodynamic control of retention, and both these quantities strongly depend on heterogeneity of the fracture network at different scales. In this contribution, we present recent analysis of flow and transport considering fracture networks with single-fracture heterogeneity described by different multivariate normal distributions. A coherent triad of fields with identical correlation length and variance are created but which greatly differ in structure, corresponding to textures with well-connected low, medium and high permeability structures. Through numerical modelling of multiple scales in a stochastic setting we quantify the relative impact of texture type and correlation length against network topological measures, and identify key thresholds for cases where flow dispersion is controlled by single-fracture heterogeneity versus network-scale heterogeneity. This is achieved by using a recently developed novel numerical discrete fracture network model. Furthermore, we highlight enhanced flow channelling for cases where correlation structure continues across intersections in a network, and discuss application to realistic fracture networks using field data of sparsely fractured crystalline rock from the Swedish candidate repository site for spent nuclear fuel.

A biologically inspired network design model.

PubMed

Zhang, Xiaoge; Adamatzky, Andrew; Chan, Felix T S; Deng, Yong; Yang, Hai; Yang, Xin-She; Tsompanas, Michail-Antisthenis I; Sirakoulis, Georgios Ch; Mahadevan, Sankaran

2015-06-04

A network design problem is to select a subset of links in a transport network that satisfy passengers or cargo transportation demands while minimizing the overall costs of the transportation. We propose a mathematical model of the foraging behaviour of slime mould P. polycephalum to solve the network design problem and construct optimal transport networks. In our algorithm, a traffic flow between any two cities is estimated using a gravity model. The flow is imitated by the model of the slime mould. The algorithm model converges to a steady state, which represents a solution of the problem. We validate our approach on examples of major transport networks in Mexico and China. By comparing networks developed in our approach with the man-made highways, networks developed by the slime mould, and a cellular automata model inspired by slime mould, we demonstrate the flexibility and efficiency of our approach.

A Biologically Inspired Network Design Model

PubMed Central

Zhang, Xiaoge; Adamatzky, Andrew; Chan, Felix T.S.; Deng, Yong; Yang, Hai; Yang, Xin-She; Tsompanas, Michail-Antisthenis I.; Sirakoulis, Georgios Ch.; Mahadevan, Sankaran

2015-01-01

A network design problem is to select a subset of links in a transport network that satisfy passengers or cargo transportation demands while minimizing the overall costs of the transportation. We propose a mathematical model of the foraging behaviour of slime mould P. polycephalum to solve the network design problem and construct optimal transport networks. In our algorithm, a traffic flow between any two cities is estimated using a gravity model. The flow is imitated by the model of the slime mould. The algorithm model converges to a steady state, which represents a solution of the problem. We validate our approach on examples of major transport networks in Mexico and China. By comparing networks developed in our approach with the man-made highways, networks developed by the slime mould, and a cellular automata model inspired by slime mould, we demonstrate the flexibility and efficiency of our approach. PMID:26041508

Social adaptation in multi-agent model of linguistic categorization is affected by network information flow.

PubMed

Zubek, Julian; Denkiewicz, Michał; Barański, Juliusz; Wróblewski, Przemysław; Rączaszek-Leonardi, Joanna; Plewczynski, Dariusz

2017-01-01

This paper explores how information flow properties of a network affect the formation of categories shared between individuals, who are communicating through that network. Our work is based on the established multi-agent model of the emergence of linguistic categories grounded in external environment. We study how network information propagation efficiency and the direction of information flow affect categorization by performing simulations with idealized network topologies optimizing certain network centrality measures. We measure dynamic social adaptation when either network topology or environment is subject to change during the experiment, and the system has to adapt to new conditions. We find that both decentralized network topology efficient in information propagation and the presence of central authority (information flow from the center to peripheries) are beneficial for the formation of global agreement between agents. Systems with central authority cope well with network topology change, but are less robust in the case of environment change. These findings help to understand which network properties affect processes of social adaptation. They are important to inform the debate on the advantages and disadvantages of centralized systems.

Social adaptation in multi-agent model of linguistic categorization is affected by network information flow

PubMed Central

Denkiewicz, Michał; Barański, Juliusz; Wróblewski, Przemysław; Rączaszek-Leonardi, Joanna; Plewczynski, Dariusz

2017-01-01

This paper explores how information flow properties of a network affect the formation of categories shared between individuals, who are communicating through that network. Our work is based on the established multi-agent model of the emergence of linguistic categories grounded in external environment. We study how network information propagation efficiency and the direction of information flow affect categorization by performing simulations with idealized network topologies optimizing certain network centrality measures. We measure dynamic social adaptation when either network topology or environment is subject to change during the experiment, and the system has to adapt to new conditions. We find that both decentralized network topology efficient in information propagation and the presence of central authority (information flow from the center to peripheries) are beneficial for the formation of global agreement between agents. Systems with central authority cope well with network topology change, but are less robust in the case of environment change. These findings help to understand which network properties affect processes of social adaptation. They are important to inform the debate on the advantages and disadvantages of centralized systems. PMID:28809957

Passenger flow analysis of Beijing urban rail transit network using fractal approach

NASA Astrophysics Data System (ADS)

Li, Xiaohong; Chen, Peiwen; Chen, Feng; Wang, Zijia

2018-04-01

To quantify the spatiotemporal distribution of passenger flow and the characteristics of an urban rail transit network, we introduce four radius fractal dimensions and two branch fractal dimensions by combining a fractal approach with passenger flow assignment model. These fractal dimensions can numerically describe the complexity of passenger flow in the urban rail transit network and its change characteristics. Based on it, we establish a fractal quantification method to measure the fractal characteristics of passenger follow in the rail transit network. Finally, we validate the reasonability of our proposed method by using the actual data of Beijing subway network. It has been shown that our proposed method can effectively measure the scale-free range of the urban rail transit network, network development and the fractal characteristics of time-varying passenger flow, which further provides a reference for network planning and analysis of passenger flow.

Structure-preserving model reduction of large-scale logistics networks. Applications for supply chains

NASA Astrophysics Data System (ADS)

Scholz-Reiter, B.; Wirth, F.; Dashkovskiy, S.; Makuschewitz, T.; Schönlein, M.; Kosmykov, M.

2011-12-01

We investigate the problem of model reduction with a view to large-scale logistics networks, specifically supply chains. Such networks are modeled by means of graphs, which describe the structure of material flow. An aim of the proposed model reduction procedure is to preserve important features within the network. As a new methodology we introduce the LogRank as a measure for the importance of locations, which is based on the structure of the flows within the network. We argue that these properties reflect relative importance of locations. Based on the LogRank we identify subgraphs of the network that can be neglected or aggregated. The effect of this is discussed for a few motifs. Using this approach we present a meta algorithm for structure-preserving model reduction that can be adapted to different mathematical modeling frameworks. The capabilities of the approach are demonstrated with a test case, where a logistics network is modeled as a Jackson network, i.e., a particular type of queueing network.

A revised model of fluid transport optimization in Physarum polycephalum.

PubMed

Bonifaci, Vincenzo

2017-02-01

Optimization of fluid transport in the slime mold Physarum polycephalum has been the subject of several modeling efforts in recent literature. Existing models assume that the tube adaptation mechanism in P. polycephalum's tubular network is controlled by the sheer amount of fluid flow through the tubes. We put forward the hypothesis that the controlling variable may instead be the flow's pressure gradient along the tube. We carry out the stability analysis of such a revised mathematical model for a parallel-edge network, proving that the revised model supports the global flow-optimizing behavior of the slime mold for a substantially wider class of response functions compared to previous models. Simulations also suggest that the same conclusion may be valid for arbitrary network topologies.

Constitutive flow behaviour of austenitic stainless steels under hot deformation: artificial neural network modelling to understand, evaluate and predict

NASA Astrophysics Data System (ADS)

Mandal, Sumantra; Sivaprasad, P. V.; Venugopal, S.; Murthy, K. P. N.

2006-09-01

An artificial neural network (ANN) model is developed to predict the constitutive flow behaviour of austenitic stainless steels during hot deformation. The input parameters are alloy composition and process variables whereas flow stress is the output. The model is based on a three-layer feed-forward ANN with a back-propagation learning algorithm. The neural network is trained with an in-house database obtained from hot compression tests on various grades of austenitic stainless steels. The performance of the model is evaluated using a wide variety of statistical indices. Good agreement between experimental and predicted data is obtained. The correlation between individual alloying elements and high temperature flow behaviour is investigated by employing the ANN model. The results are found to be consistent with the physical phenomena. The model can be used as a guideline for new alloy development.

The stationary flow in a heterogeneous compliant vessel network

NASA Astrophysics Data System (ADS)

Filoche, Marcel; Florens, Magali

2011-09-01

We introduce a mathematical model of the hydrodynamic transport into systems consisting in a network of connected flexible pipes. In each pipe of the network, the flow is assumed to be steady and one-dimensional. The fluid-structure interaction is described through tube laws which relate the pipe diameter to the pressure difference across the pipe wall. We show that the resulting one-dimensional differential equation describing the flow in the pipe can be exactly integrated if one is able to estimate averages of the Reynolds number along the pipe. The differential equation is then transformed into a non linear scalar equation relating pressures at both ends of the pipe and the flow rate in the pipe. These equations are coupled throughout the network with mass conservation equations for the flow and zero pressure losses at the branching points of the network. This allows us to derive a general model for the computation of the flow into very large inhomogeneous networks consisting of several thousands of flexible pipes. This model is then applied to perform numerical simulations of the human lung airway system at exhalation. The topology of the system and the tube laws are taken from morphometric and physiological data in the literature. We find good qualitative and quantitative agreement between the simulation results and flow-volume loops measured in real patients. In particular, expiratory flow limitation which is an essential characteristic of forced expiration is found to be well reproduced by our simulations. Finally, a mathematical model of a pathology (Chronic Obstructive Pulmonary Disease) is introduced which allows us to quantitatively assess the influence of a moderate or severe alteration of the airway compliances.

Flow control using audio tones in resonant microfluidic networks: towards cell-phone controlled lab-on-a-chip devices.

PubMed

Phillips, Reid H; Jain, Rahil; Browning, Yoni; Shah, Rachana; Kauffman, Peter; Dinh, Doan; Lutz, Barry R

2016-08-16

Fluid control remains a challenge in development of portable lab-on-a-chip devices. Here, we show that microfluidic networks driven by single-frequency audio tones create resonant oscillating flow that is predicted by equivalent electrical circuit models. We fabricated microfluidic devices with fluidic resistors (R), inductors (L), and capacitors (C) to create RLC networks with band-pass resonance in the audible frequency range available on portable audio devices. Microfluidic devices were fabricated from laser-cut adhesive plastic, and a "buzzer" was glued to a diaphragm (capacitor) to integrate the actuator on the device. The AC flowrate magnitude was measured by imaging oscillation of bead tracers to allow direct comparison to the RLC circuit model across the frequency range. We present a systematic build-up from single-channel systems to multi-channel (3-channel) networks, and show that RLC circuit models predict complex frequency-dependent interactions within multi-channel networks. Finally, we show that adding flow rectifying valves to the network creates pumps that can be driven by amplified and non-amplified audio tones from common audio devices (iPod and iPhone). This work shows that RLC circuit models predict resonant flow responses in multi-channel fluidic networks as a step towards microfluidic devices controlled by audio tones.

Regional myocardial flow heterogeneity explained with fractal networks

PubMed Central

VAN BEEK, JOHANNES H. G. M.; ROGER, STEPHEN A.; BASSINGTHWAIGHTE, JAMES B.

2010-01-01

There is explain how the distribution of flow broadens with an increase in the spatial resolution of the measurement, we developed fractal models for vascular networks. A dichotomous branching network of vessels represents the arterial tree and connects to a similar venous network. A small difference in vessel lengths and radii between the two daughter vessels, with the same degree of asymmetry at each branch generation, predicts the dependence of the relative dispersion (mean ± SD) on spatial resolution of the perfusion measurement reasonably well. When the degree of asymmetry increases with successive branching, a better fit to data on sheep and baboons results. When the asymmetry is random, a satisfactory fit is found. These models show that a difference in flow of 20% between the daughter vessels at a branch point gives a relative dispersion of flow of ~30% when the heart is divided into 100–200 pieces. Although these simple models do not represent anatomic features accurately, they provide valuable insight on the heterogeneity of flow within the heart. PMID:2589520

Effective contaminant detection networks in uncertain groundwater flow fields.

PubMed

Hudak, P F

2001-01-01

A mass transport simulation model tested seven contaminant detection-monitoring networks under a 40 degrees range of groundwater flow directions. Each monitoring network contained five wells located 40 m from a rectangular landfill. The 40-m distance (lag) was measured in different directions, depending upon the strategy used to design a particular monitoring network. Lagging the wells parallel to the central flow path was more effective than alternative design strategies. Other strategies allowed higher percentages of leaks to migrate between monitoring wells. Results of this study suggest that centrally lagged groundwater monitoring networks perform most effectively in uncertain groundwater-flow fields.

Cell transmission model of dynamic assignment for urban rail transit networks.

PubMed

Xu, Guangming; Zhao, Shuo; Shi, Feng; Zhang, Feilian

2017-01-01

For urban rail transit network, the space-time flow distribution can play an important role in evaluating and optimizing the space-time resource allocation. For obtaining the space-time flow distribution without the restriction of schedules, a dynamic assignment problem is proposed based on the concept of continuous transmission. To solve the dynamic assignment problem, the cell transmission model is built for urban rail transit networks. The priority principle, queuing process, capacity constraints and congestion effects are considered in the cell transmission mechanism. Then an efficient method is designed to solve the shortest path for an urban rail network, which decreases the computing cost for solving the cell transmission model. The instantaneous dynamic user optimal state can be reached with the method of successive average. Many evaluation indexes of passenger flow can be generated, to provide effective support for the optimization of train schedules and the capacity evaluation for urban rail transit network. Finally, the model and its potential application are demonstrated via two numerical experiments using a small-scale network and the Beijing Metro network.

Review: The state-of-art of sparse channel models and their applicability to performance assessment of radioactive waste repositories in fractured crystalline formations

NASA Astrophysics Data System (ADS)

Figueiredo, Bruno; Tsang, Chin-Fu; Niemi, Auli; Lindgren, Georg

2016-11-01

Laboratory and field experiments done on fractured rock show that flow and solute transport often occur along flow channels. `Sparse channels' refers to the case where these channels are characterised by flow in long flow paths separated from each other by large spacings relative to the size of flow domain. A literature study is presented that brings together information useful to assess whether a sparse-channel network concept is an appropriate representation of the flow system in tight fractured rock of low transmissivity, such as that around a nuclear waste repository in deep crystalline rocks. A number of observations are made in this review. First, conventional fracture network models may lead to inaccurate results for flow and solute transport in tight fractured rocks. Secondly, a flow dimension of 1, as determined by the analysis of pressure data in well testing, may be indicative of channelised flow, but such interpretation is not unique or definitive. Thirdly, in sparse channels, the percolation may be more influenced by the fracture shape than the fracture size and orientation but further studies are needed. Fourthly, the migration of radionuclides from a waste canister in a repository to the biosphere may be strongly influenced by the type of model used (e.g. discrete fracture network, channel model). Fifthly, the determination of appropriateness of representing an in situ flow system by a sparse-channel network model needs parameters usually neglected in site characterisation, such as the density of channels or fracture intersections.

Network modeling for reverse flows of end-of-life vehicles.

PubMed

Ene, Seval; Öztürk, Nursel

2015-04-01

Product recovery operations are of critical importance for the automotive industry in complying with environmental regulations concerning end-of-life products management. Manufacturers must take responsibility for their products over the entire life cycle. In this context, there is a need for network design methods for effectively managing recovery operations and waste. The purpose of this study is to develop a mathematical programming model for managing reverse flows in end-of-life vehicles' recovery network. A reverse flow is the collection of used products from consumers and the transportation of these products for the purpose of recycling, reuse or disposal. The proposed model includes all operations in a product recovery and waste management network for used vehicles and reuse for vehicle parts such as collection, disassembly, refurbishing, processing (shredding), recycling, disposal and reuse of vehicle parts. The scope of the network model is to determine the numbers and locations of facilities in the network and the material flows between these facilities. The results show the performance of the model and its applicability for use in the planning of recovery operations in the automotive industry. The main objective of recovery and waste management is to maximize revenue and minimize pollution in end-of-life product operations. This study shows that with an accurate model, these activities may provide economic benefits and incentives in addition to protecting the environment. Copyright © 2015 Elsevier Ltd. All rights reserved.

Network structure of subway passenger flows

NASA Astrophysics Data System (ADS)

Xu, Q.; Mao, B. H.; Bai, Y.

2016-03-01

The results of transportation infrastructure network analyses have been used to analyze complex networks in a topological context. However, most modeling approaches, including those based on complex network theory, do not fully account for real-life traffic patterns and may provide an incomplete view of network functions. This study utilizes trip data obtained from the Beijing Subway System to characterize individual passenger movement patterns. A directed weighted passenger flow network was constructed from the subway infrastructure network topology by incorporating trip data. The passenger flow networks exhibit several properties that can be characterized by power-law distributions based on flow size, and log-logistic distributions based on the fraction of boarding and departing passengers. The study also characterizes the temporal patterns of in-transit and waiting passengers and provides a hierarchical clustering structure for passenger flows. This hierarchical flow organization varies in the spatial domain. Ten cluster groups were identified, indicating a hierarchical urban polycentric structure composed of large concentrated flows at urban activity centers. These empirical findings provide insights regarding urban human mobility patterns within a large subway network.

Three-phase Power Flow Calculation of Low Voltage Distribution Network Considering Characteristics of Residents Load

NASA Astrophysics Data System (ADS)

Wang, Yaping; Lin, Shunjiang; Yang, Zhibin

2017-05-01

In the traditional three-phase power flow calculation of the low voltage distribution network, the load model is described as constant power. Since this model cannot reflect the characteristics of actual loads, the result of the traditional calculation is always different from the actual situation. In this paper, the load model in which dynamic load represented by air conditioners parallel with static load represented by lighting loads is used to describe characteristics of residents load, and the three-phase power flow calculation model is proposed. The power flow calculation model includes the power balance equations of three-phase (A,B,C), the current balance equations of phase 0, and the torque balancing equations of induction motors in air conditioners. And then an alternating iterative algorithm of induction motor torque balance equations with each node balance equations is proposed to solve the three-phase power flow model. This method is applied to an actual low voltage distribution network of residents load, and by the calculation of three different operating states of air conditioners, the result demonstrates the effectiveness of the proposed model and the algorithm.

Accounting for heterogeneity of nutrient dynamics in riverscapes through spatially distributed models

NASA Astrophysics Data System (ADS)

Wollheim, W. M.; Stewart, R. J.

2011-12-01

Numerous types of heterogeneity exist within river systems, leading to hotspots of nutrient sources, sinks, and impacts embedded within an underlying gradient defined by river size. This heterogeneity influences the downstream propagation of anthropogenic impacts across flow conditions. We applied a river network model to explore how nitrogen saturation at river network scales is influenced by the abundance and distribution of potential nutrient processing hotspots (lakes, beaver ponds, tributary junctions, hyporheic zones) under different flow conditions. We determined that under low flow conditions, whole network nutrient removal is relatively insensitive to the number of hotspots because the underlying river network structure has sufficient nutrient processing capacity. However, hotspots become more important at higher flows and greatly influence the spatial distribution of removal within the network at all flows, suggesting that identification of heterogeneity is critical to develop predictive understanding of nutrient removal processes under changing loading and climate conditions. New temporally intensive data from in situ sensors can potentially help to better understand and constrain these dynamics.

International Trade Modelling Using Open Flow Networks: A Flow-Distance Based Analysis.

PubMed

Shen, Bin; Zhang, Jiang; Li, Yixiao; Zheng, Qiuhua; Li, Xingsen

2015-01-01

This paper models and analyzes international trade flows using open flow networks (OFNs) with the approaches of flow distances, which provide a novel perspective and effective tools for the study of international trade. We discuss the establishment of OFNs of international trade from two coupled viewpoints: the viewpoint of trading commodity flow and that of money flow. Based on the novel model with flow distance approaches, meaningful insights are gained. First, by introducing the concepts of trade trophic levels and niches, countries' roles and positions in the global supply chains (or value-added chains) can be evaluated quantitatively. We find that the distributions of trading "trophic levels" have the similar clustering pattern for different types of commodities, and summarize some regularities between money flow and commodity flow viewpoints. Second, we find that active and competitive countries trade a wide spectrum of products, while inactive and underdeveloped countries trade a limited variety of products. Besides, some abnormal countries import many types of goods, which the vast majority of countries do not need to import. Third, harmonic node centrality is proposed and we find the phenomenon of centrality stratification. All the results illustrate the usefulness of the model of OFNs with its network approaches for investigating international trade flows.

International Trade Modelling Using Open Flow Networks: A Flow-Distance Based Analysis

PubMed Central

Shen, Bin; Zhang, Jiang; Li, Yixiao; Zheng, Qiuhua; Li, Xingsen

2015-01-01

This paper models and analyzes international trade flows using open flow networks (OFNs) with the approaches of flow distances, which provide a novel perspective and effective tools for the study of international trade. We discuss the establishment of OFNs of international trade from two coupled viewpoints: the viewpoint of trading commodity flow and that of money flow. Based on the novel model with flow distance approaches, meaningful insights are gained. First, by introducing the concepts of trade trophic levels and niches, countries’ roles and positions in the global supply chains (or value-added chains) can be evaluated quantitatively. We find that the distributions of trading “trophic levels” have the similar clustering pattern for different types of commodities, and summarize some regularities between money flow and commodity flow viewpoints. Second, we find that active and competitive countries trade a wide spectrum of products, while inactive and underdeveloped countries trade a limited variety of products. Besides, some abnormal countries import many types of goods, which the vast majority of countries do not need to import. Third, harmonic node centrality is proposed and we find the phenomenon of centrality stratification. All the results illustrate the usefulness of the model of OFNs with its network approaches for investigating international trade flows. PMID:26569618

Transportation and dynamic networks: Models, theory, and applications to supply chains, electric power, and financial networks

NASA Astrophysics Data System (ADS)

Liu, Zugang

Network systems, including transportation and logistic systems, electric power generation and distribution networks as well as financial networks, provide the critical infrastructure for the functioning of our societies and economies. The understanding of the dynamic behavior of such systems is also crucial to national security and prosperity. The identification of new connections between distinct network systems is the inspiration for the research in this dissertation. In particular, I answer two questions raised by Beckmann, McGuire, and Winsten (1956) and Copeland (1952) over half a century ago, which are, respectively, how are electric power flows related to transportation flows and does money flow like water or electricity? In addition, in this dissertation, I achieve the following: (1) I establish the relationships between transportation networks and three other classes of complex network systems: supply chain networks, electric power generation and transmission networks, and financial networks with intermediation. The establishment of such connections provides novel theoretical insights as well as new pricing mechanisms, and efficient computational methods. (2) I develop new modeling frameworks based on evolutionary variational inequality theory that capture the dynamics of such network systems in terms of the time-varying flows and incurred costs, prices, and, where applicable, profits. This dissertation studies the dynamics of such network systems by addressing both internal competition and/or cooperation, and external changes, such as varying costs and demands. (3) I focus, in depth, on electric power supply chains. By exploiting the relationships between transportation networks and electric power supply chains, I develop a large-scale network model that integrates electric power supply chains and fuel supply markets. The model captures both the economic transactions as well as the physical transmission constraints. The model is then applied to the New England electric power supply chain consisting of 6 states, 5 fuel types, 82 power generators, with a total of 573 generating units, and 10 demand markets. The empirical case study demonstrates that the regional electricity prices simulated by the model match very well the actual electricity prices in New England. I also utilize the model to study interactions between electric power supply chains and energy fuel markets.

Analysis of HRCT-derived xylem network reveals reverse flow in some vessels

USDA-ARS?s Scientific Manuscript database

Flow in xylem vessels is modeled based on constructions of three dimensional xylem networks derived from High Resolution Computed Tomography (HRCT) images of grapevine (Vitis vinifera) stems. Flow in 6-14% of the vessels was found to be oriented in the opposite direction to the bulk flow under norma...

Equivalent Relaxations of Optimal Power Flow

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bose, S; Low, SH; Teeraratkul, T

2015-03-01

Several convex relaxations of the optimal power flow (OPF) problem have recently been developed using both bus injection models and branch flow models. In this paper, we prove relations among three convex relaxations: a semidefinite relaxation that computes a full matrix, a chordal relaxation based on a chordal extension of the network graph, and a second-order cone relaxation that computes the smallest partial matrix. We prove a bijection between the feasible sets of the OPF in the bus injection model and the branch flow model, establishing the equivalence of these two models and their second-order cone relaxations. Our results implymore » that, for radial networks, all these relaxations are equivalent and one should always solve the second-order cone relaxation. For mesh networks, the semidefinite relaxation and the chordal relaxation are equally tight and both are strictly tighter than the second-order cone relaxation. Therefore, for mesh networks, one should either solve the chordal relaxation or the SOCP relaxation, trading off tightness and the required computational effort. Simulations are used to illustrate these results.« less

Microbubble transport through a bifurcating vessel network with pulsatile flow.

PubMed

Valassis, Doug T; Dodde, Robert E; Esphuniyani, Brijesh; Fowlkes, J Brian; Bull, Joseph L

2012-02-01

Motivated by two-phase microfluidics and by the clinical applications of air embolism and a developmental gas embolotherapy technique, experimental and theoretical models of microbubble transport in pulsatile flow are presented. The one-dimensional time-dependent theoretical model is developed from an unsteady Bernoulli equation that has been modified to include viscous and unsteady effects. Results of both experiments and theory show that roll angle (the angle the plane of the bifurcating network makes with the horizontal) is an important contributor to bubble splitting ratio at each bifurcation within the bifurcating network. When compared to corresponding constant flow, pulsatile flow was shown to produce insignificant changes to the overall splitting ratio of the bubble despite the order one Womersley numbers, suggesting that bubble splitting through the vasculature could be modeled adequately with a more modest constant flow model. However, bubble lodging was affected by the flow pulsatility, and the effects of pulsatile flow were evident in the dependence of splitting ratio of bubble length. The ability of bubbles to remain lodged after reaching a steady state in the bifurcations is promising for the effectiveness of gas embolotherapy to occlude blood flow to tumors, and indicates the importance of understanding where lodging will occur in air embolism. The ability to accurately predict the bubble dynamics in unsteady flow within a bifurcating network is demonstrated and suggests the potential for bubbles in microfluidics devices to encode information in both steady and unsteady aspects of their dynamics.

A distributed predictive control approach for periodic flow-based networks: application to drinking water systems

NASA Astrophysics Data System (ADS)

Grosso, Juan M.; Ocampo-Martinez, Carlos; Puig, Vicenç

2017-10-01

This paper proposes a distributed model predictive control approach designed to work in a cooperative manner for controlling flow-based networks showing periodic behaviours. Under this distributed approach, local controllers cooperate in order to enhance the performance of the whole flow network avoiding the use of a coordination layer. Alternatively, controllers use both the monolithic model of the network and the given global cost function to optimise the control inputs of the local controllers but taking into account the effect of their decisions over the remainder subsystems conforming the entire network. In this sense, a global (all-to-all) communication strategy is considered. Although the Pareto optimality cannot be reached due to the existence of non-sparse coupling constraints, the asymptotic convergence to a Nash equilibrium is guaranteed. The resultant strategy is tested and its effectiveness is shown when applied to a large-scale complex flow-based network: the Barcelona drinking water supply system.

A multi-scale network method for two-phase flow in porous media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khayrat, Karim, E-mail: khayratk@ifd.mavt.ethz.ch; Jenny, Patrick

Pore-network models of porous media are useful in the study of pore-scale flow in porous media. In order to extract macroscopic properties from flow simulations in pore-networks, it is crucial the networks are large enough to be considered representative elementary volumes. However, existing two-phase network flow solvers are limited to relatively small domains. For this purpose, a multi-scale pore-network (MSPN) method, which takes into account flow-rate effects and can simulate larger domains compared to existing methods, was developed. In our solution algorithm, a large pore network is partitioned into several smaller sub-networks. The algorithm to advance the fluid interfaces withinmore » each subnetwork consists of three steps. First, a global pressure problem on the network is solved approximately using the multiscale finite volume (MSFV) method. Next, the fluxes across the subnetworks are computed. Lastly, using fluxes as boundary conditions, a dynamic two-phase flow solver is used to advance the solution in time. Simulation results of drainage scenarios at different capillary numbers and unfavourable viscosity ratios are presented and used to validate the MSPN method against solutions obtained by an existing dynamic network flow solver.« less

Identifying Flow Networks in a Karstified Aquifer by Application of the Cellular Automata-Based Deterministic Inversion Method (Lez Aquifer, France)

NASA Astrophysics Data System (ADS)

Fischer, P.; Jardani, A.; Wang, X.; Jourde, H.; Lecoq, N.

2017-12-01

The distributed modeling of flow paths within karstic and fractured fields remains a complex task because of the high dependence of the hydraulic responses to the relative locations between observational boreholes and interconnected fractures and karstic conduits that control the main flow of the hydrosystem. The inverse problem in a distributed model is one alternative approach to interpret the hydraulic test data by mapping the karstic networks and fractured areas. In this work, we developed a Bayesian inversion approach, the Cellular Automata-based Deterministic Inversion (CADI) algorithm to infer the spatial distribution of hydraulic properties in a structurally constrained model. This method distributes hydraulic properties along linear structures (i.e., flow conduits) and iteratively modifies the structural geometry of this conduit network to progressively match the observed hydraulic data to the modeled ones. As a result, this method produces a conductivity model that is composed of a discrete conduit network embedded in the background matrix, capable of producing the same flow behavior as the investigated hydrologic system. The method is applied to invert a set of multiborehole hydraulic tests collected from a hydraulic tomography experiment conducted at the Terrieu field site in the Lez aquifer, Southern France. The emergent model shows a high consistency to field observation of hydraulic connections between boreholes. Furthermore, it provides a geologically realistic pattern of flow conduits. This method is therefore of considerable value toward an enhanced distributed modeling of the fractured and karstified aquifers.

Modelling information flow along the human connectome using maximum flow.

PubMed

Lyoo, Youngwook; Kim, Jieun E; Yoon, Sujung

2018-01-01

The human connectome is a complex network that transmits information between interlinked brain regions. Using graph theory, previously well-known network measures of integration between brain regions have been constructed under the key assumption that information flows strictly along the shortest paths possible between two nodes. However, it is now apparent that information does flow through non-shortest paths in many real-world networks such as cellular networks, social networks, and the internet. In the current hypothesis, we present a novel framework using the maximum flow to quantify information flow along all possible paths within the brain, so as to implement an analogy to network traffic. We hypothesize that the connection strengths of brain networks represent a limit on the amount of information that can flow through the connections per unit of time. This allows us to compute the maximum amount of information flow between two brain regions along all possible paths. Using this novel framework of maximum flow, previous network topological measures are expanded to account for information flow through non-shortest paths. The most important advantage of the current approach using maximum flow is that it can integrate the weighted connectivity data in a way that better reflects the real information flow of the brain network. The current framework and its concept regarding maximum flow provides insight on how network structure shapes information flow in contrast to graph theory, and suggests future applications such as investigating structural and functional connectomes at a neuronal level. Copyright © 2017 Elsevier Ltd. All rights reserved.

Optimal Concentrations in Transport Networks

NASA Astrophysics Data System (ADS)

Jensen, Kaare; Savage, Jessica; Kim, Wonjung; Bush, John; Holbrook, N. Michele

2013-03-01

Biological and man-made systems rely on effective transport networks for distribution of material and energy. Mass flow in these networks is determined by the flow rate and the concentration of material. While the most concentrated solution offers the greatest potential for mass flow, impedance grows with concentration and thus makes it the most difficult to transport. The concentration at which mass flow is optimal depends on specific physical and physiological properties of the system. We derive a simple model which is able to predict optimal concentrations observed in blood flows, sugar transport in plants, and nectar feeding animals. Our model predicts that the viscosity at the optimal concentration μopt =2nμ0 is an integer power of two times the viscosity of the pure carrier medium μ0. We show how the observed powers 1 <= n <= 6 agree well with theory and discuss how n depends on biological constraints imposed on the transport process. The model provides a universal framework for studying flows impeded by concentration and provides hints of how to optimize engineered flow systems, such as congestion in traffic flows.

An effective fractal-tree closure model for simulating blood flow in large arterial networks.

PubMed

Perdikaris, Paris; Grinberg, Leopold; Karniadakis, George Em

2015-06-01

The aim of the present work is to address the closure problem for hemodynamic simulations by developing a flexible and effective model that accurately distributes flow in the downstream vasculature and can stably provide a physiological pressure outflow boundary condition. To achieve this goal, we model blood flow in the sub-pixel vasculature by using a non-linear 1D model in self-similar networks of compliant arteries that mimic the structure and hierarchy of vessels in the meso-vascular regime (radii [Formula: see text]). We introduce a variable vessel length-to-radius ratio for small arteries and arterioles, while also addressing non-Newtonian blood rheology and arterial wall viscoelasticity effects in small arteries and arterioles. This methodology aims to overcome substantial cut-off radius sensitivities, typically arising in structured tree and linearized impedance models. The proposed model is not sensitive to outflow boundary conditions applied at the end points of the fractal network, and thus does not require calibration of resistance/capacitance parameters typically required for outflow conditions. The proposed model convergences to a periodic state in two cardiac cycles even when started from zero-flow initial conditions. The resulting fractal-trees typically consist of thousands to millions of arteries, posing the need for efficient parallel algorithms. To this end, we have scaled up a Discontinuous Galerkin solver that utilizes the MPI/OpenMP hybrid programming paradigm to thousands of computer cores, and can simulate blood flow in networks of millions of arterial segments at the rate of one cycle per 5 min. The proposed model has been extensively tested on a large and complex cranial network with 50 parent, patient-specific arteries and 21 outlets to which fractal trees where attached, resulting to a network of up to 4,392,484 vessels in total, and a detailed network of the arm with 276 parent arteries and 103 outlets (a total of 702,188 vessels after attaching the fractal trees), returning physiological flow and pressure wave predictions without requiring any parameter estimation or calibration procedures. We present a novel methodology to overcome substantial cut-off radius sensitivities.

Fracture Networks from a deterministic physical model as 'forerunners' of Maze Caves

NASA Astrophysics Data System (ADS)

Ferer, M. V.; Smith, D. H.; Lace, M. J.

2013-12-01

'Fractures are the chief forerunners of caves because they transmit water much more rapidly than intergranular pores.[1] Thus, the cave networks can follow the fracture networks from which the Karst caves formed by a variety of processes. Traditional models of continental Karst define water flow through subsurface geologic formations, slowly dissolving the rock along the pathways (e.g. water saturated with respect to carbon dioxide flowing through fractured carbonate formations). We have developed a deterministic, physical model of fracturing in a model geologic layer of a given thickness, when that layer is strained in one direction and subsequently in a perpendicular direction. It was observed that the connected fracture networks from our model visually resemble maps of maze caves. Since these detailed cave maps offer critical tools in modeling cave development patterns and conduit flow in Karst systems, we were able to test the qualitative resemblance by using statistical analyses to compare our model networks in geologic layers of four different thicknesses with the corresponding statistical analyses of four different maze caves, formed in a variety of geologic settings. The statistical studies performed are: i) standard box-counting to determine if either the caves or the model networks are fractal. We found that both are fractal with a fractal dimension Df ≈ 1.75 . ii) for each section inside a closed path, we determined the area and perimeter-length, enabling a study of the tortuosity of the networks. From the dependence of the section's area upon its perimeter-length, we have found a power-law behavior (for sufficiently large sections) characterized by a 'tortuosity' exponent. These exponents have similar values for both the model networks and the maze caves. The best agreement is between our thickest model layer and the maze-like part of Wind Cave in South Dakota where the data from the model and the cave overlie each other. For the present networks from the physical model, we assumed that the geologic layer was of uniform thickness and that the strain in both directions were the same. The latter may not be the case for the Brazilian, Toca de Boa Cave. These assumptions can be easily modified in our computer code to reflect different geologic histories. Even so the quantitative agreement suggests that our model networks are statistically realistic both for the 'forerunners' of caves and for general fracture networks in geologic layers, which should assist the study of underground fluid flow in many applications for which fracture patterns and fluid flow are difficult to determine (e.g., hydrology, watershed management, oil recovery, carbon dioxide sequestration, etc.). Keywords - Fracture Networks, Karst, Caves, Structurally Variable Pathways, hydrogeological modeling 1 Arthur N. Palmer, CAVE GEOLOGY, pub. Cave Books, Dayton OH, (2007).

Predicting commuter flows in spatial networks using a radiation model based on temporal ranges

NASA Astrophysics Data System (ADS)

Ren, Yihui; Ercsey-Ravasz, Mária; Wang, Pu; González, Marta C.; Toroczkai, Zoltán

2014-11-01

Understanding network flows such as commuter traffic in large transportation networks is an ongoing challenge due to the complex nature of the transportation infrastructure and human mobility. Here we show a first-principles based method for traffic prediction using a cost-based generalization of the radiation model for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. Using US census and highway traffic data, we show that traffic can efficiently and accurately be computed from a range-limited, network betweenness type calculation. The model based on travel time costs captures the log-normal distribution of the traffic and attains a high Pearson correlation coefficient (0.75) when compared with real traffic. Because of its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events.

Parameterisation of multi-scale continuum perfusion models from discrete vascular networks.

PubMed

Hyde, Eoin R; Michler, Christian; Lee, Jack; Cookson, Andrew N; Chabiniok, Radek; Nordsletten, David A; Smith, Nicolas P

2013-05-01

Experimental data and advanced imaging techniques are increasingly enabling the extraction of detailed vascular anatomy from biological tissues. Incorporation of anatomical data within perfusion models is non-trivial, due to heterogeneous vessel density and disparate radii scales. Furthermore, previous idealised networks have assumed a spatially repeating motif or periodic canonical cell, thereby allowing for a flow solution via homogenisation. However, such periodicity is not observed throughout anatomical networks. In this study, we apply various spatial averaging methods to discrete vascular geometries in order to parameterise a continuum model of perfusion. Specifically, a multi-compartment Darcy model was used to provide vascular scale separation for the fluid flow. Permeability tensor fields were derived from both synthetic and anatomically realistic networks using (1) porosity-scaled isotropic, (2) Huyghe and Van Campen, and (3) projected-PCA methods. The Darcy pressure fields were compared via a root-mean-square error metric to an averaged Poiseuille pressure solution over the same domain. The method of Huyghe and Van Campen performed better than the other two methods in all simulations, even for relatively coarse networks. Furthermore, inter-compartment volumetric flux fields, determined using the spatially averaged discrete flux per unit pressure difference, were shown to be accurate across a range of pressure boundary conditions. This work justifies the application of continuum flow models to characterise perfusion resulting from flow in an underlying vascular network.

Network community-based model reduction for vortical flows

NASA Astrophysics Data System (ADS)

Gopalakrishnan Meena, Muralikrishnan; Nair, Aditya G.; Taira, Kunihiko

2018-06-01

A network community-based reduced-order model is developed to capture key interactions among coherent structures in high-dimensional unsteady vortical flows. The present approach is data-inspired and founded on network-theoretic techniques to identify important vortical communities that are comprised of vortical elements that share similar dynamical behavior. The overall interaction-based physics of the high-dimensional flow field is distilled into the vortical community centroids, considerably reducing the system dimension. Taking advantage of these vortical interactions, the proposed methodology is applied to formulate reduced-order models for the inter-community dynamics of vortical flows, and predict lift and drag forces on bodies in wake flows. We demonstrate the capabilities of these models by accurately capturing the macroscopic dynamics of a collection of discrete point vortices, and the complex unsteady aerodynamic forces on a circular cylinder and an airfoil with a Gurney flap. The present formulation is found to be robust against simulated experimental noise and turbulence due to its integrating nature of the system reduction.

How important is exact knowledge of preferential flowpath locations and orientations for understanding spatiotemporally integrated spring hydrologic and transport response?

NASA Astrophysics Data System (ADS)

Henson, W.; De Rooij, R.; Graham, W. D.

2016-12-01

The Upper Floridian Aquifer is hydrogeologically complex; limestone dissolution has led to vertical and horizontal preferential flow paths. Locations of karst conduits are unknown and conduit properties are poorly constrained. Uncertainty in effects of conduit location, size, and density, network geometry and connectivity on hydrologic and transport responses is not well quantified, leading to limited use of discrete-continuum models that incorporate conduit networks for regional-scale hydrologic regulatory models. However, conduit networks typically dominate flow and contaminant transport in karst aquifers. We evaluated sensitivity of simulated water and nitrate fluxes and flow paths to karst conduit geometry in a springshed representative of Silver Springs, Florida, using a novel calcite dissolution conduit-generation algorithm coupled with a discrete-continuum flow and transport model (DisCo). Monte Carlo simulations of conduit generation, groundwater flow, and conservative solute transport indicate that, if a first magnitude spring system conduit network developed (i.e., spring flow >2.8 m3/s), the uncertainty in hydraulic and solute pulse response metrics at the spring vent was minimally related to locational uncertainty of network elements. Across the ensemble of realizations for various distributions of conduits, first magnitude spring hydraulic pulse metrics (e.g., steady-flow, peak flow, and recession coefficients) had < 0.01 coefficient of variation (CV). Similarly, spring solute breakthrough curve moments had low CV (<0.08); peak arrival had CV=0.06, mean travel time had CV=0.05, and travel time standard deviation had CV=0.08. Nevertheless, hydraulic and solute pulse response metrics were significantly different than those predicted by an equivalent porous-media model. These findings indicate that regional-scale decision models that incorporate karst preferential flow paths within an uncertainty framework can be used to better constrain aquifer-vulnerability estimates, despite lacking information about actual conduit locations.

Phase-synchronisation in continuous flow models of production networks

NASA Astrophysics Data System (ADS)

Scholz-Reiter, Bernd; Tervo, Jan Topi; Freitag, Michael

2006-04-01

To improve their position at the market, many companies concentrate on their core competences and hence cooperate with suppliers and distributors. Thus, between many independent companies strong linkages develop and production and logistics networks emerge. These networks are characterised by permanently increasing complexity, and are nowadays forced to adapt to dynamically changing markets. This factor complicates an enterprise-spreading production planning and control enormously. Therefore, a continuous flow model for production networks will be derived regarding these special logistic problems. Furthermore, phase-synchronisation effects will be presented and their dependencies to the set of network parameters will be investigated.

Spatio-temporal organization of dynamics in a two-dimensional periodically driven vortex flow: A Lagrangian flow network perspective.

PubMed

Lindner, Michael; Donner, Reik V

2017-03-01

We study the Lagrangian dynamics of passive tracers in a simple model of a driven two-dimensional vortex resembling real-world geophysical flow patterns. Using a discrete approximation of the system's transfer operator, we construct a directed network that describes the exchange of mass between distinct regions of the flow domain. By studying different measures characterizing flow network connectivity at different time-scales, we are able to identify the location of dynamically invariant structures and regions of maximum dispersion. Specifically, our approach allows us to delimit co-existing flow regimes with different dynamics. To validate our findings, we compare several network characteristics to the well-established finite-time Lyapunov exponents and apply a receiver operating characteristic analysis to identify network measures that are particularly useful for unveiling the skeleton of Lagrangian chaos.

A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks.

PubMed

Chen, Huan; Li, Lemin; Ren, Jing; Wang, Yang; Zhao, Yangming; Wang, Xiong; Wang, Sheng; Xu, Shizhong

2015-01-01

This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.

One-dimensional modelling of the interactions between heavy rainfall-runoff in an urban area and flooding flows from sewer networks and rivers.

PubMed

Kouyi, G Lipeme; Fraisse, D; Rivière, N; Guinot, V; Chocat, B

2009-01-01

Many investigations have been carried out in order to develop models which allow the linking of complex physical processes involved in urban flooding. The modelling of the interactions between overland flows on streets and flooding flows from rivers and sewer networks is one of the main objectives of recent and current research programs in hydraulics and urban hydrology. This paper outlines the original one-dimensional linking of heavy rainfall-runoff in urban areas and flooding flows from rivers and sewer networks under the RIVES project framework (Estimation of Scenario and Risks of Urban Floods). The first part of the paper highlights the capacity of Canoe software to simulate the street flows. In the second part, we show the original method of connection which enables the modelling of interactions between processes in urban flooding. Comparisons between simulated results and the results of Despotovic et al. or Gomez & Mur show a good agreement for the calibrated one-dimensional connection model. The connection operates likes a manhole with the orifice/weir coefficients used as calibration parameters. The influence of flooding flows from river was taken into account as a variable water depth boundary condition.

A flow-simulation model of the tidal Potomac River

USGS Publications Warehouse

Schaffranek, Raymond W.

1987-01-01

A one-dimensional model capable of simulating flow in a network of interconnected channels has been applied to the tidal Potomac River including its major tributaries and embayments between Washington, D.C., and Indian Head, Md. The model can be used to compute water-surface elevations and flow discharges at any of 66 predetermined locations or at any alternative river cross sections definable within the network of channels. In addition, the model can be used to provide tidal-interchange flow volumes and to evaluate tidal excursions and the flushing properties of the riverine system. Comparisons of model-computed results with measured watersurface elevations and discharges demonstrate the validity and accuracy of the model. Tidal-cycle flow volumes computed by the calibrated model have been verified to be within an accuracy of ? 10 percent. Quantitative characteristics of the hydrodynamics of the tidal river are identified and discussed. The comprehensive flow data provided by the model can be used to better understand the geochemical, biological, and other processes affecting the river's water quality.

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors.

PubMed

Cheung, Kit; Schultz, Simon R; Luk, Wayne

2015-01-01

NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation.

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors

PubMed Central

Cheung, Kit; Schultz, Simon R.; Luk, Wayne

2016-01-01

NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation. PMID:26834542

Dynamics of functional failures and recovery in complex road networks

NASA Astrophysics Data System (ADS)

Zhan, Xianyuan; Ukkusuri, Satish V.; Rao, P. Suresh C.

2017-11-01

We propose a new framework for modeling the evolution of functional failures and recoveries in complex networks, with traffic congestion on road networks as the case study. Differently from conventional approaches, we transform the evolution of functional states into an equivalent dynamic structural process: dual-vertex splitting and coalescing embedded within the original network structure. The proposed model successfully explains traffic congestion and recovery patterns at the city scale based on high-resolution data from two megacities. Numerical analysis shows that certain network structural attributes can amplify or suppress cascading functional failures. Our approach represents a new general framework to model functional failures and recoveries in flow-based networks and allows understanding of the interplay between structure and function for flow-induced failure propagation and recovery.

Percolation Network Study on the Gas Apparent Permeability of Rock

NASA Astrophysics Data System (ADS)

Wang, Y.; Tang, Y. B.; Li, M.

2017-12-01

We modeled the gas single phase transport behaviors of monomodal porous media using percolation networks. Different from the liquid absolute permeability, which is only related to topology and morphology of pore space, the gas permeability depends on pore pressure as well. A published gas flow conductance model, included usual viscous flow, slip flow and Knudsen diffusion in cylinder pipe, was used to simulated gas flow in 3D, simple cubic, body-center cubic and face-center cubic networks with different hydraulic radius, different coordination number, and different pipe radius distributions under different average pore pressure. The simulation results showed that the gas apparent permeability kapp obey the `universal' scaling law (independence of network lattices), kapp (z-zc)β, where exponent β is related to pore radius distribution, z is coordination number and zc=1.5. Following up on Bernabé et al.'s (2010) study of the effects of pore connectivity and pore size heterogeneity on liquid absolute permeability, gas apparent permeability kapp model and a new joint gas-liquid permeability (i.e., kapp/k∞) model, which could explain the Klinkenberg phenomenon, were proposed. We satisfactorily tested the models by comparison with published experimental data on glass beads and other datasets.

Information flow in a network of dispersed signalers-receivers

NASA Astrophysics Data System (ADS)

Halupka, Konrad

2017-11-01

I consider a stochastic model of multi-agent communication in regular network. The model describes how dispersed animals exchange information. Each agent can initiate and transfer the signal to its nearest neighbors, who may pass it farther. For an external observer of busy networks, signaling activity may appear random, even though information flow actually thrives. Only when signal initiation and transfer are at low levels do spatiotemporal autocorrelations emerge as clumping signaling activity in space and pink noise time series. Under such conditions, the costs of signaling are moderate, but the signaler can reach a large audience. I propose that real-world networks of dispersed signalers-receivers may self-organize into this state and the flow of information maintains their integrity.

Origin of Permeability and Structure of Flows in Fractured Media

NASA Astrophysics Data System (ADS)

De Dreuzy, J.; Darcel, C.; Davy, P.; Erhel, J.; Le Goc, R.; Maillot, J.; Meheust, Y.; Pichot, G.; Poirriez, B.

2013-12-01

After more than three decades of research, flows in fractured media have been shown to result from multi-scale geological structures. Flows result non-exclusively from the damage zone of the large faults, from the percolation within denser networks of smaller fractures, from the aperture heterogeneity within the fracture planes and from some remaining permeability within the matrix. While the effect of each of these causes has been studied independently, global assessments of the main determinisms is still needed. We propose a general approach to determine the geological structures responsible for flows, their permeability and their organization based on field data and numerical modeling [de Dreuzy et al., 2012b]. Multi-scale synthetic networks are reconstructed from field data and simplified mechanical modeling [Davy et al., 2010]. High-performance numerical methods are developed to comply with the specificities of the geometry and physical properties of the fractured media [Pichot et al., 2010; Pichot et al., 2012]. And, based on a large Monte-Carlo sampling, we determine the key determinisms of fractured permeability and flows (Figure). We illustrate our approach on the respective influence of fracture apertures and fracture correlation patterns at large scale. We show the potential role of fracture intersections, so far overlooked between the fracture and the network scales. We also demonstrate how fracture correlations reduce the bulk fracture permeability. Using this analysis, we highlight the need for more specific in-situ characterization of fracture flow structures. Fracture modeling and characterization are necessary to meet the new requirements of a growing number of applications where fractures appear both as potential advantages to enhance permeability and drawbacks for safety, e.g. in energy storage, stimulated geothermal energy and non-conventional gas productions. References Davy, P., et al. (2010), A likely universal model of fracture scaling and its consequence for crustal hydromechanics, Journal of Geophysical Research-Solid Earth, 115, 13. de Dreuzy, J.-R., et al. (2012a), Influence of fracture scale heterogeneity on the flow properties of three-dimensional Discrete Fracture Networks (DFN), J. Geophys. Res.-Earth Surf., 117(B11207), 21 PP. de Dreuzy, J.-R., et al. (2012b), Synthetic benchmark for modeling flow in 3D fractured media, Computers and Geosciences(0). Pichot, G., et al. (2010), A Mixed Hybrid Mortar Method for solving flow in Discrete Fracture Networks, Applicable Analysis, 89(10), 1729-1643. Pichot, G., et al. (2012), Flow simulation in 3D multi-scale fractured networks using non-matching meshes, SIAM Journal on Scientific Computing (SISC), 34(1). Figure: (a) Fracture network with a broad-range of fracture lengths. (b) Flows (log-scale) with homogeneous fractures. (c) Flows (log-scale) with heterogeneous fractures [de Dreuzy et al., 2012a]. The impact of the fracture apertures (c) is illustrated on the organization of flows.

Quantifying spatial and temporal patterns of flow intermittency using spatially contiguous runoff data

NASA Astrophysics Data System (ADS)

Yu (于松延), Songyan; Bond, Nick R.; Bunn, Stuart E.; Xu, Zongxue; Kennard, Mark J.

2018-04-01

River channel drying caused by intermittent stream flow is a widely-recognized factor shaping stream ecosystems. There is a strong need to quantify the distribution of intermittent streams across catchments to inform management. However, observational gauge networks provide only point estimates of streamflow variation. Increasingly, this limitation is being overcome through the use of spatially contiguous estimates of the terrestrial water-balance, which can also assist in estimating runoff and streamflow at large-spatial scales. Here we proposed an approach to quantifying spatial and temporal variation in monthly flow intermittency throughout river networks in eastern Australia. We aggregated gridded (5 × 5 km) monthly water-balance data with a hierarchically nested catchment dataset to simulate catchment runoff accumulation throughout river networks from 1900 to 2016. We also predicted zero flow duration for the entire river network by developing a robust predictive model relating measured zero flow duration (% months) to environmental predictor variables (based on 43 stream gauges). We then combined these datasets by using the predicted zero flow duration from the regression model to determine appropriate 'zero' flow thresholds for the modelled discharge data, which varied spatially across the catchments examined. Finally, based on modelled discharge data and identified actual zero flow thresholds, we derived summary metrics describing flow intermittency across the catchment (mean flow duration and coefficient-of-variation in flow permanence from 1900 to 2016). We also classified the relative degree of flow intermittency annually to characterise temporal variation in flow intermittency. Results showed that the degree of flow intermittency varied substantially across streams in eastern Australia, ranging from perennial streams flowing permanently (11-12 months) to strongly intermittent streams flowing 4 months or less of year. Results also showed that the temporal extent of flow intermittency varied dramatically inter-annually from 1900 to 2016, with the proportion of intermittent (weakly and strongly intermittent) streams ranging in length from 3% to nearly 100% of the river network, but there was no evidence of an increasing trend towards flow intermittency over this period. Our approach to generating spatially explicit and catchment-wide estimates of streamflow intermittency can facilitate improved ecological understanding and management of intermittent streams in Australia and around the world.

Tests of peak flow scaling in simulated self-similar river networks

USGS Publications Warehouse

Menabde, M.; Veitzer, S.; Gupta, V.; Sivapalan, M.

2001-01-01

The effect of linear flow routing incorporating attenuation and network topology on peak flow scaling exponent is investigated for an instantaneously applied uniform runoff on simulated deterministic and random self-similar channel networks. The flow routing is modelled by a linear mass conservation equation for a discrete set of channel links connected in parallel and series, and having the same topology as the channel network. A quasi-analytical solution for the unit hydrograph is obtained in terms of recursion relations. The analysis of this solution shows that the peak flow has an asymptotically scaling dependence on the drainage area for deterministic Mandelbrot-Vicsek (MV) and Peano networks, as well as for a subclass of random self-similar channel networks. However, the scaling exponent is shown to be different from that predicted by the scaling properties of the maxima of the width functions. ?? 2001 Elsevier Science Ltd. All rights reserved.

A source-controlled data center network model.

PubMed

Yu, Yang; Liang, Mangui; Wang, Zhe

2017-01-01

The construction of data center network by applying SDN technology has become a hot research topic. The SDN architecture has innovatively separated the control plane from the data plane which makes the network more software-oriented and agile. Moreover, it provides virtual multi-tenancy, effective scheduling resources and centralized control strategies to meet the demand for cloud computing data center. However, the explosion of network information is facing severe challenges for SDN controller. The flow storage and lookup mechanisms based on TCAM device have led to the restriction of scalability, high cost and energy consumption. In view of this, a source-controlled data center network (SCDCN) model is proposed herein. The SCDCN model applies a new type of source routing address named the vector address (VA) as the packet-switching label. The VA completely defines the communication path and the data forwarding process can be finished solely relying on VA. There are four advantages in the SCDCN architecture. 1) The model adopts hierarchical multi-controllers and abstracts large-scale data center network into some small network domains that has solved the restriction for the processing ability of single controller and reduced the computational complexity. 2) Vector switches (VS) developed in the core network no longer apply TCAM for table storage and lookup that has significantly cut down the cost and complexity for switches. Meanwhile, the problem of scalability can be solved effectively. 3) The SCDCN model simplifies the establishment process for new flows and there is no need to download flow tables to VS. The amount of control signaling consumed when establishing new flows can be significantly decreased. 4) We design the VS on the NetFPGA platform. The statistical results show that the hardware resource consumption in a VS is about 27% of that in an OFS.

A source-controlled data center network model

PubMed Central

Yu, Yang; Liang, Mangui; Wang, Zhe

2017-01-01

The construction of data center network by applying SDN technology has become a hot research topic. The SDN architecture has innovatively separated the control plane from the data plane which makes the network more software-oriented and agile. Moreover, it provides virtual multi-tenancy, effective scheduling resources and centralized control strategies to meet the demand for cloud computing data center. However, the explosion of network information is facing severe challenges for SDN controller. The flow storage and lookup mechanisms based on TCAM device have led to the restriction of scalability, high cost and energy consumption. In view of this, a source-controlled data center network (SCDCN) model is proposed herein. The SCDCN model applies a new type of source routing address named the vector address (VA) as the packet-switching label. The VA completely defines the communication path and the data forwarding process can be finished solely relying on VA. There are four advantages in the SCDCN architecture. 1) The model adopts hierarchical multi-controllers and abstracts large-scale data center network into some small network domains that has solved the restriction for the processing ability of single controller and reduced the computational complexity. 2) Vector switches (VS) developed in the core network no longer apply TCAM for table storage and lookup that has significantly cut down the cost and complexity for switches. Meanwhile, the problem of scalability can be solved effectively. 3) The SCDCN model simplifies the establishment process for new flows and there is no need to download flow tables to VS. The amount of control signaling consumed when establishing new flows can be significantly decreased. 4) We design the VS on the NetFPGA platform. The statistical results show that the hardware resource consumption in a VS is about 27% of that in an OFS. PMID:28328925

A compact model for electroosmotic flows in microfluidic devices

NASA Astrophysics Data System (ADS)

Qiao, R.; Aluru, N. R.

2002-09-01

A compact model to compute flow rate and pressure in microfluidic devices is presented. The microfluidic flow can be driven by either an applied electric field or a combined electric field and pressure gradient. A step change in the ζ-potential on a channel wall is treated by a pressure source in the compact model. The pressure source is obtained from the pressure Poisson equation and conservation of mass principle. In the proposed compact model, the complex fluidic network is simplified by an electrical circuit. The compact model can predict the flow rate, pressure distribution and other basic characteristics in microfluidic channels quickly with good accuracy when compared to detailed numerical simulation. Using the compact model, fluidic mixing and dispersion control are studied in a complex microfluidic network.

XFEM modeling of hydraulic fracture in porous rocks with natural fractures

NASA Astrophysics Data System (ADS)

Wang, Tao; Liu, ZhanLi; Zeng, QingLei; Gao, Yue; Zhuang, Zhuo

2017-08-01

Hydraulic fracture (HF) in porous rocks is a complex multi-physics coupling process which involves fluid flow, diffusion and solid deformation. In this paper, the extended finite element method (XFEM) coupling with Biot theory is developed to study the HF in permeable rocks with natural fractures (NFs). In the recent XFEM based computational HF models, the fluid flow in fractures and interstitials of the porous media are mostly solved separately, which brings difficulties in dealing with complex fracture morphology. In our new model the fluid flow is solved in a unified framework by considering the fractures as a kind of special porous media and introducing Poiseuille-type flow inside them instead of Darcy-type flow. The most advantage is that it is very convenient to deal with fluid flow inside the complex fracture network, which is important in shale gas extraction. The weak formulation for the new coupled model is derived based on virtual work principle, which includes the XFEM formulation for multiple fractures and fractures intersection in porous media and finite element formulation for the unified fluid flow. Then the plane strain Kristianovic-Geertsma-de Klerk (KGD) model and the fluid flow inside the fracture network are simulated to validate the accuracy and applicability of this method. The numerical results show that large injection rate, low rock permeability and isotropic in-situ stresses tend to lead to a more uniform and productive fracture network.

A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks

PubMed Central

Chen, Huan; Li, Lemin; Ren, Jing; Wang, Yang; Zhao, Yangming; Wang, Xiong; Wang, Sheng; Xu, Shizhong

2015-01-01

This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme. PMID:26690571

Optimizing Natural Gas Networks through Dynamic Manifold Theory and a Decentralized Algorithm: Belgium Case Study

NASA Astrophysics Data System (ADS)

Koch, Caleb; Winfrey, Leigh

2014-10-01

Natural Gas is a major energy source in Europe, yet political instabilities have the potential to disrupt access and supply. Energy resilience is an increasingly essential construct and begins with transmission network design. This study proposes a new way of thinking about modelling natural gas flow. Rather than relying on classical economic models, this problem is cast into a time-dependent Hamiltonian dynamics discussion. Traditional Natural Gas constraints, including inelastic demand and maximum/minimum pipe flows, are portrayed as energy functions and built into the dynamics of each pipe flow. Doing so allows the constraints to be built into the dynamics of each pipeline. As time progresses in the model, natural gas flow rates find the minimum energy, thus the optimal gas flow rates. The most important result of this study is using dynamical principles to ensure the output of natural gas at demand nodes remains constant, which is important for country to country natural gas transmission. Another important step in this study is building the dynamics of each flow in a decentralized algorithm format. Decentralized regulation has solved congestion problems for internet data flow, traffic flow, epidemiology, and as demonstrated in this study can solve the problem of Natural Gas congestion. A mathematical description is provided for how decentralized regulation leads to globally optimized network flow. Furthermore, the dynamical principles and decentralized algorithm are applied to a case study of the Fluxys Belgium Natural Gas Network.

Comparative empirical analysis of flow-weighted transit route networks in R-space and evolution modeling

NASA Astrophysics Data System (ADS)

Huang, Ailing; Zang, Guangzhi; He, Zhengbing; Guan, Wei

2017-05-01

Urban public transit system is a typical mixed complex network with dynamic flow, and its evolution should be a process coupling topological structure with flow dynamics, which has received little attention. This paper presents the R-space to make a comparative empirical analysis on Beijing’s flow-weighted transit route network (TRN) and we found that both the Beijing’s TRNs in the year of 2011 and 2015 exhibit the scale-free properties. As such, we propose an evolution model driven by flow to simulate the development of TRNs with consideration of the passengers’ dynamical behaviors triggered by topological change. The model simulates that the evolution of TRN is an iterative process. At each time step, a certain number of new routes are generated driven by travel demands, which leads to dynamical evolution of new routes’ flow and triggers perturbation in nearby routes that will further impact the next round of opening new routes. We present the theoretical analysis based on the mean-field theory, as well as the numerical simulation for this model. The results obtained agree well with our empirical analysis results, which indicate that our model can simulate the TRN evolution with scale-free properties for distributions of node’s strength and degree. The purpose of this paper is to illustrate the global evolutional mechanism of transit network that will be used to exploit planning and design strategies for real TRNs.

A Comprehensive Model for Real Gas Transport in Shale Formations with Complex Non-planar Fracture Networks

PubMed Central

Yang, Ruiyue; Huang, Zhongwei; Yu, Wei; Li, Gensheng; Ren, Wenxi; Zuo, Lihua; Tan, Xiaosi; Sepehrnoori, Kamy; Tian, Shouceng; Sheng, Mao

2016-01-01

A complex fracture network is generally generated during the hydraulic fracturing treatment in shale gas reservoirs. Numerous efforts have been made to model the flow behavior of such fracture networks. However, it is still challenging to predict the impacts of various gas transport mechanisms on well performance with arbitrary fracture geometry in a computationally efficient manner. We develop a robust and comprehensive model for real gas transport in shales with complex non-planar fracture network. Contributions of gas transport mechanisms and fracture complexity to well productivity and rate transient behavior are systematically analyzed. The major findings are: simple planar fracture can overestimate gas production than non-planar fracture due to less fracture interference. A “hump” that occurs in the transition period and formation linear flow with a slope less than 1/2 can infer the appearance of natural fractures. The sharpness of the “hump” can indicate the complexity and irregularity of the fracture networks. Gas flow mechanisms can extend the transition flow period. The gas desorption could make the “hump” more profound. The Knudsen diffusion and slippage effect play a dominant role in the later production time. Maximizing the fracture complexity through generating large connected networks is an effective way to increase shale gas production. PMID:27819349

A Comprehensive Model for Real Gas Transport in Shale Formations with Complex Non-planar Fracture Networks.

PubMed

Yang, Ruiyue; Huang, Zhongwei; Yu, Wei; Li, Gensheng; Ren, Wenxi; Zuo, Lihua; Tan, Xiaosi; Sepehrnoori, Kamy; Tian, Shouceng; Sheng, Mao

2016-11-07

A complex fracture network is generally generated during the hydraulic fracturing treatment in shale gas reservoirs. Numerous efforts have been made to model the flow behavior of such fracture networks. However, it is still challenging to predict the impacts of various gas transport mechanisms on well performance with arbitrary fracture geometry in a computationally efficient manner. We develop a robust and comprehensive model for real gas transport in shales with complex non-planar fracture network. Contributions of gas transport mechanisms and fracture complexity to well productivity and rate transient behavior are systematically analyzed. The major findings are: simple planar fracture can overestimate gas production than non-planar fracture due to less fracture interference. A "hump" that occurs in the transition period and formation linear flow with a slope less than 1/2 can infer the appearance of natural fractures. The sharpness of the "hump" can indicate the complexity and irregularity of the fracture networks. Gas flow mechanisms can extend the transition flow period. The gas desorption could make the "hump" more profound. The Knudsen diffusion and slippage effect play a dominant role in the later production time. Maximizing the fracture complexity through generating large connected networks is an effective way to increase shale gas production.

Variable speed limit strategies analysis with mesoscopic traffic flow model based on complex networks

NASA Astrophysics Data System (ADS)

Li, Shu-Bin; Cao, Dan-Ni; Dang, Wen-Xiu; Zhang, Lin

As a new cross-discipline, the complexity science has penetrated into every field of economy and society. With the arrival of big data, the research of the complexity science has reached its summit again. In recent years, it offers a new perspective for traffic control by using complex networks theory. The interaction course of various kinds of information in traffic system forms a huge complex system. A new mesoscopic traffic flow model is improved with variable speed limit (VSL), and the simulation process is designed, which is based on the complex networks theory combined with the proposed model. This paper studies effect of VSL on the dynamic traffic flow, and then analyzes the optimal control strategy of VSL in different network topologies. The conclusion of this research is meaningful to put forward some reasonable transportation plan and develop effective traffic management and control measures to help the department of traffic management.

How has climate change altered network connectivity in a mountain stream network?

NASA Astrophysics Data System (ADS)

Ward, A. S.; Schmadel, N.; Wondzell, S. M.; Johnson, S.

2017-12-01

Connectivity along river networks is broadly recognized as dynamic, with seasonal and event-based expansion and contraction of the network extent. Intermittently flowing streams are particularly important as they define a crucial threshold for continuously connected waters that enable migration by aquatic species. In the Pacific northwestern U.S., changes in atmospheric circulation have been found to alter rainfall patterns and result in decreased summer low-flows in the region. However, the impact of this climate dynamic on network connectivity is heretofore unstudied. Thus, we ask: How has connectivity in the riparian corridor changed in response to observed changes in climate? In this study we take the well-studied H.J. Andrews Experimental Forest as representative of mountain river networks in the Pacific northwestern U.S. First, we analyze 63 years of stream gauge information from a network of 11 gauges to document observed changes in timing and magnitude of stream discharge. We found declining magnitudes of seasonal low-flows and shifting seasonality of water export from the catchment, both of which we attribute to changes in precipitation timing and storage as snow vs. rainfall. Next, we use these discharge data to drive a reduced-complexity model of the river network to simulate network connectivity over 63 years. Model results show that network contraction (i.e., minimum network extent) has decreased over the past 63 years. Unexpectedly, the increasing winter peak flows did not correspond with increasing network expansion, suggesting a geologic control on maximum flowing network extent. We find dynamic expansion and contraction of the network primarily occurs during period of catchment discharge less than about 1 m3/s at the outlet, whereas the network extent is generally constant for discharges from 1 to 300 m3/s. Results of our study are of interest to scientists focused on connectivity as a control on ecological processes both directly (e.g., fish migration) and indirectly (e.g., stream temperature modeling). Additionally, our results inform management and regulatory needs such as estimating connectivity for entire river networks as a basis for regulation, and identifying the complexity of a shifting baseline in identifying a regulatory basis.

Microfluidic circuit analysis II: implications of ion conservation for microchannels connected in series.

PubMed

Biscombe, Christian J C; Davidson, Malcolm R; Harvie, Dalton J E

2012-01-01

A mathematical framework for analysing electrokinetic flow in microchannel networks is outlined. The model is based on conservation of volume and total charge at network junctions, but in contrast to earlier theories also incorporates conservation of ion charge there. The model is applied to mixed pressure-driven/electro-osmotic flows of binary electrolytes through homogeneous microchannels as well as a 4:1:4 contraction-expansion series network. Under conditions of specified volumetric flow rate and ion currents, non-linear steady-state phenomena may arise: when the direction of the net co-ion flux is opposite to the direction of the net volumetric flow, two different fully developed, steady-state flow solutions may be obtained. Model predictions are compared with two-dimensional computational fluid dynamics (CFD) simulations. For systems where two steady states are realisable, the ultimate steady behaviour is shown to depend in part upon the initial state of the system. Copyright © 2011 Elsevier Inc. All rights reserved.

Multiscale modelling and nonlinear simulation of vascular tumour growth

PubMed Central

Macklin, Paul; Anderson, Alexander R. A.; Chaplain, Mark A. J.; Cristini, Vittorio

2011-01-01

In this article, we present a new multiscale mathematical model for solid tumour growth which couples an improved model of tumour invasion with a model of tumour-induced angiogenesis. We perform nonlinear simulations of the multi-scale model that demonstrate the importance of the coupling between the development and remodeling of the vascular network, the blood flow through the network and the tumour progression. Consistent with clinical observations, the hydrostatic stress generated by tumour cell proliferation shuts down large portions of the vascular network dramatically affecting the flow, the subsequent network remodeling, the delivery of nutrients to the tumour and the subsequent tumour progression. In addition, extracellular matrix degradation by tumour cells is seen to have a dramatic affect on both the development of the vascular network and the growth response of the tumour. In particular, the newly developing vessels tend to encapsulate, rather than penetrate, the tumour and are thus less effective in delivering nutrients. PMID:18781303

Flow regulation in the Swiss Alps: a river network modelling approach to investigate the impacts on bed load and grain size distribution

NASA Astrophysics Data System (ADS)

Costa, A.; Molnar, P.; Schmitt, R. J. P.

2017-12-01

The grain size distribution (GSD) of river bed sediment results from the long term balance between transport capacity and sediment supply. Changes in climate and human activities may alter the spatial distribution of transport capacity and sediment supply along channels and hence impact local bedload transport and GSD. The effects of changed flow are not easily inferable due the non-linear, threshold-based nature of the relation between discharge and sediment mobilization, and the network-scale control on local sediment supply. We present a network-scale model for fractional sediment transport to quantify the impact of hydropower (HP) operations on river network GSD. We represent the river network as a series of connected links for which we extract the geometric characteristics from satellite images and a digital elevation model. We assign surface roughness based on the channel bed GSD. Bed shear stress is estimated at link-scale under the assumptions of rectangular prismatic cross sections and normal flow. The mass balance between sediment supply and transport capacity, computed with the Wilcock and Crowe model, determines transport rates of multiple grain size classes and the resulting GSD. We apply the model to the upper Rhone basin, a large Alpine basin in Switzerland. Since 1960s, changed flow conditions due to HP operations and sediment storage behind dams have potentially altered the sediment transport of the basin. However, little is known on the magnitude and spatial distribution of these changes. We force the model with time series of daily discharge derived with a spatially distributed hydrological model for pre and post HP scenarios. We initialize GSD under the assumption that coarse grains (d90) are mobilized only during mean annual maximum flows, and on the basis of ratios between d90 and characteristic diameters estimated from field measurements. Results show that effects of flow regulation vary significantly in space and in time and are grain size dependent. HP operations led to an overall reduction of sediment transport at network scale, especially in summer and for coarser grains, leading to a general coarsening of the river bed sediments at the upstream reaches. The model allows investigating the impact of modified HP operations and climate change projections on sediment dynamics at the network scale.

Scale-dependent genetic structure of the Idaho giant salamander (Dicamptodon aterrimus) in stream networks

Treesearch

Lindy B. Mullen; H. Arthur Woods; Michael K. Schwartz; Adam J. Sepulveda; Winsor H. Lowe

2010-01-01

The network architecture of streams and rivers constrains evolutionary, demographic and ecological processes of freshwater organisms. This consistent architecture also makes stream networks useful for testing general models of population genetic structure and the scaling of gene flow. We examined genetic structure and gene flow in the facultatively paedomorphic Idaho...

Real-time flood forecasts & risk assessment using a possibility-theory based fuzzy neural network

NASA Astrophysics Data System (ADS)

Khan, U. T.

2016-12-01

Globally floods are one of the most devastating natural disasters and improved flood forecasting methods are essential for better flood protection in urban areas. Given the availability of high resolution real-time datasets for flood variables (e.g. streamflow and precipitation) in many urban areas, data-driven models have been effectively used to predict peak flow rates in river; however, the selection of input parameters for these types of models is often subjective. Additionally, the inherit uncertainty associated with data models along with errors in extreme event observations means that uncertainty quantification is essential. Addressing these concerns will enable improved flood forecasting methods and provide more accurate flood risk assessments. In this research, a new type of data-driven model, a quasi-real-time updating fuzzy neural network is developed to predict peak flow rates in urban riverine watersheds. A possibility-to-probability transformation is first used to convert observed data into fuzzy numbers. A possibility theory based training regime is them used to construct the fuzzy parameters and the outputs. A new entropy-based optimisation criterion is used to train the network. Two existing methods to select the optimum input parameters are modified to account for fuzzy number inputs, and compared. These methods are: Entropy-Wavelet-based Artificial Neural Network (EWANN) and Combined Neural Pathway Strength Analysis (CNPSA). Finally, an automated algorithm design to select the optimum structure of the neural network is implemented. The overall impact of each component of training this network is to replace the traditional ad hoc network configuration methods, with one based on objective criteria. Ten years of data from the Bow River in Calgary, Canada (including two major floods in 2005 and 2013) are used to calibrate and test the network. The EWANN method selected lagged peak flow as a candidate input, whereas the CNPSA method selected lagged precipitation and lagged mean daily flow as candidate inputs. Model performance metric show that the CNPSA method had higher performance (with an efficiency of 0.76). Model output was used to assess the risk of extreme peak flows for a given day using an inverse possibility-to-probability transformation.

Finite volume solution for two-phase flow in a straight capillary

NASA Astrophysics Data System (ADS)

Yelkhovsky, Alexander; Pinczewski, W. Val

2018-04-01

The problem of two-phase flow in straight capillaries of polygonal cross section displays many of the dynamic characteristics of rapid interfacial motions associated with pore-scale displacements in porous media. Fluid inertia is known to be important in these displacements but is usually ignored in network models commonly used to predict macroscopic flow properties. This study presents a numerical model for two-phase flow which describes the spatial and temporal evolution of the interface between the fluids. The model is based on an averaged Navier-Stokes equation and is shown to be successful in predicting the complex dynamics of both capillary rise in round capillaries and imbibition along the corners of polygonal capillaries. The model can form the basis for more realistic network models which capture the effect of capillary, viscous, and inertial forces on pore-scale interfacial dynamics and consequent macroscopic flow properties.

The epidemic spreading model and the direction of information flow in brain networks.

PubMed

Meier, J; Zhou, X; Hillebrand, A; Tewarie, P; Stam, C J; Van Mieghem, P

2017-05-15

The interplay between structural connections and emerging information flow in the human brain remains an open research problem. A recent study observed global patterns of directional information flow in empirical data using the measure of transfer entropy. For higher frequency bands, the overall direction of information flow was from posterior to anterior regions whereas an anterior-to-posterior pattern was observed in lower frequency bands. In this study, we applied a simple Susceptible-Infected-Susceptible (SIS) epidemic spreading model on the human connectome with the aim to reveal the topological properties of the structural network that give rise to these global patterns. We found that direct structural connections induced higher transfer entropy between two brain regions and that transfer entropy decreased with increasing distance between nodes (in terms of hops in the structural network). Applying the SIS model, we were able to confirm the empirically observed opposite information flow patterns and posterior hubs in the structural network seem to play a dominant role in the network dynamics. For small time scales, when these hubs acted as strong receivers of information, the global pattern of information flow was in the posterior-to-anterior direction and in the opposite direction when they were strong senders. Our analysis suggests that these global patterns of directional information flow are the result of an unequal spatial distribution of the structural degree between posterior and anterior regions and their directions seem to be linked to different time scales of the spreading process. Copyright © 2017 Elsevier Inc. All rights reserved.

Bicriteria Network Optimization Problem using Priority-based Genetic Algorithm

NASA Astrophysics Data System (ADS)

Gen, Mitsuo; Lin, Lin; Cheng, Runwei

Network optimization is being an increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. In many applications, however, there are several criteria associated with traversing each edge of a network. For example, cost and flow measures are both important in the networks. As a result, there has been recent interest in solving Bicriteria Network Optimization Problem. The Bicriteria Network Optimization Problem is known a NP-hard. The efficient set of paths may be very large, possibly exponential in size. Thus the computational effort required to solve it can increase exponentially with the problem size in the worst case. In this paper, we propose a genetic algorithm (GA) approach used a priority-based chromosome for solving the bicriteria network optimization problem including maximum flow (MXF) model and minimum cost flow (MCF) model. The objective is to find the set of Pareto optimal solutions that give possible maximum flow with minimum cost. This paper also combines Adaptive Weight Approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. Computer simulations show the several numerical experiments by using some difficult-to-solve network design problems, and show the effectiveness of the proposed method.

Control of Networked Traffic Flow Distribution - A Stochastic Distribution System Perspective

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Hong; Aziz, H M Abdul; Young, Stan

Networked traffic flow is a common scenario for urban transportation, where the distribution of vehicle queues either at controlled intersections or highway segments reflect the smoothness of the traffic flow in the network. At signalized intersections, the traffic queues are controlled by traffic signal control settings and effective traffic lights control would realize both smooth traffic flow and minimize fuel consumption. Funded by the Energy Efficient Mobility Systems (EEMS) program of the Vehicle Technologies Office of the US Department of Energy, we performed a preliminary investigation on the modelling and control framework in context of urban network of signalized intersections.more » In specific, we developed a recursive input-output traffic queueing models. The queue formation can be modeled as a stochastic process where the number of vehicles entering each intersection is a random number. Further, we proposed a preliminary B-Spline stochastic model for a one-way single-lane corridor traffic system based on theory of stochastic distribution control.. It has been shown that the developed stochastic model would provide the optimal probability density function (PDF) of the traffic queueing length as a dynamic function of the traffic signal setting parameters. Based upon such a stochastic distribution model, we have proposed a preliminary closed loop framework on stochastic distribution control for the traffic queueing system to make the traffic queueing length PDF follow a target PDF that potentially realizes the smooth traffic flow distribution in a concerned corridor.« less

The Role of Small Impoundments on Flow Alteration Within River Networks

NASA Astrophysics Data System (ADS)

Brogan, C. O.; Keys, T.; Scott, D.; Burgholzer, R.; Kleiner, J.

2017-12-01

Numerous water quality and quantity models have been established to illustrate the ecologic and hydrologic effects of large reservoirs. Smaller, unregulated ponds are often assumed to have a negligible impact on watershed flow regimes even though they overwhelmingly outnumber larger waterbodies. Individually, these small impoundments impart merely a fraction of the flow alteration larger reservoirs do; however, a network of ponds may act cumulatively to alter the flow regime. Many models have attempted to study smaller impoundments but rely on selectively available rating curves or bathymetry surveys. This study created a generalized process to model impoundments of varying size across a 58 square mile watershed exclusively using satellite imagery and publicly available information as inputs. With information drawn from public Army Corps of Engineers databases and LiDAR surveys, it was found that impoundment surface and drainage area served as useful explanatory variables, capable of predicting both pond bathymetry and outlet structure area across the 37 waterbodies modeled within the study area. Working within a flow routing model with inputs from the Chesapeake Bay HSPF model and verified with USGS gauge data, flow simulations were conducted with increasing number of impoundments to quantify how small ponds affect the overall flow regime. As the total impounded volume increased, simulations showed a notable reduction in both low and peak flows. Medium-sized floods increased as the network of ponds and reservoirs stabilized the catchment's streamflow. The results of this study illustrate the importance of including ponded waters into river corridor models to improve downstream management of both water quantity and quality.

Network traffic behaviour near phase transition point

NASA Astrophysics Data System (ADS)

Lawniczak, A. T.; Tang, X.

2006-03-01

We explore packet traffic dynamics in a data network model near phase transition point from free flow to congestion. The model of data network is an abstraction of the Network Layer of the OSI (Open Systems Interconnect) Reference Model of packet switching networks. The Network Layer is responsible for routing packets across the network from their sources to their destinations and for control of congestion in data networks. Using the model we investigate spatio-temporal packets traffic dynamics near the phase transition point for various network connection topologies, and static and adaptive routing algorithms. We present selected simulation results and analyze them.

A Model of Network Porosity

DTIC Science & Technology

2016-11-09

the model does not become a full probabilistic attack graph analysis of the network , whose data requirements are currently unrealistic. The second...flow. – Untrustworthy persons may intentionally try to exfiltrate known sensitive data to ex- ternal networks . People may also unintentionally leak...section will provide details on the components, procedures, data requirements, and parameters required to instantiate the network porosity model. These

A Minimax Network Flow Model for Characterizing the Impact of Slot Restrictions

NASA Technical Reports Server (NTRS)

Lee, Douglas W.; Patek, Stephen D.; Alexandrov, Natalia; Bass, Ellen J.; Kincaid, Rex K.

2010-01-01

This paper proposes a model for evaluating long-term measures to reduce congestion at airports in the National Airspace System (NAS). This model is constructed with the goal of assessing the global impacts of congestion management strategies, specifically slot restrictions. We develop the Minimax Node Throughput Problem (MINNTHRU), a multicommodity network flow model that provides insight into air traffic patterns when one minimizes the worst-case operation across all airports in a given network. MINNTHRU is thus formulated as a model where congestion arises from network topology. It reflects not market-driven airline objectives, but those of a regulatory authority seeking a distribution of air traffic beneficial to all airports, in response to congestion management measures. After discussing an algorithm for solving MINNTHRU for moderate-sized (30 nodes) and larger networks, we use this model to study the impacts of slot restrictions on the operation of an entire hub-spoke airport network. For both a small example network and a medium-sized network based on 30 airports in the NAS, we use MINNTHRU to demonstrate that increasing the severity of slot restrictions increases the traffic around unconstrained hub airports as well as the worst-case level of operation over all airports.

Network analysis of patient flow in two UK acute care hospitals identifies key sub-networks for A&E performance

PubMed Central

Stringer, Clive; Beeknoo, Neeraj

2017-01-01

The topology of the patient flow network in a hospital is complex, comprising hundreds of overlapping patient journeys, and is a determinant of operational efficiency. To understand the network architecture of patient flow, we performed a data-driven network analysis of patient flow through two acute hospital sites of King’s College Hospital NHS Foundation Trust. Administration databases were queried for all intra-hospital patient transfers in an 18-month period and modelled as a dynamic weighted directed graph. A ‘core’ subnetwork containing only 13–17% of all edges channelled 83–90% of the patient flow, while an ‘ephemeral’ network constituted the remainder. Unsupervised cluster analysis and differential network analysis identified sub-networks where traffic is most associated with A&E performance. Increased flow to clinical decision units was associated with the best A&E performance in both sites. The component analysis also detected a weekend effect on patient transfers which was not associated with performance. We have performed the first data-driven hypothesis-free analysis of patient flow which can enhance understanding of whole healthcare systems. Such analysis can drive transformation in healthcare as it has in industries such as manufacturing. PMID:28968472

Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model.

PubMed

Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2016-10-06

Comparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison. In this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow - or the long-term relative frequency of the transitions that the random walker makes - between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment. Through evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID .

Contraction driven flow in the extended vein networks of Physarum polycephalum

NASA Astrophysics Data System (ADS)

Alim, Karen; Amselem, Gabriel; Peaudecerf, Francois; Pringle, Anne; Brenner, Michael P.

2011-11-01

The true slime mold Physarum polycephalum is a basal organism that forms an extended network of veins to forage for food. P. polycephalum is renown for its adaptive changes of vein structure and morphology in response to food sources. These rearrangements presumably occur to establish an efficient transport and mixing of resources throughout the networks thus presenting a prototype to design transport networks under the constraints of laminar flow. The physical flows of cytoplasmic fluid enclosed by the veins exhibit an oscillatory flow termed ``shuttle streaming.'' The flow exceed by far the volume required for growth at the margins suggesting that the additional energy cost for generating the flow is spent for efficient and/or targeted redistribution of resources. We show that the viscous shuttle flow is driven by the radial contractions of the veins that accompany the streaming. We present a model for the fluid flow and resource dispersion arising due to radial contractions. The transport and mixing properties of the flow are discussed.

Two problems in multiphase biological flows: Blood flow and particulate transport in microvascular network, and pseudopod-driven motility of amoeboid cells

NASA Astrophysics Data System (ADS)

Bagchi, Prosenjit

2016-11-01

In this talk, two problems in multiphase biological flows will be discussed. The first is the direct numerical simulation of whole blood and drug particulates in microvascular networks. Blood in microcirculation behaves as a dense suspension of heterogeneous cells. The erythrocytes are extremely deformable, while inactivated platelets and leukocytes are nearly rigid. A significant progress has been made in recent years in modeling blood as a dense cellular suspension. However, many of these studies considered the blood flow in simple geometry, e.g., straight tubes of uniform cross-section. In contrast, the architecture of a microvascular network is very complex with bifurcating, merging and winding vessels, posing a further challenge to numerical modeling. We have developed an immersed-boundary-based method that can consider blood cell flow in physiologically realistic and complex microvascular network. In addition to addressing many physiological issues related to network hemodynamics, this tool can be used to optimize the transport properties of drug particulates for effective organ-specific delivery. Our second problem is pseudopod-driven motility as often observed in metastatic cancer cells and other amoeboid cells. We have developed a multiscale hydrodynamic model to simulate such motility. We study the effect of cell stiffness on motility as the former has been considered as a biomarker for metastatic potential. Funded by the National Science Foundation.

Flow distribution in parallel microfluidic networks and its effect on concentration gradient

PubMed Central

Guermonprez, Cyprien; Michelin, Sébastien; Baroud, Charles N.

2015-01-01

The architecture of microfluidic networks can significantly impact the flow distribution within its different branches and thereby influence tracer transport within the network. In this paper, we study the flow rate distribution within a network of parallel microfluidic channels with a single input and single output, using a combination of theoretical modeling and microfluidic experiments. Within the ladder network, the flow rate distribution follows a U-shaped profile, with the highest flow rate occurring in the initial and final branches. The contrast with the central branches is controlled by a single dimensionless parameter, namely, the ratio of hydrodynamic resistance between the distribution channel and the side branches. This contrast in flow rates decreases when the resistance of the side branches increases relative to the resistance of the distribution channel. When the inlet flow is composed of two parallel streams, one of which transporting a diffusing species, a concentration variation is produced within the side branches of the network. The shape of this concentration gradient is fully determined by two dimensionless parameters: the ratio of resistances, which determines the flow rate distribution, and the Péclet number, which characterizes the relative speed of diffusion and advection. Depending on the values of these two control parameters, different distribution profiles can be obtained ranging from a flat profile to a step distribution of solute, with well-distributed gradients between these two limits. Our experimental results are in agreement with our numerical model predictions, based on a simplified 2D advection-diffusion problem. Finally, two possible applications of this work are presented: the first one combines the present design with self-digitization principle to encapsulate the controlled concentration in nanoliter chambers, while the second one extends the present design to create a continuous concentration gradient within an open flow chamber. PMID:26487905

Discrete Fracture Network Modeling and Simulation of Subsurface Transport for the Topopah Springs and Lava Flow Aquifers at Pahute Mesa, FY 15 Progress Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makedonska, Nataliia; Kwicklis, Edward Michael; Birdsell, Kay Hanson

This progress report for fiscal year 2015 (FY15) describes the development of discrete fracture network (DFN) models for Pahute Mesa. DFN models will be used to upscale parameters for simulations of subsurface flow and transport in fractured media in Pahute Mesa. The research focuses on modeling of groundwater flow and contaminant transport using DFNs generated according to fracture characteristics observed in the Topopah Spring Aquifer (TSA) and the Lava Flow Aquifer (LFA). This work will improve the representation of radionuclide transport processes in large-scale, regulatory-focused models with a view to reduce pessimistic bounding approximations and provide more realistic contaminant boundarymore » calculations that can be used to describe the future extent of contaminated groundwater. Our goal is to refine a modeling approach that can translate parameters to larger-scale models that account for local-scale flow and transport processes, which tend to attenuate migration.« less

Network analysis applications in hydrology

NASA Astrophysics Data System (ADS)

Price, Katie

2017-04-01

Applied network theory has seen pronounced expansion in recent years, in fields such as epidemiology, computer science, and sociology. Concurrent development of analytical methods and frameworks has increased possibilities and tools available to researchers seeking to apply network theory to a variety of problems. While water and nutrient fluxes through stream systems clearly demonstrate a directional network structure, the hydrological applications of network theory remain under­explored. This presentation covers a review of network applications in hydrology, followed by an overview of promising network analytical tools that potentially offer new insights into conceptual modeling of hydrologic systems, identifying behavioral transition zones in stream networks and thresholds of dynamical system response. Network applications were tested along an urbanization gradient in Atlanta, Georgia, USA. Peachtree Creek and Proctor Creek. Peachtree Creek contains a nest of five long­term USGS streamflow and water quality gages, allowing network application of long­term flow statistics. The watershed spans a range of suburban and heavily urbanized conditions. Summary flow statistics and water quality metrics were analyzed using a suite of network analysis techniques, to test the conceptual modeling and predictive potential of the methodologies. Storm events and low flow dynamics during Summer 2016 were analyzed using multiple network approaches, with an emphasis on tomogravity methods. Results indicate that network theory approaches offer novel perspectives for understanding long­ term and event­based hydrological data. Key future directions for network applications include 1) optimizing data collection, 2) identifying "hotspots" of contaminant and overland flow influx to stream systems, 3) defining process domains, and 4) analyzing dynamic connectivity of various system components, including groundwater­surface water interactions.

Progression of Diabetic Capillary Occlusion: A Model

PubMed Central

Gens, John Scott; Glazier, James A.; Burns, Stephen A.; Gast, Thomas J.

2016-01-01

An explanatory computational model is developed of the contiguous areas of retinal capillary loss which play a large role in diabetic maculapathy and diabetic retinal neovascularization. Strictly random leukocyte mediated capillary occlusion cannot explain the occurrence of large contiguous areas of retinal ischemia. Therefore occlusion of an individual capillary must increase the probability of occlusion of surrounding capillaries. A retinal perifoveal vascular sector as well as a peripheral retinal capillary network and a deleted hexagonal capillary network are modelled using Compucell3D. The perifoveal modelling produces a pattern of spreading capillary loss with associated macular edema. In the peripheral network, spreading ischemia results from the progressive loss of the ladder capillaries which connect peripheral arterioles and venules. System blood flow was elevated in the macular model before a later reduction in flow in cases with progression of capillary occlusions. Simulations differing only in initial vascular network structures but with identical dynamics for oxygen, growth factors and vascular occlusions, replicate key clinical observations of ischemia and macular edema in the posterior pole and ischemia in the retinal periphery. The simulation results also seem consistent with quantitative data on macular blood flow and qualitative data on venous oxygenation. One computational model applied to distinct capillary networks in different retinal regions yielded results comparable to clinical observations in those regions. PMID:27300722

Preferential paths in yield stress fluid flow through a porous medium

NASA Astrophysics Data System (ADS)

Guasto, Jeffrey; Waisbord, Nicolas; Stoop, Norbert; Dunkel, Jörn

2016-11-01

A broad range of biological, geological, and industrial materials with complex rheological properties are subjected to flow through porous media in applications ranging from oil recovery to food manufacturing. In this experimental study, we examine the flow of a model yield stress fluid (Carbopol micro-gel) through a quasi-2D porous medium, fabricated in a microfluidic channel. The flow is driven by applying a precisely-controlled pressure gradient and measured by particle tracking velocimetry, and our observations are complemented by a pore-network model of the yield stress fluid flow. While remaining unyielded at small applied pressure, the micro-gel begins to yield at a critical pressure gradient, exhibiting a single preferential flow path that percolates through the porous medium. As the applied pressure gradient increases, we observe a subsequent coarsening and invasion of the yielded, fluidized network. An examination of both the yielded network topology and pore-scale flow reveal that two cooperative phenomena are involved in sculpting the preferential flow paths: (1) the geometry of the porous microstructure, and (2) the adhesive surface interactions between the micro-gel and substrate. NSF CBET-1511340.

Sample EP Flow Analysis of Severely Damaged Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Werley, Kenneth Alan; McCown, Andrew William

These are slides for a presentation at the working group meeting of the WESC SREMP Software Product Integration Team on sample EP flow analysis of severely damaged networks. The following topics are covered: ERCOT EP Transmission Model; Zoomed in to Houston and Overlaying StreetAtlas; EMPACT Solve/Dispatch/Shedding Options; QACS BaseCase Power Flow Solution; 3 Substation Contingency; Gen. & Load/100 Optimal Dispatch; Dispatch Results; Shed Load for Low V; Network Damage Summary; Estimated Service Areas (Potential); Estimated Outage Areas (potential).

Interfacing a General Purpose Fluid Network Flow Program with the SINDA/G Thermal Analysis Program

NASA Technical Reports Server (NTRS)

Schallhorn, Paul; Popok, Daniel

1999-01-01

A general purpose, one dimensional fluid flow code is currently being interfaced with the thermal analysis program Systems Improved Numerical Differencing Analyzer/Gaski (SINDA/G). The flow code, Generalized Fluid System Simulation Program (GFSSP), is capable of analyzing steady state and transient flow in a complex network. The flow code is capable of modeling several physical phenomena including compressibility effects, phase changes, body forces (such as gravity and centrifugal) and mixture thermodynamics for multiple species. The addition of GFSSP to SINDA/G provides a significant improvement in convective heat transfer modeling for SINDA/G. The interface development is conducted in multiple phases. This paper describes the first phase of the interface which allows for steady and quasi-steady (unsteady solid, steady fluid) conjugate heat transfer modeling.

Network-optimized congestion pricing : a parable, model and algorithm

DOT National Transportation Integrated Search

1995-05-31

This paper recites a parable, formulates a model and devises an algorithm for optimizing tolls on a road network. Such tolls induce an equilibrium traffic flow that is at once system-optimal and user-optimal. The parable introduces the network-wide c...

Experimental testing and modeling analysis of solute mixing at water distribution pipe junctions.

PubMed

Shao, Yu; Jeffrey Yang, Y; Jiang, Lijie; Yu, Tingchao; Shen, Cheng

2014-06-01

Flow dynamics at a pipe junction controls particle trajectories, solute mixing and concentrations in downstream pipes. The effect can lead to different outcomes of water quality modeling and, hence, drinking water management in a distribution network. Here we have investigated solute mixing behavior in pipe junctions of five hydraulic types, for which flow distribution factors and analytical equations for network modeling are proposed. First, based on experiments, the degree of mixing at a cross is found to be a function of flow momentum ratio that defines a junction flow distribution pattern and the degree of departure from complete mixing. Corresponding analytical solutions are also validated using computational-fluid-dynamics (CFD) simulations. Second, the analytical mixing model is further extended to double-Tee junctions. Correspondingly the flow distribution factor is modified to account for hydraulic departure from a cross configuration. For a double-Tee(A) junction, CFD simulations show that the solute mixing depends on flow momentum ratio and connection pipe length, whereas the mixing at double-Tee(B) is well represented by two independent single-Tee junctions with a potential water stagnation zone in between. Notably, double-Tee junctions differ significantly from a cross in solute mixing and transport. However, it is noted that these pipe connections are widely, but incorrectly, simplified as cross junctions of assumed complete solute mixing in network skeletonization and water quality modeling. For the studied pipe junction types, analytical solutions are proposed to characterize the incomplete mixing and hence may allow better water quality simulation in a distribution network. Published by Elsevier Ltd.

Optimal Power Flow in Multiphase Radial Networks with Delta Connections: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, Changhong; Dall-Anese, Emiliano; Low, Steven H.

This paper focuses on multiphase radial distribution networks with mixed wye and delta connections, and proposes a semidefinite relaxation of the AC optimal power flow (OPF) problem. Two multiphase power-flow models are developed to facilitate the integration of delta-connected generation units/loads in the OPF problem. The first model extends traditional branch flow models - and it is referred to as extended branch flow model (EBFM). The second model leverages a linear relationship between per-phase power injections and delta connections, which holds under a balanced voltage approximation (BVA). Based on these models, pertinent OPF problems are formulated and relaxed to semidefinitemore » programs (SDPs). Numerical studies on IEEE test feeders show that SDP relaxations can be solved efficiently by a generic optimization solver. Numerical evidences indicate that solving the resultant SDP under BVA is faster than under EBFM. Moreover, both SDP solutions are numerically exact with respect to voltages and branch flows. It is also shown that the SDP solution under BVA has a small optimality gap, while the BVA model is accurate in the sense that it reflects actual system voltages.« less

A network-analysis-based comparative study of the throughput behavior of polymer melts in barrier screw geometries

NASA Astrophysics Data System (ADS)

Aigner, M.; Köpplmayr, T.; Kneidinger, C.; Miethlinger, J.

2014-05-01

Barrier screws are widely used in the plastics industry. Due to the extreme diversity of their geometries, describing the flow behavior is difficult and rarely done in practice. We present a systematic approach based on networks that uses tensor algebra and numerical methods to model and calculate selected barrier screw geometries in terms of pressure, mass flow, and residence time. In addition, we report the results of three-dimensional simulations using the commercially available ANSYS Polyflow software. The major drawbacks of three-dimensional finite-element-method (FEM) simulations are that they require vast computational power and, large quantities of memory, and consume considerable time to create a geometric model created by computer-aided design (CAD) and complete a flow calculation. Consequently, a modified 2.5-dimensional finite volume method, termed network analysis is preferable. The results obtained by network analysis and FEM simulations correlated well. Network analysis provides an efficient alternative to complex FEM software in terms of computing power and memory consumption. Furthermore, typical barrier screw geometries can be parameterized and used for flow calculations without timeconsuming CAD-constructions.

River flow modeling using artificial neural networks in Kapuas river, West Kalimantan, Indonesia

NASA Astrophysics Data System (ADS)

Herawati, Henny; Suripin, Suharyanto

2017-11-01

Kapuas River is located in the province of West Kalimantan. Kapuas river length is 1,086 km and river basin areas about 100,000 Km2. The availability of river flow data in the Long River and very wide catchments are difficult to obtain, while river flow data are essential for planning waterworks. To predict the water flow in the catchment area requires a lot of hydrology coefficient, so it is very difficult to predict and obtain results that closer to the real conditions. This paper demonstrates that artificial neural network (ANN) could be used to predict the water flow. The ANN technique can be used to predict the incidence of water discharge that occurs in the Kapuas River based on rainfall and evaporation data. With the data available to do training on the artificial neural network model is obtained mean square error (MSE) 0.00007. The river flow predictions could be carried out after the training. The results showed differences in water discharge measurement and prediction of about 4%.

Filament turnover tunes both force generation and dissipation to control long-range flows in a model actomyosin cortex

PubMed Central

McCall, Patrick M.; Gardel, Margaret L.; Munro, Edwin M.

2017-01-01

Actomyosin-based cortical flow is a fundamental engine for cellular morphogenesis. Cortical flows are generated by cross-linked networks of actin filaments and myosin motors, in which active stress produced by motor activity is opposed by passive resistance to network deformation. Continuous flow requires local remodeling through crosslink unbinding and and/or filament disassembly. But how local remodeling tunes stress production and dissipation, and how this in turn shapes long range flow, remains poorly understood. Here, we study a computational model for a cross-linked network with active motors based on minimal requirements for production and dissipation of contractile stress: Asymmetric filament compliance, spatial heterogeneity of motor activity, reversible cross-links and filament turnover. We characterize how the production and dissipation of network stress depend, individually, on cross-link dynamics and filament turnover, and how these dependencies combine to determine overall rates of cortical flow. Our analysis predicts that filament turnover is required to maintain active stress against external resistance and steady state flow in response to external stress. Steady state stress increases with filament lifetime up to a characteristic time τm, then decreases with lifetime above τm. Effective viscosity increases with filament lifetime up to a characteristic time τc, and then becomes independent of filament lifetime and sharply dependent on crosslink dynamics. These individual dependencies of active stress and effective viscosity define multiple regimes of steady state flow. In particular our model predicts that when filament lifetimes are shorter than both τc and τm, the dependencies of effective viscosity and steady state stress on filament turnover cancel one another, such that flow speed is insensitive to filament turnover, and shows a simple dependence on motor activity and crosslink dynamics. These results provide a framework for understanding how animal cells tune cortical flow through local control of network remodeling. PMID:29253848

Coupling Network Computing Applications in Air-cooled Turbine Blades Optimization

NASA Astrophysics Data System (ADS)

Shi, Liang; Yan, Peigang; Xie, Ming; Han, Wanjin

2018-05-01

Through establishing control parameters from blade outside to inside, the parametric design of air-cooled turbine blade based on airfoil has been implemented. On the basis of fast updating structure features and generating solid model, a complex cooling system has been created. Different flow units are modeled into a complex network topology with parallel and serial connection. Applying one-dimensional flow theory, programs have been composed to get pipeline network physical quantities along flow path, including flow rate, pressure, temperature and other parameters. These inner units parameters set as inner boundary conditions for external flow field calculation program HIT-3D by interpolation, thus to achieve full field thermal coupling simulation. Referring the studies in literatures to verify the effectiveness of pipeline network program and coupling algorithm. After that, on the basis of a modified design, and with the help of iSIGHT-FD, an optimization platform had been established. Through MIGA mechanism, the target of enhancing cooling efficiency has been reached, and the thermal stress has been effectively reduced. Research work in this paper has significance for rapid deploying the cooling structure design.

Simplifications for hydronic system models in modelica

DOE PAGES

Jorissen, F.; Wetter, M.; Helsen, L.

2018-01-12

Building systems and their heating, ventilation and air conditioning flow networks, are becoming increasingly complex. Some building energy simulation tools simulate these flow networks using pressure drop equations. These flow network models typically generate coupled algebraic nonlinear systems of equations, which become increasingly more difficult to solve as their sizes increase. This leads to longer computation times and can cause the solver to fail. These problems also arise when using the equation-based modelling language Modelica and Annex 60-based libraries. This may limit the applicability of the library to relatively small problems unless problems are restructured. This paper discusses two algebraicmore » loop types and presents an approach that decouples algebraic loops into smaller parts, or removes them completely. The approach is applied to a case study model where an algebraic loop of 86 iteration variables is decoupled into smaller parts with a maximum of five iteration variables.« less

Modeling Day-to-day Flow Dynamics on Degradable Transport Network

PubMed Central

Gao, Bo; Zhang, Ronghui; Lou, Xiaoming

2016-01-01

Stochastic link capacity degradations are common phenomena in transport network which can cause travel time variations and further can affect travelers’ daily route choice behaviors. This paper formulates a deterministic dynamic model, to capture the day-to-day (DTD) flow evolution process in the presence of degraded link capacity degradations. The aggregated network flow dynamics are driven by travelers’ study of uncertain travel time and their choice of risky routes. This paper applies the exponential-smoothing filter to describe travelers’ study of travel time variations, and meanwhile formulates risk attitude parameter updating equation to reflect travelers’ endogenous risk attitude evolution schema. In addition, this paper conducts theoretical analyses to investigate several significant mathematical characteristics implied in the proposed DTD model, including fixed point existence, uniqueness, stability and irreversibility. Numerical experiments are used to demonstrate the effectiveness of the DTD model and verify some important dynamic system properties. PMID:27959903

Examining Food Risk in the Large using a Complex, Networked System-of-sytems Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ambrosiano, John; Newkirk, Ryan; Mc Donald, Mark P

2010-12-03

The food production infrastructure is a highly complex system of systems. Characterizing the risks of intentional contamination in multi-ingredient manufactured foods is extremely challenging because the risks depend on the vulnerabilities of food processing facilities and on the intricacies of the supply-distribution networks that link them. A pure engineering approach to modeling the system is impractical because of the overall system complexity and paucity of data. A methodology is needed to assess food contamination risk 'in the large', based on current, high-level information about manufacturing facilities, corrunodities and markets, that will indicate which food categories are most at risk ofmore » intentional contamination and warrant deeper analysis. The approach begins by decomposing the system for producing a multi-ingredient food into instances of two subsystem archetypes: (1) the relevant manufacturing and processing facilities, and (2) the networked corrunodity flows that link them to each other and consumers. Ingredient manufacturing subsystems are modeled as generic systems dynamics models with distributions of key parameters that span the configurations of real facilities. Networks representing the distribution systems are synthesized from general information about food corrunodities. This is done in a series of steps. First, probability networks representing the aggregated flows of food from manufacturers to wholesalers, retailers, other manufacturers, and direct consumers are inferred from high-level approximate information. This is followed by disaggregation of the general flows into flows connecting 'large' and 'small' categories of manufacturers, wholesalers, retailers, and consumers. Optimization methods are then used to determine the most likely network flows consistent with given data. Vulnerability can be assessed for a potential contamination point using a modified CARVER + Shock model. Once the facility and corrunodity flow models are instantiated, a risk consequence analysis can be performed by injecting contaminant at chosen points in the system and propagating the event through the overarching system to arrive at morbidity and mortality figures. A generic chocolate snack cake model, consisting of fluid milk, liquid eggs, and cocoa, is described as an intended proof of concept for multi-ingredient food systems. We aim for an eventual tool that can be used directly by policy makers and planners.« less

Modeling a network of turloughs in lowland karst

NASA Astrophysics Data System (ADS)

Gill, L. W.; Naughton, O.; Johnston, P. M.

2013-06-01

In lowland karst areas of Ireland topographic depressions which get intermittently flooded on an annual cycle via groundwater sources are termed turloughs. These are sites of high ecological interest as they have communities and substrate characteristic of wetlands. The flooding in many turlough basins is due to insufficient capacity of the underground karst system to take increased flows following excessive precipitation events, causing the conduit-type network to surcharge. Continuous water level measurements have been taken in five linked turloughs in the lowland karst area of south Galway over a 3 year period. These water level fluctuations, in conjunction with river inputs and precipitation, were then used to elucidate the hydrogeological controls forming the hydraulic system beneath the ground. A model of the karst network has been developed using a pipe network model with the turloughs represented as ponds. The contribution to the karst network from diffuse flow through the epikarst via the matrix and fracture flow has also been modeled using a combination of an infiltration module and network of permeable pipes. The final model was calibrated against two separate hydrological years and in general provided a good simulation for all of the turloughs water levels particularly for the year with one main filling event. The model also accurately picked up the tidal response observed in these turloughs at shallow depths. The model has been used to predict the groundwater discharge to the coast via the main spring which had not heretofore been possible to measure, being below the sea level.

Numerical Simulation of Multiphase Flow in Nanoporous Organic Matter With Application to Coal and Gas Shale Systems

NASA Astrophysics Data System (ADS)

Song, Wenhui; Yao, Jun; Ma, Jingsheng; Sun, Hai; Li, Yang; Yang, Yongfei; Zhang, Lei

2018-02-01

Fluid flow in nanoscale organic pores is known to be affected by fluid transport mechanisms and properties within confined pore space. The flow of gas and water shows notably different characteristics compared with conventional continuum modeling approach. A pore network flow model is developed and implemented in this work. A 3-D organic pore network model is constructed from 3-D image that is reconstructed from 2-D shale SEM image of organic-rich sample. The 3-D pore network model is assumed to be gas-wet and to contain initially gas-filled pores only, and the flow model is concerned with drainage process. Gas flow considers a full range of gas transport mechanisms, including viscous flow, Knudsen diffusion, surface diffusion, ad/desorption, and gas PVT and viscosity using a modified van der Waals' EoS and a correlation for natural gas, respectively. The influences of slip length, contact angle, and gas adsorption layer on water flow are considered. Surface tension considers the pore size and temperature effects. Invasion percolation is applied to calculate gas-water relative permeability. The results indicate that the influences of pore pressure and temperature on water phase relative permeabilities are negligible while gas phase relative permeabilities are relatively larger in higher temperatures and lower pore pressures. Gas phase relative permeability increases while water phase relative permeability decreases with the shrinkage of pore size. This can be attributed to the fact that gas adsorption layer decreases the effective flow area of the water phase and surface diffusion capacity for adsorbed gas is enhanced in small pore size.

Modeling constitutive behavior of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel under hot compression using artificial neural network

NASA Astrophysics Data System (ADS)

Mandal, Sumantra

2006-11-01

ABSTRACT In this paper, an artificial neural network (ANN) model has been suggested to predict the constitutive flow behavior of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel under hot deformation. Hot compression tests in the temperature range 850°C- 1250°C and strain rate range 10-3-102 s-1 were carried out. These tests provided the required data for training the neural network and for subsequent testing. The inputs of the neural network are strain, log strain rate and temperature while flow stress is obtained as output. A three layer feed-forward network with ten neurons in a single hidden layer and back-propagation learning algorithm has been employed. A very good correlation between experimental and predicted result has been obtained. The effect of temperature and strain rate on flow behavior has been simulated employing the ANN model. The results have been found to be consistent with the metallurgical trend. Finally, a monte carlo analiysis has been carried out to find out the noise sensitivity of the developed model.

Modeling of Relation between Transaction Network and Production Activity for Firms

NASA Astrophysics Data System (ADS)

Iino, T.; Iyetomi, H.

Bak et al. [Ricerche Economiche 47 (1993), 3] proposed a self-organizing model for production activity of interacting firms to illustrate how large fluctuations can be triggered by small independent shocks in aggregate economy. This paper develops the original transaction model based on a regular network with layered order flow to accommodate more realistic networks. Simulations in the generalized model so obtained are then carried out for various networks to examine the influence caused by change of the network structure.

Time-Ordered Networks Reveal Limitations to Information Flow in Ant Colonies

PubMed Central

Blonder, Benjamin; Dornhaus, Anna

2011-01-01

Background An important function of many complex networks is to inhibit or promote the transmission of disease, resources, or information between individuals. However, little is known about how the temporal dynamics of individual-level interactions affect these networks and constrain their function. Ant colonies are a model comparative system for understanding general principles linking individual-level interactions to network-level functions because interactions among individuals enable integration of multiple sources of information to collectively make decisions, and allocate tasks and resources. Methodology/Findings Here we show how the temporal and spatial dynamics of such individual interactions provide upper bounds to rates of colony-level information flow in the ant Temnothorax rugatulus. We develop a general framework for analyzing dynamic networks and a mathematical model that predicts how information flow scales with individual mobility and group size. Conclusions/Significance Using thousands of time-stamped interactions between uniquely marked ants in four colonies of a range of sizes, we demonstrate that observed maximum rates of information flow are always slower than predicted, and are constrained by regulation of individual mobility and contact rate. By accounting for the ordering and timing of interactions, we can resolve important difficulties with network sampling frequency and duration, enabling a broader understanding of interaction network functioning across systems and scales. PMID:21625450

Smart social adaptation prevents catastrophic ecological regime shifts in networks of myopic harvesters

NASA Astrophysics Data System (ADS)

Donges, Jonathan; Lucht, Wolfgang; Wiedermann, Marc; Heitzig, Jobst; Kurths, Jürgen

2015-04-01

In the anthropocene, the rise of global social and economic networks with ever increasing connectivity and speed of interactions, e.g., the internet or global financial markets, is a key challenge for sustainable development. The spread of opinions, values or technologies on these networks, in conjunction with the coevolution of the network structures themselves, underlies nexuses of current concern such as anthropogenic climate change, biodiversity loss or global land use change. To isolate and quantitatively study the effects and implications of network dynamics for sustainable development, we propose an agent-based model of information flow on adaptive networks between myopic harvesters that exploit private renewable resources. In this conceptual model of a network of socio-ecological systems, information on management practices flows between agents via boundedly rational imitation depending on the state of the resource stocks involved in an interaction. Agents can also adapt the structure of their social network locally by preferentially connecting to culturally similar agents with identical management practices and, at the same time, disconnecting from culturally dissimilar agents. Investigating in detail the statistical mechanics of this model, we find that an increasing rate of information flow through faster imitation dynamics or growing density of network connectivity leads to a marked increase in the likelihood of environmental resource collapse. However, we show that an optimal rate of social network adaptation can mitigate this negative effect without loss of social cohesion through network fragmentation. Our results highlight that seemingly immaterial network dynamics of spreading opinions or values can be of large relevance for the sustainable management of socio-ecological systems and suggest smartly conservative network adaptation as a strategy for mitigating environmental collapse. Hence, facing the great acceleration, these network dynamics should be more routinely incorporated in standard models of economic development or integrated assessment models used for evaluating anthropogenic climate change.

To trade or not to trade: Link prediction in the virtual water network

NASA Astrophysics Data System (ADS)

Tuninetti, Marta; Tamea, Stefania; Laio, Francesco; Ridolfi, Luca

2017-12-01

In the international trade network, links express the (temporary) presence of a commercial exchange of goods between any two countries. Given the dynamical behaviour of the trade network, where links are created and dismissed every year, predicting the link activation/deactivation is an open research question. Through the international trade network of agricultural goods, water resources are 'virtually' transferred from the country of production to the country of consumption. We propose a novel methodology for link prediction applied to the network of virtual water trade. Starting from the assumption of having links between any two countries, we estimate the associated virtual water flows by means of a gravity-law model using country and link characteristics as drivers. We consider the links with estimated flows higher than 1000 m3/year as active links, while the others as non-active links. Flows traded along estimated active links are then re-estimated using a similar but differently-calibrated gravity-law model. We were able to correctly model 84% of the existing links and 93% of the non-existing links in year 2011. It is worth to note that the predicted active links carry 99% of the global virtual water flow; hence, missed links are mainly those where a minimum volume of virtual water is exchanged. Results indicate that, over the period from 1986 to 2011, population, geographical distances between countries, and agricultural efficiency (through fertilizers use) are the major factors driving the link activation and deactivation. As opposed to other (network-based) models for link prediction, the proposed method is able to reconstruct the network architecture without any prior knowledge of the network topology, using only the nodes and links attributes; it thus represents a general method that can be applied to other networks such as food or value trade networks.

Modeling the Effects of Cu Content and Deformation Variables on the High-Temperature Flow Behavior of Dilute Al-Fe-Si Alloys Using an Artificial Neural Network.

PubMed

Shakiba, Mohammad; Parson, Nick; Chen, X-Grant

2016-06-30

The hot deformation behavior of Al-0.12Fe-0.1Si alloys with varied amounts of Cu (0.002-0.31 wt %) was investigated by uniaxial compression tests conducted at different temperatures (400 °C-550 °C) and strain rates (0.01-10 s -1 ). The results demonstrated that flow stress decreased with increasing deformation temperature and decreasing strain rate, while flow stress increased with increasing Cu content for all deformation conditions studied due to the solute drag effect. Based on the experimental data, an artificial neural network (ANN) model was developed to study the relationship between chemical composition, deformation variables and high-temperature flow behavior. A three-layer feed-forward back-propagation artificial neural network with 20 neurons in a hidden layer was established in this study. The input parameters were Cu content, temperature, strain rate and strain, while the flow stress was the output. The performance of the proposed model was evaluated using the K-fold cross-validation method. The results showed excellent generalization capability of the developed model. Sensitivity analysis indicated that the strain rate is the most important parameter, while the Cu content exhibited a modest but significant influence on the flow stress.

Modeling the Effects of Cu Content and Deformation Variables on the High-Temperature Flow Behavior of Dilute Al-Fe-Si Alloys Using an Artificial Neural Network

PubMed Central

Shakiba, Mohammad; Parson, Nick; Chen, X.-Grant

2016-01-01

The hot deformation behavior of Al-0.12Fe-0.1Si alloys with varied amounts of Cu (0.002–0.31 wt %) was investigated by uniaxial compression tests conducted at different temperatures (400 °C–550 °C) and strain rates (0.01–10 s−1). The results demonstrated that flow stress decreased with increasing deformation temperature and decreasing strain rate, while flow stress increased with increasing Cu content for all deformation conditions studied due to the solute drag effect. Based on the experimental data, an artificial neural network (ANN) model was developed to study the relationship between chemical composition, deformation variables and high-temperature flow behavior. A three-layer feed-forward back-propagation artificial neural network with 20 neurons in a hidden layer was established in this study. The input parameters were Cu content, temperature, strain rate and strain, while the flow stress was the output. The performance of the proposed model was evaluated using the K-fold cross-validation method. The results showed excellent generalization capability of the developed model. Sensitivity analysis indicated that the strain rate is the most important parameter, while the Cu content exhibited a modest but significant influence on the flow stress. PMID:28773658

A network flow model for load balancing in circuit-switched multicomputers

NASA Technical Reports Server (NTRS)

Bokhari, Shahid H.

1990-01-01

In multicomputers that utilize circuit switching or wormhole routing, communication overhead depends largely on link contention - the variation due to distance between nodes is negligible. This has a major impact on the load balancing problem. In this case, there are some nodes with excess load (sources) and others with deficit load (sinks) and it is required to find a matching of sources to sinks that avoids contention. The problem is made complex by the hardwired routing on currently available machines: the user can control only which nodes communicate but not how the messages are routed. Network flow models of message flow in the mesh and the hypercube were developed to solve this problem. The crucial property of these models is the correspondence between minimum cost flows and correctly routed messages. To solve a given load balancing problem, a minimum cost flow algorithm is applied to the network. This permits one to determine efficiently a maximum contention free matching of sources to sinks which, in turn, tells one how much of the given imbalance can be eliminated without contention.

A probabilistic approach to quantifying spatial patterns of flow regimes and network-scale connectivity

NASA Astrophysics Data System (ADS)

Garbin, Silvia; Alessi Celegon, Elisa; Fanton, Pietro; Botter, Gianluca

2017-04-01

The temporal variability of river flow regime is a key feature structuring and controlling fluvial ecological communities and ecosystem processes. In particular, streamflow variability induced by climate/landscape heterogeneities or other anthropogenic factors significantly affects the connectivity between streams with notable implication for river fragmentation. Hydrologic connectivity is a fundamental property that guarantees species persistence and ecosystem integrity in riverine systems. In riverine landscapes, most ecological transitions are flow-dependent and the structure of flow regimes may affect ecological functions of endemic biota (i.e., fish spawning or grazing of invertebrate species). Therefore, minimum flow thresholds must be guaranteed to support specific ecosystem services, like fish migration, aquatic biodiversity and habitat suitability. In this contribution, we present a probabilistic approach aiming at a spatially-explicit, quantitative assessment of hydrologic connectivity at the network-scale as derived from river flow variability. Dynamics of daily streamflows are estimated based on catchment-scale climatic and morphological features, integrating a stochastic, physically based approach that accounts for the stochasticity of rainfall with a water balance model and a geomorphic recession flow model. The non-exceedance probability of ecologically meaningful flow thresholds is used to evaluate the fragmentation of individual stream reaches, and the ensuing network-scale connectivity metrics. A multi-dimensional Poisson Process for the stochastic generation of rainfall is used to evaluate the impact of climate signature on reach-scale and catchment-scale connectivity. The analysis shows that streamflow patterns and network-scale connectivity are influenced by the topology of the river network and the spatial variability of climatic properties (rainfall, evapotranspiration). The framework offers a robust basis for the prediction of the impact of land-use/land-cover changes and river regulation on network-scale connectivity.

Application of Geographical Information System Arc/info Grid-Based Surface Hyrologic Modeling to the Eastern Hellas Region, Mars

NASA Astrophysics Data System (ADS)

Mest, S. C.; Harbert, W.; Crown, D. A.

2001-05-01

Geographical Information System GRID-based raster modeling of surface water runoff in the eastern Hellas region of Mars has been completed. We utilized the 0.0625 by 0.0625 degree topographic map of Mars collected by the Mars Global Surveyor Mars Orbiter Laser Altimeter (MOLA) instrument to model watershed and surface runoff drainage systems. Scientific interpretation of these models with respect to ongoing geological mapping is presented in Mest et al., (2001). After importing a region of approximately 77,000,000 square kilometers into Arc/Info 8.0.2 we reprojected this digital elevation model (DEM) from a Mars sphere into a Mars ellipsoid. Using a simple cylindrical geographic projection and horizontal spatial units of decimal degrees and then an Albers projection with horizontal spatial units of meters, we completed basic hydrological modeling. Analysis of the raw DEM to determine slope, aspect, flow direction, watershed and flow accumulation grids demonstrated the need for correction of single pixel sink anomalies. After analysis of zonal elevation statistics associated with single pixel sinks, which identified 0.8 percent of the DEM points as having undefined surface water flow directions, we filled single pixel sink values of 89 meters or less. This correction is comparable with terrestrial DEMs that contain 0.9 percent to 4.7 percent of cells, which are sinks (Tarboton et al., 1991). The fill-corrected DEM was then used to determine slope, aspect, surface water flow direction and surface water flow accumulation. Within the region of interest 8,776 watersheds were identified. Using Arc/Info GRID flow direction and flow accumulation tools, regions of potential surface water flow accumulation were identified. These networks were then converted to a Strahler ordered stream network. Surface modeling produced Strahler orders one through six. As presented in Mest et al., (2001) comparisons of mapped features may prove compatible with drainage networks and watersheds derived using this methodology. Mest, Scott C., Crown, David A., and Harbert, William, 2001, Highland drainage basins and valley networks in the eastern Hellas Region of Mars, Abstract 1419, Lunar and Planetary Science XXXII Meeting Houston (CDROM). Tarboton D. G., Bras, R. L., and Rodriguez-Iturbe, 1991, On the Extraction of Channel Networks from Digital Elevation Data, Hydrological Processes, v. 5, 81-100. http://viking.eps.pitt.edu

Evaluating the effect of internal aperture variability on transport in kilometer scale discrete fracture networks

DOE PAGES

Makedonska, Nataliia; Hyman, Jeffrey D.; Karra, Satish; ...

2016-08-01

The apertures of natural fractures in fractured rock are highly heterogeneous. However, in-fracture aperture variability is often neglected in flow and transport modeling and individual fractures are assumed to have uniform aperture distribution. The relative importance of in-fracture variability in flow and transport modeling within kilometer-scale fracture networks has been under debate for a long time, since the flow in each single fracture is controlled not only by in-fracture variability but also by boundary conditions. Computational limitations have previously prohibited researchers from investigating the relative importance of in-fracture variability in flow and transport modeling within large-scale fracture networks. We addressmore » this question by incorporating internal heterogeneity of individual fractures into flow simulations within kilometer scale three-dimensional fracture networks, where fracture intensity, P 32 (ratio between total fracture area and domain volume) is between 0.027 and 0.031 [1/m]. The recently developed discrete fracture network (DFN) simulation capability, dfnWorks, is used to generate kilometer scale DFNs that include in-fracture aperture variability represented by a stationary log-normal stochastic field with various correlation lengths and variances. The Lagrangian transport parameters, non-reacting travel time, , and cumulative retention, , are calculated along particles streamlines. As a result, it is observed that due to local flow channeling early particle travel times are more sensitive to in-fracture aperture variability than the tails of travel time distributions, where no significant effect of the in-fracture aperture variations and spatial correlation length is observed.« less

Evaluating the effect of internal aperture variability on transport in kilometer scale discrete fracture networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makedonska, Nataliia; Hyman, Jeffrey D.; Karra, Satish

The apertures of natural fractures in fractured rock are highly heterogeneous. However, in-fracture aperture variability is often neglected in flow and transport modeling and individual fractures are assumed to have uniform aperture distribution. The relative importance of in-fracture variability in flow and transport modeling within kilometer-scale fracture networks has been under debate for a long time, since the flow in each single fracture is controlled not only by in-fracture variability but also by boundary conditions. Computational limitations have previously prohibited researchers from investigating the relative importance of in-fracture variability in flow and transport modeling within large-scale fracture networks. We addressmore » this question by incorporating internal heterogeneity of individual fractures into flow simulations within kilometer scale three-dimensional fracture networks, where fracture intensity, P 32 (ratio between total fracture area and domain volume) is between 0.027 and 0.031 [1/m]. The recently developed discrete fracture network (DFN) simulation capability, dfnWorks, is used to generate kilometer scale DFNs that include in-fracture aperture variability represented by a stationary log-normal stochastic field with various correlation lengths and variances. The Lagrangian transport parameters, non-reacting travel time, , and cumulative retention, , are calculated along particles streamlines. As a result, it is observed that due to local flow channeling early particle travel times are more sensitive to in-fracture aperture variability than the tails of travel time distributions, where no significant effect of the in-fracture aperture variations and spatial correlation length is observed.« less

Analysis and Reduction of Complex Networks Under Uncertainty.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghanem, Roger G

2014-07-31

This effort was a collaboration with Youssef Marzouk of MIT, Omar Knio of Duke University (at the time at Johns Hopkins University) and Habib Najm of Sandia National Laboratories. The objective of this effort was to develop the mathematical and algorithmic capacity to analyze complex networks under uncertainty. Of interest were chemical reaction networks and smart grid networks. The statements of work for USC focused on the development of stochastic reduced models for uncertain networks. The USC team was led by Professor Roger Ghanem and consisted of one graduate student and a postdoc. The contributions completed by the USC teammore » consisted of 1) methodology and algorithms to address the eigenvalue problem, a problem of significance in the stability of networks under stochastic perturbations, 2) methodology and algorithms to characterize probability measures on graph structures with random flows. This is an important problem in characterizing random demand (encountered in smart grid) and random degradation (encountered in infrastructure systems), as well as modeling errors in Markov Chains (with ubiquitous relevance !). 3) methodology and algorithms for treating inequalities in uncertain systems. This is an important problem in the context of models for material failure and network flows under uncertainty where conditions of failure or flow are described in the form of inequalities between the state variables.« less

Influence of geomorphological properties and stage on in-stream travel time

NASA Astrophysics Data System (ADS)

Åkesson, Anna; Wörman, Anders

2014-05-01

The travel time distribution within stream channels is known to vary non-linearly with stage (discharge), depending on the combined effects of geomorphologic, hydrodynamic and kinematic dispersions. This non-linearity, implying that stream network travel time generally decreases with increasing discharge is a factor that is important to account for in hydrological modelling - especially when making peak flow predictions where uncertainty is often high and large values can be at risk. Through hydraulic analysis of several stream networks, we analyse how travel time distributions varies with discharge. The principal focus is the coupling to the geomorphologic properties of stream networks with the final goal being to use this physically based information as a parameterisation tool of the streamflow component of hydrologic models. For each of the studied stream networks, a 1D, steady-state, distributed routing model was set up to determine the velocities in each reach during different flow conditions. Although the model (based in the Manning friction formula) is built on the presence of uniform conditions within sub-reaches, the model can in the stream network scale be considered to include effects of non-uniformity as supercritical conditions in sections of the stream network give rise to backwater effects that reduce the flow velocities in upstream reaches in the stream. By coupling the routing model to a particle tracking routine tracing water "parcels" through the stream network, the average travel time within the stream network can be determined quantitatively for different flow conditions. The data used to drive the model is digitised stream network maps, topographical data (DEMs). The model is not calibrated in any way, but is run for with different sets of parameters representing a span of possible friction coefficients and cross-sectional geometries as this information is not generally known. The routing model is implemented in several different stream networks (representing catchments of the spatial scale of a few hundred km2) in different geographic regions in Sweden displaying different geomorphological properties. Results show that the geomorphological properties (data that is often available in the form of maps and/or DEMs) of individual stream networks have major influence on the stream network travel times. By coupling the geomorphological information to general expressions for stage dependency, catchment-specific relationships of how the travel times within stream networks can be determined. Basing the parameterisation procedure of a hydrological model in physical catchment properties and process understanding rather than statistical parameterisation (based in how a catchment has responded in the past) - is believed to lead to more reliable hydrological predictions - during extreme conditions as well as during changing conditions such as climate change and landscape modifications, and/or when making predictions in ungauged basins.

Artificial neural network analysis based on genetic algorithm to predict the performance characteristics of a cross flow cooling tower

NASA Astrophysics Data System (ADS)

Wu, Jiasheng; Cao, Lin; Zhang, Guoqiang

2018-02-01

Cooling tower of air conditioning has been widely used as cooling equipment, and there will be broad application prospect if it can be reversibly used as heat source under heat pump heating operation condition. In view of the complex non-linear relationship of each parameter in the process of heat and mass transfer inside tower, In this paper, the BP neural network model based on genetic algorithm optimization (GABP neural network model) is established for the reverse use of cross flow cooling tower. The model adopts the structure of 6 inputs, 13 hidden nodes and 8 outputs. With this model, the outlet air dry bulb temperature, wet bulb temperature, water temperature, heat, sensible heat ratio and heat absorbing efficiency, Lewis number, a total of 8 the proportion of main performance parameters were predicted. Furthermore, the established network model is used to predict the water temperature and heat absorption of the tower at different inlet temperatures. The mean relative error MRE between BP predicted value and experimental value are 4.47%, 3.63%, 2.38%, 3.71%, 6.35%,3.14%, 13.95% and 6.80% respectively; the mean relative error MRE between GABP predicted value and experimental value are 2.66%, 3.04%, 2.27%, 3.02%, 6.89%, 3.17%, 11.50% and 6.57% respectively. The results show that the prediction results of GABP network model are better than that of BP network model; the simulation results are basically consistent with the actual situation. The GABP network model can well predict the heat and mass transfer performance of the cross flow cooling tower.

Optimal topology to minimizing congestion in connected communication complex network

NASA Astrophysics Data System (ADS)

Benyoussef, M.; Ez-Zahraouy, H.; Benyoussef, A.

In this paper, a new model of the interdependent complex network is proposed, based on two assumptions that (i) the capacity of a node depends on its degree, and (ii) the traffic load depends on the distribution of the links in the network. Based on these assumptions, the presented model proposes a method of connection not based on the node having a higher degree but on the region containing hubs. It is found that the final network exhibits two kinds of degree distribution behavior, depending on the kind and the way of the connection. This study reveals a direct relation between network structure and traffic flow. It is found that pc the point of transition between the free flow and the congested phase depends on the network structure and the degree distribution. Moreover, this new model provides an improvement in the traffic compared to the results found in a single network. The same behavior of degree distribution found in a BA network and observed in the real world is obtained; except for this model, the transition point between the free phase and congested phase is much higher than the one observed in a network of BA, for both static and dynamic protocols.

Prediction of friction factor of pure water flowing inside vertical smooth and microfin tubes by using artificial neural networks

NASA Astrophysics Data System (ADS)

Çebi, A.; Akdoğan, E.; Celen, A.; Dalkilic, A. S.

2017-02-01

An artificial neural network (ANN) model of friction factor in smooth and microfin tubes under heating, cooling and isothermal conditions was developed in this study. Data used in ANN was taken from a vertically positioned heat exchanger experimental setup. Multi-layered feed-forward neural network with backpropagation algorithm, radial basis function networks and hybrid PSO-neural network algorithm were applied to the database. Inputs were the ratio of cross sectional flow area to hydraulic diameter, experimental condition number depending on isothermal, heating, or cooling conditions and mass flow rate while the friction factor was the output of the constructed system. It was observed that such neural network based system could effectively predict the friction factor values of the flows regardless of their tube types. A dependency analysis to determine the strongest parameter that affected the network and database was also performed and tube geometry was found to be the strongest parameter of all as a result of analysis.

Evolution of weighted complex bus transit networks with flow

NASA Astrophysics Data System (ADS)

Huang, Ailing; Xiong, Jie; Shen, Jinsheng; Guan, Wei

2016-02-01

Study on the intrinsic properties and evolutional mechanism of urban public transit networks (PTNs) has great significance for transit planning and control, particularly considering passengers’ dynamic behaviors. This paper presents an empirical analysis for exploring the complex properties of Beijing’s weighted bus transit network (BTN) based on passenger flow in L-space, and proposes a bi-level evolution model to simulate the development of transit routes from the view of complex network. The model is an iterative process that is driven by passengers’ travel demands and dual-controlled interest mechanism, which is composed of passengers’ spatio-temporal requirements and cost constraint of transit agencies. Also, the flow’s dynamic behaviors, including the evolutions of travel demand, sectional flow attracted by a new link and flow perturbation triggered in nearby routes, are taken into consideration in the evolutional process. We present the numerical experiment to validate the model, where the main parameters are estimated by using distribution functions that are deduced from real-world data. The results obtained have proven that our model can generate a BTN with complex properties, such as the scale-free behavior or small-world phenomenon, which shows an agreement with our empirical results. Our study’s results can be exploited to optimize the real BTN’s structure and improve the network’s robustness.

Bifurcations: Focal Points of Particle Adhesion in Microvascular Networks

PubMed Central

Prabhakarpandian, Balabhaskar; Wang, Yi; Rea-Ramsey, Angela; Sundaram, Shivshankar; Kiani, Mohammad F.; Pant, Kapil

2011-01-01

Objective Particle adhesion in vivo is dependent on microcirculation environment which features unique anatomical (bifurcations, tortuosity, cross-sectional changes) and physiological (complex hemodynamics) characteristics. The mechanisms behind these complex phenomena are not well understood. In this study, we used a recently developed in vitro model of microvascular networks, called Synthetic Microvascular Network, for characterizing particle adhesion patterns in the microcirculation. Methods Synthetic microvascular networks were fabricated using soft lithography processes followed by particle adhesion studies using avidin and biotin-conjugated microspheres. Particle adhesion patterns were subsequently analyzed using CFD based modeling. Results Experimental and modeling studies highlighted the complex and heterogeneous fluid flow patterns encountered by particles in microvascular networks resulting in significantly higher propensity of adhesion (>1.5X) near bifurcations compared to the branches of the microvascular networks. Conclusion Bifurcations are the focal points of particle adhesion in microvascular networks. Changing flow patterns and morphology near bifurcations are the primary factors controlling the preferential adhesion of functionalized particles in microvascular networks. Synthetic microvascular networks provide an in vitro framework for understanding particle adhesion. PMID:21418388

Bandwidth turbulence control based on flow community structure in the Internet

NASA Astrophysics Data System (ADS)

Wu, Xiaoyu; Gu, Rentao; Ji, Yuefeng

2016-10-01

Bursty flows vary rapidly in short period of time, and cause fierce bandwidth turbulence in the Internet. In this letter, we model the flow bandwidth turbulence process by constructing a flow interaction network (FIN network), with nodes representing flows and edges denoting bandwidth interactions among them. To restrain the bandwidth turbulence in FIN networks, an immune control strategy based on flow community structure is proposed. Flows in community boundary positions are immunized to cut off the inter-community turbulence spreading. By applying this control strategy in the first- and the second-level flow communities separately, 97.2% flows can effectively avoid bandwidth variations by immunizing 21% flows, and the average bandwidth variation degree reaches near zero. To achieve a similar result, about 70%-90% immune flows are needed with targeted control strategy based on flow degrees and random control strategy. Moreover, simulation results showed that the control effect of the proposed strategy improves significantly if the immune flow number is relatively smaller in each control step.

A new estimation of equivalent matrix block sizes in fractured media with two-phase flow applications in dual porosity models

NASA Astrophysics Data System (ADS)

Jerbi, Chahir; Fourno, André; Noetinger, Benoit; Delay, Frederick

2017-05-01

Single and multiphase flows in fractured porous media at the scale of natural reservoirs are often handled by resorting to homogenized models that avoid the heavy computations associated with a complete discretization of both fractures and matrix blocks. For example, the two overlapping continua (fractures and matrix) of a dual porosity system are coupled by way of fluid flux exchanges that deeply condition flow at the large scale. This characteristic is a key to realistic flow simulations, especially for multiphase flow as capillary forces and contrasts of fluid mobility compete in the extraction of a fluid from a capacitive matrix then conveyed through the fractures. The exchange rate between fractures and matrix is conditioned by the so-called mean matrix block size which can be viewed as the size of a single matrix block neighboring a single fracture within a mesh of a dual porosity model. We propose a new evaluation of this matrix block size based on the analysis of discrete fracture networks. The fundaments rely upon establishing at the scale of a fractured block the equivalence between the actual fracture network and a Warren and Root network only made of three regularly spaced fracture families parallel to the facets of the fractured block. The resulting matrix block sizes are then compared via geometrical considerations and two-phase flow simulations to the few other available methods. It is shown that the new method is stable in the sense it provides accurate sizes irrespective of the type of fracture network investigated. The method also results in two-phase flow simulations from dual porosity models very close to that from references calculated in finely discretized networks. Finally, calculations of matrix block sizes by this new technique reveal very rapid, which opens the way to cumbersome applications such as preconditioning a dual porosity approach applied to regional fractured reservoirs.

System and Method for Modeling the Flow Performance Features of an Object

NASA Technical Reports Server (NTRS)

Jorgensen, Charles (Inventor); Ross, James (Inventor)

1997-01-01

The method and apparatus includes a neural network for generating a model of an object in a wind tunnel from performance data on the object. The network is trained from test input signals (e.g., leading edge flap position, trailing edge flap position, angle of attack, and other geometric configurations, and power settings) and test output signals (e.g., lift, drag, pitching moment, or other performance features). In one embodiment, the neural network training method employs a modified Levenberg-Marquardt optimization technique. The model can be generated 'real time' as wind tunnel testing proceeds. Once trained, the model is used to estimate performance features associated with the aircraft given geometric configuration and/or power setting input. The invention can also be applied in other similar static flow modeling applications in aerodynamics, hydrodynamics, fluid dynamics, and other such disciplines. For example, the static testing of cars, sails, and foils, propellers, keels, rudders, turbines, fins, and the like, in a wind tunnel, water trough, or other flowing medium.

Modelling the Flow Stress of Alloy 316L using a Multi-Layered Feed Forward Neural Network with Bayesian Regularization

NASA Astrophysics Data System (ADS)

Abiriand Bhekisipho Twala, Olufunminiyi

2017-08-01

In this paper, a multilayer feedforward neural network with Bayesian regularization constitutive model is developed for alloy 316L during high strain rate and high temperature plastic deformation. The input variables are strain rate, temperature and strain while the output value is the flow stress of the material. The results show that the use of Bayesian regularized technique reduces the potential of overfitting and overtraining. The prediction quality of the model is thereby improved. The model predictions are in good agreement with experimental measurements. The measurement data used for the network training and model comparison were taken from relevant literature. The developed model is robust as it can be generalized to deformation conditions slightly below or above the training dataset.

International Neural Network Society Annual Meeting (1994) Held in San Diego, California on 5-9 June 1994. Volume 3.

DTIC Science & Technology

1994-06-09

Competitive Neural Nets Speed Complex Fluid Flow Calculations 1-366 T. Long, E. Hanzevack Neural Networks for Steam Boiler MIMO Modeling and Advisory Control...Gallinr The Cochlear Nucleus and Primary Cortex as a Sequence of Distributed Neural Filters in Phoneme IV-607 Perception J. Antrobus, C. Tarshish, S...propulsion linear model, a fuel flow actuator modelled as a linear second order system with position and rate limits, and a thrust vectoring actuator

Single- and two-phase flow in microfluidic porous media analogs based on Voronoi tessellation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Mengjie; Xiao, Feng; Johnson-Paben, Rebecca

2012-01-01

The objective of this study was to create a microfluidic model of complex porous media for studying single and multiphase flows. Most experimental porous media models consist of periodic geometries that lend themselves to comparison with well-developed theoretical predictions. However, most real porous media such as geological formations and biological tissues contain a degree of randomness and complexity that is not adequately represented in periodic geometries. To design an experimental tool to study these complex geometries, we created microfluidic models of random homogeneous and heterogeneous networks based on Voronoi tessellations. These networks consisted of approximately 600 grains separated by amore » highly connected network of channels with an overall porosity of 0.11 0.20. We found that introducing heterogeneities in the form of large cavities within the network changed the permeability in a way that cannot be predicted by the classical porosity-permeability relationship known as the Kozeny equation. The values of permeability found in experiments were in excellent agreement with those calculated from three-dimensional lattice Boltzmann simulations. In two-phase flow experiments of oil displacement with water we found that the surface energy of channel walls determined the pattern of water invasion, while the network topology determined the residual oil saturation. These results suggest that complex network topologies lead to fluid flow behavior that is difficult to predict based solely on porosity. The microfluidic models developed in this study using a novel geometry generation algorithm based on Voronoi tessellation are a new experimental tool for studying fluid and solute transport problems within complex porous media.« less

Dynamic switching enables efficient bacterial colonization in flow.

PubMed

Kannan, Anerudh; Yang, Zhenbin; Kim, Minyoung Kevin; Stone, Howard A; Siryaporn, Albert

2018-05-22

Bacteria colonize environments that contain networks of moving fluids, including digestive pathways, blood vasculature in animals, and the xylem and phloem networks in plants. In these flow networks, bacteria form distinct biofilm structures that have an important role in pathogenesis. The physical mechanisms that determine the spatial organization of bacteria in flow are not understood. Here, we show that the bacterium P. aeruginosa colonizes flow networks using a cyclical process that consists of surface attachment, upstream movement, detachment, movement with the bulk flow, and surface reattachment. This process, which we have termed dynamic switching, distributes bacterial subpopulations upstream and downstream in flow through two phases: movement on surfaces and cellular movement via the bulk. The model equations that describe dynamic switching are identical to those that describe dynamic instability, a process that enables microtubules in eukaryotic cells to search space efficiently to capture chromosomes. Our results show that dynamic switching enables bacteria to explore flow networks efficiently, which maximizes dispersal and colonization and establishes the organizational structure of biofilms. A number of eukaryotic and mammalian cells also exhibit movement in two phases in flow, which suggests that dynamic switching is a modality that enables efficient dispersal for a broad range of cell types.

Equivalent model and power flow model for electric railway traction network

NASA Astrophysics Data System (ADS)

Wang, Feng

2018-05-01

An equivalent model of the Cable Traction Network (CTN) considering the distributed capacitance effect of the cable system is proposed. The model can be divided into 110kV side and 27.5kV side two kinds. The 110kV side equivalent model can be used to calculate the power supply capacity of the CTN. The 27.5kV side equivalent model can be used to solve the voltage of the catenary. Based on the equivalent simplified model of CTN, the power flow model of CTN which involves the reactive power compensation coefficient and the interaction of voltage and current, is derived.

Role of Unchannelized Flow in Determining Bifurcation Angle in Distributary Channel Networks

NASA Astrophysics Data System (ADS)

Coffey, T.

2016-02-01

Distributary channel bifurcations on river deltas are important features in both actively prograding river deltas and in lithified deltas within the stratigraphic record. Attributes of distributary channels have long been thought to be defined by flow velocity, grain size and channel aspect ratio where the channel enters the basin. Interestingly, bifurcations in groundwater-fed tributary networks have been shown to grow and bifurcate independent of flow within the exposed channel network. These networks possess a characteristic bifurcation angle of 72°, based on Laplacian flow (water surface concavity equals zero) in the groundwater flow field near tributary channel tips. Based on the tributary channel model, we develop and test the hypothesis that bifurcation angles in distributary channels are likewise dictated by the external flow field, in this case the surface water surrounding the subaqueous portion of distributary channel tips in a deltaic setting. We measured 64 unique distributary bifurcations in an experimental delta, yielding a characteristic angle of 70.2°±2.2° (95% confidence interval), in line with the theoretical prediction for tributary channels. This similarity between bifurcation angles suggests that (A) flow directly outside of the distributary network is Laplacian, (B) the external flow field controls the bifurcation dynamics of distributary channels, and (C) that flow within the channel plays a secondary role in network dynamics.

Flow assignment model for quantitative analysis of diverting bulk freight from road to railway

PubMed Central

Liu, Chang; Wang, Jiaxi; Xiao, Jie; Liu, Siqi; Wu, Jianping; Li, Jian

2017-01-01

Since railway transport possesses the advantage of high volume and low carbon emissions, diverting some freight from road to railway will help reduce the negative environmental impacts associated with transport. This paper develops a flow assignment model for quantitative analysis of diverting truck freight to railway. First, a general network which considers road transportation, railway transportation, handling and transferring is established according to all the steps in the whole transportation process. Then general functions which embody the factors which the shippers will pay attention to when choosing mode and path are formulated. The general functions contain the congestion cost on road, the capacity constraints of railways and freight stations. Based on the general network and general cost function, a user equilibrium flow assignment model is developed to simulate the flow distribution on the general network under the condition that all shippers choose transportation mode and path independently. Since the model is nonlinear and challenging, we adopt a method that uses tangent lines to constitute envelope curve to linearize it. Finally, a numerical example is presented to test the model and show the method of making quantitative analysis of bulk freight modal shift between road and railway. PMID:28771536

Modeling of Propagation of Interacting Cracks Under Hydraulic Pressure Gradient

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Hai; Mattson, Earl Douglas; Podgorney, Robert Karl

A robust and reliable numerical model for fracture initiation and propagation, which includes the interactions among propagating fractures and the coupling between deformation, fracturing and fluid flow in fracture apertures and in the permeable rock matrix, would be an important tool for developing a better understanding of fracturing behaviors of crystalline brittle rocks driven by thermal and (or) hydraulic pressure gradients. In this paper, we present a physics-based hydraulic fracturing simulator based on coupling a quasi-static discrete element model (DEM) for deformation and fracturing with conjugate lattice network flow model for fluid flow in both fractures and porous matrix. Fracturingmore » is represented explicitly by removing broken bonds from the network to represent microcracks. Initiation of new microfractures and growth and coalescence of the microcracks leads to the formation of macroscopic fractures when external and/or internal loads are applied. The coupled DEM-network flow model reproduces realistic growth pattern of hydraulic fractures. In particular, simulation results of perforated horizontal wellbore clearly demonstrate that elastic interactions among multiple propagating fractures, fluid viscosity, strong coupling between fluid pressure fluctuations within fractures and fracturing, and lower length scale heterogeneities, collectively lead to complicated fracturing patterns.« less

Operator splitting method for simulation of dynamic flows in natural gas pipeline networks

DOE PAGES

Dyachenko, Sergey A.; Zlotnik, Anatoly; Korotkevich, Alexander O.; ...

2017-09-19

Here, we develop an operator splitting method to simulate flows of isothermal compressible natural gas over transmission pipelines. The method solves a system of nonlinear hyperbolic partial differential equations (PDEs) of hydrodynamic type for mass flow and pressure on a metric graph, where turbulent losses of momentum are modeled by phenomenological Darcy-Weisbach friction. Mass flow balance is maintained through the boundary conditions at the network nodes, where natural gas is injected or withdrawn from the system. Gas flow through the network is controlled by compressors boosting pressure at the inlet of the adjoint pipe. Our operator splitting numerical scheme ismore » unconditionally stable and it is second order accurate in space and time. The scheme is explicit, and it is formulated to work with general networks with loops. We test the scheme over range of regimes and network configurations, also comparing its performance with performance of two other state of the art implicit schemes.« less

Information transmission and signal permutation in active flow networks

NASA Astrophysics Data System (ADS)

Woodhouse, Francis G.; Fawcett, Joanna B.; Dunkel, Jörn

2018-03-01

Recent experiments show that both natural and artificial microswimmers in narrow channel-like geometries will self-organise to form steady, directed flows. This suggests that networks of flowing active matter could function as novel autonomous microfluidic devices. However, little is known about how information propagates through these far-from-equilibrium systems. Through a mathematical analogy with spin-ice vertex models, we investigate here the input–output characteristics of generic incompressible active flow networks (AFNs). Our analysis shows that information transport through an AFN is inherently different from conventional pressure or voltage driven networks. Active flows on hexagonal arrays preserve input information over longer distances than their passive counterparts and are highly sensitive to bulk topological defects, whose presence can be inferred from marginal input–output distributions alone. This sensitivity further allows controlled permutations on parallel inputs, revealing an unexpected link between active matter and group theory that can guide new microfluidic mixing strategies facilitated by active matter and aid the design of generic autonomous information transport networks.

Inverse modeling of the hydraulic properties of fractured media : development of a flow tomography approach

NASA Astrophysics Data System (ADS)

Bour, O.; Klepikova, M.; Le Borgne, T.; De Dreuzy, J.

2013-12-01

Inverse modeling of hydraulic and geometrical properties of fractured media is a very challenging objective due to the spatial heterogeneity of the medium and the scarcity of data. Here we present a flow tomography approach that permits to characterize the location, the connectivity and the hydraulic properties of main flow paths in fractured media. The accurate characterization of the location, hydraulic properties and connectivity of major fracture zones is essential to model flow and solute transport in fractured media. Cross-borehole flowmeter tests, which consist of measuring changes in vertical borehole flows when pumping a neighboring borehole, were shown to be an efficient technique to provide information on the properties of the flow zones that connect borehole pairs [Paillet, 1998; Le Borgne et al., 2006]. The interpretation of such experiments may however be quite uncertain when multiple connections exist. In this study, we explore the potential of flow tomography (i.e., sequential cross-borehole flowmeter tests) for characterizing aquifer heterogeneity. We first propose a framework for inverting flow and drawdown data to infer fracture connectivity and transmissivities. Here we use a simplified discrete fracture network approach that highlights main connectivity structures. This conceptual model attempts to reproduce fracture network connectivity without taking fracture geometry (length, orientation, dip) into account. We then explore the potential of the method for simplified synthetic fracture network models and quantify the sensitivity of drawdown and borehole flow velocities to the transmissivity of the connecting flowpaths. Flow tomography is expected to be most effective if cross-borehole pumping induces large changes in vertical borehole velocities. The uncertainty of the transmissivity estimates increases for small borehole flow velocities. The uncertainty about the transmissivity of fractures that connect the main flowpath but not the boreholes is generally higher. We demonstrate that successively changing pumping and observation boreholes improves the quality of available information and reduces the indetermination of the problem. The inverse method is validated for different synthetic flow scenarios. It is shown to provide a good estimation of connectivity patterns and transmissivities of main flowpaths. Although the chosen fracture network geometry has been simplified, flow tomography appears to be a promising approach for characterizing connectivity patterns and transmissivities of fractured media.

Scale-dependent genetic structure of the Idaho giant salamander (Dicamptodon aterrimus) in stream networks.

PubMed

Mullen, Lindy B; Arthur Woods, H; Schwartz, Michael K; Sepulveda, Adam J; Lowe, Winsor H

2010-03-01

The network architecture of streams and rivers constrains evolutionary, demographic and ecological processes of freshwater organisms. This consistent architecture also makes stream networks useful for testing general models of population genetic structure and the scaling of gene flow. We examined genetic structure and gene flow in the facultatively paedomorphic Idaho giant salamander, Dicamptodon aterrimus, in stream networks of Idaho and Montana, USA. We used microsatellite data to test population structure models by (i) examining hierarchical partitioning of genetic variation in stream networks; and (ii) testing for genetic isolation by distance along stream corridors vs. overland pathways. Replicated sampling of streams within catchments within three river basins revealed that hierarchical scale had strong effects on genetic structure and gene flow. amova identified significant structure at all hierarchical scales (among streams, among catchments, among basins), but divergence among catchments had the greatest structural influence. Isolation by distance was detected within catchments, and in-stream distance was a strong predictor of genetic divergence. Patterns of genetic divergence suggest that differentiation among streams within catchments was driven by limited migration, consistent with a stream hierarchy model of population structure. However, there was no evidence of migration among catchments within basins, or among basins, indicating that gene flow only counters the effects of genetic drift at smaller scales (within rather than among catchments). These results show the strong influence of stream networks on population structure and genetic divergence of a salamander, with contrasting effects at different hierarchical scales.

A Network Thermodynamic Approach to Compartmental Analysis

PubMed Central

Mikulecky, D. C.; Huf, E. G.; Thomas, S. R.

1979-01-01

We introduce a general network thermodynamic method for compartmental analysis which uses a compartmental model of sodium flows through frog skin as an illustrative example (Huf and Howell, 1974a). We use network thermodynamics (Mikulecky et al., 1977b) to formulate the problem, and a circuit simulation program (ASTEC 2, SPICE2, or PCAP) for computation. In this way, the compartment concentrations and net fluxes between compartments are readily obtained for a set of experimental conditions involving a square-wave pulse of labeled sodium at the outer surface of the skin. Qualitative features of the influx at the outer surface correlate very well with those observed for the short circuit current under another similar set of conditions by Morel and LeBlanc (1975). In related work, the compartmental model is used as a basis for simulation of the short circuit current and sodium flows simultaneously using a two-port network (Mikulecky et al., 1977a, and Mikulecky et al., A network thermodynamic model for short circuit current transients in frog skin. Manuscript in preparation; Gary-Bobo et al., 1978). The network approach lends itself to computation of classic compartmental problems in a simple manner using circuit simulation programs (Chua and Lin, 1975), and it further extends the compartmental models to more complicated situations involving coupled flows and non-linearities such as concentration dependencies, chemical reaction kinetics, etc. PMID:262387

Network thermodynamic approach compartmental analysis. Na+ transients in frog skin.

PubMed

Mikulecky, D C; Huf, E G; Thomas, S R

1979-01-01

We introduce a general network thermodynamic method for compartmental analysis which uses a compartmental model of sodium flows through frog skin as an illustrative example (Huf and Howell, 1974a). We use network thermodynamics (Mikulecky et al., 1977b) to formulate the problem, and a circuit simulation program (ASTEC 2, SPICE2, or PCAP) for computation. In this way, the compartment concentrations and net fluxes between compartments are readily obtained for a set of experimental conditions involving a square-wave pulse of labeled sodium at the outer surface of the skin. Qualitative features of the influx at the outer surface correlate very well with those observed for the short circuit current under another similar set of conditions by Morel and LeBlanc (1975). In related work, the compartmental model is used as a basis for simulation of the short circuit current and sodium flows simultaneously using a two-port network (Mikulecky et al., 1977a, and Mikulecky et al., A network thermodynamic model for short circuit current transients in frog skin. Manuscript in preparation; Gary-Bobo et al., 1978). The network approach lends itself to computation of classic compartmental problems in a simple manner using circuit simulation programs (Chua and Lin, 1975), and it further extends the compartmental models to more complicated situations involving coupled flows and non-linearities such as concentration dependencies, chemical reaction kinetics, etc.

The Social Context of Adolescent Smoking: A Systems Perspective

PubMed Central

Hipp, John R.; Timberlake, David S.

2010-01-01

We used a systems science perspective to examine adolescents' personal networks, school networks, and neighborhoods as a system through which emotional support and peer influence flow, and we sought to determine whether these flows affected past-month smoking at 2 time points, 1994–1995 and 1996. To test relationships, we employed structural equation modeling and used public-use data from the National Longitudinal Study of Adolescent Health (n = 6504). Personal network properties affected past-month smoking at both time points via the flow of emotional support. We observed a feedback loop from personal network properties to emotional support and then to past-month smoking. Past-month smoking at time 1 fed back to positively affect in-degree centrality (i.e., popularity). Findings suggest that networks and neighborhoods in this system positively affected past-month smoking via flows of emotional support. PMID:20466966

Calibration of CORSIM models under saturated traffic flow conditions.

DOT National Transportation Integrated Search

2013-09-01

This study proposes a methodology to calibrate microscopic traffic flow simulation models. : The proposed methodology has the capability to calibrate simultaneously all the calibration : parameters as well as demand patterns for any network topology....

The Dual Role of Cerebral Autoregulation and Collateral Flow in the Circle of Willis After Major Vessel Occlusion.

PubMed

Kennedy McConnell, Flora; Payne, Stephen

2017-08-01

Ischaemic stroke is a leading cause of death and disability. Autoregulation and collateral blood flow through the circle of Willis both play a role in preventing tissue infarction. To investigate the interaction of these mechanisms a one-dimensional steady-state model of the cerebral arterial network was created. Structural variants of the circle of Willis that present particular risk of stroke were recreated by using a network model coupled with: 1) a steady-state physiological model of cerebral autoregulation; and 2) one wherein the cerebral vascular bed was modeled as a passive resistance. Simulations were performed in various conditions of internal carotid and vertebral artery occlusion. Collateral flow alone is unable to ensure adequate blood flow ([Formula: see text] normal flow) to the cerebral arteries in several common variants during internal carotid artery occlusion. However, compared to a passive model, cerebral autoregulation is better able to exploit available collateral flow and maintain flows within [Formula: see text] of baseline. This is true for nearly all configurations. Hence, autoregulation is a crucial facilitator of collateral flow through the circle of Willis. Impairment of this response during ischemia will severely impact cerebral blood flows and tissue survival, and hence, autoregulation should be monitored in this situation.

Modeling of workflow-engaged networks on radiology transfers across a metro network.

PubMed

Camorlinga, Sergio; Schofield, Bruce

2006-04-01

Radiology metro networks bear the challenging proposition of interconnecting several hospitals in a region to provide a comprehensive diagnostic imaging service. Consequences of a poorly designed and implemented metro network could cause delays or no access at all when health care providers try to retrieve medical cases across the network. This could translate into limited diagnostic services to patients, resulting in negative impacts to the patients' medical treatment. A workflow-engaged network (WEN) is a new network paradigm. A WEN appreciates radiology workflows and priorities in using the network. A WEN greatly improves the network performance by guaranteeing that critical image transfers experience minimal delay. It adjusts network settings to ensure the application's requirements are met. This means that high-priority image transfers will have guaranteed and known delay times, whereas lower-priority traffic will have increased delays. This paper introduces a modeling to understand the benefits that WEN brings to a radiology metro network. The modeling uses actual data patterns and flows found in a hospital metro region. The workflows considered are based on the Integrating the Healthcare Enterprise profiles. This modeling has been applied to metropolitan workflows of a health region. The modeling helps identify the kind of metro network that supports data patterns and flows in a metro area. The results of the modeling show that a 155-Mb/s metropolitan area network (MAN) with WEN operates virtually equal to a normal 622-Mb/s MAN without WEN, with potential cost savings for leased line services measured in the millions of dollars per year.

Output-feedback control of combined sewer networks through receding horizon control with moving horizon estimation

NASA Astrophysics Data System (ADS)

Joseph-Duran, Bernat; Ocampo-Martinez, Carlos; Cembrano, Gabriela

2015-10-01

An output-feedback control strategy for pollution mitigation in combined sewer networks is presented. The proposed strategy provides means to apply model-based predictive control to large-scale sewer networks, in-spite of the lack of measurements at most of the network sewers. In previous works, the authors presented a hybrid linear control-oriented model for sewer networks together with the formulation of Optimal Control Problems (OCP) and State Estimation Problems (SEP). By iteratively solving these problems, preliminary Receding Horizon Control with Moving Horizon Estimation (RHC/MHE) results, based on flow measurements, were also obtained. In this work, the RHC/MHE algorithm has been extended to take into account both flow and water level measurements and the resulting control loop has been extensively simulated to assess the system performance according different measurement availability scenarios and rain events. All simulations have been carried out using a detailed physically based model of a real case-study network as virtual reality.

Calibration of water distribution network of the Ramnagar zone in Nagpur City using online pressure and flow data

NASA Astrophysics Data System (ADS)

Jadhao, Ramrao D.; Gupta, Rajesh

2018-03-01

Calibration of hydraulic model of a water distribution network is required to match the model results of flows and pressures with those obtained in the field. This is a challenging task considering the involvement of a large number of parameters. Having more precise data helps in reducing time and results in better calibration as shown herein with a case study of one hydraulic zone served from the Ramnagar Ground Service Reservoir in Nagpur City. Flow and pressure values for the entire day were obtained through data loggers. Network details regarding pipe lengths, diameters, installation year and material were obtained with the largest possible accuracy. Locations of consumers on the network were noted and average nodal consumptions were obtained from the billing records. The non-revenue water losses were uniformly allocated to all junctions. Valve positions and their operating status were noted from the field and used. The pipe roughness coefficients were adjusted to match the model values with field values of pressures at observation nodes by minimizing the sum of square of difference between them. This paper aims at describing the entire process from collection of the required data to the calibration of the network.

Using Inspiration from Synaptic Plasticity Rules to Optimize Traffic Flow in Distributed Engineered Networks.

PubMed

Suen, Jonathan Y; Navlakha, Saket

2017-05-01

Controlling the flow and routing of data is a fundamental problem in many distributed networks, including transportation systems, integrated circuits, and the Internet. In the brain, synaptic plasticity rules have been discovered that regulate network activity in response to environmental inputs, which enable circuits to be stable yet flexible. Here, we develop a new neuro-inspired model for network flow control that depends only on modifying edge weights in an activity-dependent manner. We show how two fundamental plasticity rules, long-term potentiation and long-term depression, can be cast as a distributed gradient descent algorithm for regulating traffic flow in engineered networks. We then characterize, both by simulation and analytically, how different forms of edge-weight-update rules affect network routing efficiency and robustness. We find a close correspondence between certain classes of synaptic weight update rules derived experimentally in the brain and rules commonly used in engineering, suggesting common principles to both.

Variable disparity estimation based intermediate view reconstruction in dynamic flow allocation over EPON-based access networks

NASA Astrophysics Data System (ADS)

Bae, Kyung-Hoon; Lee, Jungjoon; Kim, Eun-Soo

2008-06-01

In this paper, a variable disparity estimation (VDE)-based intermediate view reconstruction (IVR) in dynamic flow allocation (DFA) over an Ethernet passive optical network (EPON)-based access network is proposed. In the proposed system, the stereoscopic images are estimated by a variable block-matching algorithm (VBMA), and they are transmitted to the receiver through DFA over EPON. This scheme improves a priority-based access network by converting it to a flow-based access network with a new access mechanism and scheduling algorithm, and then 16-view images are synthesized by the IVR using VDE. Some experimental results indicate that the proposed system improves the peak-signal-to-noise ratio (PSNR) to as high as 4.86 dB and reduces the processing time to 3.52 s. Additionally, the network service provider can provide upper limits of transmission delays by the flow. The modeling and simulation results, including mathematical analyses, from this scheme are also provided.

Activity flow over resting-state networks shapes cognitive task activations.

PubMed

Cole, Michael W; Ito, Takuya; Bassett, Danielle S; Schultz, Douglas H

2016-12-01

Resting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying the distributed processes that shape localized cognitive task activations may help reveal why resting-state FC is so strongly related to cognitive task activations. We found that estimating task-evoked activity flow (the spread of activation amplitudes) over resting-state FC networks allowed prediction of cognitive task activations in a large-scale neural network model. Applying this insight to empirical functional MRI data, we found that cognitive task activations can be predicted in held-out brain regions (and held-out individuals) via estimated activity flow over resting-state FC networks. This suggests that task-evoked activity flow over intrinsic networks is a large-scale mechanism explaining the relevance of resting-state FC to cognitive task activations.

Activity flow over resting-state networks shapes cognitive task activations

PubMed Central

Cole, Michael W.; Ito, Takuya; Bassett, Danielle S.; Schultz, Douglas H.

2016-01-01

Resting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying the distributed processes that shape localized cognitive task activations may help reveal why resting-state FC is so strongly related to cognitive task activations. We found that estimating task-evoked activity flow (the spread of activation amplitudes) over resting-state FC networks allows prediction of cognitive task activations in a large-scale neural network model. Applying this insight to empirical functional MRI data, we found that cognitive task activations can be predicted in held-out brain regions (and held-out individuals) via estimated activity flow over resting-state FC networks. This suggests that task-evoked activity flow over intrinsic networks is a large-scale mechanism explaining the relevance of resting-state FC to cognitive task activations. PMID:27723746

Stability and dynamical properties of material flow systems on random networks

NASA Astrophysics Data System (ADS)

Anand, K.; Galla, T.

2009-04-01

The theory of complex networks and of disordered systems is used to study the stability and dynamical properties of a simple model of material flow networks defined on random graphs. In particular we address instabilities that are characteristic of flow networks in economic, ecological and biological systems. Based on results from random matrix theory, we work out the phase diagram of such systems defined on extensively connected random graphs, and study in detail how the choice of control policies and the network structure affects stability. We also present results for more complex topologies of the underlying graph, focussing on finitely connected Erdös-Réyni graphs, Small-World Networks and Barabási-Albert scale-free networks. Results indicate that variability of input-output matrix elements, and random structures of the underlying graph tend to make the system less stable, while fast price dynamics or strong responsiveness to stock accumulation promote stability.

Two modelling approaches to water-quality simulation in a flooded iron-ore mine (Saizerais, Lorraine, France): a semi-distributed chemical reactor model and a physically based distributed reactive transport pipe network model.

PubMed

Hamm, V; Collon-Drouaillet, P; Fabriol, R

2008-02-19

The flooding of abandoned mines in the Lorraine Iron Basin (LIB) over the past 25 years has degraded the quality of the groundwater tapped for drinking water. High concentrations of dissolved sulphate have made the water unsuitable for human consumption. This problematic issue has led to the development of numerical tools to support water-resource management in mining contexts. Here we examine two modelling approaches using different numerical tools that we tested on the Saizerais flooded iron-ore mine (Lorraine, France). A first approach considers the Saizerais Mine as a network of two chemical reactors (NCR). The second approach is based on a physically distributed pipe network model (PNM) built with EPANET 2 software. This approach considers the mine as a network of pipes defined by their geometric and chemical parameters. Each reactor in the NCR model includes a detailed chemical model built to simulate quality evolution in the flooded mine water. However, in order to obtain a robust PNM, we simplified the detailed chemical model into a specific sulphate dissolution-precipitation model that is included as sulphate source/sink in both a NCR model and a pipe network model. Both the NCR model and the PNM, based on different numerical techniques, give good post-calibration agreement between the simulated and measured sulphate concentrations in the drinking-water well and overflow drift. The NCR model incorporating the detailed chemical model is useful when a detailed chemical behaviour at the overflow is needed. The PNM incorporating the simplified sulphate dissolution-precipitation model provides better information of the physics controlling the effect of flow and low flow zones, and the time of solid sulphate removal whereas the NCR model will underestimate clean-up time due to the complete mixing assumption. In conclusion, the detailed NCR model will give a first assessment of chemical processes at overflow, and in a second time, the PNM model will provide more detailed information on flow and chemical behaviour (dissolved sulphate concentrations, remaining mass of solid sulphate) in the network. Nevertheless, both modelling methods require hydrological and chemical parameters (recharge flow rate, outflows, volume of mine voids, mass of solids, kinetic constants of the dissolution-precipitation reactions), which are commonly not available for a mine and therefore call for calibration data.

Adding the 'heart' to hanging drop networks for microphysiological multi-tissue experiments.

PubMed

Rismani Yazdi, Saeed; Shadmani, Amir; Bürgel, Sebastian C; Misun, Patrick M; Hierlemann, Andreas; Frey, Olivier

2015-11-07

Microfluidic hanging-drop networks enable culturing and analysis of 3D microtissue spheroids derived from different cell types under controlled perfusion and investigating inter-tissue communication in multi-tissue formats. In this paper we introduce a compact on-chip pumping approach for flow control in hanging-drop networks. The pump includes one pneumatic chamber located directly above one of the hanging drops and uses the surface tension at the liquid-air-interface for flow actuation. Control of the pneumatic protocol provides a wide range of unidirectional pulsatile and continuous flow profiles. With the proposed concept several independent hanging-drop networks can be operated in parallel with only one single pneumatic actuation line at high fidelity. Closed-loop medium circulation between different organ models for multi-tissue formats and multiple simultaneous assays in parallel are possible. Finally, we implemented a real-time feedback control-loop of the pump actuation based on the beating of a human iPS-derived cardiac microtissue cultured in the same system. This configuration allows for simulating physiological effects on the heart and their impact on flow circulation between the organ models on chip.

A Noachian/Hesperian Hiatus and Erosive Reactivation of Martian Valley Networks

NASA Technical Reports Server (NTRS)

Irwin, R. P., III.; Maxwell, T. A.; Howard, A. D.; Craddock, R. A.; Moore, J. M.

2005-01-01

Despite new evidence for persistent flow and sedimentation on early Mars, it remains unclear whether valley networks were active over long geologic timescales (10(exp 5)-10(exp 8) yr), or if flows were persistent only during multiple discrete episodes of moderate (approx. 10(exp 4) yr) to short (<10 yr) duration. Understanding the long-term stability/variability of valley network hydrology would provide an important control on paleoclimate and groundwater models. Here we describe geologic evidence for a hiatus in highland valley network activity while the fretted terrain formed, followed by a discrete reactivation of persistent (but possibly variable) erosive flows. Additional information is included in the original extended abstract.

Hydraulic Fracturing and Production Optimization in Eagle Ford Shale Using Coupled Geomechanics and Fluid Flow Model

NASA Astrophysics Data System (ADS)

Suppachoknirun, Theerapat; Tutuncu, Azra N.

2017-12-01

With increasing production from shale gas and tight oil reservoirs, horizontal drilling and multistage hydraulic fracturing processes have become a routine procedure in unconventional field development efforts. Natural fractures play a critical role in hydraulic fracture growth, subsequently affecting stimulated reservoir volume and the production efficiency. Moreover, the existing fractures can also contribute to the pressure-dependent fluid leak-off during the operations. Hence, a reliable identification of the discrete fracture network covering the zone of interest prior to the hydraulic fracturing design needs to be incorporated into the hydraulic fracturing and reservoir simulations for realistic representation of the in situ reservoir conditions. In this research study, an integrated 3-D fracture and fluid flow model have been developed using a new approach to simulate the fluid flow and deliver reliable production forecasting in naturally fractured and hydraulically stimulated tight reservoirs. The model was created with three key modules. A complex 3-D discrete fracture network model introduces realistic natural fracture geometry with the associated fractured reservoir characteristics. A hydraulic fracturing model is created utilizing the discrete fracture network for simulation of the hydraulic fracture and flow in the complex discrete fracture network. Finally, a reservoir model with the production grid system is used allowing the user to efficiently perform the fluid flow simulation in tight formations with complex fracture networks. The complex discrete natural fracture model, the integrated discrete fracture model for the hydraulic fracturing, the fluid flow model, and the input dataset have been validated against microseismic fracture mapping and commingled production data obtained from a well pad with three horizontal production wells located in the Eagle Ford oil window in south Texas. Two other fracturing geometries were also evaluated to optimize the cumulative production and for the three wells individually. Significant reduction in the production rate in early production times is anticipated in tight reservoirs regardless of the fracturing techniques implemented. The simulations conducted using the alternating fracturing technique led to more oil production than when zipper fracturing was used for a 20-year production period. Yet, due to the decline experienced, the differences in cumulative production get smaller, and the alternating fracturing is not practically implementable while field application of zipper fracturing technique is more practical and widely used.

Evaluation of Spatial Pattern of Altered Flow Regimes on a River Network Using a Distributed Hydrological Model

PubMed Central

Ryo, Masahiro; Iwasaki, Yuichi; Yoshimura, Chihiro; Saavedra V., Oliver C.

2015-01-01

Alteration of the spatial variability of natural flow regimes has been less studied than that of the temporal variability, despite its ecological importance for river ecosystems. Here, we aimed to quantify the spatial patterns of flow regime alterations along a river network in the Sagami River, Japan, by estimating river discharge under natural and altered flow conditions. We used a distributed hydrological model, which simulates hydrological processes spatiotemporally, to estimate 20-year daily river discharge along the river network. Then, 33 hydrologic indices (i.e., Indicators of Hydrologic Alteration) were calculated from the simulated discharge to estimate the spatial patterns of their alterations. Some hydrologic indices were relatively well estimated such as the magnitude and timing of maximum flows, monthly median flows, and the frequency of low and high flow pulses. The accuracy was evaluated with correlation analysis (r > 0.4) and the Kolmogorov–Smirnov test (α = 0.05) by comparing these indices calculated from both observed and simulated discharge. The spatial patterns of the flow regime alterations varied depending on the hydrologic indices. For example, both the median flow in August and the frequency of high flow pulses were reduced by the maximum of approximately 70%, but these strongest alterations were detected at different locations (i.e., on the mainstream and the tributary, respectively). These results are likely caused by different operational purposes of multiple water control facilities. The results imply that the evaluation only at discharge gauges is insufficient to capture the alteration of the flow regime. Our findings clearly emphasize the importance of evaluating the spatial pattern of flow regime alteration on a river network where its discharge is affected by multiple water control facilities. PMID:26207997

Chaos in a dynamic model of traffic flows in an origin-destination network.

PubMed

Zhang, Xiaoyan; Jarrett, David F.

1998-06-01

In this paper we investigate the dynamic behavior of road traffic flows in an area represented by an origin-destination (O-D) network. Probably the most widely used model for estimating the distribution of O-D flows is the gravity model, [J. de D. Ortuzar and L. G. Willumsen, Modelling Transport (Wiley, New York, 1990)] which originated from an analogy with Newton's gravitational law. The conventional gravity model, however, is static. The investigation in this paper is based on a dynamic version of the gravity model proposed by Dendrinos and Sonis by modifying the conventional gravity model [D. S. Dendrinos and M. Sonis, Chaos and Social-Spatial Dynamics (Springer-Verlag, Berlin, 1990)]. The dynamic model describes the variations of O-D flows over discrete-time periods, such as each day, each week, and so on. It is shown that when the dimension of the system is one or two, the O-D flow pattern either approaches an equilibrium or oscillates. When the dimension is higher, the behavior found in the model includes equilibria, oscillations, periodic doubling, and chaos. Chaotic attractors are characterized by (positive) Liapunov exponents and fractal dimensions.(c) 1998 American Institute of Physics.

Simulation of the Flow Through Porous Layers Composed of Converging-Diverging Capillary Fissures or Tubes

NASA Astrophysics Data System (ADS)

Walicka, A.

2018-02-01

In this paper, a porous medium is modelled by a network of converging-diverging capillaries which may be considered as fissures or tubes. This model makes it necessary to consider flows through capillary fissures or tubes. Therefore an analytical method for deriving the relationships between pressure drops, volumetric flow rates and velocities for the following fluids: Newtonian, polar, power-law, pseudoplastic (DeHaven and Sisko types) and Shulmanian, was developed. Next, considerations on the models of pore network for Newtonian and non-Newtonian fluids were presented. The models, similar to the schemes of central finite differences may provide a good basis for transforming the governing equations of a flow through the porous medium into a set of linear or quasi-linear algebraic equations. It was shown that the some coefficients in these algebraic equations depend on the kind of the capillary convergence.

Application of optimization technique for flood damage modeling in river system

NASA Astrophysics Data System (ADS)

Barman, Sangita Deb; Choudhury, Parthasarathi

2018-04-01

A river system is defined as a network of channels that drains different parts of a basin uniting downstream to form a common outflow. An application of various models found in literatures, to a river system having multiple upstream flows is not always straight forward, involves a lengthy procedure; and with non-availability of data sets model calibration and applications may become difficult. In the case of a river system the flow modeling can be simplified to a large extent if the channel network is replaced by an equivalent single channel. In the present work optimization model formulations based on equivalent flow and applications of the mixed integer programming based pre-emptive goal programming model in evaluating flood control alternatives for a real life river system in India are proposed to be covered in the study.

On the linear stability of blood flow through model capillary networks.

PubMed

Davis, Jeffrey M

2014-12-01

Under the approximation that blood behaves as a continuum, a numerical implementation is presented to analyze the linear stability of capillary blood flow through model tree and honeycomb networks that are based on the microvascular structures of biological tissues. The tree network is comprised of a cascade of diverging bifurcations, in which a parent vessel bifurcates into two descendent vessels, while the honeycomb network also contains converging bifurcations, in which two parent vessels merge into one descendent vessel. At diverging bifurcations, a cell partitioning law is required to account for the nonuniform distribution of red blood cells as a function of the flow rate of blood into each descendent vessel. A linearization of the governing equations produces a system of delay differential equations involving the discharge hematocrit entering each network vessel and leads to a nonlinear eigenvalue problem. All eigenvalues in a specified region of the complex plane are captured using a transformation based on contour integrals to construct a linear eigenvalue problem with identical eigenvalues, which are then determined using a standard QR algorithm. The predicted value of the dimensionless exponent in the cell partitioning law at the instability threshold corresponds to a supercritical Hopf bifurcation in numerical simulations of the equations governing unsteady blood flow. Excellent agreement is found between the predictions of the linear stability analysis and nonlinear simulations. The relaxation of the assumption of plug flow made in previous stability analyses typically has a small, quantitative effect on the stability results that depends on the specific network structure. This implementation of the stability analysis can be applied to large networks with arbitrary structure provided only that the connectivity among the network segments is known.

Macroscopic modeling of freeway traffic using an artificial neural network

DOT National Transportation Integrated Search

1997-01-01

Traffic flow on freeways is a complex process that often is described by a set of highly nonlinear, dynamic equations in the form of a macroscopic traffic flow model. However, some of the existing macroscopic models have been found to exhibit instabi...

Impact analysis of two kinds of failure strategies in Beijing road transportation network

NASA Astrophysics Data System (ADS)

Zhang, Zundong; Xu, Xiaoyang; Zhang, Zhaoran; Zhou, Huijuan

The Beijing road transportation network (BRTN), as a large-scale technological network, exhibits very complex and complicate features during daily periods. And it has been widely highlighted that how statistical characteristics (i.e. average path length and global network efficiency) change while the network evolves. In this paper, by using different modeling concepts, three kinds of network models of BRTN namely the abstract network model, the static network model with road mileage as weights and the dynamic network model with travel time as weights — are constructed, respectively, according to the topological data and the real detected flow data. The degree distribution of the three kinds of network models are analyzed, which proves that the urban road infrastructure network and the dynamic network behavior like scale-free networks. By analyzing and comparing the important statistical characteristics of three models under random attacks and intentional attacks, it shows that the urban road infrastructure network and the dynamic network of BRTN are both robust and vulnerable.

An Emergent Bifurcation Angle on River Deltas

NASA Astrophysics Data System (ADS)

Shaw, J.; Coffey, T.

2017-12-01

Distributary channel bifurcations on river deltas are important features that control water, sediment, and nutrient routing and can dictate large-scale stratigraphic heterogeneity. We use theory originally developed for a special case of tributary networks to understand the dynamics of distributary channel bifurcations. Interestingly, bifurcations in groundwater-fed tributary networks have been shown to evolve dependent on the diffusive flow field outside the network. These networks possess a characteristic bifurcation angle of 72°, due to Laplacian flow in the groundwater flow field near tributary channel tips (gradient2h2=0, where h is water surface elevation). We develop and test the hypothesis that bifurcation angles in distributary channel networks are likewise dictated by the external flow field, in this case the shallow surface water surrounding the subaqueous portion of distributary channel bifurcations in a deltaic setting. We measured 130 unique distributary channel bifurcations in a single experimental delta and in 10 natural deltas, yielding a mean angle of 70.35°±2.59° (95% confidence interval), in line with the theoretical prediction. These data and hydrodynamic scaling arguments convince us that distributary network formation can result simply from the coupling of (Laplacian) extra-channel flow to channels along subaqueous channel networks. The simplicity of this model provides new insight into distributary network formation and its geomorphic and stratigraphic consequences.

Better Water Demand and Pipe Description Improve the Distribution Network Modeling Results

EPA Science Inventory

Distribution system modeling simplifies pipe network in skeletonization and simulates the flow and water quality by using generalized water demand patterns. While widely used, the approach has not been examined fully on how it impacts the modeling fidelity. This study intends to ...

Cellular automata model for traffic flow at intersections in internet of vehicles

NASA Astrophysics Data System (ADS)

Zhao, Han-Tao; Liu, Xin-Ru; Chen, Xiao-Xu; Lu, Jian-Cheng

2018-03-01

Considering the effect of the front vehicle's speed, the influence of the brake light and the conflict of the traffic flow, we established a cellular automata model called CE-NS for traffic flow at the intersection in the non-vehicle networking environment. According to the information interaction of Internet of Vehicles (IoV), introducing parameters describing the congestion and the accurate speed of the front vehicle into the CE-NS model, we improved the rules of acceleration, deceleration and conflict, and finally established a cellular automata model for traffic flow at intersections of IoV. The relationship between traffic parameters such as vehicle speed, flow and average travel time is obtained by numerical simulation of two models. Based on this, we compared the traffic situation of the non-vehicle networking environment with conditions of IoV environment, and analyzed the influence of the different degree of IoV on the traffic flow. The results show that the traffic speed is increased, the travel time is reduced, the flux of intersections is increased and the traffic flow is more smoothly under IoV environment. After the vehicle which achieves IoV reaches a certain proportion, the operation effect of the traffic flow begins to improve obviously.

Neural network computer simulation of medical aerosols.

PubMed

Richardson, C J; Barlow, D J

1996-06-01

Preliminary investigations have been conducted to assess the potential for using artificial neural networks to simulate aerosol behaviour, with a view to employing this type of methodology in the evaluation and design of pulmonary drug-delivery systems. Details are presented of the general purpose software developed for these tasks; it implements a feed-forward back-propagation algorithm with weight decay and connection pruning, the user having complete run-time control of the network architecture and mode of training. A series of exploratory investigations is then reported in which different network structures and training strategies are assessed in terms of their ability to simulate known patterns of fluid flow in simple model systems. The first of these involves simulations of cellular automata-generated data for fluid flow through a partially obstructed two-dimensional pipe. The artificial neural networks are shown to be highly successful in simulating the behaviour of this simple linear system, but with important provisos relating to the information content of the training data and the criteria used to judge when the network is properly trained. A second set of investigations is then reported in which similar networks are used to simulate patterns of fluid flow through aerosol generation devices, using training data furnished through rigorous computational fluid dynamics modelling. These more complex three-dimensional systems are modelled with equal success. It is concluded that carefully tailored, well trained networks could provide valuable tools not just for predicting but also for analysing the spatial dynamics of pharmaceutical aerosols.

Numerical study of groundwater flow cycling controlled by seawater/freshwater interaction in a coastal karst aquifer through conduit network using CFPv2.

PubMed

Xu, Zexuan; Hu, Bill X; Davis, Hal; Kish, Stephen

2015-11-01

In this study, a groundwater flow cycling in a karst springshed and an interaction between two springs, Spring Creek Springs and Wakulla Springs, through a subground conduit network are numerically simulated using CFPv2, the latest research version of MODFLOW-CFP (Conduit Flow Process). The Spring Creek Springs and Wakulla Springs, located in a marine estuary and 11 miles inland, respectively, are two major groundwater discharge spots in the Woodville Karst Plain (WKP), North Florida, USA. A three-phase conceptual model of groundwater flow cycling between the two springs and surface water recharge from a major surface creek (Lost Creek) was proposed in various rainfall conditions. A high permeable subground karst conduit network connecting the two springs was found by tracer tests and cave diving. Flow rate of discharge, salinity, sea level and tide height at Spring Creek Springs could significantly affect groundwater discharge and water stage at Wakulla Springs simultaneously. Based on the conceptual model, a numerical hybrid discrete-continuum groundwater flow model is developed using CFPv2 and calibrated by field measurements. Non-laminar flows in conduits and flow exchange between conduits and porous medium are implemented in the hybrid coupling numerical model. Time-variable salinity and equivalent freshwater head boundary conditions at the submarine spring as well as changing recharges have significant impacts on seawater/freshwater interaction and springs' discharges. The developed numerical model is used to simulate the dynamic hydrological process and quantitatively represent the three-phase conceptual model from June 2007 to June 2010. Simulated results of two springs' discharges match reasonably well to measurements with correlation coefficients 0.891 and 0.866 at Spring Creeks Springs and Wakulla Springs, respectively. The impacts of sea level rise on regional groundwater flow field and relationship between the inland springs and submarine springs are evaluated as well in this study. Copyright © 2015 Elsevier B.V. All rights reserved.

Numerical study of groundwater flow cycling controlled by seawater/freshwater interaction in a coastal karst aquifer through conduit network using CFPv2

NASA Astrophysics Data System (ADS)

Xu, Zexuan; Hu, Bill X.; Davis, Hal; Kish, Stephen

2015-11-01

In this study, a groundwater flow cycling in a karst springshed and an interaction between two springs, Spring Creek Springs and Wakulla Springs, through a subground conduit network are numerically simulated using CFPv2, the latest research version of MODFLOW-CFP (Conduit Flow Process). The Spring Creek Springs and Wakulla Springs, located in a marine estuary and 11 miles inland, respectively, are two major groundwater discharge spots in the Woodville Karst Plain (WKP), North Florida, USA. A three-phase conceptual model of groundwater flow cycling between the two springs and surface water recharge from a major surface creek (Lost Creek) was proposed in various rainfall conditions. A high permeable subground karst conduit network connecting the two springs was found by tracer tests and cave diving. Flow rate of discharge, salinity, sea level and tide height at Spring Creek Springs could significantly affect groundwater discharge and water stage at Wakulla Springs simultaneously. Based on the conceptual model, a numerical hybrid discrete-continuum groundwater flow model is developed using CFPv2 and calibrated by field measurements. Non-laminar flows in conduits and flow exchange between conduits and porous medium are implemented in the hybrid coupling numerical model. Time-variable salinity and equivalent freshwater head boundary conditions at the submarine spring as well as changing recharges have significant impacts on seawater/freshwater interaction and springs' discharges. The developed numerical model is used to simulate the dynamic hydrological process and quantitatively represent the three-phase conceptual model from June 2007 to June 2010. Simulated results of two springs' discharges match reasonably well to measurements with correlation coefficients 0.891 and 0.866 at Spring Creeks Springs and Wakulla Springs, respectively. The impacts of sea level rise on regional groundwater flow field and relationship between the inland springs and submarine springs are evaluated as well in this study.

Analytical transport network theory to guide the design of 3-D microstructural networks in energy materials: Part 1. Flow without reactions

NASA Astrophysics Data System (ADS)

Cocco, Alex P.; Nakajo, Arata; Chiu, Wilson K. S.

2017-12-01

We present a fully analytical, heuristic model - the "Analytical Transport Network Model" - for steady-state, diffusive, potential flow through a 3-D network. Employing a combination of graph theory, linear algebra, and geometry, the model explicitly relates a microstructural network's topology and the morphology of its channels to an effective material transport coefficient (a general term meant to encompass, e.g., conductivity or diffusion coefficient). The model's transport coefficient predictions agree well with those from electrochemical fin (ECF) theory and finite element analysis (FEA), but are computed 0.5-1.5 and 5-6 orders of magnitude faster, respectively. In addition, the theory explicitly relates a number of morphological and topological parameters directly to the transport coefficient, whereby the distributions that characterize the structure are readily available for further analysis. Furthermore, ATN's explicit development provides insight into the nature of the tortuosity factor and offers the potential to apply theory from network science and to consider the optimization of a network's effective resistance in a mathematically rigorous manner. The ATN model's speed and relative ease-of-use offer the potential to aid in accelerating the design (with respect to transport), and thus reducing the cost, of energy materials.

Network model and short circuit program for the Kennedy Space Center electric power distribution system

NASA Technical Reports Server (NTRS)

1976-01-01

Assumptions made and techniques used in modeling the power network to the 480 volt level are discussed. Basic computational techniques used in the short circuit program are described along with a flow diagram of the program and operational procedures. Procedures for incorporating network changes are included in this user's manual.

Link prediction in the network of global virtual water trade

NASA Astrophysics Data System (ADS)

Tuninetti, Marta; Tamea, Stefania; Laio, Francesco; Ridolfi, Luca

2016-04-01

Through the international food-trade, water resources are 'virtually' transferred from the country of production to the country of consumption. The international food-trade, thus, implies a network of virtual water flows from exporting to importing countries (i.e., nodes). Given the dynamical behavior of the network, where food-trade relations (i.e., links) are created and dismissed every year, link prediction becomes a challenge. In this study, we propose a novel methodology for link prediction in the virtual water network. The model aims at identifying the main factors (among 17 different variables) driving the creation of a food-trade relation between any two countries, along the period between 1986 and 2011. Furthermore, the model can be exploited to investigate the network configuration in the future, under different possible (climatic and demographic) scenarios. The model grounds the existence of a link between any two nodes on the link weight (i.e., the virtual water flow): a link exists when the nodes exchange a minimum (fixed) volume of virtual water. Starting from a set of potential links between any two nodes, we fit the associated virtual water flows (both the real and the null ones) by means of multivariate linear regressions. Then, links with estimated flows higher than a minimum value (i.e., threshold) are considered active-links, while the others are non-active ones. The discrimination between active and non-active links through the threshold introduces an error (called link-prediction error) because some real links are lost (i.e., missed links) and some non-existing links (i.e., spurious links) are inevitably introduced in the network. The major drivers are those significantly minimizing the link-prediction error. Once the structure of the unweighted virtual water network is known, we apply, again, linear regressions to assess the major factors driving the fluxes traded along (modelled) active-links. Results indicate that, on the one hand, population and fertilizer use, together with link properties (such as the distance between nodes), are the major factors driving the links creation; on the other hand, population, distance, and gross domestic product are essential to model the flux entity. The results are promising since the model is able to correctly predict the 85% of the 16422 food-trade links (15% are missed), by spuriously adding to the real network only the 5% of non-existing links. The link-prediction error, evaluated as the sum of the percentage of missed and spurious links, is around 20% and it is constant over the study period. Only the 0.01% of the global virtual water flow is traded along missed links and an even lower flow is added by the spurious links (0.003%).

A comparative study of discrete fracture network and equivalent continuum models for simulating flow and transport in the far field of a hypothetical nuclear waste repository in crystalline host rock

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hadgu, Teklu; Karra, Satish; Kalinina, Elena

One of the major challenges of simulating flow and transport in the far field of a geologic repository in crystalline host rock is related to reproducing the properties of the fracture network over the large volume of rock with sparse fracture characterization data. Various approaches have been developed to simulate flow and transport through the fractured rock. The approaches can be broadly divided into Discrete Fracture Network (DFN) and Equivalent Continuum Model (ECM). The DFN explicitly represents individual fractures, while the ECM uses fracture properties to determine equivalent continuum parameters. In this paper, we compare DFN and ECM in termsmore » of upscaled observed transport properties through generic fracture networks. The major effort was directed on making the DFN and ECM approaches similar in their conceptual representations. This allows for separating differences related to the interpretation of the test conditions and parameters from the differences between the DFN and ECM approaches. The two models are compared using a benchmark test problem that is constructed to represent the far field (1 × 1 × 1 km 3) of a hypothetical repository in fractured crystalline rock. The test problem setting uses generic fracture properties that can be expected in crystalline rocks. The models are compared in terms of the: 1) effective permeability of the domain, and 2) nonreactive solute breakthrough curves through the domain. The principal differences between the models are mesh size, network connectivity, matrix diffusion and anisotropy. We demonstrate how these differences affect the flow and transport. Finally, we identify the factors that should be taken in consideration when selecting an approach most suitable for the site-specific conditions.« less

A comparative study of discrete fracture network and equivalent continuum models for simulating flow and transport in the far field of a hypothetical nuclear waste repository in crystalline host rock

DOE PAGES

Hadgu, Teklu; Karra, Satish; Kalinina, Elena; ...

2017-07-28

One of the major challenges of simulating flow and transport in the far field of a geologic repository in crystalline host rock is related to reproducing the properties of the fracture network over the large volume of rock with sparse fracture characterization data. Various approaches have been developed to simulate flow and transport through the fractured rock. The approaches can be broadly divided into Discrete Fracture Network (DFN) and Equivalent Continuum Model (ECM). The DFN explicitly represents individual fractures, while the ECM uses fracture properties to determine equivalent continuum parameters. In this paper, we compare DFN and ECM in termsmore » of upscaled observed transport properties through generic fracture networks. The major effort was directed on making the DFN and ECM approaches similar in their conceptual representations. This allows for separating differences related to the interpretation of the test conditions and parameters from the differences between the DFN and ECM approaches. The two models are compared using a benchmark test problem that is constructed to represent the far field (1 × 1 × 1 km 3) of a hypothetical repository in fractured crystalline rock. The test problem setting uses generic fracture properties that can be expected in crystalline rocks. The models are compared in terms of the: 1) effective permeability of the domain, and 2) nonreactive solute breakthrough curves through the domain. The principal differences between the models are mesh size, network connectivity, matrix diffusion and anisotropy. We demonstrate how these differences affect the flow and transport. Finally, we identify the factors that should be taken in consideration when selecting an approach most suitable for the site-specific conditions.« less

A comparative study of discrete fracture network and equivalent continuum models for simulating flow and transport in the far field of a hypothetical nuclear waste repository in crystalline host rock

NASA Astrophysics Data System (ADS)

Hadgu, Teklu; Karra, Satish; Kalinina, Elena; Makedonska, Nataliia; Hyman, Jeffrey D.; Klise, Katherine; Viswanathan, Hari S.; Wang, Yifeng

2017-10-01

One of the major challenges of simulating flow and transport in the far field of a geologic repository in crystalline host rock is related to reproducing the properties of the fracture network over the large volume of rock with sparse fracture characterization data. Various approaches have been developed to simulate flow and transport through the fractured rock. The approaches can be broadly divided into Discrete Fracture Network (DFN) and Equivalent Continuum Model (ECM). The DFN explicitly represents individual fractures, while the ECM uses fracture properties to determine equivalent continuum parameters. We compare DFN and ECM in terms of upscaled observed transport properties through generic fracture networks. The major effort was directed on making the DFN and ECM approaches similar in their conceptual representations. This allows for separating differences related to the interpretation of the test conditions and parameters from the differences between the DFN and ECM approaches. The two models are compared using a benchmark test problem that is constructed to represent the far field (1 × 1 × 1 km3) of a hypothetical repository in fractured crystalline rock. The test problem setting uses generic fracture properties that can be expected in crystalline rocks. The models are compared in terms of the: 1) effective permeability of the domain, and 2) nonreactive solute breakthrough curves through the domain. The principal differences between the models are mesh size, network connectivity, matrix diffusion and anisotropy. We demonstrate how these differences affect the flow and transport. We identify the factors that should be taken in consideration when selecting an approach most suitable for the site-specific conditions.

A method for the stochastic modeling of karstic systems accounting for geophysical data: an example of application in the region of Tulum, Yucatan Peninsula (Mexico)

NASA Astrophysics Data System (ADS)

Vuilleumier, C.; Borghi, A.; Renard, P.; Ottowitz, D.; Schiller, A.; Supper, R.; Cornaton, F.

2013-05-01

The eastern coast of the Yucatan Peninsula, Mexico, contains one of the most developed karst systems in the world. This natural wonder is undergoing increasing pollution threat due to rapid economic development in the region of Tulum, together with a lack of wastewater treatment facilities. A preliminary numerical model has been developed to assess the vulnerability of the resource. Maps of explored caves have been completed using data from two airborne geophysical campaigns. These electromagnetic measurements allow for the mapping of unexplored karstic conduits. The completion of the network map is achieved through a stochastic pseudo-genetic karst simulator, previously developed but adapted as part of this study to account for the geophysical data. Together with the cave mapping by speleologists, the simulated networks are integrated into the finite-element flow-model mesh as pipe networks where turbulent flow is modeled. The calibration of the karstic network parameters (density, radius of the conduits) is conducted through a comparison with measured piezometric levels. Although the proposed model shows great uncertainty, it reproduces realistically the heterogeneous flow of the aquifer. Simulated velocities in conduits are greater than 1 cm s-1, suggesting that the reinjection of Tulum wastewater constitutes a pollution risk for the nearby ecosystems.

Sediment and Vegetation Controls on Delta Channel Networks

NASA Astrophysics Data System (ADS)

Lauzon, R.; Murray, A. B.; Piliouras, A.; Kim, W.

2016-12-01

Numerous factors control the patterns of distributary channels formed on a delta, including water and sediment discharge, grain size, sea level rise rates, and vegetation type. In turn, these channel networks influence the shape and evolution of a delta, including what types of plant and animal life - such as humans - it can support. Previous fluvial modeling and flume experiments, outside of the delta context, have addressed how interactions between sediment and vegetation, through their influence on lateral transport of sediment, determine what type of channel networks develops. Similar interactions likely also shape delta flow patterns. Vegetation introduces cohesion, tending to reduce channel migration rates and strengthen existing channel banks, reinforcing existing channels and resulting in localized, relatively stable flow patterns. On the other hand, sediment transport processes can result in lateral migration and frequent switching of active channels, resulting in flow resembling that of a braided stream. While previous studies of deltas have indirectly explored the effects of vegetation through the introduction of cohesive sediment, we directly incorporate key effects of vegetation on flow and sediment transport into the delta-building model DeltaRCM to explore how these effects influence delta channel network formation. Model development is informed by laboratory flume experiments at UT Austin. Here we present initial results of experiments exploring the effects of sea level rise rate, sediment grain size, vegetation type, and vegetation growth rate on delta channel network morphology. These results support the hypothesis that the ability for lateral transport of sediment to occur plays a key role in determining the evolution of delta channel networks and delta morphology.

Qualitative modeling of normal blood coagulation and its pathological states using stochastic activity networks.

PubMed

Mounts, W M; Liebman, M N

1997-07-01

We have developed a method for representing biological pathways and simulating their behavior based on the use of stochastic activity networks (SANs). SANs, an extension of the original Petri net, have been used traditionally to model flow systems including data-communications networks and manufacturing processes. We apply the methodology to the blood coagulation cascade, a biological flow system, and present the representation method as well as results of simulation studies based on published experimental data. In addition to describing the dynamic model, we also present the results of its utilization to perform simulations of clinical states including hemophilia's A and B as well as sensitivity analysis of individual factors and their impact on thrombin production.

Network Flows

DTIC Science & Technology

1988-12-01

production or service activity over time. In these instances it is often convenient to formulate a network flow problem on a "space—time network" with several...planning model in production planning, the economic lot size problem, is an important example. In this problem context, we wish to meet prescribed...demands d^ for a product in each of the T time periods. In each period, we can produce at level Xj and /or we can meet the demand by drav^g upon inventory I

Coupled prediction of flash flood response and debris flow occurrence: Application on an alpine extreme flood event

NASA Astrophysics Data System (ADS)

Destro, Elisa; Amponsah, William; Nikolopoulos, Efthymios I.; Marchi, Lorenzo; Marra, Francesco; Zoccatelli, Davide; Borga, Marco

2018-03-01

The concurrence of flash floods and debris flows is of particular concern, because it may amplify the hazard corresponding to the individual generative processes. This paper presents a coupled modelling framework for the predictions of flash flood response and of the occurrence of debris flows initiated by channel bed mobilization. The framework combines a spatially distributed flash flood response model and a debris flow initiation model to define a threshold value for the peak flow which permits identification of channelized debris flow initiation. The threshold is defined over the channel network as a function of the upslope area and of the local channel bed slope, and it is based on assumptions concerning the properties of the channel bed material and of the morphology of the channel network. The model is validated using data from an extreme rainstorm that impacted the 140 km2 Vizze basin in the Eastern Italian Alps on August 4-5, 2012. The results show that the proposed methodology has improved skill in identifying the catchments where debris-flows are triggered, compared to the use of simpler thresholds based on rainfall properties.

Numerical simulation of flow for viscoelastic neutrophil models in a rectangular capillary network: effects of capillary shape and cell stiffness on transit time.

PubMed

Shirai, Atsushi; Fujita, Ryo; Hayase, Toshiyuki

2007-01-01

The concentration of neutrophils in the pulmonary microvasculature is higher than in large systemic vessels. It is thought that the high concentration of neutrophils facilitates their effective recruitment to sites of inflammation. Thus, in order to understand the role of neutrophils in the immune system, it is important to clarify their flow characteristics in the pulmonary microvasculature. In a previous study, we developed a model to simulate the flow of neutrophils in a capillary network, in which the cells were modeled as spheres of a Maxwell material with a cortical tension and the capillary segments were modeled as arc-shaped constrictions in straight pipes. In the present paper, the flow of neutrophils in a simplified alveolar capillary network model is investigated for various constriction shapes and cell stiffnesses. Finally, it is shown that both the coefficient of variation of the transit time of the cells, which is the standard deviation divided by the mean transit time, and the mean transit time increase as the capillary segments become steep or tight, or when the cells become hard. The mean value of the transit time exceeds the median for all of the conditions that occur in real lungs, although the difference between them is small.

A Generalized Fluid System Simulation Program to Model Flow Distribution in Fluid Networks

NASA Technical Reports Server (NTRS)

Majumdar, Alok; Bailey, John W.; Schallhorn, Paul; Steadman, Todd

1998-01-01

This paper describes a general purpose computer program for analyzing steady state and transient flow in a complex network. The program is capable of modeling phase changes, compressibility, mixture thermodynamics and external body forces such as gravity and centrifugal. The program's preprocessor allows the user to interactively develop a fluid network simulation consisting of nodes and branches. Mass, energy and specie conservation equations are solved at the nodes; the momentum conservation equations are solved in the branches. The program contains subroutines for computing "real fluid" thermodynamic and thermophysical properties for 33 fluids. The fluids are: helium, methane, neon, nitrogen, carbon monoxide, oxygen, argon, carbon dioxide, fluorine, hydrogen, parahydrogen, water, kerosene (RP-1), isobutane, butane, deuterium, ethane, ethylene, hydrogen sulfide, krypton, propane, xenon, R-11, R-12, R-22, R-32, R-123, R-124, R-125, R-134A, R-152A, nitrogen trifluoride and ammonia. The program also provides the options of using any incompressible fluid with constant density and viscosity or ideal gas. Seventeen different resistance/source options are provided for modeling momentum sources or sinks in the branches. These options include: pipe flow, flow through a restriction, non-circular duct, pipe flow with entrance and/or exit losses, thin sharp orifice, thick orifice, square edge reduction, square edge expansion, rotating annular duct, rotating radial duct, labyrinth seal, parallel plates, common fittings and valves, pump characteristics, pump power, valve with a given loss coefficient, and a Joule-Thompson device. The system of equations describing the fluid network is solved by a hybrid numerical method that is a combination of the Newton-Raphson and successive substitution methods. This paper also illustrates the application and verification of the code by comparison with Hardy Cross method for steady state flow and analytical solution for unsteady flow.

Unified pipe network method for simulation of water flow in fractured porous rock

NASA Astrophysics Data System (ADS)

Ren, Feng; Ma, Guowei; Wang, Yang; Li, Tuo; Zhu, Hehua

2017-04-01

Rock masses are often conceptualized as dual-permeability media containing fractures or fracture networks with high permeability and porous matrix that is less permeable. In order to overcome the difficulties in simulating fluid flow in a highly discontinuous dual-permeability medium, an effective unified pipe network method is developed, which discretizes the dual-permeability rock mass into a virtual pipe network system. It includes fracture pipe networks and matrix pipe networks. They are constructed separately based on equivalent flow models in a representative area or volume by taking the advantage of the orthogonality of the mesh partition. Numerical examples of fluid flow in 2-D and 3-D domain including porous media and fractured porous media are presented to demonstrate the accuracy, robustness, and effectiveness of the proposed unified pipe network method. Results show that the developed method has good performance even with highly distorted mesh. Water recharge into the fractured rock mass with complex fracture network is studied. It has been found in this case that the effect of aperture change on the water recharge rate is more significant in the early stage compared to the fracture density change.

A new scripting library for modeling flow and transport in fractured rock with channel networks

NASA Astrophysics Data System (ADS)

Dessirier, Benoît; Tsang, Chin-Fu; Niemi, Auli

2018-02-01

Deep crystalline bedrock formations are targeted to host spent nuclear fuel owing to their overall low permeability. They are however highly heterogeneous and only a few preferential paths pertaining to a small set of dominant rock fractures usually carry most of the flow or mass fluxes, a behavior known as channeling that needs to be accounted for in the performance assessment of repositories. Channel network models have been developed and used to investigate the effect of channeling. They are usually simpler than discrete fracture networks based on rock fracture mappings and rely on idealized full or sparsely populated lattices of channels. This study reexamines the fundamental parameter structure required to describe a channel network in terms of groundwater flow and solute transport, leading to an extended description suitable for unstructured arbitrary networks of channels. An implementation of this formalism in a Python scripting library is presented and released along with this article. A new algebraic multigrid preconditioner delivers a significant speedup in the flow solution step compared to previous channel network codes. 3D visualization is readily available for verification and interpretation of the results by exporting the results to an open and free dedicated software. The new code is applied to three example cases to verify its results on full uncorrelated lattices of channels, sparsely populated percolation lattices and to exemplify the use of unstructured networks to accommodate knowledge on local rock fractures.

AN INTEGRATED VIEW OF GROUNDWATER FLOW CHARACTERIZATION AND MODELING IN FRACTURED GEOLOGIC MEDIA

EPA Science Inventory

The particular attributes of fractured geologic media pertaining to groundwater flow characterization and modeling are presented. These cover the issues of fracture network and hydraulic control of fracture geometry parameters, major and minor fractures, heterogeneity, anisotrop...

The topology of card transaction money flows

NASA Astrophysics Data System (ADS)

Zanin, Massimiliano; Papo, David; Romance, Miguel; Criado, Regino; Moral, Santiago

2016-11-01

Money flow models are essential tools to understand different economical phenomena, like saving propensities and wealth distributions. In spite of their importance, most of them are based on synthetic transaction networks with simple topologies, e.g. random or scale-free ones, as the characterisation of real networks is made difficult by the confidentiality and sensitivity of money transaction data. Here, we present an analysis of the topology created by real credit card transactions from one of the biggest world banks, and show how different distributions, e.g. number of transactions per card or amount, have nontrivial characteristics. We further describe a stochastic model to create transactions data sets, feeding from the obtained distributions, which will allow researchers to create more realistic money flow models.

Code System to Calculate Tornado-Induced Flow Material Transport.

DOE Office of Scientific and Technical Information (OSTI.GOV)

ANDRAE, R. W.

1999-11-18

Version: 00 TORAC models tornado-induced flows, pressures, and material transport within structures. Its use is directed toward nuclear fuel cycle facilities and their primary release pathway, the ventilation system. However, it is applicable to other structures and can model other airflow pathways within a facility. In a nuclear facility, this network system could include process cells, canyons, laboratory offices, corridors, and offgas systems. TORAC predicts flow through a network system that also includes ventilation system components such as filters, dampers, ducts, and blowers. These ventilation system components are connected to the rooms and corridors of the facility to form amore » complete network for moving air through the structure and, perhaps, maintaining pressure levels in certain areas. The material transport capability in TORAC is very basic and includes convection, depletion, entrainment, and filtration of material.« less

Selfish Routing

DTIC Science & Technology

2002-05-01

traffic models , thereby identifying types of networks for which the cost of routing selfishly is mild. The inefficiency inherent in an uncoordinated outcome...17 1.6 Bibliographic Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2 Preliminaries 18 2.1 The Model ...to Other Models 68 4.1 Flows at Approximate Nash Equilibrium . . . . . . . . . . . . . . . . 69 4.2 Finitely Many Users: Splittable Flow

Validation of model predictions of pore-scale fluid distributions during two-phase flow

NASA Astrophysics Data System (ADS)

Bultreys, Tom; Lin, Qingyang; Gao, Ying; Raeini, Ali Q.; AlRatrout, Ahmed; Bijeljic, Branko; Blunt, Martin J.

2018-05-01

Pore-scale two-phase flow modeling is an important technology to study a rock's relative permeability behavior. To investigate if these models are predictive, the calculated pore-scale fluid distributions which determine the relative permeability need to be validated. In this work, we introduce a methodology to quantitatively compare models to experimental fluid distributions in flow experiments visualized with microcomputed tomography. First, we analyzed five repeated drainage-imbibition experiments on a single sample. In these experiments, the exact fluid distributions were not fully repeatable on a pore-by-pore basis, while the global properties of the fluid distribution were. Then two fractional flow experiments were used to validate a quasistatic pore network model. The model correctly predicted the fluid present in more than 75% of pores and throats in drainage and imbibition. To quantify what this means for the relevant global properties of the fluid distribution, we compare the main flow paths and the connectivity across the different pore sizes in the modeled and experimental fluid distributions. These essential topology characteristics matched well for drainage simulations, but not for imbibition. This suggests that the pore-filling rules in the network model we used need to be improved to make reliable predictions of imbibition. The presented analysis illustrates the potential of our methodology to systematically and robustly test two-phase flow models to aid in model development and calibration.

Optimizing Virtual Network Functions Placement in Virtual Data Center Infrastructure Using Machine Learning

NASA Astrophysics Data System (ADS)

Bolodurina, I. P.; Parfenov, D. I.

2018-01-01

We have elaborated a neural network model of virtual network flow identification based on the statistical properties of flows circulating in the network of the data center and characteristics that describe the content of packets transmitted through network objects. This enabled us to establish the optimal set of attributes to identify virtual network functions. We have established an algorithm for optimizing the placement of virtual data functions using the data obtained in our research. Our approach uses a hybrid method of visualization using virtual machines and containers, which enables to reduce the infrastructure load and the response time in the network of the virtual data center. The algorithmic solution is based on neural networks, which enables to scale it at any number of the network function copies.

Planning and cost analysis of digital radiography services for a network of hospitals (the Veterans Integrated Service Network).

PubMed

Duerinckx, A J; Kenagy, J J; Grant, E G

1998-01-01

This study analysed the design and cost of a picture archiving and communications system (PACS), computerized radiography (CR) and a wide-area network for teleradiology. The Desert Pacific Healthcare Network comprises 10 facilities, including four tertiary medical centres and one small hospital. Data were collected on radiologists' workloads, and patient and image flow within and between these medical centres. These were used to estimate the size and cash flows associated with a system-wide implementation of PACS, CR and teleradiology services. A cost analysis model was used to estimate the potential cost savings in a filmless radiology environment. ATM technology was selected as the communications medium between the medical centres. A strategic plan and business plan were successfully developed. The cost model predicted the cost-effectiveness of the proposed PACS/CR configuration within four to six years, if the base costs were kept low. The experience gained in design and cost analysis of a PACS/teleradiology network will serve as a model for similar projects.

Direct numerical simulation of cellular-scale blood flow in microvascular networks

NASA Astrophysics Data System (ADS)

Balogh, Peter; Bagchi, Prosenjit

2017-11-01

A direct numerical simulation method is developed to study cellular-scale blood flow in physiologically realistic microvascular networks that are constructed in silico following published in vivo images and data, and are comprised of bifurcating, merging, and winding vessels. The model resolves large deformation of individual red blood cells (RBC) flowing in such complex networks. The vascular walls and deformable interfaces of the RBCs are modeled using the immersed-boundary methods. Time-averaged hemodynamic quantities obtained from the simulations agree quite well with published in vivo data. Our simulations reveal that in several vessels the flow rates and pressure drops could be negatively correlated. The flow resistance and hematocrit are also found to be negatively correlated in some vessels. These observations suggest a deviation from the classical Poiseuille's law in such vessels. The cells are observed to frequently jam at vascular bifurcations resulting in reductions in hematocrit and flow rate in the daughter and mother vessels. We find that RBC jamming results in several orders of magnitude increase in hemodynamic resistance, and thus provides an additional mechanism of increased in vivo blood viscosity as compared to that determined in vitro. Funded by NSF CBET 1604308.

The flow of power law fluids in elastic networks and porous media.

PubMed

Sochi, Taha

2016-02-01

The flow of power law fluids, which include shear thinning and shear thickening as well as Newtonian as a special case, in networks of interconnected elastic tubes is investigated using a residual-based pore scale network modeling method with the employment of newly derived formulae. Two relations describing the mechanical interaction between the local pressure and local cross-sectional area in distensible tubes of elastic nature are considered in the derivation of these formulae. The model can be used to describe shear dependent flows of mainly viscous nature. The behavior of the proposed model is vindicated by several tests in a number of special and limiting cases where the results can be verified quantitatively or qualitatively. The model, which is the first of its kind, incorporates more than one major nonlinearity corresponding to the fluid rheology and conduit mechanical properties, that is non-Newtonian effects and tube distensibility. The formulation, implementation, and performance indicate that the model enjoys certain advantages over the existing models such as being exact within the restricting assumptions on which the model is based, easy implementation, low computational costs, reliability, and smooth convergence. The proposed model can, therefore, be used as an alternative to the existing Newtonian distensible models; moreover, it stretches the capabilities of the existing modeling approaches to reach non-Newtonian rheologies.

High frequency sound propagation in a network of interconnecting streets

NASA Astrophysics Data System (ADS)

Hewett, D. P.

2012-12-01

We propose a new model for the propagation of acoustic energy from a time-harmonic point source through a network of interconnecting streets in the high frequency regime, in which the wavelength is small compared to typical macro-lengthscales such as street widths/lengths and building heights. Our model, which is based on geometrical acoustics (ray theory), represents the acoustic power flow from the source along any pathway through the network as the integral of a power density over the launch angle of a ray emanating from the source, and takes into account the key phenomena involved in the propagation, namely energy loss by wall absorption, energy redistribution at junctions, and, in 3D, energy loss to the atmosphere. The model predicts strongly anisotropic decay away from the source, with the power flow decaying exponentially in the number of junctions from the source, except along the axial directions of the network, where the decay is algebraic.

Calculation method of water injection forward modeling and inversion process in oilfield water injection network

NASA Astrophysics Data System (ADS)

Liu, Long; Liu, Wei

2018-04-01

A forward modeling and inversion algorithm is adopted in order to determine the water injection plan in the oilfield water injection network. The main idea of the algorithm is shown as follows: firstly, the oilfield water injection network is inversely calculated. The pumping station demand flow is calculated. Then, forward modeling calculation is carried out for judging whether all water injection wells meet the requirements of injection allocation or not. If all water injection wells meet the requirements of injection allocation, calculation is stopped, otherwise the demand injection allocation flow rate of certain step size is reduced aiming at water injection wells which do not meet requirements, and next iterative operation is started. It is not necessary to list the algorithm into water injection network system algorithm, which can be realized easily. Iterative method is used, which is suitable for computer programming. Experimental result shows that the algorithm is fast and accurate.

Percolation connectivity, pore size, and gas apparent permeability: Network simulations and comparison to experimental data

NASA Astrophysics Data System (ADS)

Li, M.; Tang, Y. B.; Bernabé, Y.; Zhao, J. Z.; Li, X. F.; Li, T.

2017-07-01

We modeled single-phase gas flow through porous media using percolation networks. Gas permeability is different from liquid permeability. The latter is only related to the geometry and topology of the pore space, while the former depends on the specific gas considered and varies with gas pressure. As gas pressure decreases, four flow regimes can be distinguished as viscous flow, slip flow, transition flow, and free molecular diffusion. Here we use a published conductance model presumably capable of predicting the flow rate of an arbitrary gas through a cylindrical pipe in the four regimes. We incorporated this model into pipe network simulations. We considered 3-D simple cubic, body-centered cubic, and face-centered cubic lattices, in which we varied the pipe radius distribution and the bond coordination number. Gas flow was simulated at different gas pressures. The simulation results showed that the gas apparent permeability kapp obeys an identical scaling law in all three lattices, kapp (z-zc)β, where the exponent β depends on the width of the pipe radius distribution, z is the mean coordination number, and zc its critical value at the percolation threshold. Surprisingly, (z-zc) had a very weak effect on the ratio of the apparent gas permeability to the absolute liquid permeability, kapp/kabs, suggesting that the Klinkenberg gas slippage correction factor is nearly independent of connectivity. We constructed models of kapp and kapp/kabs based on the observed power law and tested them by comparison with published experimental data on glass beads and other materials.

Vertex centrality as a measure of information flow in Italian Corporate Board Networks

NASA Astrophysics Data System (ADS)

Grassi, Rosanna

2010-06-01

The aim of this article is to investigate the governance models of companies listed on the Italian Stock Exchange by using a network approach, which describes the interlinks between boards of directors. Following mainstream literature, I construct a weighted graph representing the listed companies (vertices) and their relationships (weighted edges), the Corporate Board Network; I then apply three different vertex centrality measures: degree, betweenness and flow betweenness. What emerges from the network construction and by applying the degree centrality is a structure with a large number of connections but not particularly dense, where the presence of a small number of highly connected nodes (hubs) is evident. Then I focus on betweenness and flow betweenness; indeed I expect that these centrality measures may give a representation of the intensity of the relationship between companies, capturing the volume of information flowing from one vertex to another. Finally, I investigate the possible scale-free structure of the network.

Numerical Modeling of Conjugate Heat Transfer in Fluid Network

NASA Technical Reports Server (NTRS)

Majumdar, Alok

2004-01-01

Fluid network modeling with conjugate heat transfer has many applications in Aerospace engineering. In modeling unsteady flow with heat transfer, it is important to know the variation of wall temperature in time and space to calculate heat transfer between solid to fluid. Since wall temperature is a function of flow, a coupled analysis of temperature of solid and fluid is necessary. In cryogenic applications, modeling of conjugate heat transfer is of great importance to correctly predict boil-off rate in propellant tanks and chill down of transfer lines. In TFAWS 2003, the present author delivered a paper to describe a general-purpose computer program, GFSSP (Generalized Fluid System Simulation Program). GFSSP calculates flow distribution in complex flow circuit for compressible/incompressible, with or without heat transfer or phase change in all real fluids or mixtures. The flow circuit constitutes of fluid nodes and branches. The mass, energy and specie conservation equations are solved at the nodes where as momentum conservation equations are solved at the branches. The proposed paper describes the extension of GFSSP to model conjugate heat transfer. The network also includes solid nodes and conductors in addition to fluid nodes and branches. The energy conservation equations for solid nodes solves to determine the temperatures of the solid nodes simultaneously with all conservation equations governing fluid flow. The numerical scheme accounts for conduction, convection and radiation heat transfer. The paper will also describe the applications of the code to predict chill down of cryogenic transfer line and boil-off rate of cryogenic propellant storage tank.

Simulation of unsteady flow and solute transport in a tidal river network

USGS Publications Warehouse

Zhan, X.

2003-01-01

A mathematical model and numerical method for water flow and solute transport in a tidal river network is presented. The tidal river network is defined as a system of open channels of rivers with junctions and cross sections. As an example, the Pearl River in China is represented by a network of 104 channels, 62 nodes, and a total of 330 cross sections with 11 boundary section for one of the applications. The simulations are performed with a supercomputer for seven scenarios of water flow and/or solute transport in the Pearl River, China, with different hydrological and weather conditions. Comparisons with available data are shown. The intention of this study is to summarize previous works and to provide a useful tool for water environmental management in a tidal river network, particularly for the Pearl River, China.

A hydrologic network supporting spatially referenced regression modeling in the Chesapeake Bay watershed

USGS Publications Warehouse

Brakebill, J.W.; Preston, S.D.

2003-01-01

The U.S. Geological Survey has developed a methodology for statistically relating nutrient sources and land-surface characteristics to nutrient loads of streams. The methodology is referred to as SPAtially Referenced Regressions On Watershed attributes (SPARROW), and relates measured stream nutrient loads to nutrient sources using nonlinear statistical regression models. A spatially detailed digital hydrologic network of stream reaches, stream-reach characteristics such as mean streamflow, water velocity, reach length, and travel time, and their associated watersheds supports the regression models. This network serves as the primary framework for spatially referencing potential nutrient source information such as atmospheric deposition, septic systems, point-sources, land use, land cover, and agricultural sources and land-surface characteristics such as land use, land cover, average-annual precipitation and temperature, slope, and soil permeability. In the Chesapeake Bay watershed that covers parts of Delaware, Maryland, Pennsylvania, New York, Virginia, West Virginia, and Washington D.C., SPARROW was used to generate models estimating loads of total nitrogen and total phosphorus representing 1987 and 1992 land-surface conditions. The 1987 models used a hydrologic network derived from an enhanced version of the U.S. Environmental Protection Agency's digital River Reach File, and course resolution Digital Elevation Models (DEMs). A new hydrologic network was created to support the 1992 models by generating stream reaches representing surface-water pathways defined by flow direction and flow accumulation algorithms from higher resolution DEMs. On a reach-by-reach basis, stream reach characteristics essential to the modeling were transferred to the newly generated pathways or reaches from the enhanced River Reach File used to support the 1987 models. To complete the new network, watersheds for each reach were generated using the direction of surface-water flow derived from the DEMs. This network improves upon existing digital stream data by increasing the level of spatial detail and providing consistency between the reach locations and topography. The hydrologic network also aids in illustrating the spatial patterns of predicted nutrient loads and sources contributed locally to each stream, and the percentages of nutrient load that reach Chesapeake Bay.

Hydrodynamics and Connectivity of Channelized Floodplains: Insights from the Meandering East Fork White River, Indiana, USA

NASA Astrophysics Data System (ADS)

Czuba, J. A.; David, S. R.; Edmonds, D. A.

2017-12-01

High resolution topography reveals that meandering river floodplains in Indiana commonly have networks of channels. These floodplain channel networks are most prevalent in agricultural, low-gradient, wide floodplains. It appears that these networks are formed when floodplain channels connect oxbows to each other and the main river channel. Collectively, the channels in the floodplain create an interconnected network of pathways that convey water beginning at flows less than bankfull, and as stage increases, more of the floodplain becomes dissected by floodplain channels. In this work, we quantify the hydrodynamics and connectivity of the flow on the floodplain and in the main channel of the East Fork White River near Seymour, Indiana, USA. We constructed a two-dimensional numerical model using HECRAS of the river-floodplain system from LiDAR data and from main-channel river bathymetry to elucidate the behaviour of these floodplain channels across a range of flows. Model calibration and verification data included stage from a USGS gage, high-water marks at a high and medium flow, and an aerial photograph of inundation in the floodplain channels. The numerical model simulated flow depth and velocity, which was used to quantify connectivity of the floodplain channels, exchange between the main channel and floodplain channels, and residence time of water on the floodplain. Model simulations suggest that the floodplain channels convey roughly 50% of the total flow at what is typically considered "bankfull" flow. Overall, we present a process-based approach for analyzing complex floodplain-river systems where an individual floodplain-river system can be distilled down to a set of characteristic curves. Notably, we map the East Fork White River system to exchange-residence time space and argue that this characterization forms the basis for thinking about morphologic evolution (e.g., sediment deposition and erosion) and biogeochemistry (e.g., nitrate removal) in floodplain-river systems.

Statistical Model Applied to NetFlow for Network Intrusion Detection

NASA Astrophysics Data System (ADS)

Proto, André; Alexandre, Leandro A.; Batista, Maira L.; Oliveira, Isabela L.; Cansian, Adriano M.

The computers and network services became presence guaranteed in several places. These characteristics resulted in the growth of illicit events and therefore the computers and networks security has become an essential point in any computing environment. Many methodologies were created to identify these events; however, with increasing of users and services on the Internet, many difficulties are found in trying to monitor a large network environment. This paper proposes a methodology for events detection in large-scale networks. The proposal approaches the anomaly detection using the NetFlow protocol, statistical methods and monitoring the environment in a best time for the application.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dyachenko, Sergey A.; Zlotnik, Anatoly; Korotkevich, Alexander O.

Here, we develop an operator splitting method to simulate flows of isothermal compressible natural gas over transmission pipelines. The method solves a system of nonlinear hyperbolic partial differential equations (PDEs) of hydrodynamic type for mass flow and pressure on a metric graph, where turbulent losses of momentum are modeled by phenomenological Darcy-Weisbach friction. Mass flow balance is maintained through the boundary conditions at the network nodes, where natural gas is injected or withdrawn from the system. Gas flow through the network is controlled by compressors boosting pressure at the inlet of the adjoint pipe. Our operator splitting numerical scheme ismore » unconditionally stable and it is second order accurate in space and time. The scheme is explicit, and it is formulated to work with general networks with loops. We test the scheme over range of regimes and network configurations, also comparing its performance with performance of two other state of the art implicit schemes.« less

Water balance in irrigation districts. Uncertainty in on-demand pressurized networks

NASA Astrophysics Data System (ADS)

Sánchez-Calvo, Raúl; Rodríguez-Sinobas, Leonor; Juana, Luis; Laguna, Francisco Vicente

2015-04-01

In on-demand pressurized irrigation distribution networks, applied water volume is usually controlled opening a valve during a calculated time interval, and assuming constant flow rate. In general, pressure regulating devices for controlling the discharged flow rate by irrigation units are needed due to the variability of pressure conditions. A pressure regulating valve PRV is the commonly used pressure regulating device in a hydrant, which, also, executes the open and close function. A hydrant feeds several irrigation units, requiring a wide range in flow rate. In addition, some flow meters are also available, one as a component of the hydrant and the rest are placed downstream. Every land owner has one flow meter for each group of field plots downstream the hydrant. Ideal PRV performance would maintain a constant downstream pressure. However, the true performance depends on both upstream pressure and the discharged flow rate. Theoretical flow rates values have been introduced into a PRV behavioral model, validated in laboratory, coupled with an on-demand irrigation district waterworks, composed by a distribution network and a multi-pump station. Variations on flow rate are simulated by taking into account the consequences of variations on climate conditions and also decisions in irrigation operation, such us duration and frequency application. The model comprises continuity, dynamic and energy equations of the components of both the PRV and the water distribution network. In this work the estimation of water balance terms during the irrigation events in an irrigation campaign has been simulated. The effect of demand concentration peaks has been estimated.

Pore-scale simulation of liquid CO2 displacement of water using a two-phase lattice Boltzmann model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Haihu; Valocchi, Albert J.; Werth, Charles J.

A lattice Boltzmann color-fluid model, which was recently proposed by Liu et al. [H. Liu, A.J. Valocchi, and Q. Kang. Three-dimensional lattice Boltzmann model for immiscible two-phase flow simulations. Phys. Rev. E, 85:046309, 2012.] based on a concept of continuum surface force, is improved to simulate immiscible two-phase flows in porous media. The new improvements allow the model to account for different kinematic viscosities of both fluids and to model fluid-solid interactions. The capability and accuracy of this model is first validated by two benchmark tests: a layered two-phase flow with a viscosity ratio, and a dynamic capillary intrusion. Thismore » model is then used to simulate liquid CO2 (LCO2) displacing water in a dual-permeability pore network. The extent and behavior of LCO2 preferential flow (i.e., fingering) is found to depend on the capillary number (Ca), and three different displacement patterns observed in previous micromodel experiments are reproduced. The predicted variation of LCO2 saturation with Ca, as well as variation of specific interfacial length with LCO2 saturation, are both in good agreement with the experimental observations. To understand the effect of heterogeneity on pore-scale displacement, we also simulate LCO2 displacing water in a randomly heterogeneous pore network, which has the same size and porosity as the dual-permeability pore network. In comparison to the dual-permeability case, the transition from capillary fingering to viscous fingering occurs at a higher Ca, and LCO2 saturation is higher at low Ca but lower at high Ca. In either pore network, the LCO2-water specific interfacial length is found to obey a power-law dependence on LCO2 saturation.« less

Non-Newtonian fluid flow in 2D fracture networks

NASA Astrophysics Data System (ADS)

Zou, L.; Håkansson, U.; Cvetkovic, V.

2017-12-01

Modeling of non-Newtonian fluid (e.g., drilling fluids and cement grouts) flow in fractured rocks is of interest in many geophysical and industrial practices, such as drilling operations, enhanced oil recovery and rock grouting. In fractured rock masses, the flow paths are dominated by fractures, which are often represented as discrete fracture networks (DFN). In the literature, many studies have been devoted to Newtonian fluid (e.g., groundwater) flow in fractured rock using the DFN concept, but few works are dedicated to non-Newtonian fluids.In this study, a generalized flow equation for common non-Newtonian fluids (such as Bingham, power-law and Herschel-Bulkley) in a single fracture is obtained from the analytical solutions for non-Newtonian fluid discharge between smooth parallel plates. Using Monte Carlo sampling based on site characterization data for the distribution of geometrical features (e.g., density, length, aperture and orientations) in crystalline fractured rock, a two dimensional (2D) DFN model is constructed for generic flow simulations. Due to complex properties of non-Newtonian fluids, the relationship between fluid discharge and the pressure gradient is nonlinear. A Galerkin finite element method solver is developed to iteratively solve the obtained nonlinear governing equations for the 2D DFN model. Using DFN realizations, simulation results for different geometrical distributions of the fracture network and different non-Newtonian fluid properties are presented to illustrate the spatial discharge distributions. The impact of geometrical structures and the fluid properties on the non-Newtonian fluid flow in 2D DFN is examined statistically. The results generally show that modeling non-Newtonian fluid flow in fractured rock as a DFN is feasible, and that the discharge distribution may be significantly affected by the geometrical structures as well as by the fluid constitutive properties.

Modelling rapid subsurface flow at the hillslope scale with explicit representation of preferential flow paths

NASA Astrophysics Data System (ADS)

Wienhöfer, J.; Zehe, E.

2012-04-01

Rapid lateral flow processes via preferential flow paths are widely accepted to play a key role for rainfall-runoff response in temperate humid headwater catchments. A quantitative description of these processes, however, is still a major challenge in hydrological research, not least because detailed information about the architecture of subsurface flow paths are often impossible to obtain at a natural site without disturbing the system. Our study combines physically based modelling and field observations with the objective to better understand how flow network configurations influence the hydrological response of hillslopes. The system under investigation is a forested hillslope with a small perennial spring at the study area Heumöser, a headwater catchment of the Dornbirnerach in Vorarlberg, Austria. In-situ points measurements of field-saturated hydraulic conductivity and dye staining experiments at the plot scale revealed that shrinkage cracks and biogenic macropores function as preferential flow paths in the fine-textured soils of the study area, and these preferential flow structures were active in fast subsurface transport of artificial tracers at the hillslope scale. For modelling of water and solute transport, we followed the approach of implementing preferential flow paths as spatially explicit structures of high hydraulic conductivity and low retention within the 2D process-based model CATFLOW. Many potential configurations of the flow path network were generated as realisations of a stochastic process informed by macropore characteristics derived from the plot scale observations. Together with different realisations of soil hydraulic parameters, this approach results in a Monte Carlo study. The model setups were used for short-term simulation of a sprinkling and tracer experiment, and the results were evaluated against measured discharges and tracer breakthrough curves. Although both criteria were taken for model evaluation, still several model setups produced acceptable matches to the observed behaviour. These setups were selected for long-term simulation, the results of which were compared against water level measurements at two piezometers along the hillslope and the integral discharge response of the spring to reject some non-behavioural model setups and further reduce equifinality. The results of this study indicate that process-based modelling can provide a means to distinguish preferential flow networks on the hillslope scale when complementary measurements to constrain the range of behavioural model setups are available. These models can further be employed as a virtual reality to investigate the characteristics of flow path architectures and explore effective parameterisations for larger scale applications.

Connectivity of Secondary Channels in the Floodplain of a Low-Gradient Midwestern U.S. Agricultural River

NASA Astrophysics Data System (ADS)

Czuba, J. A.; David, S. R.; Edmonds, D. A.

2016-12-01

Floodplains of low-gradient Midwestern U.S. agricultural rivers are commonly dissected by a network of secondary channels that convey flow only during flood events. These networks of secondary channels have only recently been revealed by high resolution digital elevation models. Secondary channels, as referred to here, span multiple meander wavelengths and appear fundamentally different from chute channels. While secondary channels have been described to some extent in other river systems, our focus here is on those found in Indiana, which are revealed by state-wide LiDAR data acquired in 2011. In this work, we quantify how the network connectivity of the secondary channels in the floodplain develops as a function of flow stage. Secondary channels begin conveying water at stages just below bankfull, become an interconnected web of flow pathways above bankfull stage, and are completely inundated at higher stages. We construct a two-dimensional numerical model of the river/floodplain system from LiDAR data and from main-channel river bathymetry in order to obtain the extent of floodplain inundation at various flows. The inundated area within the secondary channels is then converted into a river/floodplain flow-channel network and quantified using various network metrics. Future work will explore the morphodynamics of this river/floodplain system extended to 100-1,000 year timescales. The goal is to develop a simple model to test hypotheses about how these floodplain channels evolve. Relevant research questions include: do secondary channels serve as preferential avulsion pathways? Or could secondary channels evolve to create a multi-channeled anabranching system? Furthermore, under what hydrologic and sedimentologic conditions would a river/floodplain system evolve to one state or another?

Multilane Traffic Flow Modeling Using Cellular Automata Theory

NASA Astrophysics Data System (ADS)

Chechina, Antonina; Churbanova, Natalia; Trapeznikova, Marina

2018-02-01

The paper deals with the mathematical modeling of traffic flows on urban road networks using microscopic approach. The model is based on the cellular automata theory and presents a generalization of the Nagel-Schreckenberg model to a multilane case. The created program package allows to simulate traffic on various types of road fragments (T or X type intersection, strait road elements, etc.) and on road networks that consist of these elements. Besides that, it allows to predict the consequences of various decisions regarding road infrastructure changes, such as: number of lanes increasing/decreasing, putting new traffic lights into operation, building new roads, entrances/exits, road junctions.

Model for adhesion clutch explains biphasic relationship between actin flow and traction at the cell leading edge

PubMed Central

Craig, Erin M.; Stricker, Jonathan; Gardel, Margaret L.; Mogilner, Alex

2015-01-01

Cell motility relies on the continuous reorganization of a dynamic actin-myosin-adhesion network at the leading edge of the cell, in order to generate protrusion at the leading edge and traction between the cell and its external environment. We analyze experimentally measured spatial distributions of actin flow, traction force, myosin density, and adhesion density in control and pharmacologically perturbed epithelial cells in order to develop a mechanical model of the actin-adhesion-myosin self-organization at the leading edge. A model in which the F-actin network is treated as a viscous gel, and adhesion clutch engagement is strengthened by myosin but weakened by actin flow, can explain the measured molecular distributions and correctly predict the spatial distributions of the actin flow and traction stress. We test the model by comparing its predictions with measurements of the actin flow and traction stress in cells with fast and slow actin polymerization rates. The model predicts how the location of the lamellipodium-lamellum boundary depends on the actin viscosity and adhesion strength. The model further predicts that the location of the lamellipodium-lamellum boundary is not very sensitive to the level of myosin contraction. PMID:25969948

A feedback control model for network flow with multiple pure time delays

NASA Technical Reports Server (NTRS)

Press, J.

1972-01-01

A control model describing a network flow hindered by multiple pure time (or transport) delays is formulated. Feedbacks connect each desired output with a single control sector situated at the origin. The dynamic formulation invokes the use of differential difference equations. This causes the characteristic equation of the model to consist of transcendental functions instead of a common algebraic polynomial. A general graphical criterion is developed to evaluate the stability of such a problem. A digital computer simulation confirms the validity of such criterion. An optimal decision making process with multiple delays is presented.

Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers.

PubMed

Taheri, Mahboobeh; Mohebbi, Ali

2008-08-30

In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm (GA), has been used to predict collection efficiency in venturi scrubbers. The experimental input data, including particle diameter, throat gas velocity, liquid to gas flow rate ratio, throat hydraulic diameter, pressure drop across the venturi scrubber and collection efficiency as an output, have been used to create a GA-artificial neural network (ANN) model. The testing results from the model are in good agreement with the experimental data. Comparison of the results of the GA optimized ANN model with the results from the trial-and-error calibrated ANN model indicates that the GA-ANN model is more efficient. Finally, the effects of operating parameters such as liquid to gas flow rate ratio, throat gas velocity, and particle diameter on collection efficiency were determined.

Scale-invariant feature extraction of neural network and renormalization group flow

NASA Astrophysics Data System (ADS)

Iso, Satoshi; Shiba, Shotaro; Yokoo, Sumito

2018-05-01

Theoretical understanding of how a deep neural network (DNN) extracts features from input images is still unclear, but it is widely believed that the extraction is performed hierarchically through a process of coarse graining. It reminds us of the basic renormalization group (RG) concept in statistical physics. In order to explore possible relations between DNN and RG, we use the restricted Boltzmann machine (RBM) applied to an Ising model and construct a flow of model parameters (in particular, temperature) generated by the RBM. We show that the unsupervised RBM trained by spin configurations at various temperatures from T =0 to T =6 generates a flow along which the temperature approaches the critical value Tc=2.2 7 . This behavior is the opposite of the typical RG flow of the Ising model. By analyzing various properties of the weight matrices of the trained RBM, we discuss why it flows towards Tc and how the RBM learns to extract features of spin configurations.

dfnWorks: A discrete fracture network framework for modeling subsurface flow and transport

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hyman, Jeffrey D.; Karra, Satish; Makedonska, Nataliia

DFNWORKS is a parallelized computational suite to generate three-dimensional discrete fracture networks (DFN) and simulate flow and transport. Developed at Los Alamos National Laboratory over the past five years, it has been used to study flow and transport in fractured media at scales ranging from millimeters to kilometers. The networks are created and meshed using DFNGEN, which combines FRAM (the feature rejection algorithm for meshing) methodology to stochastically generate three-dimensional DFNs with the LaGriT meshing toolbox to create a high-quality computational mesh representation. The representation produces a conforming Delaunay triangulation suitable for high performance computing finite volume solvers in anmore » intrinsically parallel fashion. Flow through the network is simulated in dfnFlow, which utilizes the massively parallel subsurface flow and reactive transport finite volume code PFLOTRAN. A Lagrangian approach to simulating transport through the DFN is adopted within DFNTRANS to determine pathlines and solute transport through the DFN. Example applications of this suite in the areas of nuclear waste repository science, hydraulic fracturing and CO 2 sequestration are also included.« less

dfnWorks: A discrete fracture network framework for modeling subsurface flow and transport

DOE PAGES

Hyman, Jeffrey D.; Karra, Satish; Makedonska, Nataliia; ...

2015-11-01

DFNWORKS is a parallelized computational suite to generate three-dimensional discrete fracture networks (DFN) and simulate flow and transport. Developed at Los Alamos National Laboratory over the past five years, it has been used to study flow and transport in fractured media at scales ranging from millimeters to kilometers. The networks are created and meshed using DFNGEN, which combines FRAM (the feature rejection algorithm for meshing) methodology to stochastically generate three-dimensional DFNs with the LaGriT meshing toolbox to create a high-quality computational mesh representation. The representation produces a conforming Delaunay triangulation suitable for high performance computing finite volume solvers in anmore » intrinsically parallel fashion. Flow through the network is simulated in dfnFlow, which utilizes the massively parallel subsurface flow and reactive transport finite volume code PFLOTRAN. A Lagrangian approach to simulating transport through the DFN is adopted within DFNTRANS to determine pathlines and solute transport through the DFN. Example applications of this suite in the areas of nuclear waste repository science, hydraulic fracturing and CO 2 sequestration are also included.« less

Multiscale model reduction for shale gas transport in poroelastic fractured media

NASA Astrophysics Data System (ADS)

Akkutlu, I. Yucel; Efendiev, Yalchin; Vasilyeva, Maria; Wang, Yuhe

2018-01-01

Inherently coupled flow and geomechanics processes in fractured shale media have implications for shale gas production. The system involves highly complex geo-textures comprised of a heterogeneous anisotropic fracture network spatially embedded in an ultra-tight matrix. In addition, nonlinearities due to viscous flow, diffusion, and desorption in the matrix and high velocity gas flow in the fractures complicates the transport. In this paper, we develop a multiscale model reduction approach to couple gas flow and geomechanics in fractured shale media. A Discrete Fracture Model (DFM) is used to treat the complex network of fractures on a fine grid. The coupled flow and geomechanics equations are solved using a fixed stress-splitting scheme by solving the pressure equation using a continuous Galerkin method and the displacement equation using an interior penalty discontinuous Galerkin method. We develop a coarse grid approximation and coupling using the Generalized Multiscale Finite Element Method (GMsFEM). GMsFEM constructs the multiscale basis functions in a systematic way to capture the fracture networks and their interactions with the shale matrix. Numerical results and an error analysis is provided showing that the proposed approach accurately captures the coupled process using a few multiscale basis functions, i.e. a small fraction of the degrees of freedom of the fine-scale problem.

Impact of trucking network flow on preferred biorefinery locations in the southern United States

Treesearch

Timothy M. Young; Lee D. Han; James H. Perdue; Stephanie R. Hargrove; Frank M. Guess; Xia Huang; Chung-Hao Chen

2017-01-01

The impact of the trucking transportation network flow was modeled for the southern United States. The study addresses a gap in existing research by applying a Bayesian logistic regression and Geographic Information System (GIS) geospatial analysis to predict biorefinery site locations. A one-way trucking cost assuming a 128.8 km (80-mile) haul distance was estimated...

Communication Dynamics in Finite Capacity Social Networks

NASA Astrophysics Data System (ADS)

Haerter, Jan O.; Jamtveit, Bjørn; Mathiesen, Joachim

2012-10-01

In communication networks, structure and dynamics are tightly coupled. The structure controls the flow of information and is itself shaped by the dynamical process of information exchanged between nodes. In order to reconcile structure and dynamics, a generic model, based on the local interaction between nodes, is considered for the communication in large social networks. In agreement with data from a large human organization, we show that the flow is non-Markovian and controlled by the temporal limitations of individuals. We confirm the versatility of our model by predicting simultaneously the degree-dependent node activity, the balance between information input and output of nodes, and the degree distribution. Finally, we quantify the limitations to network analysis when it is based on data sampled over a finite period of time.

A streamline splitting pore-network approach for computationally inexpensive and accurate simulation of transport in porous media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mehmani, Yashar; Oostrom, Martinus; Balhoff, Matthew

2014-03-20

Several approaches have been developed in the literature for solving flow and transport at the pore-scale. Some authors use a direct modeling approach where the fundamental flow and transport equations are solved on the actual pore-space geometry. Such direct modeling, while very accurate, comes at a great computational cost. Network models are computationally more efficient because the pore-space morphology is approximated. Typically, a mixed cell method (MCM) is employed for solving the flow and transport system which assumes pore-level perfect mixing. This assumption is invalid at moderate to high Peclet regimes. In this work, a novel Eulerian perspective on modelingmore » flow and transport at the pore-scale is developed. The new streamline splitting method (SSM) allows for circumventing the pore-level perfect mixing assumption, while maintaining the computational efficiency of pore-network models. SSM was verified with direct simulations and excellent matches were obtained against micromodel experiments across a wide range of pore-structure and fluid-flow parameters. The increase in the computational cost from MCM to SSM is shown to be minimal, while the accuracy of SSM is much higher than that of MCM and comparable to direct modeling approaches. Therefore, SSM can be regarded as an appropriate balance between incorporating detailed physics and controlling computational cost. The truly predictive capability of the model allows for the study of pore-level interactions of fluid flow and transport in different porous materials. In this paper, we apply SSM and MCM to study the effects of pore-level mixing on transverse dispersion in 3D disordered granular media.« less

Adding the ‘heart’ to hanging drop networks for microphysiological multi-tissue experiments†

PubMed Central

Yazdi, Saeed Rismani; Shadmani, Amir; Bürgel, Sebastian C.; Misun, Patrick M.; Hierlemann, Andreas; Frey, Olivier

2017-01-01

Microfluidic hanging-drop networks enable culturing and analysis of 3D microtissue spheroids derived from different cell types under controlled perfusion and investigating inter-tissue communication in multi-tissue formats. In this paper we introduce a compact on-chip pumping approach for flow control in hanging-drop networks. The pump includes one pneumatic chamber located directly above one of the hanging drops and uses the surface tension at the liquid–air-interface for flow actuation. Control of the pneumatic protocol provides a wide range of unidirectional pulsatile and continuous flow profiles. With the proposed concept several independent hanging-drop networks can be operated in parallel with only one single pneumatic actuation line at high fidelity. Closed-loop medium circulation between different organ models for multi-tissue formats and multiple simultaneous assays in parallel are possible. Finally, we implemented a real-time feedback control-loop of the pump actuation based on the beating of a human iPS-derived cardiac microtissue cultured in the same system. This configuration allows for simulating physiological effects on the heart and their impact on flow circulation between the organ models on chip. PMID:26401602

Thermodynamics of random reaction networks.

PubMed

Fischer, Jakob; Kleidon, Axel; Dittrich, Peter

2015-01-01

Reaction networks are useful for analyzing reaction systems occurring in chemistry, systems biology, or Earth system science. Despite the importance of thermodynamic disequilibrium for many of those systems, the general thermodynamic properties of reaction networks are poorly understood. To circumvent the problem of sparse thermodynamic data, we generate artificial reaction networks and investigate their non-equilibrium steady state for various boundary fluxes. We generate linear and nonlinear networks using four different complex network models (Erdős-Rényi, Barabási-Albert, Watts-Strogatz, Pan-Sinha) and compare their topological properties with real reaction networks. For similar boundary conditions the steady state flow through the linear networks is about one order of magnitude higher than the flow through comparable nonlinear networks. In all networks, the flow decreases with the distance between the inflow and outflow boundary species, with Watts-Strogatz networks showing a significantly smaller slope compared to the three other network types. The distribution of entropy production of the individual reactions inside the network follows a power law in the intermediate region with an exponent of circa -1.5 for linear and -1.66 for nonlinear networks. An elevated entropy production rate is found in reactions associated with weakly connected species. This effect is stronger in nonlinear networks than in the linear ones. Increasing the flow through the nonlinear networks also increases the number of cycles and leads to a narrower distribution of chemical potentials. We conclude that the relation between distribution of dissipation, network topology and strength of disequilibrium is nontrivial and can be studied systematically by artificial reaction networks.

Thermodynamics of Random Reaction Networks

PubMed Central

Fischer, Jakob; Kleidon, Axel; Dittrich, Peter

2015-01-01

Reaction networks are useful for analyzing reaction systems occurring in chemistry, systems biology, or Earth system science. Despite the importance of thermodynamic disequilibrium for many of those systems, the general thermodynamic properties of reaction networks are poorly understood. To circumvent the problem of sparse thermodynamic data, we generate artificial reaction networks and investigate their non-equilibrium steady state for various boundary fluxes. We generate linear and nonlinear networks using four different complex network models (Erdős-Rényi, Barabási-Albert, Watts-Strogatz, Pan-Sinha) and compare their topological properties with real reaction networks. For similar boundary conditions the steady state flow through the linear networks is about one order of magnitude higher than the flow through comparable nonlinear networks. In all networks, the flow decreases with the distance between the inflow and outflow boundary species, with Watts-Strogatz networks showing a significantly smaller slope compared to the three other network types. The distribution of entropy production of the individual reactions inside the network follows a power law in the intermediate region with an exponent of circa −1.5 for linear and −1.66 for nonlinear networks. An elevated entropy production rate is found in reactions associated with weakly connected species. This effect is stronger in nonlinear networks than in the linear ones. Increasing the flow through the nonlinear networks also increases the number of cycles and leads to a narrower distribution of chemical potentials. We conclude that the relation between distribution of dissipation, network topology and strength of disequilibrium is nontrivial and can be studied systematically by artificial reaction networks. PMID:25723751

Processing the Army’s Wartime Replacements: The Preferred CONUS Replacement Center Concept.

DTIC Science & Technology

1987-12-01

Replacement System. The first model, the macro model, was a network flow model which was used to analyze the flow of replacements from their source through...individual CRCs. Through the analysis of the macro model, recommendations were made on how the CRC system should be configured in terms of size, location

Routing Algorithm based on Minimum Spanning Tree and Minimum Cost Flow for Hybrid Wireless-optical Broadband Access Network

NASA Astrophysics Data System (ADS)

Le, Zichun; Suo, Kaihua; Fu, Minglei; Jiang, Ling; Dong, Wen

2012-03-01

In order to minimize the average end to end delay for data transporting in hybrid wireless optical broadband access network, a novel routing algorithm named MSTMCF (minimum spanning tree and minimum cost flow) is devised. The routing problem is described as a minimum spanning tree and minimum cost flow model and corresponding algorithm procedures are given. To verify the effectiveness of MSTMCF algorithm, extensively simulations based on OWNS have been done under different types of traffic source.

Numerical modeling of fluid and oxygen exchanges through microcirculation for the assessment of microcirculation alterations caused by type 2 diabetes.

PubMed

Tang, Yuanliang; He, Ying

2018-05-01

Type 2 diabetes mellitus (DM2) is frequently accompanied by microcirculation complications, including structural and functional alterations, which may have serious effects on substance exchanges between blood and interstitial tissue and the health of organs. In this paper, we aim to study the influence of microcirculation alterations in DM2 patients on fluid and oxygen exchanges through a model analysis. A fluid flow and oxygen transport model were developed by considering the interplay between blood in capillary network and interstitial tissue. The two regions were separately represented by 1D network model and 3D volume model, and the immersed boundary method (IBM) was adopted to solve fluid and mass transfer between these two regions. By using the model, the steady flow field and the distributions of oxygen in capillary network and surrounding tissue were firstly simulated. In the interstitial volume, fluid pressure and oxygen tension decreased with the increase of distance from the network; in the network, oxygen tension in blood plasma dropped from 100 mm Hg at the entrance to about 40 mm Hg at the exit. We further tested several structural and functional disorders related to diabetic pathological conditions. Simulated results show that the impaired connectivity of the network could result in poor robustness in maintaining blood flow and perfused surface; under high fluid permeability conditions of capillary walls, the pressure gradient was much larger around the capillary bed, and this alteration led to a saturation level of the interstitial pressure when lymphatic flow drainage can't work effectively; the variations in network connectivity and permeability of capillary wall also had unfavorable influence on oxygen distributions in interstitial tissue. In addition, when the oxygen releasing capacity of hemoglobin was confined by glycosylated hemoglobin (HbA1) in the case of diabetes, the plasma could not be complemented with adequate oxygen and thus the hypoxic tissue range will be extended. This study illustrates that when microcirculation disturbances, including the structure of capillary network, the wall osmosis property and the capacity of blood binding oxygen occur in DM2, some negative impacts are raised on microvascular hemodynamics and metabolism circumstance of interstitial tissue. Copyright © 2018 Elsevier Inc. All rights reserved.

Construction of pore network models for Berea and Fontainebleau sandstones using non-linear programing and optimization techniques

NASA Astrophysics Data System (ADS)

Sharqawy, Mostafa H.

2016-12-01

Pore network models (PNM) of Berea and Fontainebleau sandstones were constructed using nonlinear programming (NLP) and optimization methods. The constructed PNMs are considered as a digital representation of the rock samples which were based on matching the macroscopic properties of the porous media and used to conduct fluid transport simulations including single and two-phase flow. The PNMs consisted of cubic networks of randomly distributed pores and throats sizes and with various connectivity levels. The networks were optimized such that the upper and lower bounds of the pore sizes are determined using the capillary tube bundle model and the Nelder-Mead method instead of guessing them, which reduces the optimization computational time significantly. An open-source PNM framework was employed to conduct transport and percolation simulations such as invasion percolation and Darcian flow. The PNM model was subsequently used to compute the macroscopic properties; porosity, absolute permeability, specific surface area, breakthrough capillary pressure, and primary drainage curve. The pore networks were optimized to allow for the simulation results of the macroscopic properties to be in excellent agreement with the experimental measurements. This study demonstrates that non-linear programming and optimization methods provide a promising method for pore network modeling when computed tomography imaging may not be readily available.

Two-Photon Imaging of Cortical Surface Microvessels Reveals a Robust Redistribution in Blood Flow after Vascular Occlusion

PubMed Central

Schaffer, Chris B; Friedman, Beth; Nishimura, Nozomi; Schroeder, Lee F; Tsai, Philbert S; Ebner, Ford F; Lyden, Patrick D

2006-01-01

A highly interconnected network of arterioles overlies mammalian cortex to route blood to the cortical mantle. Here we test if this angioarchitecture can ensure that the supply of blood is redistributed after vascular occlusion. We use rodent parietal cortex as a model system and image the flow of red blood cells in individual microvessels. Changes in flow are quantified in response to photothrombotic occlusions to individual pial arterioles as well as to physical occlusions of the middle cerebral artery (MCA), the primary source of blood to this network. We observe that perfusion is rapidly reestablished at the first branch downstream from a photothrombotic occlusion through a reversal in flow in one vessel. More distal downstream arterioles also show reversals in flow. Further, occlusion of the MCA leads to reversals in flow through approximately half of the downstream but distant arterioles. Thus the cortical arteriolar network supports collateral flow that may mitigate the effects of vessel obstruction, as may occur secondary to neurovascular pathology. PMID:16379497

Flow model for open-channel reach or network

USGS Publications Warehouse

Schaffranek, R.W.

1987-01-01

Formulation of a one-dimensional model for simulating unsteady flow in a single open-channel reach or in a network of interconnected channels is presented. The model is both general and flexible in that it can be used to simulate a wide range of flow conditions for various channel configurations. It is based on a four-point (box), implicit, finite-difference approximation of the governing nonlinear flow equations with user-definable weighting coefficients to permit varying the solution scheme from box-centered to fully forward. Unique transformation equations are formulated that permit correlation of the unknowns at the extremities of the channels, thereby reducing coefficient matrix and execution time requirements. Discharges and water-surface elevations computed at intermediate locations within a channel are determined following solution of the transformation equations. The matrix of transformation and boundary-condition equations is solved by Gauss elimination using maximum pivot strategy. Two diverse applications of the model are presented to illustrate its broad utility. (USGS)

An algorithm for treating flat areas and depressions in digital elevation models using linear interpolation

Treesearch

F. Pan; M. Stieglitz; R.B. McKane

2012-01-01

Digital elevation model (DEM) data are essential to hydrological applications and have been widely used to calculate a variety of useful topographic characteristics, e.g., slope, flow direction, flow accumulation area, stream channel network, topographic index, and others. Except for slope, none of the other topographic characteristics can be calculated until the flow...

Landscape aridity, fire severity and rainfall intensity as controls on debris flow frequency after the 2009 Black Saturday Wildfires in Victoria

NASA Astrophysics Data System (ADS)

Nyman, Petter; Sherwin, Christopher; Sheridan, Gary; Lane, Patrick

2015-04-01

This study uses aerial imagery and field surveys to develop a statistical model for determining debris flow susceptibility in a landscape with variable terrain, soil and vegetation properties. A measure of landscape scale debris flow response was obtained by recording all debris flow affected drainage lines in the first year after fire in a ~258 000 ha forested area that was burned by the 2009 Black Saturday Wildfire in Victoria. A total of 12 500 points along the drainage network were sampled from catchments ranging in size from 0.0001 km2to 75 km2. Local slope and the attributes of the drainage areas (including the spatially averaged peak intensity) were extracted for each sample point. A logistic regression was used to model how debris flow susceptibility varies with the normalised burn ratio (dNBR, from Landsat imagery), rainfall intensity (from rainfall radar), slope (from DEM) and aridity (from long-term radiation, temperature and rainfall data).The model of debris flow susceptibility produced a good fit with the observed debris flow response of drainage networks within the burned area and was reliable in distinguishing between drainage lines which produced debris flows and those which didn't. The performance of the models was tested through multiple iterations of fitting and testing using unseen data. The local channel slope captured the effect of scale on debris flow susceptibility with debris flow probability approaching zero as the channel slope decreased with increasing drainage area. Aridity emerged as an important predictor of debris flow susceptibility, with increased likelihood of debris flows in drier parts of the landscape, thus reinforcing previous research in the region showing that post-fire surface runoff from wet Eucalypt forests is insufficient for initiating debris flows. Fire severity, measured as dNBR, was also a very important predictor. The inclusion of local channel slope as a predictor of debris flow susceptibility proved to be an effective approach for implicitly incorporating scale and relief as parameters. When combined with models of debris flow magnitude the results from this study can be used obtain continuous probability-magnitude relations of sediment flux from debris flows for drainage networks across entire burned areas.

Controls of the U.S. Virtual Water Transfer Network

NASA Astrophysics Data System (ADS)

Garcia, S.; Mejia, A.

2017-12-01

A complex interplay of human and natural factors shape the economic geography of the U.S., operating through socioeconomic forces that drive the consumption, production, and exchange of commodities. The virtual water content of a commodity represents the water embedded in its production. This work investigates the controls of national bilateral transfers of the virtual water transfer network (VWTN), through a gravity-type spatial interaction model. We use a probabilistic model to predict the binary network and investigate whether the gravity model can explain the topological properties of the empirical weighted network. In general, the gravity model relates transfer flows to the mass of the trading regions and their geographical distance. We hypothesize that properties of the nodes such as population, employment, and availability of land, together with the Euclidean distance between two trading regions, capture the main drivers of the national VWTN. The results from the model are then compared to the empirical weighted network to verify its ability to model the structure of this self-organized system. The proposed empirical model provides insight into the processes that underlie the formation of the VWTN. It can be a promising tool to study how flows are affected by changes in the generating conditions due to shocks and/or stresses.

Quantifying the Effects of Spatial Uncertainty in Fracture Permeability on CO2 Leakage through Columbia River Basalt Flow Interiors

NASA Astrophysics Data System (ADS)

Gierzynski, A.; Pollyea, R.

2016-12-01

Recent studies suggest that continental flood basalts may be suitable for geologic carbon sequestration, due to fluid-rock reactions that mineralize injected CO2 on relatively short time-scales. Flood basalts also possess a morphological structure conducive to injection, with alternating high-permeability (flow margin) and low-permeability (flow interior) layers. However, little information exists on the behavior of CO2 migration within field-scale fracture networks, particularly within flow interiors and at conditions near the critical point for CO2. In this study, numerical simulation is used to investigate the influence of fracture permeability uncertainty during gravity-driven CO2 migration within a jointed basalt flow interior as CO2 undergoes phase change from supercritical fluid to a subcritical phase. The model domain comprises a 2D fracture network mapped with terrestrial LiDAR scans of Columbia River Basalt acquired near Starbuck, WA. The model domain is 5 m × 5 m with bimodal heterogeneity (fracture and matrix), and initial conditions corresponding to a hydrostatic pressure gradient between 750 and 755 m depth. Under these conditions, the critical point for CO2 occurs 1.5 m above the bottom of the domain. For this model scenario, CO2 enters the base of the fracture network at 0.5 MPa overpressure, and matrix permeability is assumed constant. Fracture permeability follows a lognormal distribution on the basis of fracture aperture values from literature. In order to account for spatial uncertainty, the lognormal fracture permeability distribution is randomly located in the model domain and CO2 migration is simulated within the same fracture network for 50 equally probable realizations. Model results suggest that fracture connectivity, which is independent of permeability distribution, governs the path taken by buoyant CO2 as it rises through the flow interior; however, the permeability distribution strongly governs the CO2 flux magnitude. In particular, this research shows that even where fracture networks are sufficiently connected, CO2 flux is often inhibited by a cell of lower permeability, analogous to an obstruction or asperity in a natural fracture. This impresses the importance of considering spatial uncertainty in fracture apertures when modeling CO2 leakage through a caprock.

Deep Vadose Zone Flow and Transport Behavior at T-Tunnel Complex, Rainier Mesa, Nevada National Security Site

NASA Astrophysics Data System (ADS)

Parashar, R.; Reeves, D. M.

2010-12-01

Rainier Mesa, a tuffaceous plateau on the Nevada National Security Site, has been the location of numerous subsurface nuclear tests conducted in a series of tunnel complexes located approximately 450 m below the top of the mesa and 500 m above the regional groundwater flow system. The tunnels were constructed near the middle of an 800 m Tertiary sequence of faulted, low-permeability welded and non-welded bedded, vitric, and zeolitized tuff units. Water levels from wells in the vicinity of the T-tunnel complex indicate the presence of a perched saturation zone located approximately 100 m above the T-tunnel complex. This upper zone of saturation extends downward through most of the Tertiary sequence. The groundwater table is located at an elevation of 1300 m within a thrust sheet of Paleozoic carbonates, corresponding to the lower carbonate aquifer hydrostratigraphic unit (LCA3). The LCA3 is considered to be hydraulically connected to the Death Valley regional flow system. The objective of this project is to simulate complex downward patterns of fluid flow and radionuclide transport from the T-tunnel complex through the matrix and fault networks of the Tertiary tuff units to the water table. We developed an improved fracture characterization and mapping methodology consisting of displacement-length scaling relationships, simulation of realistic fault networks based on site-specific data, and the development of novel fracture network upscaling techniques that preserves fracture network flow and transport properties on coarse continuum grid. Development of upscaling method for fracture continua is based on the concepts of discrete fracture network modeling approach which performs better at honoring network connectivity and anisotropy of sparse networks in comparison to other established methods such as a tensor approach. Extensive flow simulations in the dual-continuum framework demonstrate that the characteristics of fault networks strongly influences the saturation profile and formation of perched zones, although they may not conduct a large amount of flow when compared to the matrix continua. The simulated results are found to be very sensitive to distribution of fracture aperture, density of the network, and spatial pattern of fracture clustering. The faults provide rapid pathways for radionuclide transport and the conceptual modeling of diffusional mass transfer between matrix and fracture continua plays a vital role in prediction of the overall behavior of the breakthrough curve.

Predicting bifurcation angle effect on blood flow in the microvasculature.

PubMed

Yang, Jiho; Pak, Y Eugene; Lee, Tae-Rin

2016-11-01

Since blood viscosity is a basic parameter for understanding hemodynamics in human physiology, great amount of research has been done in order to accurately predict this highly non-Newtonian flow property. However, previous works lacked in consideration of hemodynamic changes induced by heterogeneous vessel networks. In this paper, the effect of bifurcation on hemodynamics in a microvasculature is quantitatively predicted. The flow resistance in a single bifurcation microvessel was calculated by combining a new simple mathematical model with 3-dimensional flow simulation for varying bifurcation angles under physiological flow conditions. Interestingly, the results indicate that flow resistance induced by vessel bifurcation holds a constant value of approximately 0.44 over the whole single bifurcation model below diameter of 60μm regardless of geometric parameters including bifurcation angle. Flow solutions computed from this new model showed substantial decrement in flow velocity relative to other mathematical models, which do not include vessel bifurcation effects, while pressure remained the same. Furthermore, when applying the bifurcation angle effect to the entire microvascular network, the simulation results gave better agreements with recent in vivo experimental measurements. This finding suggests a new paradigm in microvascular blood flow properties, that vessel bifurcation itself, regardless of its angle, holds considerable influence on blood viscosity, and this phenomenon will help to develop new predictive tools in microvascular research. Copyright © 2016 Elsevier Inc. All rights reserved.

Robust optimal control of material flows in demand-driven supply networks

NASA Astrophysics Data System (ADS)

Laumanns, Marco; Lefeber, Erjen

2006-04-01

We develop a model based on stochastic discrete-time controlled dynamical systems in order to derive optimal policies for controlling the material flow in supply networks. Each node in the network is described as a transducer such that the dynamics of the material and information flows within the entire network can be expressed by a system of first-order difference equations, where some inputs to the system act as external disturbances. We apply methods from constrained robust optimal control to compute the explicit control law as a function of the current state. For the numerical examples considered, these control laws correspond to certain classes of optimal ordering policies from inventory management while avoiding, however, any a priori assumptions about the general form of the policy.

Modeling flow and transport in fracture networks using graphs

NASA Astrophysics Data System (ADS)

Karra, S.; O'Malley, D.; Hyman, J. D.; Viswanathan, H. S.; Srinivasan, G.

2018-03-01

Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital for improving the performance of subsurface applications. Fracture sizes in these systems can range from millimeters to kilometers. Although modeling flow and transport using the discrete fracture network (DFN) approach is known to be more accurate due to incorporation of the detailed fracture network structure over continuum-based methods, capturing the flow and transport in such a wide range of scales is still computationally intractable. Furthermore, if one has to quantify uncertainty, hundreds of realizations of these DFN models have to be run. To reduce the computational burden, we solve flow and transport on a graph representation of a DFN. We study the accuracy of the graph approach by comparing breakthrough times and tracer particle statistical data between the graph-based and the high-fidelity DFN approaches, for fracture networks with varying number of fractures and degree of heterogeneity. Due to our recent developments in capabilities to perform DFN high-fidelity simulations on fracture networks with large number of fractures, we are in a unique position to perform such a comparison. We show that the graph approach shows a consistent bias with up to an order of magnitude slower breakthrough when compared to the DFN approach. We show that this is due to graph algorithm's underprediction of the pressure gradients across intersections on a given fracture, leading to slower tracer particle speeds between intersections and longer travel times. We present a bias correction methodology to the graph algorithm that reduces the discrepancy between the DFN and graph predictions. We show that with this bias correction, the graph algorithm predictions significantly improve and the results are very accurate. The good accuracy and the low computational cost, with O (104) times lower times than the DFN, makes the graph algorithm an ideal technique to incorporate in uncertainty quantification methods.

Modeling flow and transport in fracture networks using graphs.

PubMed

Karra, S; O'Malley, D; Hyman, J D; Viswanathan, H S; Srinivasan, G

2018-03-01

Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital for improving the performance of subsurface applications. Fracture sizes in these systems can range from millimeters to kilometers. Although modeling flow and transport using the discrete fracture network (DFN) approach is known to be more accurate due to incorporation of the detailed fracture network structure over continuum-based methods, capturing the flow and transport in such a wide range of scales is still computationally intractable. Furthermore, if one has to quantify uncertainty, hundreds of realizations of these DFN models have to be run. To reduce the computational burden, we solve flow and transport on a graph representation of a DFN. We study the accuracy of the graph approach by comparing breakthrough times and tracer particle statistical data between the graph-based and the high-fidelity DFN approaches, for fracture networks with varying number of fractures and degree of heterogeneity. Due to our recent developments in capabilities to perform DFN high-fidelity simulations on fracture networks with large number of fractures, we are in a unique position to perform such a comparison. We show that the graph approach shows a consistent bias with up to an order of magnitude slower breakthrough when compared to the DFN approach. We show that this is due to graph algorithm's underprediction of the pressure gradients across intersections on a given fracture, leading to slower tracer particle speeds between intersections and longer travel times. We present a bias correction methodology to the graph algorithm that reduces the discrepancy between the DFN and graph predictions. We show that with this bias correction, the graph algorithm predictions significantly improve and the results are very accurate. The good accuracy and the low computational cost, with O(10^{4}) times lower times than the DFN, makes the graph algorithm an ideal technique to incorporate in uncertainty quantification methods.

Modeling flow and transport in fracture networks using graphs

DOE PAGES

Karra, S.; O'Malley, D.; Hyman, J. D.; ...

2018-03-09

Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital for improving the performance of subsurface applications. Fracture sizes in these systems can range from millimeters to kilometers. Although modeling flow and transport using the discrete fracture network (DFN) approach is known to be more accurate due to incorporation of the detailed fracture network structure over continuum-based methods, capturing the flow and transport in such a wide range of scales is still computationally intractable. Furthermore, if one has to quantify uncertainty, hundreds of realizationsmore » of these DFN models have to be run. To reduce the computational burden, we solve flow and transport on a graph representation of a DFN. We study the accuracy of the graph approach by comparing breakthrough times and tracer particle statistical data between the graph-based and the high-fidelity DFN approaches, for fracture networks with varying number of fractures and degree of heterogeneity. Due to our recent developments in capabilities to perform DFN high-fidelity simulations on fracture networks with large number of fractures, we are in a unique position to perform such a comparison. We show that the graph approach shows a consistent bias with up to an order of magnitude slower breakthrough when compared to the DFN approach. We show that this is due to graph algorithm's underprediction of the pressure gradients across intersections on a given fracture, leading to slower tracer particle speeds between intersections and longer travel times. We present a bias correction methodology to the graph algorithm that reduces the discrepancy between the DFN and graph predictions. We show that with this bias correction, the graph algorithm predictions significantly improve and the results are very accurate. In conclusion, the good accuracy and the low computational cost, with O(10 4) times lower times than the DFN, makes the graph algorithm an ideal technique to incorporate in uncertainty quantification methods.« less

Modeling flow and transport in fracture networks using graphs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karra, S.; O'Malley, D.; Hyman, J. D.

Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital for improving the performance of subsurface applications. Fracture sizes in these systems can range from millimeters to kilometers. Although modeling flow and transport using the discrete fracture network (DFN) approach is known to be more accurate due to incorporation of the detailed fracture network structure over continuum-based methods, capturing the flow and transport in such a wide range of scales is still computationally intractable. Furthermore, if one has to quantify uncertainty, hundreds of realizationsmore » of these DFN models have to be run. To reduce the computational burden, we solve flow and transport on a graph representation of a DFN. We study the accuracy of the graph approach by comparing breakthrough times and tracer particle statistical data between the graph-based and the high-fidelity DFN approaches, for fracture networks with varying number of fractures and degree of heterogeneity. Due to our recent developments in capabilities to perform DFN high-fidelity simulations on fracture networks with large number of fractures, we are in a unique position to perform such a comparison. We show that the graph approach shows a consistent bias with up to an order of magnitude slower breakthrough when compared to the DFN approach. We show that this is due to graph algorithm's underprediction of the pressure gradients across intersections on a given fracture, leading to slower tracer particle speeds between intersections and longer travel times. We present a bias correction methodology to the graph algorithm that reduces the discrepancy between the DFN and graph predictions. We show that with this bias correction, the graph algorithm predictions significantly improve and the results are very accurate. In conclusion, the good accuracy and the low computational cost, with O(10 4) times lower times than the DFN, makes the graph algorithm an ideal technique to incorporate in uncertainty quantification methods.« less

Sensitivity Analysis of Genetic Algorithm Parameters for Optimal Groundwater Monitoring Network Design

NASA Astrophysics Data System (ADS)

Abdeh-Kolahchi, A.; Satish, M.; Datta, B.

2004-05-01

A state art groundwater monitoring network design is introduced. The method combines groundwater flow and transport results with optimization Genetic Algorithm (GA) to identify optimal monitoring well locations. Optimization theory uses different techniques to find a set of parameter values that minimize or maximize objective functions. The suggested groundwater optimal monitoring network design is based on the objective of maximizing the probability of tracking a transient contamination plume by determining sequential monitoring locations. The MODFLOW and MT3DMS models included as separate modules within the Groundwater Modeling System (GMS) are used to develop three dimensional groundwater flow and contamination transport simulation. The groundwater flow and contamination simulation results are introduced as input to the optimization model, using Genetic Algorithm (GA) to identify the groundwater optimal monitoring network design, based on several candidate monitoring locations. The groundwater monitoring network design model is used Genetic Algorithms with binary variables representing potential monitoring location. As the number of decision variables and constraints increase, the non-linearity of the objective function also increases which make difficulty to obtain optimal solutions. The genetic algorithm is an evolutionary global optimization technique, which is capable of finding the optimal solution for many complex problems. In this study, the GA approach capable of finding the global optimal solution to a groundwater monitoring network design problem involving 18.4X 1018 feasible solutions will be discussed. However, to ensure the efficiency of the solution process and global optimality of the solution obtained using GA, it is necessary that appropriate GA parameter values be specified. The sensitivity analysis of genetic algorithms parameters such as random number, crossover probability, mutation probability, and elitism are discussed for solution of monitoring network design.

Two-Phase Acto-Cytosolic Fluid Flow in a Moving Keratocyte: A 2D Continuum Model.

PubMed

Nikmaneshi, M R; Firoozabadi, B; Saidi, M S

2015-09-01

The F-actin network and cytosol in the lamellipodia of crawling cells flow in a centripetal pattern and spout-like form, respectively. We have numerically studied this two-phase flow in the realistic geometry of a moving keratocyte. Cytosol has been treated as a low viscosity Newtonian fluid flowing through the high viscosity porous medium of F-actin network. Other involved phenomena including myosin activity, adhesion friction, and interphase interaction are also discussed to provide an overall view of this problem. Adopting a two-phase coupled model by myosin concentration, we have found new accurate perspectives of acto-cytosolic flow and pressure fields, myosin distribution, as well as the distribution of effective forces across the lamellipodia of a keratocyte with stationary shape. The order of magnitude method is also used to determine the contribution of forces in the internal dynamics of lamellipodia.

Decompositions of injection patterns for nodal flow allocation in renewable electricity networks

NASA Astrophysics Data System (ADS)

Schäfer, Mirko; Tranberg, Bo; Hempel, Sabrina; Schramm, Stefan; Greiner, Martin

2017-08-01

The large-scale integration of fluctuating renewable power generation represents a challenge to the technical and economical design of a sustainable future electricity system. In this context, the increasing significance of long-range power transmission calls for innovative methods to understand the emerging complex flow patterns and to integrate price signals about the respective infrastructure needs into the energy market design. We introduce a decomposition method of injection patterns. Contrary to standard flow tracing approaches, it provides nodal allocations of link flows and costs in electricity networks by decomposing the network injection pattern into market-inspired elementary import/export building blocks. We apply the new approach to a simplified data-driven model of a European electricity grid with a high share of renewable wind and solar power generation.

Establishing a Multi-scale Stream Gaging Network in the Whitewater River Basin, Kansas, USA

USGS Publications Warehouse

Clayton, J.A.; Kean, J.W.

2010-01-01

Investigating the routing of streamflow through a large drainage basin requires the determination of discharge at numerous locations in the channel network. Establishing a dense network of stream gages using conventional methods is both cost-prohibitive and functionally impractical for many research projects. We employ herein a previously tested, fluid-mechanically based model for generating rating curves to establish a stream gaging network in the Whitewater River basin in south-central Kansas. The model was developed for the type of channels typically found in this watershed, meaning that it is designed to handle deep, narrow geomorphically stable channels with irregular planforms, and can model overbank flow over a vegetated floodplain. We applied the model to ten previously ungaged stream reaches in the basin, ranging from third- to sixth-order channels. At each site, detailed field measurements of the channel and floodplain morphology, bed and bank roughness, and vegetation characteristics were used to quantify the roughness for a range of flow stages, from low flow to overbank flooding. Rating curves that relate stage to discharge were developed for all ten sites. Both fieldwork and modeling were completed in less than 2 years during an anomalously dry period in the region, which underscores an advantage of using theoretically based (as opposed to empirically based) discharge estimation techniques. ?? 2010 Springer Science+Business Media B.V.

A quasi steady state method for solving transient Darcy flow in complex 3D fractured networks accounting for matrix to fracture flow

NASA Astrophysics Data System (ADS)

Nœtinger, B.

2015-02-01

Modeling natural Discrete Fracture Networks (DFN) receives more and more attention in applied geosciences, from oil and gas industry, to geothermal recovery and aquifer management. The fractures may be either natural, or artificial in case of well stimulation. Accounting for the flow inside the fracture network, and accounting for the transfers between the matrix and the fractures, with the same level of accuracy is an important issue for calibrating the well architecture and for setting up optimal resources recovery strategies. Recently, we proposed an original method allowing to model transient pressure diffusion in the fracture network only [1]. The matrix was assumed to be impervious. A systematic approximation scheme was built, allowing to model the initial DFN by a set of N unknowns located at each identified intersection between fractures. The higher N, the higher the accuracy of the model. The main assumption was using a quasi steady state hypothesis, that states that the characteristic diffusion time over one single fracture is negligible compared with the characteristic time of the macroscopic problem, e.g. change of boundary conditions. In that context, the lowest order approximation N = 1 has the form of solving a transient problem in a resistor/capacitor network, a so-called pipe network. Its topology is the same as the network of geometrical intersections between fractures. In this paper, we generalize this approach in order to account for fluxes from matrix to fractures. The quasi steady state hypothesis at the fracture level is still kept. Then, we show that in the case of well separated time scales between matrix and fractures, the preceding model needs only to be slightly modified in order to incorporate these fluxes. The additional knowledge of the so-called matrix to fracture transfer function allows to modify the mass matrix that becomes a time convolution operator. This is reminiscent of existing space averaged transient dual porosity models.

A Deep Learning based Approach to Reduced Order Modeling of Fluids using LSTM Neural Networks

NASA Astrophysics Data System (ADS)

Mohan, Arvind; Gaitonde, Datta

2017-11-01

Reduced Order Modeling (ROM) can be used as surrogates to prohibitively expensive simulations to model flow behavior for long time periods. ROM is predicated on extracting dominant spatio-temporal features of the flow from CFD or experimental datasets. We explore ROM development with a deep learning approach, which comprises of learning functional relationships between different variables in large datasets for predictive modeling. Although deep learning and related artificial intelligence based predictive modeling techniques have shown varied success in other fields, such approaches are in their initial stages of application to fluid dynamics. Here, we explore the application of the Long Short Term Memory (LSTM) neural network to sequential data, specifically to predict the time coefficients of Proper Orthogonal Decomposition (POD) modes of the flow for future timesteps, by training it on data at previous timesteps. The approach is demonstrated by constructing ROMs of several canonical flows. Additionally, we show that statistical estimates of stationarity in the training data can indicate a priori how amenable a given flow-field is to this approach. Finally, the potential and limitations of deep learning based ROM approaches will be elucidated and further developments discussed.

Branching pattern in natural drainage network

NASA Astrophysics Data System (ADS)

Hooshyar, M.; Singh, A.; Wang, D.

2017-12-01

The formation and growth of river channels and their network evolution are governed by the erosional and depositional processes operating on the landscape due to movement of water. The branching structure of drainage network is an important feature related to the network topology and contain valuable information about the forming mechanisms of the landscape. We studied the branching patterns in natural drainage networks, extracted from 1 m Digital Elevation Models (DEMs) of 120 catchments with minimal human impacts across the United States. We showed that the junction angles have two distinct modes an the observed modes are physically explained as the optimal angles that result in minimum energy dissipation and are linked to the exponent characterizing slope-area curve. Our findings suggest that the flow regimes, debris-flow dominated or fluvial, have distinct characteristic angles which are functions of the scaling exponent of the slope-area curve. These findings enable us to understand the geomorphological signature of hydrological processes on drainage networks and develop more refined landscape evolution models.

An algorithm for treating flat areas and depressions in digital elevation models using linear interpolation

EPA Science Inventory

Digital elevation model (DEM) data are essential to hydrological applications and have been widely used to calculate a variety of useful topographic characteristics, e.g., slope, flow direction, flow accumulation area, stream channel network, topographic index, and others. Excep...

Numerical Experiments on Advective Transport in Large Three-Dimensional Discrete Fracture Networks

NASA Astrophysics Data System (ADS)

Makedonska, N.; Painter, S. L.; Karra, S.; Gable, C. W.

2013-12-01

Modeling of flow and solute transport in discrete fracture networks is an important approach for understanding the migration of contaminants in impermeable hard rocks such as granite, where fractures provide dominant flow and transport pathways. The discrete fracture network (DFN) model attempts to mimic discrete pathways for fluid flow through a fractured low-permeable rock mass, and may be combined with particle tracking simulations to address solute transport. However, experience has shown that it is challenging to obtain accurate transport results in three-dimensional DFNs because of the high computational burden and difficulty in constructing a high-quality unstructured computational mesh on simulated fractures. An integrated DFN meshing [1], flow, and particle tracking [2] simulation capability that enables accurate flow and particle tracking simulation on large DFNs has recently been developed. The new capability has been used in numerical experiments on advective transport in large DFNs with tens of thousands of fractures and millions of computational cells. The modeling procedure starts from the fracture network generation using a stochastic model derived from site data. A high-quality computational mesh is then generated [1]. Flow is then solved using the highly parallel PFLOTRAN [3] code. PFLOTRAN uses the finite volume approach, which is locally mass conserving and thus eliminates mass balance problems during particle tracking. The flow solver provides the scalar fluxes on each control volume face. From the obtained fluxes the Darcy velocity is reconstructed for each node in the network [4]. Velocities can then be continuously interpolated to any point in the domain of interest, thus enabling random walk particle tracking. In order to describe the flow field on fractures intersections, the control volume cells on intersections are split into four planar polygons, where each polygon corresponds to a piece of a fracture near the intersection line. Thus, computational nodes lying on fracture intersections have four associated velocities, one on each side of the intersection in each fracture plane [2]. This information is used to route particles arriving at the fracture intersection to the appropriate downstream fracture segment. Verified for small DFNs, the new simulation capability allows accurate particle tracking on more realistic representations of fractured rock sites. In the current work we focus on travel time statistics and spatial dispersion and show numerical results in DFNs of different sizes, fracture densities, and transmissivity distributions. [1] Hyman J.D., Gable C.W., Painter S.L., Automated meshing of stochastically generated discrete fracture networks, Abstract H33G-1403, 2011 AGU, San Francisco, CA, 5-9 Dec. [2] N. Makedonska, S. L. Painter, T.-L. Hsieh, Q.M. Bui, and C. W. Gable., Development and verification of a new particle tracking capability for modeling radionuclide transport in discrete fracture networks, Abstract, 2013 IHLRWM, Albuquerque, NM, Apr. 28 - May 3. [3] Lichtner, P.C., Hammond, G.E., Bisht, G., Karra, S., Mills, R.T., and Kumar, J. (2013) PFLOTRAN User's Manual: A Massively Parallel Reactive Flow Code. [4] Painter S.L., Gable C.W., Kelkar S., Pathline tracing on fully unstructured control-volume grids, Computational Geosciences, 16 (4), 2012, 1125-1134.

Role of groundwater in formation of Martian channels

NASA Technical Reports Server (NTRS)

Howard, Alan D.

1991-01-01

A global 3-D model of groundwater flow has been used to study possible behavior of groundwater on Mars and its role in creating fluvial features. Conclusions drawn from an earlier 2-D groundwater model are supplemented and expanded. Topical headings are discussed as follows: timescales of groundwater flow; wet areas on Mars and location of outflow channels; implications for valley networks; the enigma of Hellas; absence of fluvial or periglacial features on Syrtis Major; development of chaotic terrain and associated outflow channels; and structurally controlled valley networks.

A flow-control mechanism for distributed systems

NASA Technical Reports Server (NTRS)

Maitan, J.

1991-01-01

A new approach to the rate-based flow control in store-and-forward networks is evaluated. Existing methods display oscillations in the presence of transport delays. The proposed scheme is based on the explicit use of an embedded dynamic model of a store-and-forward buffer in a controller's feedback loop. It is shown that the use of the model eliminates the oscillations caused by the transport delays. The paper presents simulation examples and assesses the applicability of the scheme in the new generation of high-speed photonic networks where transport delays must be considered.

Anomalous Transport in Natural Fracture Networks Induced by Tectonic Stress

NASA Astrophysics Data System (ADS)

Kang, P. K.; Lei, Q.; Lee, S.; Dentz, M.; Juanes, R.

2017-12-01

Fluid flow and transport in fractured rock controls many natural and engineered processes in the subsurface. However, characterizing flow and transport through fractured media is challenging due to the high uncertainty and large heterogeneity associated with fractured rock properties. In addition to these "static" challenges, geologic fractures are always under significant overburden stress, and changes in the stress state can lead to changes in the fracture's ability to conduct fluids. While confining stress has been shown to impact fluid flow through fractures in a fundamental way, the impact of confining stress on transportthrough fractured rock remains poorly understood. The link between anomalous (non-Fickian) transport and confining stress has been shown, only recently, at the level of a single rough fracture [1]. Here, we investigate the impact of geologic (tectonic) stress on flow and tracer transport through natural fracture networks. We model geomechanical effects in 2D fractured rock by means of a finite-discrete element method (FEMDEM) [2], which can capture the deformation of matrix blocks, reactivation of pre-existing fractures, and propagation of new cracks, upon changes in the stress field. We apply the model to a fracture network extracted from the geological map of an actual rock outcrop to obtain the aperture field at different stress conditions. We then simulate fluid flow and particle transport through the stressed fracture networks. We observe that anomalous transport emerges in response to confining stress on the fracture network, and show that the stress state is a powerful determinant of transport behavior: (1) An anisotropic stress state induces preferential flow paths through shear dilation; (2) An increase in geologic stress increases aperture heterogeneity that induces late-time tailing of particle breakthrough curves. Finally, we develop an effective transport model that captures the anomalous transport through the stressed fracture network. Our results point to a heretofore unrecognized link between geomechanics and anomalous transport in natural fractured media. [1] P. K. Kang, S. Brown, and R. Juanes, Earth and Planetary Science Letters, 454, 46-54 (2016). [2] Q. Lei, J. P. Latham, and C. F. Tsang, Computers and Geotechnics, 85, 151-176 (2017).

Numerical study of Free Convective Viscous Dissipative flow along Vertical Cone with Influence of Radiation using Network Simulation method

NASA Astrophysics Data System (ADS)

Kannan, R. M.; Pullepu, Bapuji; Immanuel, Y.

2018-04-01

A two dimensional mathematical model is formulated for the transient laminar free convective flow with heat transfer over an incompressible viscous fluid past a vertical cone with uniform surface heat flux with combined effects of viscous dissipation and radiation. The dimensionless boundary layer equations of the flow which are transient, coupled and nonlinear Partial differential equations are solved using the Network Simulation Method (NSM), a powerful numerical technique which demonstrates high efficiency and accuracy by employing the network simulator computer code Pspice. The velocity and temperature profiles have been investigated for various factors, namely viscous dissipation parameter ε, Prandtl number Pr and radiation Rd are analyzed graphically.

Dynamics of blood flow in a microfluidic ladder network

NASA Astrophysics Data System (ADS)

Maddala, Jeevan; Zilberman-Rudenko, Jevgenia; McCarty, Owen

The dynamics of a complex mixture of cells and proteins, such as blood, in perturbed shear flow remains ill-defined. Microfluidics is a promising technology for improving the understanding of blood flow under complex conditions of shear; as found in stent implants and in tortuous blood vessels. We model the fluid dynamics of blood flow in a microfluidic ladder network with dimensions mimicking venules. Interaction of blood cells was modeled using multiagent framework, where cells of different diameters were treated as spheres. This model served as the basis for predicting transition regions, collision pathways, re-circulation zones and residence times of cells dependent on their diameters and device architecture. Based on these insights from the model, we were able to predict the clot formation configurations at various locations in the device. These predictions were supported by the experiments using whole blood. To facilitate platelet aggregation, the devices were coated with fibrillar collagen and tissue factor. Blood was perfused through the microfluidic device for 9 min at a physiologically relevant venous shear rate of 600 s-1. Using fluorescent microscopy, we observed flow transitions near the channel intersections and at the areas of blood flow obstruction, which promoted larger thrombus formation. This study of integrating model predictions with experimental design, aids in defining the dynamics of blood flow in microvasculature and in development of novel biomedical devices.

Direct Numerical Simulation of Cellular-Scale Blood Flow in 3D Microvascular Networks.

PubMed

Balogh, Peter; Bagchi, Prosenjit

2017-12-19

We present, to our knowledge, the first direct numerical simulation of 3D cellular-scale blood flow in physiologically realistic microvascular networks. The vascular networks are designed following in vivo images and data, and are comprised of bifurcating, merging, and winding vessels. Our model resolves the large deformation and dynamics of each individual red blood cell flowing through the networks with high fidelity, while simultaneously retaining the highly complex geometric details of the vascular architecture. To our knowledge, our simulations predict several novel and unexpected phenomena. We show that heterogeneity in hemodynamic quantities, which is a hallmark of microvascular blood flow, appears both in space and time, and that the temporal heterogeneity is more severe than its spatial counterpart. The cells are observed to frequently jam at vascular bifurcations resulting in reductions in hematocrit and flow rate in the daughter and mother vessels. We find that red blood cell jamming at vascular bifurcations results in several orders-of-magnitude increase in hemodynamic resistance, and thus provides an additional mechanism of increased in vivo blood viscosity as compared to that determined in vitro. A striking result from our simulations is negative pressure-flow correlations observed in several vessels, implying a significant deviation from Poiseuille's law. Furthermore, negative correlations between vascular resistance and hematocrit are observed in various vessels, also defying a major principle of particulate suspension flow. To our knowledge, these novel findings are absent in blood flow in straight tubes, and they underscore the importance of considering realistic physiological geometry and resolved cellular interactions in modeling microvascular hemodynamics. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

One-dimensional and two-dimensional hydrodynamic modelling derived flow properties: Impacts on aquatic habitat quality predictions

Treesearch

Rohan Benjankar; Daniele Tonina; James McKean

2014-01-01

Studies of the effects of hydrodynamic model dimensionality on simulated flow properties and derived quantities such as aquatic habitat quality are limited. It is important to close this knowledge gap especially now that entire river networks can be mapped at the microhabitat scale due to the advent of point-cloud techniques. This study compares flow properties, such...

Simulation of Hydraulic and Natural Fracture Interaction Using a Coupled DFN-DEM Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, J.; Huang, H.; Deo, M.

2016-03-01

The presence of natural fractures will usually result in a complex fracture network due to the interactions between hydraulic and natural fracture. The reactivation of natural fractures can generally provide additional flow paths from formation to wellbore which play a crucial role in improving the hydrocarbon recovery in these ultra-low permeability reservoir. Thus, accurate description of the geometry of discrete fractures and bedding is highly desired for accurate flow and production predictions. Compared to conventional continuum models that implicitly represent the discrete feature, Discrete Fracture Network (DFN) models could realistically model the connectivity of discontinuities at both reservoir scale andmore » well scale. In this work, a new hybrid numerical model that couples Discrete Fracture Network (DFN) and Dual-Lattice Discrete Element Method (DL-DEM) is proposed to investigate the interaction between hydraulic fracture and natural fractures. Based on the proposed model, the effects of natural fracture orientation, density and injection properties on hydraulic-natural fractures interaction are investigated.« less

Simulation of Hydraulic and Natural Fracture Interaction Using a Coupled DFN-DEM Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

J. Zhou; H. Huang; M. Deo

The presence of natural fractures will usually result in a complex fracture network due to the interactions between hydraulic and natural fracture. The reactivation of natural fractures can generally provide additional flow paths from formation to wellbore which play a crucial role in improving the hydrocarbon recovery in these ultra-low permeability reservoir. Thus, accurate description of the geometry of discrete fractures and bedding is highly desired for accurate flow and production predictions. Compared to conventional continuum models that implicitly represent the discrete feature, Discrete Fracture Network (DFN) models could realistically model the connectivity of discontinuities at both reservoir scale andmore » well scale. In this work, a new hybrid numerical model that couples Discrete Fracture Network (DFN) and Dual-Lattice Discrete Element Method (DL-DEM) is proposed to investigate the interaction between hydraulic fracture and natural fractures. Based on the proposed model, the effects of natural fracture orientation, density and injection properties on hydraulic-natural fractures interaction are investigated.« less

Identifying the best locations to install flow control devices in sewer networks to enable in-sewer storage

NASA Astrophysics Data System (ADS)

Leitão, J. P.; Carbajal, J. P.; Rieckermann, J.; Simões, N. E.; Sá Marques, A.; de Sousa, L. M.

2018-01-01

The activation of available in-sewer storage volume has been suggested as a low-cost flood and combined sewer overflow mitigation measure. However, it is currently unknown what the attributes for suitable objective functions to identify the best location for flow control devices are and the impact of those attributes on the results. In this study, we present a novel location model and efficient algorithm to identify the best location(s) to install flow limiters. The model is a screening tool that does not require hydraulic simulations but rather considers steady state instead of simplistic static flow conditions. It also maximises in-sewer storage according to different reward functions that also considers the potential impact of flow control device failure. We demonstrate its usefulness on two real sewer networks, for which an in-sewer storage potential of approximately 2,000 m3 and 500 m3 was estimated with five flow control devices installed.

Coupled Multi-physics analysis of Caprock Integrity and Fault Reactivation during CO2 Sequestration*

NASA Astrophysics Data System (ADS)

Newell, P.; Martinez, M. J.; Bishop, J.

2012-12-01

Structural/stratigraphic trapping beneath a low-permeable caprock layer is the primary trapping mechanism for long-term subsurface sequestration of CO2. Pre-existing fracture networks, injection induced fractures, and faults are of concern for possible CO2 leakage both during and after injection. In this work we model the effects of both caprock jointing and a fault on the caprock sealing integrity during various injection scenarios. The modeling effort uses a three-dimensional finite-element based coupled multiphase flow and geomechanics simulator. The joints within the caprock are idealized as equally spaced and parallel. Both the mechanical and flow behavior of the joint network are treated within an effective continuum formulation. The mechanical behavior of the joint network is linear elastic in shear and nonlinear elastic in the normal direction. The flow behavior of the joint network is treated using the classical cubic-law relating flow rate and aperture. The flow behavior is then upscaled to obtain an effective permeability. The fault is modeled as a finite-thickness layer with multiple joint sets. The joint sets within the fault region are modeled following the same mechanical and flow formulation as the joints within the caprock. Various injection schedules as well as fault and caprock jointing configurations within a proto-typical sequestration site have been investigated. The resulting leakage rates through the caprock and fault are compared to those assuming intact material. The predicted leakage rates are a strong nonlinear function of the injection rate. *This material is based upon work supported as part of the Center for Frontiers of Subsurface Energy Security, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award Number DE-SC0001114. Sandia is a multi-program laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energys National Nuclear Security Administration under Contract DE-AC04-94AL85000.

High-efficient Extraction of Drainage Networks from Digital Elevation Model Data Constrained by Enhanced Flow Enforcement from Known River Map

NASA Astrophysics Data System (ADS)

Wu, T.; Li, T.; Li, J.; Wang, G.

2017-12-01

Improved drainage network extraction can be achieved by flow enforcement whereby information of known river maps is imposed to the flow-path modeling process. However, the common elevation-based stream burning method can sometimes cause unintended topological errors and misinterpret the overall drainage pattern. We presented an enhanced flow enforcement method to facilitate accurate and efficient process of drainage network extraction. Both the topology of the mapped hydrography and the initial landscape of the DEM are well preserved and fully utilized in the proposed method. An improved stream rasterization is achieved here, yielding continuous, unambiguous and stream-collision-free raster equivalent of stream vectors for flow enforcement. By imposing priority-based enforcement with a complementary flow direction enhancement procedure, the drainage patterns of the mapped hydrography are fully represented in the derived results. The proposed method was tested over the Rogue River Basin, using DEMs with various resolutions. As indicated by the visual and statistical analyses, the proposed method has three major advantages: (1) it significantly reduces the occurrences of topological errors, yielding very accurate watershed partition and channel delineation, (2) it ensures scale-consistent performance at DEMs of various resolutions, and (3) the entire extraction process is well-designed to achieve great computational efficiency.

Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors.

PubMed

Ali, Hany S M; Blagden, Nicholas; York, Peter; Amani, Amir; Brook, Toni

2009-06-28

This study employs artificial neural networks (ANNs) to create a model to identify relationships between variables affecting drug nanoprecipitation using microfluidic reactors. The input variables examined were saturation levels of prednisolone, solvent and antisolvent flow rates, microreactor inlet angles and internal diameters, while particle size was the single output. ANNs software was used to analyse a set of data obtained by random selection of the variables. The developed model was then assessed using a separate set of validation data and provided good agreement with the observed results. The antisolvent flow rate was found to have the dominant role on determining final particle size.

CityWaterBalance: Track Flows of Water Through an Urban System

EPA Science Inventory

CityWaterBalance provides a reproducible workflow for studying an urban water system. The network of urban water flows and storages can be modeled and visualized. Any city may be modeled with preassembled data, but data for US cities can be gathered via web services using this p...

WASP7 Stream Transport - Model Theory and User's Guide: Supplement to Water Quality Analysis Simulation Program (WASP) User Documentation

EPA Science Inventory

The standard WASP7 stream transport model calculates water flow through a branching stream network that may include both free-flowing and ponded segments. This supplemental user manual documents the hydraulic algorithms, including the transport and hydrogeometry equations, the m...

GIS-based channel flow and sediment transport simulation using CCHE1D coupled with AnnAGNPS

USDA-ARS?s Scientific Manuscript database

CCHE1D (Center for Computational Hydroscience and Engineering 1-Dimensional model) simulates unsteady free-surface flows with nonequilibrium, nonuniform sediment transport in dendritic channel networks. Since early 1990’s, the model and its software packages have been developed and continuously main...

Streamflow Forecasting Using Nuero-Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Nanduri, U. V.; Swain, P. C.

2005-12-01

The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system of the river basin that takes the rainfall as an input and produces the runoff is highly nonlinear, complicated and very difficult to fully comprehend. The system is influenced by large number of factors and variables. The large spatial extent of the systems forces the uncertainty into the hydrologic information. A variety of methods have been proposed for forecasting reservoir inflows including conceptual (physical) and empirical (statistical) models (WMO 1994), but none of them can be considered as unique superior model (Shamseldin 1997). Owing to difficulties of formulating reasonable non-linear watershed models, recent attempts have resorted to Neural Network (NN) approach for complex hydrologic modeling. In recent years the use of soft computing in the field of hydrological forecasting is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference System (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the system. It is a judicious combination of the Neural Networks and fuzzy systems. It can learn and generalize highly nonlinear and uncertain phenomena due to the embedded neural network (NN). NN is efficient in learning and generalization, and the fuzzy system mimics the cognitive capability of human brain. Hence, ANFIS can learn the complicated processes involved in the basin and correlate the precipitation to the corresponding discharge. In the present study, one step ahead forecasts are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A Neuro-Fuzzy model is developed to forecast ten-daily flows into the Hirakud reservoir on River Mahanadi in the state of Orissa in India. Correlation analysis is carried out to find out the most influential variables on the ten daily flow at Hirakud. Based on this analysis, four variables, namely, flow during the previous time period, ql1, rainfall during the previous two time periods, rl1 and rl2, and flow during the same period in previous year, qpy, are identified as the most influential variables to forecast the ten daily flow. Performance measures such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and coefficient of efficiency R2 are computed for training and testing phases of the model to evaluate its performance. The results indicate that the ten-daily forecasting model is efficient in predicting the high and medium flows with reasonable accuracy. The forecast of low flows is associated with less efficiency. REFERENCES Jang, J.S.R. (1993). "ANFIS: Adaptive - network- based fuzzy inference system." IEEE Trans. on Systems, Man and Cybernetics, 23 (3), 665-685. Shamseldin, A.Y. (1997). "Application of a neural network technique to rainfall-runoff modeling." Journal of Hydrology, 199, 272-294. World Meteorological Organization (1975). Intercomparison of conceptual models used in operational hydrological forecasting. World Meteorological Organization, Technical Report No.429, Geneva, Switzerland.

Statistical properties of world investment networks

NASA Astrophysics Data System (ADS)

Song, Dong-Ming; Jiang, Zhi-Qiang; Zhou, Wei-Xing

2009-06-01

We have performed a detailed investigation on the world investment networks constructed from the Coordinated Portfolio Investment Survey (CPIS) data of the International Monetary Fund, ranging from 2001 to 2006. The distributions of degrees and node strengths are scale-free. The weight distributions can be well modeled by the Weibull distribution. The maximum flow spanning trees of the world investment networks possess two universal allometric scaling relations, independent of time and the investment type. The topological scaling exponent is 1.17±0.02 and the flow scaling exponent is 1.03±0.01.

Insights to Engineered Geothermal System Performance Using Gringarten-Witherspoon-Ohnishi Analytical Solutions and Fracture Network Models

NASA Astrophysics Data System (ADS)

Doe, T.; McLaren, R.; Finilla, A.

2017-12-01

An enduring legacy of Paul Witherspoon and his students and colleagues has been both the development of geothermal energy and the bases of modern fractured-rock hydrogeology. One of the seminal contributions to the geothermal field was Gringarten, Witherspoon, and Ohnishi's analytical models for enhanced geothermal systems. Although discrete fracture network (DFN) modeling developed somewhat independently in the late 1970s, Paul Witherspoon's foresight in promoting underground in situ testing at the Stripa Mine in Sweden was a major driver in Lawrence Berkeley Laboratory's contributions to its development.This presentation looks extensions of Gringarten's analytical model into discrete fracture network modeling as a basis for providing further insights into the challenges and opportunities of engineered geothermal systems. The analytical solution itself has many insightful applications beyond those presented in the original paper. The definition of dimensionless time by itself shows that thermal breakthrough has a second power dependence on surface area and on flow rate. The fracture intensity also plays a strong role, as it both increases the surface area and decrease his flow rate per fracture. The improvement of EGS performance with fracture intensity reaches a limit where thermal depletion of the rock lags only slightly behind the thermal breakthrough of cold water in the fracture network.Simple network models, which couple a DFN generator (FracMan) with a hydrothermally coupled flow solver (HydroGeoSphere) expand on Gringarten's concepts to show that realistic heterogeneity of spacing and transmissivity significantly degrades EGS performance. EGS production in networks of stimulated fractures initially follows Gringarten's type curves, with a later deviation is the smaller rock blocks thermally deplete and the entire stimulated volume acts as a single sink. Three-dimensional models of EGS performance show the critical importance of the relative magnitudes of fluid pressure and stress gradients, preferential growth and aperture enhancement may change with depth creating preferential pathways through rock this cooler than the injection depth.

Maintenance and Expansion: Modeling Material Stocks and Flows for Residential Buildings and Transportation Networks in the EU25.

PubMed

Wiedenhofer, Dominik; Steinberger, Julia K; Eisenmenger, Nina; Haas, Willi

2015-08-01

Material stocks are an important part of the social metabolism. Owing to long service lifetimes of stocks, they not only shape resource flows during construction, but also during use, maintenance, and at the end of their useful lifetime. This makes them an important topic for sustainable development. In this work, a model of stocks and flows for nonmetallic minerals in residential buildings, roads, and railways in the EU25, from 2004 to 2009 is presented. The changing material composition of the stock is modeled using a typology of 72 residential buildings, four road and two railway types, throughout the EU25. This allows for estimating the amounts of materials in in-use stocks of residential buildings and transportation networks, as well as input and output flows. We compare the magnitude of material demands for expansion versus those for maintenance of existing stock. Then, recycling potentials are quantitatively explored by comparing the magnitude of estimated input, waste, and recycling flows from 2004 to 2009 and in a business-as-usual scenario for 2020. Thereby, we assess the potential impacts of the European Waste Framework Directive, which strives for a significant increase in recycling. We find that in the EU25, consisting of highly industrialized countries, a large share of material inputs are directed at maintaining existing stocks. Proper management of existing transportation networks and residential buildings is therefore crucial for the future size of flows of nonmetallic minerals.

Maintenance and Expansion: Modeling Material Stocks and Flows for Residential Buildings and Transportation Networks in the EU25

PubMed Central

Steinberger, Julia K.; Eisenmenger, Nina; Haas, Willi

2015-01-01

Summary Material stocks are an important part of the social metabolism. Owing to long service lifetimes of stocks, they not only shape resource flows during construction, but also during use, maintenance, and at the end of their useful lifetime. This makes them an important topic for sustainable development. In this work, a model of stocks and flows for nonmetallic minerals in residential buildings, roads, and railways in the EU25, from 2004 to 2009 is presented. The changing material composition of the stock is modeled using a typology of 72 residential buildings, four road and two railway types, throughout the EU25. This allows for estimating the amounts of materials in in‐use stocks of residential buildings and transportation networks, as well as input and output flows. We compare the magnitude of material demands for expansion versus those for maintenance of existing stock. Then, recycling potentials are quantitatively explored by comparing the magnitude of estimated input, waste, and recycling flows from 2004 to 2009 and in a business‐as‐usual scenario for 2020. Thereby, we assess the potential impacts of the European Waste Framework Directive, which strives for a significant increase in recycling. We find that in the EU25, consisting of highly industrialized countries, a large share of material inputs are directed at maintaining existing stocks. Proper management of existing transportation networks and residential buildings is therefore crucial for the future size of flows of nonmetallic minerals. PMID:27524878

Network-induced oscillatory behavior in material flow networks and irregular business cycles

NASA Astrophysics Data System (ADS)

Helbing, Dirk; Lämmer, Stefen; Witt, Ulrich; Brenner, Thomas

2004-11-01

Network theory is rapidly changing our understanding of complex systems, but the relevance of topological features for the dynamic behavior of metabolic networks, food webs, production systems, information networks, or cascade failures of power grids remains to be explored. Based on a simple model of supply networks, we offer an interpretation of instabilities and oscillations observed in biological, ecological, economic, and engineering systems. We find that most supply networks display damped oscillations, even when their units—and linear chains of these units—behave in a nonoscillatory way. Moreover, networks of damped oscillators tend to produce growing oscillations. This surprising behavior offers, for example, a different interpretation of business cycles and of oscillating or pulsating processes. The network structure of material flows itself turns out to be a source of instability, and cyclical variations are an inherent feature of decentralized adjustments.

Simulation of Code Spectrum and Code Flow of Cultured Neuronal Networks.

PubMed

Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime

2016-01-01

It has been shown that, in cultured neuronal networks on a multielectrode, pseudorandom-like sequences (codes) are detected, and they flow with some spatial decay constant. Each cultured neuronal network is characterized by a specific spectrum curve. That is, we may consider the spectrum curve as a "signature" of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution. In the present study, we used an integrate-and-fire model of neurons with intrinsic and instantaneous fluctuations of characteristics for performing a simulation of a code spectrum from multielectrodes on a 2D mesh neural network. We showed that it is possible to estimate the characteristics of neurons such as the distribution of number of neurons around each electrode and their refractory periods. Although this process is a reverse problem and theoretically the solutions are not sufficiently guaranteed, the parameters seem to be consistent with those of neurons. That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network. Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence.

Analysis on Voltage Profile of Distribution Network with Distributed Generation

NASA Astrophysics Data System (ADS)

Shao, Hua; Shi, Yujie; Yuan, Jianpu; An, Jiakun; Yang, Jianhua

2018-02-01

Penetration of distributed generation has some impacts on a distribution network in load flow, voltage profile, reliability, power loss and so on. After the impacts and the typical structures of the grid-connected distributed generation are analyzed, the back/forward sweep method of the load flow calculation of the distribution network is modelled including distributed generation. The voltage profiles of the distribution network affected by the installation location and the capacity of distributed generation are thoroughly investigated and simulated. The impacts on the voltage profiles are summarized and some suggestions to the installation location and the capacity of distributed generation are given correspondingly.

Cascade phenomenon against subsequent failures in complex networks

NASA Astrophysics Data System (ADS)

Jiang, Zhong-Yuan; Liu, Zhi-Quan; He, Xuan; Ma, Jian-Feng

2018-06-01

Cascade phenomenon may lead to catastrophic disasters which extremely imperil the network safety or security in various complex systems such as communication networks, power grids, social networks and so on. In some flow-based networks, the load of failed nodes can be redistributed locally to their neighboring nodes to maximally preserve the traffic oscillations or large-scale cascading failures. However, in such local flow redistribution model, a small set of key nodes attacked subsequently can result in network collapse. Then it is a critical problem to effectively find the set of key nodes in the network. To our best knowledge, this work is the first to study this problem comprehensively. We first introduce the extra capacity for every node to put up with flow fluctuations from neighbors, and two extra capacity distributions including degree based distribution and average distribution are employed. Four heuristic key nodes discovering methods including High-Degree-First (HDF), Low-Degree-First (LDF), Random and Greedy Algorithms (GA) are presented. Extensive simulations are realized in both scale-free networks and random networks. The results show that the greedy algorithm can efficiently find the set of key nodes in both scale-free and random networks. Our work studies network robustness against cascading failures from a very novel perspective, and methods and results are very useful for network robustness evaluations and protections.

Spontaneous oscillations in microfluidic networks

NASA Astrophysics Data System (ADS)

Case, Daniel; Angilella, Jean-Regis; Motter, Adilson

2017-11-01

Precisely controlling flows within microfluidic systems is often difficult which typically results in systems being heavily reliant on numerous external pumps and computers. Here, I present a simple microfluidic network that exhibits flow rate switching, bistablity, and spontaneous oscillations controlled by a single pressure. That is, by solely changing the driving pressure, it is possible to switch between an oscillating and steady flow state. Such functionality does not rely on external hardware and may even serve as an on-chip memory or timing mechanism. I use an analytic model and rigorous fluid dynamics simulations to show these results.

Cascading failures in interdependent systems under a flow redistribution model

NASA Astrophysics Data System (ADS)

Zhang, Yingrui; Arenas, Alex; Yaǧan, Osman

2018-02-01

Robustness and cascading failures in interdependent systems has been an active research field in the past decade. However, most existing works use percolation-based models where only the largest component of each network remains functional throughout the cascade. Although suitable for communication networks, this assumption fails to capture the dependencies in systems carrying a flow (e.g., power systems, road transportation networks), where cascading failures are often triggered by redistribution of flows leading to overloading of lines. Here, we consider a model consisting of systems A and B with initial line loads and capacities given by {LA,i,CA ,i} i =1 n and {LB,i,CB ,i} i =1 n, respectively. When a line fails in system A , a fraction of its load is redistributed to alive lines in B , while remaining (1 -a ) fraction is redistributed equally among all functional lines in A ; a line failure in B is treated similarly with b giving the fraction to be redistributed to A . We give a thorough analysis of cascading failures of this model initiated by a random attack targeting p1 fraction of lines in A and p2 fraction in B . We show that (i) the model captures the real-world phenomenon of unexpected large scale cascades and exhibits interesting transition behavior: the final collapse is always first order, but it can be preceded by a sequence of first- and second-order transitions; (ii) network robustness tightly depends on the coupling coefficients a and b , and robustness is maximized at non-trivial a ,b values in general; (iii) unlike most existing models, interdependence has a multifaceted impact on system robustness in that interdependency can lead to an improved robustness for each individual network.

Cascading failures in interdependent systems under a flow redistribution model.

PubMed

Zhang, Yingrui; Arenas, Alex; Yağan, Osman

2018-02-01

Robustness and cascading failures in interdependent systems has been an active research field in the past decade. However, most existing works use percolation-based models where only the largest component of each network remains functional throughout the cascade. Although suitable for communication networks, this assumption fails to capture the dependencies in systems carrying a flow (e.g., power systems, road transportation networks), where cascading failures are often triggered by redistribution of flows leading to overloading of lines. Here, we consider a model consisting of systems A and B with initial line loads and capacities given by {L_{A,i},C_{A,i}}_{i=1}^{n} and {L_{B,i},C_{B,i}}_{i=1}^{n}, respectively. When a line fails in system A, a fraction of its load is redistributed to alive lines in B, while remaining (1-a) fraction is redistributed equally among all functional lines in A; a line failure in B is treated similarly with b giving the fraction to be redistributed to A. We give a thorough analysis of cascading failures of this model initiated by a random attack targeting p_{1} fraction of lines in A and p_{2} fraction in B. We show that (i) the model captures the real-world phenomenon of unexpected large scale cascades and exhibits interesting transition behavior: the final collapse is always first order, but it can be preceded by a sequence of first- and second-order transitions; (ii) network robustness tightly depends on the coupling coefficients a and b, and robustness is maximized at non-trivial a,b values in general; (iii) unlike most existing models, interdependence has a multifaceted impact on system robustness in that interdependency can lead to an improved robustness for each individual network.

A novel approach to model dynamic flow interactions between storm sewer system and overland surface for different land covers in urban areas

NASA Astrophysics Data System (ADS)

Chang, Tsang-Jung; Wang, Chia-Ho; Chen, Albert S.

2015-05-01

In this study, we developed a novel approach to simulate dynamic flow interactions between storm sewers and overland surface for different land covers in urban areas. The proposed approach couples the one-dimensional (1D) sewer flow model (SFM) and the two-dimensional (2D) overland flow model (OFM) with different techniques depending on the land cover type of the study areas. For roads, pavements, plazas, and so forth where rainfall becomes surface runoff before entering the sewer system, the rainfall-runoff process is simulated directly in the 2D OFM, and the runoff is drained to the sewer network via inlets, which is regarded as the input to 1D SFM. For green areas on which rainfall falls into the permeable ground surface and the generated direct runoff traverses terrain, the deduction rate is applied to the rainfall for reflecting the soil infiltration in the 2D OFM. For flat building roofs with drainage facilities allowing rainfall to drain directly from the roof to sewer networks, the rainfall-runoff process is simulated using the hydrological module in the 1D SFM where no rainfall is applied to these areas in the 2D OFM. The 1D SFM is used for hydraulic simulations in the sewer network. Where the flow in the drainage network exceeds its capacity, a surcharge occurs and water may spill onto the ground surface if the pressure head in a manhole exceeds the ground elevation. The overflow discharge from the sewer system is calculated by the 1D SFM and considered a point source in the 2D OFM. The overland flow will return into the sewer network when it reaches an inlet that connects to an un-surcharged manhole. In this case, the inlet is considered as a point sink in the 2D OFM and an inflow to a manhole in the 1D SFM. The proposed approach was compared to other five urban flood modelling techniques with four rainfall events that had previously recorded inundation areas. The merits and drawbacks of each modelling technique were compared and discussed. Based on the simulated results, the proposed approach was found to simulate floodings closer to the survey records than other approaches because the physical rainfall-runoff phenomena in urban environment were better reflected.

Progress on Discrete Fracture Network models with implications on the predictions of permeability and flow channeling structure

NASA Astrophysics Data System (ADS)

Darcel, C.; Davy, P.; Le Goc, R.; Maillot, J.; Selroos, J. O.

2017-12-01

We present progress on Discrete Fracture Network (DFN) flow modeling, including realistic advanced DFN spatial structures and local fracture transmissivity properties, through an application to the Forsmark site in Sweden. DFN models are a framework to combine fracture datasets from different sources and scales and to interpolate them in combining statistical distributions and stereological relations. The resulting DFN upscaling function - size density distribution - is a model component key to extrapolating fracture size densities between data gaps, from borehole core up to site scale. Another important feature of DFN models lays in the spatial correlations between fractures, with still unevaluated consequences on flow predictions. Indeed, although common Poisson (i.e. spatially random) models are widely used, they do not reflect these geological evidences for more complex structures. To model them, we define a DFN growth process from kinematic rules for nucleation, growth and stopping conditions. It mimics in a simplified way the geological fracturing processes and produces DFN characteristics -both upscaling function and spatial correlations- fully consistent with field observations. DFN structures are first compared for constant transmissivities. Flow simulations for the kinematic and equivalent Poisson DFN models show striking differences: with the kinematic DFN, connectivity and permeability are significantly smaller, down to a difference of one order of magnitude, and flow is much more channelized. Further flow analyses are performed with more realistic transmissivity distribution conditions (sealed parts, relations to fracture sizes, orientations and in-situ stress field). The relative importance of the overall DFN structure in the final flow predictions is discussed.

Microfluidic System Simulation Including the Electro-Viscous Effect

NASA Technical Reports Server (NTRS)

Rojas, Eileen; Chen, C. P.; Majumdar, Alok

2007-01-01

This paper describes a practical approach using a general purpose lumped-parameter computer program, GFSSP (Generalized Fluid System Simulation Program) for calculating flow distribution in a network of micro-channels including electro-viscous effects due to the existence of electrical double layer (EDL). In this study, an empirical formulation for calculating an effective viscosity of ionic solutions based on dimensional analysis is described to account for surface charge and bulk fluid conductivity, which give rise to electro-viscous effect in microfluidics network. Two dimensional slit micro flow data was used to determine the model coefficients. Geometry effect is then included through a Poiseuille number correlation in GFSSP. The bi-power model was used to calculate flow distribution of isotropically etched straight channel and T-junction microflows involving ionic solutions. Performance of the proposed model is assessed against experimental test data.

Survey of Human Systems Integration (HSI) Tools for USCG Acquisitions

DTIC Science & Technology

2009-04-01

an IMPRINT HPM. IMPRINT uses task network modeling to represent human performance. As the name implies, task networks use a flowchart type format...tools; and built-in tutoring support for beginners . A perceptual/motor layer extending ACT-R’s theory of cognition to perception and action is also...chisystems.com B.8 Information and Functional Flow Analysis Description In information flow analysis, a flowchart of the information and decisions

GPSS and Modeling of Computer Communication Networks.

DTIC Science & Technology

1982-04-01

chains are used to alter the normal "flows" of transactions in a user defined manner. Transaction "flow" may be controlled on the basis of group ...authors refer to loops and rings interchangeably, including those who have designed loop networks with distributed control mechanisms [8,9,10,11,121...that detailed simulation of character by character transmission does not take place; rather, [ control message--data message-- control message! groupings

Extrapolating regional probability of drying of headwater streams using discrete observations and gauging networks

NASA Astrophysics Data System (ADS)

Beaufort, Aurélien; Lamouroux, Nicolas; Pella, Hervé; Datry, Thibault; Sauquet, Eric

2018-05-01

Headwater streams represent a substantial proportion of river systems and many of them have intermittent flows due to their upstream position in the network. These intermittent rivers and ephemeral streams have recently seen a marked increase in interest, especially to assess the impact of drying on aquatic ecosystems. The objective of this paper is to quantify how discrete (in space and time) field observations of flow intermittence help to extrapolate over time the daily probability of drying (defined at the regional scale). Two empirical models based on linear or logistic regressions have been developed to predict the daily probability of intermittence at the regional scale across France. Explanatory variables were derived from available daily discharge and groundwater-level data of a dense gauging/piezometer network, and models were calibrated using discrete series of field observations of flow intermittence. The robustness of the models was tested using an independent, dense regional dataset of intermittence observations and observations of the year 2017 excluded from the calibration. The resulting models were used to extrapolate the daily regional probability of drying in France: (i) over the period 2011-2017 to identify the regions most affected by flow intermittence; (ii) over the period 1989-2017, using a reduced input dataset, to analyse temporal variability of flow intermittence at the national level. The two empirical regression models performed equally well between 2011 and 2017. The accuracy of predictions depended on the number of continuous gauging/piezometer stations and intermittence observations available to calibrate the regressions. Regions with the highest performance were located in sedimentary plains, where the monitoring network was dense and where the regional probability of drying was the highest. Conversely, the worst performances were obtained in mountainous regions. Finally, temporal projections (1989-2016) suggested the highest probabilities of intermittence (> 35 %) in 1989-1991, 2003 and 2005. A high density of intermittence observations improved the information provided by gauging stations and piezometers to extrapolate the temporal variability of intermittent rivers and ephemeral streams.

Simulation of two-phase flow in horizontal fracture networks with numerical manifold method

NASA Astrophysics Data System (ADS)

Ma, G. W.; Wang, H. D.; Fan, L. F.; Wang, B.

2017-10-01

The paper presents simulation of two-phase flow in discrete fracture networks with numerical manifold method (NMM). Each phase of fluids is considered to be confined within the assumed discrete interfaces in the present method. The homogeneous model is modified to approach the mixed fluids. A new mathematical cover formation for fracture intersection is proposed to satisfy the mass conservation. NMM simulations of two-phase flow in a single fracture, intersection, and fracture network are illustrated graphically and validated by the analytical method or the finite element method. Results show that the motion status of discrete interface significantly depends on the ratio of mobility of two fluids rather than the value of the mobility. The variation of fluid velocity in each fracture segment and the driven fluid content are also influenced by the ratio of mobility. The advantages of NMM in the simulation of two-phase flow in a fracture network are demonstrated in the present study, which can be further developed for practical engineering applications.

Betweenness centrality and its applications from modeling traffic flows to network community detection

NASA Astrophysics Data System (ADS)

Ren, Yihui

As real-world complex networks are heterogeneous structures, not all their components such as nodes, edges and subgraphs carry the same role or importance in the functions performed by the networks: some elements are more critical than others. Understanding the roles of the components of a network is crucial for understanding the behavior of the network as a whole. One the most basic function of networks is transport; transport of vehicles/people, information, materials, forces, etc., and these quantities are transported along edges between source and destination nodes. For this reason, network path-based importance measures, also called centralities, play a crucial role in the understanding of the transport functions of the network and the network's structural and dynamical behavior in general. In this thesis we study the notion of betweenness centrality, which measures the fraction of lowest-cost (or shortest) paths running through a network component, in particular through a node or an edge. High betweenness centrality nodes/edges are those that will be frequently used by the entities transported through the network and thus they play a key role in the overall transport properties of the network. In the first part of the thesis we present a first-principles based method for traffic prediction using a cost-based generalization of the radiation model (emission/absorbtion model) for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. Using US census and highway traffic data, we show that traffic can efficiently and accurately be computed from a range-limited, network betweenness type calculation. The model based on travel time costs captures the log-normal distribution of the traffic and attains a high Pearson correlation coefficient (0.75) when compared with real traffic. We then focus on studying the extent of changes in traffic flows in the wake of a localized damage or alteration to the network and we demonstrate that the changes can propagate globally, affecting traffic several hundreds of miles away. Because of its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events. In the second part of the thesis we focus on network deconstruction and community detection problems, both intensely studied topics in network science, using a weighted betweenness centrality approach. We present an algorithm that solves both problems efficiently and accurately and demonstrate that on both benchmark networks and data networks.

A knowledge-based system for controlling automobile traffic

NASA Technical Reports Server (NTRS)

Maravas, Alexander; Stengel, Robert F.

1994-01-01

Transportation network capacity variations arising from accidents, roadway maintenance activity, and special events as well as fluctuations in commuters' travel demands complicate traffic management. Artificial intelligence concepts and expert systems can be useful in framing policies for incident detection, congestion anticipation, and optimal traffic management. This paper examines the applicability of intelligent route guidance and control as decision aids for traffic management. Basic requirements for managing traffic are reviewed, concepts for studying traffic flow are introduced, and mathematical models for modeling traffic flow are examined. Measures for quantifying transportation network performance levels are chosen, and surveillance and control strategies are evaluated. It can be concluded that automated decision support holds great promise for aiding the efficient flow of automobile traffic over limited-access roadways, bridges, and tunnels.

A computational model for the flow of resin in self-healing composites

NASA Astrophysics Data System (ADS)

Hall, J.; Qamar, I. P. S.; Rendall, T. C. S.; Trask, R. S.

2015-03-01

To explore the flow characteristics of healing agent leaving a vascular network and infusing a damage site within a fibre reinforced polymer composite, a numerical model of healing agent flow from an orifice has been developed using smoothed particle hydrodynamics. As an initial validation the discharge coefficient for low Reynolds number flow from a cylindrical tank is calculated numerically, using two different viscosity formulations, and compared to existing experimental data. Results of this comparison are very favourable; the model is able to reproduce experimental results for the discharge coefficient in the high Reynolds number limit, together with the power-law behaviour for low Reynolds numbers. Results are also presented for a representative delamination geometry showing healing fluid behaviour and fraction filled inside the delamination for a variety of fluid viscosities. This work provides the foundations for the vascular self-healing community in calculating not only the flow rate through the network, but also, by simulating a representative damage site, the final location of the healing fluid within the damage site in order to assess the improvement in local and global mechanical properties and thus healing efficiency.

Serial Network Flow Monitor

NASA Technical Reports Server (NTRS)

Robinson, Julie A.; Tate-Brown, Judy M.

2009-01-01

Using a commercial software CD and minimal up-mass, SNFM monitors the Payload local area network (LAN) to analyze and troubleshoot LAN data traffic. Validating LAN traffic models may allow for faster and more reliable computer networks to sustain systems and science on future space missions. Research Summary: This experiment studies the function of the computer network onboard the ISS. On-orbit packet statistics are captured and used to validate ground based medium rate data link models and enhance the way that the local area network (LAN) is monitored. This information will allow monitoring and improvement in the data transfer capabilities of on-orbit computer networks. The Serial Network Flow Monitor (SNFM) experiment attempts to characterize the network equivalent of traffic jams on board ISS. The SNFM team is able to specifically target historical problem areas including the SAMS (Space Acceleration Measurement System) communication issues, data transmissions from the ISS to the ground teams, and multiple users on the network at the same time. By looking at how various users interact with each other on the network, conflicts can be identified and work can begin on solutions. SNFM is comprised of a commercial off the shelf software package that monitors packet traffic through the payload Ethernet LANs (local area networks) on board ISS.

U1108 performance model

NASA Technical Reports Server (NTRS)

Trachta, G.

1976-01-01

A model of Univac 1108 work flow has been developed to assist in performance evaluation studies and configuration planning. Workload profiles and system configurations are parameterized for ease of experimental modification. Outputs include capacity estimates and performance evaluation functions. The U1108 system is conceptualized as a service network; classical queueing theory is used to evaluate network dynamics.

Probability and volume of potential postwildfire debris flows in the 2012 High Park Burn Area near Fort Collins, Colorado

USGS Publications Warehouse

Verdin, Kristine L.; Dupree, Jean A.; Elliott, John G.

2012-01-01

This report presents a preliminary emergency assessment of the debris-flow hazards from drainage basins burned by the 2012 High Park fire near Fort Collins in Larimer County, Colorado. Empirical models derived from statistical evaluation of data collected from recently burned basins throughout the intermountain western United States were used to estimate the probability of debris-flow occurrence and volume of debris flows along the burned area drainage network and to estimate the same for 44 selected drainage basins along State Highway 14 and the perimeter of the burned area. Input data for the models included topographic parameters, soil characteristics, burn severity, and rainfall totals and intensities for a (1) 2-year-recurrence, 1-hour-duration rainfall (25 millimeters); (2) 10-year-recurrence, 1-hour-duration rainfall (43 millimeters); and (3) 25-year-recurrence, 1-hour-duration rainfall (51 millimeters). Estimated debris-flow probabilities along the drainage network and throughout the drainage basins of interest ranged from 1 to 84 percent in response to the 2-year-recurrence, 1-hour-duration rainfall; from 2 to 95 percent in response to the 10-year-recurrence, 1-hour-duration rainfall; and from 3 to 97 in response to the 25-year-recurrence, 1-hour-duration rainfall. Basins and drainage networks with the highest probabilities tended to be those on the eastern edge of the burn area where soils have relatively high clay contents and gradients are steep. Estimated debris-flow volumes range from a low of 1,600 cubic meters to a high of greater than 100,000 cubic meters. Estimated debris-flow volumes increase with basin size and distance along the drainage network, but some smaller drainages were also predicted to produce substantial volumes of material. The predicted probabilities and some of the volumes predicted for the modeled storms indicate a potential for substantial debris-flow impacts on structures, roads, bridges, and culverts located both within and immediately downstream from the burned area. Colorado State Highway 14 is also susceptible to impacts from debris flows.

Numerical model for learning concepts of streamflow simulation

USGS Publications Warehouse

DeLong, L.L.; ,

1993-01-01

Numerical models are useful for demonstrating principles of open-channel flow. Such models can allow experimentation with cause-and-effect relations, testing concepts of physics and numerical techniques. Four PT is a numerical model written primarily as a teaching supplement for a course in one-dimensional stream-flow modeling. Four PT options particularly useful in training include selection of governing equations, boundary-value perturbation, and user-programmable constraint equations. The model can simulate non-trivial concepts such as flow in complex interconnected channel networks, meandering channels with variable effective flow lengths, hydraulic structures defined by unique three-parameter relations, and density-driven flow.The model is coded in FORTRAN 77, and data encapsulation is used extensively to simplify maintenance and modification and to enhance the use of Four PT modules by other programs and programmers.

Computer aided approximation of flow rate through systemic-pulmonary arterial shunts (SPAS).

PubMed

Vennemann, Peter; Montag, Michael; Peters, Franz; Merzkirch, Wolfgang

2012-02-22

The discrimination of flow rates through bronchial arteries that are affected by pathological SPAS today still happens solely qualitatively. A reproducible quantification of flow rates, however, would enable the comprehension of phenomena like the intensified shunt perfusion seen in cases of chronic inflammations or the characterization of SPAS that may cause cardiovascular problems. A computational program is developed, that allows the modeling of individual bronchial arteries on the basis of the information provided by angiography. Angiographic images are available from the standard clinical assessment of SPAS. The flow through continuous and geometrically measurable vessel segments and SPAS is given by the law of Hagen-Poiseuille. The discharge through healthy branches is calculated by means of allometric scaling laws. The simulation results are verified by flow experiments in artificial vessel networks made of glass and PE tubing. The experimental set-up mimics realistic, pulsating pressure and flow conditions. When applied to the artificial vessel networks, the model described herein provides results for the volumetric flow rate that differ from values measured in laboratory experiments by <6%. The computer model is also applied to real angiographic images. Due to inaccuracies during the deduction of the geometry and due to necessary simplifications of the model, we expect significant deviations between calculated and real flow rates in bronchial systems. Nevertheless, the presented method enables the physician to objectively estimate the order of magnitude of volumetric flow through individual SPAS fairly independently from his experience and without the need of measurements additional to the mandatory angiography.

Comparison of Conventional and ANN Models for River Flow Forecasting

NASA Astrophysics Data System (ADS)

Jain, A.; Ganti, R.

2011-12-01

Hydrological models are useful in many water resources applications such as flood control, irrigation and drainage, hydro power generation, water supply, erosion and sediment control, etc. Estimates of runoff are needed in many water resources planning, design development, operation and maintenance activities. River flow is generally estimated using time series or rainfall-runoff models. Recently, soft artificial intelligence tools such as Artificial Neural Networks (ANNs) have become popular for research purposes but have not been extensively adopted in operational hydrological forecasts. There is a strong need to develop ANN models based on real catchment data and compare them with the conventional models. In this paper, a comparative study has been carried out for river flow forecasting using the conventional and ANN models. Among the conventional models, multiple linear, and non linear regression, and time series models of auto regressive (AR) type have been developed. Feed forward neural network model structure trained using the back propagation algorithm, a gradient search method, was adopted. The daily river flow data derived from Godavari Basin @ Polavaram, Andhra Pradesh, India have been employed to develop all the models included here. Two inputs, flows at two past time steps, (Q(t-1) and Q(t-2)) were selected using partial auto correlation analysis for forecasting flow at time t, Q(t). A wide range of error statistics have been used to evaluate the performance of all the models developed in this study. It has been found that the regression and AR models performed comparably, and the ANN model performed the best amongst all the models investigated in this study. It is concluded that ANN model should be adopted in real catchments for hydrological modeling and forecasting.

Parallel approach to identifying the well-test interpretation model using a neurocomputer

NASA Astrophysics Data System (ADS)

May, Edward A., Jr.; Dagli, Cihan H.

1996-03-01

The well test is one of the primary diagnostic and predictive tools used in the analysis of oil and gas wells. In these tests, a pressure recording device is placed in the well and the pressure response is recorded over time under controlled flow conditions. The interpreted results are indicators of the well's ability to flow and the damage done to the formation surrounding the wellbore during drilling and completion. The results are used for many purposes, including reservoir modeling (simulation) and economic forecasting. The first step in the analysis is the identification of the Well-Test Interpretation (WTI) model, which determines the appropriate solution method. Mis-identification of the WTI model occurs due to noise and non-ideal reservoir conditions. Previous studies have shown that a feed-forward neural network using the backpropagation algorithm can be used to identify the WTI model. One of the drawbacks to this approach is, however, training time, which can run into days of CPU time on personal computers. In this paper a similar neural network is applied using both a personal computer and a neurocomputer. Input data processing, network design, and performance are discussed and compared. The results show that the neurocomputer greatly eases the burden of training and allows the network to outperform a similar network running on a personal computer.

Experimental evidence for intraplate deformation controlled by netlike plastic-flow in central-eastern Asia

NASA Astrophysics Data System (ADS)

Wang, Sheng-zu; Li, Jian-guo; Zhou, Yong-sheng

2007-12-01

The experimental results of brittle/ductile two-layer analogue models verify that intraplate tectonic deformation in central-eastern Asia is controlled mainly by the netlike plastic-flow (NPF) occurring in the lower lithosphere, including the lower crust and lithospheric mantle. The ductile lower layer in the model, corresponding to the lower lithosphere in the natural prototype, is made of a mixture of gum rosin and turpentine oil and the brittle upper one, to the upper crust, is formed by the consolidation of talc-powder slurry. The NPF hypothesis for continental dynamics can be regarded as a combination and development of two kinds of seemingly mutually exclusive ones, which are based on the theories of slip-line field and viscous (plastic) flow, respectively. In contrast to "homogeneous" viscous (plastic) flow considered usually in fluid mechanics and rheology, NPF is a viscous (plastic) flow accompanied with shear strain localization, forming plastic-flow network in the flow field. Plastic-flow network, being composed of two families of plastic-flow belts intersecting each other with their initial conjugate angles (i.e. the included angles facing the compression direction) equal to 90°, is similar to but different from the traditional slip-line network, which is assumed as a critical state of yield in elastoplastic medium. The experiments show that there are several NPF-controlled tectonic network systems to be developed in the models and two of them correspond to those in central-eastern Asia, which have the Himalayan and Taiwan arcs as their driving boundaries, respectively. The existence of "stable blocks" in the ductile lower layer has promoted some types of tectonic deformation, including the formation of large-scale compressional basins, corresponding to the Tarim, Ordos, Sichuan basins, etc., the development of compression-shear tectonic zones between some of these basins, corresponding to those shown by the Tianshan and Altay mountain ranges, and the uplift of some areas of the "plateau", corresponding to a contribution to the formation of the Qinghai-Tibet plateau. The distributions of maximum compressive stress directions and strains in the ductile lower layer estimated using the "conjugate-angle-bisector" and "conjugate-angle-increment" methods, respectively, are coincident in general tendency and framework with those in the prototype for the major part of the central-eastern Asian continent. It is also inferred that the westward influence of the horizontal compression component of the Pacific plate has reached North China by means of the interaction between adjacent plastic-flow networks although the tectonic network resulting directly from this horizontal compression has not spread westward beyond the Japan Sea.

A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region

NASA Astrophysics Data System (ADS)

He, Zhibin; Wen, Xiaohu; Liu, Hu; Du, Jun

2014-02-01

Data driven models are very useful for river flow forecasting when the underlying physical relationships are not fully understand, but it is not clear whether these data driven models still have a good performance in the small river basin of semiarid mountain regions where have complicated topography. In this study, the potential of three different data driven methods, artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for forecasting river flow in the semiarid mountain region, northwestern China. The models analyzed different combinations of antecedent river flow values and the appropriate input vector has been selected based on the analysis of residuals. The performance of the ANN, ANFIS and SVM models in training and validation sets are compared with the observed data. The model which consists of three antecedent values of flow has been selected as the best fit model for river flow forecasting. To get more accurate evaluation of the results of ANN, ANFIS and SVM models, the four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), root mean squared error (RMSE), Nash-Sutcliffe efficiency coefficient (NS) and mean absolute relative error (MARE), were employed to evaluate the performances of various models developed. The results indicate that the performance obtained by ANN, ANFIS and SVM in terms of different evaluation criteria during the training and validation period does not vary substantially; the performance of the ANN, ANFIS and SVM models in river flow forecasting was satisfactory. A detailed comparison of the overall performance indicated that the SVM model performed better than ANN and ANFIS in river flow forecasting for the validation data sets. The results also suggest that ANN, ANFIS and SVM method can be successfully applied to establish river flow with complicated topography forecasting models in the semiarid mountain regions.

A Novel Approach to Adaptive Flow Separation Control

DTIC Science & Technology

2016-09-03

particular, it considers control of flow separation over a NACA-0025 airfoil using microjet actuators and develops Adaptive Sampling Based Model...Predictive Control ( Adaptive SBMPC), a novel approach to Nonlinear Model Predictive Control that applies the Minimal Resource Allocation Network...Distribution Unlimited UU UU UU UU 03-09-2016 1-May-2013 30-Apr-2016 Final Report: A Novel Approach to Adaptive Flow Separation Control The views, opinions

Convex Relaxation of OPF in Multiphase Radial Networks with Wye and Delta Connections

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, Changhong; Dall-Anese, Emiliano; Low, Steven

2017-08-01

This panel presentation focuses on multiphase radial distribution networks with wye and delta connections, and proposes a semidefinite relaxation of the AC optimal power flow (OPF) problem. Two multiphase power flow models are developed to facilitate the integration of delta-connected loads or generation resources in the OPF problem. The first model is referred to as the extended branch flow model (EBFM). The second model leverages a linear relationship between phase-to-ground power injections and delta connections that holds under a balanced voltage approximation (BVA). Based on these models, pertinent OPF problems are formulated and relaxed to semidefinite programs (SDPs). Numerical studiesmore » on IEEE test feeders show that the proposed SDP relaxations can be solved efficiently by a generic optimization solver. Numerical evidence also indicates that solving the resultant SDP under BVA is faster than under EBFM. Moreover, both SDP solutions are numerically exact with respect to voltages and branch flows. It is further shown that the SDP solution under BVA has a small optimality gap, and the BVA model is accurate in the sense that it reproduces actual system voltages.« less

Modeling the Sedimentation of Red Blood Cells in Flow under Strong External Magnetic Body Force using a Lattice Boltzmann Fictitious Domain Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shi, Xing; Lin, Guang

To model the sedimentation of the red blood cell (RBC) in a square duct and a circular pipe, the recently developed technique derived from the lattice Boltzmann method and the distributed Lagrange multiplier/fictitious domain method (LBM-DLM/FD) is extended to employ the mesoscopic network model for simulations of the sedimentation of the RBC in flow. The flow is simulated by the lattice Boltzmann method with a strong magnetic body force, while the network model is used for modeling RBC deformation. The fluid-RBC interactions are enforced by the Lagrange multiplier. The sedimentation of the RBC in a square duct and a circularmore » pipe is simulated, revealing the capacity of the current method for modeling the sedimentation of RBC in various flows. Numerical results illustrate that that the terminal setting velocity increases with the increment of the exerted body force. The deformation of the RBC has significant effect on the terminal setting velocity due to the change of the frontal area. The larger the exerted force is, the smaller the frontal area and the larger deformation of the RBC are.« less

Simulation of dynamic expansion, contraction, and connectivity in a mountain stream network

NASA Astrophysics Data System (ADS)

Ward, Adam S.; Schmadel, Noah M.; Wondzell, Steven M.

2018-04-01

Headwater stream networks expand and contract in response to changes in stream discharge. The changes in the extent of the stream network are also controlled by geologic or geomorphic setting - some reaches go dry even under relatively wet conditions, other reaches remain flowing under relatively dry conditions. While such patterns are well recognized, we currently lack tools to predict the extent of the stream network and the times and locations where the network is dry within large river networks. Here, we develop a perceptual model of the river corridor in a headwater mountainous catchment, translate this into a reduced-complexity mechanistic model, and implement the model to examine connectivity and network extent over an entire water year. Our model agreed reasonably well with our observations, showing that the extent and connectivity of the river network was most sensitive to hydrologic forcing under the lowest discharges (Qgauge < 1 L s-1), that at intermediate discharges (1 L s-1 < Qgauge < 10 L s-1) the extent of the network changed dramatically with changes in discharge, and that under wet conditions (Qgauge > 10 L s-1) the extent of the network was relatively insensitive to hydrologic forcing and was instead determined by the network topology. We do not expect that the specific thresholds observed in this study would be transferable to other catchments with different geology, topology, or hydrologic forcing. However, we expect that the general pattern should be robust: the dominant controls will shift from hydrologic forcing to geologic setting as discharge increases. Furthermore, our method is readily transferable as the model can be applied with minimal data requirements (a single stream gauge, a digital terrain model, and estimates of hydrogeologic properties) to estimate flow duration or connectivity along the river corridor in unstudied catchments. As the available information increases, the model could be better calibrated to match site-specific observations of network extent, locations of dry reaches, or solute break through curves as demonstrated in this study. Based on the low initial data requirements and ability to later tune the model to a specific site, we suggest example applications of this parsimonious model that may prove useful to both researchers and managers.

Landscape genetics for the empirical assessment of resistance surfaces: the European pine marten (Martes martes) as a target-species of a regional ecological network.

PubMed

Ruiz-González, Aritz; Gurrutxaga, Mikel; Cushman, Samuel A; Madeira, María José; Randi, Ettore; Gómez-Moliner, Benjamin J

2014-01-01

Coherent ecological networks (EN) composed of core areas linked by ecological corridors are being developed worldwide with the goal of promoting landscape connectivity and biodiversity conservation. However, empirical assessment of the performance of EN designs is critical to evaluate the utility of these networks to mitigate effects of habitat loss and fragmentation. Landscape genetics provides a particularly valuable framework to address the question of functional connectivity by providing a direct means to investigate the effects of landscape structure on gene flow. The goals of this study are (1) to evaluate the landscape features that drive gene flow of an EN target species (European pine marten), and (2) evaluate the optimality of a regional EN design in providing connectivity for this species within the Basque Country (North Spain). Using partial Mantel tests in a reciprocal causal modeling framework we competed 59 alternative models, including isolation by distance and the regional EN. Our analysis indicated that the regional EN was among the most supported resistance models for the pine marten, but was not the best supported model. Gene flow of pine marten in northern Spain is facilitated by natural vegetation, and is resisted by anthropogenic landcover types and roads. Our results suggest that the regional EN design being implemented in the Basque Country will effectively facilitate gene flow of forest dwelling species at regional scale.

Tuning-free controller to accurately regulate flow rates in a microfluidic network

NASA Astrophysics Data System (ADS)

Heo, Young Jin; Kang, Junsu; Kim, Min Jun; Chung, Wan Kyun

2016-03-01

We describe a control algorithm that can improve accuracy and stability of flow regulation in a microfluidic network that uses a conventional pressure pump system. The algorithm enables simultaneous and independent control of fluid flows in multiple micro-channels of a microfluidic network, but does not require any model parameters or tuning process. We investigate robustness and optimality of the proposed control algorithm and those are verified by simulations and experiments. In addition, the control algorithm is compared with a conventional PID controller to show that the proposed control algorithm resolves critical problems induced by the PID control. The capability of the control algorithm can be used not only in high-precision flow regulation in the presence of disturbance, but in some useful functions for lab-on-a-chip devices such as regulation of volumetric flow rate, interface position control of two laminar flows, valveless flow switching, droplet generation and particle manipulation. We demonstrate those functions and also suggest further potential biological applications which can be accomplished by the proposed control framework.

Tuning-free controller to accurately regulate flow rates in a microfluidic network

PubMed Central

Heo, Young Jin; Kang, Junsu; Kim, Min Jun; Chung, Wan Kyun

2016-01-01

We describe a control algorithm that can improve accuracy and stability of flow regulation in a microfluidic network that uses a conventional pressure pump system. The algorithm enables simultaneous and independent control of fluid flows in multiple micro-channels of a microfluidic network, but does not require any model parameters or tuning process. We investigate robustness and optimality of the proposed control algorithm and those are verified by simulations and experiments. In addition, the control algorithm is compared with a conventional PID controller to show that the proposed control algorithm resolves critical problems induced by the PID control. The capability of the control algorithm can be used not only in high-precision flow regulation in the presence of disturbance, but in some useful functions for lab-on-a-chip devices such as regulation of volumetric flow rate, interface position control of two laminar flows, valveless flow switching, droplet generation and particle manipulation. We demonstrate those functions and also suggest further potential biological applications which can be accomplished by the proposed control framework. PMID:26987587

The modeling of attraction characteristics regarding passenger flow in urban rail transit network based on field theory

PubMed Central

Jia, Limin

2017-01-01

Aimed at the complicated problems of attraction characteristics regarding passenger flow in urban rail transit network, the concept of the gravity field of passenger flow is proposed in this paper. We establish the computation methods of field strength and potential energy to reveal the potential attraction relationship among stations from the perspective of the collection and distribution of passenger flow and the topology of network. As for the computation methods of field strength, an optimum path concept is proposed to define betweenness centrality parameter. Regarding the computation of potential energy, Compound Simpson’s Rule Formula is applied to get a solution to the function. Taking No. 10 Beijing Subway as a practical example, an analysis of simulation and verification is conducted, and the results shows in the following ways. Firstly, the bigger field strength value between two stations is, the stronger passenger flow attraction is, and the greater probability of the formation of the largest passenger flow of section is. Secondly, there is the greatest passenger flow volume and circulation capacity between two zones of high potential energy. PMID:28863175

Diffusion Dynamics of Energy Saving Practices in Large Heterogeneous Online Networks

PubMed Central

Mohammadi, Neda; Wang, Qi; Taylor, John E.

2016-01-01

Online social networks are today’s fastest growing communications channel and a popular source of information for many, so understanding their contribution to building awareness and shaping public perceptions of climate change is of utmost importance. Today’s online social networks are composed of complex combinations of entities and communication channels and it is not clear which communicators are the most influential, what the patterns of communication flow are, or even whether the widely accepted two-step flow of communication model applies in this new arena. This study examines the diffusion of energy saving practices in a large online social network across organizations, opinion leaders, and the public by tracking 108,771 communications on energy saving practices among 1,084 communicators, then analyzing the flow of information and influence over a 28 day period. Our findings suggest that diffusion networks of messages advocating energy saving practices are predominantly led by the activities of dedicated organizations but their attempts do not result in substantial public awareness, as most of these communications are effectively trapped in organizational loops in which messages are simply shared between organizations. Despite their comparably significant influential values, opinion leaders played a weak role in diffusing energy saving practices to a wider audience. Thus, the two-step flow of communication model does not appear to describe the sharing of energy conservation practices in large online heterogeneous networks. These results shed new light on the underlying mechanisms driving the diffusion of important societal issues such as energy efficiency, particularly in the context of large online social media outlets. PMID:27736912

Diffusion Dynamics of Energy Saving Practices in Large Heterogeneous Online Networks.

PubMed

Mohammadi, Neda; Wang, Qi; Taylor, John E

2016-01-01

Online social networks are today's fastest growing communications channel and a popular source of information for many, so understanding their contribution to building awareness and shaping public perceptions of climate change is of utmost importance. Today's online social networks are composed of complex combinations of entities and communication channels and it is not clear which communicators are the most influential, what the patterns of communication flow are, or even whether the widely accepted two-step flow of communication model applies in this new arena. This study examines the diffusion of energy saving practices in a large online social network across organizations, opinion leaders, and the public by tracking 108,771 communications on energy saving practices among 1,084 communicators, then analyzing the flow of information and influence over a 28 day period. Our findings suggest that diffusion networks of messages advocating energy saving practices are predominantly led by the activities of dedicated organizations but their attempts do not result in substantial public awareness, as most of these communications are effectively trapped in organizational loops in which messages are simply shared between organizations. Despite their comparably significant influential values, opinion leaders played a weak role in diffusing energy saving practices to a wider audience. Thus, the two-step flow of communication model does not appear to describe the sharing of energy conservation practices in large online heterogeneous networks. These results shed new light on the underlying mechanisms driving the diffusion of important societal issues such as energy efficiency, particularly in the context of large online social media outlets.

Graphical models for optimal power flow

DOE PAGES

Dvijotham, Krishnamurthy; Chertkov, Michael; Van Hentenryck, Pascal; ...

2016-09-13

Optimal power flow (OPF) is the central optimization problem in electric power grids. Although solved routinely in the course of power grid operations, it is known to be strongly NP-hard in general, and weakly NP-hard over tree networks. In this paper, we formulate the optimal power flow problem over tree networks as an inference problem over a tree-structured graphical model where the nodal variables are low-dimensional vectors. We adapt the standard dynamic programming algorithm for inference over a tree-structured graphical model to the OPF problem. Combining this with an interval discretization of the nodal variables, we develop an approximation algorithmmore » for the OPF problem. Further, we use techniques from constraint programming (CP) to perform interval computations and adaptive bound propagation to obtain practically efficient algorithms. Compared to previous algorithms that solve OPF with optimality guarantees using convex relaxations, our approach is able to work for arbitrary tree-structured distribution networks and handle mixed-integer optimization problems. Further, it can be implemented in a distributed message-passing fashion that is scalable and is suitable for “smart grid” applications like control of distributed energy resources. In conclusion, numerical evaluations on several benchmark networks show that practical OPF problems can be solved effectively using this approach.« less

Node-node correlations and transport properties in scale-free networks

NASA Astrophysics Data System (ADS)

Obregon, Bibiana; Guzman, Lev

2011-03-01

We study some transport properties of complex networks. We focus our attention on transport properties of scale-free and small-world networks and compare two types of transport: Electric and max-flow cases. In particular, we construct scale-free networks, with a given degree sequence, to estimate the distribution of conductances for different values of assortative/dissortative mixing. For the electric case we find that the distributions of conductances are affect ed by the assortative mixing of the network whereas for the max-flow case, the distributions almost do not show changes when node-node correlations are altered. Finally, we compare local and global transport in terms of the average conductance for the small-world (Watts-Strogatz) model

Systematic flood modelling to support flood-proof urban design

NASA Astrophysics Data System (ADS)

Bruwier, Martin; Mustafa, Ahmed; Aliaga, Daniel; Archambeau, Pierre; Erpicum, Sébastien; Nishida, Gen; Zhang, Xiaowei; Pirotton, Michel; Teller, Jacques; Dewals, Benjamin

2017-04-01

Urban flood risk is influenced by many factors such as hydro-meteorological drivers, existing drainage systems as well as vulnerability of population and assets. The urban fabric itself has also a complex influence on inundation flows. In this research, we performed a systematic analysis on how various characteristics of urban patterns control inundation flow within the urban area and upstream of it. An urban generator tool was used to generate over 2,250 synthetic urban networks of 1 km2. This tool is based on the procedural modelling presented by Parish and Müller (2001) which was adapted to generate a broader variety of urban networks. Nine input parameters were used to control the urban geometry. Three of them define the average length, orientation and curvature of the streets. Two orthogonal major roads, for which the width constitutes the fourth input parameter, work as constraints to generate the urban network. The width of secondary streets is given by the fifth input parameter. Each parcel generated by the street network based on a parcel mean area parameter can be either a park or a building parcel depending on the park ratio parameter. Three setback parameters constraint the exact location of the building whithin a building parcel. For each of synthetic urban network, detailed two-dimensional inundation maps were computed with a hydraulic model. The computational efficiency was enhanced by means of a porosity model. This enables the use of a coarser computational grid , while preserving information on the detailed geometry of the urban network (Sanders et al. 2008). These porosity parameters reflect not only the void fraction, which influences the storage capacity of the urban area, but also the influence of buildings on flow conveyance (dynamic effects). A sensitivity analysis was performed based on the inundation maps to highlight the respective impact of each input parameter characteristizing the urban networks. The findings of the study pinpoint which properties of urban networks have a major influence on urban inundation flow, enabling better informed flood-proof urban design. References: Parish, Y. I. H., Muller, P. 2001. Procedural modeling of cities. SIGGRAPH, pp. 301—308. Sanders, B.F., Schubert, J.E., Gallegos, H.A., 2008. Integral formulation of shallow-water equations with anisotropic porosity for urban flood modeling. Journal of Hydrology 362, 19-38. Acknowledgements: The research was funded through the ARC grant for Concerted Research Actions, financed by the Wallonia-Brussels Federation.

Modelling CO2 flow in naturally fractured geological media using MINC and multiple subregion upscaling procedure

NASA Astrophysics Data System (ADS)

Tatomir, Alexandru Bogdan A. C.; Flemisch, Bernd; Class, Holger; Helmig, Rainer; Sauter, Martin

2017-04-01

Geological storage of CO2 represents one viable solution to reduce greenhouse gas emission in the atmosphere. Potential leakage of CO2 storage can occur through networks of interconnected fractures. The geometrical complexity of these networks is often very high involving fractures occurring at various scales and having hierarchical structures. Such multiphase flow systems are usually hard to solve with a discrete fracture modelling (DFM) approach. Therefore, continuum fracture models assuming average properties are usually preferred. The multiple interacting continua (MINC) model is an extension of the classic double porosity model (Warren and Root, 1963) which accounts for the non-linear behaviour of the matrix-fracture interactions. For CO2 storage applications the transient representation of the inter-porosity two phase flow plays an important role. This study tests the accuracy and computational efficiency of the MINC method complemented with the multiple sub-region (MSR) upscaling procedure versus the DFM. The two phase flow MINC simulator is implemented in the free-open source numerical toolbox DuMux (www.dumux.org). The MSR (Gong et al., 2009) determines the inter-porosity terms by solving simplified local single-phase flow problems. The DFM is considered as the reference solution. The numerical examples consider a quasi-1D reservoir with a quadratic fracture system , a five-spot radial symmetric reservoir, and a completely random generated fracture system. Keywords: MINC, upscaling, two-phase flow, fractured porous media, discrete fracture model, continuum fracture model

Modeling Physiological Systems in the Human Body as Networks of Quasi-1D Fluid Flows

NASA Astrophysics Data System (ADS)

Staples, Anne

2008-11-01

Extensive research has been done on modeling human physiology. Most of this work has been aimed at developing detailed, three-dimensional models of specific components of physiological systems, such as a cell, a vein, a molecule, or a heart valve. While efforts such as these are invaluable to our understanding of human biology, if we were to construct a global model of human physiology with this level of detail, computing even a nanosecond in this computational being's life would certainly be prohibitively expensive. With this in mind, we derive the Pulsed Flow Equations, a set of coupled one-dimensional partial differential equations, specifically designed to capture two-dimensional viscous, transport, and other effects, and aimed at providing accurate and fast-to-compute global models for physiological systems represented as networks of quasi one-dimensional fluid flows. Our goal is to be able to perform faster-than-real time simulations of global processes in the human body on desktop computers.

Mathematical-Artificial Neural Network Hybrid Model to Predict Roll Force during Hot Rolling of Steel

NASA Astrophysics Data System (ADS)

Rath, S.; Sengupta, P. P.; Singh, A. P.; Marik, A. K.; Talukdar, P.

2013-07-01

Accurate prediction of roll force during hot strip rolling is essential for model based operation of hot strip mills. Traditionally, mathematical models based on theory of plastic deformation have been used for prediction of roll force. In the last decade, data driven models like artificial neural network have been tried for prediction of roll force. Pure mathematical models have accuracy limitations whereas data driven models have difficulty in convergence when applied to industrial conditions. Hybrid models by integrating the traditional mathematical formulations and data driven methods are being developed in different parts of world. This paper discusses the methodology of development of an innovative hybrid mathematical-artificial neural network model. In mathematical model, the most important factor influencing accuracy is flow stress of steel. Coefficients of standard flow stress equation, calculated by parameter estimation technique, have been used in the model. The hybrid model has been trained and validated with input and output data collected from finishing stands of Hot Strip Mill, Bokaro Steel Plant, India. It has been found that the model accuracy has been improved with use of hybrid model, over the traditional mathematical model.

Modeling of a pitching and plunging airfoil using experimental flow field and load measurements

NASA Astrophysics Data System (ADS)

Troshin, Victor; Seifert, Avraham

2018-01-01

The main goal of the current paper is to outline a low-order modeling procedure of a heaving airfoil in a still fluid using experimental measurements. Due to its relative simplicity, the proposed procedure is applicable for the analysis of flow fields within complex and unsteady geometries and it is suitable for analyzing the data obtained by experimentation. Currently, this procedure is used to model and predict the flow field evolution using a small number of low profile load sensors and flow field measurements. A time delay neural network is used to estimate the flow field. The neural network estimates the amplitudes of the most energetic modes using four sensory inputs. The modes are calculated using proper orthogonal decomposition of the flow field data obtained experimentally by time-resolved, phase-locked particle imaging velocimetry. To permit the use of proper orthogonal decomposition, the measured flow field is mapped onto a stationary domain using volume preserving transformation. The analysis performed by the model showed good estimation quality within the parameter range used in the training procedure. However, the performance deteriorates for cases out of this range. This situation indicates that, to improve the robustness of the model, both the decomposition and the training data sets must be diverse in terms of input parameter space. In addition, the results suggest that the property of volume preservation of the mapping does not affect the model quality as long as the model is not based on the Galerkin approximation. Thus, it may be relaxed for cases with more complex geometry and kinematics.

Depicting mass flow rate of R134a /LPG refrigerant through straight and helical coiled adiabatic capillary tubes of vapor compression refrigeration system using artificial neural network approach

NASA Astrophysics Data System (ADS)

Gill, Jatinder; Singh, Jagdev

2018-07-01

In this work, an experimental investigation is carried out with R134a and LPG refrigerant mixture for depicting mass flow rate through straight and helical coil adiabatic capillary tubes in a vapor compression refrigeration system. Various experiments were conducted under steady-state conditions, by changing capillary tube length, inner diameter, coil diameter and degree of subcooling. The results showed that mass flow rate through helical coil capillary tube was found lower than straight capillary tube by about 5-16%. Dimensionless correlation and Artificial Neural Network (ANN) models were developed to predict mass flow rate. It was found that dimensionless correlation and ANN model predictions agreed well with experimental results and brought out an absolute fraction of variance of 0.961 and 0.988, root mean square error of 0.489 and 0.275 and mean absolute percentage error of 4.75% and 2.31% respectively. The results suggested that ANN model shows better statistical prediction than dimensionless correlation model.

Microrheology of highly crosslinked microtubule networks is dominated by force-induced crosslinker unbinding

PubMed Central

Yang, Yali; Bai, Mo; Klug, William S.; Levine, Alex J.

2012-01-01

We determine the time- and force-dependent viscoelastic responses of reconstituted networks of microtubules that have been strongly crosslinked by biotin-streptavidin bonds. To measure the microscale viscoelasticity of such networks, we use a magnetic tweezers device to apply localized forces. At short time scales, the networks respond nonlinearly to applied force, with stiffening at small forces, followed by a reduction in the stiffening response at high forces, which we attribute to the force-induced unbinding of crosslinks. At long time scales, force-induced bond unbinding leads to local network rearrangement and significant bead creep. Interestingly, the network retains its elastic modulus even under conditions of significant plastic flow, suggesting that crosslinker breakage is balanced by the formation of new bonds. To better understand this effect, we developed a finite element model of such a stiff filament network with labile crosslinkers obeying force-dependent Bell model unbinding dynamics. The coexistence of dissipation, due to bond breakage, and the elastic recovery of the network is possible because each filament has many crosslinkers. Recovery can occur as long as a sufficient number of the original crosslinkers are preserved under the loading period. When these remaining original crosslinkers are broken, plastic flow results. PMID:23577042

A Network Scheduling Model for Distributed Control Simulation

NASA Technical Reports Server (NTRS)

Culley, Dennis; Thomas, George; Aretskin-Hariton, Eliot

2016-01-01

Distributed engine control is a hardware technology that radically alters the architecture for aircraft engine control systems. Of its own accord, it does not change the function of control, rather it seeks to address the implementation issues for weight-constrained vehicles that can limit overall system performance and increase life-cycle cost. However, an inherent feature of this technology, digital communication networks, alters the flow of information between critical elements of the closed-loop control. Whereas control information has been available continuously in conventional centralized control architectures through virtue of analog signaling, moving forward, it will be transmitted digitally in serial fashion over the network(s) in distributed control architectures. An underlying effect is that all of the control information arrives asynchronously and may not be available every loop interval of the controller, therefore it must be scheduled. This paper proposes a methodology for modeling the nominal data flow over these networks and examines the resulting impact for an aero turbine engine system simulation.

Transient pressure analysis of a volume fracturing well in fractured tight oil reservoirs

NASA Astrophysics Data System (ADS)

Lu, Cheng; Wang, Jiahang; Zhang, Cong; Cheng, Minhua; Wang, Xiaodong; Dong, Wenxiu; Zhou, Yingfang

2017-12-01

This paper presents a semi-analytical model to simulate transient pressure curves for a vertical well with a reconstructed fracture network in fractured tight oil reservoirs. In the proposed model, the reservoir is a composite system and contains two regions. The inner region is described as a formation with a finite conductivity hydraulic fracture network and the flow in the fracture is assumed to be linear, while the outer region is modeled using the classical Warren-Root model where radial flow is applied. The transient pressure curves of a vertical well in the proposed reservoir model are calculated semi-analytically using the Laplace transform and Stehfest numerical inversion. As shown in the type curves, the flow is divided into several regimes: (a) linear flow in artificial main fractures; (b) coupled boundary flow; (c) early linear flow in a fractured formation; (d) mid radial flow in the semi-fractures of the formation; (e) mid radial flow or pseudo steady flow; (f) mid cross-flow; (g) closed boundary flow. Based on our newly proposed model, the effects of some sensitive parameters, such as elastic storativity ratio, cross-flow coefficient, fracture conductivity and skin factor, on the type curves were also analyzed extensively. The simulated type curves show that for a vertical fractured well in a tight reservoir, the elastic storativity ratios and crossflow coefficients affect the time and the degree of crossflow respectively. The pressure loss increases with an increase in the fracture conductivity. To a certain extent, the effect of the fracture conductivity is more obvious than that of the half length of the fracture on improving the production effect. With an increase in the wellbore storage coefficient, the fluid compressibility is so large that it might cover the early stage fracturing characteristics. Linear or bilinear flow may not be recognized, and the pressure and pressure derivative gradually shift to the right. With an increase in the skin effect, the pressure loss increases gradually.

Optimization of the graph model of the water conduit network, based on the approach of search space reducing

NASA Astrophysics Data System (ADS)

Korovin, Iakov S.; Tkachenko, Maxim G.

2018-03-01

In this paper we present a heuristic approach, improving the efficiency of methods, used for creation of efficient architecture of water distribution networks. The essence of the approach is a procedure of search space reduction the by limiting the range of available pipe diameters that can be used for each edge of the network graph. In order to proceed the reduction, two opposite boundary scenarios for the distribution of flows are analysed, after which the resulting range is further narrowed by applying a flow rate limitation for each edge of the network. The first boundary scenario provides the most uniform distribution of the flow in the network, the opposite scenario created the net with the highest possible flow level. The parameters of both distributions are calculated by optimizing systems of quadratic functions in a confined space, which can be effectively performed with small time costs. This approach was used to modify the genetic algorithm (GA). The proposed GA provides a variable number of variants of each gene, according to the number of diameters in list, taking into account flow restrictions. The proposed approach was implemented to the evaluation of a well-known test network - the Hanoi water distribution network [1], the results of research were compared with a classical GA with an unlimited search space. On the test data, the proposed trip significantly reduced the search space and provided faster and more obvious convergence in comparison with the classical version of GA.

Autonomous Agent-Based Systems and Their Applications in Fluid Dynamics, Particle Separation, and Co-evolving Networks

NASA Astrophysics Data System (ADS)

Graeser, Oliver

This thesis comprises three parts, reporting research results in Fluid Dynamics (Part I), Particle Separation (Part II) and Co-evolving Networks (Part III). Part I deals with the simulation of fluid dynamics using the lattice-Boltzmann method. Microfluidic devices often feature two-dimensional, repetitive arrays. Flows through such devices are pressure-driven and confined by solid walls. We have defined new adaptive generalised periodic boundary conditions to represent the effects of outer solid walls, and are thus able to exploit the periodicity of the array by simulating the flow through one unit cell in lieu of the entire device. The so-calculated fully developed flow describes the flow through the entire array accurately, but with computational requirements that are reduced according to the dimensions of the array. Part II discusses the problem of separating macromolecules like proteins or DNA coils. The reliable separation of such molecules is a crucial task in molecular biology. The use of Brownian ratchets as mechanisms for the separation of such particles has been proposed and discussed during the last decade. Pressure-driven flows have so far been dismissed as possible driving forces for Brownian ratchets, as they do not generate ratchet asymmetry. We propose a microfluidic design that uses pressure-driven flows to create asymmetry and hence allows particle separation. The dependence of the asymmetry on various factors of the microfluidic geometry is discussed. We further exemplify the feasibility of our approach using Brownian dynamics simulations of particles of different sizes in such a device. The results show that ratchet-based particle separation using flows as the driving force is possible. Simulation results and ratchet theory predictions are in excellent agreement. Part III deals with the co-evolution of networks and dynamic models. A group of agents occupies the nodes of a network, which defines the relationship between these agents. The evolution of the agents is defined by the rules of the dynamic model and depends on the relationship between agents, i.e., the state of the network. In return, the evolution of the network depends on the state of the dynamic model. The concept is introduced through the adaptive SIS model. We show that the previously used criterion determining the critical infected fraction, i.e., the number of infected agents required to sustain the epidemic, is inappropriate for this model. We introduce a different criterion and show that the critical infected fraction so determined is in good agreement with results obtained by numerical simulations. We further discuss the concept of co-evolving dynamics using the Snowdrift Game as a model paradigm. Co-evolution occurs through agents cutting dissatisfied links and rewiring to other agents at random. The effect of co-evolution on the emergence of cooperation is discussed using a mean-field theory and numerical simulations. A transition between a connected and a disconnected, highly cooperative state of the system is observed, and explained using the mean-field model. Quantitative deviations regarding the level of cooperation in the disconnected regime can be fully resolved through an improved mean-field theory that includes the effect of random fluctuations into its model.

Tumor Blood Vessel Dynamics

NASA Astrophysics Data System (ADS)

Munn, Lance

2009-11-01

``Normalization'' of tumor blood vessels has shown promise to improve the efficacy of chemotherapeutics. In theory, anti-angiogenic drugs targeting endothelial VEGF signaling can improve vessel network structure and function, enhancing the transport of subsequent cytotoxic drugs to cancer cells. In practice, the effects are unpredictable, with varying levels of success. The predominant effects of anti-VEGF therapies are decreased vessel leakiness (hydraulic conductivity), decreased vessel diameters and pruning of the immature vessel network. It is thought that each of these can influence perfusion of the vessel network, inducing flow in regions that were previously sluggish or stagnant. Unfortunately, when anti-VEGF therapies affect vessel structure and function, the changes are dynamic and overlapping in time, and it has been difficult to identify a consistent and predictable normalization ``window'' during which perfusion and subsequent drug delivery is optimal. This is largely due to the non-linearity in the system, and the inability to distinguish the effects of decreased vessel leakiness from those due to network structural changes in clinical trials or animal studies. We have developed a mathematical model to calculate blood flow in complex tumor networks imaged by two-photon microscopy. The model incorporates the necessary and sufficient components for addressing the problem of normalization of tumor vasculature: i) lattice-Boltzmann calculations of the full flow field within the vasculature and within the tissue, ii) diffusion and convection of soluble species such as oxygen or drugs within vessels and the tissue domain, iii) distinct and spatially-resolved vessel hydraulic conductivities and permeabilities for each species, iv) erythrocyte particles advecting in the flow and delivering oxygen with real oxygen release kinetics, v) shear stress-mediated vascular remodeling. This model, guided by multi-parameter intravital imaging of tumor vessel structure and function, provides a tool for identifying the structural and functional determinants of tumor vessel normalization.

Screening Models of Aquifer Heterogeneity Using the Flow Dimension

NASA Astrophysics Data System (ADS)

Walker, D. D.; Cello, P. A.; Roberts, R. M.; Valocchi, A. J.

2007-12-01

Despite advances in test interpretation and modeling, typical groundwater modeling studies only indirectly use the parameters and information inferred from hydraulic tests. In particular, the Generalized Radial Flow approach to test interpretation infers the flow dimension, a parameter describing the geometry of the flow field during a hydraulic test. Noninteger values of the flow dimension often are inferred for tests in highly heterogeneous aquifers, yet subsequent modeling studies typically ignore the flow dimension. Monte Carlo analyses of detailed numerical models of aquifer tests examine the flow dimension for several stochastic models of heterogeneous transmissivity, T(x). These include multivariate lognormal, fractional Brownian motion, a site percolation network, and discrete linear features with lengths distributed as power-law. The behavior of the simulated flow dimensions are compared to the flow dimensions observed for multiple aquifer tests in a fractured dolomite aquifer in the Great Lakes region of North America. The combination of multiple hydraulic tests, observed fracture patterns, and the Monte Carlo results are used to screen models of heterogeneity and their parameters for subsequent groundwater flow modeling.

Computational network model prediction of hemodynamic alterations due to arteriolar remodeling in interval sprint trained skeletal muscle.

PubMed

Binder, Kyle W; Murfee, Walter L; Song, Ji; Laughlin, M Harold; Price, Richard J

2007-01-01

Exercise training is known to enhance skeletal muscle blood flow capacity, with high-intensity interval sprint training (IST) primarily affecting muscles with a high proportion of fast twitch glycolytic fibers. The objective of this study was to determine the relative contributions of new arteriole formation and lumenal arteriolar remodeling to enhanced flow capacity and the impact of these adaptations on local microvascular hemodynamics deep within the muscle. The authors studied arteriolar adaptation in the white/mixed-fiber portion of gastrocnemius muscles of IST (6 bouts of running/day; 2.5 min/bout; 60 m/min speed; 15% grade; 4.5 min rest between bouts; 5 training days/wk; 10 wks total) and sedentary (SED) control rats using whole-muscle Microfil casts. Dimensional and topological data were then used to construct a series of computational hemodynamic network models that incorporated physiological red blood cell distributions and hematocrit and diameter dependent apparent viscosities. In comparison to SED controls, IST elicited a significant increase in arterioles/order in the 3A through 6A generations. Predicted IST and SED flows through the 2A generation agreed closely with in vivo measurements made in a previous study, illustrating the accuracy of the model. IST shifted the bulk of the pressure drop across the network from the 3As to the 4As and 5As, and flow capacity increased from 0.7 mL/min in SED to 1.5 mL/min in IST when a driving pressure of 80 mmHg was applied. The primary adaptation to IST is an increase in arterioles in the 3A through 6A generations, which, in turn, creates an approximate doubling of flow capacity and a deeper penetration of high pressure into the arteriolar network.

Modeling the Inhomogeneous Response of Steady and Transient Flows of Entangled Micellar Solutions

NASA Astrophysics Data System (ADS)

McKinley, Gareth

2008-03-01

Surfactant molecules can self-assemble in solution into long flexible structures known as wormlike micelles. These structures entangle, forming a viscoelastic network similar to those in entangled polymer melts and solutions. However, in contrast to `inert' polymeric networks, wormlike micelles continuously break and reform leading to an additional relaxation mechanism and the name `living polymers'. Observations in both classes of entangled fluids have shown that steady and transient shearing flows of these solutions exhibit spatial inhomogeneities such as `shear-bands' at sufficiently large applied strains. In the present work, we investigate the dynamical response of a class of two-species elastic network models which can capture, in a self-consistent manner, the creation and destruction of elastically-active network segments, as well as diffusive coupling between the microstructural conformations and the local state of stress in regions with large spatial gradients of local deformation. These models incorporate a discrete version of the micellar breakage and reforming dynamics originally proposed by Cates and capture, at least qualitatively, non-affine tube deformation and chain disentanglement. The `flow curves' of stress and apparent shear rate resulting from an assumption of homogeneous deformation is non-monotonic and linear stability analysis shows that the region of non-monotonic response is unstable. Calculation of the full inhomogeneous flow field results in localized shear bands that grow linearly in extent across the gap as the apparent shear rate increases. Time-dependent calculations in step strain, large amplitude oscillatory shear (LAOS) and in start up of steady shear flow show that the velocity profile in the gap and the total stress measured at the bounding surfaces are coupled and evolve in a complex non-monotonic manner as the shear bands develop and propagate.

A physically-based Distributed Hydrologic Model for Tropical Catchments

NASA Astrophysics Data System (ADS)

Abebe, N. A.; Ogden, F. L.

2010-12-01

Hydrological models are mathematical formulations intended to represent observed hydrological processes in a watershed. Simulated watersheds in turn vary in their nature based on their geographic location, altitude, climatic variables and geology and soil formation. Due to these variations, available hydrologic models vary in process formulation, spatial and temporal resolution and data demand. Many tropical watersheds are characterized by extensive and persistent biological activity and a large amount of rain. The Agua Salud catchments located within the Panama Canal Watershed, Panama, are such catchments identified by steep rolling topography, deep soils derived from weathered bedrock, and limited exposed bedrock. Tropical soils are highly affected by soil cracks, decayed tree roots and earthworm burrows forming a network of preferential flow paths that drain to a perched water table, which forms at a depth where the vertical hydraulic conductivity is significantly reduced near the bottom of the bioturbation layer. We have developed a physics-based, spatially distributed, multi-layered hydrologic model to simulate the dominant processes in these tropical watersheds. The model incorporates the major flow processes including overland flow, channel flow, matrix and non-Richards film flow infiltration, lateral downslope saturated matrix and non-Darcian pipe flow in the bioturbation layer, and deep saturated groundwater flow. Emphasis is given to the modeling of subsurface unsaturated zone soil moisture dynamics and the saturated preferential lateral flow from the network of macrospores. Preliminary results indicate that the model has the capability to simulate the complex hydrological processes in the catchment and will be a useful tool in the ongoing comprehensive ecohydrological studies in tropical catchments, and help improve our understanding of the hydrological effects of deforestation and aforestation.

Oscillations and Multiple Equilibria in Microvascular Blood Flow.

PubMed

Karst, Nathaniel J; Storey, Brian D; Geddes, John B

2015-07-01

We investigate the existence of oscillatory dynamics and multiple steady-state flow rates in a network with a simple topology and in vivo microvascular blood flow constitutive laws. Unlike many previous analytic studies, we employ the most biologically relevant models of the physical properties of whole blood. Through a combination of analytic and numeric techniques, we predict in a series of two-parameter bifurcation diagrams a range of dynamical behaviors, including multiple equilibria flow configurations, simple oscillations in volumetric flow rate, and multiple coexistent limit cycles at physically realizable parameters. We show that complexity in network topology is not necessary for complex behaviors to arise and that nonlinear rheology, in particular the plasma skimming effect, is sufficient to support oscillatory dynamics similar to those observed in vivo.

Meta-modeling of the pesticide fate model MACRO for groundwater exposure assessments using artificial neural networks

NASA Astrophysics Data System (ADS)

Stenemo, Fredrik; Lindahl, Anna M. L.; Gärdenäs, Annemieke; Jarvis, Nicholas

2007-08-01

Several simple index methods that use easily accessible data have been developed and included in decision-support systems to estimate pesticide leaching across larger areas. However, these methods often lack important process descriptions (e.g. macropore flow), which brings into question their reliability. Descriptions of macropore flow have been included in simulation models, but these are too complex and demanding for spatial applications. To resolve this dilemma, a neural network simulation meta-model of the dual-permeability macropore flow model MACRO was created for pesticide groundwater exposure assessment. The model was parameterized using pedotransfer functions that require as input the clay and sand content of the topsoil and subsoil, and the topsoil organic carbon content. The meta-model also requires the topsoil pesticide half-life and the soil organic carbon sorption coefficient as input. A fully connected feed-forward multilayer perceptron classification network with two hidden layers, linked to fully connected feed-forward multilayer perceptron neural networks with one hidden layer, trained on sub-sets of the target variable, was shown to be a suitable meta-model for the intended purpose. A Fourier amplitude sensitivity test showed that the model output (the 80th percentile average yearly pesticide concentration at 1 m depth for a 20 year simulation period) was sensitive to all input parameters. The two input parameters related to pesticide characteristics (i.e. soil organic carbon sorption coefficient and topsoil pesticide half-life) were the most influential, but texture in the topsoil was also quite important since it was assumed to control the mass exchange coefficient that regulates the strength of macropore flow. This is in contrast to models based on the advection-dispersion equation where soil texture is relatively unimportant. The use of the meta-model is exemplified with a case-study where the spatial variability of pesticide leaching is mapped for a small field. It was shown that the area of the field that contributes most to leaching depends on the properties of the compound in question. It is concluded that the simulation meta-model of MACRO should prove useful for mapping relative pesticide leaching risks at large scales.

Fuzzy neural network for flow estimation in sewer systems during wet weather.

PubMed

Shen, Jun; Shen, Wei; Chang, Jian; Gong, Ning

2006-02-01

Estimation of the water flow from rainfall intensity during storm events is important in hydrology, sewer system control, and environmental protection. The runoff-producing behavior of a sewer system changes from one storm event to another because rainfall loss depends not only on rainfall intensities, but also on the state of the soil and vegetation, the general condition of the climate, and so on. As such, it would be difficult to obtain a precise flowrate estimation without sufficient a priori knowledge of these factors. To establish a model for flow estimation, one can also use statistical methods, such as the neural network STORMNET, software developed at Lyonnaise des Eaux, France, analyzing the relation between rainfall intensity and flowrate data of the known storm events registered in the past for a given sewer system. In this study, the authors propose a fuzzy neural network to estimate the flowrate from rainfall intensity. The fuzzy neural network combines four STORMNETs and fuzzy deduction to better estimate the flowrates. This study's system for flow estimation can be calibrated automatically by using known storm events; no data regarding the physical characteristics of the drainage basins are required. Compared with the neural network STORMNET, this method reduces the mean square error of the flow estimates by approximately 20%. Experimental results are reported herein.

Prediction of silicon oxynitride plasma etching using a generalized regression neural network

NASA Astrophysics Data System (ADS)

Kim, Byungwhan; Lee, Byung Teak

2005-08-01

A prediction model of silicon oxynitride (SiON) etching was constructed using a neural network. Model prediction performance was improved by means of genetic algorithm. The etching was conducted in a C2F6 inductively coupled plasma. A 24 full factorial experiment was employed to systematically characterize parameter effects on SiON etching. The process parameters include radio frequency source power, bias power, pressure, and C2F6 flow rate. To test the appropriateness of the trained model, additional 16 experiments were conducted. For comparison, four types of statistical regression models were built. Compared to the best regression model, the optimized neural network model demonstrated an improvement of about 52%. The optimized model was used to infer etch mechanisms as a function of parameters. The pressure effect was noticeably large only as relatively large ion bombardment was maintained in the process chamber. Ion-bombardment-activated polymer deposition played the most significant role in interpreting the complex effect of bias power or C2F6 flow rate. Moreover, [CF2] was expected to be the predominant precursor to polymer deposition.

Value flow mapping: Using networks to inform stakeholder analysis

NASA Astrophysics Data System (ADS)

Cameron, Bruce G.; Crawley, Edward F.; Loureiro, Geilson; Rebentisch, Eric S.

2008-02-01

Stakeholder theory has garnered significant interest from the corporate community, but has proved difficult to apply to large government programs. A detailed value flow exercise was conducted to identify the value delivery mechanisms among stakeholders for the current Vision for Space Exploration. We propose a method for capturing stakeholder needs that explicitly recognizes the outcomes required of the value creating organization. The captured stakeholder needs are then translated into input-output models for each stakeholder, which are then aggregated into a network model. Analysis of this network suggests that benefits are infrequently linked to the root provider of value. Furthermore, it is noted that requirements should not only be written to influence the organization's outputs, but also to influence the propagation of benefit further along the value chain. A number of future applications of this model to systems architecture and requirement analysis are discussed.

Economic model for QoS guarantee on the Internet

NASA Astrophysics Data System (ADS)

Zhang, Chi; Wei, Jiaolong

2001-09-01

This paper describes a QoS guarantee architecture suited for best-effort environments, based on ideas from microeconomics and non-cooperative game theory. First, an analytic model is developed for the study of the resource allocation in the Internet. Then we show that with a simple pricing mechanism (from network implementation and users' points-of-view), we were able to provide QoS guarantee at per flow level without resource allocation or complicated scheduling mechanisms or maintaining per flow state in the core network. Unlike the previous work on this area, we extend the basic model to support inelastic applications which require minimum bandwidth guarantees for a given time period by introducing derivative market.

Innovation flow through social networks: productivity distribution in France and Italy

NASA Astrophysics Data System (ADS)

di Matteo, T.; Aste, T.; Gallegati, M.

2005-10-01

From a detailed empirical analysis of the productivity of non financial firms across several countries and years we show that productivity follows a non-Gaussian distribution with `fat tails' in the large productivity region which are well mimicked by power law behaviors. We discuss how these empirical findings can be linked to a mechanism of exchanges in a social network where firms improve their productivity by direct innovation and/or by imitation of other firm's technological and organizational solutions. The type of network-connectivity determines how fast and how efficiently information can diffuse and how quickly innovation will permeate or behaviors will be imitated. From a model for innovation flow through a complex network we show that the expectation values of the productivity of each firm are proportional to its connectivity in the network of links between firms. The comparison with the empirical distributions in France and Italy reveals that in this model, such a network must be of a scale-free type with a power-law degree distribution in the large connectivity range.

Near-optimality of special periodic protocols for fluid models of single server switched networks with switchover times

NASA Astrophysics Data System (ADS)

Matveev, A. S.; Ishchenko, R.

2017-11-01

We consider a generic deterministic time-invariant fluid model of a single server switched network, which consists of finitely many infinite size buffers (queues) and receives constant rate inflows of jobs from the outside. Any flow undergoes a multi-phase service, entering a specific buffer after every phase, and ultimately leaves the network; the route of the flow over the buffers is pre-specified, and flows may merge inside the network. They share a common source of service, which can serve at most one buffer at a time and has to switch among buffers from time to time; any switch consumes a nonzero switchover period. With respect to the long-run maximal scaled wip (work in progress) performance metric, near-optimality of periodic scheduling and service protocols is established: the deepest optimum (that is over all feasible processes in the network, irrespective of the initial state) is furnished by such a protocol up to as small error as desired. Moreover, this can be achieved with a special periodic protocol introduced in the paper. It is also shown that the exhaustive policy is optimal for any buffer whose service at the maximal rate does not cause growth of the scaled wip.

Traffic jam dynamics in stochastic cellular automata

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nagel, K.; Schreckenberg, M.

1995-09-01

Simple models for particles hopping on a grid (cellular automata) are used to simulate (single lane) traffic flow. Despite their simplicity, these models are astonishingly realistic in reproducing start-stop-waves and realistic fundamental diagrams. One can use these models to investigate traffic phenomena near maximum flow. A so-called phase transition at average maximum flow is visible in the life-times of jams. The resulting dynamic picture is consistent with recent fluid-dynamical results by Kuehne/Kerner/Konhaeuser, and with Treiterer`s hysteresis description. This places CA models between car-following models and fluid-dynamical models for traffic flow. CA models are tested in projects in Los Alamos (USA)more » and in NRW (Germany) for large scale microsimulations of network traffic.« less

Communication efficiency and congestion of signal traffic in large-scale brain networks.

PubMed

Mišić, Bratislav; Sporns, Olaf; McIntosh, Anthony R

2014-01-01

The complex connectivity of the cerebral cortex suggests that inter-regional communication is a primary function. Using computational modeling, we show that anatomical connectivity may be a major determinant for global information flow in brain networks. A macaque brain network was implemented as a communication network in which signal units flowed between grey matter nodes along white matter paths. Compared to degree-matched surrogate networks, information flow on the macaque brain network was characterized by higher loss rates, faster transit times and lower throughput, suggesting that neural connectivity may be optimized for speed rather than fidelity. Much of global communication was mediated by a "rich club" of hub regions: a sub-graph comprised of high-degree nodes that are more densely interconnected with each other than predicted by chance. First, macaque communication patterns most closely resembled those observed for a synthetic rich club network, but were less similar to those seen in a synthetic small world network, suggesting that the former is a more fundamental feature of brain network topology. Second, rich club regions attracted the most signal traffic and likewise, connections between rich club regions carried more traffic than connections between non-rich club regions. Third, a number of rich club regions were significantly under-congested, suggesting that macaque connectivity actively shapes information flow, funneling traffic towards some nodes and away from others. Together, our results indicate a critical role of the rich club of hub nodes in dynamic aspects of global brain communication.

Communication Efficiency and Congestion of Signal Traffic in Large-Scale Brain Networks

PubMed Central

Mišić, Bratislav; Sporns, Olaf; McIntosh, Anthony R.

2014-01-01

The complex connectivity of the cerebral cortex suggests that inter-regional communication is a primary function. Using computational modeling, we show that anatomical connectivity may be a major determinant for global information flow in brain networks. A macaque brain network was implemented as a communication network in which signal units flowed between grey matter nodes along white matter paths. Compared to degree-matched surrogate networks, information flow on the macaque brain network was characterized by higher loss rates, faster transit times and lower throughput, suggesting that neural connectivity may be optimized for speed rather than fidelity. Much of global communication was mediated by a “rich club” of hub regions: a sub-graph comprised of high-degree nodes that are more densely interconnected with each other than predicted by chance. First, macaque communication patterns most closely resembled those observed for a synthetic rich club network, but were less similar to those seen in a synthetic small world network, suggesting that the former is a more fundamental feature of brain network topology. Second, rich club regions attracted the most signal traffic and likewise, connections between rich club regions carried more traffic than connections between non-rich club regions. Third, a number of rich club regions were significantly under-congested, suggesting that macaque connectivity actively shapes information flow, funneling traffic towards some nodes and away from others. Together, our results indicate a critical role of the rich club of hub nodes in dynamic aspects of global brain communication. PMID:24415931

Network evolution model for supply chain with manufactures as the core.

PubMed

Fang, Haiyang; Jiang, Dali; Yang, Tinghong; Fang, Ling; Yang, Jian; Li, Wu; Zhao, Jing

2018-01-01

Building evolution model of supply chain networks could be helpful to understand its development law. However, specific characteristics and attributes of real supply chains are often neglected in existing evolution models. This work proposes a new evolution model of supply chain with manufactures as the core, based on external market demand and internal competition-cooperation. The evolution model assumes the external market environment is relatively stable, considers several factors, including specific topology of supply chain, external market demand, ecological growth and flow conservation. The simulation results suggest that the networks evolved by our model have similar structures as real supply chains. Meanwhile, the influences of external market demand and internal competition-cooperation to network evolution are analyzed. Additionally, 38 benchmark data sets are applied to validate the rationality of our evolution model, in which, nine manufacturing supply chains match the features of the networks constructed by our model.

Network evolution model for supply chain with manufactures as the core

PubMed Central

Jiang, Dali; Fang, Ling; Yang, Jian; Li, Wu; Zhao, Jing

2018-01-01

Building evolution model of supply chain networks could be helpful to understand its development law. However, specific characteristics and attributes of real supply chains are often neglected in existing evolution models. This work proposes a new evolution model of supply chain with manufactures as the core, based on external market demand and internal competition-cooperation. The evolution model assumes the external market environment is relatively stable, considers several factors, including specific topology of supply chain, external market demand, ecological growth and flow conservation. The simulation results suggest that the networks evolved by our model have similar structures as real supply chains. Meanwhile, the influences of external market demand and internal competition-cooperation to network evolution are analyzed. Additionally, 38 benchmark data sets are applied to validate the rationality of our evolution model, in which, nine manufacturing supply chains match the features of the networks constructed by our model. PMID:29370201

Packet Traffic Dynamics Near Onset of Congestion in Data Communication Network Model

NASA Astrophysics Data System (ADS)

Lawniczak, A. T.; Tang, X.

2006-05-01

The dominant technology of data communication networks is the Packet Switching Network (PSN). It is a complex technology organized as various hierarchical layers according to the International Standard Organization (ISO) Open Systems Interconnect (OSI) Reference Model. The Network Layer of the ISO OSI Reference Model is responsible for delivering packets from their sources to their destinations and for dealing with congestion if it arises in a network. Thus, we focus on this layer and present an abstraction of the Network Layer of the ISO OSI Reference Model. Using this abstraction we investigate how onset of traffic congestion is affected for various routing algorithms by changes in network connection topology. We study how aggregate measures of network performance depend on network connection topology and routing. We explore packets traffic spatio-temporal dynamics near the phase transition point from free flow to congestion for various network connection topologies and routing algorithms. We consider static and adaptive routings. We present selected simulation results.

Comparative 1D and 3D numerical investigation of open-channel junction flows and energy losses

NASA Astrophysics Data System (ADS)

Luo, Hao; Fytanidis, Dimitrios K.; Schmidt, Arthur R.; García, Marcelo H.

2018-07-01

The complexity of open channel confluences stems from flow mixing, secondary circulation, post-confluence flow separation, contraction and backwater effects. These effects in turn result in a large number of parameters required to adequately quantify the junction induced hydraulic resistance and describe mean flow pattern and turbulent flow structures due to flow merging. The recent development in computing power advances the application of 3D Computational Fluid Dynamics (CFD) codes to visualize and understand the Confluence Hydrodynamic Zone (CHZ). Nevertheless, 1D approaches remain the mainstay in large drainage network or waterway system modeling considering computational efficiency and data availability. This paper presents (i) a modified 1D nonlinear dynamic model; (ii) a fully 3D non-hydrostatic, Reynolds-averaged Navier-Stokes Equations (RANS)-based, Computational Fluid Dynamics (CFD) model; (iii) an analysis of changing confluence hydrodynamics and 3D turbulent flow structure under various controls; (iv) a comparison of flow features (i.e. upstream water depths, energy losses and post-confluence contraction) predicted by 1D and 3D models; and (v) parameterization of 3D flow characteristics in 1D modeling through the computation of correction coefficients associated with contraction, energy and momentum. The present comprehensive 3D numerical investigation highlights the driving mechanisms for junction induced energy losses. Moreover, the comparative 1D and 3D study quantifies the deviation of 1D approximations and associated underlying assumptions from the 'true' resultant flow field. The study may also shed light on improving the accuracy of the 1D large network modeling through the parameterization of the complex 3D feature of the flow field and correction of interior boundary conditions at junctions of larger angles and/or with substantial lateral inflows. Moreover, the enclosed numerical investigations may enhance the understanding of the primary mechanisms contributing to hydraulic structure induced turbulent flow behavior and increased hydraulic resistance.

Optimization of flow modeling in fractured media with discrete fracture network via percolation theory

NASA Astrophysics Data System (ADS)

Donado-Garzon, L. D.; Pardo, Y.

2013-12-01

Fractured media are very heterogeneous systems where occur complex physical and chemical processes to model. One of the possible approaches to conceptualize this type of massifs is the Discrete Fracture Network (DFN). Donado et al., modeled flow and transport in a granitic batholith based on this approach and found good fitting with hydraulic and tracer tests, but the computational cost was excessive due to a gigantic amount of elements to model. We present in this work a methodology based on percolation theory for reducing the number of elements and in consequence, to reduce the bandwidth of the conductance matrix and the execution time of each network. DFN poses as an excellent representation of all the set of fractures of the media, but not all the fractures of the media are part of the conductive network. Percolation theory is used to identify which nodes or fractures are not conductive, based on the occupation probability or percolation threshold. In a fractured system, connectivity determines the flow pattern in the fractured rock mass. This volume of fluid is driven through connection paths formed by the fractures, when the permeability of the rock is negligible compared to the fractures. In a population of distributed fractures, each of this that has no intersection with any connected fracture do not contribute to generate a flow field. This algorithm also permits us to erase these elements however they are water conducting and hence, refine even more the backbone of the network. We used 100 different generations of DFN that were optimized in this study using percolation theory. In each of the networks calibrate hydrodynamic parameters as hydraulic conductivity and specific storage coefficient, for each of the five families of fractures, yielding a total of 10 parameters to estimate, at each generation. Since the effects of the distribution of fault orientation changes the value of the percolation threshold, but not the universal laws of classical percolation theory, the latter is applicable to such networks. Under these conditions, percolation theory permit us to reduced the number of elements (90% in average) that form clusters of the 100 DFNs, preserving the so-called backbone. In this way the calibration runs in these networks changed from several hours to just a second obtaining much better results.

Design trade-offs among shunt current, pumping loss and compactness in the piping system of a multi-stack vanadium flow battery

NASA Astrophysics Data System (ADS)

Ye, Qiang; Hu, Jing; Cheng, Ping; Ma, Zhiqi

2015-11-01

Trade-off between shunt current loss and pumping loss is a major challenge in the design of the electrolyte piping network in a flow battery system. It is generally recognized that longer and thinner ducts are beneficial to reduce shunt current but detrimental to minimize pumping power. Base on the developed analog circuit model and the flow network model, we make case studies of multi-stack vanadium flow battery piping systems and demonstrate that both shunt current and electrolyte flow resistance can be simultaneously minimized by using longer and thicker ducts in the piping network. However, extremely long and/or thick ducts lead to a bulky system and may be prohibited by the stack structure. Accordingly, the intrinsic design trade-off is between system efficiency and compactness. Since multi-stack configurations bring both flexibility and complexity to the design process, we perform systematic comparisons among representative piping system designs to illustrate the complicated trade-offs among numerous parameters including stack number, intra-stack channel resistance and inter-stack pipe resistance. As the final design depends on various technical and economical requirements, this paper aims to provide guidelines rather than solutions for designers to locate the optimal trade-off points according to their specific cases.

Impact of Stress on Anomalous Transport in Fractured Rock

NASA Astrophysics Data System (ADS)

Kang, P. K.; Lei, Q.; Lee, S.; Dentz, M.; Juanes, R.

2016-12-01

Fluid flow and transport in fractured rock controls many natural and engineered processes in the subsurface. However, characterizing flow and transport through fractured media is challenging due to the large heterogeneity of fractured rock properties. In addition to these "static" challenges, geologic fractures are always under significant overburden stress, and changes in the stress state can lead to changes in the fracture's ability to conduct fluids. While confining stress has been shown to impact fluid flow through fractures in a fundamental way, the impact of confining stress on transport through fractured rock remains largely unexplored. The link between anomalous (non-Fickian) transport and confining stress has been shown only recently, at the level of a single rough fracture [1]. Here, we investigate the impact of confining stress on flow and transport through discrete fracture networks. We model geomechanical effects in 2D fractured rock by means of a finite-discrete element method (FEMDEM), which can capture the deformation of matrix blocks, reactivation and propagation of cracks. We implement a joint constitutive model within the FEMDEM framework to simulate the effect of fracture roughness. We apply the model to a fracture network extracted from the geological map of an actual outcrop to obtain the aperture field at different stress conditions (Figure 1). We then simulate fluid flow and particle transport through the stressed fracture networks. We observe that anomalous transport emerges in response to confining stress on the fracture networks, and show that this anomalous behavior can be linked to the stress state of the rock. Finally, we develop an effective transport model that captures the anomalous transport through stressed fractures. Our results point to a heretofore unrecognized link between geomechanics and anomalous transport in discrete fractured networks. [1] P. K. Kang, S. Brown, and R. Juanes, Emergence of anomalous transport in stressed rough fractures. Earth and Planetary Science Letters, to appear (2016). Figure (a) Map of maximum principal stress with a vertical normal compressive stress of 3 MPa at top and bottom boundaries, and 1MPa at left and right boundaries. (b) Normal compressive stress of 15 MPa at top and bottom boundaries, and 5MPa at left and right boundaries.

Current challenges in quantifying preferential flow through the vadose zone

NASA Astrophysics Data System (ADS)

Koestel, John; Larsbo, Mats; Jarvis, Nick

2017-04-01

In this presentation, we give an overview of current challenges in quantifying preferential flow through the vadose zone. A review of the literature suggests that current generation models do not fully reflect the present state of process understanding and empirical knowledge of preferential flow. We believe that the development of improved models will be stimulated by the increasingly widespread application of novel imaging technologies as well as future advances in computational power and numerical techniques. One of the main challenges in this respect is to bridge the large gap between the scales at which preferential flow occurs (pore to Darcy scales) and the scale of interest for management (fields, catchments, regions). Studies at the pore scale are being supported by the development of 3-D non-invasive imaging and numerical simulation techniques. These studies are leading to a better understanding of how macropore network topology and initial/boundary conditions control key state variables like matric potential and thus the strength of preferential flow. Extrapolation of this knowledge to larger scales would require support from theoretical frameworks such as key concepts from percolation and network theory, since we lack measurement technologies to quantify macropore networks at these large scales. Linked hydro-geophysical measurement techniques that produce highly spatially and temporally resolved data enable investigation of the larger-scale heterogeneities that can generate preferential flow patterns at pedon, hillslope and field scales. At larger regional and global scales, improved methods of data-mining and analyses of large datasets (machine learning) may help in parameterizing models as well as lead to new insights into the relationships between soil susceptibility to preferential flow and site attributes (climate, land uses, soil types).

Transitions from trees to cycles in adaptive flow networks

NASA Astrophysics Data System (ADS)

Martens, Erik A.; Klemm, Konstantin

2017-11-01

Transport networks are crucial to the functioning of natural and technological systems. Nature features transport networks that are adaptive over a vast range of parameters, thus providing an impressive level of robustness in supply. Theoretical and experimental studies have found that real-world transport networks exhibit both tree-like motifs and cycles. When the network is subject to load fluctuations, the presence of cyclic motifs may help to reduce flow fluctuations and, thus, render supply in the network more robust. While previous studies considered network topology via optimization principles, here, we take a dynamical systems approach and study a simple model of a flow network with dynamically adapting weights (conductances). We assume a spatially non-uniform distribution of rapidly fluctuating loads in the sinks and investigate what network configurations are dynamically stable. The network converges to a spatially non-uniform stable configuration composed of both cyclic and tree-like structures. Cyclic structures emerge locally in a transcritical bifurcation as the amplitude of the load fluctuations is increased. The resulting adaptive dynamics thus partitions the network into two distinct regions with cyclic and tree-like structures. The location of the boundary between these two regions is determined by the amplitude of the fluctuations. These findings may explain why natural transport networks display cyclic structures in the micro-vascular regions near terminal nodes, but tree-like features in the regions with larger veins.

Consistent initial conditions for the Saint-Venant equations in river network modeling

NASA Astrophysics Data System (ADS)

Yu, Cheng-Wei; Liu, Frank; Hodges, Ben R.

2017-09-01

Initial conditions for flows and depths (cross-sectional areas) throughout a river network are required for any time-marching (unsteady) solution of the one-dimensional (1-D) hydrodynamic Saint-Venant equations. For a river network modeled with several Strahler orders of tributaries, comprehensive and consistent synoptic data are typically lacking and synthetic starting conditions are needed. Because of underlying nonlinearity, poorly defined or inconsistent initial conditions can lead to convergence problems and long spin-up times in an unsteady solver. Two new approaches are defined and demonstrated herein for computing flows and cross-sectional areas (or depths). These methods can produce an initial condition data set that is consistent with modeled landscape runoff and river geometry boundary conditions at the initial time. These new methods are (1) the pseudo time-marching method (PTM) that iterates toward a steady-state initial condition using an unsteady Saint-Venant solver and (2) the steady-solution method (SSM) that makes use of graph theory for initial flow rates and solution of a steady-state 1-D momentum equation for the channel cross-sectional areas. The PTM is shown to be adequate for short river reaches but is significantly slower and has occasional non-convergent behavior for large river networks. The SSM approach is shown to provide a rapid solution of consistent initial conditions for both small and large networks, albeit with the requirement that additional code must be written rather than applying an existing unsteady Saint-Venant solver.

Signaling mechanisms underlying the robustness and tunability of the plant immune network

PubMed Central

Kim, Yungil; Tsuda, Kenichi; Igarashi, Daisuke; Hillmer, Rachel A.; Sakakibara, Hitoshi; Myers, Chad L.; Katagiri, Fumiaki

2014-01-01

Summary How does robust and tunable behavior emerge in a complex biological network? We sought to understand this for the signaling network controlling pattern-triggered immunity (PTI) in Arabidopsis. A dynamic network model containing four major signaling sectors, the jasmonate, ethylene, PAD4, and salicylate sectors, which together explain up to 80% of the PTI level, was built using data for dynamic sector activities and PTI levels under exhaustive combinatorial sector perturbations. Our regularized multiple regression model had a high level of predictive power and captured known and unexpected signal flows in the network. The sole inhibitory sector in the model, the ethylene sector, was central to the network robustness via its inhibition of the jasmonate sector. The model's multiple input sites linked specific signal input patterns varying in strength and timing to different network response patterns, indicating a mechanism enabling tunability. PMID:24439900

Intelligent Control via Wireless Sensor Networks for Advanced Coal Combustion Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aman Behal; Sunil Kumar; Goodarz Ahmadi

2007-08-05

Numerical Modeling of Solid Gas Flow, System Identification for purposes of modeling and control, and Wireless Sensor and Actor Network design were pursued as part of this project. Time series input-output data was obtained from NETL's Morgantown CFB facility courtesy of Dr. Lawrence Shadle. It was run through a nonlinear kernel estimator and nonparametric models were obtained for the system. Linear and first-order nonlinear kernels were then utilized to obtain a state-space description of the system. Neural networks were trained that performed better at capturing the plant dynamics. It is possible to use these networks to find a plant modelmore » and the inversion of this model can be used to control the system. These models allow one to compare with physics based models whose parameters can then be determined by comparing them against the available data based model. On a parallel track, Dr. Kumar designed an energy-efficient and reliable transport protocol for wireless sensor and actor networks, where the sensors could be different types of wireless sensors used in CFB based coal combustion systems and actors are more powerful wireless nodes to set up a communication network while avoiding the data congestion. Dr. Ahmadi's group studied gas solid flow in a duct. It was seen that particle concentration clearly shows a preferential distribution. The particles strongly interact with the turbulence eddies and are concentrated in narrow bands that are evolving with time. It is believed that observed preferential concentration is due to the fact that these particles are flung out of eddies by centrifugal force.« less

Spatial modeling of potential woody biomass flow

Treesearch

Woodam Chung; Nathaniel Anderson

2012-01-01

The flow of woody biomass to end users is determined by economic factors, especially the amount available across a landscape and delivery costs of bioenergy facilities. The objective of this study develop methodology to quantify landscape-level stocks and potential biomass flows using the currently available spatial database road network analysis tool. We applied this...

A network application for modeling a centrifugal compressor performance map

NASA Astrophysics Data System (ADS)

Nikiforov, A.; Popova, D.; Soldatova, K.

2017-08-01

The approximation of aerodynamic performance of a centrifugal compressor stage and vaneless diffuser by neural networks is presented. Advantages, difficulties and specific features of the method are described. An example of a neural network and its structure is shown. The performances in terms of efficiency, pressure ratio and work coefficient of 39 model stages within the range of flow coefficient from 0.01 to 0.08 were modeled with mean squared error 1.5 %. In addition, the loss and friction coefficients of vaneless diffusers of relative widths 0.014-0.10 are modeled with mean squared error 2.45 %.

Flow distribution analysis on the cooling tube network of ITER thermal shield

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nam, Kwanwoo; Chung, Wooho; Noh, Chang Hyun

2014-01-29

Thermal shield (TS) is to be installed between the vacuum vessel or the cryostat and the magnets in ITER tokamak to reduce the thermal radiation load to the magnets operating at 4.2K. The TS is cooled by pressurized helium gas at the inlet temperature of 80K. The cooling tube is welded on the TS panel surface and the composed flow network of the TS cooling tubes is complex. The flow rate in each panel should be matched to the thermal design value for effective radiation shielding. This paper presents one dimensional analysis on the flow distribution of cooling tube networkmore » for the ITER TS. The hydraulic cooling tube network is modeled by an electrical analogy. Only the cooling tube on the TS surface and its connecting pipe from the manifold are considered in the analysis model. Considering the frictional factor and the local loss in the cooling tube, the hydraulic resistance is expressed as a linear function with respect to mass flow rate. Sub-circuits in the TS are analyzed separately because each circuit is controlled by its own control valve independently. It is found that flow rates in some panels are insufficient compared with the design values. In order to improve the flow distribution, two kinds of design modifications are proposed. The first one is to connect the tubes of the adjacent panels. This will increase the resistance of the tube on the panel where the flow rate is excessive. The other design suggestion is that an orifice is installed at the exit of tube routing where the flow rate is to be reduced. The analysis for the design suggestions shows that the flow mal-distribution is improved significantly.« less

Effects of spatial configuration of imperviousness and green infrastructure networks on hydrologic response in a residential sewershed

NASA Astrophysics Data System (ADS)

Lim, Theodore C.; Welty, Claire

2017-09-01

Green infrastructure (GI) is an approach to stormwater management that promotes natural processes of infiltration and evapotranspiration, reducing surface runoff to conventional stormwater drainage infrastructure. As more urban areas incorporate GI into their stormwater management plans, greater understanding is needed on the effects of spatial configuration of GI networks on hydrological performance, especially in the context of potential subsurface and lateral interactions between distributed facilities. In this research, we apply a three-dimensional, coupled surface-subsurface, land-atmosphere model, ParFlow.CLM, to a residential urban sewershed in Washington DC that was retrofitted with a network of GI installations between 2009 and 2015. The model was used to test nine additional GI and imperviousness spatial network configurations for the site and was compared with monitored pipe-flow data. Results from the simulations show that GI located in higher flow-accumulation areas of the site intercepted more surface runoff, even during wetter and multiday events. However, a comparison of the differences between scenarios and levels of variation and noise in monitored data suggests that the differences would only be detectable between the most and least optimal GI/imperviousness configurations.

Scaling of Average Weighted Receiving Time on Double-Weighted Koch Networks

NASA Astrophysics Data System (ADS)

Dai, Meifeng; Ye, Dandan; Hou, Jie; Li, Xingyi

2015-03-01

In this paper, we introduce a model of the double-weighted Koch networks based on actual road networks depending on the two weight factors w,r ∈ (0, 1]. The double weights represent the capacity-flowing weight and the cost-traveling weight, respectively. Denote by wFij the capacity-flowing weight connecting the nodes i and j, and denote by wCij the cost-traveling weight connecting the nodes i and j. Let wFij be related to the weight factor w, and let wCij be related to the weight factor r. This paper assumes that the walker, at each step, starting from its current node, moves to any of its neighbors with probability proportional to the capacity-flowing weight of edge linking them. The weighted time for two adjacency nodes is the cost-traveling weight connecting the two nodes. We define the average weighted receiving time (AWRT) on the double-weighted Koch networks. The obtained result displays that in the large network, the AWRT grows as power-law function of the network order with the exponent, represented by θ(w,r) = ½ log2(1 + 3wr). We show that the AWRT exhibits a sublinear or linear dependence on network order. Thus, the double-weighted Koch networks are more efficient than classic Koch networks in receiving information.

Mechanics and control of the cytoskeleton in Amoeba proteus.

PubMed Central

Dembo, M

1989-01-01

Many models of the cytoskeletal motility of Amoeba proteus can be formulated in terms of the theory of reactive interpenetrating flow (Dembo and Harlow, 1986). We have devised numerical methodology for testing such models against the phenomenon of steady axisymmetric fountain flow. The simplest workable scheme revealed by such tests (the minimal model) is the main preoccupation of this study. All parameters of the minimal model are determined from available data. Using these parameters the model quantitatively accounts for the self assembly of the cytoskeleton of A. proteus: for the formation and detailed morphology of the endoplasmic channel, the ectoplasmic tube, the uropod, the plasma gel sheet, and the hyaline cap. The model accounts for the kinematics of the cytoskeleton: the detailed velocity field of the forward flow of the endoplasm, the contraction of the ectoplasmic tube, and the inversion of the flow in the fountain zone. The model also gives a satisfactory account of measurements of pressure gradients, measurements of heat dissipation, and measurements of the output of useful work by amoeba. Finally, the model suggests a very promising (but still hypothetical) continuum formulation of the free boundary problem of amoeboid motion. by balancing normal forces on the plasma membrane as closely as possible, the minimal model is able to predict the turgor pressure and surface tension of A. proteus. Several dynamical factors are crucial to the success of the minimal model and are likely to be general features of cytoskeletal mechanics and control in amoeboid cells. These are: a constitutive law for the viscosity of the contractile network that includes an automatic process of gelation as the network density gets large; a very vigorous cycle of network polymerization and depolymerization (in the case of A. proteus, the time constant for this reaction is approximately 12 s); control of network contractility by a diffusible factor (probably calcium ion); and control of the adhesive interaction between the cytoskeleton and the inner surface of the plasma membrane. Images FIGURE 1 FIGURE 2 FIGURE 7 PMID:2765645

NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities.

PubMed

da Rocha, Edroaldo Lummertz; Ung, Choong Yong; McGehee, Cordelia D; Correia, Cristina; Li, Hu

2016-06-02

The sequential chain of interactions altering the binary state of a biomolecule represents the 'information flow' within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein-protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes-network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

A numerical approach for assessing effects of shear on equivalent permeability and nonlinear flow characteristics of 2-D fracture networks

NASA Astrophysics Data System (ADS)

Liu, Richeng; Li, Bo; Jiang, Yujing; Yu, Liyuan

2018-01-01

Hydro-mechanical properties of rock fractures are core issues for many geoscience and geo-engineering practices. Previous experimental and numerical studies have revealed that shear processes could greatly enhance the permeability of single rock fractures, yet the shear effects on hydraulic properties of fractured rock masses have received little attention. In most previous fracture network models, single fractures are typically presumed to be formed by parallel plates and flow is presumed to obey the cubic law. However, related studies have suggested that the parallel plate model cannot realistically represent the surface characters of natural rock fractures, and the relationship between flow rate and pressure drop will no longer be linear at sufficiently large Reynolds numbers. In the present study, a numerical approach was established to assess the effects of shear on the hydraulic properties of 2-D discrete fracture networks (DFNs) in both linear and nonlinear regimes. DFNs considering fracture surface roughness and variation of aperture in space were generated using an originally developed code DFNGEN. Numerical simulations by solving Navier-Stokes equations were performed to simulate the fluid flow through these DFNs. A fracture that cuts through each model was sheared and by varying the shear and normal displacements, effects of shear on equivalent permeability and nonlinear flow characteristics of DFNs were estimated. The results show that the critical condition of quantifying the transition from a linear flow regime to a nonlinear flow regime is: 10-4 〈 J < 10-3, where J is the hydraulic gradient. When the fluid flow is in a linear regime (i.e., J < 10-4), the relative deviation of equivalent permeability induced by shear, δ2, is linearly correlated with J with small variations, while for fluid flow in the nonlinear regime (J 〉 10-3), δ2 is nonlinearly correlated with J. A shear process would reduce the equivalent permeability significantly in the orientation perpendicular to the sheared fracture as much as 53.86% when J = 1, shear displacement Ds = 7 mm, and normal displacement Dn = 1 mm. By fitting the calculated results, the mathematical expression for δ2 is established to help choose proper governing equations when solving fluid flow problems in fracture networks.

CCSDS Advanced Orbiting Systems Virtual Channel Access Service for QoS MACHETE Model

NASA Technical Reports Server (NTRS)

Jennings, Esther H.; Segui, John S.

2011-01-01

To support various communications requirements imposed by different missions, interplanetary communication protocols need to be designed, validated, and evaluated carefully. Multimission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE), described in "Simulator of Space Communication Networks" (NPO-41373), NASA Tech Briefs, Vol. 29, No. 8 (August 2005), p. 44, combines various tools for simulation and performance analysis of space networks. The MACHETE environment supports orbital analysis, link budget analysis, communications network simulations, and hardware-in-the-loop testing. By building abstract behavioral models of network protocols, one can validate performance after identifying the appropriate metrics of interest. The innovators have extended the MACHETE model library to include a generic link-layer Virtual Channel (VC) model supporting quality-of-service (QoS) controls based on IP streams. The main purpose of this generic Virtual Channel model addition was to interface fine-grain flow-based QoS (quality of service) between the network and MAC layers of the QualNet simulator, a commercial component of MACHETE. This software model adds the capability of mapping IP streams, based on header fields, to virtual channel numbers, allowing extended QoS handling at link layer. This feature further refines the QoS v existing at the network layer. QoS at the network layer (e.g. diffserv) supports few QoS classes, so data from one class will be aggregated together; differentiating between flows internal to a class/priority is not supported. By adding QoS classification capability between network and MAC layers through VC, one maps multiple VCs onto the same physical link. Users then specify different VC weights, and different queuing and scheduling policies at the link layer. This VC model supports system performance analysis of various virtual channel link-layer QoS queuing schemes independent of the network-layer QoS systems.

A Unified Framework for Analyzing and Designing for Stationary Arterial Networks

DOT National Transportation Integrated Search

2017-05-17

This research aims to develop a unified theoretical and simulation framework for analyzing and designing signals for stationary arterial networks. Existing traffic flow models used in design and analysis of signal control strategies are either too si...

One-dimensional model for the intracranial pulse morphological analysis during hyperventilation and CO2 inhalation tests

NASA Astrophysics Data System (ADS)

Ryu, Jaiyoung; Hu, Xiao; Shadden, Shawn C.

2015-11-01

The brain's CO2 reactivity mechanism is coupled with cerebral autoregulation and other unique features of cerebral hemodynamics. We developed a one-dimensional nonlinear model of blood flow in the cerebral arteries coupled to lumped parameter (LP) networks. The LP networks incorporate cerebral autoregulation, CO2 reactivity, intracranial pressure, cerebrospinal fluid, and cortical collateral blood flow models. The model was used to evaluate hemodynamic variables (arterial deformation, blood velocity and pressure) in the cerebral vasculature during hyperventilation and CO2 inhalation test. Tests were performed for various arterial blood pressure (ABP) representing normal and hypotensive conditions. The increase of the cerebral blood flow rates agreed well with the published measurements for various ABP measurements taken during clinical CO2 reactivity tests. The changes in distal vasculature affected the reflected pulse wave energy, which caused the waveform morphological changes at the middle cerebral, common and internal carotid arteries. The pulse morphological analysis demonstrated agreement with previous clinical measurements for cerebral vasoconstriction and vasodilation.

Computer Simulations of the Tumor Vasculature: Applications to Interstitial Fluid Flow, Drug Delivery, and Oxygen Supply.

PubMed

Welter, Michael; Rieger, Heiko

2016-01-01

Tumor vasculature, the blood vessel network supplying a growing tumor with nutrients such as oxygen or glucose, is in many respects different from the hierarchically organized arterio-venous blood vessel network in normal tissues. Angiogenesis (the formation of new blood vessels), vessel cooption (the integration of existing blood vessels into the tumor vasculature), and vessel regression remodel the healthy vascular network into a tumor-specific vasculature. Integrative models, based on detailed experimental data and physical laws, implement, in silico, the complex interplay of molecular pathways, cell proliferation, migration, and death, tissue microenvironment, mechanical and hydrodynamic forces, and the fine structure of the host tissue vasculature. With the help of computer simulations high-precision information about blood flow patterns, interstitial fluid flow, drug distribution, oxygen and nutrient distribution can be obtained and a plethora of therapeutic protocols can be tested before clinical trials. This chapter provides an overview over the current status of computer simulations of vascular remodeling during tumor growth including interstitial fluid flow, drug delivery, and oxygen supply within the tumor. The model predictions are compared with experimental and clinical data and a number of longstanding physiological paradigms about tumor vasculature and intratumoral solute transport are critically scrutinized.

Multi-scale modeling of urban air pollution: development of a Street-in-Grid model

NASA Astrophysics Data System (ADS)

Kim, Youngseob; Wu, You; Seigneur, Christian; Roustan, Yelva

2016-04-01

A new multi-scale model of urban air pollution is presented. This model combines a chemical-transport model (CTM) that includes a comprehensive treatment of atmospheric chemistry and transport at spatial scales greater than 1 km and a street-network model that describes the atmospheric concentrations of pollutants in an urban street network. The street-network model is based on the general formulation of the SIRANE model and consists of two main components: a street-canyon component and a street-intersection component. The street-canyon component calculates the mass transfer velocity at the top of the street canyon (roof top) and the mean wind velocity within the street canyon. The estimation of the mass transfer velocity depends on the intensity of the standard deviation of the vertical velocity at roof top. The effect of various formulations of this mass transfer velocity on the pollutant transport at roof-top level is examined. The street-intersection component calculates the mass transfer from a given street to other streets across the intersection. These mass transfer rates among the streets are calculated using the mean wind velocity calculated for each street and are balanced so that the total incoming flow rate is equal to the total outgoing flow rate from the intersection including the flow between the intersection and the overlying atmosphere at roof top. In the default option, the Leighton photostationary cycle among ozone (O3) and nitrogen oxides (NO and NO2) is used to represent the chemical reactions within the street network. However, the influence of volatile organic compounds (VOC) on the pollutant concentrations increases when the nitrogen oxides (NOx) concentrations are low. To account for the possible VOC influence on street-canyon chemistry, the CB05 chemical kinetic mechanism, which includes 35 VOC model species, is implemented in this street-network model. A sensitivity study is conducted to assess the uncertainties associated with the use of the Leighton cycle chemistry. The street-network model is coupled to the CTM Polair3D of the Polyphemus air quality modeling platform to constitute a Street-in-Grid (SinG) model. The street-network model is used to simulate the concentrations of the chemical species in the lowest layer in the urban area and the simulation for the upper layers is then performed by Polair3D. Interactions between the street-network model and the host CTM occur at roof-top and depend on the vertical mass transfer described above. The SinG model is used to simulate the concentrations of gas-phase pollutants (O3 and NOx) in a Paris suburb. The emission data for each street that are needed for the street-network model were obtained from a dynamic traffic model. Topographic data, such as street length/width and building height, were obtained from a geographic database (BD TOPO). Simulated concentrations are compared to concentrations measured at two monitoring stations that were located on each side of a large avenue.

Coupling the Near and Far Field Models for Performance Assessment of Repositories for Spent Nuclear Fuel

NASA Astrophysics Data System (ADS)

Liu, L.; Neretnieks, I.

2006-12-01

ABSTRACT In our conceptualisation, water flows in channels in fractures in fractured rocks such as granites. In the Swedish concept for a repository for spent nuclear fuel the canisters containing the spent fuel are embedded in a buffer in holes below the floor of tunnels. The deposition holes can be intersected by fractures with channels with flowing water. The flow in individual channels is determined by the transmissivity properties of the network of the channels. The flowrate around a deposition hole and in the excavation damaged zone around the tunnels will control the rate of mass transfer of corrosive agents and of escaping nuclides. We call the carrying capacity of the solutes an equivalent flowrate. An escaping nuclide will reach the flowing water in the channel and be transported further into the channel network, mixing with water from other channels at some channel intersections and dividing into several channels at other intersection. In order to follow a nuclide from any leaking canister to the effluent points at the ground surface we have integrated our channel network model CHAN3D with our near field mass transfer model NUCTRAN. The NUCTRAN code, based on a compartment model can calculate the release of nuclides from a defective canister through different pathways into the near field of a repository from the local flowrates in the channels near the deposition hole obtained from CHAN3D. The network model CHAN3D uses observed transmissivity distributions and flowing fracture frequencies in boreholes to set up the 3-dimensional network of stochastic fractures. Deterministic fracture zones are described as such with their hydraulic, properties, sizes, locations and extensions. When available, information on fracture length distributions e.g. power law distributions and correlations between sizes and transmissivities are included in the network model. Once flowrates in all channels in the network have been calculated all equivalent flowrates for all canister positions can be calculated. The rate of transport of corrosive agents to and the releases of nuclides from any damaged canister are then calculated. For any given canister location the channel network model is then used to calculate the paths of the nuclides from the canister through the network by particle tracking. A large number of particles are released one by one from the canister and followed from one channel intersection to the next. A mixing rule is used at an intersection to decide which exit the particle takes. We mostly assume full mixing at intersections. The residence time and the ratio of flow wetted surface to flowrate along every path the particles traverse is summed. This information is sufficient to determine the residence time distribution (RTD) of the nuclides along that path also when they are subject to retardation by surface sorption and matrix diffusion. Actually this information is also sufficient to determine the RTD of arbitrary length decay chains subject to some minor (unimportant) simplifying assumptions. In this paper, we discuss in detail the coupling concept of how to integrate the near and far field models, together with the method of how to include transmissive fractures following a power law length distribution and fracture zones into CHAN3D in order to significantly decrease the computer time without loss of important features of the far field. The simulation results regarding a hypothetical repository located at the Forsmark area, Sweden, are also presented and discussed. Our study suggests that the integrated model can be used as an efficient tool to simulate the release of nuclides, including decay chains, from a repository and the transport to recipients.

Mechanistic principles of colloidal crystal growth by evaporation-induced convective steering.

PubMed

Brewer, Damien D; Allen, Joshua; Miller, Michael R; de Santos, Juan M; Kumar, Satish; Norris, David J; Tsapatsis, Michael; Scriven, L E

2008-12-02

We simulate evaporation-driven self-assembly of colloidal crystals using an equivalent network model. Relationships between a regular hexagonally close-packed array of hard, monodisperse spheres, the associated pore space, and selectivity mechanisms for face-centered cubic microstructure propagation are described. By accounting for contact line rearrangement and evaporation at a series of exposed menisci, the equivalent network model describes creeping flow of solvent into and through a rigid colloidal crystal. Observations concerning colloidal crystal growth are interpreted in terms of the convective steering hypothesis, which posits that solvent flow into and through the pore space of the crystal may play a major role in colloidal self-assembly. Aspects of the convective steering and deposition of high-Peclet-number rigid spherical particles at a crystal boundary are inferred from spatially resolved solvent flow into the crystal. Gradients in local flow through boundary channels were predicted due to the channels' spatial distribution relative to a pinned free surface contact line. On the basis of a uniform solvent and particle flux as the criterion for stability of a particular growth plane, these network simulations suggest the stability of a declining {311} crystal interface, a symmetry plane which exclusively propagates fcc microstructure. Network simulations of alternate crystal planes suggest preferential growth front evolution to the declining {311} interface, in consistent agreement with the proposed stability mechanism for preferential fcc microstructure propagation in convective assembly.

A kinetic Monte Carlo approach to study fluid transport in pore networks

NASA Astrophysics Data System (ADS)

Apostolopoulou, M.; Day, R.; Hull, R.; Stamatakis, M.; Striolo, A.

2017-10-01

The mechanism of fluid migration in porous networks continues to attract great interest. Darcy's law (phenomenological continuum theory), which is often used to describe macroscopically fluid flow through a porous material, is thought to fail in nano-channels. Transport through heterogeneous and anisotropic systems, characterized by a broad distribution of pores, occurs via a contribution of different transport mechanisms, all of which need to be accounted for. The situation is likely more complicated when immiscible fluid mixtures are present. To generalize the study of fluid transport through a porous network, we developed a stochastic kinetic Monte Carlo (KMC) model. In our lattice model, the pore network is represented as a set of connected finite volumes (voxels), and transport is simulated as a random walk of molecules, which "hop" from voxel to voxel. We simulated fluid transport along an effectively 1D pore and we compared the results to those expected by solving analytically the diffusion equation. The KMC model was then implemented to quantify the transport of methane through hydrated micropores, in which case atomistic molecular dynamic simulation results were reproduced. The model was then used to study flow through pore networks, where it was able to quantify the effect of the pore length and the effect of the network's connectivity. The results are consistent with experiments but also provide additional physical insights. Extension of the model will be useful to better understand fluid transport in shale rocks.

Criticism of generally accepted fundamentals and methodologies of traffic and transportation theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kerner, Boris S.

It is explained why the set of the fundamental empirical features of traffic breakdown (a transition from free flow to congested traffic) should be the empirical basis for any traffic and transportation theory that can be reliable used for control and optimization in traffic networks. It is shown that generally accepted fundamentals and methodologies of traffic and transportation theory are not consistent with the set of the fundamental empirical features of traffic breakdown at a highway bottleneck. To these fundamentals and methodologies of traffic and transportation theory belong (i) Lighthill-Whitham-Richards (LWR) theory, (ii) the General Motors (GM) model class (formore » example, Herman, Gazis et al. GM model, Gipps’s model, Payne’s model, Newell’s optimal velocity (OV) model, Wiedemann’s model, Bando et al. OV model, Treiber’s IDM, Krauß’s model), (iii) the understanding of highway capacity as a particular stochastic value, and (iv) principles for traffic and transportation network optimization and control (for example, Wardrop’s user equilibrium (UE) and system optimum (SO) principles). Alternatively to these generally accepted fundamentals and methodologies of traffic and transportation theory, we discuss three-phase traffic theory as the basis for traffic flow modeling as well as briefly consider the network breakdown minimization (BM) principle for the optimization of traffic and transportation networks with road bottlenecks.« less

A soft porous drop in linear flows

NASA Astrophysics Data System (ADS)

Young, Yuan-Nan; Miksis, Michael; Mori, Yoichiro; Shelley, Michael

2017-11-01

The cellular cytoplasm consists a viscous fluid filled with fibrous networks that also have their own dynamics. Such fluid-structure interactions have been modeled as a soft porous material immersed in a viscous fluid. In this talk we focus on the hydrodynamics of a viscous drop filled with soft porous material inside. Suspended in a Stokes flow, such a porous viscous drop is allowed to deform, both the drop interface and the porous structures inside. Special focus is on the deformation dynamics of both the porosity and the shape of the drop under simple flows such as a uniform streaming flow and linear flows. We examine the effects of flow boundary conditions at interface between the porous drop and the surrounding viscous fluid. We also examine the dynamics of a porous drop with active stress from the porous network.

Traffic protection in MPLS networks using an off-line flow optimization model

NASA Astrophysics Data System (ADS)

Krzesinski, Anthony E.; Muller, Karen E.

2002-07-01

MPLS-based recovery is intended to effect rapid and complete restoration of traffic affected by a fault in an MPLS network. Two MPLS-based recovery models have been proposed: IP re-routing which establishes recovery paths on demand, and protection switching which works with pre-established recovery paths. IP re-routing is robust and frugal since no resources are pre-committed but is inherently slower than protection switching which is intended to offer high reliability to premium services where fault recovery takes place at the 100 ms time scale. We present a model of protection switching in MPLS networks. A variant of the flow deviation method is used to find and capacitate a set of optimal label switched paths. The traffic is routed over a set of working LSPs. Global repair is implemented by reserving a set of pre-established recovery LSPs. An analytic model is used to evaluate the MPLS-based recovery mechanisms in response to bi-directional link failures. A simulation model is used to evaluate the MPLS recovery cycle in terms of the time needed to restore the traffic after a uni-directional link failure. The models are applied to evaluate the effectiveness of protection switching in networks consisting of between 20 and 100 nodes.

Computational modeling for prediction of the shear stress of three-dimensional isotropic and aligned fiber networks.

PubMed

Park, Seungman

2017-09-01

Interstitial flow (IF) is a creeping flow through the interstitial space of the extracellular matrix (ECM). IF plays a key role in diverse biological functions, such as tissue homeostasis, cell function and behavior. Currently, most studies that have characterized IF have focused on the permeability of ECM or shear stress distribution on the cells, but less is known about the prediction of shear stress on the individual fibers or fiber networks despite its significance in the alignment of matrix fibers and cells observed in fibrotic or wound tissues. In this study, I developed a computational model to predict shear stress for different structured fibrous networks. To generate isotropic models, a random growth algorithm and a second-order orientation tensor were employed. Then, a three-dimensional (3D) solid model was created using computer-aided design (CAD) software for the aligned models (i.e., parallel, perpendicular and cubic models). Subsequently, a tetrahedral unstructured mesh was generated and flow solutions were calculated by solving equations for mass and momentum conservation for all models. Through the flow solutions, I estimated permeability using Darcy's law. Average shear stress (ASS) on the fibers was calculated by averaging the wall shear stress of the fibers. By using nonlinear surface fitting of permeability, viscosity, velocity, porosity and ASS, I devised new computational models. Overall, the developed models showed that higher porosity induced higher permeability, as previous empirical and theoretical models have shown. For comparison of the permeability, the present computational models were matched well with previous models, which justify our computational approach. ASS tended to increase linearly with respect to inlet velocity and dynamic viscosity, whereas permeability was almost the same. Finally, the developed model nicely predicted the ASS values that had been directly estimated from computational fluid dynamics (CFD). The present computational models will provide new tools for predicting accurate functional properties and designing fibrous porous materials, thereby significantly advancing tissue engineering. Copyright © 2017 Elsevier B.V. All rights reserved.

Hybrid network modeling and the effect of image resolution on digitally-obtained petrophysical and two-phase flow properties

NASA Astrophysics Data System (ADS)

Aghaei, A.

2017-12-01

Digital imaging and modeling of rocks and subsequent simulation of physical phenomena in digitally-constructed rock models are becoming an integral part of core analysis workflows. One of the inherent limitations of image-based analysis, at any given scale, is image resolution. This limitation becomes more evident when the rock has multiple scales of porosity such as in carbonates and tight sandstones. Multi-scale imaging and constructions of hybrid models that encompass images acquired at multiple scales and resolutions are proposed as a solution to this problem. In this study, we investigate the effect of image resolution and unresolved porosity on petrophysical and two-phase flow properties calculated based on images. A helical X-ray micro-CT scanner with a high cone-angle is used to acquire digital rock images that are free of geometric distortion. To remove subjectivity from the analyses, a semi-automated image processing technique is used to process and segment the acquired data into multiple phases. Direct and pore network based models are used to simulate physical phenomena and obtain absolute permeability, formation factor and two-phase flow properties such as relative permeability and capillary pressure. The effect of image resolution on each property is investigated. Finally a hybrid network model incorporating images at multiple resolutions is built and used for simulations. The results from the hybrid model are compared against results from the model built at the highest resolution and those from laboratory tests.

Upscaling permeability for three-dimensional fractured porous rocks with the multiple boundary method

NASA Astrophysics Data System (ADS)

Chen, Tao; Clauser, Christoph; Marquart, Gabriele; Willbrand, Karen; Hiller, Thomas

2018-02-01

Upscaling permeability of grid blocks is crucial for groundwater models. A novel upscaling method for three-dimensional fractured porous rocks is presented. The objective of the study was to compare this method with the commonly used Oda upscaling method and the volume averaging method. First, the multiple boundary method and its computational framework were defined for three-dimensional stochastic fracture networks. Then, the different upscaling methods were compared for a set of rotated fractures, for tortuous fractures, and for two discrete fracture networks. The results computed by the multiple boundary method are comparable with those of the other two methods and fit best the analytical solution for a set of rotated fractures. The errors in flow rate of the equivalent fracture model decrease when using the multiple boundary method. Furthermore, the errors of the equivalent fracture models increase from well-connected fracture networks to poorly connected ones. Finally, the diagonal components of the equivalent permeability tensors tend to follow a normal or log-normal distribution for the well-connected fracture network model with infinite fracture size. By contrast, they exhibit a power-law distribution for the poorly connected fracture network with multiple scale fractures. The study demonstrates the accuracy and the flexibility of the multiple boundary upscaling concept. This makes it attractive for being incorporated into any existing flow-based upscaling procedures, which helps in reducing the uncertainty of groundwater models.

Network aggregation in transportation planning models

DOT National Transportation Integrated Search

1979-06-01

This report contains six papers addressed at mathematical and computation aspects of an extraction aggregation model often employed in transportation planning studies. This model concerns the optimal flowing of an extracted subnetwork of a given netw...

Modeling thermal stress propagation during hydraulic stimulation of geothermal wells

NASA Astrophysics Data System (ADS)

Jansen, Gunnar; Miller, Stephen A.

2017-04-01

A large fraction of the world's water and energy resources are located in naturally fractured reservoirs within the earth's crust. Depending on the lithology and tectonic history of a formation, fracture networks can range from dense and homogeneous highly fractured networks to single large scale fractures dominating the flow behavior. Understanding the dynamics of such reservoirs in terms of flow and transport is crucial to successful application of engineered geothermal systems (also known as enhanced geothermal systems or EGS) for geothermal energy production in the future. Fractured reservoirs are considered to consist of two distinct separate media, namely the fracture and matrix space respectively. Fractures are generally thin, highly conductive containing only small amounts of fluid, whereas the matrix rock provides high fluid storage but typically has much smaller permeability. Simulation of flow and transport through fractured porous media is challenging due to the high permeability contrast between the fractures and the surrounding rock matrix. However, accurate and efficient simulation of flow through a fracture network is crucial in order to understand, optimize and engineer reservoirs. It has been a research topic for several decades and is still under active research. Accurate fluid flow simulations through field-scale fractured reservoirs are still limited by the power of current computer processing units (CPU). We present an efficient implementation of the embedded discrete fracture model, which is a promising new technique in modeling the behavior of enhanced geothermal systems. An efficient coupling strategy is determined for numerical performance of the model. We provide new insight into the coupled modeling of fluid flow, heat transport of engineered geothermal reservoirs with focus on the thermal stress changes during the stimulation process. We further investigate the interplay of thermal and poro-elastic stress changes in the reservoir. Combined with a analytical formulation for the injection temperatures in the open hole section of a geothermal well, the stress changes induced during the injection period of reservoir development can be studied.

Cumulative Significance of Hyporheic Exchange and Biogeochemical Processing in River Networks

NASA Astrophysics Data System (ADS)

Harvey, J. W.; Gomez-Velez, J. D.

2014-12-01

Biogeochemical reactions in rivers that decrease excessive loads of nutrients, metals, organic compounds, etc. are enhanced by hydrologic interactions with microbially and geochemically active sediments of the hyporheic zone. The significance of reactions in individual hyporheic flow paths has been shown to be controlled by the contact time between river water and sediment and the intrinsic reaction rate in the sediment. However, little is known about how the cumulative effects of hyporheic processing in large river basins. We used the river network model NEXSS (Gomez-Velez and Harvey, submitted) to simulate hyporheic exchange through synthetic river networks based on the best available models of network topology, hydraulic geometry and scaling of geomorphic features, grain size, hydraulic conductivity, and intrinsic reaction rates of nutrients and metals in river sediment. The dimensionless reaction significance factor, RSF (Harvey et al., 2013) was used to quantify the cumulative removal fraction of a reactive solute by hyporheic processing. SF scales reaction progress in a single pass through the hyporheic zone with the proportion of stream discharge passing through the hyporheic zone for a specified distance. Reaction progress is optimal where the intrinsic reaction timescale in sediment matches the residence time of hyporheic flow and is less efficient in longer residence time hyporheic flow as a result of the decreasing proportion of river flow that is processed by longer residence time hyporheic flow paths. In contrast, higher fluxes through short residence time hyporheic flow paths may be inefficient because of the repeated surface-subsurface exchanges required to complete the reaction. Using NEXSS we found that reaction efficiency may be high in both small streams and large rivers, although for different reasons. In small streams reaction progress generally is dominated by faster pathways of vertical exchange beneath submerged bedforms. Slower exchange beneath meandering river banks mainly has importance only in large rivers. For solutes entering networks in proportion to water inputs it is the lower order streams that tend to dominate cumulative reaction progress.

Landscape Genetics for the Empirical Assessment of Resistance Surfaces: The European Pine Marten (Martes martes) as a Target-Species of a Regional Ecological Network

PubMed Central

Ruiz-González, Aritz; Gurrutxaga, Mikel; Cushman, Samuel A.; Madeira, María José; Randi, Ettore; Gómez-Moliner, Benjamin J.

2014-01-01

Coherent ecological networks (EN) composed of core areas linked by ecological corridors are being developed worldwide with the goal of promoting landscape connectivity and biodiversity conservation. However, empirical assessment of the performance of EN designs is critical to evaluate the utility of these networks to mitigate effects of habitat loss and fragmentation. Landscape genetics provides a particularly valuable framework to address the question of functional connectivity by providing a direct means to investigate the effects of landscape structure on gene flow. The goals of this study are (1) to evaluate the landscape features that drive gene flow of an EN target species (European pine marten), and (2) evaluate the optimality of a regional EN design in providing connectivity for this species within the Basque Country (North Spain). Using partial Mantel tests in a reciprocal causal modeling framework we competed 59 alternative models, including isolation by distance and the regional EN. Our analysis indicated that the regional EN was among the most supported resistance models for the pine marten, but was not the best supported model. Gene flow of pine marten in northern Spain is facilitated by natural vegetation, and is resisted by anthropogenic landcover types and roads. Our results suggest that the regional EN design being implemented in the Basque Country will effectively facilitate gene flow of forest dwelling species at regional scale. PMID:25329047

Nonlinear relative-proportion-based route adjustment process for day-to-day traffic dynamics: modeling, equilibrium and stability analysis

NASA Astrophysics Data System (ADS)

Zhu, Wenlong; Ma, Shoufeng; Tian, Junfang; Li, Geng

2016-11-01

Travelers' route adjustment behaviors in a congested road traffic network are acknowledged as a dynamic game process between them. Existing Proportional-Switch Adjustment Process (PSAP) models have been extensively investigated to characterize travelers' route choice behaviors; PSAP has concise structure and intuitive behavior rule. Unfortunately most of which have some limitations, i.e., the flow over adjustment problem for the discrete PSAP model, the absolute cost differences route adjustment problem, etc. This paper proposes a relative-Proportion-based Route Adjustment Process (rePRAP) maintains the advantages of PSAP and overcomes these limitations. The rePRAP describes the situation that travelers on higher cost route switch to those with lower cost at the rate that is unilaterally depended on the relative cost differences between higher cost route and its alternatives. It is verified to be consistent with the principle of the rational behavior adjustment process. The equivalence among user equilibrium, stationary path flow pattern and stationary link flow pattern is established, which can be applied to judge whether a given network traffic flow has reached UE or not by detecting the stationary or non-stationary state of link flow pattern. The stability theorem is proved by the Lyapunov function approach. A simple example is tested to demonstrate the effectiveness of the rePRAP model.

RELAP-7 Progress Report: A Mathematical Model for 1-D Compressible, Single-Phase Flow Through a Branching Junction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Berry, R. A.

In the literature, the abundance of pipe network junction models, as well as inclusion of dissipative losses between connected pipes with loss coefficients, has been treated using the incompressible flow assumption of constant density. This approach is fundamentally, physically wrong for compressible flow with density change. This report introduces a mathematical modeling approach for general junctions in piping network systems for which the transient flows are compressible and single-phase. The junction could be as simple as a 1-pipe input and 1-pipe output with differing pipe cross-sectional areas for which a dissipative loss is necessary, or it could include an activemore » component, between an inlet pipe and an outlet pipe, such as a pump or turbine. In this report, discussion will be limited to the former. A more general branching junction connecting an arbitrary number of pipes with transient, 1-D compressible single-phase flows is also presented. These models will be developed in a manner consistent with the use of a general equation of state like, for example, the recent Spline-Based Table Look-up method [1] for incorporating the IAPWS-95 formulation [2] to give accurate and efficient calculations for properties for water and steam with RELAP-7 [3].« less

Stochastic methods for analysis of power flow in electric networks

NASA Astrophysics Data System (ADS)

1982-09-01

The modeling and effects of probabilistic behavior on steady state power system operation were analyzed. A solution to the steady state network flow equations which adhere both to Kirchoff's Laws and probabilistic laws, using either combinatorial or functional approximation techniques was obtained. The development of sound techniques for producing meaningful data to serve as input is examined. Electric demand modeling, equipment failure analysis, and algorithm development are investigated. Two major development areas are described: a decomposition of stochastic processes which gives stationarity, ergodicity, and even normality; and a powerful surrogate probability approach using proportions of time which allows the calculation of joint events from one dimensional probability spaces.

Integrative models of vascular remodeling during tumor growth

PubMed Central

Rieger, Heiko; Welter, Michael

2015-01-01

Malignant solid tumors recruit the blood vessel network of the host tissue for nutrient supply, continuous growth, and gain of metastatic potential. Angiogenesis (the formation of new blood vessels), vessel cooption (the integration of existing blood vessels into the tumor vasculature), and vessel regression remodel the healthy vascular network into a tumor-specific vasculature that is in many respects different from the hierarchically organized arterio-venous blood vessel network of the host tissues. Integrative models based on detailed experimental data and physical laws implement in silico the complex interplay of molecular pathways, cell proliferation, migration, and death, tissue microenvironment, mechanical and hydrodynamic forces, and the fine structure of the host tissue vasculature. With the help of computer simulations high-precision information about blood flow patterns, interstitial fluid flow, drug distribution, oxygen and nutrient distribution can be obtained and a plethora of therapeutic protocols can be tested before clinical trials. In this review, we give an overview over the current status of integrative models describing tumor growth, vascular remodeling, blood and interstitial fluid flow, drug delivery, and concomitant transformations of the microenvironment. © 2015 The Authors. WIREs Systems Biology and Medicine published by Wiley Periodicals, Inc. PMID:25808551

"Time-dependent flow-networks"

NASA Astrophysics Data System (ADS)

Tupikina, Liubov; Molkentin, Nora; Lopez, Cristobal; Hernandez-Garcia, Emilio; Marwan, Norbert; Kurths, Jürgen

2015-04-01

Complex networks have been successfully applied to various systems such as society, technology, and recently climate. Links in a climate network are defined between two geographical locations if the correlation between the time series of some climate variable is higher than a threshold. Therefore, network links are considered to imply information or heat exchange. However, the relationship between the oceanic and atmospheric flows and the climate network's structure is still unclear. Recently, a theoretical approach verifying the correlation between ocean currents and surface air temperature networks has been introduced, where the Pearson correlation networks were constructed from advection-diffusion dynamics on an underlying flow. Since the continuous approach has its limitations, i.e. high computational complexity and fixed variety of the flows in the underlying system, we introduce a new, method of flow-networks for changing in time velocity fields including external forcing in the system, noise and temperature-decay. Method of the flow-network construction can be divided into several steps: first we obtain the linear recursive equation for the temperature time-series. Then we compute the correlation matrix for time-series averaging the tensor product over all realizations of the noise, which we interpret as a weighted adjacency matrix of the flow-network and analyze using network measures. We apply the method to different types of moving flows with geographical relevance such as meandering flow. Analyzing the flow-networks using network measures we find that our approach can highlight zones of high velocity by degree and transition zones by betweenness, while the combination of these network measures can uncover how the flow propagates within time. Flow-networks can be powerful tool to understand the connection between system's dynamics and network's topology analyzed using network measures in order to shed light on different climatic phenomena.

Frequency tuning allows flow direction control in microfluidic networks with passive features.

PubMed

Jain, Rahil; Lutz, Barry

2017-05-02

Frequency tuning has emerged as an attractive alternative to conventional pumping techniques in microfluidics. Oscillating (AC) flow driven through a passive valve can be rectified to create steady (DC) flow, and tuning the excitation frequency to the characteristic (resonance) frequency of the underlying microfluidic network allows control of flow magnitude using simple hardware, such as an on-chip piezo buzzer. In this paper, we report that frequency tuning can also be used to control the direction (forward or backward) of the rectified DC flow in a single device. Initially, we observed that certain devices provided DC flow in the "forward" direction expected from previous work with a similar valve geometry, and the maximum DC flow occurred at the same frequency as a prominent peak in the AC flow magnitude, as expected. However, devices of a slightly different geometry provided the DC flow in the opposite direction and at a frequency well below the peak AC flow. Using an equivalent electrical circuit model, we found that the "forward" DC flow occurred at the series resonance frequency (with large AC flow peak), while the "backward" DC flow occurred at a less obvious parallel resonance (a valley in AC flow magnitude). We also observed that the DC flow occurred only when there was a measurable differential in the AC flow magnitude across the valve, and the DC flow direction was from the channel with large AC flow magnitude to that with small AC flow magnitude. Using these observations and the AC flow predictions from the equivalent circuit model, we designed a device with an AC flowrate frequency profile that was expected to allow the DC flow in opposite directions at two distinct frequencies. The fabricated device showed the expected flow reversal at the expected frequencies. This approach expands the flow control toolkit to include both magnitude and direction control in frequency-tuned microfluidic pumps. The work also raises interesting questions about the origin of flow reversal behavior that may be addressed by the further study of the circuit model behavior or dynamic modeling of the fluid-solid mechanics of the valve under the AC flow.

Internal Catchment Process Simulation in a Snow-Dominated Basin: Performance Evaluation with Spatiotemporally Variable Runoff Generation and Groundwater Dynamics

NASA Astrophysics Data System (ADS)

Kuras, P. K.; Weiler, M.; Alila, Y.; Spittlehouse, D.; Winkler, R.

2006-12-01

Hydrologic models have been increasingly used in forest hydrology to overcome the limitations of paired watershed experiments, where vegetative recovery and natural variability obscure the inferences and conclusions that can be drawn from such studies. Models, however, are also plagued by uncertainty stemming from a limited understanding of hydrological processes in forested catchments and parameter equifinality is a common concern. This has created the necessity to improve our understanding of how hydrological systems work, through the development of hydrological measures, analyses and models that address the question: are we getting the right answers for the right reasons? Hence, physically-based, spatially-distributed hydrologic models should be validated with high-quality experimental data describing multiple concurrent internal catchment processes under a range of hydrologic regimes. The distributed hydrology soil vegetation model (DHSVM) frequently used in forest management applications is an example of a process-based model used to address the aforementioned circumstances, and this study takes a novel approach at collectively examining the ability of a pre-calibrated model application to realistically simulate outlet flows along with the spatial-temporal variation of internal catchment processes including: continuous groundwater dynamics at 9 locations, stream and road network flow at 67 locations for six individual days throughout the freshet, and pre-melt season snow distribution. Model efficiency was improved over prior evaluations due to continuous efforts in improving the quality of meteorological data in the watershed. Road and stream network flows were very well simulated for a range of hydrological conditions, and the spatial distribution of the pre-melt season snowpack was in general agreement with observed values. The model was effective in simulating the spatial variability of subsurface flow generation, except at locations where strong stream-groundwater interactions existed, as the model is not capable of simulating such processes and subsurface flows always drain to the stream network. The model has proven overall to be quite capable in realistically simulating internal catchment processes in the watershed, which creates more confidence in future model applications exploring the effects of various forest management scenarios on the watershed's hydrological processes.

The Flow Dimension and Aquifer Heterogeneity: Field evidence and Numerical Analyses

NASA Astrophysics Data System (ADS)

Walker, D. D.; Cello, P. A.; Valocchi, A. J.; Roberts, R. M.; Loftis, B.

2008-12-01

The Generalized Radial Flow approach to hydraulic test interpretation infers the flow dimension to describe the geometry of the flow field during a hydraulic test. Noninteger values of the flow dimension often are inferred for tests in highly heterogeneous aquifers, yet subsequent modeling studies typically ignore the flow dimension. Monte Carlo analyses of detailed numerical models of aquifer tests examine the flow dimension for several stochastic models of heterogeneous transmissivity, T(x). These include multivariate lognormal, fractional Brownian motion, a site percolation network, and discrete linear features with lengths distributed as power-law. The behavior of the simulated flow dimensions are compared to the flow dimensions observed for multiple aquifer tests in a fractured dolomite aquifer in the Great Lakes region of North America. The combination of multiple hydraulic tests, observed fracture patterns, and the Monte Carlo results are used to screen models of heterogeneity and their parameters for subsequent groundwater flow modeling. The comparison shows that discrete linear features with lengths distributed as a power-law appear to be the most consistent with observations of the flow dimension in fractured dolomite aquifers.

Effect of hydro mechanical coupling on natural fracture network formation in sedimentary basins

NASA Astrophysics Data System (ADS)

Ouraga, Zady; Guy, Nicolas; Pouya, Amade

2018-05-01

In sedimentary basin context, numerous phenomena, depending on the geological time span, can result in natural fracture network formation. In this paper, fracture network and dynamic fracture spacing triggered by significant sedimentation rate are studied considering mode I fracture propagation using a coupled hydro-mechanical numerical methods. The focus is put on synthetic geological structure under a constant sedimentation rate on its top. This model contains vertical fracture network initially closed and homogeneously distributed. The fractures are modelled with cohesive zone model undergoing damage and the flow is described by Poiseuille's law. The effect of the behaviour of the rock is studied and the analysis leads to a pattern of fracture network and fracture spacing in the geological layer.

Representing macropore flow at the catchment scale: a comparative modeling study

NASA Astrophysics Data System (ADS)

Liu, D.; Li, H. Y.; Tian, F.; Leung, L. R.

2017-12-01

Macropore flow is an important hydrological process that generally enhances the soil infiltration capacity and velocity of subsurface water. Up till now, macropore flow is mostly simulated with high-resolution models. One possible drawback of this modeling approach is the difficulty to effectively represent the overall typology and connectivity of the macropore networks. We hypothesize that modeling macropore flow directly at the catchment scale may be complementary to the existing modeling strategy and offer some new insights. Tsinghua Representative Elementary Watershed model (THREW model) is a semi-distributed hydrology model, where the fundamental building blocks are representative elementary watersheds (REW) linked by the river channel network. In THREW, all the hydrological processes are described with constitutive relationships established directly at the REW level, i.e., catchment scale. In this study, the constitutive relationship of macropore flow drainage is established as part of THREW. The enhanced THREW model is then applied at two catchments with deep soils but distinct climates, the humid Asu catchment in the Amazon River basin, and the arid Wei catchment in the Yellow River basin. The Asu catchment has an area of 12.43km2 with mean annual precipitation of 2442mm. The larger Wei catchment has an area of 24800km2 but with mean annual precipitation of only 512mm. The rainfall-runoff processes are simulated at a hourly time step from 2002 to 2005 in the Asu catchment and from 2001 to 2012 in the Wei catchment. The role of macropore flow on the catchment hydrology will be analyzed comparatively over the Asu and Wei catchments against the observed streamflow, evapotranspiration and other auxiliary data.

Application of Fuzzy-Logic Controller and Neural Networks Controller in Gas Turbine Speed Control and Overheating Control and Surge Control on Transient Performance

NASA Astrophysics Data System (ADS)

Torghabeh, A. A.; Tousi, A. M.

2007-08-01

This paper presents Fuzzy Logic and Neural Networks approach to Gas Turbine Fuel schedules. Modeling of non-linear system using feed forward artificial Neural Networks using data generated by a simulated gas turbine program is introduced. Two artificial Neural Networks are used , depicting the non-linear relationship between gas generator speed and fuel flow, and turbine inlet temperature and fuel flow respectively . Off-line fast simulations are used for engine controller design for turbojet engine based on repeated simulation. The Mamdani and Sugeno models are used to expression the Fuzzy system . The linguistic Fuzzy rules and membership functions are presents and a Fuzzy controller will be proposed to provide an Open-Loop control for the gas turbine engine during acceleration and deceleration . MATLAB Simulink was used to apply the Fuzzy Logic and Neural Networks analysis. Both systems were able to approximate functions characterizing the acceleration and deceleration schedules . Surge and Flame-out avoidance during acceleration and deceleration phases are then checked . Turbine Inlet Temperature also checked and controls by Neural Networks controller. This Fuzzy Logic and Neural Network Controllers output results are validated and evaluated by GSP software . The validation results are used to evaluate the generalization ability of these artificial Neural Networks and Fuzzy Logic controllers.

Inverse modeling of flow tomography experiments in fractured media

NASA Astrophysics Data System (ADS)

Klepikova, Maria; Le Borgne, Tanguy; Bour, Olivier; de Dreuzy, Jean-Raynald

2014-05-01

Inverse modeling of fracture hydraulic properties and connectivity is a very challenging objective due to the strong heterogeneity of the medium at multiple scales and the scarcity of data. Cross-borehole flowmeter tests, which consist of measuring changes in vertical borehole flows when pumping a neighboring borehole, were shown to be an efficient technique to provide information on the properties of the flow zones that connect borehole pairs (Paillet, 1998, Le Borgne et al., 2007). The interpretation of such experiments may, however, be quite uncertain when multiple connections exist. We propose the flow tomography approach (i.e., sequential cross-borehole flowmeter tests) to characterize the connectivity and transmissivity of preferential permeable flow paths in fractured aquifers (Klepikova et al., 2013). An inverse model approach is developed to estimate log-transformed transmissivity values of hydraulically active fractures between the pumping and observation wells by inverting cross-borehole flow and water level data. Here a simplified discrete fracture network approach that highlights main connectivity structures is used. This conceptual model attempts to reproduce fracture network connectivity without taking fracture geometry (length, orientation, dip) into account. We demonstrate that successively exchanging the roles of pumping and observation boreholes improves the quality of available information and reduces the under-determination of the problem. The inverse method is validated for several synthetic flow scenarios. It is shown to provide a good estimation of connectivity patterns and transmissivities of main flow paths. It also allows the estimation of the transmissivity of fractures that connect the flow paths but do not cross the boreholes, although the associated uncertainty may be high for some geometries. The results of this investigation encourage the application of flow tomography to natural fractured aquifers.

Comparative Analysis of River Flow Modelling by Using Supervised Learning Technique

NASA Astrophysics Data System (ADS)

Ismail, Shuhaida; Mohamad Pandiahi, Siraj; Shabri, Ani; Mustapha, Aida

2018-04-01

The goal of this research is to investigate the efficiency of three supervised learning algorithms for forecasting monthly river flow of the Indus River in Pakistan, spread over 550 square miles or 1800 square kilometres. The algorithms include the Least Square Support Vector Machine (LSSVM), Artificial Neural Network (ANN) and Wavelet Regression (WR). The forecasting models predict the monthly river flow obtained from the three models individually for river flow data and the accuracy of the all models were then compared against each other. The monthly river flow of the said river has been forecasted using these three models. The obtained results were compared and statistically analysed. Then, the results of this analytical comparison showed that LSSVM model is more precise in the monthly river flow forecasting. It was found that LSSVM has he higher r with the value of 0.934 compared to other models. This indicate that LSSVM is more accurate and efficient as compared to the ANN and WR model.

A comparative study of manhole hydraulics using stereoscopic PIV and different RANS models.

PubMed

Beg, Md Nazmul Azim; Carvalho, Rita F; Tait, Simon; Brevis, Wernher; Rubinato, Matteo; Schellart, Alma; Leandro, Jorge

2017-04-01

Flows in manholes are complex and may include swirling and recirculation flow with significant turbulence and vorticity. However, how these complex 3D flow patterns could generate different energy losses and so affect flow quantity in the wider sewer network is unknown. In this work, 2D3C stereo Particle Image Velocimetry measurements are made in a surcharged scaled circular manhole. A computational fluid dynamics (CFD) model in OpenFOAM ® with four different Reynolds Averaged Navier Stokes (RANS) turbulence model is constructed using a volume of fluid model, to represent flows in this manhole. Velocity profiles and pressure distributions from the models are compared with the experimental data in view of finding the best modelling approach. It was found among four different RANS models that the re-normalization group (RNG) k-ɛ and k-ω shear stress transport (SST) gave a better approximation for velocity and pressure.

Stochastic flux analysis of chemical reaction networks

PubMed Central

2013-01-01

Background Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes. Results We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology. Conclusions We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network. PMID:24314153

Stochastic flux analysis of chemical reaction networks.

PubMed

Kahramanoğulları, Ozan; Lynch, James F

2013-12-07

Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes. We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology. We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network.

A generalized optimization principle for asymmetric branching in fluidic networks

PubMed Central

Stephenson, David

2016-01-01

When applied to a branching network, Murray’s law states that the optimal branching of vascular networks is achieved when the cube of the parent channel radius is equal to the sum of the cubes of the daughter channel radii. It is considered integral to understanding biological networks and for the biomimetic design of artificial fluidic systems. However, despite its ubiquity, we demonstrate that Murray’s law is only optimal (i.e. maximizes flow conductance per unit volume) for symmetric branching, where the local optimization of each individual channel corresponds to the global optimum of the network as a whole. In this paper, we present a generalized law that is valid for asymmetric branching, for any cross-sectional shape, and for a range of fluidic models. We verify our analytical solutions with the numerical optimization of a bifurcating fluidic network for the examples of laminar, turbulent and non-Newtonian fluid flows. PMID:27493583

Network reliability maximization for stochastic-flow network subject to correlated failures using genetic algorithm and tabu\\xA0search

NASA Astrophysics Data System (ADS)

Yeh, Cheng-Ta; Lin, Yi-Kuei; Yang, Jo-Yun

2018-07-01

Network reliability is an important performance index for many real-life systems, such as electric power systems, computer systems and transportation systems. These systems can be modelled as stochastic-flow networks (SFNs) composed of arcs and nodes. Most system supervisors respect the network reliability maximization by finding the optimal multi-state resource assignment, which is one resource to each arc. However, a disaster may cause correlated failures for the assigned resources, affecting the network reliability. This article focuses on determining the optimal resource assignment with maximal network reliability for SFNs. To solve the problem, this study proposes a hybrid algorithm integrating the genetic algorithm and tabu search to determine the optimal assignment, called the hybrid GA-TS algorithm (HGTA), and integrates minimal paths, recursive sum of disjoint products and the correlated binomial distribution to calculate network reliability. Several practical numerical experiments are adopted to demonstrate that HGTA has better computational quality than several popular soft computing algorithms.

Discrete Dual Porosity Modeling of Electrical Current Flow in Fractured Media

NASA Astrophysics Data System (ADS)

Roubinet, D.; Irving, J.

2013-12-01

The study of fractured rocks is highly important in a variety of research fields and applications such as hydrogeology, geothermal energy, hydrocarbon extraction, and the long-term storage of toxic waste. Fractured media are characterized by a large contrast in permeability between the fractures and the rock matrix. For hydrocarbon extraction, the presence of highly conductive fractures is an advantage as they allow for quick and easy access to the resource. For toxic waste storage, however, the fractures represent a significant drawback as there is an increased risk of leakage and migration of pollutants deep into the subsurface. In both cases, the identification of fracture network characteristics is a critical, challenging, and required step. A number of previous studies have indicated that the presence of fractures in geological materials can have a significant impact on geophysical electrical resistivity measurements. It thus appears that, in some cases, geoelectrical surveys might be used to obtain useful information regarding fracture network characteristics. However, existing geoelectrical modeling tools and inversion methods are not properly adapted to deal with the specific challenges of fractured media. This prevents us from fully exploring the potential of the method to characterize fracture network properties. We thus require, as a first step, the development of accurate and efficient numerical modeling tools specifically designed for fractured domains. Building on the discrete fracture network (DFN) approach that has been widely used for modeling groundwater flow in fractured rocks, we have developed a discrete dual-porosity model for electrical current flow in fractured media. Our novel approach combines an explicit representation of the fractures with fracture-matrix electrical flow exchange at the block-scale. Tests in two dimensions show the ability of our method to deal with highly heterogeneous fracture networks in a highly computationally efficient manner, which permits us to study the impact of fractures and their properties on the electrical response of the domain. With additional development, the method will be extended to three dimensions and used in the context of geoelectrical field investigations.

A novel algorithm for delineating wetland depressions and ...

EPA Pesticide Factsheets

In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features that are seldom fully filled with water. For instance, wetland depressions in the Prairie Pothole Region (PPR) are seasonally to permanently flooded wetlands characterized by nested hierarchical structures with dynamic filling- spilling-merging surface-water hydrological processes. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution LiDAR data and aerial imagery. We proposed a novel algorithm delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost path algorithm. The resulting flow network delineated putative temporary or seasonal flow paths connecting wetland depressions to each other or to the river network at scales finer than available through the National Hydrography Dataset. The results demonstrated that our proposed framework is promising for improving overland flow modeling and hydrologic connectivity analysis. Presentation at AWRA Spring Specialty Conference in Sn

Modeling regional freight flow assignment through intermodal terminals

DOT National Transportation Integrated Search

2005-03-01

An analytical model is developed to assign regional freight across a multimodal highway and railway network using geographic information systems. As part of the regional planning process, the model is an iterative procedure that assigns multimodal fr...

Heterogeneous mechanics of the mouse pulmonary arterial network.

PubMed

Lee, Pilhwa; Carlson, Brian E; Chesler, Naomi; Olufsen, Mette S; Qureshi, M Umar; Smith, Nicolas P; Sochi, Taha; Beard, Daniel A

2016-10-01

Individualized modeling and simulation of blood flow mechanics find applications in both animal research and patient care. Individual animal or patient models for blood vessel mechanics are based on combining measured vascular geometry with a fluid structure model coupling formulations describing dynamics of the fluid and mechanics of the wall. For example, one-dimensional fluid flow modeling requires a constitutive law relating vessel cross-sectional deformation to pressure in the lumen. To investigate means of identifying appropriate constitutive relationships, an automated segmentation algorithm was applied to micro-computerized tomography images from a mouse lung obtained at four different static pressures to identify the static pressure-radius relationship for four generations of vessels in the pulmonary arterial network. A shape-fitting function was parameterized for each vessel in the network to characterize the nonlinear and heterogeneous nature of vessel distensibility in the pulmonary arteries. These data on morphometric and mechanical properties were used to simulate pressure and flow velocity propagation in the network using one-dimensional representations of fluid and vessel wall mechanics. Moreover, wave intensity analysis was used to study effects of wall mechanics on generation and propagation of pressure wave reflections. Simulations were conducted to investigate the role of linear versus nonlinear formulations of wall elasticity and homogeneous versus heterogeneous treatments of vessel wall properties. Accounting for heterogeneity, by parameterizing the pressure/distention equation of state individually for each vessel segment, was found to have little effect on the predicted pressure profiles and wave propagation compared to a homogeneous parameterization based on average behavior. However, substantially different results were obtained using a linear elastic thin-shell model than were obtained using a nonlinear model that has a more physiologically realistic pressure versus radius relationship.

Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks.

PubMed

Gao, Zhongke; Jin, Ningde

2009-06-01

The identification of flow pattern is a basic and important issue in multiphase systems. Because of the complexity of phase interaction in gas-liquid two-phase flow, it is difficult to discern its flow pattern objectively. In this paper, we make a systematic study on the vertical upward gas-liquid two-phase flow using complex network. Three unique network construction methods are proposed to build three types of networks, i.e., flow pattern complex network (FPCN), fluid dynamic complex network (FDCN), and fluid structure complex network (FSCN). Through detecting the community structure of FPCN by the community-detection algorithm based on K -mean clustering, useful and interesting results are found which can be used for identifying five vertical upward gas-liquid two-phase flow patterns. To investigate the dynamic characteristics of gas-liquid two-phase flow, we construct 50 FDCNs under different flow conditions, and find that the power-law exponent and the network information entropy, which are sensitive to the flow pattern transition, can both characterize the nonlinear dynamics of gas-liquid two-phase flow. Furthermore, we construct FSCN and demonstrate how network statistic can be used to reveal the fluid structure of gas-liquid two-phase flow. In this paper, from a different perspective, we not only introduce complex network theory to the study of gas-liquid two-phase flow but also indicate that complex network may be a powerful tool for exploring nonlinear time series in practice.

Simulating groundwater flow in karst aquifers with distributed parameter models—Comparison of porous-equivalent media and hybrid flow approaches

USGS Publications Warehouse

Kuniansky, Eve L.

2016-09-22

Understanding karst aquifers, for purposes of their management and protection, poses unique challenges. Karst aquifers are characterized by groundwater flow through conduits (tertiary porosity), and (or) layers with interconnected pores (secondary porosity) and through intergranular porosity (primary or matrix porosity). Since the late 1960s, advances have been made in the development of numerical computer codes and the use of mathematical model applications towards the understanding of dual (primary [matrix] and secondary [fractures and conduits]) porosity groundwater flow processes, as well as characterization and management of karst aquifers. The Floridan aquifer system (FAS) in Florida and parts of Alabama, Georgia, and South Carolina is composed of a thick sequence of predominantly carbonate rocks. Karst features are present over much of its area, especially in Florida where more than 30 first-magnitude springs occur, numerous sinkholes and submerged conduits have been mapped, and numerous circular lakes within sinkhole depressions are present. Different types of mathematical models have been applied for simulation of the FAS. Most of these models are distributed parameter models based on the assumption that, like a sponge, water flows through connected pores within the aquifer system and can be simulated with the same mathematical methods applied to flow through sand and gravel aquifers; these models are usually referred to as porous-equivalent media models. The partial differential equation solved for groundwater flow is the potential flow equation of fluid mechanics, which is used when flow is dominated by potential energy and has been applied for many fluid problems in which kinetic energy terms are dropped from the differential equation solved. In many groundwater model codes (basic MODFLOW), it is assumed that the water has a constant temperature and density and that flow is laminar, such that kinetic energy has minimal impact on flow. Some models have been developed that incorporate the submerged conduits as a one-dimensional pipe network within the aquifer rather than as discrete, extremely transmissive features in a porous-equivalent medium; these submerged conduit models are usually referred to as hybrid models and may include the capability to simulate both laminar and turbulent flow in the one-dimensional pipe network. Comparisons of the application of a porous-equivalent media model with and without turbulence (MODFLOW-Conduit Flow Process mode 2 and basic MODFLOW, respectively) and a hybrid (MODFLOW-Conduit Flow Process mode 1) model to the Woodville Karst Plain near Tallahassee, Florida, indicated that for annual, monthly, or seasonal average hydrologic conditions, all methods met calibration criteria (matched observed groundwater levels and average flows). Thus, the increased effort required, such as the collection of data on conduit location, to develop a hybrid model and its increased computational burden, is not necessary for simulation of average hydrologic conditions (non-laminar flow effects on simulated head and spring discharge were minimal). However, simulation of a large storm event in the Woodville Karst Plain with daily stress periods indicated that turbulence is important for matching daily springflow hydrographs. Thus, if matching streamflow hydrographs over a storm event is required, the simulation of non-laminar flow and the location of conduits are required. The main challenge in application of the methods and approaches for developing hybrid models relates to the difficulty of mapping conduit networks or having high-quality datasets to calibrate these models. Additionally, hybrid models have long simulation times, which can preclude the use of parameter estimation for calibration. Simulation of contaminant transport that does not account for preferential flow through conduits or extremely permeable zones in any approach is ill-advised. Simulation results in other karst aquifers or other parts of the FAS may differ from the comparison demonstrated herein.

Evaluating the hydraulic and transport properties of peat soil using pore network modeling and X-ray micro computed tomography

NASA Astrophysics Data System (ADS)

Gharedaghloo, Behrad; Price, Jonathan S.; Rezanezhad, Fereidoun; Quinton, William L.

2018-06-01

Micro-scale properties of peat pore space and their influence on hydraulic and transport properties of peat soils have been given little attention so far. Characterizing the variation of these properties in a peat profile can increase our knowledge on the processes controlling contaminant transport through peatlands. As opposed to the common macro-scale (or bulk) representation of groundwater flow and transport processes, a pore network model (PNM) simulates flow and transport processes within individual pores. Here, a pore network modeling code capable of simulating advective and diffusive transport processes through a 3D unstructured pore network was developed; its predictive performance was evaluated by comparing its results to empirical values and to the results of computational fluid dynamics (CFD) simulations. This is the first time that peat pore networks have been extracted from X-ray micro-computed tomography (μCT) images of peat deposits and peat pore characteristics evaluated in a 3D approach. Water flow and solute transport were modeled in the unstructured pore networks mapped directly from μCT images. The modeling results were processed to determine the bulk properties of peat deposits. Results portray the commonly observed decrease in hydraulic conductivity with depth, which was attributed to the reduction of pore radius and increase in pore tortuosity. The increase in pore tortuosity with depth was associated with more decomposed peat soil and decreasing pore coordination number with depth, which extended the flow path of fluid particles. Results also revealed that hydraulic conductivity is isotropic locally, but becomes anisotropic after upscaling to core-scale; this suggests the anisotropy of peat hydraulic conductivity observed in core-scale and field-scale is due to the strong heterogeneity in the vertical dimension that is imposed by the layered structure of peat soils. Transport simulations revealed that for a given solute, the effective diffusion coefficient decreases with depth due to the corresponding increase of diffusional tortuosity. Longitudinal dispersivity of peat also was computed by analyzing advective-dominant transport simulations that showed peat dispersivity is similar to the empirical values reported in the same peat soil; it is not sensitive to soil depth and does not vary much along the soil profile.

Topological structure and mechanics of glassy polymer networks.

PubMed

Elder, Robert M; Sirk, Timothy W

2017-11-22

The influence of chain-level network architecture (i.e., topology) on mechanics was explored for unentangled polymer networks using a blend of coarse-grained molecular simulations and graph-theoretic concepts. A simple extension of the Watts-Strogatz model is proposed to control the graph properties of the network such that the corresponding physical properties can be studied with simulations. The architecture of polymer networks assembled with a dynamic curing approach were compared with the extended Watts-Strogatz model, and found to agree surprisingly well. The final cured structures of the dynamically-assembled networks were nearly an intermediate between lattice and random connections due to restrictions imposed by the finite length of the chains. Further, the uni-axial stress response, character of the bond breaking, and non-affine displacements of fully-cured glassy networks were analyzed as a function of the degree of disorder in the network architecture. It is shown that the architecture strongly affects the network stability, flow stress, onset of bond breaking, and ultimate stress while leaving the modulus and yield point nearly unchanged. The results show that internal restrictions imposed by the network architecture alter the chain-level response through changes to the crosslink dynamics in the flow regime and through the degree of coordinated chain failure at the ultimate stress. The properties considered here are shown to be sensitive to even incremental changes to the architecture and, therefore, the overall network architecture, beyond simple defects, is predicted to be a meaningful physical parameter in the mechanics of glassy polymer networks.

Flow Regime Identification of Horizontal Two Phase Refrigerant R-134a Flow Using Neural Networks (Postprint)

DTIC Science & Technology

2013-11-01

Flows in Microchannels ," Heat Transfer Engineering, Vol. 27, No. 9, 2006, pp. 4-19. 2Kandlikar, S. G., " Heat Transfer Mechanisms During Flow...Boiling in Microchannels ," Journal of Heat Transfer , Vol. 126, No. 1, 2004, pp. 8-16. 3Kreitzer, P. J., Byrd, L., and Willebrand, B. J., "Initial...an integral aspect of modeling two phase flows as most pressure drop and heat transfer correlations rely on a priori knowledge of the flow regime for

Gravitational effects on global hemodynamics in different postures: A closed-loop multiscale mathematical analysis

NASA Astrophysics Data System (ADS)

Zhang, Xiancheng; Noda, Shigeho; Himeno, Ryutaro; Liu, Hao

2017-06-01

We present a novel methodology and strategy to predict pressures and flow rates in the global cardiovascular network in different postures varying from supine to upright. A closed-loop, multiscale mathematical model of the entire cardiovascular system (CVS) is developed through an integration of one-dimensional (1D) modeling of the large systemic arteries and veins, and zero-dimensional (0D) lumped-parameter modeling of the heart, the cardiac-pulmonary circulation, the cardiac and venous valves, as well as the microcirculation. A versatile junction model is proposed and incorporated into the 1D model to cope with splitting and/or merging flows across a multibranched junction, which is validated to be capable of estimating both subcritical and supercritical flows while ensuring the mass conservation and total pressure continuity. To model gravitational effects on global hemodynamics during postural change, a robust venous valve model is further established for the 1D venous flows and distributed throughout the entire venous network with consideration of its anatomically realistic numbers and locations. The present integrated model is proven to enable reasonable prediction of pressure and flow rate waveforms associated with cardiopulmonary circulation, systemic circulation in arteries and veins, as well as microcirculation within normal physiological ranges, particularly in mean venous pressures, which well match the in vivo measurements. Applications of the cardiovascular model at different postures demonstrate that gravity exerts remarkable influence on arterial and venous pressures, venous returns and cardiac outputs whereas venous pressures below the heart level show a specific correlation between central venous and hydrostatic pressures in right atrium and veins.

Geomorphological origin of recession curves

NASA Astrophysics Data System (ADS)

Biswal, Basudev; Marani, Marco

2010-12-01

We identify a previously undetected link between the river network morphology and key recession curves properties through a conceptual-physical model of the drainage process of the riparian unconfined aquifer. We show that the power-law exponent, α, of -dQ/dt vs. Q curves is related to the power-law exponent of N(l) vs. G(l) curves (which we show to be connected to Hack's law), where l is the downstream distance from the channel heads, N(l) is the number of channel reaches exactly located at a distance l from their channel head, and G(l) is the total length of the network located at a distance greater or equal to l from channel heads. Using Digital Terrain Models and daily discharge observations from 67 US basins we find that geomorphologic α estimates match well the values obtained from recession curves analyses. Finally, we argue that the link between recession flows and network morphology points to an important role of low-flow discharges in shaping the channel network.

Discrete Mathematical Approaches to Graph-Based Traffic Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joslyn, Cliff A.; Cowley, Wendy E.; Hogan, Emilie A.

2014-04-01

Modern cyber defense and anlaytics requires general, formal models of cyber systems. Multi-scale network models are prime candidates for such formalisms, using discrete mathematical methods based in hierarchically-structured directed multigraphs which also include rich sets of labels. An exemplar of an application of such an approach is traffic analysis, that is, observing and analyzing connections between clients, servers, hosts, and actors within IP networks, over time, to identify characteristic or suspicious patterns. Towards that end, NetFlow (or more generically, IPFLOW) data are available from routers and servers which summarize coherent groups of IP packets flowing through the network. In thismore » paper, we consider traffic analysis of Netflow using both basic graph statistics and two new mathematical measures involving labeled degree distributions and time interval overlap measures. We do all of this over the VAST test data set of 96M synthetic Netflow graph edges, against which we can identify characteristic patterns of simulated ground-truth network attacks.« less

Inclusion-based effective medium models for the field-scale permeability of 3D fractured rock masses

NASA Astrophysics Data System (ADS)

Ebigbo, Anozie; Lang, Philipp S.; Paluszny, Adriana; Zimmerman, Robert W.

2016-04-01

Fractures that are more permeable than their host rock can act as preferential, or at least additional, pathways for fluid to flow through the rock. The additional transmissivity contributed by these fractures will be of great relevance in several areas of earth science and engineering, such as radioactive waste disposal in crystalline rock, exploitation of fractured hydrocarbon and geothermal reservoirs, or hydraulic fracturing. In describing or predicting flow through fractured rock, the effective permeability of the rock mass, comprising both the rock matrix and a network of fractures, is a crucial parameter, and will depend on several geometric properties of the fractures/networks, such as lateral extent, aperture, orientation, and fracture density. This study investigates the ability of classical inclusion-based effective medium models (following the work of Sævik et al., Transp. Porous Media, 2013) to predict this permeability. In these models, the fractures are represented as thin, spheroidal inclusions, the interiors of which are treated as porous media having a high (but finite) permeability. The predictions of various effective medium models, such as the symmetric and asymmetric self-consistent schemes, the differential scheme, and Maxwell's method, are tested against the results of explicit numerical simulations of mono- and polydisperse isotropic fracture networks embedded in a permeable rock matrix. Comparisons are also made with the Hashin-Shrikman bounds, Snow's model, and Mourzenko's heuristic model (Mourzenko et al., Phys. Rev. E, 2011). This problem is characterised mathematically by two small parameters, the aspect ratio of the spheroidal fractures, α, and the ratio between matrix and fracture permeability, κ. Two different regimes can be identified, corresponding to α/κ < 1 and α/κ > 1. The lower the value of α/κ, the more significant is flow through the matrix. Due to differing flow patterns, the dependence of effective permeability on fracture density differs in the two regimes. When α/κ > 1, a distinct percolation threshold is observed, whereas for α/κ < 1, the matrix is sufficiently transmissive that a percolation-like transition is not observed. The self-consistent effective medium methods show good accuracy for both mono- and polydisperse isotropic fracture networks. Mourzenko's equation is also found to be very accurate, particularly for monodisperse networks. Finally, it is shown that Snow's model essentially coincides with the Hashin-Shtrikman upper bound.

Exploring 3D optimal channel networks by multiple organizing principles

NASA Astrophysics Data System (ADS)

Mason, Emanuele; Bizzi, Simone; Cominola, Andrea; Castelletti, Andrea; Paik, Kyungrock

2017-04-01

Catchment topography and flow networks are shaped by the interactions of water and sediment across various spatial and temporal scales. The complexity of these processes hinders the development of models able to assess the validity of general principles governing such phenomena. The theory of Optimal Channel Networks (OCNs) proved that it is possible to generate drainage networks statistically comparable to those observed in nature by minimizing the energy spent by the water flowing through them. So far, the OCN theory has been developed for planar 2D domains, assuming equal energy expenditure per unit area of channel and, correspondingly, a constant slope-discharge relationship. In this work, we apply the OCN theory to 3D problems by introducing a multi-principle minimization starting from an artificial digital elevation model of pyramidal shape. The OCN theory assumption of constant slope-area relationship is relaxed and embedded into a second-order principle. The modelled 3D channel networks achieve lower total energy expenditure corresponding to 2D sub-optimal OCNs bound to specific slope-area relationships. This is the first time we are able to explore accessible 3D OCNs starting from a general DEM. By contrasting the modelled 3D OCNs and natural river networks, we found statistical similarities of two indexes, namely the area exponent index and the profile concavity index. Among the wide range of alternative and sub-optimal river networks, a minimum degree of 3D network organization is found to guarantee the indexes values within the natural range. These networks simultaneously possess topological and topographic properties of real river networks. We found a pivotal functional link between slope-area relationship and accessible sub-optimal 2D river network paths, which suggests that geological and climate conditions producing slope-area relationships in natural basins co-determine the degree of optimality of accessible network paths.

Climate change poised to threaten hydrologic connectivity and endemic fishes in dryland streams

PubMed Central

Jaeger, Kristin L.; Olden, Julian D.; Pelland, Noel A.

2014-01-01

Protecting hydrologic connectivity of freshwater ecosystems is fundamental to ensuring species persistence, ecosystem integrity, and human well-being. More frequent and severe droughts associated with climate change are poised to significantly alter flow intermittence patterns and hydrologic connectivity in dryland streams of the American Southwest, with deleterious effects on highly endangered fishes. By integrating local-scale hydrologic modeling with emerging approaches in landscape ecology, we quantify fine-resolution, watershed-scale changes in habitat size, spacing, and connectance under forecasted climate change in the Verde River Basin, United States. Model simulations project annual zero-flow day frequency to increase by 27% by midcentury, with differential seasonal consequences on continuity (temporal continuity at discrete locations) and connectivity (spatial continuity within the network). A 17% increase in the frequency of stream drying events is expected throughout the network with associated increases in the duration of these events. Flowing portions of the river network will diminish between 8% and 20% in spring and early summer and become increasingly isolated by more frequent and longer stretches of dry channel fragments, thus limiting the opportunity for native fishes to access spawning habitats and seasonally available refuges. Model predictions suggest that midcentury and late century climate will reduce network-wide hydrologic connectivity for native fishes by 6–9% over the course of a year and up to 12–18% during spring spawning months. Our work quantifies climate-induced shifts in stream drying and connectivity across a large river network and demonstrates their implications for the persistence of a globally endemic fish fauna. PMID:25136090

Climate change poised to threaten hydrologic connectivity and endemic fishes in dryland streams.

PubMed

Jaeger, Kristin L; Olden, Julian D; Pelland, Noel A

2014-09-23

Protecting hydrologic connectivity of freshwater ecosystems is fundamental to ensuring species persistence, ecosystem integrity, and human well-being. More frequent and severe droughts associated with climate change are poised to significantly alter flow intermittence patterns and hydrologic connectivity in dryland streams of the American Southwest, with deleterious effects on highly endangered fishes. By integrating local-scale hydrologic modeling with emerging approaches in landscape ecology, we quantify fine-resolution, watershed-scale changes in habitat size, spacing, and connectance under forecasted climate change in the Verde River Basin, United States. Model simulations project annual zero-flow day frequency to increase by 27% by midcentury, with differential seasonal consequences on continuity (temporal continuity at discrete locations) and connectivity (spatial continuity within the network). A 17% increase in the frequency of stream drying events is expected throughout the network with associated increases in the duration of these events. Flowing portions of the river network will diminish between 8% and 20% in spring and early summer and become increasingly isolated by more frequent and longer stretches of dry channel fragments, thus limiting the opportunity for native fishes to access spawning habitats and seasonally available refuges. Model predictions suggest that midcentury and late century climate will reduce network-wide hydrologic connectivity for native fishes by 6-9% over the course of a year and up to 12-18% during spring spawning months. Our work quantifies climate-induced shifts in stream drying and connectivity across a large river network and demonstrates their implications for the persistence of a globally endemic fish fauna.

Study on Coagulant Dosing Control System of Micro Vortex Water Treatment

NASA Astrophysics Data System (ADS)

Fengping, Hu; Qi, Fan; Wenjie, Hu; Xizhen, He; Hongling, Dai

2018-03-01

In view of the characteristics of nonlinearity, large time delay and multi disturbance in the process of coagulant dosing in water treatment, it is difficult to control the dosage of coagulant. According to the four indexes of raw water quality parameters (raw water flow, turbidity, pH value) and turbidity of sedimentation tank, the micro vortex coagulation dosing control model is constructed based on BP neural network and GA. The forecast results of BP neural network model are ideal, and after the optimization of GA, the prediction accuracy of the model is partly improved. The prediction error of the optimized network is ±0.5 mg/L, and has a better performance than non-optimized network.

How transfer flights shape the structure of the airline network.

PubMed

Ryczkowski, Tomasz; Fronczak, Agata; Fronczak, Piotr

2017-07-17

In this paper, we analyse the gravity model in the global passenger air-transport network. We show that in the standard form, the model is inadequate for correctly describing the relationship between passenger flows and typical geo-economic variables that characterize connected countries. We propose a model for transfer flights that allows exploitation of these discrepancies in order to discover hidden subflows in the network. We illustrate its usefulness by retrieving the distance coefficient in the gravity model, which is one of the determinants of the globalization process. Finally, we discuss the correctness of the presented approach by comparing the distance coefficient to several well-known economic events.

Field Effect Flow Control in a Polymer T-Intersection Microfluidic Network

NASA Technical Reports Server (NTRS)

Sniadecki, Nathan J.; Chang, Richard; Beamesderfer, Mike; Lee, Cheng S.; DeVoe, Don L.

2003-01-01

We present a study of induced pressure pumping in a polymer microchannel due to differential electroosmotic flow @OF) rates via field-effect flow control (FEFC). The experimental results demonstrate that the induced pressure pumping is dependent on the distance of the FEFC gate from the cathodic gate. A proposed flow model based on a linearly-decaying zeta potential profile is found to successfully predict experimental trends.

Predicting dynamics and rheology of blood flow: A comparative study of multiscale and low-dimensional models of red blood cells

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pan, Wenxiao; Fedosov, Dmitry A.; Caswell, Bruce

In this work we compare the predictive capability of two mathematical models for red blood cells (RBCs) focusing on blood flow in capillaries and arterioles. Both RBC models as well as their corresponding blood flows are based on the dissipative particle dynamics (DPD) method, a coarse-grained molecular dynamics approach. The first model employs a multiscale description of the RBC (MS-RBC), with its membrane represented by hundreds or even thousands of DPD-particles connected by springs into a triangular network in combination with out-of-plane elastic bending resistance. Extra dissipation within the network accounts for membrane viscosity, while the characteristic biconcave RBC shapemore » is achieved by imposition of constraints for constant membrane area and constant cell volume. The second model is based on a low-dimensional description (LD-RBC) constructed as a closed torus-like ring of only 10 large DPD colloidal particles. They are connected into a ring by worm-like chain (WLC) springs combined with bending resistance. The LD-RBC model can be fitted to represent the entire range of nonlinear elastic deformations as measured by optical-tweezers for healthy and for infected RBCs in malaria. MS-RBCs suspensions model the dynamics and rheology of blood flow accurately for any size vessel but this approach is computationally expensive above 100 microns. Surprisingly, the much more economical suspensions of LD-RBCs also capture the blood flow dynamics and rheology accurately except for vessels with sizes comparable to RBC diameter. In particular, the LD-RBC suspensions are shown to properly capture the experimental data for the apparent viscosity of blood and its cell-free layer (CFL) in tube flow. Taken together, these findings suggest a hierarchical approach in modeling blood flow in the arterial tree, whereby the MS-RBC model should be employed for capillaries and arterioles below 100 microns, the LD-RBC model for arterioles, and the continuum description for arteries.« less

Design of robust flow processing networks with time-programmed responses

NASA Astrophysics Data System (ADS)

Kaluza, P.; Mikhailov, A. S.

2012-04-01

Can artificially designed networks reach the levels of robustness against local damage which are comparable with those of the biochemical networks of a living cell? We consider a simple model where the flow applied to an input node propagates through the network and arrives at different times to the output nodes, thus generating a pattern of coordinated responses. By using evolutionary optimization algorithms, functional networks - with required time-programmed responses - were constructed. Then, continuing the evolution, such networks were additionally optimized for robustness against deletion of individual nodes or links. In this manner, large ensembles of functional networks with different kinds of robustness were obtained, making statistical investigations and comparison of their structural properties possible. We have found that, generally, different architectures are needed for various kinds of robustness. The differences are statistically revealed, for example, in the Laplacian spectra of the respective graphs. On the other hand, motif distributions of robust networks do not differ from those of the merely functional networks; they are found to belong to the first Alon superfamily, the same as that of the gene transcription networks of single-cell organisms.

Network Simulation solution of free convective flow from a vertical cone with combined effect of non- uniform surface heat flux and heat generation or absorption

NASA Astrophysics Data System (ADS)

Immanuel, Y.; Pullepu, Bapuji; Sambath, P.

2018-04-01

A two dimensional mathematical model is formulated for the transitive laminar free convective, incompressible viscous fluid flow over vertical cone with variable surface heat flux combined with the effects of heat generation and absorption is considered . using a powerful computational method based on thermoelectric analogy called Network Simulation Method (NSM0, the solutions of governing nondimensionl coupled, unsteady and nonlinear partial differential conservation equations of the flow that are obtained. The numerical technique is always stable and convergent which establish high efficiency and accuracy by employing network simulator computer code Pspice. The effects of velocity and temperature profiles have been analyzed for various factors, namely Prandtl number Pr, heat flux power law exponent n and heat generation/absorption parameter Δ are analyzed graphically.

Parallel ALLSPD-3D: Speeding Up Combustor Analysis Via Parallel Processing

NASA Technical Reports Server (NTRS)

Fricker, David M.

1997-01-01

The ALLSPD-3D Computational Fluid Dynamics code for reacting flow simulation was run on a set of benchmark test cases to determine its parallel efficiency. These test cases included non-reacting and reacting flow simulations with varying numbers of processors. Also, the tests explored the effects of scaling the simulation with the number of processors in addition to distributing a constant size problem over an increasing number of processors. The test cases were run on a cluster of IBM RS/6000 Model 590 workstations with ethernet and ATM networking plus a shared memory SGI Power Challenge L workstation. The results indicate that the network capabilities significantly influence the parallel efficiency, i.e., a shared memory machine is fastest and ATM networking provides acceptable performance. The limitations of ethernet greatly hamper the rapid calculation of flows using ALLSPD-3D.

Quantification of groundwater infiltration and surface water inflows in urban sewer networks based on a multiple model approach.

PubMed

Karpf, Christian; Krebs, Peter

2011-05-01

The management of sewer systems requires information about discharge and variability of typical wastewater sources in urban catchments. Especially the infiltration of groundwater and the inflow of surface water (I/I) are important for making decisions about the rehabilitation and operation of sewer networks. This paper presents a methodology to identify I/I and estimate its quantity. For each flow fraction in sewer networks, an individual model approach is formulated whose parameters are optimised by the method of least squares. This method was applied to estimate the contributions to the wastewater flow in the sewer system of the City of Dresden (Germany), where data availability is good. Absolute flows of I/I and their temporal variations are estimated. Further information on the characteristics of infiltration is gained by clustering and grouping sewer pipes according to the attributes construction year and groundwater influence and relating these resulting classes to infiltration behaviour. Further, it is shown that condition classes based on CCTV-data can be used to estimate the infiltration potential of sewer pipes. Copyright © 2011 Elsevier Ltd. All rights reserved.

Engineering-Aligned 3D Neural Circuit in Microfluidic Device.

PubMed

Bang, Seokyoung; Na, Sangcheol; Jang, Jae Myung; Kim, Jinhyun; Jeon, Noo Li

2016-01-07

The brain is one of the most important and complex organs in the human body. Although various neural network models have been proposed for in vitro 3D neuronal networks, it has been difficult to mimic functional and structural complexity of the in vitro neural circuit. Here, a microfluidic model of a simplified 3D neural circuit is reported. First, the microfluidic device is filled with Matrigel and continuous flow is delivered across the device during gelation. The fluidic flow aligns the extracellular matrix (ECM) components along the flow direction. Following the alignment of ECM fibers, neurites of primary rat cortical neurons are grown into the Matrigel at the average speed of 250 μm d(-1) and form axon bundles approximately 1500 μm in length at 6 days in vitro (DIV). Additionally, neural networks are developed from presynaptic to postsynaptic neurons at 14 DIV. The establishment of aligned 3D neural circuits is confirmed with the immunostaining of PSD-95 and synaptophysin and the observation of calcium signal transmission. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

User's Guide for Mixed-Size Sediment Transport Model for Networks of One-Dimensional Open Channels

USGS Publications Warehouse

Bennett, James P.

2001-01-01

This user's guide describes a mathematical model for predicting the transport of mixed sizes of sediment by flow in networks of one-dimensional open channels. The simulation package is useful for general sediment routing problems, prediction of erosion and deposition following dam removal, and scour in channels at road embankment crossings or other artificial structures. The model treats input hydrographs as stepwise steady-state, and the flow computation algorithm automatically switches between sub- and supercritical flow as dictated by channel geometry and discharge. A variety of boundary conditions including weirs and rating curves may be applied both external and internal to the flow network. The model may be used to compute flow around islands and through multiple openings in embankments, but the network must be 'simple' in the sense that the flow directions in all channels can be specified before simulation commences. The location and shape of channel banks are user specified, and all bedelevation changes take place between these banks and above a user-specified bedrock elevation. Computation of sediment-transport emphasizes the sand-size range (0.0625-2.0 millimeter) but the user may select any desired range of particle diameters including silt and finer (<0.0625 millimeter). As part of data input, the user may set the original bed-sediment composition of any number of layers of known thickness. The model computes the time evolution of total transport and the size composition of bed- and suspended-load sand through any cross section of interest. It also tracks bed -surface elevation and size composition. The model is written in the FORTRAN programming language for implementation on personal computers using the WINDOWS operating system and, along with certain graphical output display capability, is accessed from a graphical user interface (GUI). The GUI provides a framework for selecting input files and parameters of a number of components of the sediment-transport process. There are no restrictions in the use of the model as to numbers of channels, channel junctions, cross sections per channel, or points defining the cross sections. Following completion of the simulation computations, the GUI accommodates display of longitudinal plots of either bed elevation and size composition, or of transport rate and size composition of the various components, for individual channels and selected times during the simulation period. For individual cross sections, the GUI also allows display of time series of transport rate and size composition of the various components and of bed elevation and size composition.

Computer-aided-engineering system for modeling and analysis of ECLSS integration testing

NASA Technical Reports Server (NTRS)

Sepahban, Sonbol

1987-01-01

The accurate modeling and analysis of two-phase fluid networks found in environmental control and life support systems is presently undertaken by computer-aided engineering (CAE) techniques whose generalized fluid dynamics package can solve arbitrary flow networks. The CAE system for integrated test bed modeling and analysis will also furnish interfaces and subsystem/test-article mathematical models. Three-dimensional diagrams of the test bed are generated by the system after performing the requisite simulation and analysis.

Capillary-Driven Flow in Liquid Filaments Connecting Orthogonal Channels

NASA Technical Reports Server (NTRS)

Allen, Jeffrey S.

2005-01-01

Capillary phenomena plays an important role in the management of product water in PEM fuel cells because of the length scales associated with the porous layers and the gas flow channels. The distribution of liquid water within the network of gas flow channels can be dramatically altered by capillary flow. We experimentally demonstrate the rapid movement of significant volumes of liquid via capillarity through thin liquid films which connect orthogonal channels. The microfluidic experiments discussed provide a good benchmark against which the proper modeling of capillarity by computational models may be tested. The effect of surface wettability, as expressed through the contact angle, on capillary flow will also be discussed.

A Markovian model of evolving world input-output network

PubMed Central

Isacchini, Giulio

2017-01-01

The initial theoretical connections between Leontief input-output models and Markov chains were established back in 1950s. However, considering the wide variety of mathematical properties of Markov chains, so far there has not been a full investigation of evolving world economic networks with Markov chain formalism. In this work, using the recently available world input-output database, we investigated the evolution of the world economic network from 1995 to 2011 through analysis of a time series of finite Markov chains. We assessed different aspects of this evolving system via different known properties of the Markov chains such as mixing time, Kemeny constant, steady state probabilities and perturbation analysis of the transition matrices. First, we showed how the time series of mixing times and Kemeny constants could be used as an aggregate index of globalization. Next, we focused on the steady state probabilities as a measure of structural power of the economies that are comparable to GDP shares of economies as the traditional index of economies welfare. Further, we introduced two measures of systemic risk, called systemic influence and systemic fragility, where the former is the ratio of number of influenced nodes to the total number of nodes, caused by a shock in the activity of a node, and the latter is based on the number of times a specific economic node is affected by a shock in the activity of any of the other nodes. Finally, focusing on Kemeny constant as a global indicator of monetary flow across the network, we showed that there is a paradoxical effect of a change in activity levels of economic nodes on the overall flow of the world economic network. While the economic slowdown of the majority of nodes with high structural power results to a slower average monetary flow over the network, there are some nodes, where their slowdowns improve the overall quality of the network in terms of connectivity and the average flow of the money. PMID:29065145

Modeling of Hydraulic Fracture Propagation at the kISMET Site Using a Fully Coupled 3D Network-Flow and Quasi- Static Discrete Element Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Jing; Huang, Hai; Mattson, Earl

Aimed at supporting the design of hydraulic fracturing experiments at the kISMET site, ~1500 m below ground in a deep mine, we performed pre-experimental hydraulic fracturing simulations in order to estimate the breakdown pressure, propagation pressure, fracture geometry, and the magnitude of induced seismicity using a newly developed fully coupled three-dimensional (3D) network flow and quasi-static discrete element model (DEM). The quasi-static DEM model, which is constructed by Delaunay tessellation of the rock volume, considers rock fabric heterogeneities by using the “disordered” DEM mesh and adding random perturbations to the stiffness and tensile/shear strengths of individual DEM elements and themore » elastic beams between them. A conjugate 3D flow network based on the DEM lattice is constructed to calculate the fluid flow in both the fracture and porous matrix. One distinctive advantage of the model is that fracturing is naturally described by the breakage of elastic beams between DEM elements. It is also extremely convenient to introduce mechanical anisotropy into the model by simply assigning orientation-dependent tensile/shear strengths to the elastic beams. In this paper, the 3D hydraulic fracturing model was verified against the analytic solution for a penny-shaped crack model. We applied the model to simulate fracture propagation from a vertical open borehole based on initial estimates of rock mechanical properties and in-situ stress conditions. The breakdown pressure and propagation pressure are directly obtained from the simulation. In addition, the released elastic strain energies of individual fracturing events were calculated and used as a conservative estimate for the magnitudes of the potential induced seismic activities associated with fracturing. The comparisons between model predictions and experimental results are still ongoing.« less

The freight landscape : using secondary data sources to describe metropolitan freight flows.

DOT National Transportation Integrated Search

2010-01-01

Freight flows depend on the spatial organization of freight supply and demand, and on the transportation facilities within the metropolitan area. We use network model data for both the Los Angeles region and San Francisco region, and estimate two set...

Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery

NASA Astrophysics Data System (ADS)

Wu, Qiusheng; Lane, Charles R.

2017-07-01

In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features with seasonal to permanent inundation patterning characterized by nested hierarchical structures and dynamic filling-spilling-merging surface-water hydrological processes. Differentiating and appropriately processing such ecohydrologically meaningful features remains a major technical terrain-processing challenge, particularly as high-resolution spatial data are increasingly used to support modeling and geographic analysis needs. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution lidar data and aerial imagery. The graph-theory-based contour tree method was used to delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost-path algorithm. The resulting flow network delineated potential flow paths connecting wetland depressions to each other or to the river network on scales finer than those available through the National Hydrography Dataset. The results demonstrated that our proposed framework is promising for improving overland flow simulation and hydrologic connectivity analysis.

Augmenting groundwater monitoring networks near landfills with slurry cutoff walls.

PubMed

Hudak, Paul F

2004-01-01

This study investigated the use of slurry cutoff walls in conjunction with monitoring wells to detect contaminant releases from a solid waste landfill. The 50 m wide by 75 m long landfill was oriented oblique to regional groundwater flow in a shallow sand aquifer. Computer models calculated flow fields and the detection capability of six monitoring networks, four including a 1 m wide by 50 m long cutoff wall at various positions along the landfill's downgradient boundaries and upgradient of the landfill. Wells were positioned to take advantage of convergent flow induced downgradient of the cutoff walls. A five-well network with no cutoff wall detected 81% of contaminant plumes originating within the landfill's footprint before they reached a buffer zone boundary located 50 m from the landfill's downgradient corner. By comparison, detection efficiencies of networks augmented with cutoff walls ranged from 81 to 100%. The most efficient network detected 100% of contaminant releases with four wells, with a centrally located, downgradient cutoff wall. In general, cutoff walls increased detection efficiency by delaying transport of contaminant plumes to the buffer zone boundary, thereby allowing them to increase in size, and by inducing convergent flow at downgradient areas, thereby funneling contaminant plumes toward monitoring wells. However, increases in detection efficiency were too small to offset construction costs for cutoff walls. A 100% detection efficiency was also attained by an eight-well network with no cutoff wall, at approximately one-third the cost of the most efficient wall-augmented network.

Mechanics of blood supply to the heart: wave reflection effects in a right coronary artery.

PubMed Central

Zamir, M

1998-01-01

Mechanics of blood flow in the coronary circulation have in the past been based largely on models in which the detailed architecture of the coronary network is not included because of lack of data: properties of individual vessels do not appear individually in the model but are represented collectively by the elements of a single electric circuit. Recent data from the human heart make it possible, for the first time, to examine the dynamics of flow in the coronary network based on detailed, measured vascular architecture. In particular, admittance values along the full course of the right coronary artery are computed based on actual lengths and diameters of the many thousands of branches which make up the distribution system of this vessel. The results indicate that effects of wave reflections on this flow are far more significant than those generally suspected to occur in coronary blood flow and that they are actually the reverse of the well known wave reflection effects in the aorta. PMID:9523440

A regional-scale ecological risk framework for environmental flow evaluations

NASA Astrophysics Data System (ADS)

O'Brien, Gordon C.; Dickens, Chris; Hines, Eleanor; Wepener, Victor; Stassen, Retha; Quayle, Leo; Fouchy, Kelly; MacKenzie, James; Graham, P. Mark; Landis, Wayne G.

2018-02-01

Environmental flow (E-flow) frameworks advocate holistic, regional-scale, probabilistic E-flow assessments that consider flow and non-flow drivers of change in a socio-ecological context as best practice. Regional-scale ecological risk assessments of multiple stressors to social and ecological endpoints, which address ecosystem dynamism, have been undertaken internationally at different spatial scales using the relative-risk model since the mid-1990s. With the recent incorporation of Bayesian belief networks into the relative-risk model, a robust regional-scale ecological risk assessment approach is available that can contribute to achieving the best practice recommendations of E-flow frameworks. PROBFLO is a holistic E-flow assessment method that incorporates the relative-risk model and Bayesian belief networks (BN-RRM) into a transparent probabilistic modelling tool that addresses uncertainty explicitly. PROBFLO has been developed to evaluate the socio-ecological consequences of historical, current and future water resource use scenarios and generate E-flow requirements on regional spatial scales. The approach has been implemented in two regional-scale case studies in Africa where its flexibility and functionality has been demonstrated. In both case studies the evidence-based outcomes facilitated informed environmental management decision making, with trade-off considerations in the context of social and ecological aspirations. This paper presents the PROBFLO approach as applied to the Senqu River catchment in Lesotho and further developments and application in the Mara River catchment in Kenya and Tanzania. The 10 BN-RRM procedural steps incorporated in PROBFLO are demonstrated with examples from both case studies. PROBFLO can contribute to the adaptive management of water resources and contribute to the allocation of resources for sustainable use of resources and address protection requirements.

Freight Transportation Energy Use : Volume 2. Methodology and Program Documentation.

DOT National Transportation Integrated Search

1978-07-01

The structure and logic of the transportation network model component of the TSC Freight Energy Model are presented. The model assigns given origin-destination commodity flows to specific transport modes and routes, thereby determining the traffic lo...

Improving Simulations of Extreme Flows by Coupling a Physically-based Hydrologic Model with a Machine Learning Model

NASA Astrophysics Data System (ADS)

Mohammed, K.; Islam, A. S.; Khan, M. J. U.; Das, M. K.

2017-12-01

With the large number of hydrologic models presently available along with the global weather and geographic datasets, streamflows of almost any river in the world can be easily modeled. And if a reasonable amount of observed data from that river is available, then simulations of high accuracy can sometimes be performed after calibrating the model parameters against those observed data through inverse modeling. Although such calibrated models can succeed in simulating the general trend or mean of the observed flows very well, more often than not they fail to adequately simulate the extreme flows. This causes difficulty in tasks such as generating reliable projections of future changes in extreme flows due to climate change, which is obviously an important task due to floods and droughts being closely connected to people's lives and livelihoods. We propose an approach where the outputs of a physically-based hydrologic model are used as an input to a machine learning model to try and better simulate the extreme flows. To demonstrate this offline-coupling approach, the Soil and Water Assessment Tool (SWAT) was selected as the physically-based hydrologic model, the Artificial Neural Network (ANN) as the machine learning model and the Ganges-Brahmaputra-Meghna (GBM) river system as the study area. The GBM river system, located in South Asia, is the third largest in the world in terms of freshwater generated and forms the largest delta in the world. The flows of the GBM rivers were simulated separately in order to test the performance of this proposed approach in accurately simulating the extreme flows generated by different basins that vary in size, climate, hydrology and anthropogenic intervention on stream networks. Results show that by post-processing the simulated flows of the SWAT models with ANN models, simulations of extreme flows can be significantly improved. The mean absolute errors in simulating annual maximum/minimum daily flows were minimized from 4967 cusecs to 1294 cusecs for Ganges, from 5695 cusecs to 2115 cusecs for Brahmaputra and from 689 cusecs to 321 cusecs for Meghna. Using this approach, simulations of hydrologic variables other than streamflow can also be improved given that a decent amount of observed data for that variable is available.

Modeling a full-scale primary sedimentation tank using artificial neural networks.

PubMed

Gamal El-Din, A; Smith, D W

2002-05-01

Modeling the performance of full-scale primary sedimentation tanks has been commonly done using regression-based models, which are empirical relationships derived strictly from observed daily average influent and effluent data. Another approach to model a sedimentation tank is using a hydraulic efficiency model that utilizes tracer studies to characterize the performance of model sedimentation tanks based on eddy diffusion. However, the use of hydraulic efficiency models to predict the dynamic behavior of a full-scale sedimentation tank is very difficult as the development of such models has been done using controlled studies of model tanks. In this paper, another type of model, namely artificial neural network modeling approach, is used to predict the dynamic response of a full-scale primary sedimentation tank. The neuralmodel consists of two separate networks, one uses flow and influent total suspended solids data in order to predict the effluent total suspended solids from the tank, and the other makes predictions of the effluent chemical oxygen demand using data of the flow and influent chemical oxygen demand as inputs. An extensive sampling program was conducted in order to collect a data set to be used in training and validating the networks. A systematic approach was used in the building process of the model which allowed the identification of a parsimonious neural model that is able to learn (and not memorize) from past data and generalize very well to unseen data that were used to validate the model. Theresults seem very promising. The potential of using the model as part of a real-time process control system isalso discussed.

Augmenting an observation network to facilitate flow and transport model discrimination.

USDA-ARS?s Scientific Manuscript database

Improving understanding of subsurface conditions includes performance comparison for competing models, independently developed or obtained via model abstraction. The model comparison and discrimination can be improved if additional observations will be included. The objective of this work was to i...

Machine Learning and Deep Learning Models to Predict Runoff Water Quantity and Quality

NASA Astrophysics Data System (ADS)

Bradford, S. A.; Liang, J.; Li, W.; Murata, T.; Simunek, J.

2017-12-01

Contaminants can be rapidly transported at the soil surface by runoff to surface water bodies. Physically-based models, which are based on the mathematical description of main hydrological processes, are key tools for predicting surface water impairment. Along with physically-based models, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models. In this presentation we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. First, we have developed a physically-based numerical model to simulate overland flow and contaminant transport (the HYDRUS-1D overland flow module). A large number of numerical simulations were carried out to develop a database containing information about the impact of various input parameters (weather patterns, surface topography, vegetation, soil conditions, contaminants, and best management practices) on runoff water quantity and quality outputs. This database was used to train data-driven models. Three different methods (Neural Networks, Support Vector Machines, and Recurrence Neural Networks) were explored to prepare input- output functional relations. Results demonstrate the ability and limitations of machine learning and deep learning models to predict runoff water quantity and quality.

Netflow Theory Manual

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bozinoski, Radoslav; Winters, William

2016-01-01

The purpose of this report is to document the theoretical models utilized by the computer code NETFLOW. This report will focus on the theoretical models used to analyze high Mach number fully compressible transonic flows in piping networks.

Cascading failures in interconnected networks with dynamical redistribution of loads

NASA Astrophysics Data System (ADS)

Zhao, Zhuang; Zhang, Peng; Yang, Hujiang

2015-09-01

Cascading failures of loads in isolated networks and coupled networks have been studied in the past few years. In most of the corresponding results, the topologies of the networks are destroyed. Here, we present an interconnected network model considering cascading failures based on the dynamic redistribution of flow in the networks. Compared with the results of single scale-free networks, we find that interconnected scale-free networks have higher vulnerability. Additionally, the network heterogeneity plays an important role in the robustness of interconnected networks under intentional attacks. Considering the effects of various coupling preferences, the results show that there are almost no differences. Finally, the application of our model to the Beijing interconnected traffic network, which consists of a subway network and a bus network, shows that the subway network suffers more damage under the attack. Moreover, the interconnected traffic network may be more exposed to damage after initial attacks on the bus network. These discussions are important for the design and optimization of interconnected networks.

Research on configuration of railway self-equipped tanker based on minimum cost maximum flow model

NASA Astrophysics Data System (ADS)

Yang, Yuefang; Gan, Chunhui; Shen, Tingting

2017-05-01

In the study of the configuration of the tanker of chemical logistics park, the minimum cost maximum flow model is adopted. Firstly, the transport capacity of the park loading and unloading area and the transportation demand of the dangerous goods are taken as the constraint condition of the model; then the transport arc capacity, the transport arc flow and the transport arc edge weight are determined in the transportation network diagram; finally, the software calculations. The calculation results show that the configuration issue of the tankers can be effectively solved by the minimum cost maximum flow model, which has theoretical and practical application value for tanker management of railway transportation of dangerous goods in the chemical logistics park.

Information Flow in Interaction Networks II: Channels, Path Lengths, and Potentials

PubMed Central

Stojmirović, Aleksandar

2012-01-01

Abstract In our previous publication, a framework for information flow in interaction networks based on random walks with damping was formulated with two fundamental modes: emitting and absorbing. While many other network analysis methods based on random walks or equivalent notions have been developed before and after our earlier work, one can show that they can all be mapped to one of the two modes. In addition to these two fundamental modes, a major strength of our earlier formalism was its accommodation of context-specific directed information flow that yielded plausible and meaningful biological interpretation of protein functions and pathways. However, the directed flow from origins to destinations was induced via a potential function that was heuristic. Here, with a theoretically sound approach called the channel mode, we extend our earlier work for directed information flow. This is achieved by constructing a potential function facilitating a purely probabilistic interpretation of the channel mode. For each network node, the channel mode combines the solutions of emitting and absorbing modes in the same context, producing what we call a channel tensor. The entries of the channel tensor at each node can be interpreted as the amount of flow passing through that node from an origin to a destination. Similarly to our earlier model, the channel mode encompasses damping as a free parameter that controls the locality of information flow. Through examples involving the yeast pheromone response pathway, we illustrate the versatility and stability of our new framework. PMID:22409812

Volume of the steady-state space of financial flows in a monetary stock-flow-consistent model

NASA Astrophysics Data System (ADS)

Hazan, Aurélien

2017-05-01

We show that a steady-state stock-flow consistent macro-economic model can be represented as a Constraint Satisfaction Problem (CSP). The set of solutions is a polytope, which volume depends on the constraints applied and reveals the potential fragility of the economic circuit, with no need to study the dynamics. Several methods to compute the volume are compared, inspired by operations research methods and the analysis of metabolic networks, both exact and approximate. We also introduce a random transaction matrix, and study the particular case of linear flows with respect to money stocks.

Dynamic network expansion, contraction, and connectivity in the river corridor of mountain stream network

NASA Astrophysics Data System (ADS)

Ward, A. S.; Schmadel, N.; Wondzell, S. M.

2017-12-01

River networks are broadly recognized to expand and contract in response to hydrologic forcing. Additionally, the individual controls on river corridor dynamics of hydrologic forcing and geologic setting are well recognized. However, we currently lack tools to integrate our understanding of process dynamics in the river corridor and make predictions at the scale of river networks. In this study, we develop a perceptual model of the river corridor in mountain river networks, translate this into a reduced-complexity mechanistic model, and implement the model in a well-studied headwater catchment. We found that the river network was most sensitive to hydrologic dynamics under the lowest discharges (Qgauge < 1 L s-1). We also demonstrate a discharge-dependence on the dominant controls on network expansion, contraction, and river corridor exchange. Finally, we suggest this parsimonious model will be useful to managers of water resources who need to estimate connectivity and flow initiation location along the river corridor over broad, unstudied catchments.

Fracture Network Characteristics Informed by Detailed Studies of Chlorinated Solvent Plumes in Sedimentary Rock Aquifers

NASA Astrophysics Data System (ADS)

Parker, B. L.; Chapman, S.

2015-12-01

Various numerical approaches have been used to simulate contaminant plumes in fractured porous rock, but the one that allows field and laboratory measurements to be most directly used as inputs to these models is the Discrete Fracture Network (DFN) Approach. To effectively account for fracture-matrix interactions, emphasis must be placed on identifying and parameterizing all of the fractures that participate substantially in groundwater flow and contaminated transport. High resolution plume studies at four primary research sites, where chlorinated solvent plumes serve as long-term (several decades) tracer tests, provide insight concerning the density of the fracture network unattainable by conventional methods. Datasets include contaminant profiles from detailed VOC subsampling informed by continuous core logs, hydraulic head and transmissivity profiles, packer testing and sensitive temperature logging methods in FLUTe™ lined holes. These show presence of many more transmissive fractures, contrasting observations of only a few flow zones per borehole obtained from conventional hydraulic tests including flow metering in open boreholes. Incorporating many more fractures with a wider range of transmissivities is key to predicting contaminant migration. This new understanding of dense fracture networks combined with matrix property measurements have informed 2-D DFN flow and transport modelling using Fractran and HydroGeosphere to simulate plume characteristics ground-truthed by detailed field site plume characterization. These process-based simulations corroborate field findings that plumes in sedimentary rock after decades of transport show limited plume front distances and strong internal plume attenuation by diffusion, transverse dispersion and slow degradation. This successful application of DFN modeling informed by field-derived parameters demonstrates how the DFN Approach can be applied to other sites to inform plume migration rates and remedial efficacy.

Simulating the Effects of Drainage and Agriculture on Hydrology and Sediment in the Minnesota River Basin

NASA Astrophysics Data System (ADS)

Downer, C. W.; Pradhan, N. R.; Skahill, B. E.; Banitt, A. M.; Eggers, G.; Pickett, R. E.

2014-12-01

Throughout the Midwest region of the United States, slopes are relatively flat, soils tend to have low permeability, and local water tables are high. In order to make the region suitable for agriculture, farmers have installed extensive networks of ditches to drain off excess surface water and subsurface tiles to lower the water table and remove excess soil water in the root zone that can stress common row crops, such as corn and soybeans. The combination of tiles, ditches, and intensive agricultural land practices radically alters the landscape and hydrology. Within the watershed, tiles have outlets to both the ditch/stream network as well as overland locations, where the tile discharge appears to initiate gullies and exacerbate overland erosion. As part of the Minnesota River Basin Integrated Study we are explicitly simulating the tile and drainage systems in the watershed at multiple scales using the physics-based watershed model GSSHA (Gridded Surface Subsurface Hydrologic Analysis). The tile drainage system is simulated as a network of pipes that collect water from the local water table. Within the watershed, testing of the methods on smaller basins shows the ability of the model to simulate tile flow, however, application at the larger scale is hampered by the computational burden of simulating the flow in the complex tile drain networks that drain the agricultural fields. Modeling indicates the subsurface drains account for approximately 40% of the stream flow in the Seven Mile Creek sub-basin account in the late spring and early summer when the tile is flowing. Preliminary results indicate that agricultural tile drains increase overland erosion in the Seven Mile Creek watershed.

Application of AFINCH as a tool for evaluating the effects of streamflow-gaging-network size and composition on the accuracy and precision of streamflow estimates at ungaged locations in the southeast Lake Michigan hydrologic subregion

USGS Publications Warehouse

Koltun, G.F.; Holtschlag, David J.

2010-01-01

Bootstrapping techniques employing random subsampling were used with the AFINCH (Analysis of Flows In Networks of CHannels) model to gain insights into the effects of variation in streamflow-gaging-network size and composition on the accuracy and precision of streamflow estimates at ungaged locations in the 0405 (Southeast Lake Michigan) hydrologic subregion. AFINCH uses stepwise-regression techniques to estimate monthly water yields from catchments based on geospatial-climate and land-cover data in combination with available streamflow and water-use data. Calculations are performed on a hydrologic-subregion scale for each catchment and stream reach contained in a National Hydrography Dataset Plus (NHDPlus) subregion. Water yields from contributing catchments are multiplied by catchment areas and resulting flow values are accumulated to compute streamflows in stream reaches which are referred to as flow lines. AFINCH imposes constraints on water yields to ensure that observed streamflows are conserved at gaged locations.  Data from the 0405 hydrologic subregion (referred to as Southeast Lake Michigan) were used for the analyses. Daily streamflow data were measured in the subregion for 1 or more years at a total of 75 streamflow-gaging stations during the analysis period which spanned water years 1971–2003. The number of streamflow gages in operation each year during the analysis period ranged from 42 to 56 and averaged 47. Six sets (one set for each censoring level), each composed of 30 random subsets of the 75 streamflow gages, were created by censoring (removing) approximately 10, 20, 30, 40, 50, and 75 percent of the streamflow gages (the actual percentage of operating streamflow gages censored for each set varied from year to year, and within the year from subset to subset, but averaged approximately the indicated percentages).Streamflow estimates for six flow lines each were aggregated by censoring level, and results were analyzed to assess (a) how the size and composition of the streamflow-gaging network affected the average apparent errors and variability of the estimated flows and (b) whether results for certain months were more variable than for others. The six flow lines were categorized into one of three types depending upon their network topology and position relative to operating streamflow-gaging stations.    Statistical analysis of the model results indicates that (1) less precise (that is, more variable) estimates resulted from smaller streamflow-gaging networks as compared to larger streamflow-gaging networks, (2) precision of AFINCH flow estimates at an ungaged flow line is improved by operation of one or more streamflow gages upstream and (or) downstream in the enclosing basin, (3) no consistent seasonal trend in estimate variability was evident, and (4) flow lines from ungaged basins appeared to exhibit the smallest absolute apparent percent errors (APEs) and smallest changes in average APE as a function of increasing censoring level. The counterintuitive results described in item (4) above likely reflect both the nature of the base-streamflow estimate from which the errors were computed and insensitivity in the average model-derived estimates to changes in the streamflow-gaging-network size and composition. Another analysis demonstrated that errors for flow lines in ungaged basins have the potential to be much larger than indicated by their APEs if measured relative to their true (but unknown) flows.     “Missing gage” analyses, based on examination of censoring subset results where the streamflow gage of interest was omitted from the calibration data set, were done to better understand the true error characteristics for ungaged flow lines as a function of network size. Results examined for 2 water years indicated that the probability of computing a monthly streamflow estimate within 10 percent of the true value with AFINCH decreased from greater than 0.9 at about a 10-percent network-censoring level to less than 0.6 as the censoring level approached 75 percent. In addition, estimates for typically dry months tended to be characterized by larger percent errors than typically wetter months.

A mixed-mode traffic assignment model with new time-flow impedance function

NASA Astrophysics Data System (ADS)

Lin, Gui-Hua; Hu, Yu; Zou, Yuan-Yang

2018-01-01

Recently, with the wide adoption of electric vehicles, transportation network has shown different characteristics and been further developed. In this paper, we present a new time-flow impedance function, which may be more realistic than the existing time-flow impedance functions. Based on this new impedance function, we present an optimization model for a mixed-mode traffic network in which battery electric vehicles (BEVs) and gasoline vehicles (GVs) are chosen. We suggest two approaches to handle the model: One is to use the interior point (IP) algorithm and the other is to employ the sequential quadratic programming (SQP) algorithm. Three numerical examples are presented to illustrate the efficiency of these approaches. In particular, our numerical results show that more travelers prefer to choosing BEVs when the distance limit of BEVs is long enough and the unit operating cost of GVs is higher than that of BEVs, and the SQP algorithm is faster than the IP algorithm.

The prediction of shallow landslide location and size using a multidimensional landslide analysis in a digital terrain model

Treesearch

W. E. Dietrich; J. McKean; D. Bellugi; T. Perron

2007-01-01

Shallow landslides on steep slopes often mobilize as debris flows. The size of the landslide controls the initial size of the debris flows, defines the sediment discharge to the channel network, affects rates and scales of landform development, and influences the relative hazard potential. Currently the common practice in digital terrain-based models is to set the...

Numerical Modeling of Unsteady Thermofluid Dynamics in Cryogenic Systems

NASA Technical Reports Server (NTRS)

Majumdar, Alok

2003-01-01

A finite volume based network analysis procedure has been applied to model unsteady flow without and with heat transfer. Liquid has been modeled as compressible fluid where the compressibility factor is computed from the equation of state for a real fluid. The modeling approach recognizes that the pressure oscillation is linked with the variation of the compressibility factor; therefore, the speed of sound does not explicitly appear in the governing equations. The numerical results of chilldown process also suggest that the flow and heat transfer are strongly coupled. This is evident by observing that the mass flow rate during 90-second chilldown process increases by factor of ten.

On the thresholds in modeling of high flows via artificial neural networks - A bootstrapping analysis

NASA Astrophysics Data System (ADS)

Panagoulia, D.; Trichakis, I.

2012-04-01

Considering the growing interest in simulating hydrological phenomena with artificial neural networks (ANNs), it is useful to figure out the potential and limits of these models. In this study, the main objective is to examine how to improve the ability of an ANN model to simulate extreme values of flow utilizing a priori knowledge of threshold values. A three-layer feedforward ANN was trained by using the back propagation algorithm and the logistic function as activation function. By using the thresholds, the flow was partitioned in low (x < μ), medium (μ ≤ x ≤ μ + 2σ) and high (x > μ + 2σ) values. The employed ANN model was trained for high flow partition and all flow data too. The developed methodology was implemented over a mountainous river catchment (the Mesochora catchment in northwestern Greece). The ANN model received as inputs pseudo-precipitation (rain plus melt) and previous observed flow data. After the training was completed the bootstrapping methodology was applied to calculate the ANN confidence intervals (CIs) for a 95% nominal coverage. The calculated CIs included only the uncertainty, which comes from the calibration procedure. The results showed that an ANN model trained specifically for high flows, with a priori knowledge of the thresholds, can simulate these extreme values much better (RMSE is 31.4% less) than an ANN model trained with all data of the available time series and using a posteriori threshold values. On the other hand the width of CIs increases by 54.9% with a simultaneous increase by 64.4% of the actual coverage for the high flows (a priori partition). The narrower CIs of the high flows trained with all data may be attributed to the smoothing effect produced from the use of the full data sets. Overall, the results suggest that an ANN model trained with a priori knowledge of the threshold values has an increased ability in simulating extreme values compared with an ANN model trained with all the data and a posteriori knowledge of the thresholds.

Drainage fracture networks in elastic solids with internal fluid generation

NASA Astrophysics Data System (ADS)

Kobchenko, Maya; Hafver, Andreas; Jettestuen, Espen; Galland, Olivier; Renard, François; Meakin, Paul; Jamtveit, Bjørn; Dysthe, Dag K.

2013-06-01

Experiments in which CO2 gas was generated by the yeast fermentation of sugar in an elastic layer of gelatine gel confined between two glass plates are described and analyzed theoretically. The CO2 gas pressure causes the gel layer to fracture. The gas produced is drained on short length scales by diffusion and on long length scales by flow in a fracture network, which has topological properties that are intermediate between river networks and hierarchical-fracture networks. A simple model for the experimental system with two parameters that characterize the disorder and the intermediate (river-fracture) topology of the network was developed and the results of the model were compared with the experimental results.

Spatial reasoning to determine stream network from LANDSAT imagery

NASA Technical Reports Server (NTRS)

Haralick, R. M.; Wang, S.; Elliott, D. B.

1983-01-01

In LANDSAT imagery, spectral and spatial information can be used to detect the drainage network as well as the relative elevation model in mountainous terrain. To do this, mixed information of material reflectance in the original LANDSAT imagery must be separated. From the material reflectance information, big visible rivers can be detected. From the topographic modulation information, ridges and valleys can be detected and assigned relative elevations. A complete elevation model can be generated by interpolating values for nonridge and non-valley pixels. The small streams not detectable from material reflectance information can be located in the valleys with flow direction known from the elevation model. Finally, the flow directions of big visible rivers can be inferred by solving a consistent labeling problem based on a set of spatial reasoning constraints.

Computational Network Model Prediction of Hemodynamic Alterations Due to Arteriolar Remodeling in Interval Sprint Trained Skeletal Muscle

PubMed Central

BINDER, KYLE W.; MURFEE, WALTER L.; SONG, JI; LAUGHLIN, M. HAROLD; PRICE, RICHARD J.

2009-01-01

Objectives Exercise training is known to enhance skeletal muscle blood flow capacity, with high-intensity interval sprint training (IST) primarily affecting muscles with a high proportion of fast twitch glycolytic fibers. The objective of this study was to determine the relative contributions of new arteriole formation and lumenal arteriolar remodeling to enhanced flow capacity and the impact of these adaptations on local microvascular hemodynamics deep within the muscle. Methods The authors studied arteriolar adaptation in the white/mixed-fiber portion of gastrocnemius muscles of IST (6 bouts of running/day; 2.5 min/bout; 60 m/min speed; 15% grade; 4.5 min rest between bouts; 5 training days/wk; 10 wks total) and sedentary (SED) control rats using whole-muscle Microfil casts. Dimensional and topological data were then used to construct a series of computational hemodynamic network models that incorporated physiological red blood cell distributions and hematocrit and diameter dependent apparent viscosities. Results In comparison to SED controls, IST elicited a significant increase in arterioles/order in the 3A through 6A generations. Predicted IST and SED flows through the 2A generation agreed closely with in vivo measurements made in a previous study, illustrating the accuracy of the model. IST shifted the bulk of the pressure drop across the network from the 3As to the 4As and 5As, and flow capacity increased from 0.7 mL/min in SED to 1.5 mL/min in IST when a driving pressure of 80 mmHg was applied. Conclusions The primary adaptation to IST is an increase in arterioles in the 3A through 6A generations, which, in turn, creates an approximate doubling of flow capacity and a deeper penetration of high pressure into the arteriolar network. PMID:17454671

Method and Apparatus for Predicting Unsteady Pressure and Flow Rate Distribution in a Fluid Network

NASA Technical Reports Server (NTRS)

Majumdar, Alok K. (Inventor)

2009-01-01

A method and apparatus for analyzing steady state and transient flow in a complex fluid network, modeling phase changes, compressibility, mixture thermodynamics, external body forces such as gravity and centrifugal force and conjugate heat transfer. In some embodiments, a graphical user interface provides for the interactive development of a fluid network simulation having nodes and branches. In some embodiments, mass, energy, and specific conservation equations are solved at the nodes, and momentum conservation equations are solved in the branches. In some embodiments, contained herein are data objects for computing thermodynamic and thermophysical properties for fluids. In some embodiments, the systems of equations describing the fluid network are solved by a hybrid numerical method that is a combination of the Newton-Raphson and successive substitution methods.

Reinforcement Learning of Two-Joint Virtual Arm Reaching in a Computer Model of Sensorimotor Cortex

PubMed Central

Neymotin, Samuel A.; Chadderdon, George L.; Kerr, Cliff C.; Francis, Joseph T.; Lytton, William W.

2014-01-01

Neocortical mechanisms of learning sensorimotor control involve a complex series of interactions at multiple levels, from synaptic mechanisms to cellular dynamics to network connectomics. We developed a model of sensory and motor neocortex consisting of 704 spiking model neurons. Sensory and motor populations included excitatory cells and two types of interneurons. Neurons were interconnected with AMPA/NMDA and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a two-joint virtual arm to reach to a fixed target. For each of 125 trained networks, we used 200 training sessions, each involving 15 s reaches to the target from 16 starting positions. Learning altered network dynamics, with enhancements to neuronal synchrony and behaviorally relevant information flow between neurons. After learning, networks demonstrated retention of behaviorally relevant memories by using proprioceptive information to perform reach-to-target from multiple starting positions. Networks dynamically controlled which joint rotations to use to reach a target, depending on current arm position. Learning-dependent network reorganization was evident in both sensory and motor populations: learned synaptic weights showed target-specific patterning optimized for particular reach movements. Our model embodies an integrative hypothesis of sensorimotor cortical learning that could be used to interpret future electrophysiological data recorded in vivo from sensorimotor learning experiments. We used our model to make the following predictions: learning enhances synchrony in neuronal populations and behaviorally relevant information flow across neuronal populations, enhanced sensory processing aids task-relevant motor performance and the relative ease of a particular movement in vivo depends on the amount of sensory information required to complete the movement. PMID:24047323

Path Flow Estimation Using Time Varying Coefficient State Space Model

NASA Astrophysics Data System (ADS)

Jou, Yow-Jen; Lan, Chien-Lun

2009-08-01

The dynamic path flow information is very crucial in the field of transportation operation and management, i.e., dynamic traffic assignment, scheduling plan, and signal timing. Time-dependent path information, which is important in many aspects, is nearly impossible to be obtained. Consequently, researchers have been seeking estimation methods for deriving valuable path flow information from less expensive traffic data, primarily link traffic counts of surveillance systems. This investigation considers a path flow estimation problem involving the time varying coefficient state space model, Gibbs sampler, and Kalman filter. Numerical examples with part of a real network of the Taipei Mass Rapid Transit with real O-D matrices is demonstrated to address the accuracy of proposed model. Results of this study show that this time-varying coefficient state space model is very effective in the estimation of path flow compared to time-invariant model.

Hierarchicality of trade flow networks reveals complexity of products.

PubMed

Shi, Peiteng; Zhang, Jiang; Yang, Bo; Luo, Jingfei

2014-01-01

With globalization, countries are more connected than before by trading flows, which amounts to at least 36 trillion dollars today. Interestingly, around 30-60 percents of exports consist of intermediate products in global. Therefore, the trade flow network of particular product with high added values can be regarded as value chains. The problem is weather we can discriminate between these products from their unique flow network structure? This paper applies the flow analysis method developed in ecology to 638 trading flow networks of different products. We claim that the allometric scaling exponent η can be used to characterize the degree of hierarchicality of a flow network, i.e., whether the trading products flow on long hierarchical chains. Then, it is pointed out that the flow networks of products with higher added values and complexity like machinary, transport equipment etc. have larger exponents, meaning that their trade flow networks are more hierarchical. As a result, without the extra data like global input-output table, we can identify the product categories with higher complexity, and the relative importance of a country in the global value chain by the trading network solely.

Hierarchicality of Trade Flow Networks Reveals Complexity of Products

PubMed Central

Shi, Peiteng; Zhang, Jiang; Yang, Bo; Luo, Jingfei

2014-01-01

With globalization, countries are more connected than before by trading flows, which amounts to at least trillion dollars today. Interestingly, around percents of exports consist of intermediate products in global. Therefore, the trade flow network of particular product with high added values can be regarded as value chains. The problem is weather we can discriminate between these products from their unique flow network structure? This paper applies the flow analysis method developed in ecology to 638 trading flow networks of different products. We claim that the allometric scaling exponent can be used to characterize the degree of hierarchicality of a flow network, i.e., whether the trading products flow on long hierarchical chains. Then, it is pointed out that the flow networks of products with higher added values and complexity like machinary, transport equipment etc. have larger exponents, meaning that their trade flow networks are more hierarchical. As a result, without the extra data like global input-output table, we can identify the product categories with higher complexity, and the relative importance of a country in the global value chain by the trading network solely. PMID:24905753

Impacts of logging on storm peak flows, flow volumes and suspended sediment loads in Caspar Creek, California

Treesearch

Jack Lewis; Sylvia R. Mori; Elizabeth T. Keppeler; Robert R. Ziemer

2001-01-01

Abstract - Models are fit to 11 years of storm peak flows, flow volumes, and suspended sediment loads on a network of 14 stream gaging stations in the North Fork Caspar Creek, a 473-ha coastal watershed bearing a second-growth forest of redwood and Douglas-fir. For the first 4 years of monitoring, the watershed was in a relatively undisturbed state, having last been...

Dynamics of wood in stream networks of the western Cascades Range, Oregon

Treesearch

Nicole M. Czarnomski; David M. Dreher; Kai U. Snyder; Julia A. Jones; Frederick J. Swanson

2008-01-01

We develop and test a conceptual model of wood dynamics in stream networks that considers legacies of forest management practices, floods, and debris flows. We combine an observational study of wood in 25 km of 2nd- through 5th-order streams in a steep, forested watershed of the western Cascade Range of Oregon with whole-network studies of forest cutting, roads, and...

Formation Timescales of the Martian Valley Networks

NASA Astrophysics Data System (ADS)

Hoke, M. T.; Hynek, B. M.

2010-12-01

The presence of valley networks across much of the ancient surface of Mars [e.g. 1] together with the locations and morphologies of the Martian deltas [e.g. 2] and ancient paleolakes [e.g. 3, 4], provides strong evidence that the Martian surface environment was once capable of sustaining long-lived flowing water. Many of the larger Martian valley networks exhibit characteristics consistent with their formation primarily from surface runoff of precipitated water [5-7]. Their formation likely followed similar processes as those that formed terrestrial river valleys, including the gradual erosion and transport of sediment downstream by bed load, suspended load, and wash load processes. When quantifying flow rates on Mars, some researchers have modified the Manning equation for depth- and width-averaged flow velocity in an attempt to better-fit Martian conditions [e.g. 3, 8-10]. These attempts, however, often result in flow velocities on Mars that are overestimated by up to a factor of two [10]. An alternative to the Manning equation that is often overlooked in the planetary science community is the Darcy-Weisbach (D-W) equation [11], which, unlike the Manning equation, maintains a dependence on the acceleration due to gravity. Although the D-W equation relies on a dimensionless friction function that has been fitted to terrestrial data, it is not a constant like the Manning coefficient. Rather, the D-W friction factor is a function of bed slope, flow depth, and median grain size [e.g. 8, 10, 12-14], and therefore it is better suited to model flow velocity on Mars. In this work, we investigate the formation timescales of the Martian valley networks through the use of four different sediment transport models [14], the D-W equation for average flow velocity, and a variety of parameters to encompass a range of possible formation conditions. This is done specific to each of eight large valley networks, all of which have crater densities that place their formation in the Late Noachian and Early Hesperian [15, 16], approximately 3.6 to 3.8 billion years ago. The preferred model scenario includes bankfull flows of 4-5 m depths corresponding to precipitation rates of 5 to 36 mm/day, depending on the valley network, and occurring intermittently 5% of the time. Results of the preferred model include formation timescales of 104 years (3°S, 5°E) to 108 years (east branch of Naktong Valles and 6°S, 45°E). References: [1] Hynek et al. (2010) JGR, doi:10.1029/2009JE003548; [2] Di Achille and Hynek (2010) Nature Geoscience, 3, 459-463; [3] Irwin et al. (2005) JGR, 110, E12S15; [4] Fassett and Head (2008) Icarus, 198, 37-56; [5] Craddock and Howard (2002) JGR, 107, 5111; [6] Howard et al. (2005) JGR, 110, E12S14; [7] Barnhart et al. (2009) JGR, 114, E01003; [8] Komar (1979) Icarus, 37, 156-181; [9] Goldspiel and Squyres (1991) Icarus, 89, 392-410; [10] Wilson et al. (2004) JGR, 109, E09003; [11] Leopold et al. (1964) Fluvial Processes in Geomorphology, 522pp; [12] Bathurst (1993) in Channel Network Hydrology, eds. Beven and Kirkby, p69-98; [13] Komar (1980) Icarus, 42, 317-329; [14] Kleinhans (2005) JGR, 110, E12003; [15] Fassett and Head (2008) Icarus, 195, 61-89; [16] Hoke and Hynek (2009) JGR, 114, E08002.

Blood Perfusion in Microfluidic Models of Pulmonary Capillary Networks: Role of Geometry and Hematocrit

NASA Astrophysics Data System (ADS)

Stauber, Hagit; Waisman, Dan; Sznitman, Josue; Technion-IIT Team; Department of Neonatology Carmel Medical Center; Faculty of Medicine-Technion IIT Collaboration

2015-11-01

Microfluidic platforms are increasingly used to study blood microflows at true physiological scale due to their ability to overcome manufacturing obstacle of complex anatomical morphologies, such as the organ-specific architectures of the microcirculation. In the present work, we utilize microfluidic platforms to devise in vitro models of the underlying pulmonary capillary networks (PCN), where capillary lengths and diameters are similar to the size of RBCs (~ 5-10 μm). To better understand flow characteristics and dispersion of red blood cells (RBCs) in PCNs, we have designed microfluidic models of alveolar capillary beds inspired by the seminal ``sheet flow'' model of Fung and Sobin (1969). Our microfluidic PCNs feature confined arrays of staggered pillars with diameters of ~ 5,7 and 10 μm, mimicking the dense structure of pulmonary capillary meshes. The devices are perfused with suspensions of RBCs at varying hematocrit levels under different flow rates. Whole-field velocity patterns using micro-PIV and single-cell tracking using PTV are obtained with fluorescently-labelled RBCs and discussed. Our experiments deliver a real-scale quantitative description of RBC perfusion characteristics across the pulmonary capillary microcirculation.