Implementation of Finite Volume based Navier Stokes Algorithm Within General Purpose Flow Network Code

NASA Technical Reports Server (NTRS)

Schallhorn, Paul; Majumdar, Alok

2012-01-01

This paper describes a finite volume based numerical algorithm that allows multi-dimensional computation of fluid flow within a system level network flow analysis. There are several thermo-fluid engineering problems where higher fidelity solutions are needed that are not within the capacity of system level codes. The proposed algorithm will allow NASA's Generalized Fluid System Simulation Program (GFSSP) to perform multi-dimensional flow calculation within the framework of GFSSP s typical system level flow network consisting of fluid nodes and branches. The paper presents several classical two-dimensional fluid dynamics problems that have been solved by GFSSP's multi-dimensional flow solver. The numerical solutions are compared with the analytical and benchmark solution of Poiseulle, Couette and flow in a driven cavity.

Estimation of Blood Flow Rates in Large Microvascular Networks

PubMed Central

Fry, Brendan C.; Lee, Jack; Smith, Nicolas P.; Secomb, Timothy W.

2012-01-01

Objective Recent methods for imaging microvascular structures provide geometrical data on networks containing thousands of segments. Prediction of functional properties, such as solute transport, requires information on blood flow rates also, but experimental measurement of many individual flows is difficult. Here, a method is presented for estimating flow rates in a microvascular network based on incomplete information on the flows in the boundary segments that feed and drain the network. Methods With incomplete boundary data, the equations governing blood flow form an underdetermined linear system. An algorithm was developed that uses independent information about the distribution of wall shear stresses and pressures in microvessels to resolve this indeterminacy, by minimizing the deviation of pressures and wall shear stresses from target values. Results The algorithm was tested using previously obtained experimental flow data from four microvascular networks in the rat mesentery. With two or three prescribed boundary conditions, predicted flows showed relatively small errors in most segments and fewer than 10% incorrect flow directions on average. Conclusions The proposed method can be used to estimate flow rates in microvascular networks, based on incomplete boundary data and provides a basis for deducing functional properties of microvessel networks. PMID:22506980

Constraints of nonresponding flows based on cross layers in the networks

NASA Astrophysics Data System (ADS)

Zhou, Zhi-Chao; Xiao, Yang; Wang, Dong

2016-02-01

In the active queue management (AQM) scheme, core routers cannot manage and constrain user datagram protocol (UDP) data flows by the sliding window control mechanism in the transport layer due to the nonresponsive nature of such traffic flows. However, the UDP traffics occupy a large part of the network service nowadays which brings a great challenge to the stability of the more and more complex networks. To solve the uncontrollable problem, this paper proposes a cross layers random early detection (CLRED) scheme, which can control the nonresponding UDP-like flows rate effectively when congestion occurs in the access point (AP). The CLRED makes use of the MAC frame acknowledgement (ACK) transmitting congestion information to the sources nodes and utilizes the back-off windows of the MAC layer throttling data rate. Consequently, the UDP-like flows data rate can be restrained timely by the sources nodes in order to alleviate congestion in the complex networks. The proposed CLRED can constrain the nonresponsive flows availably and make the communication expedite, so that the network can sustain stable. The simulation results of network simulator-2 (NS2) verify the proposed CLRED scheme.

PageRank versatility analysis of multilayer modality-based network for exploring the evolution of oil-water slug flow.

PubMed

Gao, Zhong-Ke; Dang, Wei-Dong; Li, Shan; Yang, Yu-Xuan; Wang, Hong-Tao; Sheng, Jing-Ran; Wang, Xiao-Fan

2017-07-14

Numerous irregular flow structures exist in the complicated multiphase flow and result in lots of disparate spatial dynamical flow behaviors. The vertical oil-water slug flow continually attracts plenty of research interests on account of its significant importance. Based on the spatial transient flow information acquired through our designed double-layer distributed-sector conductance sensor, we construct multilayer modality-based network to encode the intricate spatial flow behavior. Particularly, we calculate the PageRank versatility and multilayer weighted clustering coefficient to quantitatively explore the inferred multilayer modality-based networks. Our analysis allows characterizing the complicated evolution of oil-water slug flow, from the opening formation of oil slugs, to the succedent inter-collision and coalescence among oil slugs, and then to the dispersed oil bubbles. These properties render our developed method particularly powerful for mining the essential flow features from the multilayer sensor measurements.

Design and Evaluation of a Proxy-Based Monitoring System for OpenFlow Networks.

PubMed

Taniguchi, Yoshiaki; Tsutsumi, Hiroaki; Iguchi, Nobukazu; Watanabe, Kenzi

2016-01-01

Software-Defined Networking (SDN) has attracted attention along with the popularization of cloud environment and server virtualization. In SDN, the control plane and the data plane are decoupled so that the logical topology and routing control can be configured dynamically depending on network conditions. To obtain network conditions precisely, a network monitoring mechanism is necessary. In this paper, we focus on OpenFlow which is a core technology to realize SDN. We propose, design, implement, and evaluate a network monitoring system for OpenFlow networks. Our proposed system acts as a proxy between an OpenFlow controller and OpenFlow switches. Through experimental evaluations, we confirm that our proposed system can capture packets and monitor traffic information depending on administrator's configuration. In addition, we show that our proposed system does not influence significant performance degradation to overall network performance.

Design and Evaluation of a Proxy-Based Monitoring System for OpenFlow Networks

PubMed Central

Taniguchi, Yoshiaki; Tsutsumi, Hiroaki; Iguchi, Nobukazu; Watanabe, Kenzi

2016-01-01

Software-Defined Networking (SDN) has attracted attention along with the popularization of cloud environment and server virtualization. In SDN, the control plane and the data plane are decoupled so that the logical topology and routing control can be configured dynamically depending on network conditions. To obtain network conditions precisely, a network monitoring mechanism is necessary. In this paper, we focus on OpenFlow which is a core technology to realize SDN. We propose, design, implement, and evaluate a network monitoring system for OpenFlow networks. Our proposed system acts as a proxy between an OpenFlow controller and OpenFlow switches. Through experimental evaluations, we confirm that our proposed system can capture packets and monitor traffic information depending on administrator's configuration. In addition, we show that our proposed system does not influence significant performance degradation to overall network performance. PMID:27006977

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.

Interest communities and flow roles in directed networks: the Twitter network of the UK riots

PubMed Central

Beguerisse-Díaz, Mariano; Garduño-Hernández, Guillermo; Vangelov, Borislav; Yaliraki, Sophia N.; Barahona, Mauricio

2014-01-01

Directionality is a crucial ingredient in many complex networks in which information, energy or influence are transmitted. In such directed networks, analysing flows (and not only the strength of connections) is crucial to reveal important features of the network that might go undetected if the orientation of connections is ignored. We showcase here a flow-based approach for community detection through the study of the network of the most influential Twitter users during the 2011 riots in England. Firstly, we use directed Markov Stability to extract descriptions of the network at different levels of coarseness in terms of interest communities, i.e. groups of nodes within which flows of information are contained and reinforced. Such interest communities reveal user groupings according to location, profession, employer and topic. The study of flows also allows us to generate an interest distance, which affords a personalized view of the attention in the network as viewed from the vantage point of any given user. Secondly, we analyse the profiles of incoming and outgoing long-range flows with a combined approach of role-based similarity and the novel relaxed minimum spanning tree algorithm to reveal that the users in the network can be classified into five roles. These flow roles go beyond the standard leader/follower dichotomy and differ from classifications based on regular/structural equivalence. We then show that the interest communities fall into distinct informational organigrams characterized by a different mix of user roles reflecting the quality of dialogue within them. Our generic framework can be used to provide insight into how flows are generated, distributed, preserved and consumed in directed networks. PMID:25297320

Flow networks for Ocean currents

NASA Astrophysics Data System (ADS)

Tupikina, Liubov; Molkenthin, Nora; Marwan, Norbert; Kurths, Jürgen

2014-05-01

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 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., by its high computational complexity, we here introduce a new, discrete construction of flow-networks, which is then applied to static and dynamic velocity fields. Analyzing the flow-networks of prototypical flows we find that our approach can highlight the 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. We also apply the method to time series data of the Equatorial Pacific Ocean Current and the Gulf Stream ocean current for the changing velocity fields, which could not been done before, and analyse the properties of the dynamical system. Flow-networks can be powerful tools to theoretically understand the step from system's dynamics to network's topology that can be analyzed using network measures and is used for shading light on different climatic phenomena.

Framework based on communicability and flow to analyze complex network dynamics

NASA Astrophysics Data System (ADS)

Gilson, M.; Kouvaris, N. E.; Deco, G.; Zamora-López, G.

2018-05-01

Graph theory constitutes a widely used and established field providing powerful tools for the characterization of complex networks. The intricate topology of networks can also be investigated by means of the collective dynamics observed in the interactions of self-sustained oscillations (synchronization patterns) or propagationlike processes such as random walks. However, networks are often inferred from real-data-forming dynamic systems, which are different from those employed to reveal their topological characteristics. This stresses the necessity for a theoretical framework dedicated to the mutual relationship between the structure and dynamics in complex networks, as the two sides of the same coin. Here we propose a rigorous framework based on the network response over time (i.e., Green function) to study interactions between nodes across time. For this purpose we define the flow that describes the interplay between the network connectivity and external inputs. This multivariate measure relates to the concepts of graph communicability and the map equation. We illustrate our theory using the multivariate Ornstein-Uhlenbeck process, which describes stable and non-conservative dynamics, but the formalism can be adapted to other local dynamics for which the Green function is known. We provide applications to classical network examples, such as small-world ring and hierarchical networks. Our theory defines a comprehensive framework that is canonically related to directed and weighted networks, thus paving a way to revise the standards for network analysis, from the pairwise interactions between nodes to the global properties of networks including community detection.

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.

Weighted complex network analysis of the Beijing subway system: Train and passenger flows

NASA Astrophysics Data System (ADS)

Feng, Jia; Li, Xiamiao; Mao, Baohua; Xu, Qi; Bai, Yun

2017-05-01

In recent years, complex network theory has become an important approach to the study of the structure and dynamics of traffic networks. However, because traffic data is difficult to collect, previous studies have usually focused on the physical topology of subway systems, whereas few studies have considered the characteristics of traffic flows through the network. Therefore, in this paper, we present a multi-layer model to analyze traffic flow patterns in subway networks, based on trip data and an operation timetable obtained from the Beijing Subway System. We characterize the patterns in terms of the spatiotemporal flow size distributions of both the train flow network and the passenger flow network. In addition, we describe the essential interactions between these two networks based on statistical analyses. The results of this study suggest that layered models of transportation systems can elucidate fundamental differences between the coexisting traffic flows and can also clarify the mechanism that causes these differences.

Multi-frequency complex network from time series for uncovering oil-water flow structure.

PubMed

Gao, Zhong-Ke; Yang, Yu-Xuan; Fang, Peng-Cheng; Jin, Ning-De; Xia, Cheng-Yi; Hu, Li-Dan

2015-02-04

Uncovering complex oil-water flow structure represents a challenge in diverse scientific disciplines. This challenge stimulates us to develop a new distributed conductance sensor for measuring local flow signals at different positions and then propose a novel approach based on multi-frequency complex network to uncover the flow structures from experimental multivariate measurements. In particular, based on the Fast Fourier transform, we demonstrate how to derive multi-frequency complex network from multivariate time series. We construct complex networks at different frequencies and then detect community structures. Our results indicate that the community structures faithfully represent the structural features of oil-water flow patterns. Furthermore, we investigate the network statistic at different frequencies for each derived network and find that the frequency clustering coefficient enables to uncover the evolution of flow patterns and yield deep insights into the formation of flow structures. Current results present a first step towards a network visualization of complex flow patterns from a community structure perspective.

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%.

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.

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.

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).

Damage Response in Fluid Flow Networks

NASA Astrophysics Data System (ADS)

Gavrilchenko, Tatyana; Katifori, Eleni

The networks found in biological fluid flow systems such as leaf venation and animal vasculature are characterized by hierarchically nested loops. This structure allows the system to be resilient against fluctuations in the flow of fluid and to be robust against damage. We analytically and computationally investigate how this loopy hierarchy determines the extent of disruption in fluid flow in the vicinity of a damage site. Perturbing the network with the removal of a single edge results in the differential flow as a function of distance from the perturbation decaying as a power law. The power law exponent is generally around -2 in 2D, but we find that it varies due to edge effects, initial edge conductivity, and local topology. We expect that these network flow findings, directly applicable to plant and animal veins, will have analogues in electrical grids, traffic flow and other transport networks.

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.

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.

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.

Treelike networks accelerating capillary flow.

PubMed

Shou, Dahua; Ye, Lin; Fan, Jintu

2014-05-01

Transport in treelike networks has received wide attention in natural systems, oil recovery, microelectronic cooling systems, and textiles. Existing studies are focused on transport behaviors under a constant potential difference (including pressure, temperature, and voltage) in a steady state [B. Yu and B. Li, Phys. Rev. E 73, 066302 (2006); J. Chen, B. Yu, P. Xu, and Y. Li, Phys. Rev. E 75, 056301 (2007)]. However, dynamic (time-dependent) transport in such systems has rarely been concerned. In this work, we theoretically investigate the dynamics of capillary flow in treelike networks and design the distribution of radius and length of local branches for the fastest capillary flow. It is demonstrated that capillary flow in the optimized tree networks is faster than in traditional parallel tube nets under fixed constraints. As well, the flow time of the liquid is found to increase approximately linearly with penetration distance, which differs from Washburn's classic description that flow time increases as the square of penetration distance in a uniform tube.

Electric Current Flow Through Two-Dimensional Networks

NASA Astrophysics Data System (ADS)

Gaspard, Mallory

In modern nanotechnology, two-dimensional atomic network structures boast promising applications as nanoscale circuit boards to serve as the building blocks of more sustainable and efficient, electronic devices. However, properties associated with the network connectivity can be beneficial or deleterious to the current flow. Taking a computational approach, we will study large uniform networks, as well as large random networks using Kirchhoff's Equations in conjunction with graph theoretical measures of network connectedness and flows, to understand how network connectivity affects overall ability for successful current flow throughout a network. By understanding how connectedness affects flow, we may develop new ways to design more efficient two-dimensional materials for the next generation of nanoscale electronic devices, and we will gain a deeper insight into the intricate balance between order and chaos in the universe. Rensselaer Polytechnic Institute, SURP Institutional Grant.

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.

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

Practical application of game theory based production flow planning method in virtual manufacturing networks

NASA Astrophysics Data System (ADS)

Olender, M.; Krenczyk, D.

2016-08-01

Modern enterprises have to react quickly to dynamic changes in the market, due to changing customer requirements and expectations. One of the key area of production management, that must continuously evolve by searching for new methods and tools for increasing the efficiency of manufacturing systems is the area of production flow planning and control. These aspects are closely connected with the ability to implement the concept of Virtual Enterprises (VE) and Virtual Manufacturing Network (VMN) in which integrated infrastructure of flexible resources are created. In the proposed approach, the players role perform the objects associated with the objective functions, allowing to solve the multiobjective production flow planning problems based on the game theory, which is based on the theory of the strategic situation. For defined production system and production order models ways of solving the problem of production route planning in VMN on computational examples for different variants of production flow is presented. Possible decision strategy to use together with an analysis of calculation results is shown.

Design and performance evaluation of an OpenFlow-based control plane for software-defined elastic optical networks with direct-detection optical OFDM (DDO-OFDM) transmission.

PubMed

Liu, Lei; Peng, Wei-Ren; Casellas, Ramon; Tsuritani, Takehiro; Morita, Itsuro; Martínez, Ricardo; Muñoz, Raül; Yoo, S J B

2014-01-13

Optical Orthogonal Frequency Division Multiplexing (O-OFDM), which transmits high speed optical signals using multiple spectrally overlapped lower-speed subcarriers, is a promising candidate for supporting future elastic optical networks. In contrast to previous works which focus on Coherent Optical OFDM (CO-OFDM), in this paper, we consider the direct-detection optical OFDM (DDO-OFDM) as the transport technique, which leads to simpler hardware and software realizations, potentially offering a low-cost solution for elastic optical networks, especially in metro networks, and short or medium distance core networks. Based on this network scenario, we design and deploy a software-defined networking (SDN) control plane enabled by extending OpenFlow, detailing the network architecture, the routing and spectrum assignment algorithm, OpenFlow protocol extensions and the experimental validation. To the best of our knowledge, it is the first time that an OpenFlow-based control plane is reported and its performance is quantitatively measured in an elastic optical network with DDO-OFDM transmission.

Flow Pattern Identification of Horizontal Two-Phase Refrigerant Flow Using Neural Networks

DTIC Science & Technology

2015-12-31

AFRL-RQ-WP-TP-2016-0079 FLOW PATTERN IDENTIFICATION OF HORIZONTAL TWO-PHASE REFRIGERANT FLOW USING NEURAL NETWORKS (POSTPRINT) Abdeel J...Journal Article Postprint 01 October 2013 – 22 June 2015 4. TITLE AND SUBTITLE FLOW PATTERN IDENTIFICATION OF HORIZONTAL TWO-PHASE REFRIGERANT FLOW USING...networks were used to automatically identify two-phase flow patterns for refrigerant R-134a flowing in a horizontal tube. In laboratory experiments

Hodge Decomposition of Information Flow on Small-World Networks.

PubMed

Haruna, Taichi; Fujiki, Yuuya

2016-01-01

We investigate the influence of the small-world topology on the composition of information flow on networks. By appealing to the combinatorial Hodge theory, we decompose information flow generated by random threshold networks on the Watts-Strogatz model into three components: gradient, harmonic and curl flows. The harmonic and curl flows represent globally circular and locally circular components, respectively. The Watts-Strogatz model bridges the two extreme network topologies, a lattice network and a random network, by a single parameter that is the probability of random rewiring. The small-world topology is realized within a certain range between them. By numerical simulation we found that as networks become more random the ratio of harmonic flow to the total magnitude of information flow increases whereas the ratio of curl flow decreases. Furthermore, both quantities are significantly enhanced from the level when only network structure is considered for the network close to a random network and a lattice network, respectively. Finally, the sum of these two ratios takes its maximum value within the small-world region. These findings suggest that the dynamical information counterpart of global integration and that of local segregation are the harmonic flow and the curl flow, respectively, and that a part of the small-world region is dominated by internal circulation of information flow.

Airport Network Flow Simulator

DOT National Transportation Integrated Search

1978-10-01

The Airport Network Flow Simulator is a FORTRAN IV simulation of the flow of air traffic in the nation's 600 commercial airports. It calculates for any group of selected airports: (a) the landing and take-off (Type A) delays; and (b) the gate departu...

Scaling of peak flows with constant flow velocity in random self-similar networks

USGS Publications Warehouse

Troutman, Brent M.; Mantilla, Ricardo; Gupta, Vijay K.

2011-01-01

A methodology is presented to understand the role of the statistical self-similar topology of real river networks on scaling, or power law, in peak flows for rainfall-runoff events. We created Monte Carlo generated sets of ensembles of 1000 random self-similar networks (RSNs) with geometrically distributed interior and exterior generators having parameters pi and pe, respectively. The parameter values were chosen to replicate the observed topology of real river networks. We calculated flow hydrographs in each of these networks by numerically solving the link-based mass and momentum conservation equation under the assumption of constant flow velocity. From these simulated RSNs and hydrographs, the scaling exponents β and φ characterizing power laws with respect to drainage area, and corresponding to the width functions and flow hydrographs respectively, were estimated. We found that, in general, φ > β, which supports a similar finding first reported for simulations in the river network of the Walnut Gulch basin, Arizona. Theoretical estimation of β and φ in RSNs is a complex open problem. Therefore, using results for a simpler problem associated with the expected width function and expected hydrograph for an ensemble of RSNs, we give heuristic arguments for theoretical derivations of the scaling exponents β(E) and φ(E) that depend on the Horton ratios for stream lengths and areas. These ratios in turn have a known dependence on the parameters of the geometric distributions of RSN generators. Good agreement was found between the analytically conjectured values of β(E) and φ(E) and the values estimated by the simulated ensembles of RSNs and hydrographs. The independence of the scaling exponents φ(E) and φ with respect to the value of flow velocity and runoff intensity implies an interesting connection between unit hydrograph theory and flow dynamics. Our results provide a reference framework to study scaling exponents under more complex scenarios

Novel flat datacenter network architecture based on scalable and flow-controlled optical switch system.

PubMed

Miao, Wang; Luo, Jun; Di Lucente, Stefano; Dorren, Harm; Calabretta, Nicola

2014-02-10

We propose and demonstrate an optical flat datacenter network based on scalable optical switch system with optical flow control. Modular structure with distributed control results in port-count independent optical switch reconfiguration time. RF tone in-band labeling technique allowing parallel processing of the label bits ensures the low latency operation regardless of the switch port-count. Hardware flow control is conducted at optical level by re-using the label wavelength without occupying extra bandwidth, space, and network resources which further improves the performance of latency within a simple structure. Dynamic switching including multicasting operation is validated for a 4 x 4 system. Error free operation of 40 Gb/s data packets has been achieved with only 1 dB penalty. The system could handle an input load up to 0.5 providing a packet loss lower that 10(-5) and an average latency less that 500 ns when a buffer size of 16 packets is employed. Investigation on scalability also indicates that the proposed system could potentially scale up to large port count with limited power penalty.

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

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.

Understanding characteristics in multivariate traffic flow time series from complex network structure

NASA Astrophysics Data System (ADS)

Yan, Ying; Zhang, Shen; Tang, Jinjun; Wang, Xiaofei

2017-07-01

Discovering dynamic characteristics in traffic flow is the significant step to design effective traffic managing and controlling strategy for relieving traffic congestion in urban cities. A new method based on complex network theory is proposed to study multivariate traffic flow time series. The data were collected from loop detectors on freeway during a year. In order to construct complex network from original traffic flow, a weighted Froenius norm is adopt to estimate similarity between multivariate time series, and Principal Component Analysis is implemented to determine the weights. We discuss how to select optimal critical threshold for networks at different hour in term of cumulative probability distribution of degree. Furthermore, two statistical properties of networks: normalized network structure entropy and cumulative probability of degree, are utilized to explore hourly variation in traffic flow. The results demonstrate these two statistical quantities express similar pattern to traffic flow parameters with morning and evening peak hours. Accordingly, we detect three traffic states: trough, peak and transitional hours, according to the correlation between two aforementioned properties. The classifying results of states can actually represent hourly fluctuation in traffic flow by analyzing annual average hourly values of traffic volume, occupancy and speed in corresponding hours.

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.

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

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.

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.

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...

Predicting Information Flows in Network Traffic.

ERIC Educational Resources Information Center

Hinich, Melvin J.; Molyneux, Robert E.

2003-01-01

Discusses information flow in networks and predicting network traffic and describes a study that uses time series analysis on a day's worth of Internet log data. Examines nonlinearity and traffic invariants, and suggests that prediction of network traffic may not be possible with current techniques. (Author/LRW)

Carbon Emission Flow in Networks

PubMed Central

Kang, Chongqing; Zhou, Tianrui; Chen, Qixin; Xu, Qianyao; Xia, Qing; Ji, Zhen

2012-01-01

As the human population increases and production expands, energy demand and anthropogenic carbon emission rates have been growing rapidly, and the need to decrease carbon emission levels has drawn increasing attention. The link between energy production and consumption has required the large-scale transport of energy within energy transmission networks. Within this energy flow, there is a virtual circulation of carbon emissions. To understand this circulation and account for the relationship between energy consumption and carbon emissions, this paper introduces the concept of “carbon emission flow in networks” and establishes a method to calculate carbon emission flow in networks. Using an actual analysis of China's energy pattern, the authors discuss the significance of this new concept, not only as a feasible approach but also as an innovative theoretical perspective. PMID:22761988

An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework

PubMed Central

Zhang, Xuejun; Lei, Jiaxing

2015-01-01

Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology. PMID:26180840

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

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

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

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

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

Software defined network architecture based research on load balancing strategy

NASA Astrophysics Data System (ADS)

You, Xiaoqian; Wu, Yang

2018-05-01

As a new type network architecture, software defined network has the key idea of separating the control place of the network from the transmission plane, to manage and control the network in a concentrated way; in addition, the network interface is opened on the control layer and the data layer, so as to achieve programmable control of the network. Considering that only the single shortest route is taken into the calculation of traditional network data flow transmission, and congestion and resource consumption caused by excessive load of link circuits are ignored, a link circuit load based flow media business QoS gurantee system is proposed in this article to divide the flow in the network into ordinary data flow and QoS flow. In this way, it supervises the link circuit load with the controller so as to calculate reasonable route rapidly and issue the flow table to the exchanger, to finish rapid data transmission. In addition, it establishes a simulation platform to acquire optimized result through simulation experiment.

Social networks and trade of services: modelling interregional flows with spatial and network autocorrelation effects

NASA Astrophysics Data System (ADS)

de la Mata, Tamara; Llano, Carlos

2013-07-01

Recent literature on border effect has fostered research on informal barriers to trade and the role played by network dependencies. In relation to social networks, it has been shown that intensity of trade in goods is positively correlated with migration flows between pairs of countries/regions. In this article, we investigate whether such a relation also holds for interregional trade of services. We also consider whether interregional trade flows in services linked with tourism exhibit spatial and/or social network dependence. Conventional empirical gravity models assume the magnitude of bilateral flows between regions is independent of flows to/from regions located nearby in space, or flows to/from regions related through social/cultural/ethic network connections. With this aim, we provide estimates from a set of gravity models showing evidence of statistically significant spatial and network (demographic) dependence in the bilateral flows of the trade of services considered. The analysis has been applied to the Spanish intra- and interregional monetary flows of services from the accommodation, restaurants and travel agencies for the period 2000-2009, using alternative datasets for the migration stocks and definitions of network effects.

Availability improvement of layer 2 seamless networks using OpenFlow.

PubMed

Molina, Elias; Jacob, Eduardo; Matias, Jon; Moreira, Naiara; Astarloa, Armando

2015-01-01

The network robustness and reliability are strongly influenced by the implementation of redundancy and its ability of reacting to changes. In situations where packet loss or maximum latency requirements are critical, replication of resources and information may become the optimal technique. To this end, the IEC 62439-3 Parallel Redundancy Protocol (PRP) provides seamless recovery in layer 2 networks by delegating the redundancy management to the end-nodes. In this paper, we present a combination of the Software-Defined Networking (SDN) approach and PRP topologies to establish a higher level of redundancy and thereby, through several active paths provisioned via the OpenFlow protocol, the global reliability is increased, as well as data flows are managed efficiently. Hence, the experiments with multiple failure scenarios, which have been run over the Mininet network emulator, show the improvement in the availability and responsiveness over other traditional technologies based on a single active path.

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 .

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.

Building the Material Flow Networks of Aluminum in the 2007 U.S. Economy.

PubMed

Chen, Wei-Qiang; Graedel, T E; Nuss, Philip; Ohno, Hajime

2016-04-05

Based on the combination of the U.S. economic input-output table and the stocks and flows framework for characterizing anthropogenic metal cycles, this study presents a methodology for building material flow networks of bulk metals in the U.S. economy and applies it to aluminum. The results, which we term the Input-Output Material Flow Networks (IO-MFNs), achieve a complete picture of aluminum flow in the entire U.S. economy and for any chosen industrial sector (illustrated for the Automobile Manufacturing sector). The results are compared with information from our former study on U.S. aluminum stocks and flows to demonstrate the robustness and value of this new methodology. We find that the IO-MFN approach has the following advantages: (1) it helps to uncover the network of material flows in the manufacturing stage in the life cycle of metals; (2) it provides a method that may be less time-consuming but more complete and accurate in estimating new scrap generation, process loss, domestic final demand, and trade of final products of metals, than existing material flow analysis approaches; and, most importantly, (3) it enables the analysis of the material flows of metals in the U.S. economy from a network perspective, rather than merely that of a life cycle chain.

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.

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

Spike Code Flow in Cultured Neuronal Networks.

PubMed

Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime; Kamimura, Takuya; Yagi, Yasushi; Mizuno-Matsumoto, Yuko; Chen, Yen-Wei

2016-01-01

We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of "1101" and "1011," which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the "maximum cross-correlations" among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network.

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.

Availability Improvement of Layer 2 Seamless Networks Using OpenFlow

PubMed Central

Molina, Elias; Jacob, Eduardo; Matias, Jon; Moreira, Naiara; Astarloa, Armando

2015-01-01

The network robustness and reliability are strongly influenced by the implementation of redundancy and its ability of reacting to changes. In situations where packet loss or maximum latency requirements are critical, replication of resources and information may become the optimal technique. To this end, the IEC 62439-3 Parallel Redundancy Protocol (PRP) provides seamless recovery in layer 2 networks by delegating the redundancy management to the end-nodes. In this paper, we present a combination of the Software-Defined Networking (SDN) approach and PRP topologies to establish a higher level of redundancy and thereby, through several active paths provisioned via the OpenFlow protocol, the global reliability is increased, as well as data flows are managed efficiently. Hence, the experiments with multiple failure scenarios, which have been run over the Mininet network emulator, show the improvement in the availability and responsiveness over other traditional technologies based on a single active path. PMID:25759861

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.

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.

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.

Relationship between microscopic dynamics in traffic flow and complexity in networks.

PubMed

Li, Xin-Gang; Gao, Zi-You; Li, Ke-Ping; Zhao, Xiao-Mei

2007-07-01

Complex networks are constructed in the evolution process of traffic flow, and the states of traffic flow are represented by nodes in the network. The traffic dynamics can then be studied by investigating the statistical properties of those networks. According to Kerner's three-phase theory, there are two different phases in congested traffic, synchronized flow and wide moving jam. In the framework of this theory, we study different properties of synchronized flow and moving jam in relation to complex network. Scale-free network is constructed in stop-and-go traffic, i.e., a sequence of moving jams [Chin. Phys. Lett. 10, 2711 (2005)]. In this work, the networks generated in synchronized flow are investigated in detail. Simulation results show that the degree distribution of the networks constructed in synchronized flow has two power law regions, so the distinction in topological structure can really reflect the different dynamics in traffic flow. Furthermore, the real traffic data are investigated by this method, and the results are consistent with the simulations.

A network analysis of indirect carbon emission flows among different industries in China.

PubMed

Du, Qiang; Xu, Yadan; Wu, Min; Sun, Qiang; Bai, Libiao; Yu, Ming

2018-06-17

Indirect carbon emissions account for a large ratio of the total carbon emissions in processes to make the final products, and this implies indirect carbon emission flow across industries. Understanding these flows is crucial for allocating a carbon allowance for each industry. By combining input-output analysis and complex network theory, this study establishes an indirect carbon emission flow network (ICEFN) for 41 industries from 2005 to 2014 to investigate the interrelationships among different industries. The results show that the ICEFN was consistent with a small-world nature based on an analysis of the average path lengths and the clustering coefficients. Moreover, key industries in the ICEFN were identified using complex network theory on the basis of degree centrality and betweenness centrality. Furthermore, the 41 industries of the ICEFN were divided into four industrial subgroups that are related closely to one another. Finally, possible policy implications were provided based on the knowledge of the structure of the ICEFN and its trend.

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.

A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

NASA Astrophysics Data System (ADS)

Huang, Darong; Bai, Xing-Rong

Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

Robustness analysis of complex networks with power decentralization strategy via flow-sensitive centrality against cascading failures

NASA Astrophysics Data System (ADS)

Guo, Wenzhang; Wang, Hao; Wu, Zhengping

2018-03-01

Most existing cascading failure mitigation strategy of power grids based on complex network ignores the impact of electrical characteristics on dynamic performance. In this paper, the robustness of the power grid under a power decentralization strategy is analysed through cascading failure simulation based on AC flow theory. The flow-sensitive (FS) centrality is introduced by integrating topological features and electrical properties to help determine the siting of the generation nodes. The simulation results of the IEEE-bus systems show that the flow-sensitive centrality method is a more stable and accurate approach and can enhance the robustness of the network remarkably. Through the study of the optimal flow-sensitive centrality selection for different networks, we find that the robustness of the network with obvious small-world effect depends more on contribution of the generation nodes detected by community structure, otherwise, contribution of the generation nodes with important influence on power flow is more critical. In addition, community structure plays a significant role in balancing the power flow distribution and further slowing the propagation of failures. These results are useful in power grid planning and cascading failure prevention.

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

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

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.

Information flow in the auditory cortical network

PubMed Central

Hackett, Troy A.

2011-01-01

Auditory processing in the cerebral cortex is comprised of an interconnected network of auditory and auditory-related areas distributed throughout the forebrain. The nexus of auditory activity is located in temporal cortex among several specialized areas, or fields, that receive dense inputs from the medial geniculate complex. These areas are collectively referred to as auditory cortex. Auditory activity is extended beyond auditory cortex via connections with auditory-related areas elsewhere in the cortex. Within this network, information flows between areas to and from countless targets, but in a manner that is characterized by orderly regional, areal and laminar patterns. These patterns reflect some of the structural constraints that passively govern the flow of information at all levels of the network. In addition, the exchange of information within these circuits is dynamically regulated by intrinsic neurochemical properties of projecting neurons and their targets. This article begins with an overview of the principal circuits and how each is related to information flow along major axes of the network. The discussion then turns to a description of neurochemical gradients along these axes, highlighting recent work on glutamate transporters in the thalamocortical projections to auditory cortex. The article concludes with a brief discussion of relevant neurophysiological findings as they relate to structural gradients in the network. PMID:20116421

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

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.

Toward an optimal design principle in symmetric and asymmetric tree flow networks.

PubMed

Miguel, Antonio F

2016-01-21

Fluid flow in tree-shaped networks plays an important role in both natural and engineered systems. This paper focuses on laminar flows of Newtonian and non-Newtonian power law fluids in symmetric and asymmetric bifurcating trees. Based on the constructal law, we predict the tree-shaped architecture that provides greater access to the flow subjected to the total network volume constraint. The relationships between the sizes of parent and daughter tubes are presented both for symmetric and asymmetric branching tubes. We also approach the wall-shear stresses and the flow resistance in terms of first tube size, degree of asymmetry between daughter branches, and rheological behavior of the fluid. The influence of tubes obstructing the fluid flow is also accounted for. The predictions obtained by our theory-driven approach find clear support in the findings of previous experimental studies. Copyright © 2015 Elsevier Ltd. All rights reserved.

Self-control of traffic lights and vehicle flows in urban road networks

NASA Astrophysics Data System (ADS)

Lämmer, Stefan; Helbing, Dirk

2008-04-01

Based on fluid-dynamic and many-particle (car-following) simulations of traffic flows in (urban) networks, we study the problem of coordinating incompatible traffic flows at intersections. Inspired by the observation of self-organized oscillations of pedestrian flows at bottlenecks, we propose a self-organization approach to traffic light control. The problem can be treated as a multi-agent problem with interactions between vehicles and traffic lights. Specifically, our approach assumes a priority-based control of traffic lights by the vehicle flows themselves, taking into account short-sighted anticipation of vehicle flows and platoons. The considered local interactions lead to emergent coordination patterns such as 'green waves' and achieve an efficient, decentralized traffic light control. While the proposed self-control adapts flexibly to local flow conditions and often leads to non-cyclical switching patterns with changing service sequences of different traffic flows, an almost periodic service may evolve under certain conditions and suggests the existence of a spontaneous synchronization of traffic lights despite the varying delays due to variable vehicle queues and travel times. The self-organized traffic light control is based on an optimization and a stabilization rule, each of which performs poorly at high utilizations of the road network, while their proper combination reaches a superior performance. The result is a considerable reduction not only in the average travel times, but also of their variation. Similar control approaches could be applied to the coordination of logistic and production processes.

Overland flow erosion inferred from Martian channel network geometry

NASA Astrophysics Data System (ADS)

Seybold, Hansjörg; Kirchner, James

2016-04-01

The controversy about the origin of Mars' channel networks is almost as old as their discovery 150 years ago. Over the last few decades, new Mars probes have revealed more detailed structures in Martian The controversy about the origin of Mars' channel networks is almost as old as their discovery 150 years ago. Over the last few decades, new Mars probes have revealed more detailed structures in Martian drainage networks, and new studies suggest that Mars once had large volumes of surface water. But how this water flowed, and how it could have carved the channels, remains unclear. Simple scaling arguments show that networks formed by similar mechanisms should have similar branching angles on Earth and Mars, suggesting that Earth analogues can be informative here. A recent analysis of high-resolution data for the continental United States shows that climate leaves a characteristic imprint in the branching geometry of stream networks. Networks growing in humid regions have an average branching angle of α = 2π/5 = 72° [1], which is characteristic of network growth by groundwater sapping [2]. Networks in arid regions, where overland flow erosion is more dominant, show much smaller branching angles. Here we show that the channel networks on Mars have branching angles that resemble those created by surficial flows on Earth. This result implies that the growth of Martian channel networks was dominated by near-surface flow, and suggests that deeper infiltration was inhibited, potentially by permafrost or by impermeable weathered soils. [1] Climate's Watermark in the Geometry of River Networks, Seybold et al.; under review [2] Ramification of stream networks, Devauchelle et al.; PNAS (2012)

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

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

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.

Numerical Modeling of Interstitial Fluid Flow Coupled with Blood Flow through a Remodeled Solid Tumor Microvascular Network

PubMed Central

Soltani, M.; Chen, P.

2013-01-01

Modeling of interstitial fluid flow involves processes such as fluid diffusion, convective transport in extracellular matrix, and extravasation from blood vessels. To date, majority of microvascular flow modeling has been done at different levels and scales mostly on simple tumor shapes with their capillaries. However, with our proposed numerical model, more complex and realistic tumor shapes and capillary networks can be studied. Both blood flow through a capillary network, which is induced by a solid tumor, and fluid flow in tumor’s surrounding tissue are formulated. First, governing equations of angiogenesis are implemented to specify the different domains for the network and interstitium. Then, governing equations for flow modeling are introduced for different domains. The conservation laws for mass and momentum (including continuity equation, Darcy’s law for tissue, and simplified Navier–Stokes equation for blood flow through capillaries) are used for simulating interstitial and intravascular flows and Starling’s law is used for closing this system of equations and coupling the intravascular and extravascular flows. This is the first study of flow modeling in solid tumors to naturalistically couple intravascular and extravascular flow through a network. This network is generated by sprouting angiogenesis and consisting of one parent vessel connected to the network while taking into account the non-continuous behavior of blood, adaptability of capillary diameter to hemodynamics and metabolic stimuli, non-Newtonian blood flow, and phase separation of blood flow in capillary bifurcation. The incorporation of the outlined components beyond the previous models provides a more realistic prediction of interstitial fluid flow pattern in solid tumors and surrounding tissues. Results predict higher interstitial pressure, almost two times, for realistic model compared to the simplified model. PMID:23840579

Information and material flows in complex networks

NASA Astrophysics Data System (ADS)

Helbing, Dirk; Armbruster, Dieter; Mikhailov, Alexander S.; Lefeber, Erjen

2006-04-01

In this special issue, an overview of the Thematic Institute (TI) on Information and Material Flows in Complex Systems is given. The TI was carried out within EXYSTENCE, the first EU Network of Excellence in the area of complex systems. Its motivation, research approach and subjects are presented here. Among the various methods used are many-particle and statistical physics, nonlinear dynamics, as well as complex systems, network and control theory. The contributions are relevant for complex systems as diverse as vehicle and data traffic in networks, logistics, production, and material flows in biological systems. The key disciplines involved are socio-, econo-, traffic- and bio-physics, and a new research area that could be called “biologistics”.

Experimental demonstration of an OpenFlow based software-defined optical network employing packet, fixed and flexible DWDM grid technologies on an international multi-domain testbed.

PubMed

Channegowda, M; Nejabati, R; Rashidi Fard, M; Peng, S; Amaya, N; Zervas, G; Simeonidou, D; Vilalta, R; Casellas, R; Martínez, R; Muñoz, R; Liu, L; Tsuritani, T; Morita, I; Autenrieth, A; Elbers, J P; Kostecki, P; Kaczmarek, P

2013-03-11

Software defined networking (SDN) and flexible grid optical transport technology are two key technologies that allow network operators to customize their infrastructure based on application requirements and therefore minimizing the extra capital and operational costs required for hosting new applications. In this paper, for the first time we report on design, implementation & demonstration of a novel OpenFlow based SDN unified control plane allowing seamless operation across heterogeneous state-of-the-art optical and packet transport domains. We verify and experimentally evaluate OpenFlow protocol extensions for flexible DWDM grid transport technology along with its integration with fixed DWDM grid and layer-2 packet switching.

Forecasting the Short-Term Passenger Flow on High-Speed Railway with Neural Networks

PubMed Central

Xie, Mei-Quan; Li, Xia-Miao; Zhou, Wen-Liang; Fu, Yan-Bing

2014-01-01

Short-term passenger flow forecasting is an important component of transportation systems. The forecasting result can be applied to support transportation system operation and management such as operation planning and revenue management. In this paper, a divide-and-conquer method based on neural network and origin-destination (OD) matrix estimation is developed to forecast the short-term passenger flow in high-speed railway system. There are three steps in the forecasting method. Firstly, the numbers of passengers who arrive at each station or depart from each station are obtained from historical passenger flow data, which are OD matrices in this paper. Secondly, short-term passenger flow forecasting of the numbers of passengers who arrive at each station or depart from each station based on neural network is realized. At last, the OD matrices in short-term time are obtained with an OD matrix estimation method. The experimental results indicate that the proposed divide-and-conquer method performs well in forecasting the short-term passenger flow on high-speed railway. PMID:25544838

Forecasting the short-term passenger flow on high-speed railway with neural networks.

PubMed

Xie, Mei-Quan; Li, Xia-Miao; Zhou, Wen-Liang; Fu, Yan-Bing

2014-01-01

Short-term passenger flow forecasting is an important component of transportation systems. The forecasting result can be applied to support transportation system operation and management such as operation planning and revenue management. In this paper, a divide-and-conquer method based on neural network and origin-destination (OD) matrix estimation is developed to forecast the short-term passenger flow in high-speed railway system. There are three steps in the forecasting method. Firstly, the numbers of passengers who arrive at each station or depart from each station are obtained from historical passenger flow data, which are OD matrices in this paper. Secondly, short-term passenger flow forecasting of the numbers of passengers who arrive at each station or depart from each station based on neural network is realized. At last, the OD matrices in short-term time are obtained with an OD matrix estimation method. The experimental results indicate that the proposed divide-and-conquer method performs well in forecasting the short-term passenger flow on high-speed railway.

Dynamics of comb-of-comb-network polymers in random layered flows

NASA Astrophysics Data System (ADS)

Katyal, Divya; Kant, Rama

2016-12-01

We analyze the dynamics of comb-of-comb-network polymers in the presence of external random flows. The dynamics of such structures is evaluated through relevant physical quantities, viz., average square displacement (ASD) and the velocity autocorrelation function (VACF). We focus on comparing the dynamics of the comb-of-comb network with the linear polymer. The present work displays an anomalous diffusive behavior of this flexible network in the random layered flows. The effect of the polymer topology on the dynamics is analyzed by varying the number of generations and branch lengths in these networks. In addition, we investigate the influence of external flow on the dynamics by varying flow parameters, like the flow exponent α and flow strength Wα. Our analysis highlights two anomalous power-law regimes, viz., subdiffusive (intermediate-time polymer stretching and flow-induced diffusion) and superdiffusive (long-time flow-induced diffusion). The anomalous long-time dynamics is governed by the temporal exponent ν of ASD, viz., ν =2 -α /2 . Compared to a linear polymer, the comb-of-comb network shows a shorter crossover time (from the subdiffusive to superdiffusive regime) but a reduced magnitude of ASD. Our theory displays an anomalous VACF in the random layered flows that scales as t-α /2. We show that the network with greater total mass moves faster.

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

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.

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.

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.

Generalized network modeling of capillary-dominated two-phase flow

NASA Astrophysics Data System (ADS)

Raeini, Ali Q.; Bijeljic, Branko; Blunt, Martin J.

2018-02-01

We present a generalized network model for simulating capillary-dominated two-phase flow through porous media at the pore scale. Three-dimensional images of the pore space are discretized using a generalized network—described in a companion paper [A. Q. Raeini, B. Bijeljic, and M. J. Blunt, Phys. Rev. E 96, 013312 (2017), 10.1103/PhysRevE.96.013312]—which comprises pores that are divided into smaller elements called half-throats and subsequently into corners. Half-throats define the connectivity of the network at the coarsest level, connecting each pore to half-throats of its neighboring pores from their narrower ends, while corners define the connectivity of pore crevices. The corners are discretized at different levels for accurate calculation of entry pressures, fluid volumes, and flow conductivities that are obtained using direct simulation of flow on the underlying image. This paper discusses the two-phase flow model that is used to compute the averaged flow properties of the generalized network, including relative permeability and capillary pressure. We validate the model using direct finite-volume two-phase flow simulations on synthetic geometries, and then present a comparison of the model predictions with a conventional pore-network model and experimental measurements of relative permeability in the literature.

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

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

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.

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

Compression of Flow Can Reveal Overlapping-Module Organization in Networks

NASA Astrophysics Data System (ADS)

Viamontes Esquivel, Alcides; Rosvall, Martin

2011-10-01

To better understand the organization of overlapping modules in large networks with respect to flow, we introduce the map equation for overlapping modules. In this information-theoretic framework, we use the correspondence between compression and regularity detection. The generalized map equation measures how well we can compress a description of flow in the network when we partition it into modules with possible overlaps. When we minimize the generalized map equation over overlapping network partitions, we detect modules that capture flow and determine which nodes at the boundaries between modules should be classified in multiple modules and to what degree. With a novel greedy-search algorithm, we find that some networks, for example, the neural network of the nematode Caenorhabditis elegans, are best described by modules dominated by hard boundaries, but that others, for example, the sparse European-roads network, have an organization of highly overlapping modules.

On understanding nuclear reaction network flows with branchings on directed graphs

NASA Astrophysics Data System (ADS)

Meyer, Bradley S.

2018-04-01

Nuclear reaction network flow diagrams are useful for understanding which reactions are governing the abundance changes at a particular time during nucleosynthesis. This is especially true when the flows are largely unidirectional, such as during the s-process of nucleosynthesis. In explosive nucleosynthesis, when reaction flows are large, and when forward reactions are nearly balanced by their reverses, reaction flows no longer give a clear picture of the abundance evolution in the network. This paper presents a way of understanding network evolution in terms of sums of branchings on a directed graph, which extends the concept of reaction flows to allow for multiple reaction pathways.

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.

Flow-rate control for managing communications in tracking and surveillance networks

NASA Astrophysics Data System (ADS)

Miller, Scott A.; Chong, Edwin K. P.

2007-09-01

This paper describes a primal-dual distributed algorithm for managing communications in a bandwidth-limited sensor network for tracking and surveillance. The algorithm possesses some scale-invariance properties and adaptive gains that make it more practical for applications such as tracking where the conditions change over time. A simulation study comparing this algorithm with a priority-queue-based approach in a network tracking scenario shows significant improvement in the resulting track quality when using flow control to manage communications.

Recurrence networks from multivariate signals for uncovering dynamic transitions of horizontal oil-water stratified flows

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Zhang, Xin-Wang; Jin, Ning-De; Donner, Reik V.; Marwan, Norbert; Kurths, Jürgen

2013-09-01

Characterizing the mechanism of drop formation at the interface of horizontal oil-water stratified flows is a fundamental problem eliciting a great deal of attention from different disciplines. We experimentally and theoretically investigate the formation and transition of horizontal oil-water stratified flows. We design a new multi-sector conductance sensor and measure multivariate signals from two different stratified flow patterns. Using the Adaptive Optimal Kernel Time-Frequency Representation (AOK TFR) we first characterize the flow behavior from an energy and frequency point of view. Then, we infer multivariate recurrence networks from the experimental data and investigate the cross-transitivity for each constructed network. We find that the cross-transitivity allows quantitatively uncovering the flow behavior when the stratified flow evolves from a stable state to an unstable one and recovers deeper insights into the mechanism governing the formation of droplets at the interface of stratified flows, a task that existing methods based on AOK TFR fail to work. These findings present a first step towards an improved understanding of the dynamic mechanism leading to the transition of horizontal oil-water stratified flows from a complex-network perspective.

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.

An Amorphous Network Model for Capillary Flow and Dispersion in a Partially Saturated Porous Medium

NASA Astrophysics Data System (ADS)

Simmons, C. S.; Rockhold, M. L.

2013-12-01

Network models of capillary flow are commonly used to represent conduction of fluids at pore scales. Typically, a flow system is described by a regular geometric lattice of interconnected tubes. Tubes constitute the pore throats, while connection junctions (nodes) are pore bodies. Such conceptualization of the geometry, however, is questionable for the pore scale, where irregularity clearly prevails, although prior published models using a regular lattice have demonstrated successful descriptions of the flow in the bulk medium. Here a network is allowed to be amorphous, and is not subject to any particular lattice structure. Few network flow models have treated partially saturated or even multiphase conditions. The research trend is toward using capillary tubes with triangular or square cross sections that have corners and always retain some fluid by capillarity when drained. In contrast, this model uses only circular capillaries, whose filled state is controlled by a capillary pressure rule for the junctions. The rule determines which capillary participate in the flow under an imposed matric potential gradient during steady flow conditions. Poiseuille's Law and Laplace equation are used to describe flow and water retention in the capillary units of the model. A modified conjugate gradient solution for steady flow that tracks which capillary in an amorphous network contribute to fluid conduction was devised for partially saturated conditions. The model thus retains the features of classical capillary models for determining hydraulic flow properties under unsaturated conditions based on distribution of non-interacting tubes, but now accounts for flow exchange at junctions. Continuity of the flow balance at every junction is solved simultaneously. The effective water retention relationship and unsaturated permeability are evaluated for an extensive enough network to represent a small bulk sample of porous medium. The model is applied for both a hypothetically

Peak-flow frequency relations and evaluation of the peak-flow gaging network in Nebraska

USGS Publications Warehouse

Soenksen, Philip J.; Miller, Lisa D.; Sharpe, Jennifer B.; Watton, Jason R.

1999-01-01

Estimates of peak-flow magnitude and frequency are required for the efficient design of structures that convey flood flows or occupy floodways, such as bridges, culverts, and roads. The U.S. Geological Survey, in cooperation with the Nebraska Department of Roads, conducted a study to update peak-flow frequency analyses for selected streamflow-gaging stations, develop a new set of peak-flow frequency relations for ungaged streams, and evaluate the peak-flow gaging-station network for Nebraska. Data from stations located in or within about 50 miles of Nebraska were analyzed using guidelines of the Interagency Advisory Committee on Water Data in Bulletin 17B. New generalized skew relations were developed for use in frequency analyses of unregulated streams. Thirty-three drainage-basin characteristics related to morphology, soils, and precipitation were quantified using a geographic information system, related computer programs, and digital spatial data.For unregulated streams, eight sets of regional regression equations relating drainage-basin to peak-flow characteristics were developed for seven regions of the state using a generalized least squares procedure. Two sets of regional peak-flow frequency equations were developed for basins with average soil permeability greater than 4 inches per hour, and six sets of equations were developed for specific geographic areas, usually based on drainage-basin boundaries. Standard errors of estimate for the 100-year frequency equations (1percent probability) ranged from 12.1 to 63.8 percent. For regulated reaches of nine streams, graphs of peak flow for standard frequencies and distance upstream of the mouth were estimated.The regional networks of streamflow-gaging stations on unregulated streams were analyzed to evaluate how additional data might affect the average sampling errors of the newly developed peak-flow equations for the 100-year frequency occurrence. Results indicated that data from new stations, rather than more

Load Forecasting Based Distribution System Network Reconfiguration -- A Distributed Data-Driven Approach

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

Jiang, Huaiguang; Zhang, Yingchen; Muljadi, Eduard

In this paper, a short-term load forecasting approach based network reconfiguration is proposed in a parallel manner. Specifically, a support vector regression (SVR) based short-term load forecasting approach is designed to provide an accurate load prediction and benefit the network reconfiguration. Because of the nonconvexity of the three-phase balanced optimal power flow, a second-order cone program (SOCP) based approach is used to relax the optimal power flow problem. Then, the alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the limited number of the switches and the increasing computation capability, themore » proposed network reconfiguration is solved in a parallel way. The numerical results demonstrate the feasible and effectiveness of the proposed approach.« less

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

NASA Astrophysics Data System (ADS)

Coffey, T.

2016-12-01

Distributary channel bifurcations on river deltas are important features in both modern systems, where the channels control water, sediment, and nutrient routing, and in ancient deltas, where the channel networks can dictate large-scale stratigraphic heterogeneity. Geometric features of distributary channels, such as channel dimensions and network structure, have long been thought to be defined by factors such as flow velocity, grain size, or channel aspect ratio where the channel enters the basin. We use theory originally developed for tributary networks fed by groundwater seepage 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 patterns around the channel network. These networks possess a characteristic bifurcation angle of 72°, due to Laplacian flow (gradient2h2=0, where h is water surface elevation) in the groundwater flow field near tributary channel tips. 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. This similarity implies that flow outside of the distributary channel network is also Laplacian, which we use scaling arguments to justify. We conclude that the dynamics of the unchannelized flow control bifurcation formation in distributary networks.

Eavesdropping-aware routing and spectrum allocation based on multi-flow virtual concatenation for confidential information service in elastic optical networks

NASA Astrophysics Data System (ADS)

Bai, Wei; Yang, Hui; Yu, Ao; Xiao, Hongyun; He, Linkuan; Feng, Lei; Zhang, Jie

2018-01-01

The leakage of confidential information is one of important issues in the network security area. Elastic Optical Networks (EON) as a promising technology in the optical transport network is under threat from eavesdropping attacks. It is a great demand to support confidential information service (CIS) and design efficient security strategy against the eavesdropping attacks. In this paper, we propose a solution to cope with the eavesdropping attacks in routing and spectrum allocation. Firstly, we introduce probability theory to describe eavesdropping issue and achieve awareness of eavesdropping attacks. Then we propose an eavesdropping-aware routing and spectrum allocation (ES-RSA) algorithm to guarantee information security. For further improving security and network performance, we employ multi-flow virtual concatenation (MFVC) and propose an eavesdropping-aware MFVC-based secure routing and spectrum allocation (MES-RSA) algorithm. The presented simulation results show that the proposed two RSA algorithms can both achieve greater security against the eavesdropping attacks and MES-RSA can also improve the network performance efficiently.

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.

Altered Cerebral Blood Flow Covariance Network in Schizophrenia.

PubMed

Liu, Feng; Zhuo, Chuanjun; Yu, Chunshui

2016-01-01

Many studies have shown abnormal cerebral blood flow (CBF) in schizophrenia; however, it remains unclear how topological properties of CBF network are altered in this disorder. Here, arterial spin labeling (ASL) MRI was employed to measure resting-state CBF in 96 schizophrenia patients and 91 healthy controls. CBF covariance network of each group was constructed by calculating across-subject CBF covariance between 90 brain regions. Graph theory was used to compare intergroup differences in global and nodal topological measures of the network. Both schizophrenia patients and healthy controls had small-world topology in CBF covariance networks, implying an optimal balance between functional segregation and integration. Compared with healthy controls, schizophrenia patients showed reduced small-worldness, normalized clustering coefficient and local efficiency of the network, suggesting a shift toward randomized network topology in schizophrenia. Furthermore, schizophrenia patients exhibited altered nodal centrality in the perceptual-, affective-, language-, and spatial-related regions, indicating functional disturbance of these systems in schizophrenia. This study demonstrated for the first time that schizophrenia patients have disrupted topological properties in CBF covariance network, which provides a new perspective (efficiency of blood flow distribution between brain regions) for understanding neural mechanisms of schizophrenia.

Intra-Urban Movement Flow Estimation Using Location Based Social Networking Data

NASA Astrophysics Data System (ADS)

Kheiri, A.; Karimipour, F.; Forghani, M.

2015-12-01

In recent years, there has been a rapid growth of location-based social networking services, such as Foursquare and Facebook, which have attracted an increasing number of users and greatly enriched their urban experience. Location-based social network data, as a new travel demand data source, seems to be an alternative or complement to survey data in the study of mobility behavior and activity analysis because of its relatively high access and low cost. In this paper, three OD estimation models have been utilized in order to investigate their relative performance when using Location-Based Social Networking (LBSN) data. For this, the Foursquare LBSN data was used to analyze the intra-urban movement behavioral patterns for the study area, Manhattan, the most densely populated of the five boroughs of New York city. The outputs of models are evaluated using real observations based on different criterions including distance distribution, destination travel constraints. The results demonstrate the promising potential of using LBSN data for urban travel demand analysis and monitoring.

Hour-Glass Neural Network Based Daily Money Flow Estimation for Automatic Teller Machines

NASA Astrophysics Data System (ADS)

Karungaru, Stephen; Akashi, Takuya; Nakano, Miyoko; Fukumi, Minoru

Monetary transactions using Automated Teller Machines (ATMs) have become a normal part of our daily lives. At ATMs, one can withdraw, send or debit money and even update passbooks among many other possible functions. ATMs are turning the banking sector into a ubiquitous service. However, while the advantages for the ATM users (financial institution customers) are many, the financial institution side faces an uphill task in management and maintaining the cash flow in the ATMs. On one hand, too much money in a rarely used ATM is wasteful, while on the other, insufficient amounts would adversely affect the customers and may result in a lost business opportunity for the financial institution. Therefore, in this paper, we propose a daily cash flow estimation system using neural networks that enables better daily forecasting of the money required at the ATMs. The neural network used in this work is a five layered hour glass shaped structure that achieves fast learning, even for the time series data for which seasonality and trend feature extraction is difficult. Feature extraction is carried out using the Akamatsu Integral and Differential transforms. This work achieves an average estimation accuracy of 92.6%.

Evaluation of multilayer perceptron algorithms for an analysis of network flow data

NASA Astrophysics Data System (ADS)

Bieniasz, Jedrzej; Rawski, Mariusz; Skowron, Krzysztof; Trzepiński, Mateusz

2016-09-01

The volume of exchanged information through IP networks is larger than ever and still growing. It creates a space for both benign and malicious activities. The second one raises awareness on security network devices, as well as network infrastructure and a system as a whole. One of the basic tools to prevent cyber attacks is Network Instrusion Detection System (NIDS). NIDS could be realized as a signature-based detector or an anomaly-based one. In the last few years the emphasis has been placed on the latter type, because of the possibility of applying smart and intelligent solutions. An ideal NIDS of next generation should be composed of self-learning algorithms that could react on known and unknown malicious network activities respectively. In this paper we evaluated a machine learning approach for detection of anomalies in IP network data represented as NetFlow records. We considered Multilayer Perceptron (MLP) as the classifier and we used two types of learning algorithms - Backpropagation (BP) and Particle Swarm Optimization (PSO). This paper includes a comprehensive survey on determining the most optimal MLP learning algorithm for the classification problem in application to network flow data. The performance, training time and convergence of BP and PSO methods were compared. The results show that PSO algorithm implemented by the authors outperformed other solutions if accuracy of classifications is considered. The major disadvantage of PSO is training time, which could be not acceptable for larger data sets or in real network applications. At the end we compared some key findings with the results from the other papers to show that in all cases results from this study outperformed them.

Field-effect Flow Control in Polymer Microchannel Networks

NASA Technical Reports Server (NTRS)

Sniadecki, Nathan; Lee, Cheng S.; Beamesderfer, Mike; DeVoe, Don L.

2003-01-01

A new Bio-MEMS electroosmotic flow (EOF) modulator for plastic microchannel networks has been developed. The EOF modulator uses field-effect flow control (FEFC) to adjust the zeta potential at the Parylene C microchannel wall. By setting a differential EOF pumping rate in two of the three microchannels at a T-intersection with EOF modulators, the induced pressure at the intersection generated pumping in the third, field-free microchannel. The EOF modulators are able to change the magnitude and direction of the pressure pumping by inducing either a negative or positive pressure at the intersection. The flow velocity is tracked by neutralized fluorescent microbeads in the microchannels. The proof-of-concept of the EOF modulator described here may be applied to complex plastic ,microchannel networks where individual microchannel flow rates are addressable by localized induced-pressure pumping.

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.

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.

Solution of weakly compressible isothermal flow in landfill gas collection networks

NASA Astrophysics Data System (ADS)

Nec, Y.; Huculak, G.

2017-12-01

Pipe networks collecting gas in sanitary landfills operate under the regime of a weakly compressible isothermal flow of ideal gas. The effect of compressibility has been traditionally neglected in this application in favour of simplicity, thereby creating a conceptual incongruity between the flow equations and thermodynamic equation of state. Here the flow is solved by generalisation of the classic Darcy-Weisbach equation for an incompressible steady flow in a pipe to an ordinary differential equation, permitting continuous variation of density, viscosity and related fluid parameters, as well as head loss or gain due to gravity, in isothermal flow. The differential equation is solved analytically in the case of ideal gas for a single edge in the network. Thereafter the solution is used in an algorithm developed to construct the flow equations automatically for a network characterised by an incidence matrix, and determine pressure distribution, flow rates and all associated parameters therein.

OpenFlow arbitrated programmable network channels for managing quantum metadata

DOE PAGES

Dasari, Venkat R.; Humble, Travis S.

2016-10-10

Quantum networks must classically exchange complex metadata between devices in order to carry out information for protocols such as teleportation, super-dense coding, and quantum key distribution. Demonstrating the integration of these new communication methods with existing network protocols, channels, and data forwarding mechanisms remains an open challenge. Software-defined networking (SDN) offers robust and flexible strategies for managing diverse network devices and uses. We adapt the principles of SDN to the deployment of quantum networks, which are composed from unique devices that operate according to the laws of quantum mechanics. We show how quantum metadata can be managed within a software-definedmore » network using the OpenFlow protocol, and we describe how OpenFlow management of classical optical channels is compatible with emerging quantum communication protocols. We next give an example specification of the metadata needed to manage and control quantum physical layer (QPHY) behavior and we extend the OpenFlow interface to accommodate this quantum metadata. Here, we conclude by discussing near-term experimental efforts that can realize SDN’s principles for quantum communication.« less

OpenFlow arbitrated programmable network channels for managing quantum metadata

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

Dasari, Venkat R.; Humble, Travis S.

Quantum networks must classically exchange complex metadata between devices in order to carry out information for protocols such as teleportation, super-dense coding, and quantum key distribution. Demonstrating the integration of these new communication methods with existing network protocols, channels, and data forwarding mechanisms remains an open challenge. Software-defined networking (SDN) offers robust and flexible strategies for managing diverse network devices and uses. We adapt the principles of SDN to the deployment of quantum networks, which are composed from unique devices that operate according to the laws of quantum mechanics. We show how quantum metadata can be managed within a software-definedmore » network using the OpenFlow protocol, and we describe how OpenFlow management of classical optical channels is compatible with emerging quantum communication protocols. We next give an example specification of the metadata needed to manage and control quantum physical layer (QPHY) behavior and we extend the OpenFlow interface to accommodate this quantum metadata. Here, we conclude by discussing near-term experimental efforts that can realize SDN’s principles for quantum communication.« less

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.

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

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

Bus-based park-and-ride system: a stochastic model on multimodal network with congestion pricing schemes

NASA Astrophysics Data System (ADS)

Liu, Zhiyuan; Meng, Qiang

2014-05-01

This paper focuses on modelling the network flow equilibrium problem on a multimodal transport network with bus-based park-and-ride (P&R) system and congestion pricing charges. The multimodal network has three travel modes: auto mode, transit mode and P&R mode. A continuously distributed value-of-time is assumed to convert toll charges and transit fares to time unit, and the users' route choice behaviour is assumed to follow the probit-based stochastic user equilibrium principle with elastic demand. These two assumptions have caused randomness to the users' generalised travel times on the multimodal network. A comprehensive network framework is first defined for the flow equilibrium problem with consideration of interactions between auto flows and transit (bus) flows. Then, a fixed-point model with unique solution is proposed for the equilibrium flows, which can be solved by a convergent cost averaging method. Finally, the proposed methodology is tested by a network example.

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)

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.

Analyzing the international exergy flow network of ferrous metal ores.

PubMed

Qi, Hai; An, Haizhong; Hao, Xiaoqing; Zhong, Weiqiong; Zhang, Yanbing

2014-01-01

This paper employs an un-weighted and weighted exergy network to study the properties of ferrous metal ores in countries worldwide and their evolution from 2002 to 2012. We find that there are few countries controlling most of the ferrous metal ore exports in terms of exergy and that the entire exergy flow network is becoming more heterogeneous though the addition of new nodes. The increasing of the average clustering coefficient indicates that the formation of an international exergy flow system and regional integration is improving. When we contrast the average out strength of exergy and the average out strength of currency, we find both similarities and differences. Prices are affected largely by human factors; thus, the growth rate of the average out strength of currency has fluctuated acutely in the eleven years from 2002 to 2012. Exergy is defined as the maximum work that can be extracted from a system and can reflect the true cost in the world, and this parameter fluctuates much less. Performing an analysis based on the two aspects of exergy and currency, we find that the network is becoming uneven.

Analyzing the International Exergy Flow Network of Ferrous Metal Ores

PubMed Central

Qi, Hai; An, Haizhong; Hao, Xiaoqing; Zhong, Weiqiong; Zhang, Yanbing

2014-01-01

This paper employs an un-weighted and weighted exergy network to study the properties of ferrous metal ores in countries worldwide and their evolution from 2002 to 2012. We find that there are few countries controlling most of the ferrous metal ore exports in terms of exergy and that the entire exergy flow network is becoming more heterogeneous though the addition of new nodes. The increasing of the average clustering coefficient indicates that the formation of an international exergy flow system and regional integration is improving. When we contrast the average out strength of exergy and the average out strength of currency, we find both similarities and differences. Prices are affected largely by human factors; thus, the growth rate of the average out strength of currency has fluctuated acutely in the eleven years from 2002 to 2012. Exergy is defined as the maximum work that can be extracted from a system and can reflect the true cost in the world, and this parameter fluctuates much less. Performing an analysis based on the two aspects of exergy and currency, we find that the network is becoming uneven. PMID:25188407

Transnational cocaine and heroin flow networks in western Europe: A comparison.

PubMed

Chandra, Siddharth; Joba, Johnathan

2015-08-01

A comparison of the properties of drug flow networks for cocaine and heroin in a group of 17 western European countries is provided with the aim of understanding the implications of their similarities and differences for drug policy. Drug flow data for the cocaine and heroin networks were analyzed using the UCINET software package. Country-level characteristics including hub and authority scores, core and periphery membership, and centrality, and network-level characteristics including network density, the results of a triad census, and the final fitness of the core-periphery structure of the network, were computed and compared between the two networks. The cocaine network contains fewer path redundancies and a smaller, more tightly knit core than the heroin network. Authorities, hubs and countries central to the cocaine network tend to have higher hub, authority, and centrality scores than those in the heroin network. The core-periphery and hub-authority structures of the cocaine and heroin networks reflect the west-to-east and east-to-west patterns of flow of cocaine and heroin respectively across Europe. The key nodes in the cocaine and heroin networks are generally distinct from one another. The analysis of drug flow networks can reveal important structural features of trafficking networks that can be useful for the allocation of scarce drug control resources. The identification of authorities, hubs, network cores, and network-central nodes can suggest foci for the allocation of these resources. In the case of Europe, while some countries are important to both cocaine and heroin networks, different sets of countries occupy positions of prominence in the two networks. The distinct nature of the cocaine and heroin networks also suggests that a one-size-fits-all supply- and interdiction-focused policy may not work as well as an approach that takes into account the particular characteristics of each network. Copyright © 2015 Elsevier B.V. All rights reserved.

Network-based study of Lagrangian transport and mixing

NASA Astrophysics Data System (ADS)

Padberg-Gehle, Kathrin; Schneide, Christiane

2017-10-01

Transport and mixing processes in fluid flows are crucially influenced by coherent structures and the characterization of these Lagrangian objects is a topic of intense current research. While established mathematical approaches such as variational methods or transfer-operator-based schemes require full knowledge of the flow field or at least high-resolution trajectory data, this information may not be available in applications. Recently, different computational methods have been proposed to identify coherent behavior in flows directly from Lagrangian trajectory data, that is, numerical or measured time series of particle positions in a fluid flow. In this context, spatio-temporal clustering algorithms have been proven to be very effective for the extraction of coherent sets from sparse and possibly incomplete trajectory data. Inspired by these recent approaches, we consider an unweighted, undirected network, where Lagrangian particle trajectories serve as network nodes. A link is established between two nodes if the respective trajectories come close to each other at least once in the course of time. Classical graph concepts are then employed to analyze the resulting network. In particular, local network measures such as the node degree, the average degree of neighboring nodes, and the clustering coefficient serve as indicators of highly mixing regions, whereas spectral graph partitioning schemes allow us to extract coherent sets. The proposed methodology is very fast to run and we demonstrate its applicability in two geophysical flows - the Bickley jet as well as the Antarctic stratospheric polar vortex.

Cubic law with aperture-length correlation: implications for network scale fluid flow

NASA Astrophysics Data System (ADS)

Klimczak, Christian; Schultz, Richard A.; Parashar, Rishi; Reeves, Donald M.

2010-06-01

Previous studies have computed and modeled fluid flow through fractured rock with the parallel plate approach where the volumetric flow per unit width normal to the direction of flow is proportional to the cubed aperture between the plates, referred to as the traditional cubic law. When combined with the square root relationship of displacement to length scaling of opening-mode fractures, total flow rates through natural opening-mode fractures are found to be proportional to apertures to the fifth power. This new relationship was explored by examining a suite of flow simulations through fracture networks using the discrete fracture network model (DFN). Flow was modeled through fracture networks with the same spatial distribution of fractures for both correlated and uncorrelated fracture length-to-aperture relationships. Results indicate that flow rates are significantly higher for correlated DFNs. Furthermore, the length-to-aperture relations lead to power-law distributions of network hydraulic conductivity which greatly influence equivalent permeability tensor values. These results confirm the importance of the correlated square root relationship of displacement to length scaling for total flow through natural opening-mode fractures and, hence, emphasize the role of these correlations for flow modeling.

Traffic flow collection wireless sensor network node for intersection light control

NASA Astrophysics Data System (ADS)

Li, Xu; Li, Xue

2011-10-01

Wireless sensor network (WSN) is expected to be deployed in intersection to monitor the traffic flow continuously, and the monitoring datum can be used as the foundation of traffic light control. In this paper, a WSN based on ZigBee protocol for monitoring traffic flow is proposed. Structure, hardware and work flow of WSN nodes are designed. CC2431 from Texas Instrument is chosen as the main computational and transmission unit, and CC2591 as the amplification unit. The stability experiment and the actual environment experiment are carried out in the last of the paper. The results of experiments show that WSN has the ability to collect traffic flow information quickly and transmit the datum to the processing center in real time.

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%.

Loan and nonloan flows in the Australian interbank network

NASA Astrophysics Data System (ADS)

Sokolov, Andrey; Webster, Rachel; Melatos, Andrew; Kieu, Tien

2012-05-01

High-value transactions between banks in Australia are settled in the Reserve Bank Information and Transfer System (RITS) administered by the Reserve Bank of Australia. RITS operates on a real-time gross settlement (RTGS) basis and settles payments and transfers sourced from the SWIFT payment delivery system, the Austraclear securities settlement system, and the interbank transactions entered directly into RITS. In this paper, we analyse a dataset received from the Reserve Bank of Australia that includes all interbank transactions settled in RITS on an RTGS basis during five consecutive weekdays from 19 February 2007 inclusive, a week of relatively quiescent market conditions. The source, destination, and value of each transaction are known, which allows us to separate overnight loans from other transactions (nonloans) and reconstruct monetary flows between banks for every day in our sample. We conduct a novel analysis of the flow stability and examine the connection between loan and nonloan flows. Our aim is to understand the underlying causal mechanism connecting loan and nonloan flows. We find that the imbalances in the banks' exchange settlement funds resulting from the daily flows of nonloan transactions are almost exactly counterbalanced by the flows of overnight loans. The correlation coefficient between loan and nonloan imbalances is about -0.9 on most days. Some flows that persist over two consecutive days can be highly variable, but overall the flows are moderately stable in value. The nonloan network is characterised by a large fraction of persistent flows, whereas only half of the flows persist over any two consecutive days in the loan network. Moreover, we observe an unusual degree of coherence between persistent loan flow values on Tuesday and Wednesday. We probe static topological properties of the Australian interbank network and find them consistent with those observed in other countries.

Consistent maximum entropy representations of pipe flow networks

NASA Astrophysics Data System (ADS)

Waldrip, Steven H.; Niven, Robert K.; Abel, Markus; Schlegel, Michael

2017-06-01

The maximum entropy method is used to predict flows on water distribution networks. This analysis extends the water distribution network formulation of Waldrip et al. (2016) Journal of Hydraulic Engineering (ASCE), by the use of a continuous relative entropy defined on a reduced parameter set. This reduction in the parameters that the entropy is defined over ensures consistency between different representations of the same network. The performance of the proposed reduced parameter method is demonstrated with a one-loop network case study.

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

Dynamic autonomous routing technology for IP-based satellite ad hoc networks

NASA Astrophysics Data System (ADS)

Wang, Xiaofei; Deng, Jing; Kostas, Theresa; Rajappan, Gowri

2014-06-01

IP-based routing for military LEO/MEO satellite ad hoc networks is very challenging due to network and traffic heterogeneity, network topology and traffic dynamics. In this paper, we describe a traffic priority-aware routing scheme for such networks, namely Dynamic Autonomous Routing Technology (DART) for satellite ad hoc networks. DART has a cross-layer design, and conducts routing and resource reservation concurrently for optimal performance in the fluid but predictable satellite ad hoc networks. DART ensures end-to-end data delivery with QoS assurances by only choosing routing paths that have sufficient resources, supporting different packet priority levels. In order to do so, DART incorporates several resource management and innovative routing mechanisms, which dynamically adapt to best fit the prevailing conditions. In particular, DART integrates a resource reservation mechanism to reserve network bandwidth resources; a proactive routing mechanism to set up non-overlapping spanning trees to segregate high priority traffic flows from lower priority flows so that the high priority flows do not face contention from low priority flows; a reactive routing mechanism to arbitrate resources between various traffic priorities when needed; a predictive routing mechanism to set up routes for scheduled missions and for anticipated topology changes for QoS assurance. We present simulation results showing the performance of DART. We have conducted these simulations using the Iridium constellation and trajectories as well as realistic military communications scenarios. The simulation results demonstrate DART's ability to discriminate between high-priority and low-priority traffic flows and ensure disparate QoS requirements of these traffic flows.

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.

Power Grid Maintenance Scheduling Intelligence Arrangement Supporting System Based on Power Flow Forecasting

NASA Astrophysics Data System (ADS)

Xie, Chang; Wen, Jing; Liu, Wenying; Wang, Jiaming

With the development of intelligent dispatching, the intelligence level of network control center full-service urgent need to raise. As an important daily work of network control center, the application of maintenance scheduling intelligent arrangement to achieve high-quality and safety operation of power grid is very important. By analyzing the shortages of the traditional maintenance scheduling software, this paper designs a power grid maintenance scheduling intelligence arrangement supporting system based on power flow forecasting, which uses the advanced technologies in maintenance scheduling, such as artificial intelligence, online security checking, intelligent visualization techniques. It implements the online security checking of maintenance scheduling based on power flow forecasting and power flow adjusting based on visualization, in order to make the maintenance scheduling arrangement moreintelligent and visual.

Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch

PubMed Central

Huang, Tao; Yan, Siyu; Yang, Fan; Pan, Tian; Liu, Jiang

2016-01-01

Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, in a wireless environment, the main controller node may leave the sensor network due to the dynamic topology change or the unstable wireless signal, leaving the rest of network devices without control, e.g., a sensor node as a switch may forward packets according to stale rules until the controller updates the flow table entries. To solve this problem, this paper proposes a novel SDN-based vehicular sensor networks architecture which can minimize the performance penalty of controller connection loss. We achieve this by designing a connection state detection and self-learning mechanism. We build prototypes based on extended Open vSwitch and Ryu. The experimental results show that the recovery time from controller connection loss is under 100 ms and it keeps rule updating in real time with a stable throughput. This architecture enhances the survivability and stability of SDN-based vehicular sensor networks in precision agriculture. PMID:26797616

Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch.

PubMed

Huang, Tao; Yan, Siyu; Yang, Fan; Pan, Tian; Liu, Jiang

2016-01-19

Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, in a wireless environment, the main controller node may leave the sensor network due to the dynamic topology change or the unstable wireless signal, leaving the rest of network devices without control, e.g., a sensor node as a switch may forward packets according to stale rules until the controller updates the flow table entries. To solve this problem, this paper proposes a novel SDN-based vehicular sensor networks architecture which can minimize the performance penalty of controller connection loss. We achieve this by designing a connection state detection and self-learning mechanism. We build prototypes based on extended Open vSwitch and Ryu. The experimental results show that the recovery time from controller connection loss is under 100 ms and it keeps rule updating in real time with a stable throughput. This architecture enhances the survivability and stability of SDN-based vehicular sensor networks in precision agriculture.

SACFIR: SDN-Based Application-Aware Centralized Adaptive Flow Iterative Reconfiguring Routing Protocol for WSNs.

PubMed

Aslam, Muhammad; Hu, Xiaopeng; Wang, Fan

2017-12-13

Smart reconfiguration of a dynamic networking environment is offered by the central control of Software-Defined Networking (SDN). Centralized SDN-based management architectures are capable of retrieving global topology intelligence and decoupling the forwarding plane from the control plane. Routing protocols developed for conventional Wireless Sensor Networks (WSNs) utilize limited iterative reconfiguration methods to optimize environmental reporting. However, the challenging networking scenarios of WSNs involve a performance overhead due to constant periodic iterative reconfigurations. In this paper, we propose the SDN-based Application-aware Centralized adaptive Flow Iterative Reconfiguring (SACFIR) routing protocol with the centralized SDN iterative solver controller to maintain the load-balancing between flow reconfigurations and flow allocation cost. The proposed SACFIR's routing protocol offers a unique iterative path-selection algorithm, which initially computes suitable clustering based on residual resources at the control layer and then implements application-aware threshold-based multi-hop report transmissions on the forwarding plane. The operation of the SACFIR algorithm is centrally supervised by the SDN controller residing at the Base Station (BS). This paper extends SACFIR to SDN-based Application-aware Main-value Centralized adaptive Flow Iterative Reconfiguring (SAMCFIR) to establish both proactive and reactive reporting. The SAMCFIR transmission phase enables sensor nodes to trigger direct transmissions for main-value reports, while in the case of SACFIR, all reports follow computed routes. Our SDN-enabled proposed models adjust the reconfiguration period according to the traffic burden on sensor nodes, which results in heterogeneity awareness, load-balancing and application-specific reconfigurations of WSNs. Extensive experimental simulation-based results show that SACFIR and SAMCFIR yield the maximum scalability, network lifetime and stability

SACFIR: SDN-Based Application-Aware Centralized Adaptive Flow Iterative Reconfiguring Routing Protocol for WSNs

PubMed Central

Hu, Xiaopeng; Wang, Fan

2017-01-01

Smart reconfiguration of a dynamic networking environment is offered by the central control of Software-Defined Networking (SDN). Centralized SDN-based management architectures are capable of retrieving global topology intelligence and decoupling the forwarding plane from the control plane. Routing protocols developed for conventional Wireless Sensor Networks (WSNs) utilize limited iterative reconfiguration methods to optimize environmental reporting. However, the challenging networking scenarios of WSNs involve a performance overhead due to constant periodic iterative reconfigurations. In this paper, we propose the SDN-based Application-aware Centralized adaptive Flow Iterative Reconfiguring (SACFIR) routing protocol with the centralized SDN iterative solver controller to maintain the load-balancing between flow reconfigurations and flow allocation cost. The proposed SACFIR’s routing protocol offers a unique iterative path-selection algorithm, which initially computes suitable clustering based on residual resources at the control layer and then implements application-aware threshold-based multi-hop report transmissions on the forwarding plane. The operation of the SACFIR algorithm is centrally supervised by the SDN controller residing at the Base Station (BS). This paper extends SACFIR to SDN-based Application-aware Main-value Centralized adaptive Flow Iterative Reconfiguring (SAMCFIR) to establish both proactive and reactive reporting. The SAMCFIR transmission phase enables sensor nodes to trigger direct transmissions for main-value reports, while in the case of SACFIR, all reports follow computed routes. Our SDN-enabled proposed models adjust the reconfiguration period according to the traffic burden on sensor nodes, which results in heterogeneity awareness, load-balancing and application-specific reconfigurations of WSNs. Extensive experimental simulation-based results show that SACFIR and SAMCFIR yield the maximum scalability, network lifetime and stability

New approach for simulating groundwater flow in discrete fracture network

NASA Astrophysics Data System (ADS)

Fang, H.; Zhu, J.

2017-12-01

In this study, we develop a new approach to calculate groundwater flowrate and hydraulic head distribution in two-dimensional discrete fracture network (DFN) where both laminar and turbulent flows co-exist in individual fractures. The cubic law is used to calculate hydraulic head distribution and flow behaviors in fractures where flow is laminar, while the Forchheimer's law is used to quantify turbulent flow behaviors. Reynolds number is used to distinguish flow characteristics in individual fractures. The combination of linear and non-linear equations is solved iteratively to determine flowrates in all fractures and hydraulic heads at all intersections. We examine potential errors in both flowrate and hydraulic head from the approach of uniform flow assumption. Applying the cubic law in all fractures regardless of actual flow conditions overestimates the flowrate when turbulent flow may exist while applying the Forchheimer's law indiscriminately underestimate the flowrate when laminar flows exist in the network. The contrast of apertures of large and small fractures in the DFN has significant impact on the potential errors of using only the cubic law or the Forchheimer's law. Both the cubic law and Forchheimer's law simulate similar hydraulic head distributions as the main difference between these two approaches lies in predicting different flowrates. Fracture irregularity does not significantly affect the potential errors from using only the cubic law or the Forchheimer's law if network configuration remains similar. Relative density of fractures does not significantly affect the relative performance of the cubic law and Forchheimer's law.

Mass transport enhancement in redox flow batteries with corrugated fluidic networks

NASA Astrophysics Data System (ADS)

Lisboa, Kleber Marques; Marschewski, Julian; Ebejer, Neil; Ruch, Patrick; Cotta, Renato Machado; Michel, Bruno; Poulikakos, Dimos

2017-08-01

We propose a facile, novel concept of mass transfer enhancement in flow batteries based on electrolyte guidance in rationally designed corrugated channel systems. The proposed fluidic networks employ periodic throttling of the flow to optimally deflect the electrolytes into the porous electrode, targeting enhancement of the electrolyte-electrode interaction. Theoretical analysis is conducted with channels in the form of trapezoidal waves, confirming and detailing the mass transport enhancement mechanism. In dilute concentration experiments with an alkaline quinone redox chemistry, a scaling of the limiting current with Re0.74 is identified, which compares favourably against the Re0.33 scaling typical of diffusion-limited laminar processes. Experimental IR-corrected polarization curves are presented for high concentration conditions, and a significant performance improvement is observed with the narrowing of the nozzles. The adverse effects of periodic throttling on the pumping power are compared with the benefits in terms of power density, and an improvement of up to 102% in net power density is obtained in comparison with the flow-by case employing straight parallel channels. The proposed novel concept of corrugated fluidic networks comes with facile fabrication and contributes to the improvement of the transport characteristics and overall performance of redox flow battery systems.

Catchment organisation, free energy dynamics and network control on critical zone water flows

NASA Astrophysics Data System (ADS)

Zehe, E.; Ehret, U.; Kleidon, A.; Jackisch, C.; Scherer, U.; Blume, T.

2012-04-01

as that these flow structures organize and dominate flows of water, dissolved matter and sediments during rainfall driven conditions at various scales: - Surface connected vertical flow structures of anecic worm burrows or soil cracks organize and dominated vertical flows at the plot scale - this is usually referred to as preferential flow; - Rill networks at the soil surface organise and dominate hillslope scale overland flow response and sediment yields; - Subsurface pipe networks at the bedrock interface organize and dominate hillslope scale lateral subsurface water and tracer flows; - The river net organizes and dominates flows of water, dissolved matter and sediments to the catchment outlet and finally across continental gradients to the sea. Fundamental progress with respect to the parameterization of hydrological models, subscale flow networks and to understand the adaptation of hydro-geo ecosystems to change could be achieved by discovering principles that govern the organization of catchments flow networks in particular at least during steady state conditions. This insight has inspired various scientists to suggest principles for organization of ecosystems, landscapes and flow networks; as Bejans constructural law, Minimum Energy Expenditure , Maximum Entropy Production. In line with these studies we suggest that a thermodynamic/energetic treatment of the catchment is might be a key for understanding the underlying principles that govern organisation of flow and transport. Our approach is to employ a) physically based hydrological model that address at least all the relevant hydrological processes in the critical zone in a coupled way, behavioural representations of the observed organisation of flow structures and textural elements, that are consistent with observations in two well investigated research catchments and have been tested against distributed observations of soil moisture and catchment scale discharge; to simulate the full concert of hydrological

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.

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.

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

Clustering and Flow Conservation Monitoring Tool for Software Defined Networks

PubMed Central

Puente Fernández, Jesús Antonio

2018-01-01

Prediction systems present some challenges on two fronts: the relation between video quality and observed session features and on the other hand, dynamics changes on the video quality. Software Defined Networks (SDN) is a new concept of network architecture that provides the separation of control plane (controller) and data plane (switches) in network devices. Due to the existence of the southbound interface, it is possible to deploy monitoring tools to obtain the network status and retrieve a statistics collection. Therefore, achieving the most accurate statistics depends on a strategy of monitoring and information requests of network devices. In this paper, we propose an enhanced algorithm for requesting statistics to measure the traffic flow in SDN networks. Such an algorithm is based on grouping network switches in clusters focusing on their number of ports to apply different monitoring techniques. Such grouping occurs by avoiding monitoring queries in network switches with common characteristics and then, by omitting redundant information. In this way, the present proposal decreases the number of monitoring queries to switches, improving the network traffic and preventing the switching overload. We have tested our optimization in a video streaming simulation using different types of videos. The experiments and comparison with traditional monitoring techniques demonstrate the feasibility of our proposal maintaining similar values decreasing the number of queries to the switches. PMID:29614049

Clustering and Flow Conservation Monitoring Tool for Software Defined Networks.

PubMed

Puente Fernández, Jesús Antonio; García Villalba, Luis Javier; Kim, Tai-Hoon

2018-04-03

Prediction systems present some challenges on two fronts: the relation between video quality and observed session features and on the other hand, dynamics changes on the video quality. Software Defined Networks (SDN) is a new concept of network architecture that provides the separation of control plane (controller) and data plane (switches) in network devices. Due to the existence of the southbound interface, it is possible to deploy monitoring tools to obtain the network status and retrieve a statistics collection. Therefore, achieving the most accurate statistics depends on a strategy of monitoring and information requests of network devices. In this paper, we propose an enhanced algorithm for requesting statistics to measure the traffic flow in SDN networks. Such an algorithm is based on grouping network switches in clusters focusing on their number of ports to apply different monitoring techniques. Such grouping occurs by avoiding monitoring queries in network switches with common characteristics and then, by omitting redundant information. In this way, the present proposal decreases the number of monitoring queries to switches, improving the network traffic and preventing the switching overload. We have tested our optimization in a video streaming simulation using different types of videos. The experiments and comparison with traditional monitoring techniques demonstrate the feasibility of our proposal maintaining similar values decreasing the number of queries to the switches.

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.

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.

Network flow of mobile agents enhances the evolution of cooperation

NASA Astrophysics Data System (ADS)

Ichinose, G.; Satotani, Y.; Nagatani, T.

2018-01-01

We study the effect of contingent movement on the persistence of cooperation on complex networks with empty nodes. Each agent plays the Prisoner's Dilemma game with its neighbors and then it either updates the strategy depending on the payoff difference with neighbors or it moves to another empty node if not satisfied with its own payoff. If no neighboring node is empty, each agent stays at the same site. By extensive evolutionary simulations, we show that the medium density of agents enhances cooperation where the network flow of mobile agents is also medium. Moreover, if the movements of agents are more frequent than the strategy updating, cooperation is further promoted. In scale-free networks, the optimal density for cooperation is lower than other networks because agents get stuck at hubs. Our study suggests that keeping a smooth network flow is significant for the persistence of cooperation in ever-changing societies.

Multiscale fracture network characterization and impact on flow: A case study on the Latemar carbonate platform

NASA Astrophysics Data System (ADS)

Hardebol, N. J.; Maier, C.; Nick, H.; Geiger, S.; Bertotti, G.; Boro, H.

2015-12-01

A fracture network arrangement is quantified across an isolated carbonate platform from outcrop and aerial imagery to address its impact on fluid flow. The network is described in terms of fracture density, orientation, and length distribution parameters. Of particular interest is the role of fracture cross connections and abutments on the effective permeability. Hence, the flow simulations explicitly account for network topology by adopting Discrete-Fracture-and-Matrix description. The interior of the Latemar carbonate platform (Dolomites, Italy) is taken as outcrop analogue for subsurface reservoirs of isolated carbonate build-ups that exhibit a fracture-dominated permeability. New is our dual strategy to describe the fracture network both as deterministic- and stochastic-based inputs for flow simulations. The fracture geometries are captured explicitly and form a multiscale data set by integration of interpretations from outcrops, airborne imagery, and lidar. The deterministic network descriptions form the basis for descriptive rules that are diagnostic of the complex natural fracture arrangement. The fracture networks exhibit a variable degree of multitier hierarchies with smaller-sized fractures abutting against larger fractures under both right and oblique angles. The influence of network topology on connectivity is quantified using Discrete-Fracture-Single phase fluid flow simulations. The simulation results show that the effective permeability for the fracture and matrix ensemble can be 50 to 400 times higher than the matrix permeability of 1.0 · 10-14 m2. The permeability enhancement is strongly controlled by the connectivity of the fracture network. Therefore, the degree of intersecting and abutting fractures should be captured from outcrops with accuracy to be of value as analogue.

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.

A Study on Market-based Strategic Procurement Planning in Convergent Supply Networks

NASA Astrophysics Data System (ADS)

Opadiji, Jayeola Femi; Kaihara, Toshiya

We present a market-based decentralized approach which uses a market-oriented programming algorithm to obtain Pareto-optimal allocation of resources traded among agents which represent enterprise units in a supply network. The proposed method divides the network into a series of Walrsian markets in order to obtain procurement budgets for enterprises in the network. An interaction protocol based on market value propagation is constructed to coordinate the flow of resources across the network layers. The method mitigates the effect of product complementarity in convergent network by allowing for enterprises to hold private valuations of resources in the markets.

Random network peristalsis in Physarum polycephalum organizes fluid flows across an individual

PubMed Central

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

2013-01-01

Individuals can function as integrated organisms only when information and resources are shared across a body. Signals and substrates are commonly moved using fluids, often channeled through a network of tubes. Peristalsis is one mechanism for fluid transport and is caused by a wave of cross-sectional contractions along a tube. We extend the concept of peristalsis from the canonical case of one tube to a random network. Transport is maximized within the network when the wavelength of the peristaltic wave is of the order of the size of the network. The slime mold Physarum polycephalum grows as a random network of tubes, and our experiments confirm peristalsis is used by the slime mold to drive internal cytoplasmic flows. Comparisons of theoretically generated contraction patterns with the patterns exhibited by individuals of P. polycephalum demonstrate that individuals maximize internal flows by adapting patterns of contraction to size, thus optimizing transport throughout an organism. This control of fluid flow may be the key to coordinating growth and behavior, including the dynamic changes in network architecture seen over time in an individual. PMID:23898203

Random network peristalsis in Physarum polycephalum organizes fluid flows across an individual.

PubMed

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

2013-08-13

Individuals can function as integrated organisms only when information and resources are shared across a body. Signals and substrates are commonly moved using fluids, often channeled through a network of tubes. Peristalsis is one mechanism for fluid transport and is caused by a wave of cross-sectional contractions along a tube. We extend the concept of peristalsis from the canonical case of one tube to a random network. Transport is maximized within the network when the wavelength of the peristaltic wave is of the order of the size of the network. The slime mold Physarum polycephalum grows as a random network of tubes, and our experiments confirm peristalsis is used by the slime mold to drive internal cytoplasmic flows. Comparisons of theoretically generated contraction patterns with the patterns exhibited by individuals of P. polycephalum demonstrate that individuals maximize internal flows by adapting patterns of contraction to size, thus optimizing transport throughout an organism. This control of fluid flow may be the key to coordinating growth and behavior, including the dynamic changes in network architecture seen over time in an individual.

Polyvinylpyrrolidone-based semi-interpenetrating polymer networks as highly selective and chemically stable membranes for all vanadium redox flow batteries

NASA Astrophysics Data System (ADS)

Zeng, L.; Zhao, T. S.; Wei, L.; Zeng, Y. K.; Zhang, Z. H.

2016-09-01

Vanadium redox flow batteries (VRFBs) with their high flexibility in configuration and operation, as well as long cycle life are competent for the requirement of future energy storage systems. Nevertheless, due to the application of perfluorinated membranes, VRFBs are plagued by not only the severe migration issue of vanadium ions, but also their high cost. Herein, we fabricate semi-interpenetrating polymer networks (SIPNs), consisting of cross-linked polyvinylpyrrolidone (PVP) and polysulfone (PSF), as alternative membranes for VRFBs. It is demonstrated that the PVP-based SIPNs exhibit extremely low vanadium permeabilities, which contribute to the well-established hydrophilic/hydrophobic microstructures and the Donnan exclusion effect. As a result, the coulombic efficiencies of VRFBs with PVP-based SIPNs reach almost 100% at 40 mA cm-2 to 100 mA cm-2; the energy efficiencies are more than 3% higher than those of VRFBs with Nafion 212. More importantly, the PVP-based SIPNs exhibit a superior chemical stability, as demonstrated both by an ex situ immersion test and continuously cycling test. Hence, all the characterizations and performance tests reported here suggest that PVP-based SIPNs are a promising alternative membrane for redox flow batteries to achieve superior cell performance and excellent cycling stability at the fraction of the cost of perfluorinated membranes.

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.

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

Analysis of Cisco Open Network Environment (ONE) OpenFlow Controller Implementation

DTIC Science & Technology

2014-08-01

Software - Defined Networking ( SDN ), when fully realized, offer many improvements over the current rigid and...functionalities like handshake, connection setup, switch management, and security. 15. SUBJECT TERMS OpenFlow, software - defined networking , Cisco ONE, SDN ...innovating packet-forwarding technologies. Network device roles are strictly defined with little or no flexibility. In Software - Defined Networks ( SDNs ),

Sonification of network traffic flow for monitoring and situational awareness

PubMed Central

2018-01-01

Maintaining situational awareness of what is happening within a computer network is challenging, not only because the behaviour happens within machines, but also because data traffic speeds and volumes are beyond human ability to process. Visualisation techniques are widely used to present information about network traffic dynamics. Although they provide operators with an overall view and specific information about particular traffic or attacks on the network, they often still fail to represent the events in an understandable way. Also, because they require visual attention they are not well suited to continuous monitoring scenarios in which network administrators must carry out other tasks. Here we present SoNSTAR (Sonification of Networks for SiTuational AwaReness), a real-time sonification system for monitoring computer networks to support network administrators’ situational awareness. SoNSTAR provides an auditory representation of all the TCP/IP traffic within a network based on the different traffic flows between between network hosts. A user study showed that SoNSTAR raises situational awareness levels by enabling operators to understand network behaviour and with the benefit of lower workload demands (as measured by the NASA TLX method) than visual techniques. SoNSTAR identifies network traffic features by inspecting the status flags of TCP/IP packet headers. Combinations of these features define particular traffic events which are mapped to recorded sounds to generate a soundscape that represents the real-time status of the network traffic environment. The sequence, timing, and loudness of the different sounds allow the network to be monitored and anomalous behaviour to be detected without the need to continuously watch a monitor screen. PMID:29672543

Sonification of network traffic flow for monitoring and situational awareness.

PubMed

Debashi, Mohamed; Vickers, Paul

2018-01-01

Maintaining situational awareness of what is happening within a computer network is challenging, not only because the behaviour happens within machines, but also because data traffic speeds and volumes are beyond human ability to process. Visualisation techniques are widely used to present information about network traffic dynamics. Although they provide operators with an overall view and specific information about particular traffic or attacks on the network, they often still fail to represent the events in an understandable way. Also, because they require visual attention they are not well suited to continuous monitoring scenarios in which network administrators must carry out other tasks. Here we present SoNSTAR (Sonification of Networks for SiTuational AwaReness), a real-time sonification system for monitoring computer networks to support network administrators' situational awareness. SoNSTAR provides an auditory representation of all the TCP/IP traffic within a network based on the different traffic flows between between network hosts. A user study showed that SoNSTAR raises situational awareness levels by enabling operators to understand network behaviour and with the benefit of lower workload demands (as measured by the NASA TLX method) than visual techniques. SoNSTAR identifies network traffic features by inspecting the status flags of TCP/IP packet headers. Combinations of these features define particular traffic events which are mapped to recorded sounds to generate a soundscape that represents the real-time status of the network traffic environment. The sequence, timing, and loudness of the different sounds allow the network to be monitored and anomalous behaviour to be detected without the need to continuously watch a monitor screen.

Internet-Based Approaches to Building Stakeholder Networks for Conservation and Natural Resource Management

EPA Science Inventory

Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing i...

Internet-Based Approaches to Building Stakeholder Networks for Conservation and Natural Resource Management.

EPA Science Inventory

Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing in...

No Snow No Flow: How Montane Stream Networks Respond to Drought

NASA Astrophysics Data System (ADS)

Grant, G.; Nolin, A. W.; Selker, J. S.; Lewis, S.; Hempel, L. A.; Jefferson, A.; Walter, C.; Roques, C.

2015-12-01

Hydrologic extremes, such as drought, offer an exceptional opportunity to explore how runoff generation mechanisms and stream networks respond to changing precipitation regimes. The winter of 2014-2015 was the warmest on record in western Oregon, US, with record low snowpacks, and was followed by an anomalously warm, dry spring, resulting in historically low streamflows. But a year like 2015 is more than an outlier meteorological year. It provides a unique opportunity to test fundamental hypotheses for how montane hydrologic systems will respond to anticipated changes in amount and timing of recharge. In particular, the volcanic Cascade Mountains represent a "landscape laboratory" comprised of two distinct runoff regimes: the surface-flow dominated Western Cascade watersheds, with flashy streamflow regimes, rapid baseflow recession, and very low summer flows; and (b) the spring-fed High Cascade watersheds, with a slow-responding streamflow regime, and a long and sustained baseflow recession that maintains late summer streamflow through deep-groundwater contributions to high volume, coldwater springs. We hypothesize that stream network response to the extremely low snowpack and recharge varies sharply in these two regions. In surface flow dominated streams, the location of channel heads can migrate downstream, contracting the network longitudinally; wetted channel width and depth contract laterally as summer recession proceeds and flows diminish. In contrast, in spring-fed streams, channel heads "jump" to the next downstream spring when upper basin spring flow diminishes to zero. Downstream of flowing springs, wetted channel width and depth contract laterally as flows recede. To test these hypotheses, we conducted a field campaign to measure changing discharge, hydraulic geometry, and channel head location in both types of watersheds throughout the summer and early fall. Multiple cross-section sites were established on 6 streams representing both flow regime types

Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems

DOE PAGES

Mudunuru, Maruti Kumar; Karra, Satish; Makedonska, Nataliia; ...

2017-09-05

Subsurface applications, including geothermal, geological carbon sequestration, and oil and gas, typically involve maximizing either the extraction of energy or the storage of fluids. Fractures form the main pathways for flow in these systems, and locating these fractures is critical for predicting flow. However, fracture characterization is a highly uncertain process, and data from multiple sources, such as flow and geophysical are needed to reduce this uncertainty. We present a nonintrusive, sequential inversion framework for integrating data from geophysical and flow sources to constrain fracture networks in the subsurface. In this framework, we first estimate bounds on the statistics formore » the fracture orientations using microseismic data. These bounds are estimated through a combination of a focal mechanism (physics-based approach) and clustering analysis (statistical approach) of seismic data. Then, the fracture lengths are constrained using flow data. In conclusion, the efficacy of this inversion is demonstrated through a representative example.« less

Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems

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

Mudunuru, Maruti Kumar; Karra, Satish; Makedonska, Nataliia

Subsurface applications, including geothermal, geological carbon sequestration, and oil and gas, typically involve maximizing either the extraction of energy or the storage of fluids. Fractures form the main pathways for flow in these systems, and locating these fractures is critical for predicting flow. However, fracture characterization is a highly uncertain process, and data from multiple sources, such as flow and geophysical are needed to reduce this uncertainty. We present a nonintrusive, sequential inversion framework for integrating data from geophysical and flow sources to constrain fracture networks in the subsurface. In this framework, we first estimate bounds on the statistics formore » the fracture orientations using microseismic data. These bounds are estimated through a combination of a focal mechanism (physics-based approach) and clustering analysis (statistical approach) of seismic data. Then, the fracture lengths are constrained using flow data. In conclusion, the efficacy of this inversion is demonstrated through a representative example.« less

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.

NetFlow Dynamics

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

Corbet Jr., Thomas F; Beyeler, Walter E; Vanwestrienen, Dirk

NetFlow Dynamics is a web-accessible analysis environment for simulating dynamic flows of materials on model networks. Performing a simulation requires both the NetFlow Dynamics application and a network model which is a description of the structure of the nodes and edges of a network including the flow capacity of each edge and the storage capacity of each node, and the sources and sinks of the material flowing on the network. NetFlow Dynamics consists of databases for storing network models, algorithms to calculate flows on networks, and a GIS-based graphical interface for performing simulations and viewing simulation results. Simulated flows aremore » dynamic in the sense that flows on each edge of the network and inventories at each node change with time and can be out of equilibrium with boundary conditions. Any number of network models could be simulated using Net Flow Dynamics. To date, the models simulated have been models of petroleum infrastructure. The main model has been the National Transportation Fuels Model (NTFM), a network of U.S. oil fields, transmission pipelines, rail lines, refineries, tank farms, and distribution terminals. NetFlow Dynamics supports two different flow algorithms, the Gradient Flow algorithm and the Inventory Control algorithm, that were developed specifically for the NetFlow Dynamics application. The intent is to add additional algorithms in the future as needed. The ability to select from multiple algorithms is desirable because a single algorithm never covers all analysis needs. The current algorithms use a demand-driven capacity-constrained formulation which means that the algorithms strive to use all available capacity and stored inventory to meet desired flows to sinks, subject to the capacity constraints of each network component. The current flow algorithms are best suited for problems in which a material flows on a capacity-constrained network representing a supply chain in which the material supplied can be stored

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

Confidence intervals in Flow Forecasting by using artificial neural networks

NASA Astrophysics Data System (ADS)

Panagoulia, Dionysia; Tsekouras, George

2014-05-01

One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input

Subsurface lateral preferential flow network revealed by time-lapse ground-penetrating radar in a hillslope

NASA Astrophysics Data System (ADS)

Guo, Li; Chen, Jin; Lin, Henry

2014-12-01

Subsurface lateral preferential flow (LPF) has been observed to contribute substantially to hillslope and catchment runoff. However, the complex nature of LPF and the lack of an appropriate investigation method have hindered direct LPF observation in the field. Thus, the initiation, persistence, and dynamics of LPF networks remain poorly understood. This study explored the application of time-lapse ground-penetrating radar (GPR) together with an artificial infiltration to shed light on the nature of LPF and its dynamics in a hillslope. Based on our enhanced field experimental setup and carefully refined GPR data postprocessing algorithms, we developed a new protocol to reconstruct LPF networks with centimeter resolution. This is the first time that a detailed LPF network and its dynamics have been revealed noninvasively along a hillslope. Real-time soil water monitoring and field soil investigation confirmed the locations of LPF mapped by time-lapse GPR surveys. Our results indicated the following: (1) Increased spatial variations of radar signals after infiltration suggested heterogeneous soil water changes within the studied soil, which reflected the generation and dynamics of LPF; (2) Two types of LPF networks were identified, the network at the location of soil permeability contrasts and that formed via a series of connected preferential flow paths; and (3) The formation and distribution of LPF networks were influenced by antecedent soil water condition. Overall, this study demonstrates clearly that carefully designed time-lapse GPR surveys with enhanced data postprocessing offer a practical and nondestructive way of mapping LPF networks in the field, thereby providing a potentially significant enhancement in our ability to study complex subsurface flow processes across the landscape.

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.

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.

Backbone of complex networks of corporations: the flow of control.

PubMed

Glattfelder, J B; Battiston, S

2009-09-01

We present a methodology to extract the backbone of complex networks based on the weight and direction of links, as well as on nontopological properties of nodes. We show how the methodology can be applied in general to networks in which mass or energy is flowing along the links. In particular, the procedure enables us to address important questions in economics, namely, how control and wealth are structured and concentrated across national markets. We report on the first cross-country investigation of ownership networks, focusing on the stock markets of 48 countries around the world. On the one hand, our analysis confirms results expected on the basis of the literature on corporate control, namely, that in Anglo-Saxon countries control tends to be dispersed among numerous shareholders. On the other hand, it also reveals that in the same countries, control is found to be highly concentrated at the global level, namely, lying in the hands of very few important shareholders. Interestingly, the exact opposite is observed for European countries. These results have previously not been reported as they are not observable without the kind of network analysis developed here.

Backbone of complex networks of corporations: The flow of control

NASA Astrophysics Data System (ADS)

Glattfelder, J. B.; Battiston, S.

2009-09-01

We present a methodology to extract the backbone of complex networks based on the weight and direction of links, as well as on nontopological properties of nodes. We show how the methodology can be applied in general to networks in which mass or energy is flowing along the links. In particular, the procedure enables us to address important questions in economics, namely, how control and wealth are structured and concentrated across national markets. We report on the first cross-country investigation of ownership networks, focusing on the stock markets of 48 countries around the world. On the one hand, our analysis confirms results expected on the basis of the literature on corporate control, namely, that in Anglo-Saxon countries control tends to be dispersed among numerous shareholders. On the other hand, it also reveals that in the same countries, control is found to be highly concentrated at the global level, namely, lying in the hands of very few important shareholders. Interestingly, the exact opposite is observed for European countries. These results have previously not been reported as they are not observable without the kind of network analysis developed here.

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

Controls on debris flow bulking in proglacial gully networks on Mount Rainier, WA

NASA Astrophysics Data System (ADS)

Legg, N. T.; Meigs, A.; Grant, G. E.; Kennard, P.

2012-12-01

Conversion of floodwaters to debris flows due to sediment bulking continues to be a poorly understood phenomenon. This study examines the initiation zone of a series of six debris flows that originated in proglacial areas of catchments on the flank of Mount Rainier during one storm in 2006. One-meter spatial resolution aerial photographs and LiDAR DEMs acquired before and after the storm reveal the lack of a single mass failure to explain the debris flow deposits. Rather, the imagery show appreciable gully widening along reaches up to approximately 1.5 km in length. Based on gully discharges estimated from rainfall rates and estimates of sediment contribution from gully wall width change, we find that the sediment volumes contributed from gully walls are sufficient to bulk floodwaters up to debris flow concentrations. Points in gullies where width change began (upstream limit) in 2006 have a power law trend (R2 = 0.58) in terms of slope-drainage area. Reaches with noticeable width change, which we refer to as bulking reaches (BR), plot along a similar trend with greater drainage areas and gentler slopes. We then extracted slope and drainage area of all proglacial drainage networks to examine differences in morphology between debris flow basins (DFB) and non-debris flow basins (NDFB), hypothesizing that DFB would have a greater portion of their drainage networks with similar morphology to BR than NDFB. A comparison of total network length with greater slope and area than BR reveals that the two basins types are not statistically different. Lengths of the longest reaches with greater slope and drainage area than the BR trend, however, are statistically longer in DFB than in the NDFBs (p<0.05). These results suggest that debris flow initiation by sediment bulking does not operate as a simple threshold phenomenon in slope-area space. Instead debris flow initiation via bulking depends upon slope, drainage area, and gully length. We suspect the dependence on length

Graph Representations of Flow and Transport in Fracture Networks using Machine Learning

NASA Astrophysics Data System (ADS)

Srinivasan, G.; Viswanathan, H. S.; Karra, S.; O'Malley, D.; Godinez, H. C.; Hagberg, A.; Osthus, D.; Mohd-Yusof, J.

2017-12-01

Flow and transport of fluids through fractured systems is governed by the properties and interactions at the micro-scale. Retaining information about the micro-structure such as fracture length, orientation, aperture and connectivity in mesh-based computational models results in solving for millions to billions of degrees of freedom and quickly renders the problem computationally intractable. Our approach depicts fracture networks graphically, by mapping fractures to nodes and intersections to edges, thereby greatly reducing computational burden. Additionally, we use machine learning techniques to build simulators on the graph representation, trained on data from the mesh-based high fidelity simulations to speed up computation by orders of magnitude. We demonstrate our methodology on ensembles of discrete fracture networks, dividing up the data into training and validation sets. Our machine learned graph-based solvers result in over 3 orders of magnitude speedup without any significant sacrifice in accuracy.

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

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 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

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.

Intercluster Connection in Cognitive Wireless Mesh Networks Based on Intelligent Network Coding

NASA Astrophysics Data System (ADS)

Chen, Xianfu; Zhao, Zhifeng; Jiang, Tao; Grace, David; Zhang, Honggang

2009-12-01

Cognitive wireless mesh networks have great flexibility to improve spectrum resource utilization, within which secondary users (SUs) can opportunistically access the authorized frequency bands while being complying with the interference constraint as well as the QoS (Quality-of-Service) requirement of primary users (PUs). In this paper, we consider intercluster connection between the neighboring clusters under the framework of cognitive wireless mesh networks. Corresponding to the collocated clusters, data flow which includes the exchanging of control channel messages usually needs four time slots in traditional relaying schemes since all involved nodes operate in half-duplex mode, resulting in significant bandwidth efficiency loss. The situation is even worse at the gateway node connecting the two colocated clusters. A novel scheme based on network coding is proposed in this paper, which needs only two time slots to exchange the same amount of information mentioned above. Our simulation shows that the network coding-based intercluster connection has the advantage of higher bandwidth efficiency compared with the traditional strategy. Furthermore, how to choose an optimal relaying transmission power level at the gateway node in an environment of coexisting primary and secondary users is discussed. We present intelligent approaches based on reinforcement learning to solve the problem. Theoretical analysis and simulation results both show that the intelligent approaches can achieve optimal throughput for the intercluster relaying in the long run.

Spatially dynamic recurrent information flow across long-range dorsal motor network encodes selective motor goals.

PubMed

Yoo, Peter E; Hagan, Maureen A; John, Sam E; Opie, Nicholas L; Ordidge, Roger J; O'Brien, Terence J; Oxley, Thomas J; Moffat, Bradford A; Wong, Yan T

2018-06-01

Performing voluntary movements involves many regions of the brain, but it is unknown how they work together to plan and execute specific movements. We recorded high-resolution ultra-high-field blood-oxygen-level-dependent signal during a cued ankle-dorsiflexion task. The spatiotemporal dynamics and the patterns of task-relevant information flow across the dorsal motor network were investigated. We show that task-relevant information appears and decays earlier in the higher order areas of the dorsal motor network then in the primary motor cortex. Furthermore, the results show that task-relevant information is encoded in general initially, and then selective goals are subsequently encoded in specifics subregions across the network. Importantly, the patterns of recurrent information flow across the network vary across different subregions depending on the goal. Recurrent information flow was observed across all higher order areas of the dorsal motor network in the subregions encoding for the current goal. In contrast, only the top-down information flow from the supplementary motor cortex to the frontoparietal regions, with weakened recurrent information flow between the frontoparietal regions and bottom-up information flow from the frontoparietal regions to the supplementary cortex were observed in the subregions encoding for the opposing goal. We conclude that selective motor goal encoding and execution rely on goal-dependent differences in subregional recurrent information flow patterns across the long-range dorsal motor network areas that exhibit graded functional specialization. © 2018 Wiley Periodicals, Inc.

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

Dynamic mobility applications policy analysis : policy and institutional issues for intelligent network flow optimization (INFLO).

DOT National Transportation Integrated Search

2014-12-01

The report documents policy considerations for the Intelligent Network Flow Optimization (INFLO) connected vehicle applications bundle. INFLO aims to optimize network flow on freeways and arterials by informing motorists of existing and impendi...

Internet-Based Approaches to Building Stakeholder Networks for Conservation and Natural Resource Management.

PubMed

Kreakie, B J; Hychka, K C; Belaire, J A; Minor, E; Walker, H A

2016-02-01

Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing internet-based social networks, and use an existing traditional (survey-based) case study to illustrate in a familiar context the deviations in methods and results. Internet-based approaches to SNA offer a means to overcome institutional hurdles to conducting survey-based SNA, provide unique insight into an institution's web presences, allow for easy snowballing (iterative process that incorporates new nodes in the network), and afford monitoring of social networks through time. The internet-based approaches differ in link definition: hyperlink is based on links on a website that redirect to a different website and relatedness links are based on a Google's "relatedness" operator that identifies pages "similar" to a URL. All networks were initiated with the same start nodes [members of a conservation alliance for the Calumet region around Chicago (n = 130)], but the resulting networks vary drastically from one another. Interpretation of the resulting networks is highly contingent upon how the links were defined.

Internet-Based Approaches to Building Stakeholder Networks for Conservation and Natural Resource Management

NASA Astrophysics Data System (ADS)

Kreakie, B. J.; Hychka, K. C.; Belaire, J. A.; Minor, E.; Walker, H. A.

2016-02-01

Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing internet-based social networks, and use an existing traditional (survey-based) case study to illustrate in a familiar context the deviations in methods and results. Internet-based approaches to SNA offer a means to overcome institutional hurdles to conducting survey-based SNA, provide unique insight into an institution's web presences, allow for easy snowballing (iterative process that incorporates new nodes in the network), and afford monitoring of social networks through time. The internet-based approaches differ in link definition: hyperlink is based on links on a website that redirect to a different website and relatedness links are based on a Google's "relatedness" operator that identifies pages "similar" to a URL. All networks were initiated with the same start nodes [members of a conservation alliance for the Calumet region around Chicago ( n = 130)], but the resulting networks vary drastically from one another. Interpretation of the resulting networks is highly contingent upon how the links were defined.

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.

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.

Intelligent Network Flow Optimization (INFLO) prototype acceptance test summary.

DOT National Transportation Integrated Search

2015-05-01

This report summarizes the results of System Acceptance Testing for the implementation of the Intelligent Network Flow Optimization (INFLO) Prototype bundle within the Dynamic Mobility Applications (DMA) portion of the Connected Vehicle Program. This...

Experimental demonstration of OpenFlow-enabled media ecosystem architecture for high-end applications over metro and core networks.

PubMed

Ntofon, Okung-Dike; Channegowda, Mayur P; Efstathiou, Nikolaos; Rashidi Fard, Mehdi; Nejabati, Reza; Hunter, David K; Simeonidou, Dimitra

2013-02-25

In this paper, a novel Software-Defined Networking (SDN) architecture is proposed for high-end Ultra High Definition (UHD) media applications. UHD media applications require huge amounts of bandwidth that can only be met with high-capacity optical networks. In addition, there are requirements for control frameworks capable of delivering effective application performance with efficient network utilization. A novel SDN-based Controller that tightly integrates application-awareness with network control and management is proposed for such applications. An OpenFlow-enabled test-bed demonstrator is reported with performance evaluations of advanced online and offline media- and network-aware schedulers.

Ramification of Channel Networks Incised by Groundwater Flow

NASA Astrophysics Data System (ADS)

Yi, R. S.; Seybold, H. F.; Petroff, A. P.; Devauchelle, O.; Rothman, D.

2011-12-01

The geometry of channel networks has been a source of fascination since at least Leonardo da Vinci's time. Yet a comprehensive understanding of ramification---the mechanism of branching by which a stream network acquires its geometric complexity---remains elusive. To investigate the mechanisms of ramification and network growth, we consider channel growth driven by groundwater flow as a model system, analogous to a medical scientist's laboratory rat. We test our theoretical predictions through analysis of a particularly compelling example found on the Florida Panhandle north of Bristol. As our ultimate goal is to understand ramification and growth dynamics of the entire network, we build a computational model based on the following growth hypothesis: Channels grow in the direction that captures the maximum water flux. When there are two such directions, tips bifurcate. The direction of growth can be determined from the expansion of the ground water field around each tip, where each coefficient in this expansion has a physical interpretation. The first coefficient in the expansion determines the ground water discharge, leading to a straight growth of the channel. The second term describes the asymmetry in the water field leading to a bending of the stream in the direction of maximal water flux. The ratio between the first and the third coefficient determines a critical distance rc over which the tip feels inhomogeneities in the ground water table. This initiates then the splitting of the tip. In order to test our growth hypothesis and to determine rc, we grow the Florida network backward. At each time step we calculate the solution of the ground water field and determine the appropriate expansion coefficients around each tip. Comparing this simulation result to the predicted values provides us with a stringent measure for rc and the significance of our growth hypothesis.

How Did the Information Flow in the #AlphaGo Hashtag Network? A Social Network Analysis of the Large-Scale Information Network on Twitter.

PubMed

Kim, Jinyoung

2017-12-01

As it becomes common for Internet users to use hashtags when posting and searching information on social media, it is important to understand who builds a hashtag network and how information is circulated within the network. This article focused on unlocking the potential of the #AlphaGo hashtag network by addressing the following questions. First, the current study examined whether traditional opinion leadership (i.e., the influentials hypothesis) or grassroot participation by the public (i.e., the interpersonal hypothesis) drove dissemination of information in the hashtag network. Second, several unique patterns of information distribution by key users were identified. Finally, the association between attributes of key users who exerted great influence on information distribution (i.e., the number of followers and follows) and their central status in the network was tested. To answer the proffered research questions, a social network analysis was conducted using a large-scale hashtag network data set from Twitter (n = 21,870). The results showed that the leading actors in the network were actively receiving information from their followers rather than serving as intermediaries between the original information sources and the public. Moreover, the leading actors played several roles (i.e., conversation starters, influencers, and active engagers) in the network. Furthermore, the number of their follows and followers were significantly associated with their central status in the hashtag network. Based on the results, the current research explained how the information was exchanged in the hashtag network by proposing the reciprocal model of information flow.

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

Intelligent Sensing and Classification in DSR-Based Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Dempsey, Tae; Sahin, Gokhan; Morton, Yu T. (Jade

Wireless ad hoc networks have fundamentally altered today's battlefield, with applications ranging from unmanned air vehicles to randomly deployed sensor networks. Security and vulnerabilities in wireless ad hoc networks have been considered at different layers, and many attack strategies have been proposed, including denial of service (DoS) through the intelligent jamming of the most critical packet types of flows in a network. This paper investigates the effectiveness of intelligent jamming in wireless ad hoc networks using the Dynamic Source Routing (DSR) and TCP protocols and introduces an intelligent classifier to facilitate the jamming of such networks. Assuming encrypted packet headers and contents, our classifier is based solely on the observable characteristics of size, inter-arrival timing, and direction and classifies packets with up to 99.4% accuracy in our experiments.

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.

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.

The influence of passenger flow on the topology characteristics of urban rail transit networks

NASA Astrophysics Data System (ADS)

Hu, Yingyue; Chen, Feng; Chen, Peiwen; Tan, Yurong

2017-05-01

Current researches on the network characteristics of metro networks are generally carried out on topology networks without passenger flows running on it, thus more complex features of the networks with ridership loaded on it cannot be captured. In this study, we incorporated the load of metro networks, passenger volume, into the exploration of network features. Thus, the network can be examined in the context of operation, which is the ultimate purpose of the existence of a metro network. To this end, section load was selected as an edge weight to demonstrate the influence of ridership on the network, and a weighted calculation method for complex network indicators and robustness were proposed to capture the unique behaviors of a metro network with passengers flowing in it. The proposed method was applied on Beijing Subway. Firstly, the passenger volume in terms of daily origin and destination matrix was extracted from exhausted transit smart card data. Using the established approach and the matrix as weighting, common indicators of complex network including clustering coefficient, betweenness and degree were calculated, and network robustness were evaluated under potential attacks. The results were further compared to that of unweighted networks, and it suggests indicators of the network with consideration of passenger volumes differ from that without ridership to some extent, and networks tend to be more vulnerable than that without load on it. The significance sequence for the stations can be changed. By introducing passenger flow weighting, actual operation status of the network can be reflected more accurately. It is beneficial to determine the crucial stations and make precautionary measures for the entire network’s operation security.

The Flow of International Students from a Macro Perspective: A Network Analysis

ERIC Educational Resources Information Center

Barnett, George A.; Lee, Moosung; Jiang, Ke; Park, Han Woo

2016-01-01

This paper provides a network analysis of the international flow of students among 210 countries and the factors determining the structure of this flow. Among these factors, bilateral hyperlink connections between countries and the number of telephone minutes (communication variables) are the most important predictors of the flow's structure,…

Identifying High-Rate Flows Based on Sequential Sampling

NASA Astrophysics Data System (ADS)

Zhang, Yu; Fang, Binxing; Luo, Hao

We consider the problem of fast identification of high-rate flows in backbone links with possibly millions of flows. Accurate identification of high-rate flows is important for active queue management, traffic measurement and network security such as detection of distributed denial of service attacks. It is difficult to directly identify high-rate flows in backbone links because tracking the possible millions of flows needs correspondingly large high speed memories. To reduce the measurement overhead, the deterministic 1-out-of-k sampling technique is adopted which is also implemented in Cisco routers (NetFlow). Ideally, a high-rate flow identification method should have short identification time, low memory cost and processing cost. Most importantly, it should be able to specify the identification accuracy. We develop two such methods. The first method is based on fixed sample size test (FSST) which is able to identify high-rate flows with user-specified identification accuracy. However, since FSST has to record every sampled flow during the measurement period, it is not memory efficient. Therefore the second novel method based on truncated sequential probability ratio test (TSPRT) is proposed. Through sequential sampling, TSPRT is able to remove the low-rate flows and identify the high-rate flows at the early stage which can reduce the memory cost and identification time respectively. According to the way to determine the parameters in TSPRT, two versions of TSPRT are proposed: TSPRT-M which is suitable when low memory cost is preferred and TSPRT-T which is suitable when short identification time is preferred. The experimental results show that TSPRT requires less memory and identification time in identifying high-rate flows while satisfying the accuracy requirement as compared to previously proposed methods.

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.

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.

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.

Deterministic and stochastic algorithms for resolving the flow fields in ducts and networks using energy minimization

NASA Astrophysics Data System (ADS)

Sochi, Taha

2016-09-01

Several deterministic and stochastic multi-variable global optimization algorithms (Conjugate Gradient, Nelder-Mead, Quasi-Newton and global) are investigated in conjunction with energy minimization principle to resolve the pressure and volumetric flow rate fields in single ducts and networks of interconnected ducts. The algorithms are tested with seven types of fluid: Newtonian, power law, Bingham, Herschel-Bulkley, Ellis, Ree-Eyring and Casson. The results obtained from all those algorithms for all these types of fluid agree very well with the analytically derived solutions as obtained from the traditional methods which are based on the conservation principles and fluid constitutive relations. The results confirm and generalize the findings of our previous investigations that the energy minimization principle is at the heart of the flow dynamics systems. The investigation also enriches the methods of computational fluid dynamics for solving the flow fields in tubes and networks for various types of Newtonian and non-Newtonian fluids.

The International Postal Network and Other Global Flows as Proxies for National Wellbeing.

PubMed

Hristova, Desislava; Rutherford, Alex; Anson, Jose; Luengo-Oroz, Miguel; Mascolo, Cecilia

2016-01-01

The digital exhaust left by flows of physical and digital commodities provides a rich measure of the nature, strength and significance of relationships between countries in the global network. With this work, we examine how these traces and the network structure can reveal the socioeconomic profile of different countries. We take into account multiple international networks of physical and digital flows, including the previously unexplored international postal network. By measuring the position of each country in the Trade, Postal, Migration, International Flights, IP and Digital Communications networks, we are able to build proxies for a number of crucial socioeconomic indicators such as GDP per capita and the Human Development Index ranking along with twelve other indicators used as benchmarks of national well-being by the United Nations and other international organisations. In this context, we have also proposed and evaluated a global connectivity degree measure applying multiplex theory across the six networks that accounts for the strength of relationships between countries. We conclude by showing how countries with shared community membership over multiple networks have similar socioeconomic profiles. Combining multiple flow data sources can help understand the forces which drive economic activity on a global level. Such an ability to infer proxy indicators in a context of incomplete information is extremely timely in light of recent discussions on measurement of indicators relevant to the Sustainable Development Goals.

The International Postal Network and Other Global Flows as Proxies for National Wellbeing

PubMed Central

Rutherford, Alex; Anson, Jose; Luengo-Oroz, Miguel; Mascolo, Cecilia

2016-01-01

The digital exhaust left by flows of physical and digital commodities provides a rich measure of the nature, strength and significance of relationships between countries in the global network. With this work, we examine how these traces and the network structure can reveal the socioeconomic profile of different countries. We take into account multiple international networks of physical and digital flows, including the previously unexplored international postal network. By measuring the position of each country in the Trade, Postal, Migration, International Flights, IP and Digital Communications networks, we are able to build proxies for a number of crucial socioeconomic indicators such as GDP per capita and the Human Development Index ranking along with twelve other indicators used as benchmarks of national well-being by the United Nations and other international organisations. In this context, we have also proposed and evaluated a global connectivity degree measure applying multiplex theory across the six networks that accounts for the strength of relationships between countries. We conclude by showing how countries with shared community membership over multiple networks have similar socioeconomic profiles. Combining multiple flow data sources can help understand the forces which drive economic activity on a global level. Such an ability to infer proxy indicators in a context of incomplete information is extremely timely in light of recent discussions on measurement of indicators relevant to the Sustainable Development Goals. PMID:27248142

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.

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.

Numerical Simulation of Unsteady Blood Flow through Capillary Networks.

PubMed

Davis, J M; Pozrikidis, C

2011-08-01

A numerical method is implemented for computing unsteady blood flow through a branching capillary network. The evolution of the discharge hematocrit along each capillary segment is computed by integrating in time a one-dimensional convection equation using a finite-difference method. The convection velocity is determined by the local and instantaneous effective capillary blood viscosity, while the tube to discharge hematocrit ratio is deduced from available correlations. Boundary conditions for the discharge hematocrit at divergent bifurcations arise from the partitioning law proposed by Klitzman and Johnson involving a dimensionless exponent, q≥1. When q=1, the cells are partitioned in proportion to the flow rate; as q tends to infinity, the cells are channeled into the branch with the highest flow rate. Simulations are performed for a tree-like, perfectly symmetric or randomly perturbed capillary network with m generations. When the tree involves more than a few generations, a supercritical Hopf bifurcation occurs at a critical value of q, yielding spontaneous self-sustained oscillations in the absence of external forcing. A phase diagram in the m-q plane is presented to establish conditions for unsteady flow, and the effect of various geometrical and physical parameters is examined. For a given network tree order, m, oscillations can be induced for a sufficiently high value of q by increasing the apparent intrinsic viscosity, decreasing the ratio of the vessel diameter from one generation to the next, or by decreasing the diameter of the terminal vessels. With other parameters fixed, oscillations are inhibited by increasing m. The results of the continuum model are in excellent agreement with the predictions of a discrete model where the motion of individual cells is followed from inlet to outlet.

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.

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

Passive and Active Analysis in DSR-Based Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Dempsey, Tae; Sahin, Gokhan; Morton, Y. T. (Jade)

Security and vulnerabilities in wireless ad hoc networks have been considered at different layers, and many attack strategies have been proposed, including denial of service (DoS) through the intelligent jamming of the most critical packet types of flows in a network. This paper investigates the effectiveness of intelligent jamming in wireless ad hoc networks using the Dynamic Source Routing (DSR) and TCP protocols and introduces an intelligent classifier to facilitate the jamming of such networks. Assuming encrypted packet headers and contents, our classifier is based solely on the observable characteristics of size, inter-arrival timing, and direction and classifies packets with up to 99.4% accuracy in our experiments. Furthermore, we investigate active analysis, which is the combination of a classifier and intelligent jammer to invoke specific responses from a victim network.

Direction of information flow in large-scale resting-state networks is frequency-dependent.

PubMed

Hillebrand, Arjan; Tewarie, Prejaas; van Dellen, Edwin; Yu, Meichen; Carbo, Ellen W S; Douw, Linda; Gouw, Alida A; van Straaten, Elisabeth C W; Stam, Cornelis J

2016-04-05

Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-to-posterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequency-dependent reentry loops that are dominated by flow from parieto-occipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.

Impact Analysis of Flow Shaping in Ethernet-AVB/TSN and AFDX from Network Calculus and Simulation Perspective

PubMed Central

He, Feng; Zhao, Lin; Li, Ershuai

2017-01-01

Ethernet-AVB/TSN (Audio Video Bridging/Time-Sensitive Networking) and AFDX (Avionics Full DupleX switched Ethernet) are switched Ethernet technologies, which are both candidates for real-time communication in the context of transportation systems. AFDX implements a fixed priority scheduling strategy with two priority levels. Ethernet-AVB/TSN supports a similar fixed priority scheduling with an additional Credit-Based Shaper (CBS) mechanism. Besides, TSN can support time-triggered scheduling strategy. One direct effect of CBS mechanism is to increase the delay of its flows while decreasing the delay of other priority ones. The former effect can be seen as the shaping restriction and the latter effect can be seen as the shaping benefit from CBS. The goal of this paper is to investigate the impact of CBS on different priority flows, especially on the intermediate priority ones, as well as the effect of CBS bandwidth allocation. It is based on a performance comparison of AVB/TSN and AFDX by simulation in an automotive case study. Furthermore, the shaping benefit is modeled based on integral operation from network calculus perspective. Combing with the analysis of shaping restriction and shaping benefit, some configuration suggestions on the setting of CBS bandwidth are given. Results show that the effect of CBS depends on flow loads and CBS configurations. A larger load of high priority flows in AVB tends to a better performance for the intermediate priority flows when compared with AFDX. Shaping benefit can be explained and calculated according to the changing from the permitted maximum burst. PMID:28531158

Flow rate of transport network controls uniform metabolite supply to tissue

PubMed Central

Meigel, Felix J.

2018-01-01

Life and functioning of higher organisms depends on the continuous supply of metabolites to tissues and organs. What are the requirements on the transport network pervading a tissue to provide a uniform supply of nutrients, minerals or hormones? To theoretically answer this question, we present an analytical scaling argument and numerical simulations on how flow dynamics and network architecture control active spread and uniform supply of metabolites by studying the example of xylem vessels in plants. We identify the fluid inflow rate as the key factor for uniform supply. While at low inflow rates metabolites are already exhausted close to flow inlets, too high inflow flushes metabolites through the network and deprives tissue close to inlets of supply. In between these two regimes, there exists an optimal inflow rate that yields a uniform supply of metabolites. We determine this optimal inflow analytically in quantitative agreement with numerical results. Optimizing network architecture by reducing the supply variance over all network tubes, we identify patterns of tube dilation or contraction that compensate sub-optimal supply for the case of too low or too high inflow rate. PMID:29720455

Neural networks application to divergence-based passive ranging

NASA Technical Reports Server (NTRS)

Barniv, Yair

1992-01-01

The purpose of this report is to summarize the state of knowledge and outline the planned work in divergence-based/neural networks approach to the problem of passive ranging derived from optical flow. Work in this and closely related areas is reviewed in order to provide the necessary background for further developments. New ideas about devising a monocular passive-ranging system are then introduced. It is shown that image-plan divergence is independent of image-plan location with respect to the focus of expansion and of camera maneuvers because it directly measures the object's expansion which, in turn, is related to the time-to-collision. Thus, a divergence-based method has the potential of providing a reliable range complementing other monocular passive-ranging methods which encounter difficulties in image areas close to the focus of expansion. Image-plan divergence can be thought of as some spatial/temporal pattern. A neural network realization was chosen for this task because neural networks have generally performed well in various other pattern recognition applications. The main goal of this work is to teach a neural network to derive the divergence from the imagery.

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

Effects of iterative learning based signal control strategies on macroscopic fundamental diagrams of urban road networks

NASA Astrophysics Data System (ADS)

Yan, Fei; Tian, Fuli; Shi, Zhongke

2016-10-01

Urban traffic flows are inherently repeated on a daily or weekly basis. This repeatability can help improve the traffic conditions if it is used properly by the control system. In this paper, we propose a novel iterative learning control (ILC) strategy for traffic signals of urban road networks using the repeatability feature of traffic flow. To improve the control robustness, the ILC strategy is further integrated with an error feedback control law in a complementary manner. Theoretical analysis indicates that the ILC-based traffic signal control methods can guarantee the asymptotic learning convergence, despite the presence of modeling uncertainties and exogenous disturbances. Finally, the impacts of the ILC-based signal control strategies on the network macroscopic fundamental diagram (MFD) are examined. The results show that the proposed ILC-based control strategies can homogenously distribute the network accumulation by controlling the vehicle numbers in each link to the desired levels under different traffic demands, which can result in the network with high capacity and mobility.

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

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.

Compressing Test and Evaluation by Using Flow Data for Scalable Network Traffic Analysis

DTIC Science & Technology

2014-10-01

test events, quality of service and other key metrics of military systems and networks are evaluated. Network data captured in standard flow formats...mentioned here. The Ozone Widget Framework (Next Century, n.d.) has proven to be very useful. Also, an extensive, clean, and optimized JavaScript ...library for visualizing many types of data can be found in D3–Data Driven Documents (Bostock, 2013). Quality of Service from Flow Two essential metrics of

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.

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.

Timescale analysis of rule-based biochemical reaction networks

PubMed Central

Klinke, David J.; Finley, Stacey D.

2012-01-01

The flow of information within a cell is governed by a series of protein-protein interactions that can be described as a reaction network. Mathematical models of biochemical reaction networks can be constructed by repetitively applying specific rules that define how reactants interact and what new species are formed upon reaction. To aid in understanding the underlying biochemistry, timescale analysis is one method developed to prune the size of the reaction network. In this work, we extend the methods associated with timescale analysis to reaction rules instead of the species contained within the network. To illustrate this approach, we applied timescale analysis to a simple receptor-ligand binding model and a rule-based model of Interleukin-12 (IL-12) signaling in näive CD4+ T cells. The IL-12 signaling pathway includes multiple protein-protein interactions that collectively transmit information; however, the level of mechanistic detail sufficient to capture the observed dynamics has not been justified based upon the available data. The analysis correctly predicted that reactions associated with JAK2 and TYK2 binding to their corresponding receptor exist at a pseudo-equilibrium. In contrast, reactions associated with ligand binding and receptor turnover regulate cellular response to IL-12. An empirical Bayesian approach was used to estimate the uncertainty in the timescales. This approach complements existing rank- and flux-based methods that can be used to interrogate complex reaction networks. Ultimately, timescale analysis of rule-based models is a computational tool that can be used to reveal the biochemical steps that regulate signaling dynamics. PMID:21954150

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.

Application guide for AFINCH (Analysis of Flows in Networks of Channels) described by NHDPlus

USGS Publications Warehouse

Holtschlag, David J.

2009-01-01

AFINCH (Analysis of Flows in Networks of CHannels) is a computer application that can be used to generate a time series of monthly flows at stream segments (flowlines) and water yields for catchments defined in the National Hydrography Dataset Plus (NHDPlus) value-added attribute system. AFINCH provides a basis for integrating monthly flow data from streamgages, water-use data, monthly climatic data, and land-cover characteristics to estimate natural monthly water yields from catchments by user-defined regression equations. Images of monthly water yields for active streamgages are generated in AFINCH and provide a basis for detecting anomalies in water yields, which may be associated with undocumented flow diversions or augmentations. Water yields are multiplied by the drainage areas of the corresponding catchments to estimate monthly flows. Flows from catchments are accumulated downstream through the streamflow network described by the stream segments. For stream segments where streamgages are active, ratios of measured to accumulated flows are computed. These ratios are applied to upstream water yields to proportionally adjust estimated flows to match measured flows. Flow is conserved through the NHDPlus network. A time series of monthly flows can be generated for stream segments that average about 1-mile long, or monthly water yields from catchments that average about 1 square mile. Estimated monthly flows can be displayed within AFINCH, examined for nonstationarity, and tested for monotonic trends. Monthly flows also can be used to estimate flow-duration characteristics at stream segments. AFINCH generates output files of monthly flows and water yields that are compatible with ArcMap, a geographical information system analysis and display environment. Chloropleth maps of monthly water yield and flow can be generated and analyzed within ArcMap by joining NHDPlus data structures with AFINCH output. Matlab code for the AFINCH application is presented.

Maximum flow-based resilience analysis: From component to system

PubMed Central

Jin, Chong; Li, Ruiying; Kang, Rui

2017-01-01

Resilience, the ability to withstand disruptions and recover quickly, must be considered during system design because any disruption of the system may cause considerable loss, including economic and societal. This work develops analytic maximum flow-based resilience models for series and parallel systems using Zobel’s resilience measure. The two analytic models can be used to evaluate quantitatively and compare the resilience of the systems with the corresponding performance structures. For systems with identical components, the resilience of the parallel system increases with increasing number of components, while the resilience remains constant in the series system. A Monte Carlo-based simulation method is also provided to verify the correctness of our analytic resilience models and to analyze the resilience of networked systems based on that of components. A road network example is used to illustrate the analysis process, and the resilience comparison among networks with different topologies but the same components indicates that a system with redundant performance is usually more resilient than one without redundant performance. However, not all redundant capacities of components can improve the system resilience, the effectiveness of the capacity redundancy depends on where the redundant capacity is located. PMID:28545135

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.

Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks

PubMed Central

Artuñedo, Antonio; del Toro, Raúl M.; Haber, Rodolfo E.

2017-01-01

Nowadays many studies are being conducted to develop solutions for improving the performance of urban traffic networks. One of the main challenges is the necessary cooperation among different entities such as vehicles or infrastructure systems and how to exploit the information available through networks of sensors deployed as infrastructures for smart cities. In this work an algorithm for cooperative control of urban subsystems is proposed to provide a solution for mobility problems in cities. The interconnected traffic lights controller (TLC) network adapts traffic lights cycles, based on traffic and air pollution sensory information, in order to improve the performance of urban traffic networks. The presence of air pollution in cities is not only caused by road traffic but there are other pollution sources that contribute to increase or decrease the pollution level. Due to the distributed and heterogeneous nature of the different components involved, a system of systems engineering approach is applied to design a consensus-based control algorithm. The designed control strategy contains a consensus-based component that uses the information shared in the network for reaching a consensus in the state of TLC network components. Discrete event systems specification is applied for modelling and simulation. The proposed solution is assessed by simulation studies with very promising results to deal with simultaneous responses to both pollution levels and traffic flows in urban traffic networks. PMID:28445398

Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks.

PubMed

Artuñedo, Antonio; Del Toro, Raúl M; Haber, Rodolfo E

2017-04-26

Nowadays many studies are being conducted to develop solutions for improving the performance of urban traffic networks. One of the main challenges is the necessary cooperation among different entities such as vehicles or infrastructure systems and how to exploit the information available through networks of sensors deployed as infrastructures for smart cities. In this work an algorithm for cooperative control of urban subsystems is proposed to provide a solution for mobility problems in cities. The interconnected traffic lights controller ( TLC ) network adapts traffic lights cycles, based on traffic and air pollution sensory information, in order to improve the performance of urban traffic networks. The presence of air pollution in cities is not only caused by road traffic but there are other pollution sources that contribute to increase or decrease the pollution level. Due to the distributed and heterogeneous nature of the different components involved, a system of systems engineering approach is applied to design a consensus-based control algorithm. The designed control strategy contains a consensus-based component that uses the information shared in the network for reaching a consensus in the state of TLC network components. Discrete event systems specification is applied for modelling and simulation. The proposed solution is assessed by simulation studies with very promising results to deal with simultaneous responses to both pollution levels and traffic flows in urban traffic networks.

Transport of sediment through a channel network during a post-fire debris flow

NASA Astrophysics Data System (ADS)

Nyman, P.; Box, W. A. C.; Langhans, C.; Stout, J. C.; Keesstra, S.; Sheridan, G. J.

2017-12-01

Transport processes linking sediment in steep headwaters with rivers during high magnitude events are rarely examined in detail, particularly in forested settings where major erosion events are rare and opportunities for collecting data are limited. Yet high magnitude events in headwaters are known to drive landscape change. This study examines how a debris flow after wildfire impacts on sediment transport from small headwaters (0.02 km2) through a step pool stream system within a larger 14 km2 catchment, which drains into the East Ovens River in SE Australia. Sediment delivery from debris flows was modelled and downstream deposition of sediment was measured using a combination of aerial imagery and field surveys. Particle size distributions were measured for all major deposits. These data were summarised to map sediment flux as a continuous variable over the drainage network. Total deposition throughout the stream network was 39 x 103 m3. Catchment efflux was 61 x 103 m3 (specific sediment yield of 78 ton ha-1), which equates to 400-800 years of background erosion, based on measurements in nearby catchments. Despite the low gradient (ca. 0.1 m m-1) of the main channel there was no systematic downstream sorting in sediment deposits in the catchment. This is due to debris flow processes operating throughout the stream network, with lateral inputs sustaining the process in low gradient channels, except in the most downstream reaches where the flow transitioned towards hyper-concentrated flow. Overall, a large proportion ( 88%) of the eroded fine fraction (<63 micron) exited the catchment, when compared to the overall ratio (55%) of erosion to deposition. The geomorphic legacy of this post-wildfire event depends on scale. In the lower channels (steam order 4-5), where erosion was nearly equal to deposition, the event had no real impact on total sediment volumes stored. In upper channels (stream orders < 3) erosion was widespread but deposition rates were low. So

40-Gbps optical backbone network deep packet inspection based on FPGA

NASA Astrophysics Data System (ADS)

Zuo, Yuan; Huang, Zhiping; Su, Shaojing

2014-11-01

In the era of information, the big data, which contains huge information, brings about some problems, such as high speed transmission, storage and real-time analysis and process. As the important media for data transmission, the Internet is the significant part for big data processing research. With the large-scale usage of the Internet, the data streaming of network is increasing rapidly. The speed level in the main fiber optic communication of the present has reached 40Gbps, even 100Gbps, therefore data on the optical backbone network shows some features of massive data. Generally, data services are provided via IP packets on the optical backbone network, which is constituted with SDH (Synchronous Digital Hierarchy). Hence this method that IP packets are directly mapped into SDH payload is named POS (Packet over SDH) technology. Aiming at the problems of real time process of high speed massive data, this paper designs a process system platform based on ATCA for 40Gbps POS signal data stream recognition and packet content capture, which employs the FPGA as the CPU. This platform offers pre-processing of clustering algorithms, service traffic identification and data mining for the following big data storage and analysis with high efficiency. Also, the operational procedure is proposed in this paper. Four channels of 10Gbps POS signal decomposed by the analysis module, which chooses FPGA as the kernel, are inputted to the flow classification module and the pattern matching component based on TCAM. Based on the properties of the length of payload and net flows, buffer management is added to the platform to keep the key flow information. According to data stream analysis, DPI (deep packet inspection) and flow balance distribute, the signal is transmitted to the backend machine through the giga Ethernet ports on back board. Practice shows that the proposed platform is superior to the traditional applications based on ASIC and NP.

Exploiting Bounded Signal Flow for Graph Orientation Based on Cause-Effect Pairs

NASA Astrophysics Data System (ADS)

Dorn, Britta; Hüffner, Falk; Krüger, Dominikus; Niedermeier, Rolf; Uhlmann, Johannes

We consider the following problem: Given an undirected network and a set of sender-receiver pairs, direct all edges such that the maximum number of "signal flows" defined by the pairs can be routed respecting edge directions. This problem has applications in communication networks and in understanding protein interaction based cell regulation mechanisms. Since this problem is NP-hard, research so far concentrated on polynomial-time approximation algorithms and tractable special cases. We take the viewpoint of parameterized algorithmics and examine several parameters related to the maximum signal flow over vertices or edges. We provide several fixed-parameter tractability results, and in one case a sharp complexity dichotomy between a linear-time solvable case and a slightly more general NP-hard case. We examine the value of these parameters for several real-world network instances. For many relevant cases, the NP-hard problem can be solved to optimality. In this way, parameterized analysis yields both deeper insight into the computational complexity and practical solving strategies.

Cascades in interdependent flow networks

NASA Astrophysics Data System (ADS)

Scala, Antonio; De Sanctis Lucentini, Pier Giorgio; Caldarelli, Guido; D'Agostino, Gregorio

2016-06-01

In this manuscript, we investigate the abrupt breakdown behavior of coupled distribution grids under load growth. This scenario mimics the ever-increasing customer demand and the foreseen introduction of energy hubs interconnecting the different energy vectors. We extend an analytical model of cascading behavior due to line overloads to the case of interdependent networks and find evidence of first order transitions due to the long-range nature of the flows. Our results indicate that the foreseen increase in the couplings between the grids has two competing effects: on the one hand, it increases the safety region where grids can operate without withstanding systemic failures; on the other hand, it increases the possibility of a joint systems' failure.

Base Flow Model Validation

NASA Technical Reports Server (NTRS)

Sinha, Neeraj; Brinckman, Kevin; Jansen, Bernard; Seiner, John

2011-01-01

A method was developed of obtaining propulsive base flow data in both hot and cold jet environments, at Mach numbers and altitude of relevance to NASA launcher designs. The base flow data was used to perform computational fluid dynamics (CFD) turbulence model assessments of base flow predictive capabilities in order to provide increased confidence in base thermal and pressure load predictions obtained from computational modeling efforts. Predictive CFD analyses were used in the design of the experiments, available propulsive models were used to reduce program costs and increase success, and a wind tunnel facility was used. The data obtained allowed assessment of CFD/turbulence models in a complex flow environment, working within a building-block procedure to validation, where cold, non-reacting test data was first used for validation, followed by more complex reacting base flow validation.

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.

Assessment of topographic and drainage network controls on debris-flow travel distance along the west coast of the United States

USGS Publications Warehouse

Coe, Jeffrey A.; Reid, Mark E.; Brien, Dainne L.; Michael, John A.

2011-01-01

To better understand controls on debris-flow entrainment and travel distance, we examined topographic and drainage network characteristics of initiation locations in two separate debris-flow prone areas located 700 km apart along the west coast of the U.S. One area was located in northern California, the other in southern Oregon. In both areas, debris flows mobilized from slides during large storms, but, when stratified by number of contributing initiation locations, median debris-flow travel distances in Oregon were 5 to 8 times longer than median distances in California. Debris flows in Oregon readily entrained channel material; entrainment in California was minimal. To elucidate this difference, we registered initiation locations to high-resolution airborne LiDAR, and then examined travel distances with respect to values of slope, upslope contributing area, planform curvature, distance from initiation locations to the drainage network, and number of initiation areas that contributed to flows. Results show distinct differences in the topographic and drainage network characteristics of debris-flow initiation locations between the two study areas. Slope and planform curvature of initiation locations (landslide headscarps), commonly used to predict landslide-prone areas, were not useful for predicting debris-flow travel distances. However, a positive, power-law relation exists between median debris-flow travel distance and the number of contributing debris-flow initiation locations. Moreover, contributing area and the proximity of the initiation locations to the drainage network both influenced travel distances, but proximity to the drainage network was the better predictor of travel distance. In both study areas, flows that interacted with the drainage network flowed significantly farther than those that did not. In California, initiation sites within 60 m of the network were likely to reach the network and generate longtraveled flows; in Oregon, the threshold was

Detection of flow limitation in obstructive sleep apnea with an artificial neural network.

PubMed

Norman, Robert G; Rapoport, David M; Ayappa, Indu

2007-09-01

During sleep, the development of a plateau on the inspiratory airflow/time contour provides a non-invasive indicator of airway collapsibility. Humans recognize this abnormal contour easily, and this study replicates this with an artificial neural network (ANN) using a normalized shape. Five 10 min segments were selected from each of 18 sleep records (respiratory airflow measured with a nasal cannula) with varying degrees of sleep disordered breathing. Each breath was visually scored for shape, and breaths split randomly into a training and test set. Equally spaced, peak amplitude normalized flow values (representing breath shape) formed the only input to a back propagation ANN. Following training, breath-by-breath agreement of the ANN with the manual classification was tabulated for the training and test sets separately. Agreement of the ANN was 89% in the training set and 70.6% in the test set. When the categories of 'probably normal' and 'normal', and 'probably flow limited' and 'flow limited' were combined, the agreement increased to 92.7% and 89.4% respectively, similar to the intra- and inter-rater agreements obtained by a visual classification of these breaths. On a naive dataset, the agreement of the ANN to visual classification was 57.7% overall and 82.4% when the categories were collapsed. A neural network based only on the shape of inspiratory airflow succeeded in classifying breaths as to the presence/absence of flow limitation. This approach could be used to provide a standardized, reproducible and automated means of detecting elevated upper airway resistance.

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.

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.

Flows, scaling, and the control of moment hierarchies for stochastic chemical reaction networks

NASA Astrophysics Data System (ADS)

Smith, Eric; Krishnamurthy, Supriya

2017-12-01

Stochastic chemical reaction networks (CRNs) are complex systems that combine the features of concurrent transformation of multiple variables in each elementary reaction event and nonlinear relations between states and their rates of change. Most general results concerning CRNs are limited to restricted cases where a topological characteristic known as deficiency takes a value 0 or 1, implying uniqueness and positivity of steady states and surprising, low-information forms for their associated probability distributions. Here we derive equations of motion for fluctuation moments at all orders for stochastic CRNs at general deficiency. We show, for the standard base case of proportional sampling without replacement (which underlies the mass-action rate law), that the generator of the stochastic process acts on the hierarchy of factorial moments with a finite representation. Whereas simulation of high-order moments for many-particle systems is costly, this representation reduces the solution of moment hierarchies to a complexity comparable to solving a heat equation. At steady states, moment hierarchies for finite CRNs interpolate between low-order and high-order scaling regimes, which may be approximated separately by distributions similar to those for deficiency-zero networks and connected through matched asymptotic expansions. In CRNs with multiple stable or metastable steady states, boundedness of high-order moments provides the starting condition for recursive solution downward to low-order moments, reversing the order usually used to solve moment hierarchies. A basis for a subset of network flows defined by having the same mean-regressing property as the flows in deficiency-zero networks gives the leading contribution to low-order moments in CRNs at general deficiency, in a 1 /n expansion in large particle numbers. Our results give a physical picture of the different informational roles of mean-regressing and non-mean-regressing flows and clarify the dynamical

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.

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.

Nonbinary Tree-Based Phylogenetic Networks.

PubMed

Jetten, Laura; van Iersel, Leo

2018-01-01

Rooted phylogenetic networks are used to describe evolutionary histories that contain non-treelike evolutionary events such as hybridization and horizontal gene transfer. In some cases, such histories can be described by a phylogenetic base-tree with additional linking arcs, which can, for example, represent gene transfer events. Such phylogenetic networks are called tree-based. Here, we consider two possible generalizations of this concept to nonbinary networks, which we call tree-based and strictly-tree-based nonbinary phylogenetic networks. We give simple graph-theoretic characterizations of tree-based and strictly-tree-based nonbinary phylogenetic networks. Moreover, we show for each of these two classes that it can be decided in polynomial time whether a given network is contained in the class. Our approach also provides a new view on tree-based binary phylogenetic networks. Finally, we discuss two examples of nonbinary phylogenetic networks in biology and show how our results can be applied to them.

Virtual CO2 Emission Flows in the Global Electricity Trade Network.

PubMed

Qu, Shen; Li, Yun; Liang, Sai; Yuan, Jiahai; Xu, Ming

2018-06-05

Quantifying greenhouse gas emissions due to electricity consumption is crucial for climate mitigation in the electric power sector. Current practices primarily use production-based emission factors to quantify emissions for electricity consumption, assuming production and consumption of electricity take place within the same region. The increasingly intensified cross-border electricity trade complicates the accounting for emissions of electricity consumption. This study employs a network approach to account for the flows in the whole electricity trade network to estimate CO 2 emissions of electricity consumption for 137 major countries/regions in 2014. Results show that in some countries, especially those in Europe and Southern Africa, the impacts of electricity trade on the estimation of emission factors and embodied emissions are significant. The changes made to emission factors by considering intergrid electricity trade can have significant implications for emission accounting and climate mitigation when multiplied by total electricity consumption of the corresponding countries/regions.

Structural Efficiency of Percolated Landscapes in Flow Networks

PubMed Central

Serrano, M. Ángeles; De Los Rios, Paolo

2008-01-01

The large-scale structure of complex systems is intimately related to their functionality and evolution. In particular, global transport processes in flow networks rely on the presence of directed pathways from input to output nodes and edges, which organize in macroscopic connected components. However, the precise relation between such structures and functional or evolutionary aspects remains to be understood. Here, we investigate which are the constraints that the global structure of directed networks imposes on transport phenomena. We define quantitatively under minimal assumptions the structural efficiency of networks to determine how robust communication between the core and the peripheral components through interface edges could be. Furthermore, we assess that optimal topologies in terms of access to the core should look like “hairy balls” so to minimize bottleneck effects and the sensitivity to failures. We illustrate our investigation with the analysis of three real networks with very different purposes and shaped by very different dynamics and time-scales–the Internet customer-provider set of relationships, the nervous system of the worm Caenorhabditis elegans, and the metabolism of the bacterium Escherichia coli. Our findings prove that different global connectivity structures result in different levels of structural efficiency. In particular, biological networks seem to be close to the optimal layout. PMID:18985157

On Tree-Based Phylogenetic Networks.

PubMed

Zhang, Louxin

2016-07-01

A large class of phylogenetic networks can be obtained from trees by the addition of horizontal edges between the tree edges. These networks are called tree-based networks. We present a simple necessary and sufficient condition for tree-based networks and prove that a universal tree-based network exists for any number of taxa that contains as its base every phylogenetic tree on the same set of taxa. This answers two problems posted by Francis and Steel recently. A byproduct is a computer program for generating random binary phylogenetic networks under the uniform distribution model.

The influence of underlying topography on lava channel networks and flow behavior (Invited)

NASA Astrophysics Data System (ADS)

Dietterich, H. R.; Cashman, K. V.; Rust, A.

2013-12-01

New high resolution mapping of historical lava flows in Hawai';i reveals complex topographically controlled channel networks. Network morphologies range from distributary systems dominated by branching around local obstacles, to tributary systems constricted by topographic confinement. Because channel networks govern the distribution of lava within the flow, they can dramatically alter the effective volumetric flux, which affects both flow length and advance rate. The influence of flow bifurcations is evidenced by (1) channelized flows from Pu';u ';O';o episodes 1-20 at Kilauea Volcano, where flow front velocities decreased by approximately half each time a flow split, and (2) the length of confined flows, such as the Mauna Loa 1859 flow, which traveled twice as far as the distributary Mauna Loa 1984 flow, despite similar effusion rates and durations. To study the underlying controls on flow bifurcations, we have undertaken a series of analogue experiments with golden syrup (a Newtonian fluid) to better understand the physics of obstacle interaction and its influence on flow behavior and morphology. Controlling the effusion rate and surface slope, we extrude flows onto a surface with a cylindrical or V-shaped obstacle of variable angle. When the flow is sufficiently fast, a stationary wave forms upslope of the obstacle; if the stationary wave is sufficiently high, the flow can overtop, rather than split around, the obstacle. The stationary wave height increases with flow velocity and with the effective obstacle width. Evidence for stationary waves in Hawaiian lava flows comes from both photographs of active flows and waveforms frozen into solidified flows. We have also performed a preliminary set of similar experiments with molten basalt to identify the effect of cooling and investigate flow merging. In these experiments, a stationary wave develops upslope of the obstacle, which allows the surface to cool and thicken. After splitting, the syrup experiments show

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.

Explosive percolation on directed networks due to monotonic flow of activity

NASA Astrophysics Data System (ADS)

Waagen, Alex; D'Souza, Raissa M.; Lu, Tsai-Ching

2017-07-01

An important class of real-world networks has directed edges, and in addition, some rank ordering on the nodes, for instance the popularity of users in online social networks. Yet, nearly all research related to explosive percolation has been restricted to undirected networks. Furthermore, information on such rank-ordered networks typically flows from higher-ranked to lower-ranked individuals, such as follower relations, replies, and retweets on Twitter. Here we introduce a simple percolation process on an ordered, directed network where edges are added monotonically with respect to the rank ordering. We show with a numerical approach that the emergence of a dominant strongly connected component appears to be discontinuous. Large-scale connectivity occurs at very high density compared with most percolation processes, and this holds not just for the strongly connected component structure but for the weakly connected component structure as well. We present analysis with branching processes, which explains this unusual behavior and gives basic intuition for the underlying mechanisms. We also show that before the emergence of a dominant strongly connected component, multiple giant strongly connected components may exist simultaneously. By adding a competitive percolation rule with a small bias to link uses of similar rank, we show this leads to formation of two distinct components, one of high-ranked users, and one of low-ranked users, with little flow between the two components.

Low-flow, base-flow, and mean-flow regression equations for Pennsylvania streams

USGS Publications Warehouse

Stuckey, Marla H.

2006-01-01

Low-flow, base-flow, and mean-flow characteristics are an important part of assessing water resources in a watershed. These streamflow characteristics can be used by watershed planners and regulators to determine water availability, water-use allocations, assimilative capacities of streams, and aquatic-habitat needs. Streamflow characteristics are commonly predicted by use of regression equations when a nearby streamflow-gaging station is not available. Regression equations for predicting low-flow, base-flow, and mean-flow characteristics for Pennsylvania streams were developed from data collected at 293 continuous- and partial-record streamflow-gaging stations with flow unaffected by upstream regulation, diversion, or mining. Continuous-record stations used in the regression analysis had 9 years or more of data, and partial-record stations used had seven or more measurements collected during base-flow conditions. The state was divided into five low-flow regions and regional regression equations were developed for the 7-day, 10-year; 7-day, 2-year; 30-day, 10-year; 30-day, 2-year; and 90-day, 10-year low flows using generalized least-squares regression. Statewide regression equations were developed for the 10-year, 25-year, and 50-year base flows using generalized least-squares regression. Statewide regression equations were developed for harmonic mean and mean annual flow using weighted least-squares regression. Basin characteristics found to be significant explanatory variables at the 95-percent confidence level for one or more regression equations were drainage area, basin slope, thickness of soil, stream density, mean annual precipitation, mean elevation, and the percentage of glaciation, carbonate bedrock, forested area, and urban area within a basin. Standard errors of prediction ranged from 33 to 66 percent for the n-day, T-year low flows; 21 to 23 percent for the base flows; and 12 to 38 percent for the mean annual flow and harmonic mean, respectively. The

Performance evaluation of multi-stratum resources optimization with network functions virtualization for cloud-based radio over optical fiber networks.

PubMed

Yang, Hui; He, Yongqi; Zhang, Jie; Ji, Yuefeng; Bai, Wei; Lee, Young

2016-04-18

Cloud radio access network (C-RAN) has become a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing using cloud BBUs. In our previous work, we implemented cross stratum optimization of optical network and application stratums resources that allows to accommodate the services in optical networks. In view of this, this study extends to consider the multiple dimensional resources optimization of radio, optical and BBU processing in 5G age. We propose a novel multi-stratum resources optimization (MSRO) architecture with network functions virtualization for cloud-based radio over optical fiber networks (C-RoFN) using software defined control. A global evaluation scheme (GES) for MSRO in C-RoFN is introduced based on the proposed architecture. The MSRO can enhance the responsiveness to dynamic end-to-end user demands and globally optimize radio frequency, optical and BBU resources effectively to maximize radio coverage. The efficiency and feasibility of the proposed architecture are experimentally demonstrated on OpenFlow-based enhanced SDN testbed. The performance of GES under heavy traffic load scenario is also quantitatively evaluated based on MSRO architecture in terms of resource occupation rate and path provisioning latency, compared with other provisioning scheme.

Non-invasive classification of gas-liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

NASA Astrophysics Data System (ADS)

Musa Abbagoni, Baba; Yeung, Hoi

2016-08-01

The identification of flow pattern is a key issue in multiphase flow which is encountered in the petrochemical industry. It is difficult to identify the gas-liquid flow regimes objectively with the gas-liquid two-phase flow. This paper presents the feasibility of a clamp-on instrument for an objective flow regime classification of two-phase flow using an ultrasonic Doppler sensor and an artificial neural network, which records and processes the ultrasonic signals reflected from the two-phase flow. Experimental data is obtained on a horizontal test rig with a total pipe length of 21 m and 5.08 cm internal diameter carrying air-water two-phase flow under slug, elongated bubble, stratified-wavy and, stratified flow regimes. Multilayer perceptron neural networks (MLPNNs) are used to develop the classification model. The classifier requires features as an input which is representative of the signals. Ultrasound signal features are extracted by applying both power spectral density (PSD) and discrete wavelet transform (DWT) methods to the flow signals. A classification scheme of ‘1-of-C coding method for classification’ was adopted to classify features extracted into one of four flow regime categories. To improve the performance of the flow regime classifier network, a second level neural network was incorporated by using the output of a first level networks feature as an input feature. The addition of the two network models provided a combined neural network model which has achieved a higher accuracy than single neural network models. Classification accuracies are evaluated in the form of both the PSD and DWT features. The success rates of the two models are: (1) using PSD features, the classifier missed 3 datasets out of 24 test datasets of the classification and scored 87.5% accuracy; (2) with the DWT features, the network misclassified only one data point and it was able to classify the flow patterns up to 95.8% accuracy. This approach has demonstrated the

Global network of embodied water flow by systems input-output simulation

NASA Astrophysics Data System (ADS)

Chen, Zhanming; Chen, Guoqian; Xia, Xiaohua; Xu, Shiyun

2012-09-01

The global water resources network is simulated in the present work for the latest target year with statistical data available and with the most detailed data disaggregation. A top-down approach of systems inputoutput simulation is employed to track the embodied water flows associated with economic flows for the globalized economy in 2004. The numerical simulation provides a database of embodied water intensities for all economic commodities from 4928 producers, based on which the differences between direct and indirect water using efficiencies at the global scale are discussed. The direct and embodied water uses are analyzed at continental level. Besides, the commodity demand in terms of monetary expenditure and the water demand in terms of embodied water use are compared for the world as well as for three major water using regions, i.e., India, China, and the United States. Results show that food product contributes to a significant fraction for water demand, despite the value varies significantly with respect to the economic status of region.

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.

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.

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.

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

Spatial confinement of active microtubule networks induces large-scale rotational cytoplasmic flow

PubMed Central

Suzuki, Kazuya; Miyazaki, Makito; Takagi, Jun; Itabashi, Takeshi; Ishiwata, Shin’ichi

2017-01-01

Collective behaviors of motile units through hydrodynamic interactions induce directed fluid flow on a larger length scale than individual units. In cells, active cytoskeletal systems composed of polar filaments and molecular motors drive fluid flow, a process known as cytoplasmic streaming. The motor-driven elongation of microtubule bundles generates turbulent-like flow in purified systems; however, it remains unclear whether and how microtubule bundles induce large-scale directed flow like the cytoplasmic streaming observed in cells. Here, we adopted Xenopus egg extracts as a model system of the cytoplasm and found that microtubule bundle elongation induces directed flow for which the length scale and timescale depend on the existence of geometrical constraints. At the lower activity of dynein, kinesins bundle and slide microtubules, organizing extensile microtubule bundles. In bulk extracts, the extensile bundles connected with each other and formed a random network, and vortex flows with a length scale comparable to the bundle length continually emerged and persisted for 1 min at multiple places. When the extracts were encapsulated in droplets, the extensile bundles pushed the droplet boundary. This pushing force initiated symmetry breaking of the randomly oriented bundle network, leading to bundles aligning into a rotating vortex structure. This vortex induced rotational cytoplasmic flows on the length scale and timescale that were 10- to 100-fold longer than the vortex flows emerging in bulk extracts. Our results suggest that microtubule systems use not only hydrodynamic interactions but also mechanical interactions to induce large-scale temporally stable cytoplasmic flow. PMID:28265076

Cross layer optimization for cloud-based radio over optical fiber networks

NASA Astrophysics Data System (ADS)

Shao, Sujie; Guo, Shaoyong; Qiu, Xuesong; Yang, Hui; Meng, Luoming

2016-07-01

To adapt the 5G communication, the cloud radio access network is a paradigm introduced by operators which aggregates all base stations computational resources into a cloud BBU pool. The interaction between RRH and BBU or resource schedule among BBUs in cloud have become more frequent and complex with the development of system scale and user requirement. It can promote the networking demand among RRHs and BBUs, and force to form elastic optical fiber switching and networking. In such network, multiple stratum resources of radio, optical and BBU processing unit have interweaved with each other. In this paper, we propose a novel multiple stratum optimization (MSO) architecture for cloud-based radio over optical fiber networks (C-RoFN) with software defined networking. Additionally, a global evaluation strategy (GES) is introduced in the proposed architecture. MSO can enhance the responsiveness to end-to-end user demands and globally optimize radio frequency, optical spectrum and BBU processing resources effectively to maximize radio coverage. The feasibility and efficiency of the proposed architecture with GES strategy are experimentally verified on OpenFlow-enabled testbed in terms of resource occupation and path provisioning latency.

A microfluidic device for simultaneous measurement of viscosity and flow rate of blood in a complex fluidic network

PubMed Central

Jun Kang, Yang; Yeom, Eunseop; Lee, Sang-Joon

2013-01-01

Blood viscosity has been considered as one of important biophysical parameters for effectively monitoring variations in physiological and pathological conditions of circulatory disorders. Standard previous methods make it difficult to evaluate variations of blood viscosity under cardiopulmonary bypass procedures or hemodialysis. In this study, we proposed a unique microfluidic device for simultaneously measuring viscosity and flow rate of whole blood circulating in a complex fluidic network including a rat, a reservoir, a pinch valve, and a peristaltic pump. To demonstrate the proposed method, a twin-shaped microfluidic device, which is composed of two half-circular chambers, two side channels with multiple indicating channels, and one bridge channel, was carefully designed. Based on the microfluidic device, three sequential flow controls were applied to identify viscosity and flow rate of blood, with label-free and sensorless detection. The half-circular chamber was employed to achieve mechanical membrane compliance for flow stabilization in the microfluidic device. To quantify the effect of flow stabilization on flow fluctuations, a formula of pulsation index (PI) was analytically derived using a discrete fluidic circuit model. Using the PI formula, the time constant contributed by the half-circular chamber is estimated to be 8 s. Furthermore, flow fluctuations resulting from the peristaltic pumps are completely removed, especially under periodic flow conditions within short periods (T < 10 s). For performance demonstrations, the proposed method was applied to evaluate blood viscosity with respect to varying flow rate conditions [(a) known blood flow rate via a syringe pump, (b) unknown blood flow rate via a peristaltic pump]. As a result, the flow rate and viscosity of blood can be simultaneously measured with satisfactory accuracy. In addition, the proposed method was successfully applied to identify the viscosity of rat blood, which circulates in a

A microfluidic device for simultaneous measurement of viscosity and flow rate of blood in a complex fluidic network.

PubMed

Jun Kang, Yang; Yeom, Eunseop; Lee, Sang-Joon

2013-01-01

Blood viscosity has been considered as one of important biophysical parameters for effectively monitoring variations in physiological and pathological conditions of circulatory disorders. Standard previous methods make it difficult to evaluate variations of blood viscosity under cardiopulmonary bypass procedures or hemodialysis. In this study, we proposed a unique microfluidic device for simultaneously measuring viscosity and flow rate of whole blood circulating in a complex fluidic network including a rat, a reservoir, a pinch valve, and a peristaltic pump. To demonstrate the proposed method, a twin-shaped microfluidic device, which is composed of two half-circular chambers, two side channels with multiple indicating channels, and one bridge channel, was carefully designed. Based on the microfluidic device, three sequential flow controls were applied to identify viscosity and flow rate of blood, with label-free and sensorless detection. The half-circular chamber was employed to achieve mechanical membrane compliance for flow stabilization in the microfluidic device. To quantify the effect of flow stabilization on flow fluctuations, a formula of pulsation index (PI) was analytically derived using a discrete fluidic circuit model. Using the PI formula, the time constant contributed by the half-circular chamber is estimated to be 8 s. Furthermore, flow fluctuations resulting from the peristaltic pumps are completely removed, especially under periodic flow conditions within short periods (T < 10 s). For performance demonstrations, the proposed method was applied to evaluate blood viscosity with respect to varying flow rate conditions [(a) known blood flow rate via a syringe pump, (b) unknown blood flow rate via a peristaltic pump]. As a result, the flow rate and viscosity of blood can be simultaneously measured with satisfactory accuracy. In addition, the proposed method was successfully applied to identify the viscosity of rat blood, which circulates in a

Cascade-based attacks on complex networks

NASA Astrophysics Data System (ADS)

Motter, Adilson E.; Lai, Ying-Cheng

2002-12-01

We live in a modern world supported by large, complex networks. Examples range from financial markets to communication and transportation systems. In many realistic situations the flow of physical quantities in the network, as characterized by the loads on nodes, is important. We show that for such networks where loads can redistribute among the nodes, intentional attacks can lead to a cascade of overload failures, which can in turn cause the entire or a substantial part of the network to collapse. This is relevant for real-world networks that possess a highly heterogeneous distribution of loads, such as the Internet and power grids. We demonstrate that the heterogeneity of these networks makes them particularly vulnerable to attacks in that a large-scale cascade may be triggered by disabling a single key node. This brings obvious concerns on the security of such systems.

Cross-Layer Scheme to Control Contention Window for Per-Flow in Asymmetric Multi-Hop Networks

NASA Astrophysics Data System (ADS)

Giang, Pham Thanh; Nakagawa, Kenji

The IEEE 802.11 MAC standard for wireless ad hoc networks adopts Binary Exponential Back-off (BEB) mechanism to resolve bandwidth contention between stations. BEB mechanism controls the bandwidth allocation for each station by choosing a back-off value from one to CW according to the uniform random distribution, where CW is the contention window size. However, in asymmetric multi-hop networks, some stations are disadvantaged in opportunity of access to the shared channel and may suffer severe throughput degradation when the traffic load is large. Then, the network performance is degraded in terms of throughput and fairness. In this paper, we propose a new cross-layer scheme aiming to solve the per-flow unfairness problem and achieve good throughput performance in IEEE 802.11 multi-hop ad hoc networks. Our cross-layer scheme collects useful information from the physical, MAC and link layers of own station. This information is used to determine the optimal Contention Window (CW) size for per-station fairness. We also use this information to adjust CW size for each flow in the station in order to achieve per-flow fairness. Performance of our cross-layer scheme is examined on various asymmetric multi-hop network topologies by using Network Simulator (NS-2).

Information flow in layered networks of non-monotonic units

NASA Astrophysics Data System (ADS)

Schittler Neves, Fabio; Martim Schubert, Benno; Erichsen, Rubem, Jr.

2015-07-01

Layered neural networks are feedforward structures that yield robust parallel and distributed pattern recognition. Even though much attention has been paid to pattern retrieval properties in such systems, many aspects of their dynamics are not yet well characterized or understood. In this work we study, at different temperatures, the memory activity and information flows through layered networks in which the elements are the simplest binary odd non-monotonic function. Our results show that, considering a standard Hebbian learning approach, the network information content has its maximum always at the monotonic limit, even though the maximum memory capacity can be found at non-monotonic values for small enough temperatures. Furthermore, we show that such systems exhibit rich macroscopic dynamics, including not only fixed point solutions of its iterative map, but also cyclic and chaotic attractors that also carry information.

The queueing perspective of asynchronous network coding in two-way relay network

NASA Astrophysics Data System (ADS)

Liang, Yaping; Chang, Qing; Li, Xianxu

2018-04-01

Asynchronous network coding (NC) has potential to improve the wireless network performance compared with a routing or the synchronous network coding. Recent researches concentrate on the optimization between throughput/energy consuming and delay with a couple of independent input flow. However, the implementation of NC requires a thorough investigation of its impact on relevant queueing systems where few work focuses on. Moreover, few works study the probability density function (pdf) in network coding scenario. In this paper, the scenario with two independent Poisson input flows and one output flow is considered. The asynchronous NC-based strategy is that a new arrival evicts a head packet holding in its queue when waiting for another packet from the other flow to encode. The pdf for the output flow which contains both coded and uncoded packets is derived. Besides, the statistic characteristics of this strategy are analyzed. These results are verified by numerical simulations.

Smooth information flow in temperature climate network reflects mass transport

NASA Astrophysics Data System (ADS)

Hlinka, Jaroslav; Jajcay, Nikola; Hartman, David; Paluš, Milan

2017-03-01

A directed climate network is constructed by Granger causality analysis of air temperature time series from a regular grid covering the whole Earth. Using winner-takes-all network thresholding approach, a structure of a smooth information flow is revealed, hidden to previous studies. The relevance of this observation is confirmed by comparison with the air mass transfer defined by the wind field. Their close relation illustrates that although the information transferred due to the causal influence is not a physical quantity, the information transfer is tied to the transfer of mass and energy.

77 FR 3544 - Meeting and Webinar on the Active Traffic and Demand Management and Intelligent Network Flow...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-24

... Intelligent Network Flow Optimization Operational Concepts; Notice of Public Meeting AGENCY: Research and... Demand Management (ADTM) and Intelligent Network Flow Optimization (INFLO) operational concepts. The ADTM... February 8, 2012, 8:30 to 4:30 p.m. The location for both meetings is the Hall of States, 444 North Capitol...

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.

Role of Distance-Based Routing in Traffic Dynamics on Mobile Networks

NASA Astrophysics Data System (ADS)

Yang, Han-Xin; Wang, Wen-Xu

2013-06-01

Despite of intensive investigations on transportation dynamics taking place on complex networks with fixed structures, a deep understanding of networks consisting of mobile nodes is challenging yet, especially the lacking of insight into the effects of routing strategies on transmission efficiency. We introduce a distance-based routing strategy for networks of mobile agents toward enhancing the network throughput and the transmission efficiency. We study the transportation capacity and delivering time of data packets associated with mobility and communication ability. Interestingly, we find that the transportation capacity is optimized at moderate moving speed, which is quite different from random routing strategy. In addition, both continuous and discontinuous transitions from free flow to congestions are observed. Degree distributions are explored in order to explain the enhancement of network throughput and other observations. Our work is valuable toward understanding complex transportation dynamics and designing effective routing protocols.

Employment growth through labor flow networks.

PubMed

Guerrero, Omar A; Axtell, Robert L

2013-01-01

It is conventional in labor economics to treat all workers who are seeking new jobs as belonging to a labor pool, and all firms that have job vacancies as an employer pool, and then match workers to jobs. Here we develop a new approach to study labor and firm dynamics. By combining the emerging science of networks with newly available employment micro-data, comprehensive at the level of whole countries, we are able to broadly characterize the process through which workers move between firms. Specifically, for each firm in an economy as a node in a graph, we draw edges between firms if a worker has migrated between them, possibly with a spell of unemployment in between. An economy's overall graph of firm-worker interactions is an object we call the labor flow network (LFN). This is the first study that characterizes a LFN for an entire economy. We explore the properties of this network, including its topology, its community structure, and its relationship to economic variables. It is shown that LFNs can be useful in identifying firms with high growth potential. We relate LFNs to other notions of high performance firms. Specifically, it is shown that fewer than 10% of firms account for nearly 90% of all employment growth. We conclude with a model in which empirically-salient LFNs emerge from the interaction of heterogeneous adaptive agents in a decentralized labor market.

Employment Growth through Labor Flow Networks

PubMed Central

Guerrero, Omar A.; Axtell, Robert L.

2013-01-01

It is conventional in labor economics to treat all workers who are seeking new jobs as belonging to a labor pool, and all firms that have job vacancies as an employer pool, and then match workers to jobs. Here we develop a new approach to study labor and firm dynamics. By combining the emerging science of networks with newly available employment micro-data, comprehensive at the level of whole countries, we are able to broadly characterize the process through which workers move between firms. Specifically, for each firm in an economy as a node in a graph, we draw edges between firms if a worker has migrated between them, possibly with a spell of unemployment in between. An economy's overall graph of firm-worker interactions is an object we call the labor flow network (LFN). This is the first study that characterizes a LFN for an entire economy. We explore the properties of this network, including its topology, its community structure, and its relationship to economic variables. It is shown that LFNs can be useful in identifying firms with high growth potential. We relate LFNs to other notions of high performance firms. Specifically, it is shown that fewer than 10% of firms account for nearly 90% of all employment growth. We conclude with a model in which empirically-salient LFNs emerge from the interaction of heterogeneous adaptive agents in a decentralized labor market. PMID:23658682

Creating Turbulent Flow Realizations with Generative Adversarial Networks

NASA Astrophysics Data System (ADS)

King, Ryan; Graf, Peter; Chertkov, Michael

2017-11-01

Generating valid inflow conditions is a crucial, yet computationally expensive, step in unsteady turbulent flow simulations. We demonstrate a new technique for rapid generation of turbulent inflow realizations that leverages recent advances in machine learning for image generation using a deep convolutional generative adversarial network (DCGAN). The DCGAN is an unsupervised machine learning technique consisting of two competing neural networks that are trained against each other using backpropagation. One network, the generator, tries to produce samples from the true distribution of states, while the discriminator tries to distinguish between true and synthetic samples. We present results from a fully-trained DCGAN that is able to rapidly draw random samples from the full distribution of possible inflow states without needing to solve the Navier-Stokes equations, eliminating the costly process of spinning up inflow turbulence. This suggests a new paradigm in physics informed machine learning where the turbulence physics can be encoded in either the discriminator or generator. Finally, we also propose additional applications such as feature identification and subgrid scale modeling.

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

Graphene-based battery electrodes having continuous flow paths

DOEpatents

Zhang, Jiguang; Xiao, Jie; Liu, Jun; Xu, Wu; Li, Xiaolin; Wang, Deyu

2014-05-24

Some batteries can exhibit greatly improved performance by utilizing electrodes having randomly arranged graphene nanosheets forming a network of channels defining continuous flow paths through the electrode. The network of channels can provide a diffusion pathway for the liquid electrolyte and/or for reactant gases. Metal-air batteries can benefit from such electrodes. In particular Li-air batteries show extremely high capacities, wherein the network of channels allow oxygen to diffuse through the electrode and mesopores in the electrode can store discharge products.

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

Flow-oriented dynamic assembly algorithm in TCP over OBS networks

NASA Astrophysics Data System (ADS)

Peng, Shuping; Li, Zhengbin; He, Yongqi; Xu, Anshi

2008-11-01

OBS is envisioned as a promising infrastructure for the next generation optical network, and TCP is likely to be the dominant transport protocol in the next generation network. Therefore, it is necessary to evaluate the performance of TCP over OBS networks. The assembly at the ingress edge nodes will impact the network performance. There have been several Fixed Assembly Period (FAP) algorithms proposed. However, the assembly period in FAP is fixed, and it can not be adjusted according to the network condition. Moreover, in FAP, the packets from different TCP sources are assembled into one burst. In that case, if such a burst is dropped, the TCP windows of the corresponding sources will shrink and the throughput will be reduced. In this paper, we introduced a flow-oriented Dynamic Assembly Period (DAP) algorithm for TCP over OBS networks. Through comparing the previous and current burst lengths, DAP can track the variation of TCP window, and update the assembly period dynamically for the next assembly. The performance of DAP is evaluated over a single TCP connection and multiple connections, respectively. The simulation results show that DAP performs better than FAP at almost the whole range of burst dropping probability.

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

A recurrence network approach for the analysis of skin blood flow dynamics in response to loading pressure.

PubMed

Liao, Fuyuan; Jan, Yih-Kuen

2012-06-01

This paper presents a recurrence network approach for the analysis of skin blood flow dynamics in response to loading pressure. Recurrence is a fundamental property of many dynamical systems, which can be explored in phase spaces constructed from observational time series. A visualization tool of recurrence analysis called recurrence plot (RP) has been proved to be highly effective to detect transitions in the dynamics of the system. However, it was found that delay embedding can produce spurious structures in RPs. Network-based concepts have been applied for the analysis of nonlinear time series recently. We demonstrate that time series with different types of dynamics exhibit distinct global clustering coefficients and distributions of local clustering coefficients and that the global clustering coefficient is robust to the embedding parameters. We applied the approach to study skin blood flow oscillations (BFO) response to loading pressure. The results showed that global clustering coefficients of BFO significantly decreased in response to loading pressure (p<0.01). Moreover, surrogate tests indicated that such a decrease was associated with a loss of nonlinearity of BFO. Our results suggest that the recurrence network approach can practically quantify the nonlinear dynamics of BFO.

An Agent-Based Model of Private Woodland Owner Management Behavior Using Social Interactions, Information Flow, and Peer-To-Peer Networks

PubMed Central

Huff, Emily Silver; Leahy, Jessica E.; Hiebeler, David; Weiskittel, Aaron R.; Noblet, Caroline L.

2015-01-01

Privately owned woodlands are an important source of timber and ecosystem services in North America and worldwide. Impacts of management on these ecosystems and timber supply from these woodlands are difficult to estimate because complex behavioral theory informs the owner’s management decisions. The decision-making environment consists of exogenous market factors, internal cognitive processes, and social interactions with fellow landowners, foresters, and other rural community members. This study seeks to understand how social interactions, information flow, and peer-to-peer networks influence timber harvesting behavior using an agent-based model. This theoretical model includes forested polygons in various states of ‘harvest readiness’ and three types of agents: forest landowners, foresters, and peer leaders (individuals trained in conservation who use peer-to-peer networking). Agent rules, interactions, and characteristics were parameterized with values from existing literature and an empirical survey of forest landowner attitudes, intentions, and demographics. The model demonstrates that as trust in foresters and peer leaders increases, the percentage of the forest that is harvested sustainably increases. Furthermore, peer leaders can serve to increase landowner trust in foresters. Model output and equations will inform forest policy and extension/outreach efforts. The model also serves as an important testing ground for new theories of landowner decision making and behavior. PMID:26562429

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.

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.

Estimation of selected streamflow statistics for a network of low-flow partial-record stations in areas affected by Base Realignment and Closure (BRAC) in Maryland

USGS Publications Warehouse

Ries, Kernell G.; Eng, Ken

2010-01-01

The U.S. Geological Survey, in cooperation with the Maryland Department of the Environment, operated a network of 20 low-flow partial-record stations during 2008 in a region that extends from southwest of Baltimore to the northeastern corner of Maryland to obtain estimates of selected streamflow statistics at the station locations. The study area is expected to face a substantial influx of new residents and businesses as a result of military and civilian personnel transfers associated with the Federal Base Realignment and Closure Act of 2005. The estimated streamflow statistics, which include monthly 85-percent duration flows, the 10-year recurrence-interval minimum base flow, and the 7-day, 10-year low flow, are needed to provide a better understanding of the availability of water resources in the area to be affected by base-realignment activities. Streamflow measurements collected for this study at the low-flow partial-record stations and measurements collected previously for 8 of the 20 stations were related to concurrent daily flows at nearby index streamgages to estimate the streamflow statistics. Three methods were used to estimate the streamflow statistics and two methods were used to select the index streamgages. Of the three methods used to estimate the streamflow statistics, two of them--the Moments and MOVE1 methods--rely on correlating the streamflow measurements at the low-flow partial-record stations with concurrent streamflows at nearby, hydrologically similar index streamgages to determine the estimates. These methods, recommended for use by the U.S. Geological Survey, generally require about 10 streamflow measurements at the low-flow partial-record station. The third method transfers the streamflow statistics from the index streamgage to the partial-record station based on the average of the ratios of the measured streamflows at the partial-record station to the concurrent streamflows at the index streamgage. This method can be used with as few as

From time-series to complex networks: Application to the cerebrovascular flow patterns in atrial fibrillation

NASA Astrophysics Data System (ADS)

Scarsoglio, Stefania; Cazzato, Fabio; Ridolfi, Luca

2017-09-01

A network-based approach is presented to investigate the cerebrovascular flow patterns during atrial fibrillation (AF) with respect to normal sinus rhythm (NSR). AF, the most common cardiac arrhythmia with faster and irregular beating, has been recently and independently associated with the increased risk of dementia. However, the underlying hemodynamic mechanisms relating the two pathologies remain mainly undetermined so far; thus, the contribution of modeling and refined statistical tools is valuable. Pressure and flow rate temporal series in NSR and AF are here evaluated along representative cerebral sites (from carotid arteries to capillary brain circulation), exploiting reliable artificially built signals recently obtained from an in silico approach. The complex network analysis evidences, in a synthetic and original way, a dramatic signal variation towards the distal/capillary cerebral regions during AF, which has no counterpart in NSR conditions. At the large artery level, networks obtained from both AF and NSR hemodynamic signals exhibit elongated and chained features, which are typical of pseudo-periodic series. These aspects are almost completely lost towards the microcirculation during AF, where the networks are topologically more circular and present random-like characteristics. As a consequence, all the physiological phenomena at the microcerebral level ruled by periodicity—such as regular perfusion, mean pressure per beat, and average nutrient supply at the cellular level—can be strongly compromised, since the AF hemodynamic signals assume irregular behaviour and random-like features. Through a powerful approach which is complementary to the classical statistical tools, the present findings further strengthen the potential link between AF hemodynamic and cognitive decline.

Connectomics-based analysis of information flow in the Drosophila brain.

PubMed

Shih, Chi-Tin; Sporns, Olaf; Yuan, Shou-Li; Su, Ta-Shun; Lin, Yen-Jen; Chuang, Chao-Chun; Wang, Ting-Yuan; Lo, Chung-Chuang; Greenspan, Ralph J; Chiang, Ann-Shyn

2015-05-18

Understanding the overall patterns of information flow within the brain has become a major goal of neuroscience. In the current study, we produced a first draft of the Drosophila connectome at the mesoscopic scale, reconstructed from 12,995 images of neuron projections collected in FlyCircuit (version 1.1). Neuron polarities were predicted according to morphological criteria, with nodes of the network corresponding to brain regions designated as local processing units (LPUs). The weight of each directed edge linking a pair of LPUs was determined by the number of neuron terminals that connected one LPU to the other. The resulting network showed hierarchical structure and small-world characteristics and consisted of five functional modules that corresponded to sensory modalities (olfactory, mechanoauditory, and two visual) and the pre-motor center. Rich-club organization was present in this network and involved LPUs in all sensory centers, and rich-club members formed a putative motor center of the brain. Major intra- and inter-modular loops were also identified that could play important roles for recurrent and reverberant information flow. The present analysis revealed whole-brain patterns of network structure and information flow. Additionally, we propose that the overall organizational scheme showed fundamental similarities to the network structure of the mammalian brain. Copyright © 2015 Elsevier Ltd. All rights reserved.

Simulation of Electromigration Based on Resistor Networks

NASA Astrophysics Data System (ADS)

Patrinos, Anthony John

A two dimensional computer simulation of electromigration based on resistor networks was designed and implemented. The model utilizes a realistic grain structure generated by the Monte Carlo method and takes specific account of the local effects through which electromigration damage progresses. The dynamic evolution of the simulated thin film is governed by the local current and temperature distributions. The current distribution is calculated by superimposing a two dimensional electrical network on the lattice whose nodes correspond to the particles in the lattice and the branches to interparticle bonds. Current is assumed to flow from site to site via nearest neighbor bonds. The current distribution problem is solved by applying Kirchhoff's rules on the resulting electrical network. The calculation of the temperature distribution in the lattice proceeds by discretizing the partial differential equation for heat conduction, with appropriate material parameters chosen for the lattice and its defects. SEReNe (for Simulation of Electromigration using Resistor Networks) was tested by applying it to common situations arising in experiments with real films with satisfactory results. Specifically, the model successfully reproduces the expected grain size, line width and bamboo effects, the lognormal failure time distribution and the relationship between current density exponent and current density. It has also been modified to simulate temperature ramp experiments but with mixed, in this case, results.

Mobility based multicast routing in wireless mesh networks

NASA Astrophysics Data System (ADS)

Jain, Sanjeev; Tripathi, Vijay S.; Tiwari, Sudarshan

2013-01-01

There exist two fundamental approaches to multicast routing namely minimum cost trees and shortest path trees. The (MCT's) minimum cost tree is one which connects receiver and sources by providing a minimum number of transmissions (MNTs) the MNTs approach is generally used for energy constraint sensor and mobile ad hoc networks. In this paper we have considered node mobility and try to find out simulation based comparison of the (SPT's) shortest path tree, (MST's) minimum steiner trees and minimum number of transmission trees in wireless mesh networks by using the performance metrics like as an end to end delay, average jitter, throughput and packet delivery ratio, average unicast packet delivery ratio, etc. We have also evaluated multicast performance in the small and large wireless mesh networks. In case of multicast performance in the small networks we have found that when the traffic load is moderate or high the SPTs outperform the MSTs and MNTs in all cases. The SPTs have lowest end to end delay and average jitter in almost all cases. In case of multicast performance in the large network we have seen that the MSTs provide minimum total edge cost and minimum number of transmissions. We have also found that the one drawback of SPTs, when the group size is large and rate of multicast sending is high SPTs causes more packet losses to other flows as MCTs.

Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis

NASA Astrophysics Data System (ADS)

Li, Yuanyuan; Jin, Suoqin; Lei, Lei; Pan, Zishu; Zou, Xiufen

2015-03-01

The early diagnosis and investigation of the pathogenic mechanisms of complex diseases are the most challenging problems in the fields of biology and medicine. Network-based systems biology is an important technique for the study of complex diseases. The present study constructed dynamic protein-protein interaction (PPI) networks to identify dynamical network biomarkers (DNBs) and analyze the underlying mechanisms of complex diseases from a systems level. We developed a model-based framework for the construction of a series of time-sequenced networks by integrating high-throughput gene expression data into PPI data. By combining the dynamic networks and molecular modules, we identified significant DNBs for four complex diseases, including influenza caused by either H3N2 or H1N1, acute lung injury and type 2 diabetes mellitus, which can serve as warning signals for disease deterioration. Function and pathway analyses revealed that the identified DNBs were significantly enriched during key events in early disease development. Correlation and information flow analyses revealed that DNBs effectively discriminated between different disease processes and that dysfunctional regulation and disproportional information flow may contribute to the increased disease severity. This study provides a general paradigm for revealing the deterioration mechanisms of complex diseases and offers new insights into their early diagnoses.

Identifying and characterizing key nodes among communities based on electrical-circuit networks.

PubMed

Zhu, Fenghui; Wang, Wenxu; Di, Zengru; Fan, Ying

2014-01-01

Complex networks with community structures are ubiquitous in the real world. Despite many approaches developed for detecting communities, we continue to lack tools for identifying overlapping and bridging nodes that play crucial roles in the interactions and communications among communities in complex networks. Here we develop an algorithm based on the local flow conservation to effectively and efficiently identify and distinguish the two types of nodes. Our method is applicable in both undirected and directed networks without a priori knowledge of the community structure. Our method bypasses the extremely challenging problem of partitioning communities in the presence of overlapping nodes that may belong to multiple communities. Due to the fact that overlapping and bridging nodes are of paramount importance in maintaining the function of many social and biological networks, our tools open new avenues towards understanding and controlling real complex networks with communities accompanied with the key nodes.

Network-Based Management Procedures.

ERIC Educational Resources Information Center

Buckner, Allen L.

Network-based management procedures serve as valuable aids in organizational management, achievement of objectives, problem solving, and decisionmaking. Network techniques especially applicable to educational management systems are the program evaluation and review technique (PERT) and the critical path method (CPM). Other network charting…

Neural Action Fields for Optic Flow Based Navigation: A Simulation Study of the Fly Lobula Plate Network

PubMed Central

Borst, Alexander; Weber, Franz

2011-01-01

Optic flow based navigation is a fundamental way of visual course control described in many different species including man. In the fly, an essential part of optic flow analysis is performed in the lobula plate, a retinotopic map of motion in the environment. There, the so-called lobula plate tangential cells possess large receptive fields with different preferred directions in different parts of the visual field. Previous studies demonstrated an extensive connectivity between different tangential cells, providing, in principle, the structural basis for their large and complex receptive fields. We present a network simulation of the tangential cells, comprising most of the neurons studied so far (22 on each hemisphere) with all the known connectivity between them. On their dendrite, model neurons receive input from a retinotopic array of Reichardt-type motion detectors. Model neurons exhibit receptive fields much like their natural counterparts, demonstrating that the connectivity between the lobula plate tangential cells indeed can account for their complex receptive field structure. We describe the tuning of a model neuron to particular types of ego-motion (rotation as well as translation around/along a given body axis) by its ‘action field’. As we show for model neurons of the vertical system (VS-cells), each of them displays a different type of action field, i.e., responds maximally when the fly is rotating around a particular body axis. However, the tuning width of the rotational action fields is relatively broad, comparable to the one with dendritic input only. The additional intra-lobula-plate connectivity mainly reduces their translational action field amplitude, i.e., their sensitivity to translational movements along any body axis of the fly. PMID:21305019

Influence of Turbulent Flow and Fractal Scaling on Effective Permeability of Fracture Network

NASA Astrophysics Data System (ADS)

Zhu, J.

2017-12-01

A new approach is developed to calculate hydraulic gradient dependent effective permeability of a fractal fracture network where both laminar and turbulent flows may occur in individual fractures. A critical fracture length is used to distinguish flow characteristics in individual fractures. The developed new solutions can be used for the case of a general scaling relationship, an extension to the linear scaling. We examine the impact on the effective permeability of the network of fractal fracture network characteristics, which include the fractal scaling coefficient and exponent, fractal dimension, ratio of minimum over maximum fracture lengths. Results demonstrate that the developed solution can explain more variations of the effective permeability in relation to the fractal dimensions estimated from the field observations. At high hydraulic gradient the effective permeability decreases with the fractal scaling exponent, but increases with the fractal scaling exponent at low gradient. The effective permeability increases with the scaling coefficient, fractal dimension, fracture length ratio and maximum fracture length.

Network-based Approaches in Pharmacology.

PubMed

Boezio, Baptiste; Audouze, Karine; Ducrot, Pierre; Taboureau, Olivier

2017-10-01

In drug discovery, network-based approaches are expected to spotlight our understanding of drug action across multiple layers of information. On one hand, network pharmacology considers the drug response in the context of a cellular or phenotypic network. On the other hand, a chemical-based network is a promising alternative for characterizing the chemical space. Both can provide complementary support for the development of rational drug design and better knowledge of the mechanisms underlying the multiple actions of drugs. Recent progress in both concepts is discussed here. In addition, a network-based approach using drug-target-therapy data is introduced as an example. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

A network function-based definition of communities in complex networks.

PubMed

Chauhan, Sanjeev; Girvan, Michelle; Ott, Edward

2012-09-01

We consider an alternate definition of community structure that is functionally motivated. We define network community structure based on the function the network system is intended to perform. In particular, as a specific example of this approach, we consider communities whose function is enhanced by the ability to synchronize and/or by resilience to node failures. Previous work has shown that, in many cases, the largest eigenvalue of the network's adjacency matrix controls the onset of both synchronization and percolation processes. Thus, for networks whose functional performance is dependent on these processes, we propose a method that divides a given network into communities based on maximizing a function of the largest eigenvalues of the adjacency matrices of the resulting communities. We also explore the differences between the partitions obtained by our method and the modularity approach (which is based solely on consideration of network structure). We do this for several different classes of networks. We find that, in many cases, modularity-based partitions do almost as well as our function-based method in finding functional communities, even though modularity does not specifically incorporate consideration of function.

Mapping edge-based traffic measurements onto the internal links in MPLS network

NASA Astrophysics Data System (ADS)

Zhao, Guofeng; Tang, Hong; Zhang, Yi

2004-09-01

Applying multi-protocol label switching techniques to IP-based backbone for traffic engineering goals has shown advantageous. Obtaining a volume of load on each internal link of the network is crucial for traffic engineering applying. Though collecting can be available for each link, such as applying traditional SNMP scheme, the approach may cause heavy processing load and sharply degrade the throughput of the core routers. Then monitoring merely at the edge of the network and mapping the measurements onto the core provides a good alternative way. In this paper, we explore a scheme for traffic mapping with edge-based measurements in MPLS network. It is supposed that the volume of traffic on each internal link over the domain would be mapped onto by measurements available only at ingress nodes. We apply path-based measurements at ingress nodes without enabling measurements in the core of the network. We propose a method that can infer a path from the ingress to the egress node using label distribution protocol without collecting routing data from core routers. Based on flow theory and queuing theory, we prove that our approach is effective and present the algorithm for traffic mapping. We also show performance simulation results that indicate potential of our approach.

A neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine

NASA Astrophysics Data System (ADS)

Guo, T. H.; Musgrave, J.

1992-11-01

In order to properly utilize the available fuel and oxidizer of a liquid propellant rocket engine, the mixture ratio is closed loop controlled during main stage (65 percent - 109 percent power) operation. However, because of the lack of flight-capable instrumentation for measuring mixture ratio, the value of mixture ratio in the control loop is estimated using available sensor measurements such as the combustion chamber pressure and the volumetric flow, and the temperature and pressure at the exit duct on the low pressure fuel pump. This estimation scheme has two limitations. First, the estimation formula is based on an empirical curve fitting which is accurate only within a narrow operating range. Second, the mixture ratio estimate relies on a few sensor measurements and loss of any of these measurements will make the estimate invalid. In this paper, we propose a neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine. The estimator is an extension of a previously developed neural network based sensor failure detection and recovery algorithm (sensor validation). This neural network uses an auto associative structure which utilizes the redundant information of dissimilar sensors to detect inconsistent measurements. Two approaches have been identified for synthesizing mixture ratio from measurement data using a neural network. The first approach uses an auto associative neural network for sensor validation which is modified to include the mixture ratio as an additional output. The second uses a new network for the mixture ratio estimation in addition to the sensor validation network. Although mixture ratio is not directly measured in flight, it is generally available in simulation and in test bed firing data from facility measurements of fuel and oxidizer volumetric flows. The pros and cons of these two approaches will be discussed in terms of robustness to sensor failures and accuracy of the estimate during typical transients using

A neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine

NASA Technical Reports Server (NTRS)

Guo, T. H.; Musgrave, J.

1992-01-01

In order to properly utilize the available fuel and oxidizer of a liquid propellant rocket engine, the mixture ratio is closed loop controlled during main stage (65 percent - 109 percent power) operation. However, because of the lack of flight-capable instrumentation for measuring mixture ratio, the value of mixture ratio in the control loop is estimated using available sensor measurements such as the combustion chamber pressure and the volumetric flow, and the temperature and pressure at the exit duct on the low pressure fuel pump. This estimation scheme has two limitations. First, the estimation formula is based on an empirical curve fitting which is accurate only within a narrow operating range. Second, the mixture ratio estimate relies on a few sensor measurements and loss of any of these measurements will make the estimate invalid. In this paper, we propose a neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine. The estimator is an extension of a previously developed neural network based sensor failure detection and recovery algorithm (sensor validation). This neural network uses an auto associative structure which utilizes the redundant information of dissimilar sensors to detect inconsistent measurements. Two approaches have been identified for synthesizing mixture ratio from measurement data using a neural network. The first approach uses an auto associative neural network for sensor validation which is modified to include the mixture ratio as an additional output. The second uses a new network for the mixture ratio estimation in addition to the sensor validation network. Although mixture ratio is not directly measured in flight, it is generally available in simulation and in test bed firing data from facility measurements of fuel and oxidizer volumetric flows. The pros and cons of these two approaches will be discussed in terms of robustness to sensor failures and accuracy of the estimate during typical transients using

Spatiotemporal image correlation analysis of blood flow in branched vessel networks of zebrafish embryos

NASA Astrophysics Data System (ADS)

Ceffa, Nicolo G.; Cesana, Ilaria; Collini, Maddalena; D'Alfonso, Laura; Carra, Silvia; Cotelli, Franco; Sironi, Laura; Chirico, Giuseppe

2017-10-01

Ramification of blood circulation is relevant in a number of physiological and pathological conditions. The oxygen exchange occurs largely in the capillary bed, and the cancer progression is closely linked to the angiogenesis around the tumor mass. Optical microscopy has made impressive improvements in in vivo imaging and dynamic studies based on correlation analysis of time stacks of images. Here, we develop and test advanced methods that allow mapping the flow fields in branched vessel networks at the resolution of 10 to 20 μm. The methods, based on the application of spatiotemporal image correlation spectroscopy and its extension to cross-correlation analysis, are applied here to the case of early stage embryos of zebrafish.

Evaluation of a method of estimating low-flow frequencies from base-flow measurements at Indiana streams

USGS Publications Warehouse

Wilson, John Thomas

2000-01-01

A mathematical technique of estimating low-flow frequencies from base-flow measurements was evaluated by using data for streams in Indiana. Low-flow frequencies at low- flow partial-record stations were estimated by relating base-flow measurements to concurrent daily flows at nearby streamflow-gaging stations (index stations) for which low-flowfrequency curves had been developed. A network of long-term streamflow-gaging stations in Indiana provided a sample of sites with observed low-flow frequencies. Observed values of 7-day, 10-year low flow and 7-day, 2-year low flow were compared to predicted values to evaluate the accuracy of the method. Five test cases were used to evaluate the method under a variety of conditions in which the location of the index station and its drainage area varied relative to the partial-record station. A total of 141 pairs of streamflow-gaging stations were used in the five test cases. Four of the test cases used one index station, the fifth test case used two index stations. The number of base-flow measurements was varied for each test case to see if the accuracy of the method was affected by the number of measurements used. The most accurate and least variable results were produced when two index stations on the same stream or tributaries of the partial-record station were used. All but one value of the predicted 7-day, 10-year low flow were within 15 percent of the values observed for the long-term continuous record, and all of the predicted values of the 7-day, 2-year lowflow were within 15 percent of the observed values. This apparent accuracy, to some extent, may be a result of the small sample set of 15. Of the four test cases that used one index station, the most accurate and least variable results were produced in the test case where the index station and partial-record station were on the same stream or on streams tributary to each other and where the index station had a larger drainage area than the partial-record station. In

[Software-based visualization of patient flow at a university eye clinic].

PubMed

Greb, O; Abou Moulig, W; Hufendiek, K; Junker, B; Framme, C

2017-03-01

This article presents a method for visualization and navigation of patient flow in outpatient eye clinics with a high level of complexity. A network-based software solution was developed targeting long-term process optimization by structural analysis and temporal coordination of process navigation. Each examination unit receives a separate waiting list of patients in which the patient flow for every patient is recorded in a timeline. Time periods and points in time can be executed by mouse clicks and the desired diagnostic procedure can be entered. Recent progress in any of these diagnostic requests, as well as a variety of information on patient progress are collated and drawn into the corresponding timeline which can be viewed by any of the personnel involved. The software called TimeElement has been successfully tested in the practical implemenation for several months. As an example the patient flow regarding time stamps of defined events for intravitreous injections on 250 patients was recorded and an average attendance time of 169.71 min was found, whereby the time was also automatically recorded for each individual stage. Recording of patient flow data is a fundamental component of patient flow management, waiting time reduction, patient flow navigation with time and coordination in particular regarding timeline-based visualization for each individual patient. Long-term changes in process management can be planned and evaluated by comparing patient flow data. As using the software itself causes structural changes within the organization, a questionnaire is being planned for appraisal by the personnel involved.

Thermodynamics-based Metabolite Sensitivity Analysis in metabolic networks.

PubMed

Kiparissides, A; Hatzimanikatis, V

2017-01-01

The increasing availability of large metabolomics datasets enhances the need for computational methodologies that can organize the data in a way that can lead to the inference of meaningful relationships. Knowledge of the metabolic state of a cell and how it responds to various stimuli and extracellular conditions can offer significant insight in the regulatory functions and how to manipulate them. Constraint based methods, such as Flux Balance Analysis (FBA) and Thermodynamics-based flux analysis (TFA), are commonly used to estimate the flow of metabolites through genome-wide metabolic networks, making it possible to identify the ranges of flux values that are consistent with the studied physiological and thermodynamic conditions. However, unless key intracellular fluxes and metabolite concentrations are known, constraint-based models lead to underdetermined problem formulations. This lack of information propagates as uncertainty in the estimation of fluxes and basic reaction properties such as the determination of reaction directionalities. Therefore, knowledge of which metabolites, if measured, would contribute the most to reducing this uncertainty can significantly improve our ability to define the internal state of the cell. In the present work we combine constraint based modeling, Design of Experiments (DoE) and Global Sensitivity Analysis (GSA) into the Thermodynamics-based Metabolite Sensitivity Analysis (TMSA) method. TMSA ranks metabolites comprising a metabolic network based on their ability to constrain the gamut of possible solutions to a limited, thermodynamically consistent set of internal states. TMSA is modular and can be applied to a single reaction, a metabolic pathway or an entire metabolic network. This is, to our knowledge, the first attempt to use metabolic modeling in order to provide a significance ranking of metabolites to guide experimental measurements. Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier

Predictions of first passage times in sparse discrete fracture networks using graph-based reductions

NASA Astrophysics Data System (ADS)

Hyman, J.; Hagberg, A.; Srinivasan, G.; Mohd-Yusof, J.; Viswanathan, H. S.

2017-12-01

We present a graph-based methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We derive graph representations of generic three-dimensional discrete fracture networks (DFNs) using the DFN topology and flow boundary conditions. Subgraphs corresponding to the union of the k shortest paths between the inflow and outflow boundaries are identified and transport on their equivalent subnetworks is compared to transport through the full network. The number of paths included in the subgraphs is based on the scaling behavior of the number of edges in the graph with the number of shortest paths. First passage times through the subnetworks are in good agreement with those obtained in the full network, both for individual realizations and in distribution. Accurate estimates of first passage times are obtained with an order of magnitude reduction of CPU time and mesh size using the proposed method.

Predictions of first passage times in sparse discrete fracture networks using graph-based reductions

NASA Astrophysics Data System (ADS)

Hyman, Jeffrey D.; Hagberg, Aric; Srinivasan, Gowri; Mohd-Yusof, Jamaludin; Viswanathan, Hari

2017-07-01

We present a graph-based methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We derive graph representations of generic three-dimensional discrete fracture networks (DFNs) using the DFN topology and flow boundary conditions. Subgraphs corresponding to the union of the k shortest paths between the inflow and outflow boundaries are identified and transport on their equivalent subnetworks is compared to transport through the full network. The number of paths included in the subgraphs is based on the scaling behavior of the number of edges in the graph with the number of shortest paths. First passage times through the subnetworks are in good agreement with those obtained in the full network, both for individual realizations and in distribution. Accurate estimates of first passage times are obtained with an order of magnitude reduction of CPU time and mesh size using the proposed method.

Flow Analysis Tool White Paper

NASA Technical Reports Server (NTRS)

Boscia, Nichole K.

2012-01-01

Faster networks are continually being built to accommodate larger data transfers. While it is intuitive to think that implementing faster networks will result in higher throughput rates, this is often not the case. There are many elements involved in data transfer, many of which are beyond the scope of the network itself. Although networks may get bigger and support faster technologies, the presence of other legacy components, such as older application software or kernel parameters, can often cause bottlenecks. Engineers must be able to identify when data flows are reaching a bottleneck that is not imposed by the network and then troubleshoot it using the tools available to them. The current best practice is to collect as much information as possible on the network traffic flows so that analysis is quick and easy. Unfortunately, no single method of collecting this information can sufficiently capture the whole endto- end picture. This becomes even more of a hurdle when large, multi-user systems are involved. In order to capture all the necessary information, multiple data sources are required. This paper presents a method for developing a flow analysis tool to effectively collect network flow data from multiple sources and provide that information to engineers in a clear, concise way for analysis. The purpose of this method is to collect enough information to quickly (and automatically) identify poorly performing flows along with the cause of the problem. The method involves the development of a set of database tables that can be populated with flow data from multiple sources, along with an easyto- use, web-based front-end interface to help network engineers access, organize, analyze, and manage all the information.

Event-Based Computation of Motion Flow on a Neuromorphic Analog Neural Platform

PubMed Central

Giulioni, Massimiliano; Lagorce, Xavier; Galluppi, Francesco; Benosman, Ryad B.

2016-01-01

Estimating the speed and direction of moving objects is a crucial component of agents behaving in a dynamic world. Biological organisms perform this task by means of the neural connections originating from their retinal ganglion cells. In artificial systems the optic flow is usually extracted by comparing activity of two or more frames captured with a vision sensor. Designing artificial motion flow detectors which are as fast, robust, and efficient as the ones found in biological systems is however a challenging task. Inspired by the architecture proposed by Barlow and Levick in 1965 to explain the spiking activity of the direction-selective ganglion cells in the rabbit's retina, we introduce an architecture for robust optical flow extraction with an analog neuromorphic multi-chip system. The task is performed by a feed-forward network of analog integrate-and-fire neurons whose inputs are provided by contrast-sensitive photoreceptors. Computation is supported by the precise time of spike emission, and the extraction of the optical flow is based on time lag in the activation of nearby retinal neurons. Mimicking ganglion cells our neuromorphic detectors encode the amplitude and the direction of the apparent visual motion in their output spiking pattern. Hereby we describe the architectural aspects, discuss its latency, scalability, and robustness properties and demonstrate that a network of mismatched delicate analog elements can reliably extract the optical flow from a simple visual scene. This work shows how precise time of spike emission used as a computational basis, biological inspiration, and neuromorphic systems can be used together for solving specific tasks. PMID:26909015

Event-Based Computation of Motion Flow on a Neuromorphic Analog Neural Platform.

PubMed

Giulioni, Massimiliano; Lagorce, Xavier; Galluppi, Francesco; Benosman, Ryad B

2016-01-01

Estimating the speed and direction of moving objects is a crucial component of agents behaving in a dynamic world. Biological organisms perform this task by means of the neural connections originating from their retinal ganglion cells. In artificial systems the optic flow is usually extracted by comparing activity of two or more frames captured with a vision sensor. Designing artificial motion flow detectors which are as fast, robust, and efficient as the ones found in biological systems is however a challenging task. Inspired by the architecture proposed by Barlow and Levick in 1965 to explain the spiking activity of the direction-selective ganglion cells in the rabbit's retina, we introduce an architecture for robust optical flow extraction with an analog neuromorphic multi-chip system. The task is performed by a feed-forward network of analog integrate-and-fire neurons whose inputs are provided by contrast-sensitive photoreceptors. Computation is supported by the precise time of spike emission, and the extraction of the optical flow is based on time lag in the activation of nearby retinal neurons. Mimicking ganglion cells our neuromorphic detectors encode the amplitude and the direction of the apparent visual motion in their output spiking pattern. Hereby we describe the architectural aspects, discuss its latency, scalability, and robustness properties and demonstrate that a network of mismatched delicate analog elements can reliably extract the optical flow from a simple visual scene. This work shows how precise time of spike emission used as a computational basis, biological inspiration, and neuromorphic systems can be used together for solving specific tasks.

Denitrification in the Mississippi River network controlled by flow through river bedforms

USGS Publications Warehouse

Gomez-Velez, Jesus D.; Harvey, Judson W.; Cardenas, M. Bayani; Kiel, Brian

2015-01-01

Increasing nitrogen concentrations in the world’s major rivers have led to over-fertilization of sensitive downstream waters. Flow through channel bed and bank sediments acts to remove riverine nitrogen through microbe-mediated denitrification reactions. However, little is understood about where in the channel network this biophysical process is most efficient, why certain channels are more effective nitrogen reactors, and how management practices can enhance the removal of nitrogen in regions where water circulates through sediment and mixes with groundwater - hyporheic zones. Here we present numerical simulations of hyporheic flow and denitrification throughout the Mississippi River network using a hydrogeomorphic model. We find that vertical exchange with sediments beneath the riverbed in hyporheic zones, driven by submerged bedforms, has denitrification potential that far exceeds lateral hyporheic exchange with sediments alongside river channels, driven by river bars and meandering banks. We propose that geomorphic differences along river corridors can explain why denitrification efficiency varies between basins in the Mississippi River network. Our findings suggest that promoting the development of permeable bedforms at the streambed - and thus vertical hyporheic exchange - would be more effective at enhancing river denitrification in large river basins than promoting lateral exchange through induced channel meandering.

Network Anomaly Detection Based on Wavelet Analysis

NASA Astrophysics Data System (ADS)

Lu, Wei; Ghorbani, Ali A.

2008-12-01

Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.

A Laminar Flow-Based Microfluidic Tesla Pump via Lithography Enabled 3D Printing.

PubMed

Habhab, Mohammed-Baker; Ismail, Tania; Lo, Joe Fujiou

2016-11-23

Tesla turbine and its applications in power generation and fluid flow were demonstrated by Nicholas Tesla in 1913. However, its real-world implementations were limited by the difficulty to maintain laminar flow between rotor disks, transient efficiencies during rotor acceleration, and the lack of other applications that fully utilize the continuous flow outputs. All of the aforementioned limits of Tesla turbines can be addressed by scaling to the microfluidic flow regime. Demonstrated here is a microscale Tesla pump designed and fabricated using a Digital Light Processing (DLP) based 3D printer with 43 µm lateral and 30 µm thickness resolutions. The miniaturized pump is characterized by low Reynolds number of 1000 and a flow rate of up to 12.6 mL/min at 1200 rpm, unloaded. It is capable of driving a mixer network to generate microfluidic gradient. The continuous, laminar flow from Tesla turbines is well-suited to the needs of flow-sensitive microfluidics, where the integrated pump will enable numerous compact lab-on-a-chip applications.

A Laminar Flow-Based Microfluidic Tesla Pump via Lithography Enabled 3D Printing

PubMed Central

Habhab, Mohammed-Baker; Ismail, Tania; Lo, Joe Fujiou

2016-01-01

Tesla turbine and its applications in power generation and fluid flow were demonstrated by Nicholas Tesla in 1913. However, its real-world implementations were limited by the difficulty to maintain laminar flow between rotor disks, transient efficiencies during rotor acceleration, and the lack of other applications that fully utilize the continuous flow outputs. All of the aforementioned limits of Tesla turbines can be addressed by scaling to the microfluidic flow regime. Demonstrated here is a microscale Tesla pump designed and fabricated using a Digital Light Processing (DLP) based 3D printer with 43 µm lateral and 30 µm thickness resolutions. The miniaturized pump is characterized by low Reynolds number of 1000 and a flow rate of up to 12.6 mL/min at 1200 rpm, unloaded. It is capable of driving a mixer network to generate microfluidic gradient. The continuous, laminar flow from Tesla turbines is well-suited to the needs of flow-sensitive microfluidics, where the integrated pump will enable numerous compact lab-on-a-chip applications. PMID:27886051

A Network Coding Based Routing Protocol for Underwater Sensor Networks

PubMed Central

Wu, Huayang; Chen, Min; Guan, Xin

2012-01-01

Due to the particularities of the underwater environment, some negative factors will seriously interfere with data transmission rates, reliability of data communication, communication range, and network throughput and energy consumption of underwater sensor networks (UWSNs). Thus, full consideration of node energy savings, while maintaining a quick, correct and effective data transmission, extending the network life cycle are essential when routing protocols for underwater sensor networks are studied. In this paper, we have proposed a novel routing algorithm for UWSNs. To increase energy consumption efficiency and extend network lifetime, we propose a time-slot based routing algorithm (TSR).We designed a probability balanced mechanism and applied it to TSR. The theory of network coding is introduced to TSBR to meet the requirement of further reducing node energy consumption and extending network lifetime. Hence, time-slot based balanced network coding (TSBNC) comes into being. We evaluated the proposed time-slot based balancing routing algorithm and compared it with other classical underwater routing protocols. The simulation results show that the proposed protocol can reduce the probability of node conflicts, shorten the process of routing construction, balance energy consumption of each node and effectively prolong the network lifetime. PMID:22666045

A network coding based routing protocol for underwater sensor networks.

PubMed

Wu, Huayang; Chen, Min; Guan, Xin

2012-01-01

Due to the particularities of the underwater environment, some negative factors will seriously interfere with data transmission rates, reliability of data communication, communication range, and network throughput and energy consumption of underwater sensor networks (UWSNs). Thus, full consideration of node energy savings, while maintaining a quick, correct and effective data transmission, extending the network life cycle are essential when routing protocols for underwater sensor networks are studied. In this paper, we have proposed a novel routing algorithm for UWSNs. To increase energy consumption efficiency and extend network lifetime, we propose a time-slot based routing algorithm (TSR).We designed a probability balanced mechanism and applied it to TSR. The theory of network coding is introduced to TSBR to meet the requirement of further reducing node energy consumption and extending network lifetime. Hence, time-slot based balanced network coding (TSBNC) comes into being. We evaluated the proposed time-slot based balancing routing algorithm and compared it with other classical underwater routing protocols. The simulation results show that the proposed protocol can reduce the probability of node conflicts, shorten the process of routing construction, balance energy consumption of each node and effectively prolong the network lifetime.

Where and why hyporheic exchange is important: Inferences from a parsimonious, physically-based river network model

NASA Astrophysics Data System (ADS)

Gomez-Velez, J. D.; Harvey, J. W.

2014-12-01

Hyporheic exchange has been hypothesized to have basin-scale consequences; however, predictions throughout river networks are limited by available geomorphic and hydrogeologic data as well as models that can analyze and aggregate hyporheic exchange flows across large spatial scales. We developed a parsimonious but physically-based model of hyporheic flow for application in large river basins: Networks with EXchange and Subsurface Storage (NEXSS). At the core of NEXSS is a characterization of the channel geometry, geomorphic features, and related hydraulic drivers based on scaling equations from the literature and readily accessible information such as river discharge, bankfull width, median grain size, sinuosity, channel slope, and regional groundwater gradients. Multi-scale hyporheic flow is computed based on combining simple but powerful analytical and numerical expressions that have been previously published. We applied NEXSS across a broad range of geomorphic diversity in river reaches and synthetic river networks. NEXSS demonstrates that vertical exchange beneath submerged bedforms dominates hyporheic fluxes and turnover rates along the river corridor. Moreover, the hyporheic zone's potential for biogeochemical transformations is comparable across stream orders, but the abundance of lower-order channels results in a considerably higher cumulative effect for low-order streams. Thus, vertical exchange beneath submerged bedforms has more potential for biogeochemical transformations than lateral exchange beneath banks, although lateral exchange through meanders may be important in large rivers. These results have implications for predicting outcomes of river and basin management practices.

Design of pressure-driven microfluidic networks using electric circuit analogy.

PubMed

Oh, Kwang W; Lee, Kangsun; Ahn, Byungwook; Furlani, Edward P

2012-02-07

This article reviews the application of electric circuit methods for the analysis of pressure-driven microfluidic networks with an emphasis on concentration- and flow-dependent systems. The application of circuit methods to microfluidics is based on the analogous behaviour of hydraulic and electric circuits with correlations of pressure to voltage, volumetric flow rate to current, and hydraulic to electric resistance. Circuit analysis enables rapid predictions of pressure-driven laminar flow in microchannels and is very useful for designing complex microfluidic networks in advance of fabrication. This article provides a comprehensive overview of the physics of pressure-driven laminar flow, the formal analogy between electric and hydraulic circuits, applications of circuit theory to microfluidic network-based devices, recent development and applications of concentration- and flow-dependent microfluidic networks, and promising future applications. The lab-on-a-chip (LOC) and microfluidics community will gain insightful ideas and practical design strategies for developing unique microfluidic network-based devices to address a broad range of biological, chemical, pharmaceutical, and other scientific and technical challenges.

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.

Individual-based approach to epidemic processes on arbitrary dynamic contact networks

NASA Astrophysics Data System (ADS)

Rocha, Luis E. C.; Masuda, Naoki

2016-08-01

The dynamics of contact networks and epidemics of infectious diseases often occur on comparable time scales. Ignoring one of these time scales may provide an incomplete understanding of the population dynamics of the infection process. We develop an individual-based approximation for the susceptible-infected-recovered epidemic model applicable to arbitrary dynamic networks. Our framework provides, at the individual-level, the probability flow over time associated with the infection dynamics. This computationally efficient framework discards the correlation between the states of different nodes, yet provides accurate results in approximating direct numerical simulations. It naturally captures the temporal heterogeneities and correlations of contact sequences, fundamental ingredients regulating the timing and size of an epidemic outbreak, and the number of secondary infections. The high accuracy of our approximation further allows us to detect the index individual of an epidemic outbreak in real-life network data.

PolNet: A Tool to Quantify Network-Level Cell Polarity and Blood Flow in Vascular Remodeling.

PubMed

Bernabeu, Miguel O; Jones, Martin L; Nash, Rupert W; Pezzarossa, Anna; Coveney, Peter V; Gerhardt, Holger; Franco, Claudio A

2018-05-08

In this article, we present PolNet, an open-source software tool for the study of blood flow and cell-level biological activity during vessel morphogenesis. We provide an image acquisition, segmentation, and analysis protocol to quantify endothelial cell polarity in entire in vivo vascular networks. In combination, we use computational fluid dynamics to characterize the hemodynamics of the vascular networks under study. The tool enables, to our knowledge for the first time, a network-level analysis of polarity and flow for individual endothelial cells. To date, PolNet has proven invaluable for the study of endothelial cell polarization and migration during vascular patterning, as demonstrated by two recent publications. Additionally, the tool can be easily extended to correlate blood flow with other experimental observations at the cellular/molecular level. We release the source code of our tool under the Lesser General Public License. Copyright © 2018 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Identifying the Critical Links in Road Transportation Networks: Centrality-based approach utilizing structural properties