Hopkins, Matthew Morgan; DeChant, Lawrence Justin.; Piekos, Edward Stanley; Pointon, Timothy David
2009-02-01
This report summarizes the work completed during FY2007 and FY2008 for the LDRD project ''Hybrid Plasma Modeling''. The goal of this project was to develop hybrid methods to model plasmas across the non-continuum-to-continuum collisionality spectrum. The primary methodology to span these regimes was to couple a kinetic method (e.g., Particle-In-Cell) in the non-continuum regions to a continuum PDE-based method (e.g., finite differences) in continuum regions. The interface between the two would be adjusted dynamically ased on statistical sampling of the kinetic results. Although originally a three-year project, it became clear during the second year (FY2008) that there were not sufficient resources to complete the project and it was terminated mid-year.
Competitive hybridization models.
Cherepinsky, Vera; Hashmi, Ghazala; Mishra, Bud
2010-11-01
Microarray technology, in its simplest form, allows one to gather abundance data for target DNA molecules, associated with genomes or gene-expressions, and relies on hybridizing the target to many short probe oligonucleotides arrayed on a surface. While for such multiplexed reactions conditions are optimized to make the most of each individual probe-target interaction, subsequent analysis of these experiments is based on the implicit assumption that a given experiment yields the same result regardless of whether it was conducted in isolation or in parallel with many others. It has been discussed in the literature that this assumption is frequently false, and its validity depends on the types of probes and their interactions with each other. We present a detailed physical model of hybridization as a means of understanding probe interactions in a multiplexed reaction. Ultimately, the model can be derived from a system of ordinary differential equations (ODE's) describing kinetic mass action with conservation-of-mass equations completing the system. We examine pairwise probe interactions in detail and present a model of "competition" between the probes for the target--especially, when the target is effectively in short supply. These effects are shown to be predictable from the affinity constants for each of the four probe sequences involved, namely, the match and mismatch sequences for both probes. These affinity constants are calculated from the thermodynamic parameters such as the free energy of hybridization, which are in turn computed according to the nearest neighbor (NN) model for each probe and target sequence. Simulations based on the competitive hybridization model explain the observed variability in the signal of a given probe when measured in parallel with different groupings of other probes or individually. The results of the simulations can be used for experiment design and pooling strategies, based on which probes have been shown to have a strong effect
Competitive hybridization models
NASA Astrophysics Data System (ADS)
Cherepinsky, Vera; Hashmi, Ghazala; Mishra, Bud
2010-11-01
Microarray technology, in its simplest form, allows one to gather abundance data for target DNA molecules, associated with genomes or gene-expressions, and relies on hybridizing the target to many short probe oligonucleotides arrayed on a surface. While for such multiplexed reactions conditions are optimized to make the most of each individual probe-target interaction, subsequent analysis of these experiments is based on the implicit assumption that a given experiment yields the same result regardless of whether it was conducted in isolation or in parallel with many others. It has been discussed in the literature that this assumption is frequently false, and its validity depends on the types of probes and their interactions with each other. We present a detailed physical model of hybridization as a means of understanding probe interactions in a multiplexed reaction. Ultimately, the model can be derived from a system of ordinary differential equations (ODE’s) describing kinetic mass action with conservation-of-mass equations completing the system. We examine pairwise probe interactions in detail and present a model of “competition” between the probes for the target—especially, when the target is effectively in short supply. These effects are shown to be predictable from the affinity constants for each of the four probe sequences involved, namely, the match and mismatch sequences for both probes. These affinity constants are calculated from the thermodynamic parameters such as the free energy of hybridization, which are in turn computed according to the nearest neighbor (NN) model for each probe and target sequence. Simulations based on the competitive hybridization model explain the observed variability in the signal of a given probe when measured in parallel with different groupings of other probes or individually. The results of the simulations can be used for experiment design and pooling strategies, based on which probes have been shown to have a strong
Hybrid2: The hybrid power system simulation model
Baring-Gould, E I; Green, H J; van Dijk, V A.P.; Manwell, J F
1996-07-01
There is a large-scale need and desire for energy in remote communities, especially in the developing world; however the lack of a user friendly, flexible performance prediction model for hybrid power systems incorporating renewables hindered the analysis of hybrids (including wind turbines, PV, diesel generators, AC/DC energy storage) as options to conventional solutions. A user friendly model was needed with the versatility to simulate the many system locations, widely varying hardware configurations, and differing control options for potential hybrid power systems. To meet these ends, NREL and U. Mass. researchers developed the Hybrid2 software. This paper provides an overview of the capabilities, features, and functionality of the Hybrid2 code, discusses its validation and future plans. Model availability and technical support provided to Hybrid2 users are also discussed.
Hybrid2 - The hybrid power system simulation model
Baring-Gould, E.I.; Green, H.J.; Dijk, V.A.P. van; Manwell, J.F.
1996-12-31
There is a large-scale need and desire for energy in remote communities, especially in the developing world; however the lack of a user friendly, flexible performance prediction model for hybrid power systems incorporating renewables hindered the analysis of hybrids as options to conventional solutions. A user friendly model was needed with the versatility to simulate the many system locations, widely varying hardware configurations, and differing control options for potential hybrid power systems. To meet these ends, researchers from the National Renewable Energy Laboratory (NREL) and the University of Massachusetts (UMass) developed the Hybrid2 software. This paper provides an overview of the capabilities, features, and functionality of the Hybrid2 code, discusses its validation and future plans. Model availability and technical support provided to Hybrid2 users are also discussed. 12 refs., 3 figs., 4 tabs.
Hybrid regional air pollution models
Drake, R.L.
1980-03-01
This discussion deals with a family of air quality models for predicting and analyzing the fine particulate loading in the atmosphere, for assessing the extent and degree of visibility impairment, and for determining the potential of pollutants for increasing the acidity of soils and water. The major horizontal scales of interest are from 400km to 2000km; and the time scales may vary from several hours, to days, weeks, and a few months or years, depending on the EPA regulations being addressed. First the role air quality models play in the general family of atmospheric simulation models is described. Then, the characteristics of a well-designed, comprehensive air quality model are discussed. Following this, the specific objectives of this workshop are outlined, and their modeling implications are summarized. There are significant modeling differences produced by the choice of the coordinate system, whether it be the fixed Eulerian system, the moving Lagrangian system, or some hybrid of the two. These three systems are briefly discussed, and a list of hybrid models that are currently in use are given. Finally, the PNL regional transport model is outlined and a number of research needs are listed.
Using Hybrid Modeling to Develop Innovative Activities
ERIC Educational Resources Information Center
Lichtman, Brenda; Avans, Diana
2005-01-01
This article describes a hybrid activities model that physical educators can use with students in grades four and above to create virtually a limitless array of novel games. A brief introduction to the basic theory is followed by descriptions of some hybrid games. Hybrid games are typically the result of merging two traditional sports or other…
Using Hybrid Modeling to Develop Innovative Activities
ERIC Educational Resources Information Center
Lichtman, Brenda; Avans, Diana
2005-01-01
This article describes a hybrid activities model that physical educators can use with students in grades four and above to create virtually a limitless array of novel games. A brief introduction to the basic theory is followed by descriptions of some hybrid games. Hybrid games are typically the result of merging two traditional sports or other…
Model-Based Prognostics of Hybrid Systems
NASA Technical Reports Server (NTRS)
Daigle, Matthew; Roychoudhury, Indranil; Bregon, Anibal
2015-01-01
Model-based prognostics has become a popular approach to solving the prognostics problem. However, almost all work has focused on prognostics of systems with continuous dynamics. In this paper, we extend the model-based prognostics framework to hybrid systems models that combine both continuous and discrete dynamics. In general, most systems are hybrid in nature, including those that combine physical processes with software. We generalize the model-based prognostics formulation to hybrid systems, and describe the challenges involved. We present a general approach for modeling hybrid systems, and overview methods for solving estimation and prediction in hybrid systems. As a case study, we consider the problem of conflict (i.e., loss of separation) prediction in the National Airspace System, in which the aircraft models are hybrid dynamical systems.
Hybrid model for solidification and convection
NASA Technical Reports Server (NTRS)
Oldenburg, Curtis M.; Spera, Frank J.
1992-01-01
A hybrid model for continuum phase-change problems is presented. The hybrid model accounts for flow in regions of concentrated mush, dilute mush, and single-phase liquid. Scale analysis shows that, in dilute mush, the Blake-Kozeny-Carman relation may lead to inaccuracy in the continuum formulation for certain values of the Rayleigh and Darcy numbers. The hybrid model uses arctangent switching functions to switch on the Darcy flow and variable viscosity terms depending on the local value of the fraction solid. Two-dimensional example calculations suggest that the hybrid model more accurately accounts for transport in the dilute mush region.
Hadron rapidity spectra within a hybrid model
NASA Astrophysics Data System (ADS)
Khvorostukhin, A. S.; Toneev, V. D.
2017-01-01
A 2-stage hybrid model is proposed that joins the fast initial state of interaction, described by the hadron string dynamics (HSD) model, to subsequent evolution of the expanding system at the second stage, treated within ideal hydrodynamics. The developed hybrid model is assigned to describe heavy-ion collisions in the energy range of the NICA collider under construction in Dubna. Generally, the model is in reasonable agreement with the available data on proton rapidity spectra. However, reproducing proton rapidity spectra, our hybrid model cannot describe the rapidity distributions of pions. The model should be improved by taking into consideration viscosity effects at the hydrodynamical stage of system evolution.
Evaluating the Pedagogical Potential of Hybrid Models
ERIC Educational Resources Information Center
Levin, Tzur; Levin, Ilya
2013-01-01
The paper examines how the use of hybrid models--that consist of the interacting continuous and discrete processes--may assist in teaching system thinking. We report an experiment in which undergraduate students were asked to choose between a hybrid and a continuous solution for a number of control problems. A correlation has been found between…
Modeling hybrid perovskites by molecular dynamics.
Mattoni, Alessandro; Filippetti, Alessio; Caddeo, Claudia
2017-02-01
The topical review describes the recent progress in the modeling of hybrid perovskites by molecular dynamics simulations. Hybrid perovskites and in particular methylammonium lead halide (MAPI) have a tremendous technological relevance representing the fastest-advancing solar material to date. They also represent the paradigm of an organic-inorganic crystalline material with some conceptual peculiarities: an inorganic semiconductor for what concerns the electronic and absorption properties with a hybrid and solution processable organic-inorganic body. After briefly explaining the basic concepts of ab initio and classical molecular dynamics, the model potential recently developed for hybrid perovskites is described together with its physical motivation as a simple ionic model able to reproduce the main dynamical properties of the material. Advantages and limits of the two strategies (either ab initio or classical) are discussed in comparison with the time and length scales (from pico to microsecond scale) necessary to comprehensively study the relevant properties of hybrid perovskites from molecular reorientations to electrocaloric effects. The state-of-the-art of the molecular dynamics modeling of hybrid perovskites is reviewed by focusing on a selection of showcase applications of methylammonium lead halide: molecular cations disorder; temperature evolution of vibrations; thermally activated defects diffusion; thermal transport. We finally discuss the perspectives in the modeling of hybrid perovskites by molecular dynamics.
Modeling hybrid perovskites by molecular dynamics
NASA Astrophysics Data System (ADS)
Mattoni, Alessandro; Filippetti, Alessio; Caddeo, Claudia
2017-02-01
The topical review describes the recent progress in the modeling of hybrid perovskites by molecular dynamics simulations. Hybrid perovskites and in particular methylammonium lead halide (MAPI) have a tremendous technological relevance representing the fastest-advancing solar material to date. They also represent the paradigm of an organic-inorganic crystalline material with some conceptual peculiarities: an inorganic semiconductor for what concerns the electronic and absorption properties with a hybrid and solution processable organic-inorganic body. After briefly explaining the basic concepts of ab initio and classical molecular dynamics, the model potential recently developed for hybrid perovskites is described together with its physical motivation as a simple ionic model able to reproduce the main dynamical properties of the material. Advantages and limits of the two strategies (either ab initio or classical) are discussed in comparison with the time and length scales (from pico to microsecond scale) necessary to comprehensively study the relevant properties of hybrid perovskites from molecular reorientations to electrocaloric effects. The state-of-the-art of the molecular dynamics modeling of hybrid perovskites is reviewed by focusing on a selection of showcase applications of methylammonium lead halide: molecular cations disorder; temperature evolution of vibrations; thermally activated defects diffusion; thermal transport. We finally discuss the perspectives in the modeling of hybrid perovskites by molecular dynamics.
Travelling waves in hybrid chemotaxis models.
Franz, Benjamin; Xue, Chuan; Painter, Kevin J; Erban, Radek
2014-02-01
Hybrid models of chemotaxis combine agent-based models of cells with partial differential equation models of extracellular chemical signals. In this paper, travelling wave properties of hybrid models of bacterial chemotaxis are investigated. Bacteria are modelled using an agent-based (individual-based) approach with internal dynamics describing signal transduction. In addition to the chemotactic behaviour of the bacteria, the individual-based model also includes cell proliferation and death. Cells consume the extracellular nutrient field (chemoattractant), which is modelled using a partial differential equation. Mesoscopic and macroscopic equations representing the behaviour of the hybrid model are derived and the existence of travelling wave solutions for these models is established. It is shown that cell proliferation is necessary for the existence of non-transient (stationary) travelling waves in hybrid models. Additionally, a numerical comparison between the wave speeds of the continuum models and the hybrid models shows good agreement in the case of weak chemotaxis and qualitative agreement for the strong chemotaxis case. In the case of slow cell adaptation, we detect oscillating behaviour of the wave, which cannot be explained by mean-field approximations.
Hybrid rocket engine, theoretical model and experiment
NASA Astrophysics Data System (ADS)
Chelaru, Teodor-Viorel; Mingireanu, Florin
2011-06-01
The purpose of this paper is to build a theoretical model for the hybrid rocket engine/motor and to validate it using experimental results. The work approaches the main problems of the hybrid motor: the scalability, the stability/controllability of the operating parameters and the increasing of the solid fuel regression rate. At first, we focus on theoretical models for hybrid rocket motor and compare the results with already available experimental data from various research groups. A primary computation model is presented together with results from a numerical algorithm based on a computational model. We present theoretical predictions for several commercial hybrid rocket motors, having different scales and compare them with experimental measurements of those hybrid rocket motors. Next the paper focuses on tribrid rocket motor concept, which by supplementary liquid fuel injection can improve the thrust controllability. A complementary computation model is also presented to estimate regression rate increase of solid fuel doped with oxidizer. Finally, the stability of the hybrid rocket motor is investigated using Liapunov theory. Stability coefficients obtained are dependent on burning parameters while the stability and command matrixes are identified. The paper presents thoroughly the input data of the model, which ensures the reproducibility of the numerical results by independent researchers.
A Hybrid 3D Indoor Space Model
NASA Astrophysics Data System (ADS)
Jamali, Ali; Rahman, Alias Abdul; Boguslawski, Pawel
2016-10-01
GIS integrates spatial information and spatial analysis. An important example of such integration is for emergency response which requires route planning inside and outside of a building. Route planning requires detailed information related to indoor and outdoor environment. Indoor navigation network models including Geometric Network Model (GNM), Navigable Space Model, sub-division model and regular-grid model lack indoor data sources and abstraction methods. In this paper, a hybrid indoor space model is proposed. In the proposed method, 3D modeling of indoor navigation network is based on surveying control points and it is less dependent on the 3D geometrical building model. This research proposes a method of indoor space modeling for the buildings which do not have proper 2D/3D geometrical models or they lack semantic or topological information. The proposed hybrid model consists of topological, geometrical and semantical space.
Hybrid modelling of anaerobic wastewater treatment processes.
Karama, A; Bernard, O; Genovesi, A; Dochain, D; Benhammou, A; Steyer, J P
2001-01-01
This paper presents a hybrid approach for the modelling of an anaerobic digestion process. The hybrid model combines a feed-forward network, describing the bacterial kinetics, and the a priori knowledge based on the mass balances of the process components. We have considered an architecture which incorporates the neural network as a static model of unmeasured process parameters (kinetic growth rate) and an integrator for the dynamic representation of the process using a set of dynamic differential equations. The paper contains a description of the neural network component training procedure. The performance of this approach is illustrated with experimental data.
Weather forecasting based on hybrid neural model
NASA Astrophysics Data System (ADS)
Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.
2017-02-01
Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.
Hybrid models in loop quantum cosmology
NASA Astrophysics Data System (ADS)
Elizaga Navascués, Beatriz; Martín-Benito, Mercedes; Mena Marugán, Guillermo A.
2016-06-01
In the framework of Loop Quantum Cosmology (LQC), inhomogeneous models are usually quantized by means of a hybrid approach that combines loop quantization techniques with standard quantum field theory methods. This approach is based on a splitting of the phase space in a homogeneous sector, formed by global, zero-modes and an inhomogeneous sector, formed by the remaining, infinite number of modes, that describe the local degrees of freedom. Then, the hybrid quantization is attained by adopting a loop representation for the homogeneous gravitational sector, while a Fock representation is used for the inhomogeneities. The zero-mode of the Hamiltonian constraint operator couples the homogeneous and inhomogeneous sectors. The hybrid approach, therefore, is expected to provide a suitable quantum theory in regimes where the main quantum effects of the geometry are those affecting the zero-modes, while the inhomogeneities, still being quantum, can be treated in a more conventional way. This hybrid strategy was first proposed for the simplest cosmological midisuperspaces: the Gowdy models, and it has been later applied to the case of cosmological perturbations. This paper reviews the construction and main applications of hybrid LQC.
Hybrid quantum teleportation: A theoretical model
Takeda, Shuntaro; Mizuta, Takahiro; Fuwa, Maria; Yoshikawa, Jun-ichi; Yonezawa, Hidehiro; Furusawa, Akira
2014-12-04
Hybrid quantum teleportation – continuous-variable teleportation of qubits – is a promising approach for deterministically teleporting photonic qubits. We propose how to implement it with current technology. Our theoretical model shows that faithful qubit transfer can be achieved for this teleportation by choosing an optimal gain for the teleporter’s classical channel.
Dynamic modeling of lower hybrid current drive
Ignat, D.W.; Valeo, E.J.; Jardin, S.C.
1993-10-01
A computational model of lower hybrid current drive in the presence of an electric field is described and some results are given. Details of geometry, plasma profiles and circuit equations are treated carefully. Two-dimensional velocity space effects are approximated in a one-dimensional Fokker-Planck treatment.
CORSICA modelling of ITER hybrid operation scenarios
NASA Astrophysics Data System (ADS)
Kim, S. H.; Bulmer, R. H.; Campbell, D. J.; Casper, T. A.; LoDestro, L. L.; Meyer, W. H.; Pearlstein, L. D.; Snipes, J. A.
2016-12-01
The hybrid operating mode observed in several tokamaks is characterized by further enhancement over the high plasma confinement (H-mode) associated with reduced magneto-hydro-dynamic (MHD) instabilities linked to a stationary flat safety factor (q ) profile in the core region. The proposed ITER hybrid operation is currently aiming at operating for a long burn duration (>1000 s) with a moderate fusion power multiplication factor, Q , of at least 5. This paper presents candidate ITER hybrid operation scenarios developed using a free-boundary transport modelling code, CORSICA, taking all relevant physics and engineering constraints into account. The ITER hybrid operation scenarios have been developed by tailoring the 15 MA baseline ITER inductive H-mode scenario. Accessible operation conditions for ITER hybrid operation and achievable range of plasma parameters have been investigated considering uncertainties on the plasma confinement and transport. ITER operation capability for avoiding the poloidal field coil current, field and force limits has been examined by applying different current ramp rates, flat-top plasma currents and densities, and pre-magnetization of the poloidal field coils. Various combinations of heating and current drive (H&CD) schemes have been applied to study several physics issues, such as the plasma current density profile tailoring, enhancement of the plasma energy confinement and fusion power generation. A parameterized edge pedestal model based on EPED1 added to the CORSICA code has been applied to hybrid operation scenarios. Finally, fully self-consistent free-boundary transport simulations have been performed to provide information on the poloidal field coil voltage demands and to study the controllability with the ITER controllers. Extended from Proc. 24th Int. Conf. on Fusion Energy (San Diego, 2012) IT/P1-13.
Hybrid Model of IRT and Latent Class Models.
ERIC Educational Resources Information Center
Yamamoto, Kentaro
This study developed a hybrid of item response theory (IRT) models and latent class models, which combined the strengths of each type of model. The primary motivation for developing the new model is to describe characteristics of examinees' knowledge at the time of the examination. Hence, the application of the model lies mainly in so-called…
Hybrid model for QCD deconfining phase boundary
NASA Astrophysics Data System (ADS)
Srivastava, P. K.; Singh, C. P.
2012-06-01
Intensive search for a proper and realistic equations of state (EOS) is still continued for studying the phase diagram existing between quark gluon plasma (QGP) and hadron gas (HG) phases. Lattice calculations provide such EOS for the strongly interacting matter at finite temperature (T) and vanishing baryon chemical potential (μB). These calculations are of limited use at finite μB due to the appearance of notorious sign problem. In the recent past, we had constructed a hybrid model description for the QGP as well as HG phases where we make use of a new excluded-volume model for HG and a thermodynamically-consistent quasiparticle model for the QGP phase and used them further to get QCD phase boundary and a critical point. Since then many lattice calculations have appeared showing various thermal and transport properties of QCD matter at finite T and μB=0. We test our hybrid model by reproducing the entire data for strongly interacting matter and predict our results at finite μB so that they can be tested in future. Finally we demonstrate the utility of the model in fixing the precise location, the order of the phase transition and the nature of CP existing on the QCD phase diagram. We thus emphasize the suitability of the hybrid model as formulated here in providing a realistic EOS for the strongly interacting matter.
Hybrid modeling and prediction of dynamical systems
Lloyd, Alun L.; Flores, Kevin B.
2017-01-01
Scientific analysis often relies on the ability to make accurate predictions of a system’s dynamics. Mechanistic models, parameterized by a number of unknown parameters, are often used for this purpose. Accurate estimation of the model state and parameters prior to prediction is necessary, but may be complicated by issues such as noisy data and uncertainty in parameters and initial conditions. At the other end of the spectrum exist nonparametric methods, which rely solely on data to build their predictions. While these nonparametric methods do not require a model of the system, their performance is strongly influenced by the amount and noisiness of the data. In this article, we consider a hybrid approach to modeling and prediction which merges recent advancements in nonparametric analysis with standard parametric methods. The general idea is to replace a subset of a mechanistic model’s equations with their corresponding nonparametric representations, resulting in a hybrid modeling and prediction scheme. Overall, we find that this hybrid approach allows for more robust parameter estimation and improved short-term prediction in situations where there is a large uncertainty in model parameters. We demonstrate these advantages in the classical Lorenz-63 chaotic system and in networks of Hindmarsh-Rose neurons before application to experimentally collected structured population data. PMID:28692642
Hybrid Energy System Modeling in Modelica
William R. Binder; Christiaan J. J. Paredis; Humberto E. Garcia
2014-03-01
In this paper, a Hybrid Energy System (HES) configuration is modeled in Modelica. Hybrid Energy Systems (HES) have as their defining characteristic the use of one or more energy inputs, combined with the potential for multiple energy outputs. Compared to traditional energy systems, HES provide additional operational flexibility so that high variability in both energy production and consumption levels can be absorbed more effectively. This is particularly important when including renewable energy sources, whose output levels are inherently variable, determined by nature. The specific HES configuration modeled in this paper include two energy inputs: a nuclear plant, and a series of wind turbines. In addition, the system produces two energy outputs: electricity and synthetic fuel. The models are verified through simulations of the individual components, and the system as a whole. The simulations are performed for a range of component sizes, operating conditions, and control schemes.
Hybrid Modeling of Plasma Discharges
2010-04-01
govern some critical aspects of the dynamics. • Significant deviations from thermal (Boltzmann) and ionization (Saha) equilibrium. • Significant... thermal non-equilibrium. Otherwise, one must use a particle method such as DSMC or PIC to model its dynamics. Typically, the energetic component is...non- thermal ) electrons, are themselves not treated as particles but are part of the bulk. This is inconsistent because these can have sufficient
A Mathematical Model for Suppression Subtractive Hybridization
Gadgil, Chetan; Rink, Anette; Beattie, Craig
2002-01-01
Suppression subtractive hybridization (SSH) is frequently used to unearth differentially expressed genes on a whole-genome scale. Its versatility is based on combining cDNA library subtraction and normalization, which allows the isolation of sequences of varying degrees of abundance and differential expression. SSH is a complex process with many adjustable parameters that affect the outcome of gene isolation.We present a mathematical model of SSH based on DNA hybridization kinetics for assessing the effect of various parameters to facilitate its optimization. We derive an equation for the probability that a particular differentially expressed species is successfully isolated and use this to quantify the effect of the following parameters related to the cDNA sample: (a) mRNA abundance; (b) partial sequence complementarity to other species; and (3) degree of differential expression. We also evaluate the effect of parameters related to the process, including: (a) reaction times; and (b) extent of driver excess used in the two hybridization reactions. The optimum set of process parameters for successful isolation of differentially expressed species depends on transcript abundance. We show that the reaction conditions have a significant effect on the occurrence of false-positives and formulate strategies to isolate specific subsets of differentially expressed genes. We also quantify the effect of non-specific hybridization on the false-positive results and present strategies for spiking cDNA sequences to address this problem. PMID:18629052
Advanced Hybrid Modeling of Hall Thruster Plumes
2010-06-16
LVTF. A direct simulation Monte Carlo (DSMC) method3 is used to model collision dynamics, and a Particle-in-Cell ( PIC ) method4 is used to capture...cell ( PIC ) numerical methods on an axisymmetric grid.7 The code has been found to be effective in creating either time-averaged outputs of performance...here. The HPHall code performs an axisymmetric simulation, commonly referred to as “hybrid- PIC ,” treating the electrons via fluid approximation
Hybrid approaches to physiologic modeling and prediction
NASA Astrophysics Data System (ADS)
Olengü, Nicholas O.; Reifman, Jaques
2005-05-01
This paper explores how the accuracy of a first-principles physiological model can be enhanced by integrating data-driven, "black-box" models with the original model to form a "hybrid" model system. Both linear (autoregressive) and nonlinear (neural network) data-driven techniques are separately combined with a first-principles model to predict human body core temperature. Rectal core temperature data from nine volunteers, subject to four 30/10-minute cycles of moderate exercise/rest regimen in both CONTROL and HUMID environmental conditions, are used to develop and test the approach. The results show significant improvements in prediction accuracy, with average improvements of up to 30% for prediction horizons of 20 minutes. The models developed from one subject's data are also used in the prediction of another subject's core temperature. Initial results for this approach for a 20-minute horizon show no significant improvement over the first-principles model by itself.
A hybrid modeling approach for option pricing
NASA Astrophysics Data System (ADS)
Hajizadeh, Ehsan; Seifi, Abbas
2011-11-01
The complexity of option pricing has led many researchers to develop sophisticated models for such purposes. The commonly used Black-Scholes model suffers from a number of limitations. One of these limitations is the assumption that the underlying probability distribution is lognormal and this is so controversial. We propose a couple of hybrid models to reduce these limitations and enhance the ability of option pricing. The key input to option pricing model is volatility. In this paper, we use three popular GARCH type model for estimating volatility. Then, we develop two non-parametric models based on neural networks and neuro-fuzzy networks to price call options for S&P 500 index. We compare the results with those of Black-Scholes model and show that both neural network and neuro-fuzzy network models outperform Black-Scholes model. Furthermore, comparing the neural network and neuro-fuzzy approaches, we observe that for at-the-money options, neural network model performs better and for both in-the-money and an out-of-the money option, neuro-fuzzy model provides better results.
From a Harmonious Unifying Hybrid Preferential Model Toward a Large Unifying Hybrid Network Model
NASA Astrophysics Data System (ADS)
Fang, Jinqing; Li, Yong; Bi, Qiao
The motivation of this work raises four challenging questions: (1) Why is it that so many generalized random network models exist but they cannot be completely consistent with real-world networks? (2) Are these complex networks fundamentally attached in a random preferential manner without any deterministic attachment for both un-weighted and weighted networks? To answer the first two questions, we propose a harmonious unifying hybrid preferential model (HUHPM) controlled by a total hybrid ratio. (3) Why are social networks mostly positive degree-degree correlation but biological and technological networks tend to possess negative degree-degree correlation? (4) Are there coherent physical ideas and a unification formation mechanism for studies of complex networks? To seek a better answer of all these questions, especially the last two above, we extend the HUHPM to a large unifying hybrid network model (LUHNM), based on introducing two new hybrid ratios. We study the two models above, both numerically and analytically. All findings of topological properties in the network models above can give a certain universally meaningful result, which reveals some nontrivial topological properties, new phenomena, and give a relatively satisfactory answer.
Fluid and hybrid models for streamers
NASA Astrophysics Data System (ADS)
Bonaventura, Zdeněk
2016-09-01
Streamers are contracted ionizing waves with self-generated field enhancement that propagate into a low-ionized medium exposed to high electric field leaving filamentary trails of plasma behind. The widely used model to study streamer dynamics is based on drift-diffusion equations for electrons and ions, assuming local field approximation, coupled with Poisson's equation. For problems where presence of energetic electrons become important a fluid approach needs to be extended by a particle model, accompanied also with Monte Carlo Collision technique, that takes care of motion of these electrons. A combined fluid-particle approach is used to study an influence of surface emission processes on a fast-pulsed dielectric barrier discharge in air at atmospheric pressure. It is found that fluid-only model predicts substantially faster reignition dynamics compared to coupled fluid-particle model. Furthermore, a hybrid model can be created in which the population of electrons is divided in the energy space into two distinct groups: (1) low energy `bulk' electrons that are treated with fluid model, and (2) high energy `beam' electrons, followed as particles. The hybrid model is then capable not only to deal with streamer discharges in laboratory conditions, but also allows us to study electron acceleration in streamer zone of lighting leaders. There, the production of fast electrons from streamers is investigated, since these (runaway) electrons act as seeds for the relativistic runaway electron avalanche (RREA) mechanism, important for high-energy atmospheric physics phenomena. Results suggest that high energy electrons effect the streamer propagation, namely the velocity, the peak electric field, and thus also the production rate of runaway electrons. This work has been supported by the Czech Science Foundation research project 15-04023S.
A Cause of Ahistorical Science Teaching: Use of Hybrid Models.
ERIC Educational Resources Information Center
Justi, Rosaria; Gilbert, John
1999-01-01
Presents eight historical consensus models for teaching chemical kinetics. Models put forth by teachers and textbooks in a Brazilian secondary classroom were analyzed and discussed and found to be hybrids of the historical models. The existence of hybrid models in science teaching is proposed as a new component in science teachers' training…
Models of transition regions in hybrid stars
NASA Technical Reports Server (NTRS)
Brosius, J. W.; Mullan, D. J.
1986-01-01
Models for the transition regions of six hybrid stars, four bright giants and two supergiants, are calculated. The models include mass loss and prescribe Alfven waves as the source of mechanical energy. The momentum and energy deposition rates required at each level of the atmosphere are evaluated. The final models for all six stars have mass loss rates lying below the current VLA upper limits by factors of two to ten, and have densities which agree with those derived by density-sensitive line ratios. The density vs. temperature structure in Alpha TrA agree well with that derived by Hartmann et al. (1985). Wave amplitudes and magnetic field strengths are derived as functions of height, and the amplitudes are found to agree well with the observed line widths in Alpha TrA.
Hybrid2: The hybrid system simulation model, Version 1.0, user manual
Baring-Gould, E.I.
1996-06-01
In light of the large scale desire for energy in remote communities, especially in the developing world, the need for a detailed long term performance prediction model for hybrid power systems was seen. To meet these ends, engineers from the National Renewable Energy Laboratory (NREL) and the University of Massachusetts (UMass) have spent the last three years developing the Hybrid2 software. The Hybrid2 code provides a means to conduct long term, detailed simulations of the performance of a large array of hybrid power systems. This work acts as an introduction and users manual to the Hybrid2 software. The manual describes the Hybrid2 code, what is included with the software and instructs the user on the structure of the code. The manual also describes some of the major features of the Hybrid2 code as well as how to create projects and run hybrid system simulations. The Hybrid2 code test program is also discussed. Although every attempt has been made to make the Hybrid2 code easy to understand and use, this manual will allow many organizations to consider the long term advantages of using hybrid power systems instead of conventional petroleum based systems for remote power generation.
Hybrid Concurrent Constraint Simulation Models of Several Systems
NASA Technical Reports Server (NTRS)
Sweet, Adam
2003-01-01
This distribution contains several simulation models created for the hybrid simulation language, Hybrid Concurrent Constraint (HCC). An HCC model contains the information specified in the widely-accepted academic definition of a hybrid system: this includes expressions for the modes of the systems to be simulated and the differential equations that apply in each mode. These expressions are written in the HCC syntax. The models included here were created by either applying basic physical laws or implementing equations listed in previously published papers.
Hybrid Concurrent Constraint Simulation Models of Several Systems
NASA Technical Reports Server (NTRS)
Sweet, Adam
2003-01-01
This distribution contains several simulation models created for the hybrid simulation language, Hybrid Concurrent Constraint (HCC). An HCC model contains the information specified in the widely-accepted academic definition of a hybrid system: this includes expressions for the modes of the systems to be simulated and the differential equations that apply in each mode. These expressions are written in the HCC syntax. The models included here were created by either applying basic physical laws or implementing equations listed in previously published papers.
Gyrofluid-Gyrokinetic Hybrid Turbulence Model
NASA Astrophysics Data System (ADS)
Dorland, William; Mandell, Noah
2015-11-01
Gyrofluid models of tokamak turbulence are efficient compared to gyrokinetic models, in three senses. First, it is typically easier to develop one's intuition from fluid equations than kinetic equations. Second, because gyrofluid equations are only three-dimensional (instead of 5D or 6D), simulations with gyrofluid models require less memory than kinetic simulations and can therefore more easily fit on highly-optimized computing hardware, such as graphics processors. The third advantage is a result of the first two: one can develop and test ideas quickly with gyrofluid models. The disadvantage of gyrofluid models is their potential lack of physics fidelity. In this poster, we present our attempt to take full advantage of gyrofluid models, without sacrificing physics fidelity. Our approach is encapsulated in the Gryf-X code, which is an implementation of hybrid gyrofluid/gyrokinetic equations. The key improvements that we have brought to bear are: an improved understanding of the cascade of free energy simultaneously in k⊥ and v⊥ an improved model of zonal flow physics; and an implementation of the equations on modern heterogeneous computing platforms, both as a standalone simulation tool and as a component of TRINITY (a transport modeling code for tokamaks).
Hybrid Modeling Improves Health and Performance Monitoring
NASA Technical Reports Server (NTRS)
2007-01-01
Scientific Monitoring Inc. was awarded a Phase I Small Business Innovation Research (SBIR) project by NASA's Dryden Flight Research Center to create a new, simplified health-monitoring approach for flight vehicles and flight equipment. The project developed a hybrid physical model concept that provided a structured approach to simplifying complex design models for use in health monitoring, allowing the output or performance of the equipment to be compared to what the design models predicted, so that deterioration or impending failure could be detected before there would be an impact on the equipment's operational capability. Based on the original modeling technology, Scientific Monitoring released I-Trend, a commercial health- and performance-monitoring software product named for its intelligent trending, diagnostics, and prognostics capabilities, as part of the company's complete ICEMS (Intelligent Condition-based Equipment Management System) suite of monitoring and advanced alerting software. I-Trend uses the hybrid physical model to better characterize the nature of health or performance alarms that result in "no fault found" false alarms. Additionally, the use of physical principles helps I-Trend identify problems sooner. I-Trend technology is currently in use in several commercial aviation programs, and the U.S. Air Force recently tapped Scientific Monitoring to develop next-generation engine health-management software for monitoring its fleet of jet engines. Scientific Monitoring has continued the original NASA work, this time under a Phase III SBIR contract with a joint NASA-Pratt & Whitney aviation security program on propulsion-controlled aircraft under missile-damaged aircraft conditions.
Hybrid adaptive control of a dragonfly model
NASA Astrophysics Data System (ADS)
Couceiro, Micael S.; Ferreira, Nuno M. F.; Machado, J. A. Tenreiro
2012-02-01
Dragonflies show unique and superior flight performances than most of other insect species and birds. They are equipped with two pairs of independently controlled wings granting an unmatchable flying performance and robustness. In this paper, it is presented an adaptive scheme controlling a nonlinear model inspired in a dragonfly-like robot. It is proposed a hybrid adaptive ( HA) law for adjusting the parameters analyzing the tracking error. At the current stage of the project it is considered essential the development of computational simulation models based in the dynamics to test whether strategies or algorithms of control, parts of the system (such as different wing configurations, tail) as well as the complete system. The performance analysis proves the superiority of the HA law over the direct adaptive ( DA) method in terms of faster and improved tracking and parameter convergence.
A Hybrid Teaching and Learning Model
NASA Astrophysics Data System (ADS)
Juhary, Jowati Binti
This paper aims at analysing the needs for a specific teaching and learning model for the National Defence University of Malaysia (NDUM). The main argument is that whether there are differences between teaching and learning for academic component versus military component at the university. It is further argued that in order to achieve excellence, there should be one teaching and learning culture. Data were collected through interviews with military cadets. It is found that there are variations of teaching and learning strategies for academic courses, in comparison to a dominant teaching and learning style for military courses. Thus, in the interest of delivering quality education and training for students at the university, the paper argues that possibly a hybrid model for teaching and learning is fundamental in order to generate a one culture of academic and military excellence for the NDUM.
A Hybrid Tsunami Risk Model for Japan
NASA Astrophysics Data System (ADS)
Haseemkunju, A. V.; Smith, D. F.; Khater, M.; Khemici, O.; Betov, B.; Scott, J.
2014-12-01
Around the margins of the Pacific Ocean, denser oceanic plates slipping under continental plates cause subduction earthquakes generating large tsunami waves. The subducting Pacific and Philippine Sea plates create damaging interplate earthquakes followed by huge tsunami waves. It was a rupture of the Japan Trench subduction zone (JTSZ) and the resultant M9.0 Tohoku-Oki earthquake that caused the unprecedented tsunami along the Pacific coast of Japan on March 11, 2011. EQECAT's Japan Earthquake model is a fully probabilistic model which includes a seismo-tectonic model describing the geometries, magnitudes, and frequencies of all potential earthquake events; a ground motion model; and a tsunami model. Within the much larger set of all modeled earthquake events, fault rupture parameters for about 24000 stochastic and 25 historical tsunamigenic earthquake events are defined to simulate tsunami footprints using the numerical tsunami model COMCOT. A hybrid approach using COMCOT simulated tsunami waves is used to generate inundation footprints, including the impact of tides and flood defenses. Modeled tsunami waves of major historical events are validated against observed data. Modeled tsunami flood depths on 30 m grids together with tsunami vulnerability and financial models are then used to estimate insured loss in Japan from the 2011 tsunami. The primary direct report of damage from the 2011 tsunami is in terms of the number of buildings damaged by municipality in the tsunami affected area. Modeled loss in Japan from the 2011 tsunami is proportional to the number of buildings damaged. A 1000-year return period map of tsunami waves shows high hazard along the west coast of southern Honshu, on the Pacific coast of Shikoku, and on the east coast of Kyushu, primarily associated with major earthquake events on the Nankai Trough subduction zone (NTSZ). The highest tsunami hazard of more than 20m is seen on the Sanriku coast in northern Honshu, associated with the JTSZ.
HYBRID2 -- A versatile model of the performance of hybrid power systems
Green, H.J.; Manwell, J.
1995-04-01
In 1993, the National Renewable Laboratory (NREL) made an assessment of the available tools from the United States and Europe for predicting the long-term performance of hybrid power systems. By hybrid power the authors mean combinations of two or more power sources wind turbines, photovoltaics (PV), diesel gensets, or other generators into integrated systems for electric power generation in remote locations. Their conclusion was that there was no single, user-friendly tool capable of modeling the full range of hybrid power technologies being considered for the 1990s and beyond. The existing tools were, in particular, lacking flexibility in system configuration and in dispatch of components. As a result, NREL developed a specification for a model, called HYBRID2, for making comparisons of competing technology options on a level playing field. This specification was prepared with a range of potential users in mind including not only the US Department of Energy (DOE) renewable energy programs, but also the US wind industry, technical consultants, international development institutions/banks, and rural electrification programs in developing countries. During 1994, NREL and subcontractor, the University of Massachusetts (UMass), began development of HYBRID2 with funding from the DOE Wind Energy Program. It builds on the wind/diesel model, HYBRID1, developed previously by UMass, and expands that model to accommodate the wider array of technologies used in hybrid power systems. This paper will provide an overview of the model`s features, functions, and status.
Retrospective tests of hybrid operational earthquake forecasting models for Canterbury
NASA Astrophysics Data System (ADS)
Rhoades, D. A.; Liukis, M.; Christophersen, A.; Gerstenberger, M. C.
2016-01-01
The Canterbury, New Zealand, earthquake sequence, which began in September 2010, occurred in a region of low crustal deformation and previously low seismicity. Because, the ensuing seismicity in the region is likely to remain above previous levels for many years, a hybrid operational earthquake forecasting model for Canterbury was developed to inform decisions on building standards and urban planning for the rebuilding of Christchurch. The model estimates occurrence probabilities for magnitudes M ≥ 5.0 in the Canterbury region for each of the next 50 yr. It combines two short-term, two medium-term and four long-term forecasting models. The weight accorded to each individual model in the operational hybrid was determined by an expert elicitation process. A retrospective test of the operational hybrid model and of an earlier informally developed hybrid model in the whole New Zealand region has been carried out. The individual and hybrid models were installed in the New Zealand Earthquake Forecast Testing Centre and used to make retrospective annual forecasts of earthquakes with magnitude M > 4.95 from 1986 on, for time-lags up to 25 yr. All models underpredict the number of earthquakes due to an abnormally large number of earthquakes in the testing period since 2008 compared to those in the learning period. However, the operational hybrid model is more informative than any of the individual time-varying models for nearly all time-lags. Its information gain relative to a reference model of least information decreases as the time-lag increases to become zero at a time-lag of about 20 yr. An optimal hybrid model with the same mathematical form as the operational hybrid model was computed for each time-lag from the 26-yr test period. The time-varying component of the optimal hybrid is dominated by the medium-term models for time-lags up to 12 yr and has hardly any impact on the optimal hybrid model for greater time-lags. The optimal hybrid model is considerably more
HYBRID MESOSCALE MODELING OF DYNAMIC GRAIN FRAGMENTATION
R. SWIFT; C. HAGELBERG; M. HILTL
2001-04-01
Fines created by grain fragmentation from shaped-charge, jet perforation treatment often plug-up pores in the vicinity of the perforation tunnel. We analyze and model grain damage on samples recovered from impact tests of dry and water saturated sandstone at stress levels and duration similar to that of perforation loading. Analyses of Scanning Electron Microscope (SEM) images and laser particle size measurements on portions of the recovered samples characterize grain damage and changes in grain size distribution. Hybrid modeling that combines the Discrete Element Method (DEM) with Smooth Particle Hydrodynamics (SPH), and includes mesoscale representation of grain/pore structure, shows how grain damage evolves for dry and wet conditions. Modeling defines behavior in accord with recovered sample analyses as follows: (1) Increase in grain damage is obtained with an increase in stress level and pulse duration. (2) The grains in dry samples are extremely and irregularly fragmented with extensive reduced porosity. (3) Less grain damage and higher porosity is obtained in saturated samples. The influence of pore fluid mitigates the interaction between grains, thus reducing fragmentation damage. (4) Computed particle size distributions are similar in character to measurements.
Modeling material interfaces with hybrid adhesion method
Brown, Nicholas Taylor; Qu, Jianmin; Martinez, Enrique
2017-01-27
A molecular dynamics simulation approach is presented to approximate layered material structures using discrete interatomic potentials through classical mechanics and the underlying principles of quantum mechanics. This method isolates the energetic contributions of the system into two pure material layers and an interfacial region used to simulate the adhesive properties of the diffused interface. The strength relationship of the adhesion contribution is calculated through small-scale separation calculations and applied to the molecular surfaces through an inter-layer bond criterion. By segregating the contributions into three regions and accounting for the interfacial excess energies through the adhesive surface bonds, it is possiblemore » to model each material with an independent potential while maintaining an acceptable level of accuracy in the calculation of mechanical properties. This method is intended for the atomistic study of the delamination mechanics, typically observed in thin-film applications. Therefore, the work presented in this paper focuses on mechanical tensile behaviors, with observations in the elastic modulus and the delamination failure mode. To introduce the hybrid adhesion method, we apply the approach to an ideal bulk copper sample, where an interface is created by disassociating the force potential in the middle of the structure. Various mechanical behaviors are compared to a standard EAM control model to demonstrate the adequacy of this approach in a simple setting. In addition, we demonstrate the robustness of this approach by applying it on (1) a Cu-Cu2O interface with interactions between two atom types, and (2) an Al-Cu interface with two dissimilar FCC lattices. These additional examples are verified against EAM and COMB control models to demonstrate the accurate simulation of failure through delamination, and the formation and propagation of dislocations under loads. Finally, the results conclude that by modeling the energy
A range extender hybrid electric vehicle dynamic model
Powell, B.K.; Pilutti, T.E.
1994-12-31
This paper describes a dynamic model possessing the key system components of a Range Extender Hybrid Electric Vehicle. The model is suitable for dynamic analysis, control law synthesis, and prototype simulation.
SCAN-based hybrid and double-hybrid density functionals from models without fitted parameters
NASA Astrophysics Data System (ADS)
Hui, Kerwin; Chai, Jeng-Da
2016-01-01
By incorporating the nonempirical strongly constrained and appropriately normed (SCAN) semilocal density functional [J. Sun, A. Ruzsinszky, and J. P. Perdew, Phys. Rev. Lett. 115, 036402 (2015)] in the underlying expression of four existing hybrid and double-hybrid models, we propose one hybrid (SCAN0) and three double-hybrid (SCAN0-DH, SCAN-QIDH, and SCAN0-2) density functionals, which are free from any fitted parameters. The SCAN-based double-hybrid functionals consistently outperform their parent SCAN semilocal functional for self-interaction problems and noncovalent interactions. In particular, SCAN0-2, which includes about 79% of Hartree-Fock exchange and 50% of second-order Møller-Plesset correlation, is shown to be reliably accurate for a very diverse range of applications, such as thermochemistry, kinetics, noncovalent interactions, and self-interaction problems.
Theoretical aspects of hybrid chiral bag models
NASA Astrophysics Data System (ADS)
Mulders, P. J.
1984-09-01
In hybrid chiral bag models (HCBM's) the quarks are the source for the pion field outside the bag. If we want to solve this model with a classical external soliton solution and quantized fermions, it is necessary to evaluate the vacuum expectation values (VEV's) of those operators that contain fermion fields and appear in the boundary conditions. When the external solution is the so-called hedgehog solution, π-->(r-->,t)=fπθ(r)r^, the relevant VEV is i16π<0 | d2s[ψ―,(τ-->.r^)γ5exp(iτ-->.r^γ5θ)ψ] | 0>=2θ16πη+C0(θ)R, where η is a cutoff parameter (η-->0). To obtain this result we have used a multiple-reflection expansion of the Green's function, while C0(θ) is evaluated numerically. We discuss the infinite contribution in the above VEV, and show that 4πC0(θ)R is precisely the derivative of the Casimir energy with respect to θ. We also discuss some solutions of the HCBM for bag radii varying from 0 to ∞.
Intensive Scheduling: A Hybrid Model for the Junior High.
ERIC Educational Resources Information Center
McGorry, Eugene; McGorry, Susan Y.
1998-01-01
Discusses Intensive Scheduling as an approach to learning. Describes how educators in the Pocono Mountain School District in Pennsylvania searched for a more effective way to schedule classes. Describes how the junior high administration and teachers piloted a hybrid modified intensive schedule. Presents student opinions about the hybrid model,…
NASA Astrophysics Data System (ADS)
Wu, Guang; Dong, Zuomin
2017-09-01
Hybrid electric vehicles are widely accepted as a promising short to mid-term technical solution due to noticeably improved efficiency and lower emissions at competitive costs. In recent years, various hybrid powertrain systems were proposed and implemented based on different types of conventional transmission. Power-split system, including Toyota Hybrid System and Ford Hybrid System, are well-known examples. However, their relatively low torque capacity, and the drive of alternative and more advanced designs encouraged other innovative hybrid system designs. In this work, a new type of hybrid powertrain system based hybridized automated manual transmission (HAMT) is proposed. By using the concept of torque gap filler (TGF), this new hybrid powertrain type has the potential to overcome issue of torque gap during gearshift. The HAMT design (patent pending) is described in details, from gear layout and design of gear ratios (EV mode and HEV mode) to torque paths at different gears. As an analytical tool, mutli-body model of vehicle equipped with this HAMT was built to analyze powertrain dynamics at various steady and transient modes. A gearshift was decomposed and analyzed based basic modes. Furthermore, a Simulink-SimDriveline hybrid vehicle model was built for the new transmission, driveline and vehicle modular. Control strategy has also been built to harmonically coordinate different powertrain components to realize TGF function. A vehicle launch simulation test has been completed under 30% of accelerator pedal position to reveal details during gearshift. Simulation results showed that this HAMT can eliminate most torque gap that has been persistent issue of traditional AMT, improving both drivability and performance. This work demonstrated a new type of transmission that features high torque capacity, high efficiency and improved drivability.
A Structural Model Decomposition Framework for Hybrid Systems Diagnosis
NASA Technical Reports Server (NTRS)
Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil
2015-01-01
Nowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study.
The Hybrid Model for Implementing the Continuing Education Mission.
ERIC Educational Resources Information Center
Hentschel, Doe
1991-01-01
Models through which higher education provides outreach include centralized, decentralized, and hybrid. The latter, academically integrated and administratively decentralized, meshes continuing education programs with the academic mission while maximizing cost effectiveness. (SK)
Hybrid ODE/SSA methods and the cell cycle model
NASA Astrophysics Data System (ADS)
Wang, S.; Chen, M.; Cao, Y.
2017-07-01
Stochastic effect in cellular systems has been an important topic in systems biology. Stochastic modeling and simulation methods are important tools to study stochastic effect. Given the low efficiency of stochastic simulation algorithms, the hybrid method, which combines an ordinary differential equation (ODE) system with a stochastic chemically reacting system, shows its unique advantages in the modeling and simulation of biochemical systems. The efficiency of hybrid method is usually limited by reactions in the stochastic subsystem, which are modeled and simulated using Gillespie's framework and frequently interrupt the integration of the ODE subsystem. In this paper we develop an efficient implementation approach for the hybrid method coupled with traditional ODE solvers. We also compare the efficiency of hybrid methods with three widely used ODE solvers RADAU5, DASSL, and DLSODAR. Numerical experiments with three biochemical models are presented. A detailed discussion is presented for the performances of three ODE solvers.
Nuclear Hybrid Energy System Modeling: RELAP5 Dynamic Coupling Capabilities
Piyush Sabharwall; Nolan Anderson; Haihua Zhao; Shannon Bragg-Sitton; George Mesina
2012-09-01
The nuclear hybrid energy systems (NHES) research team is currently developing a dynamic simulation of an integrated hybrid energy system. A detailed simulation of proposed NHES architectures will allow initial computational demonstration of a tightly coupled NHES to identify key reactor subsystem requirements, identify candidate reactor technologies for a hybrid system, and identify key challenges to operation of the coupled system. This work will provide a baseline for later coupling of design-specific reactor models through industry collaboration. The modeling capability addressed in this report focuses on the reactor subsystem simulation.
Van Kirk, Robert W; Battle, Laurie; Schrader, William C
2010-03-01
Native salmonid fish have been displaced worldwide by nonnatives through hybridization, competition, and predation, but the dynamics of these factors are poorly understood. We apply stochastic Lotka-Volterra models to the displacement of cutthroat trout by rainbow/hybrid trout in the Snake River, Idaho, USA. Cutthroat trout are susceptible to hybridization in the river but are reproductively isolated in tributaries via removal of migratory rainbow/hybrid spawners at weirs. Based on information-theoretic analysis, population data provide evidence that hybridization was the primary mechanism for cutthroat trout displacement in the first 17 years of the invasion. However, under some parameter values, the data provide evidence for a model in which interaction occurs among fish from both river and tributary subpopulations. This situation is likely to occur when tributary-spawned cutthroat trout out-migrate to the river as fry. The resulting competition with rainbow/hybrid trout can result in the extinction of cutthroat trout even when reproductive segregation is maintained.
Two-compartment model for competitive hybridization on molecular biochips
NASA Astrophysics Data System (ADS)
Chechetkin, V. R.
2007-01-01
During competitive hybridization the specific and non-specific fractions of tested biomolecules in solution bind jointly with the specific probes immobilized in a separate cell of a microchip. The application of two-compartment model to the two-component hybridization allows analytically investigating the underlying kinetics. It is shown that the behaviour with the non-monotonous growth of complexes formed by the non-specific fraction on a probe cell is a typical feature of competitive hybridization for both diffusion-limited and reaction-limited kinetics. The physical reason behind such an evolution consists in the fact that the characteristic hybridization time for the perfect complexes turns out longer with respect to that for the mismatch complexes. This behaviour should be taken into account for the choice of optimum hybridization and washing conditions for the analysis of specific fraction.
The development of a mathematical model of a hybrid airship
NASA Astrophysics Data System (ADS)
Abdul Ghaffar, Alia Farhana
The mathematical model of a winged hybrid airship is developed for the analysis of its dynamic stability characteristics. A full nonlinear equation of motion that describes the dynamics of the hybrid airship is determined and for completeness, some of the components in the equations are estimated using the appropriate methods that has been established and used in the past. Adequate assumptions are made in order to apply any relevant computation and estimation methods. While this hybrid airship design is unique, its modeling and stability analysis were done according to the typical procedure of conventional airships and aircrafts. All computations pertaining to the hybrid airship's equation of motion are carried out and any issues related to the integration of the wing to the conventional airship design are discussed in this thesis. The design of the hybrid airship is also slightly modified to suit the demanding requirement of a complete and feasible mathematical model. Then, linearization is performed under a chosen trim condition, and eigenvalue analysis is carried out to determine the general dynamic stability characteristics of the winged hybrid airship. The result shows that the winged hybrid airship possesses dynamic instability in longitudinal pitch motion and lateral-directional slow roll motion. This is due to the strong coupling between the aerostatic lift from the buoyant gas and aerodynamic lift from the wing.
Multimedia Learning Design Pedagogy: A Hybrid Learning Model
ERIC Educational Resources Information Center
Tsoi, Mun Fie; Goh, Ngoh Khang; Chia, Lian Sai
2005-01-01
This paper provides insights on a hybrid learning model for multimedia learning design conceptualized from the Piagetian science learning cycle model and the Kolb's experiential learning model. This model represents learning as a cognitive process in a cycle of four phases, namely, Translating, Sculpting, Operationalizing, and Integrating and is…
Hybrid modeling of xanthan gum bioproduction in batch bioreactor.
Zabot, Giovani L; Mecca, Jaqueline; Mesomo, Michele; Silva, Marceli F; Prá, Valéria Dal; de Oliveira, Débora; Oliveira, J Vladimir; Castilhos, Fernanda; Treichel, Helen; Mazutti, Marcio A
2011-10-01
This work is focused on hybrid modeling of xanthan gum bioproduction process by Xanthomonas campestris pv. mangiferaeindicae. Experiments were carried out to evaluate the effects of stirred speed and superficial gas velocity on the kinetics of cell growth, lactose consumption and xanthan gum production in a batch bioreactor using cheese whey as substrate. A hybrid model was employed to simulate the bio-process making use of an artificial neural network (ANN) as a kinetic parameter estimator for the phenomenological model. The hybrid modeling of the process provided a satisfactory fitting quality of the experimental data, since this approach makes possible the incorporation of the effects of operational variables on model parameters. The applicability of the validated model was investigated, using the model as a process simulator to evaluate the effects of initial cell and lactose concentration in the xanthan gum production.
Pedestrian navigation data modeling for hybrid travel patterns
NASA Astrophysics Data System (ADS)
Zheng, Jianghua; Tao, Jianwei; Ding, Jianli; Abuliz, Abudukim; Xiang, Hanyu
2008-10-01
At present, navigation data models, such as GDF4.0, KIWI, SDAL and WI 19134, didn't pay attention to form pedestrian transport infrastructure into their models. With the development of navigation, pedestrian navigation has become a hot topic. The research team put forward their pilot research on pedestrian data modeling for hybrid travel patters, mainly including subway, bus and feet. Pedestrian road network modeling was made. Based on this, it carried out the discussion on multi-level navigation data modeling of hybrid travel patterns. It also gave algorithm suggestion to operate the optimal route computing more efficient. The future work is just to focus on demonstrate the algorithm.
Strategies for Energy Efficient Resource Management of Hybrid Programming Models
Li, Dong; Supinski, Bronis de; Schulz, Martin; Nikolopoulos, Dimitrios S; Cameron, Kirk W.
2013-01-01
Many scientific applications are programmed using hybrid programming models that use both message-passing and shared-memory, due to the increasing prevalence of large-scale systems with multicore, multisocket nodes. Previous work has shown that energy efficiency can be improved using software-controlled execution schemes that consider both the programming model and the power-aware execution capabilities of the system. However, such approaches have focused on identifying optimal resource utilization for one programming model, either shared-memory or message-passing, in isolation. The potential solution space, thus the challenge, increases substantially when optimizing hybrid models since the possible resource configurations increase exponentially. Nonetheless, with the accelerating adoption of hybrid programming models, we increasingly need improved energy efficiency in hybrid parallel applications on large-scale systems. In this work, we present new software-controlled execution schemes that consider the effects of dynamic concurrency throttling (DCT) and dynamic voltage and frequency scaling (DVFS) in the context of hybrid programming models. Specifically, we present predictive models and novel algorithms based on statistical analysis that anticipate application power and time requirements under different concurrency and frequency configurations. We apply our models and methods to the NPB MZ benchmarks and selected applications from the ASC Sequoia codes. Overall, we achieve substantial energy savings (8.74% on average and up to 13.8%) with some performance gain (up to 7.5%) or negligible performance loss.
Exact energy conservation in hybrid meshless model/code
NASA Astrophysics Data System (ADS)
Galkin, Sergei A.
2008-11-01
Energy conservation is an important issue for both PIC and hybrid models. In hybrid codes the ions are treated kinetically and the electrons are described as a massless charge-neutralizing fluid. Our recently developed Particle-In-Cloud-Of-Points (PICOP) approach [1], which uses an adaptive meshless technique to compute electromagnetic fields on a cloud of computational points, is applied to a hybrid model. An exact energy conservation numerical scheme, which describes the interaction between geometrical space, where the electromagnetic fields are computed, and particle/velocity space, is presented. Having being utilized in a new PICOP hybrid code, the algorithm had demonstrated accurate energy conservation in the numerical simulation of two counter streaming plasma beams instability. [1] S. A. Galkin, B. P. Cluggish, J. S. Kim, S. Yu. Medvedev ``Advansed PICOP Algorithm with Adaptive Meshless Field Solver'', Published in the IEEE PPPS/ICOP 2007 Conference proceedings, pp. 1445-1448, Albuquerque, New Mexico, June 17-22, 2007.
Nonlinear lower hybrid modeling in tokamak plasmas
Napoli, F.; Schettini, G.; Castaldo, C.; Cesario, R.
2014-02-12
We present here new results concerning the nonlinear mechanism underlying the observed spectral broadening produced by parametric instabilities occurring at the edge of tokamak plasmas in present day LHCD (lower hybrid current drive) experiments. Low frequency (LF) ion-sound evanescent modes (quasi-modes) are the main parametric decay channel which drives a nonlinear mode coupling of lower hybrid (LH) waves. The spectrum of the LF fluctuations is calculated here considering the beating of the launched LH wave at the radiofrequency (RF) operating line frequency (pump wave) with the noisy background of the RF power generator. This spectrum is calculated in the frame of the kinetic theory, following a perturbative approach. Numerical solutions of the nonlinear LH wave equation show the evolution of the nonlinear mode coupling in condition of a finite depletion of the pump power. The role of the presence of heavy ions in a Deuterium plasma in mitigating the nonlinear effects is analyzed.
A Hybridization Model for the Plasmon Response of Complex Nanostructures
NASA Astrophysics Data System (ADS)
Prodan, E.; Radloff, C.; Halas, N. J.; Nordlander, P.
2003-10-01
We present a simple and intuitive picture, an electromagnetic analog of molecular orbital theory, that describes the plasmon response of complex nanostructures of arbitrary shape. Our model can be understood as the interaction or ``hybridization'' of elementary plasmons supported by nanostructures of elementary geometries. As an example, the approach is applied to the important case of a four-layer concentric nanoshell, where the hybridization of the plasmons of the inner and outer nanoshells determines the resonant frequencies of the multilayer nanostructure.
A hybridization model for the plasmon response of complex nanostructures.
Prodan, E; Radloff, C; Halas, N J; Nordlander, P
2003-10-17
We present a simple and intuitive picture, an electromagnetic analog of molecular orbital theory, that describes the plasmon response of complex nanostructures of arbitrary shape. Our model can be understood as the interaction or "hybridization" of elementary plasmons supported by nanostructures of elementary geometries. As an example, the approach is applied to the important case of a four-layer concentric nanoshell, where the hybridization of the plasmons of the inner and outer nanoshells determines the resonant frequencies of the multilayer nanostructure.
Mechanisms Underlying Mammalian Hybrid Sterility in Two Feline Interspecies Models
Davis, Brian W.; Seabury, Christopher M.; Brashear, Wesley A.; Li, Gang; Roelke-Parker, Melody; Murphy, William J.
2015-01-01
The phenomenon of male sterility in interspecies hybrids has been observed for over a century, however, few genes influencing this recurrent phenotype have been identified. Genetic investigations have been primarily limited to a small number of model organisms, thus limiting our understanding of the underlying molecular basis of this well-documented “rule of speciation.” We utilized two interspecies hybrid cat breeds in a genome-wide association study employing the Illumina 63 K single-nucleotide polymorphism array. Collectively, we identified eight autosomal genes/gene regions underlying associations with hybrid male sterility (HMS) involved in the function of the blood-testis barrier, gamete structural development, and transcriptional regulation. We also identified several candidate hybrid sterility regions on the X chromosome, with most residing in close proximity to complex duplicated regions. Differential gene expression analyses revealed significant chromosome-wide upregulation of X chromosome transcripts in testes of sterile hybrids, which were enriched for genes involved in chromatin regulation of gene expression. Our expression results parallel those reported in Mus hybrids, supporting the “Large X-Effect” in mammalian HMS and the potential epigenetic basis for this phenomenon. These results support the value of the interspecies feline model as a powerful tool for comparison to rodent models of HMS, demonstrating unique aspects and potential commonalities that underpin mammalian reproductive isolation. PMID:26006188
Mechanisms Underlying Mammalian Hybrid Sterility in Two Feline Interspecies Models.
Davis, Brian W; Seabury, Christopher M; Brashear, Wesley A; Li, Gang; Roelke-Parker, Melody; Murphy, William J
2015-10-01
The phenomenon of male sterility in interspecies hybrids has been observed for over a century, however, few genes influencing this recurrent phenotype have been identified. Genetic investigations have been primarily limited to a small number of model organisms, thus limiting our understanding of the underlying molecular basis of this well-documented "rule of speciation." We utilized two interspecies hybrid cat breeds in a genome-wide association study employing the Illumina 63 K single-nucleotide polymorphism array. Collectively, we identified eight autosomal genes/gene regions underlying associations with hybrid male sterility (HMS) involved in the function of the blood-testis barrier, gamete structural development, and transcriptional regulation. We also identified several candidate hybrid sterility regions on the X chromosome, with most residing in close proximity to complex duplicated regions. Differential gene expression analyses revealed significant chromosome-wide upregulation of X chromosome transcripts in testes of sterile hybrids, which were enriched for genes involved in chromatin regulation of gene expression. Our expression results parallel those reported in Mus hybrids, supporting the "Large X-Effect" in mammalian HMS and the potential epigenetic basis for this phenomenon. These results support the value of the interspecies feline model as a powerful tool for comparison to rodent models of HMS, demonstrating unique aspects and potential commonalities that underpin mammalian reproductive isolation.
Fitzpatrick, B M
2008-01-01
Speciation may result from 'complementary' genetic differences that cause dysfunction when brought together in hybrids despite having no deleterious effects within pure species genomes. The theory of complementary genes, independently proposed by Dobzhansky and Muller, yields specific predictions about the genetics of hybrid fitness. Here, I show how alternative models of hybrid dysfunction can be compared using a simple multivariate analysis of hybrid indices calculated from molecular markers. I use the approach to fit models of hybrid dysfunction to swimming performance in hybrid tiger salamander larvae. Poor burst-speed performance is a dysfunction suggesting low vigour and could translate directly into low survival. My analyses show that the Dobzhansky-Muller model fits these data better than heterozygote disadvantage. The approach demonstrated here can be applied to a broad array of nonmodel species, potentially leading to important generalizations about the genetics of hybrid dysfunction.
A hybrid agent-based approach for modeling microbiological systems.
Guo, Zaiyi; Sloot, Peter M A; Tay, Joc Cing
2008-11-21
Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent-based approach where biological cells are modeled as individuals (agents) while molecules are represented by quantities. This hybridization in entity representation entails a combined modeling strategy with agent-based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability. We demonstrate the efficacy of this approach with models of chemotaxis involving an assay of 10(3) cells and 1.2x10(6) molecules. The model produces cell migration patterns that are comparable to laboratory observations.
Hybrid Models and Biological Model Reduction with PyDSTool
Clewley, Robert
2012-01-01
The PyDSTool software environment is designed to develop, simulate, and analyze dynamical systems models, particularly for biological applications. Unlike the engineering application focus and graphical specification environments of most general purpose simulation tools, PyDSTool provides a programmatic environment well suited to exploratory data- and hypothesis-driven biological modeling problems. In this work, we show how the environment facilitates the application of hybrid dynamical modeling to the reverse engineering of complex biophysical dynamics; in this case, of an excitable membrane. The example demonstrates how the software provides novel tools that support the inference and validation of mechanistic hypotheses and the inclusion of data constraints in both quantitative and qualitative ways. The biophysical application is broadly relevant to models in the biosciences. The open source and platform-independent PyDSTool package is freely available under the BSD license from http://sourceforge.net/projects/pydstool/. The hosting service provides links to documentation and online forums for user support. PMID:22912566
Diagnosing Hybrid Systems: a Bayesian Model Selection Approach
NASA Technical Reports Server (NTRS)
McIlraith, Sheila A.
2005-01-01
In this paper we examine the problem of monitoring and diagnosing noisy complex dynamical systems that are modeled as hybrid systems-models of continuous behavior, interleaved by discrete transitions. In particular, we examine continuous systems with embedded supervisory controllers that experience abrupt, partial or full failure of component devices. Building on our previous work in this area (MBCG99;MBCG00), our specific focus in this paper ins on the mathematical formulation of the hybrid monitoring and diagnosis task as a Bayesian model tracking algorithm. The nonlinear dynamics of many hybrid systems present challenges to probabilistic tracking. Further, probabilistic tracking of a system for the purposes of diagnosis is problematic because the models of the system corresponding to failure modes are numerous and generally very unlikely. To focus tracking on these unlikely models and to reduce the number of potential models under consideration, we exploit logic-based techniques for qualitative model-based diagnosis to conjecture a limited initial set of consistent candidate models. In this paper we discuss alternative tracking techniques that are relevant to different classes of hybrid systems, focusing specifically on a method for tracking multiple models of nonlinear behavior simultaneously using factored sampling and conditional density propagation. To illustrate and motivate the approach described in this paper we examine the problem of monitoring and diganosing NASA's Sprint AERCam, a small spherical robotic camera unit with 12 thrusters that enable both linear and rotational motion.
Runoff prediction using an integrated hybrid modelling scheme
NASA Astrophysics Data System (ADS)
Remesan, Renji; Shamim, Muhammad Ali; Han, Dawei; Mathew, Jimson
2009-06-01
SummaryRainfall runoff is a very complicated process due to its nonlinear and multidimensional dynamics, and hence difficult to model. There are several options for a modeller to consider, for example: the type of input data to be used, the length of model calibration (training) data and whether or not the input data be treated as signals with different frequency bands so that they can be modelled separately. This paper describes a new hybrid modelling scheme to answer the above mentioned questions. The proposed methodology is based on a hybrid model integrating wavelet transformation, a modelling engine (Artificial Neural Network) and the Gamma Test. First, the Gamma Test is used to decide the required input data dimensions and its length. Second, the wavelet transformation decomposes the input signals into different frequency bands. Finally, a modelling engine (ANN in this study) is used to model the decomposed signals separately. The proposed scheme was tested using the Brue catchment, Southwest England, as a case study and has produced very positive results. The hybrid model outperforms all other models tested. This study has a wider implication in the hydrological modelling field since its general framework could be applied to other model combinations (e.g., model engine could be Support Vector Machines, neuro-fuzzy systems, or even a conceptual model. The signal decomposition could be carried out by Fourier transformation).
A new adaptive hybrid electromagnetic damper: modelling, optimization, and experiment
NASA Astrophysics Data System (ADS)
Asadi, Ehsan; Ribeiro, Roberto; Behrad Khamesee, Mir; Khajepour, Amir
2015-07-01
This paper presents the development of a new electromagnetic hybrid damper which provides regenerative adaptive damping force for various applications. Recently, the introduction of electromagnetic technologies to the damping systems has provided researchers with new opportunities for the realization of adaptive semi-active damping systems with the added benefit of energy recovery. In this research, a hybrid electromagnetic damper is proposed. The hybrid damper is configured to operate with viscous and electromagnetic subsystems. The viscous medium provides a bias and fail-safe damping force while the electromagnetic component adds adaptability and the capacity for regeneration to the hybrid design. The electromagnetic component is modeled and analyzed using analytical (lumped equivalent magnetic circuit) and electromagnetic finite element method (FEM) (COMSOL® software package) approaches. By implementing both modeling approaches, an optimization for the geometric aspects of the electromagnetic subsystem is obtained. Based on the proposed electromagnetic hybrid damping concept and the preliminary optimization solution, a prototype is designed and fabricated. A good agreement is observed between the experimental and FEM results for the magnetic field distribution and electromagnetic damping forces. These results validate the accuracy of the modeling approach and the preliminary optimization solution. An analytical model is also presented for viscous damping force, and is compared with experimental results The results show that the damper is able to produce damping coefficients of 1300 and 0-238 N s m-1 through the viscous and electromagnetic components, respectively.
Nuclear Hybrid Energy Systems FY16 Modeling Efforts at ORNL
Cetiner, Sacit M.; Greenwood, Michael Scott; Harrison, Thomas J.; Qualls, A. L.; Guler Yigitoglu, Askin; Fugate, David W.
2016-09-01
A nuclear hybrid system uses a nuclear reactor as the basic power generation unit. The power generated by the nuclear reactor is utilized by one or more power customers as either thermal power, electrical power, or both. In general, a nuclear hybrid system will couple the nuclear reactor to at least one thermal power user in addition to the power conversion system. The definition and architecture of a particular nuclear hybrid system is flexible depending on local markets needs and opportunities. For example, locations in need of potable water may be best served by coupling a desalination plant to the nuclear system. Similarly, an area near oil refineries may have a need for emission-free hydrogen production. A nuclear hybrid system expands the nuclear power plant from its more familiar central power station role by diversifying its immediately and directly connected customer base. The definition, design, analysis, and optimization work currently performed with respect to the nuclear hybrid systems represents the work of three national laboratories. Idaho National Laboratory (INL) is the lead lab working with Argonne National Laboratory (ANL) and Oak Ridge National Laboratory. Each laboratory is providing modeling and simulation expertise for the integration of the hybrid system.
A Lower Hybrid Fluid Model and Asymptotic Solutions
NASA Astrophysics Data System (ADS)
Wang, Xiaogang
2016-10-01
Hall MHD is for ion dynamics with a zero mass electron fluid. EMHD is for electron dynamics with fixed (infinity mass) ions. Also, other approximations such as electron incompressibility and low frequency appraisal (by ignoring the displacement current) have limited the application of EMHD. We then introduce a ``Lower Hybrid Fluid'' model by keeping the higher order mass ratio terms in the two-fluid model to investigate the problems in a hybrid scale range between the electron skin depth and the ion inertial length.
Verification of biological models with Timed Hybrid Petri Nets
NASA Astrophysics Data System (ADS)
Troncale, S.; Comet, J.-P.; Bernot, G.
2007-11-01
The formalism of Hybrid Functional Petri Nets (HFPN) has proved its convenience for simulating biological systems. The drawback of the noticeable expressiveness of HFPN is the difficulty to perform formal verifications of dynamical properties. In this article, we propose a model-checking procedure for Timed Hybrid Petri Nets (THPN), a sub-class of HFPN. This procedure is based on the translation of the THPN model and of the studied property into real-time automata. It is applied to a sub-network involved in amphibian metamorphosis.
Modeling of hybrid vehicle fuel economy and fuel engine efficiency
NASA Astrophysics Data System (ADS)
Wu, Wei
"Near-CV" (i.e., near-conventional vehicle) hybrid vehicles, with an internal combustion engine, and a supplementary storage with low-weight, low-energy but high-power capacity, are analyzed. This design avoids the shortcoming of the "near-EV" and the "dual-mode" hybrid vehicles that need a large energy storage system (in terms of energy capacity and weight). The small storage is used to optimize engine energy management and can provide power when needed. The energy advantage of the "near-CV" design is to reduce reliance on the engine at low power, to enable regenerative braking, and to provide good performance with a small engine. The fuel consumption of internal combustion engines, which might be applied to hybrid vehicles, is analyzed by building simple analytical models that reflect the engines' energy loss characteristics. Both diesel and gasoline engines are modeled. The simple analytical models describe engine fuel consumption at any speed and load point by describing the engine's indicated efficiency and friction. The engine's indicated efficiency and heat loss are described in terms of several easy-to-obtain engine parameters, e.g., compression ratio, displacement, bore and stroke. Engine friction is described in terms of parameters obtained by fitting available fuel measurements on several diesel and spark-ignition engines. The engine models developed are shown to conform closely to experimental fuel consumption and motored friction data. A model of the energy use of "near-CV" hybrid vehicles with different storage mechanism is created, based on simple algebraic description of the components. With powertrain downsizing and hybridization, a "near-CV" hybrid vehicle can obtain a factor of approximately two in overall fuel efficiency (mpg) improvement, without considering reductions in the vehicle load.
Energy-efficient container handling using hybrid model predictive control
NASA Astrophysics Data System (ADS)
Xin, Jianbin; Negenborn, Rudy R.; Lodewijks, Gabriel
2015-11-01
The performance of container terminals needs to be improved to adapt the growth of containers while maintaining sustainability. This paper provides a methodology for determining the trajectory of three key interacting machines for carrying out the so-called bay handling task, involving transporting containers between a vessel and the stacking area in an automated container terminal. The behaviours of the interacting machines are modelled as a collection of interconnected hybrid systems. Hybrid model predictive control (MPC) is proposed to achieve optimal performance, balancing the handling capacity and energy consumption. The underlying control problem is hereby formulated as a mixed-integer linear programming problem. Simulation studies illustrate that a higher penalty on energy consumption indeed leads to improved sustainability using less energy. Moreover, simulations illustrate how the proposed energy-efficient hybrid MPC controller performs under different types of uncertainties.
Hybrid attacks on model-based social recommender systems
NASA Astrophysics Data System (ADS)
Yu, Junliang; Gao, Min; Rong, Wenge; Li, Wentao; Xiong, Qingyu; Wen, Junhao
2017-10-01
With the growing popularity of the online social platform, the social network based approaches to recommendation emerged. However, because of the open nature of rating systems and social networks, the social recommender systems are susceptible to malicious attacks. In this paper, we present a certain novel attack, which inherits characteristics of the rating attack and the relation attack, and term it hybrid attack. Furtherly, we explore the impact of the hybrid attack on model-based social recommender systems in multiple aspects. The experimental results show that, the hybrid attack is more destructive than the rating attack in most cases. In addition, users and items with fewer ratings will be influenced more when attacked. Last but not the least, the findings suggest that spammers do not depend on the feedback links from normal users to become more powerful, the unilateral links can make the hybrid attack effective enough. Since unilateral links are much cheaper, the hybrid attack will be a great threat to model-based social recommender systems.
The innovative concept of three-dimensional hybrid receptor modeling
NASA Astrophysics Data System (ADS)
Stojić, A.; Stanišić Stojić, S.
2017-09-01
The aim of this study was to improve the current understanding of air pollution transport processes at regional and long-range scale. For this purpose, three-dimensional (3D) potential source contribution function and concentration weighted trajectory models, as well as new hybrid receptor model, concentration weighted boundary layer (CWBL), which uses a two-dimensional grid and a planetary boundary layer height as a frame of reference, are presented. The refined approach to hybrid receptor modeling has two advantages. At first, it considers whether each trajectory endpoint meets the inclusion criteria based on planetary boundary layer height, which is expected to provide a more realistic representation of the spatial distribution of emission sources and pollutant transport pathways. Secondly, it includes pollutant time series preprocessing to make hybrid receptor models more applicable for suburban and urban locations. The 3D hybrid receptor models presented herein are designed to identify altitude distribution of potential sources, whereas CWBL can be used for analyzing the vertical distribution of pollutant concentrations along the transport pathway.
Battery thermal models for hybrid vehicle simulations
NASA Astrophysics Data System (ADS)
Pesaran, Ahmad A.
This paper summarizes battery thermal modeling capabilities for: (1) an advanced vehicle simulator (ADVISOR); and (2) battery module and pack thermal design. The National Renewable Energy Laboratory's (NREL's) ADVISOR is developed in the Matlab/Simulink environment. There are several battery models in ADVISOR for various chemistry types. Each one of these models requires a thermal model to predict the temperature change that could affect battery performance parameters, such as resistance, capacity and state of charges. A lumped capacitance battery thermal model in the Matlab/Simulink environment was developed that included the ADVISOR battery performance models. For thermal evaluation and design of battery modules and packs, NREL has been using various computer aided engineering tools including commercial finite element analysis software. This paper will discuss the thermal ADVISOR battery model and its results, along with the results of finite element modeling that were presented at the workshop on "Development of Advanced Battery Engineering Models" in August 2001.
Hybrid Scheduling Model for Independent Grid Tasks
Shanthini, J.; Kalaikumaran, T.; Karthik, S.
2015-01-01
Grid computing facilitates the resource sharing through the administrative domains which are geographically distributed. Scheduling in a distributed heterogeneous environment is intrinsically very hard because of the heterogeneous nature of resource collection. Makespan and tardiness are two different measures of scheduling, and many of the previous researches concentrated much on reduction of makespan, which measures the machine utilization. In this paper, we propose a hybrid scheduling algorithm for scheduling independent grid tasks with the objective of reducing total weighted tardiness of grid tasks. Tardiness is to measure the due date performance, which has a direct impact on cost for executing the jobs. In this paper we propose BG_ATC algorithm which is a combination of best gap (BG) search and Apparent Tardiness Cost (ATC) indexing algorithm. Furthermore, we implemented these two algorithms in two different phases of the scheduling process. In addition to that, the comparison was made on results with various benchmark algorithms and the experimental results show that our algorithm outperforms the benchmark algorithms. PMID:26543897
Hybrid Scheduling Model for Independent Grid Tasks.
Shanthini, J; Kalaikumaran, T; Karthik, S
2015-01-01
Grid computing facilitates the resource sharing through the administrative domains which are geographically distributed. Scheduling in a distributed heterogeneous environment is intrinsically very hard because of the heterogeneous nature of resource collection. Makespan and tardiness are two different measures of scheduling, and many of the previous researches concentrated much on reduction of makespan, which measures the machine utilization. In this paper, we propose a hybrid scheduling algorithm for scheduling independent grid tasks with the objective of reducing total weighted tardiness of grid tasks. Tardiness is to measure the due date performance, which has a direct impact on cost for executing the jobs. In this paper we propose BG_ATC algorithm which is a combination of best gap (BG) search and Apparent Tardiness Cost (ATC) indexing algorithm. Furthermore, we implemented these two algorithms in two different phases of the scheduling process. In addition to that, the comparison was made on results with various benchmark algorithms and the experimental results show that our algorithm outperforms the benchmark algorithms.
A New Model for Baryogenesis through Hybrid Inflation
Delepine, D.; Prieto, C. Martinez; Lopez, L. A. Urena
2009-04-17
We propose a hybrid inflation model with a complex waterfall field which contains an interaction term that breaks the U(1) global symmetry associated to the waterfall field charge. The asymmetric evolution of the real and imaginary parts of the complex field during the phase transition at the end of inflation translates into a charge asymmetry.
Incorporating RTI in a Hybrid Model of Reading Disability
ERIC Educational Resources Information Center
Spencer, Mercedes; Wagner, Richard K.; Schatschneider, Christopher; Quinn, Jamie M.; Lopez, Danielle; Petscher, Yaacov
2014-01-01
The present study seeks to evaluate a hybrid model of identification that incorporates response to instruction and intervention (RTI) as one of the key symptoms of reading disability. The 1-year stability of alternative operational definitions of reading disability was examined in a large-scale sample of students who were followed longitudinally…
New Models of Hybrid Leadership in Global Higher Education
ERIC Educational Resources Information Center
Tonini, Donna C.; Burbules, Nicholas C.; Gunsalus, C. K.
2016-01-01
This manuscript highlights the development of a leadership preparation program known as the Nanyang Technological University Leadership Academy (NTULA), exploring the leadership challenges unique to a university undergoing rapid growth in a highly multicultural context, and the hybrid model of leadership it developed in response to globalization.…
Incorporating RTI in a Hybrid Model of Reading Disability
ERIC Educational Resources Information Center
Spencer, Mercedes; Wagner, Richard K.; Schatschneider, Christopher; Quinn, Jamie M.; Lopez, Danielle; Petscher, Yaacov
2014-01-01
The present study seeks to evaluate a hybrid model of identification that incorporates response to instruction and intervention (RTI) as one of the key symptoms of reading disability. The 1-year stability of alternative operational definitions of reading disability was examined in a large-scale sample of students who were followed longitudinally…
New Models of Hybrid Leadership in Global Higher Education
ERIC Educational Resources Information Center
Tonini, Donna C.; Burbules, Nicholas C.; Gunsalus, C. K.
2016-01-01
This manuscript highlights the development of a leadership preparation program known as the Nanyang Technological University Leadership Academy (NTULA), exploring the leadership challenges unique to a university undergoing rapid growth in a highly multicultural context, and the hybrid model of leadership it developed in response to globalization.…
Hybrid mice as genetic models of high alcohol consumption.
Blednov, Y A; Ozburn, A R; Walker, D; Ahmed, S; Belknap, J K; Harris, R A
2010-01-01
We showed that F1 hybrid genotypes may provide a broader variety of ethanol drinking phenotypes than the inbred progenitor strains used to create the hybrids (Blednov et al. in Alcohol Clin Exp Res 29:1949-1958, 2005). To extend this work, we characterized alcohol consumption as well as intake of other tastants (saccharin, quinine and sodium chloride) in five inbred strains of mice (FVB, SJL, B6, BUB, NZB) and in their reciprocal F1 hybrids with B6 (FVBxB6; B6xFVB; NZBxB6; B6xNZB; BUBxB6; B6xBUB; SJLxB6; B6xSJL). We also compared ethanol intake in these mice for several concentrations before and after two periods of abstinence. F1 hybrid mice derived from the crosses of B6 and FVB and also B6 and SJL drank higher levels of ethanol than their progenitor strains, demonstrating overdominance for two-bottle choice drinking test. The B6 and NZB hybrid showed additivity in two-bottle choice drinking, whereas the hybrid of B6 and BUB demonstrated full or complete dominance. Genealogical origin, as well as non-alcohol taste preferences (sodium chloride), predicted ethanol consumption. Mice derived from the crosses of B6 and FVB showed high sustained alcohol preference and the B6 and NZB hybrids showed reduced alcohol preference after periods of abstinence. These new genetic models offer some advantages over inbred strains because they provide high, sustained, alcohol intake, and should allow mapping of loci important for the genetic architecture of these traits.
A hybrid wind farm parameterization for mesoscale and climate models
NASA Astrophysics Data System (ADS)
Pan, Y.; Archer, C. L.
2016-12-01
To better understand the potential impacts of wind farms on weather and climate at the local to regional scale, a new hybrid wind farm parameterization is proposed here for mesoscale models, such as the Weather Research and Forecasting Model (WRF), or climate models, such as the Community Atmosphere Model (CAM). All previous wind farm parameterizations treat all the wind turbines in the same grid cell as identical (i.e., they all share the same upstream wind velocity) and ignore the effect of wind direction. By contrast, the new hybrid model considers each individual wind turbine, based on its position in the layout and on wind direction. The new parameterization is developed starting from large eddy simulations (LES) of existing wind farms, in which the local flow around each wind turbine is directly simulated at high spatial ( 3.5 m) and temporal ( 0.1 s) resolutions and the effects of subgrid-scale processes are modeled. Based on analytic and statistical relationships between the LES results and several geometric properties of the wind farm layout (such as blockage ratio and blocking distance), the new hybrid parameterization predicts the local upstream wind speed of each individual wind turbine in the same grid cell, and thus successfully account for the effects of layout and wind direction with little computational cost. With the newly predicted upstream velocity, the turbine-induced forces and added turbulence kinetic energy (TKE) in the atmosphere are derived analytically. The wind speed, wind speed deficit, and TKE profiles and power production obtained with the hybrid parameterization for the test case (the 48-turbine Lillgrund wind farm in Sweden) are in better agreement with the LES results than previous parameterizations. Future work includes the insertion of the hybrid parameterization into the WRF code to assess impacts on near-surface properties, such as temperature and heat and momentum fluxes, in the region surrounding the wind farm.
Hierarchical hybrid modelling and control of an unmanned helicopter
NASA Astrophysics Data System (ADS)
Karimoddini, Ali; Lin, Hai; Chen, Ben M.; Lee, Tong H.
2014-09-01
In this paper, we propose a hybrid modelling and control design scheme for an unmanned helicopter. This control structure has a hierarchical form with three layers: the regulation layer, the motion planning layer, and the supervision layer. For each layer, a separate hybrid controller has been developed. Then, a composition operator is adopted to capture the interactions between these layers. The resulting closed-loop system can flexibly command the helicopter to perform different tasks, autonomously. The designed controller is embedded in the avionic system of an unmanned helicopter, and actual flight test results are presented to demonstrate the effectiveness of the proposed control structure.
Integrated Modelling of Iter Hybrid Scenarios with Eccd
NASA Astrophysics Data System (ADS)
Giruzzi, G.; Artaud, J. F.; Basiuk, V.; Garcia, J.; Imbeaux, F.; Schneider, M.
2009-04-01
ITER hybrid scenarios may require off-axis current drive in order to keep the safety factor above 1. In this type of applications, alignment of the current sources and self-consistency of current and temperature profiles are critical issues, which can only be addressed by integrated modelling. To this end, the CRONOS suite of codes has been applied to the simulation of these scenarios. Results of simulations of ITER hybrid scenarios assisted by ECCD, using the ITER equatorial launcher, for both co- and counter-ECCD, are presented.
Hybrid multiscale modeling and prediction of cancer cell behavior.
Zangooei, Mohammad Hossein; Habibi, Jafar
2017-01-01
Understanding cancer development crossing several spatial-temporal scales is of great practical significance to better understand and treat cancers. It is difficult to tackle this challenge with pure biological means. Moreover, hybrid modeling techniques have been proposed that combine the advantages of the continuum and the discrete methods to model multiscale problems. In light of these problems, we have proposed a new hybrid vascular model to facilitate the multiscale modeling and simulation of cancer development with respect to the agent-based, cellular automata and machine learning methods. The purpose of this simulation is to create a dataset that can be used for prediction of cell phenotypes. By using a proposed Q-learning based on SVR-NSGA-II method, the cells have the capability to predict their phenotypes autonomously that is, to act on its own without external direction in response to situations it encounters. Computational simulations of the model were performed in order to analyze its performance. The most striking feature of our results is that each cell can select its phenotype at each time step according to its condition. We provide evidence that the prediction of cell phenotypes is reliable. Our proposed model, which we term a hybrid multiscale modeling of cancer cell behavior, has the potential to combine the best features of both continuum and discrete models. The in silico results indicate that the 3D model can represent key features of cancer growth, angiogenesis, and its related micro-environment and show that the findings are in good agreement with biological tumor behavior. To the best of our knowledge, this paper is the first hybrid vascular multiscale modeling of cancer cell behavior that has the capability to predict cell phenotypes individually by a self-generated dataset.
Brain anatomical structure segmentation by hybrid discriminative/generative models.
Tu, Z; Narr, K L; Dollar, P; Dinov, I; Thompson, P M; Toga, A W
2008-04-01
In this paper, a hybrid discriminative/generative model for brain anatomical structure segmentation is proposed. The learning aspect of the approach is emphasized. In the discriminative appearance models, various cues such as intensity and curvatures are combined to locally capture the complex appearances of different anatomical structures. A probabilistic boosting tree (PBT) framework is adopted to learn multiclass discriminative models that combine hundreds of features across different scales. On the generative model side, both global and local shape models are used to capture the shape information about each anatomical structure. The parameters to combine the discriminative appearance and generative shape models are also automatically learned. Thus, low-level and high-level information is learned and integrated in a hybrid model. Segmentations are obtained by minimizing an energy function associated with the proposed hybrid model. Finally, a grid-face structure is designed to explicitly represent the 3-D region topology. This representation handles an arbitrary number of regions and facilitates fast surface evolution. Our system was trained and tested on a set of 3-D magnetic resonance imaging (MRI) volumes and the results obtained are encouraging.
A hybrid parallel framework for the cellular Potts model simulations
Jiang, Yi; He, Kejing; Dong, Shoubin
2009-01-01
The Cellular Potts Model (CPM) has been widely used for biological simulations. However, most current implementations are either sequential or approximated, which can't be used for large scale complex 3D simulation. In this paper we present a hybrid parallel framework for CPM simulations. The time-consuming POE solving, cell division, and cell reaction operation are distributed to clusters using the Message Passing Interface (MPI). The Monte Carlo lattice update is parallelized on shared-memory SMP system using OpenMP. Because the Monte Carlo lattice update is much faster than the POE solving and SMP systems are more and more common, this hybrid approach achieves good performance and high accuracy at the same time. Based on the parallel Cellular Potts Model, we studied the avascular tumor growth using a multiscale model. The application and performance analysis show that the hybrid parallel framework is quite efficient. The hybrid parallel CPM can be used for the large scale simulation ({approx}10{sup 8} sites) of complex collective behavior of numerous cells ({approx}10{sup 6}).
Strongly Interacting Matter at Finite Chemical Potential: Hybrid Model Approach
NASA Astrophysics Data System (ADS)
Srivastava, P. K.; Singh, C. P.
2013-06-01
Search for a proper and realistic equation of state (EOS) for strongly interacting matter used in the study of the QCD phase diagram still appears as a challenging problem. Recently, we constructed a hybrid model description for the quark-gluon plasma (QGP) as well as hadron gas (HG) phases where we used an excluded volume model for HG and a thermodynamically consistent quasiparticle model for the QGP phase. The hybrid model suitably describes the recent lattice results of various thermodynamical as well as transport properties of the QCD matter at zero baryon chemical potential (μB). In this paper, we extend our investigations further in obtaining the properties of QCD matter at finite value of μB and compare our results with the most recent results of lattice QCD calculation.
Dynamical modeling of drug effect using hybrid systems
2012-01-01
Drug discovery today is a complex, expensive, and time-consuming process with high attrition rate. A more systematic approach is needed to combine innovative approaches in order to lead to more effective and efficient drug development. This article provides systematic mathematical analysis and dynamical modeling of drug effect under gene regulatory network contexts. A hybrid systems model, which merges together discrete and continuous dynamics into a single dynamical model, is proposed to study dynamics of the underlying regulatory network under drug perturbations. The major goal is to understand how the system changes when perturbed by drugs and give suggestions for better therapeutic interventions. A realistic periodic drug intake scenario is considered, drug pharmacokinetics and pharmacodynamics information being taken into account in the proposed hybrid systems model. Simulations are performed using MATLAB/SIMULINK to corroborate the analytical results. PMID:23268741
Hierarchical models and iterative optimization of hybrid systems
Rasina, Irina V.; Baturina, Olga V.; Nasatueva, Soelma N.
2016-06-08
A class of hybrid control systems on the base of two-level discrete-continuous model is considered. The concept of this model was proposed and developed in preceding works as a concretization of the general multi-step system with related optimality conditions. A new iterative optimization procedure for such systems is developed on the base of localization of the global optimality conditions via contraction the control set.
Li, Ben Q; Liu, Changhong
2011-01-15
A hybridization model for the localized surface plasmon resonance of a nanoshell is developed within the framework of long-wave approximation. Compared with the existing hybridization model derived from the hydrodynamic simulation of free electron gas, this approach is much simpler and gives identical results for a concentric nanoshell. Also, with this approach, the limitations associated with the original hybridization model are succinctly stated. Extension of this approach to hybridization modeling of more complicated structures such as multiplayered nanoshells is straightforward.
Hybrid reduced order modeling for assembly calculations
Bang, Youngsuk; Abdel-Khalik, Hany S.; Jessee, Matthew A.; Mertyurek, Ugur
2015-08-14
While the accuracy of assembly calculations has greatly improved due to the increase in computer power enabling more refined description of the phase space and use of more sophisticated numerical algorithms, the computational cost continues to increase which limits the full utilization of their effectiveness for routine engineering analysis. Reduced order modeling is a mathematical vehicle that scales down the dimensionality of large-scale numerical problems to enable their repeated executions on small computing environment, often available to end users. This is done by capturing the most dominant underlying relationships between the model's inputs and outputs. Previous works demonstrated the use of the reduced order modeling for a single physics code, such as a radiation transport calculation. This paper extends those works to coupled code systems as currently employed in assembly calculations. Finally, numerical tests are conducted using realistic SCALE assembly models with resonance self-shielding, neutron transport, and nuclides transmutation/depletion models representing the components of the coupled code system.
Hybrid reduced order modeling for assembly calculations
Bang, Youngsuk; Abdel-Khalik, Hany S.; Jessee, Matthew A.; ...
2015-08-14
While the accuracy of assembly calculations has greatly improved due to the increase in computer power enabling more refined description of the phase space and use of more sophisticated numerical algorithms, the computational cost continues to increase which limits the full utilization of their effectiveness for routine engineering analysis. Reduced order modeling is a mathematical vehicle that scales down the dimensionality of large-scale numerical problems to enable their repeated executions on small computing environment, often available to end users. This is done by capturing the most dominant underlying relationships between the model's inputs and outputs. Previous works demonstrated the usemore » of the reduced order modeling for a single physics code, such as a radiation transport calculation. This paper extends those works to coupled code systems as currently employed in assembly calculations. Finally, numerical tests are conducted using realistic SCALE assembly models with resonance self-shielding, neutron transport, and nuclides transmutation/depletion models representing the components of the coupled code system.« less
A simplified model for hybrid rocket performance prediction
NASA Astrophysics Data System (ADS)
Wolf, Robert S.; Wagner, John W.
1992-02-01
A computer code to predict hybrid rocket performance was developed and validated. The algorithm used is a simplification of the model derived by Marxman and Wooldridge. This model assumes the fuel regression rate to be controlled by convective heat transfer to the solid fuel from a relatively thin diffusion flame in a turbulent boundary layer. The model further assumes that the Reynolds analogy applies with mass addition at the wall. The computer code incorporates variable combustion product properties (temperature, molecular weight, and ratio of specific heats) as a function of the instantaneous global oxidizer/fuel ratio. The code was validated by constructing and firing a hybrid rocket motor. This motor used gaseous oxygen and hydroxyl-terminated polybutadiene as propellants. The oxygen flow rate used in the test was given as an input to the computer code, which then calculated chamber pressures and thrust. The agreement between test data and computer predictions was excellent.
Multiview coding mode decision with hybrid optimal stopping model.
Zhao, Tiesong; Kwong, Sam; Wang, Hanli; Wang, Zhou; Pan, Zhaoqing; Kuo, C-C Jay
2013-04-01
In a generic decision process, optimal stopping theory aims to achieve a good tradeoff between decision performance and time consumed, with the advantages of theoretical decision-making and predictable decision performance. In this paper, optimal stopping theory is employed to develop an effective hybrid model for the mode decision problem, which aims to theoretically achieve a good tradeoff between the two interrelated measurements in mode decision, as computational complexity reduction and rate-distortion degradation. The proposed hybrid model is implemented and examined with a multiview encoder. To support the model and further promote coding performance, the multiview coding mode characteristics, including predicted mode probability and estimated coding time, are jointly investigated with inter-view correlations. Exhaustive experimental results with a wide range of video resolutions reveal the efficiency and robustness of our method, with high decision accuracy, negligible computational overhead, and almost intact rate-distortion performance compared to the original encoder.
A hybrid likelihood algorithm for risk modelling.
Kellerer, A M; Kreisheimer, M; Chmelevsky, D; Barclay, D
1995-03-01
The risk of radiation-induced cancer is assessed through the follow-up of large cohorts, such as atomic bomb survivors or underground miners who have been occupationally exposed to radon and its decay products. The models relate to the dose, age and time dependence of the excess tumour rates, and they contain parameters that are estimated in terms of maximum likelihood computations. The computations are performed with the software package EPI-CURE, which contains the two main options of person-by person regression or of Poisson regression with grouped data. The Poisson regression is most frequently employed, but there are certain models that require an excessive number of cells when grouped data are used. One example involves computations that account explicitly for the temporal distribution of continuous exposures, as they occur with underground miners. In past work such models had to be approximated, but it is shown here that they can be treated explicitly in a suitably reformulated person-by person computation of the likelihood. The algorithm uses the familiar partitioning of the log-likelihood into two terms, L1 and L0. The first term, L1, represents the contribution of the 'events' (tumours). It needs to be evaluated in the usual way, but constitutes no computational problem. The second term, L0, represents the event-free periods of observation. It is, in its usual form, unmanageable for large cohorts. However, it can be reduced to a simple form, in which the number of computational steps is independent of cohort size. The method requires less computing time and computer memory, but more importantly it leads to more stable numerical results by obviating the need for grouping the data. The algorithm may be most relevant to radiation risk modelling, but it can facilitate the modelling of failure-time data in general.
A Hybrid Tool for User Interface Modeling and Prototyping
NASA Astrophysics Data System (ADS)
Trætteberg, Hallvard
Although many methods have been proposed, model-based development methods have only to some extent been adopted for UI design. In particular, they are not easy to combine with user-centered design methods. In this paper, we present a hybrid UI modeling and GUI prototyping tool, which is designed to fit better with IS development and UI design traditions. The tool includes a diagram editor for domain and UI models and an execution engine that integrates UI behavior, live UI components and sample data. Thus, both model-based user interface design and prototyping-based iterative design are supported
Hybrid modeling of tumor-induced angiogenesis
NASA Astrophysics Data System (ADS)
Bonilla, L. L.; Capasso, V.; Alvaro, M.; Carretero, M.
2014-12-01
When modeling of tumor-driven angiogenesis, a major source of analytical and computational complexity is the strong coupling between the kinetic parameters of the relevant stochastic branching-and-growth of the capillary network, and the family of interacting underlying fields. To reduce this complexity, we take advantage of the system intrinsic multiscale structure: we describe the stochastic dynamics of the cells at the vessel tip at their natural mesoscale, whereas we describe the deterministic dynamics of the underlying fields at a larger macroscale. Here, we set up a conceptual stochastic model including branching, elongation, and anastomosis of vessels and derive a mean field approximation for their densities. This leads to a deterministic integropartial differential system that describes the formation of the stochastic vessel network. We discuss the proper capillary injecting boundary conditions and include the results of relevant numerical simulations.
Hybrid configuration mixing model for odd nuclei
NASA Astrophysics Data System (ADS)
Colò, G.; Bortignon, P. F.; Bocchi, G.
2017-03-01
In this work, we introduce a new approach which is meant to be a first step towards complete self-consistent low-lying spectroscopy of odd nuclei. So far, we essentially limit ourselves to the description of a double-magic core plus an extra nucleon. The model does not contain any free adjustable parameter and is instead based on a Hartree-Fock (HF) description of the particle states in the core, together with self-consistent random-phase approximation (RPA) calculations for the core excitations. We include both collective and noncollective excitations, with proper care of the corrections due to the overlap between them (i.e., due to the nonorthonormality of the basis). As a consequence, with respect to traditional particle-vibration coupling calculations in which one can only address single-nucleon states and particle-vibration multiplets, we can also describe states of shell-model types like 2 particle-1 hole. We will report results for 49Ca and 133Sb and discuss future perspectives.
Multiscale Modeling of Hybrid Structural Composites with Integrated Damping Features
NASA Astrophysics Data System (ADS)
Martone, Alfonso; Giordano, Michele
2008-08-01
The aim of this work is to propose a design approach for a multifunctional hybrid composite material that integrates high damping performances while withstanding the required structural features. Hybrid composite consists in a three phases composite where a viscoelastic material is added to the conventional structural long fibers/polymeric matrix laminate. Design addresses the problem of integrating the viscoelastic material within the laminate architecture to exploit its maximum damping efficiency. Key aspect is the definition of a viscoelastic multiscale model starting from the constituents to the lamina, and further to the hybrid laminate properties. An analytical procedure has been developed that uses the strain energy method to evaluate the specific damping capacity for all dimensional scales and classical lamination theory was extended to include the transverse shear effects. The method potentiality has been tested against experimental data from Literature. Possible configurations of hybrid laminates have been simulated where viscoelastic material is added as laminae or distributed as long fibers within the structural laminate.
A hybrid neural network model for consciousness.
Lin, Jie; Jin, Xiao-gang; Yang, Jian-gang
2004-11-01
A new framework for consciousness is introduced based upon traditional artificial neural network models. This framework reflects explicit connections between two parts of the brain: one global working memory and distributed modular cerebral networks relating to specific brain functions. Accordingly this framework is composed of three layers, physical mnemonic layer and abstract thinking layer, which cooperate together through a recognition layer to accomplish information storage and cognition using algorithms of how these interactions contribute to consciousness: (1) the reception process whereby cerebral subsystems group distributed signals into coherent object patterns; (2) the partial recognition process whereby patterns from particular subsystems are compared or stored as knowledge; and (3) the resonant learning process whereby global workspace stably adjusts its structure to adapt to patterns' changes. Using this framework, various sorts of human actions can be explained, leading to a general approach for analyzing brain functions.
A hybrid (numerical-physical) model of the left ventricle.
Ferrari, G; Kozarski, M; De Lazzari, C; Clemente, F; Merolli, M; Tosti, G; Guaragno, M; Mimmo, R; Ambrosi, D; Glapinski, J
2001-07-01
Hydraulic models of the circulation are used to test mechanical devices and for training and research purposes; when compared to numerical models, however, they are not flexible enough and rather expensive. The solution proposed here is to merge the characteristics and the flexibility of numerical models with the functions of physical models. The result is a hybrid model with numerical and physical sections connected by an electro-hydraulic interface - which is to some extent the main problem since the numerical model can be easily changed or modified. The concept of hybrid model is applied to the representation of ventricular function by a variable elastance numerical model. This prototype is an open loop circuit and the physical section is built out of a reservoir (atrium) and a modified windkessel (arterial tree). The corresponding equations are solved numerically using the variables (atrial and arterial pressures) coming from the physical circuit. Ventricular output flow is the computed variable and is sent to a servo amplifier connected to a DC motor-gear pump system. The gear pump, behaving roughly as a flow source, is the interface to the physical circuit. Results obtained under different hemodynamic conditions demonstrate the behaviour of the ventricular model on the pressure-volume plane and the time course of output flow and arterial pressure.
Controllability in Hybrid Kinetic Equations Modeling Nonequilibrium Multicellular Systems
Bianca, Carlo
2013-01-01
This paper is concerned with the derivation of hybrid kinetic partial integrodifferential equations that can be proposed for the mathematical modeling of multicellular systems subjected to external force fields and characterized by nonconservative interactions. In order to prevent an uncontrolled time evolution of the moments of the solution, a control operator is introduced which is based on the Gaussian thermostat. Specifically, the analysis shows that the moments are solution of a Riccati-type differential equation. PMID:24191137
Baryon spectroscopy in a hybrid model with quantized pions
Price, C.E.; McNeil, J.A.
1993-04-01
The authors extend their earlier calculations of baryon properties in a hybrid quark model based on the Gell-Mann-Levy linear sigma model. They have avoided the projection problems associated with the standard hedgehog ansatz by solving the model using a Fock-space configuration which explicitly incorporates the correct isospin and angular momentum couplings in every component. This Fock-space configuration has components involving three quarks and various numbers of quantal pions. They minimize the ground-state expectation value of their Hamiltonian to obtain the equations of motion which they solve self-consistently. They calculate the canonical set of nucleon observables and compare with previous work.
A hybrid model for reducing ecological bias.
Salway, Ruth; Wakefield, Jon
2008-01-01
A major drawback of epidemiological ecological studies, in which the association between area-level summaries of risk and exposure is used to make inference about individual risk, is the difficulty in characterizing within-area variability in exposure and confounder variables. To avoid ecological bias, samples of individual exposure/confounder data within each area are required. Unfortunately, these may be difficult or expensive to obtain, particularly if large samples are required. In this paper, we propose a new approach suitable for use with small samples. We combine a Bayesian nonparametric Dirichlet process prior with an estimating functions' approach and show that this model gives a compromise between 2 previously described methods. The method is investigated using simulated data, and a practical illustration is provided through an analysis of lung cancer mortality and residential radon exposure in counties of Minnesota. We conclude that we require good quality prior information about the exposure/confounder distributions and a large between- to within-area variability ratio for an ecological study to be feasible using only small samples of individual data.
Hybrid Surface Mesh Adaptation for Climate Modeling
Ahmed Khamayseh; Valmor de Almeida; Glen Hansen
2008-10-01
Solution-driven mesh adaptation is becoming quite popular for spatial error control in the numerical simulation of complex computational physics applications, such as climate modeling. Typically, spatial adaptation is achieved by element subdivision (h adaptation) with a primary goal of resolving the local length scales of interest. A second, less-popular method of spatial adaptivity is called “mesh motion” (r adaptation); the smooth repositioning of mesh node points aimed at resizing existing elements to capture the local length scales. This paper proposes an adaptation method based on a combination of both element subdivision and node point repositioning (rh adaptation). By combining these two methods using the notion of a mobility function, the proposed approach seeks to increase the flexibility and extensibility of mesh motion algorithms while providing a somewhat smoother transition between refined regions than is produced by element subdivision alone. Further, in an attempt to support the requirements of a very general class of climate simulation applications, the proposed method is designed to accommodate unstructured, polygonal mesh topologies in addition to the most popular mesh types.
Electric/hybrid vehicle model for establishing optimal battery requirements
NASA Astrophysics Data System (ADS)
Marr, W. W.; Walsh, W. J.
1986-04-01
A microcomputer program (HELEN) for establishing battery requirements for a heat engine/battery hybrid vehicle is described. The program permits least-cost analyses to identify the optimum combination of battery and heat engine characteristics for different vehicle types and missions. It can also be used for cost comparisons between heat-engine vehicles, all-electric (battery) vehicles, and hybrid vehicles. Simplified models are used for the transmission, motor/generator, controller, and other vehicle components, while a rather comprehensive model is employed for the battery. The heat engine performance model is based on engineering data for a production engine. A series/parallel configuration for the hybrid vehicle system is presently simulated. Energy management in the operation of the vehicle depends on the specified mission requirements, type and size of the battery, allowable battery depth of discharge, type and size of the heat engine, and the energy management strategy used. The program is written in PL/I language and can be run interactively on an IBM PC, COMPAQ, or other compatible microcomputer.
A hybrid double-observer sightability model for aerial surveys
Griffin, Paul C.; Lubow, Bruce C.; Jenkins, Kurt J.; Vales, David J.; Moeller, Barbara J.; Reid, Mason; Happe, Patricia J.; Mccorquodale, Scott M.; Tirhi, Michelle J.; Schaberi, Jim P.; Beirne, Katherine
2013-01-01
Raw counts from aerial surveys make no correction for undetected animals and provide no estimate of precision with which to judge the utility of the counts. Sightability modeling and double-observer (DO) modeling are 2 commonly used approaches to account for detection bias and to estimate precision in aerial surveys. We developed a hybrid DO sightability model (model MH) that uses the strength of each approach to overcome the weakness in the other, for aerial surveys of elk (Cervus elaphus). The hybrid approach uses detection patterns of 2 independent observer pairs in a helicopter and telemetry-based detections of collared elk groups. Candidate MH models reflected hypotheses about effects of recorded covariates and unmodeled heterogeneity on the separate front-seat observer pair and back-seat observer pair detection probabilities. Group size and concealing vegetation cover strongly influenced detection probabilities. The pilot's previous experience participating in aerial surveys influenced detection by the front pair of observers if the elk group was on the pilot's side of the helicopter flight path. In 9 surveys in Mount Rainier National Park, the raw number of elk counted was approximately 80–93% of the abundance estimated by model MH. Uncorrected ratios of bulls per 100 cows generally were low compared to estimates adjusted for detection bias, but ratios of calves per 100 cows were comparable whether based on raw survey counts or adjusted estimates. The hybrid method was an improvement over commonly used alternatives, with improved precision compared to sightability modeling and reduced bias compared to DO modeling.
HYBRIST Mobility Model - A Novel Hybrid Mobility Model for VANET Simulations
NASA Astrophysics Data System (ADS)
ManfeDanquah, Wiseborn; Turgay Altilar, D.
2014-01-01
Simulations play a vital role in implementing, testing and validating proposed algorithms and protocols in VANET. Mobility model, defined as the movement pattern of vehicles, is one of the main factors that contribute towards the efficient implementation of VANET algorithms and protocols. Using near reality mobility models ensure that accurate results are obtained from simulations. Mobility models that have been proposed and used to implement and test VANET protocols and algorithms are either the urban mobility model or highway mobility model. Algorithms and protocols implemented using urban or highway mobility models may not produce accurate results in hybrid mobility models without enhancement due to the vast differences in mobility patterns. It is on this score the Hybrist, a novel hybrid mobility model is proposed. The realistic mobility pattern trace file of the proposed Hybrist hybrid mobility model can be imported to VANET simulators such as Veins and network simulators such as ns2 and Qualnet to simulate VANET algorithms and protocols.
NASA Technical Reports Server (NTRS)
Uccellini, L. W.; Johnson, D. R.; Schlesinger, R. E.
1979-01-01
A solution is presented for matching boundary conditions across the interface of an isentropic and sigma coordinate hybrid model. A hybrid model based on the flux form of the primitive equations is developed which allows direct vertical exchange between the model domains, satisfies conservation principles with respect to transport processes, and maintains a smooth transition across the interface without need for artificial adjustment or parameterization schemes. The initial hybrid model simulations of a jet streak propagating in a zonal channel are used to test the feasibility of the hybrid model approach. High efficiency of the hybrid model is demonstrated.
Hybrid Geoid Model: Theory and Application in Brazil.
Arana, Daniel; Camargo, Paulo O; Guimarães, Gabriel N
2017-01-01
Determination of the ellipsoidal height by Global Navigation Satellite Systems (GNSS) is becoming better known and used for purposes of leveling with the aid of geoid models. However, the disadvantage of this method is the quality of the geoid models, which degrade heights and limit the application of the method. In order to provide better quality in transforming height using GNSS leveling, this research aims to develop a hybridization methodology of gravimetric geoid models EGM08, MAPGEO2015 and GEOIDSP2014 for the State of São Paulo, providing more consistent models with GNSS technology. Radial Basis Function (RBF) neural networks were used to obtain the corrector surface, based on differences between geoid model undulations and the undulations obtained by GNSS tracking in benchmarks. The experiments showed that the most suitable interpolation for correction modeling is the linear RBF. Checkpoints indicate that the geoid hybrid models feature root mean square deviation ± 0.107, ± 0.104 and ± 0.098 m, respectively. The results shows an improvement of 30 to 40% in consistencies compared with the gravimetric geoids, providing users with better quality in transformation of geometric to orthometric heights.
RCS and antenna modeling with MOM using hybrid meshes
Putnam, J.M.; Kotulski, J.D.
1997-02-01
In this presentation, the authors will investigate the use of hybrid meshes for modeling RCS and antenna problems in three dimensions. They will consider two classes of hybrid basis functions. These include combinations of quadrilateral and triangular meshes for arbitrary 3D geometries, and combinations of axisymmetric body-of-revolution (BOR) basis functions and triangular facets. In particular, they will focus on the problem of enforcing current continuity between two surfaces which are represented by different types of surface discretizations and unknown basis function representations. They will illustrate the use of an operator-based code architecture for the implementation of these formulations, and how it facilitates the incorporation of the various types of boundary conditions in the code. Both serial and parallel code implementation issues for the formulations will be discussed. Results will be presented for both scattering and antenna problems. The emphasis will be on accuracy, and robustness of the techniques. Comparisons of accuracy between triangular meshed and quadrilateral meshed geometries will be shown. The use of hybrid meshes for modeling BORs with attached appendages will also be presented.
Transient Modeling of Hybrid Rocket Low Frequency Instabilities
NASA Technical Reports Server (NTRS)
Karabeyoglu, M. Arif; DeZilwa, Shane; Cantwell, Brian; Zilliac, Greg
2003-01-01
A comprehensive dynamic model of a hybrid rocket has been developed in order to understand and predict the transient behavior including instabilities. A linearized version of the transient model predicted the low-frequency chamber pressure oscillations that are commonly observed in hybrids. The source of the instabilities is based on a complex coupling of thermal transients in the solid fuel, wall heat transfer blocking due to fuel regression rate and the transients in the boundary layer that forms on the fuel surface. The oscillation frequencies predicted by the linearized theory are in very good agreement with 43 motor test results obtained from the hybrid propulsion literature. The motor test results used in the comparison cover a very wide spectrum of parameters including: 1) four separate research and development programs, 2) three different oxidizers (LOX, GOX, N2O), 3) a wide range of motor dimensions (i.e. from 5 inch diameter to 72 inch diameter) and operating conditions and 4) several fuel formulations. A simple universal scaling formula for the frequency of the primary oscillation mode is suggested.
Time series modeling of autonomous hybrid power systems
Quinlan, P.J.; Beckman, W.A.; Mitchell, J.W.; Klein, S.A.; Blair, N.J.
1997-12-31
The Solar Energy Laboratory (SEL) has developed a wind diesel PV hybrid systems simulator, UW-HYBRID 1.0, as an application of the TRNSYS 14.2 time-series simulation environment. The simulator provides a customizable user interface. The simulation provides an AC/DC buss, diesel generators, wind turbines, PV modules, a battery bank, and power converter. PV system simulations include solar angle and peak power tracking options. Diesel simulations include estimated fuel-use and waste heat output, and are dispatched using a least-cost of fuel strategy. Wind system simulations include varying air density, wind shear and wake effects. Time step duration is user-selectable. This paper provides a description of the simulation models and example output.
Higher hybrid bottomonia in an extended potential model
NASA Astrophysics Data System (ADS)
Akbar, Nosheen; Sultan, M. Atif; Masud, Bilal; Akram, Faisal
2017-04-01
Using our extension of the quark potential model to hybrid mesons that fits well with the available lattice results, we now calculate the masses, radii, wave functions at the origin, leptonic and two-photon decay widths, and E 1 and M 1 radiative transitions for a significant number of bottomonium mesons. These mesons include both conventional and hybrid ones with radial and angular excitations. Our numerical solutions of the Schrödinger equation are related to QCD through the Born-Oppenheimer approach. Relativistic corrections in masses and decay widths are also calculated by applying the leading-order perturbation theory. The calculated results are compared with available experimental data and the theoretical results by other groups. We also identify the states of ϒ (10860 ) , ϒ (11020 ) , and Yb(10890 ) mesons by comparing their experimental masses and decay widths to our results.
A hybrid generative and predictive model of the motor cortex.
Weber, Cornelius; Wermter, Stefan; Elshaw, Mark
2006-05-01
We describe a hybrid generative and predictive model of the motor cortex. The generative model is related to the hierarchically directed cortico-cortical (or thalamo-cortical) connections and unsupervised training leads to a topographic and sparse hidden representation of its sensory and motor input. The predictive model is related to lateral intra-area and inter-area cortical connections, functions as a hetero-associator attractor network and is trained to predict the future state of the network. Applying partial input, the generative model can map sensory input to motor actions and can thereby perform learnt action sequences of the agent within the environment. The predictive model can additionally predict a longer perception- and action sequence (mental simulation). The models' performance is demonstrated on a visually guided robot docking manoeuvre. We propose that the motor cortex might take over functions previously learnt by reinforcement in the basal ganglia and relate this to mirror neurons and imitation.
A new hybrid star model in Krori-Barua spacetime
NASA Astrophysics Data System (ADS)
Bhar, Piyali
2015-05-01
Present paper provides a new model of hybrid star with strange quark matter along with normal baryonic matter. The relation between pressure and density of the quark matter is given by the MIT bag model equation of state. The model is developed in the framework of Krori and Barua (KB) ansatz (Krori and Barua, Phys. A, Math. Gen. 8:508, 1975). All the physical requirements are satisfied by our model. The value of mass calculated from our model is close to the observational data which gives the validity of our present model. We match our interior solution to the exterior Schwarzschild metric where negative surface pressure is required to hold the thin shell against collapsing.
Hybrid first-principles/neural networks model for column flotation
Gupta, S.; Liu, P.H.; Svoronos, S.A.; Sharma, R.; Abdel-Khalek, N.A.; Cheng, Y.; El-Shall, H.
1999-03-01
A new model for phosphate column flotation is presented which for the first time relates the effects of operating variables such as frother concentration on column performance. This is a hybrid model that combines a first-principles model with artificial neural networks. The first-principles model is obtained from material balances on both phosphate particles and gangue (undesired material containing mostly silica). First-order rates of net attachment are assumed for both. Artificial neural networks relate the attachment rate constants to the operating variables. Experiments were conducted in a 6-in.-dia. (152-mm-dia.) laboratory column to provide data for neural network training and model validation. The model successfully predicts the effects of frother concentration, particle size, air flow rate and bubble diameter on grade and recovery.
System Modeling and Diagnostics for Liquefying-Fuel Hybrid Rockets
NASA Technical Reports Server (NTRS)
Poll, Scott; Iverson, David; Ou, Jeremy; Sanderfer, Dwight; Patterson-Hine, Ann
2003-01-01
A Hybrid Combustion Facility (HCF) was recently built at NASA Ames Research Center to study the combustion properties of a new fuel formulation that burns approximately three times faster than conventional hybrid fuels. Researchers at Ames working in the area of Integrated Vehicle Health Management recognized a good opportunity to apply IVHM techniques to a candidate technology for next generation launch systems. Five tools were selected to examine various IVHM techniques for the HCF. Three of the tools, TEAMS (Testability Engineering and Maintenance System), L2 (Livingstone2), and RODON, are model-based reasoning (or diagnostic) systems. Two other tools in this study, ICS (Interval Constraint Simulator) and IMS (Inductive Monitoring System) do not attempt to isolate the cause of the failure but may be used for fault detection. Models of varying scope and completeness were created, both qualitative and quantitative. In each of the models, the structure and behavior of the physical system are captured. In the qualitative models, the temporal aspects of the system behavior and the abstraction of sensor data are handled outside of the model and require the development of additional code. In the quantitative model, less extensive processing code is also necessary. Examples of fault diagnoses are given.
Pope, Bernard J; Fitch, Blake G; Pitman, Michael C; Rice, John J; Reumann, Matthias
2011-01-01
Future multiscale and multiphysics models must use the power of high performance computing (HPC) systems to enable research into human disease, translational medical science, and treatment. Previously we showed that computationally efficient multiscale models will require the use of sophisticated hybrid programming models, mixing distributed message passing processes (e.g. the message passing interface (MPI)) with multithreading (e.g. OpenMP, POSIX pthreads). The objective of this work is to compare the performance of such hybrid programming models when applied to the simulation of a lightweight multiscale cardiac model. Our results show that the hybrid models do not perform favourably when compared to an implementation using only MPI which is in contrast to our results using complex physiological models. Thus, with regards to lightweight multiscale cardiac models, the user may not need to increase programming complexity by using a hybrid programming approach. However, considering that model complexity will increase as well as the HPC system size in both node count and number of cores per node, it is still foreseeable that we will achieve faster than real time multiscale cardiac simulations on these systems using hybrid programming models.
A New Hybrid STEP/Coulomb model for Aftershock Forecasting
NASA Astrophysics Data System (ADS)
Steacy, S.; Jimenez, A.; Gerstenberger, M.
2014-12-01
Aftershock forecasting models tend to fall into two classes - purely statistical approaches based on clustering, b-value, and the Omori-Utsu law; and Coulomb rate-state models which relate the forecast increase in rate to the magnitude of the Coulomb stress change. Recently, hybrid models combining physical and statistical forecasts have begun to be developed, for example by Bach and Hainzl (2012) and Steacy et al. (2013). The latter approach combined Coulomb stress patterns with the STEP (short-term earthquake probability) model by redistributing expected rate from areas with decreased stress to regions where the stress had increased. The chosen 'Coulomb Redistribution Parameter' (CRP) was 0.93, based on California earthquakes, which meant that 93% of the total rate was expected to occur where the stress had increased. The model was tested against the Canterbury sequence and the main result was that the new model performed at least as well as, and often better than, STEP when tested against retrospective data but that STEP was generally better in pseudo-prospective tests that involved data actually available within the first 10 days of each event of interest. The authors suggested that the major reason for this discrepancy was uncertainty in the slip models and, particularly, in the geometries of the faults involved in each complex major event. Here we develop a variant of the STEP/Coulomb model in which the CRP varies based on the percentage of aftershocks that occur in the positively stressed areas during the forecast learning period. We find that this variant significantly outperforms both STEP and the previous hybrid model in almost all cases, even when the input Coulomb model is quite poor. Our results suggest that this approach might be more useful than Coulomb rate-state when the underlying slip model is not well constrained due to the dependence of that method on the magnitude of the Coulomb stress change.
Hybrid Stars in the Framework of NJL Models
NASA Astrophysics Data System (ADS)
Contrera, Gustavo A.; Orsaria, Milva; Ranea-Sandoval, I. F.; Weber, Fridolin
We compute models for the equation of state (EoS) of the matter in the cores of hybrid stars. Hadronic matter is treated in the non-linear relativistic mean-field approximation, and quark matter is modeled by three-flavor local and non-local Nambu‑Jona-Lasinio (NJL) models with repulsive vector interactions. The transition from hadronic to quark matter is constructed by considering either a soft phase transition (Gibbs construction) or a sharp phase transition (Maxwell construction). We find that high-mass neutron stars with masses up to 2.1 ‑ 2.4M⊙ may contain a mixed phase with hadrons and quarks in their cores, if global charge conservation is imposed via the Gibbs conditions. However, if the Maxwell conditions is considered, the appearance of a pure quark matter core either destabilizes the star immediately (commonly for non-local NJL models) or leads to a very short hybrid star branch in the mass-radius relation (generally for local NJL models).
Hybrid Adaptive Flight Control with Model Inversion Adaptation
NASA Technical Reports Server (NTRS)
Nguyen, Nhan
2011-01-01
This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.
Multiobjective muffler shape optimization with hybrid acoustics modeling.
Airaksinen, Tuomas; Heikkola, Erkki
2011-09-01
This paper considers the combined use of a hybrid numerical method for the modeling of acoustic mufflers and a genetic algorithm for multiobjective optimization. The hybrid numerical method provides accurate modeling of sound propagation in uniform waveguides with non-uniform obstructions. It is based on coupling a wave based modal solution in the uniform sections of the waveguide to a finite element solution in the non-uniform component. Finite element method provides flexible modeling of complicated geometries, varying material parameters, and boundary conditions, while the wave based solution leads to accurate treatment of non-reflecting boundaries and straightforward computation of the transmission loss (TL) of the muffler. The goal of optimization is to maximize TL at multiple frequency ranges simultaneously by adjusting chosen shape parameters of the muffler. This task is formulated as a multiobjective optimization problem with the objectives depending on the solution of the simulation model. NSGA-II genetic algorithm is used for solving the multiobjective optimization problem. Genetic algorithms can be easily combined with different simulation methods, and they are not sensitive to the smoothness properties of the objective functions. Numerical experiments demonstrate the accuracy and feasibility of the model-based optimization method in muffler design.
Probabilistic logic modeling of network reliability for hybrid network architectures
Wyss, G.D.; Schriner, H.K.; Gaylor, T.R.
1996-10-01
Sandia National Laboratories has found that the reliability and failure modes of current-generation network technologies can be effectively modeled using fault tree-based probabilistic logic modeling (PLM) techniques. We have developed fault tree models that include various hierarchical networking technologies and classes of components interconnected in a wide variety of typical and atypical configurations. In this paper we discuss the types of results that can be obtained from PLMs and why these results are of great practical value to network designers and analysts. After providing some mathematical background, we describe the `plug-and-play` fault tree analysis methodology that we have developed for modeling connectivity and the provision of network services in several current- generation network architectures. Finally, we demonstrate the flexibility of the method by modeling the reliability of a hybrid example network that contains several interconnected ethernet, FDDI, and token ring segments. 11 refs., 3 figs., 1 tab.
Hybrid radiative-transfer-diffusion model for optical tomography
NASA Astrophysics Data System (ADS)
Tarvainen, Tanja; Vauhkonen, Marko; Kolehmainen, Ville; Kaipio, Jari P.
2005-02-01
A hybrid radiative-transfer-diffusion model for optical tomography is proposed. The light propagation is modeled with the radiative-transfer equation in the vicinity of the laser sources, and the diffusion approximation is used elsewhere in the domain. The solution of the radiative-transfer equation is used to construct a Dirichlet boundary condition for the diffusion approximation on a fictitious interface within the object. This boundary condition constitutes an approximative distributed source model for the diffusion approximation in the remaining area. The results from the proposed approach are compared with finite-element solutions of the radiative-transfer equation and the diffusion approximation and Monte Carlo simulation. The results show that the method improves the accuracy of the forward model compared with the conventional diffusion model.
Experimental Validation of a Thermoelastic Model for SMA Hybrid Composites
NASA Technical Reports Server (NTRS)
Turner, Travis L.
2001-01-01
This study presents results from experimental validation of a recently developed model for predicting the thermomechanical behavior of shape memory alloy hybrid composite (SMAHC) structures, composite structures with an embedded SMA constituent. The model captures the material nonlinearity of the material system with temperature and is capable of modeling constrained, restrained, or free recovery behavior from experimental measurement of fundamental engineering properties. A brief description of the model and analysis procedures is given, followed by an overview of a parallel effort to fabricate and characterize the material system of SMAHC specimens. Static and dynamic experimental configurations for the SMAHC specimens are described and experimental results for thermal post-buckling and random response are presented. Excellent agreement is achieved between the measured and predicted results, fully validating the theoretical model for constrained recovery behavior of SMAHC structures.
Design, test and model of a hybrid magnetostrictive hydraulic actuator
NASA Astrophysics Data System (ADS)
Chaudhuri, Anirban; Yoo, Jin-Hyeong; Wereley, Norman M.
2009-08-01
The basic operation of hybrid hydraulic actuators involves high frequency bi-directional operation of an active material that is converted to uni-directional motion of hydraulic fluid using valves. A hybrid actuator was developed using magnetostrictive material Terfenol-D as the driving element and hydraulic oil as the working fluid. Two different lengths of Terfenol-D rod, 51 and 102 mm, with the same diameter, 12.7 mm, were used. Tests with no load and with load were carried out to measure the performance for uni-directional motion of the output piston at different pumping frequencies. The maximum no-load flow rates were 24.8 cm3 s-1 and 22.7 cm3 s-1 with the 51 mm and 102 mm long rods respectively, and the peaks were noted around 325 Hz pumping frequency. The blocked force of the actuator was close to 89 N in both cases. A key observation was that, at these high pumping frequencies, the inertial effects of the fluid mass dominate over the viscous effects and the problem becomes unsteady in nature. In this study, we also develop a mathematical model of the hydraulic hybrid actuator in the time domain to show the basic operational principle under varying conditions and to capture phenomena affecting system performance. Governing equations for the pumping piston and output shaft were obtained from force equilibrium considerations, while compressibility of the working fluid was taken into account by incorporating the bulk modulus. Fluid inertia was represented by a lumped parameter approach to the transmission line model, giving rise to strongly coupled ordinary differential equations. The model was then used to calculate the no-load velocities of the actuator at different pumping frequencies and simulation results were compared with experimental data for model validation.
Hybrid perturbation methods based on statistical time series models
NASA Astrophysics Data System (ADS)
San-Juan, Juan Félix; San-Martín, Montserrat; Pérez, Iván; López, Rosario
2016-04-01
In this work we present a new methodology for orbit propagation, the hybrid perturbation theory, based on the combination of an integration method and a prediction technique. The former, which can be a numerical, analytical or semianalytical theory, generates an initial approximation that contains some inaccuracies derived from the fact that, in order to simplify the expressions and subsequent computations, not all the involved forces are taken into account and only low-order terms are considered, not to mention the fact that mathematical models of perturbations not always reproduce physical phenomena with absolute precision. The prediction technique, which can be based on either statistical time series models or computational intelligence methods, is aimed at modelling and reproducing missing dynamics in the previously integrated approximation. This combination results in the precision improvement of conventional numerical, analytical and semianalytical theories for determining the position and velocity of any artificial satellite or space debris object. In order to validate this methodology, we present a family of three hybrid orbit propagators formed by the combination of three different orders of approximation of an analytical theory and a statistical time series model, and analyse their capability to process the effect produced by the flattening of the Earth. The three considered analytical components are the integration of the Kepler problem, a first-order and a second-order analytical theories, whereas the prediction technique is the same in the three cases, namely an additive Holt-Winters method.
Model-adaptive hybrid dynamic control for robotic assembly tasks
Austin, D.J.; McCarragher, B.J.
1999-10-01
A new task-level adaptive controller is presented for the hybrid dynamic control of robotic assembly tasks. Using a hybrid dynamic model of the assembly task, velocity constraints are derived from which satisfactory velocity commands are obtained. Due to modeling errors and parametric uncertainties, the velocity commands may be erroneous and may result in suboptimal performance. Task-level adaptive control schemes, based on the occurrence of discrete events, are used to change the model parameters from which the velocity commands are determined. Two adaptive schemes are presented: the first is based on intuitive reasoning about the vector spaces involved whereas the second uses a search region that is reduced with each iteration. For the first adaptation law, asymptotic convergence to the correct model parameters is proven except for one case. This weakness motivated the development of the second adaptation law, for which asymptotic convergence is proven in all cases. Automated control of a peg-in-hole assembly task is given as an example, and simulations and experiments for this task are presented. These results demonstrate the success of the method and also indicate properties for rapid convergence.
Constructing biological pathway models with hybrid functional Petri nets.
Doi, Atsushi; Fujita, Sachie; Matsuno, Hiroshi; Nagasaki, Masao; Miyano, Satoru
2004-01-01
In many research projects on modeling and analyzing biological pathways, the Petri net has been recognized as a promising method for representing biological pathways. From the pioneering works by Reddy et al., 1993, and Hofestädt, 1994, that model metabolic pathways by traditional Petri net, several enhanced Petri nets such as colored Petri net, stochastic Petri net, and hybrid Petri net have been used for modeling biological phenomena. Recently, Matsuno et al., 2003b, introduced the hybrid functional Petri net (HFPN) in order to give a more intuitive and natural modeling method for biological pathways than these existing Petri nets. Although the paper demonstrates the effectiveness of HFPN with two examples of gene regulation mechanism for circadian rhythms and apoptosis signaling pathway, there has been no detailed explanation about the method of HFPN construction for these examples. The purpose of this paper is to describe method to construct biological pathways with the HFPN step-by-step. The method is demonstrated by the well-known glycolytic pathway controlled by the lac operon gene regulatory mechanism.
Constructing biological pathway models with hybrid functional petri nets.
Doi, Atsushi; Fujita, Sachie; Matsuno, Hiroshi; Nagasaki, Masao; Miyano, Satoru
2011-01-01
In many research projects on modeling and analyzing biological pathways, the Petri net has been recognized as a promising method for representing biological pathways. From the pioneering works by Reddy et al., 1993, and Hofestädt, 1994, that model metabolic pathways by traditional Petri net, several enhanced Petri nets such as colored Petri net, stochastic Petri net, and hybrid Petri net have been used for modeling biological phenomena. Recently, Matsuno et al., 2003b, introduced the hybrid functional Petri net (HFPN) in order to give a more intuitive and natural modeling method for biological pathways than these existing Petri nets. Although the paper demonstrates the effectiveness of HFPN with two examples of gene regulation mechanism for circadian rhythms and apoptosis signaling pathway, there has been no detailed explanation about the method of HFPN construction for these examples. The purpose of this paper is to describe method to construct biological pathways with the HFPN step-by-step. The method is demonstrated by the well-known glycolytic pathway controlled by the lac operon gene regulatory mechanism.
KGEOID10: A New Hybrid Geoid Model in Korea
NASA Astrophysics Data System (ADS)
Lee, D. H.; Yun, H. S.; Suh, Y. C.; Hwang, J. S.; Min, B. I.
2012-04-01
This study describes in brief the construction of a new hybrid geoid model, KGEOID10, which can be used as an accurate vertical datum in Korea. The hybrid geoid model should be determined by fitting the gravimetric geoid to the geometric geoid undulations from GPS/Levelling data which were presented the local vertical level. For developing the gravimetric geoid model, we determined all frequency parts (long, middle and short-frequency) of gravimetric geoid using all available data with optimal remove-restore technique based on EGM2008 reference surface. In remove-restore technique, the EGM2008 model to degree 2,160, 4-band spherical FFT with modified stokes kernel and RTM reduction method were used for calculating the long, middle and short-frequency part of gravimetric geoid, respectively. A number of gravity data compiled for modelling the middle-frequency part, residual geoid, containing 8,296 points gravity data on land and ocean areas. And, the DEM data gridded by 100m×100m were used for short-frequency part, is the topographic effect on the geoid generated by RTM method. The accuracy of gravimetric geoid model were evaluated by comparison with GPS/Levelling data was about -1.63m ± 0.123m. Finally, we developed the hybrid geoid model in Korea, KGEOID10, corrected to gravimetric geoid with the correction term by fitting the 1,185 GPS/leveling data. The correction term is modelled using the difference between GPS/Levelling derived geoidal heights and gravimetric geoidal heights. The stochastic model used in the calculation of correction term is the LSC technique based on second-order Markov covariance function. The post-fit error (mean and std. dev.) of the KGEOID10 model was evaluated as 0.001m ± 0.054m. Concerning the result of this study, the accurate orthometric height at any points in Korea will be easily and precisely calculated using the KGEOID10 and GPS technique. Therefore, the KGEOID10 could be used as a vertical datum for determining the vertical
KNGEOID14: A national hybrid geoid model in Korea
NASA Astrophysics Data System (ADS)
Kang, S.; Sung, Y. M.; KIM, H.; Kim, Y. S.
2016-12-01
This study describes in brief the construction of a national hybrid geoid model in Korea, KNGEOID14, which can be used as an accurate vertical datum in/around Korea. The hybrid geoid model should be determined by fitting the gravimetric geoid to the geometric geoid undulations from GNSS/Leveling data which were presented the local vertical level. For developing the gravimetric geoid model, we determined all frequency parts (long, middle and short-frequency) of gravimetric geoid using all available data with optimal remove-restore technique based on EGM2008 reference surface. In remove-restore technique, the EGM2008 model to degree 360, RTM reduction method were used for calculating the long, middle and short-frequency part of gravimetric geoid, respectively. A number of gravity data compiled for modeling the middle-frequency part, residual geoid, containing 8,866 points gravity data on land and ocean areas. And, the DEM data gridded by 100m×100m were used for short-frequency part, is the topographic effect on the geoid generated by RTM method. The accuracy of gravimetric geoid model were evaluated by comparison with GNSS/Leveling data was about -0.362m ± 0.055m. Finally, we developed the national hybrid geoid model in Korea, KNGEOID14, corrected to gravimetric geoid with the correction term by fitting the about 1,200 GNSS/Leveling data on Korean bench marks. The correction term is modeled using the difference between GNSS/Leveling derived geoidal heights and gravimetric geoidal heights. The stochastic model used in the calculation of correction term is the LSC technique based on second-order Markov covariance function. The post-fit error (mean and std. dev.) of the KNGEOID14 model was evaluated as 0.001m ± 0.033m. Concerning the result of this study, the accurate orthometric height at any points in Korea will be easily and precisely calculated by combining the geoidal height from KNGEOID14 and ellipsoidal height from GPS observation technique.
Computational modeling of brain tumors: discrete, continuum or hybrid?
NASA Astrophysics Data System (ADS)
Wang, Zhihui; Deisboeck, Thomas S.
In spite of all efforts, patients diagnosed with highly malignant brain tumors (gliomas), continue to face a grim prognosis. Achieving significant therapeutic advances will also require a more detailed quantitative understanding of the dynamic interactions among tumor cells, and between these cells and their biological microenvironment. Data-driven computational brain tumor models have the potential to provide experimental tumor biologists with such quantitative and cost-efficient tools to generate and test hypotheses on tumor progression, and to infer fundamental operating principles governing bidirectional signal propagation in multicellular cancer systems. This review highlights the modeling objectives of and challenges with developing such in silico brain tumor models by outlining two distinct computational approaches: discrete and continuum, each with representative examples. Future directions of this integrative computational neuro-oncology field, such as hybrid multiscale multiresolution modeling are discussed.
Computational modeling of brain tumors: discrete, continuum or hybrid?
NASA Astrophysics Data System (ADS)
Wang, Zhihui; Deisboeck, Thomas S.
2008-04-01
In spite of all efforts, patients diagnosed with highly malignant brain tumors (gliomas), continue to face a grim prognosis. Achieving significant therapeutic advances will also require a more detailed quantitative understanding of the dynamic interactions among tumor cells, and between these cells and their biological microenvironment. Data-driven computational brain tumor models have the potential to provide experimental tumor biologists with such quantitative and cost-efficient tools to generate and test hypotheses on tumor progression, and to infer fundamental operating principles governing bidirectional signal propagation in multicellular cancer systems. This review highlights the modeling objectives of and challenges with developing such in silicobrain tumor models by outlining two distinct computational approaches: discrete and continuum, each with representative examples. Future directions of this integrative computational neuro-oncology field, such as hybrid multiscale multiresolution modeling are discussed.
Ionocovalency and applications 1. Ionocovalency model and orbital hybrid scales.
Zhang, Yonghe
2010-11-03
Ionocovalency (IC), a quantitative dual nature of the atom, is defined and correlated with quantum-mechanical potential to describe quantitatively the dual properties of the bond. Orbiotal hybrid IC model scale, IC, and IC electronegativity scale, X(IC), are proposed, wherein the ionicity and the covalent radius are determined by spectroscopy. Being composed of the ionic function I and the covalent function C, the model describes quantitatively the dual properties of bond strengths, charge density and ionic potential. Based on the atomic electron configuration and the various quantum-mechanical built-up dual parameters, the model formed a Dual Method of the multiple-functional prediction, which has much more versatile and exceptional applications than traditional electronegativity scales and molecular properties. Hydrogen has unconventional values of IC and X(IC), lower than that of boron. The IC model can agree fairly well with the data of bond properties and satisfactorily explain chemical observations of elements throughout the Periodic Table.
Ionocovalency and Applications 1. Ionocovalency Model and Orbital Hybrid Scales
Zhang, Yonghe
2010-01-01
Ionocovalency (IC), a quantitative dual nature of the atom, is defined and correlated with quantum-mechanical potential to describe quantitatively the dual properties of the bond. Orbiotal hybrid IC model scale, IC, and IC electronegativity scale, XIC, are proposed, wherein the ionicity and the covalent radius are determined by spectroscopy. Being composed of the ionic function I and the covalent function C, the model describes quantitatively the dual properties of bond strengths, charge density and ionic potential. Based on the atomic electron configuration and the various quantum-mechanical built-up dual parameters, the model formed a Dual Method of the multiple-functional prediction, which has much more versatile and exceptional applications than traditional electronegativity scales and molecular properties. Hydrogen has unconventional values of IC and XIC, lower than that of boron. The IC model can agree fairly well with the data of bond properties and satisfactorily explain chemical observations of elements throughout the Periodic Table. PMID:21151444
Nonlinear Thermoelastic Model for SMAs and SMA Hybrid Composites
NASA Technical Reports Server (NTRS)
Turner, Travis L.
2004-01-01
A constitutive mathematical model has been developed that predicts the nonlinear thermomechanical behaviors of shape-memory-alloys (SMAs) and of shape-memory-alloy hybrid composite (SMAHC) structures, which are composite-material structures that contain embedded SMA actuators. SMAHC structures have been investigated for their potential utility in a variety of applications in which there are requirements for static or dynamic control of the shapes of structures, control of the thermoelastic responses of structures, or control of noise and vibrations. The present model overcomes deficiencies of prior, overly simplistic or qualitative models that have proven ineffective or intractable for engineering of SMAHC structures. The model is sophisticated enough to capture the essential features of the mechanics of SMAHC structures yet simple enough to accommodate input from fundamental engineering measurements and is in a form that is amenable to implementation in general-purpose structural analysis environments.
Software development infrastructure for the HYBRID modeling and simulation project
Aaron S. Epiney; Robert A. Kinoshita; Jong Suk Kim; Cristian Rabiti; M. Scott Greenwood
2016-09-01
One of the goals of the HYBRID modeling and simulation project is to assess the economic viability of hybrid systems in a market that contains renewable energy sources like wind. The idea is that it is possible for the nuclear plant to sell non-electric energy cushions, which absorb (at least partially) the volatility introduced by the renewable energy sources. This system is currently modeled in the Modelica programming language. To assess the economics of the system, an optimization procedure is trying to find the minimal cost of electricity production. The RAVEN code is used as a driver for the whole problem. It is assumed that at this stage, the HYBRID modeling and simulation framework can be classified as non-safety “research and development” software. The associated quality level is Quality Level 3 software. This imposes low requirements on quality control, testing and documentation. The quality level could change as the application development continues.Despite the low quality requirement level, a workflow for the HYBRID developers has been defined that include a coding standard and some documentation and testing requirements. The repository performs automated unit testing of contributed models. The automated testing is achieved via an open-source python script called BuildingsP from Lawrence Berkeley National Lab. BuildingsPy runs Modelica simulation tests using Dymola in an automated manner and generates and runs unit tests from Modelica scripts written by developers. In order to assure effective communication between the different national laboratories a biweekly videoconference has been set-up, where developers can report their progress and issues. In addition, periodic face-face meetings are organized intended to discuss high-level strategy decisions with management. A second means of communication is the developer email list. This is a list to which everybody can send emails that will be received by the collective of the developers and managers
Exploring the lambda model of the hybrid superstring
NASA Astrophysics Data System (ADS)
Schmidtt, David M.
2016-10-01
The purpose of this contribution is to initiate the study of integrable deformations for different superstring theory formalisms that manifest the property of (classical) integrability. In this paper we choose the hybrid formalism of the superstring in the background AdS 2 × S 2 and explore in detail the most immediate consequences of its λ-deformation. The resulting action functional corresponds to the λ-model of the matter part of the fairly more sophisticated pure spinor formalism, which is also known to be classical integrable. In particular, the deformation preserves the integrability and the one-loop conformal invariance of its parent theory, hence being a marginal deformation.
Rotating hybrid stars with the Dyson-Schwinger quark model
NASA Astrophysics Data System (ADS)
Wei, J.-B.; Chen, H.; Burgio, G. F.; Schulze, H.-J.
2017-08-01
We study rapidly rotating hybrid stars with the Dyson-Schwinger model for quark matter and the Brueckner-Hartree-Fock many-body theory with realistic two-body and three-body forces for nuclear matter. We determine the maximum gravitational mass, equatorial radius, and rotation frequency of stable stellar configurations by considering the constraints of the Keplerian limit and the secular axisymmetric instability, and compare with observational data. We also discuss the rotational evolution for constant baryonic mass and find a spin-up phenomenon for supramassive stars before they collapse to black holes.
Modeling the Mixture of IRT and Pattern Responses by a Modified HYBRID Model.
ERIC Educational Resources Information Center
Yamamoto, Kentaro; Everson, Howard T.
This study demonstrates the utility of a HYBRID psychometric model, which incorporates both item response theoretic and latent class models, for detecting test speededness. The model isolates where in a sequence of test items examinee response patterns shift from one providing reasonable estimates of ability to those best characterized by a random…
A High Precision Prediction Model Using Hybrid Grey Dynamic Model
ERIC Educational Resources Information Center
Li, Guo-Dong; Yamaguchi, Daisuke; Nagai, Masatake; Masuda, Shiro
2008-01-01
In this paper, we propose a new prediction analysis model which combines the first order one variable Grey differential equation Model (abbreviated as GM(1,1) model) from grey system theory and time series Autoregressive Integrated Moving Average (ARIMA) model from statistics theory. We abbreviate the combined GM(1,1) ARIMA model as ARGM(1,1)…
A High Precision Prediction Model Using Hybrid Grey Dynamic Model
ERIC Educational Resources Information Center
Li, Guo-Dong; Yamaguchi, Daisuke; Nagai, Masatake; Masuda, Shiro
2008-01-01
In this paper, we propose a new prediction analysis model which combines the first order one variable Grey differential equation Model (abbreviated as GM(1,1) model) from grey system theory and time series Autoregressive Integrated Moving Average (ARIMA) model from statistics theory. We abbreviate the combined GM(1,1) ARIMA model as ARGM(1,1)…
Hybrid CFD/CAA Modeling for Liftoff Acoustic Predictions
NASA Technical Reports Server (NTRS)
Strutzenberg, Louise L.; Liever, Peter A.
2011-01-01
This paper presents development efforts at the NASA Marshall Space flight Center to establish a hybrid Computational Fluid Dynamics and Computational Aero-Acoustics (CFD/CAA) simulation system for launch vehicle liftoff acoustics environment analysis. Acoustic prediction engineering tools based on empirical jet acoustic strength and directivity models or scaled historical measurements are of limited value in efforts to proactively design and optimize launch vehicles and launch facility configurations for liftoff acoustics. CFD based modeling approaches are now able to capture the important details of vehicle specific plume flow environment, identifY the noise generation sources, and allow assessment of the influence of launch pad geometric details and sound mitigation measures such as water injection. However, CFD methodologies are numerically too dissipative to accurately capture the propagation of the acoustic waves in the large CFD models. The hybrid CFD/CAA approach combines the high-fidelity CFD analysis capable of identifYing the acoustic sources with a fast and efficient Boundary Element Method (BEM) that accurately propagates the acoustic field from the source locations. The BEM approach was chosen for its ability to properly account for reflections and scattering of acoustic waves from launch pad structures. The paper will present an overview of the technology components of the CFD/CAA framework and discuss plans for demonstration and validation against test data.
A Concept Analysis of Holistic Care by Hybrid Model
Jasemi, Madineh; Valizadeh, Leila; Zamanzadeh, Vahid; Keogh, Brian
2017-01-01
Purpose: Even though holistic care has been widely discussed in the health care and professional nursing literature, there is no comprehensive definition of it. Therefore, the aim of this article is to present a concept analysis of holistic care which was developed using the hybrid model. Methods: The hybrid model comprises three phases. In the theoretical phase, characteristics of holistic care were identified through a review of the literature from CINAHL, MEDLINE, PubMed, OVID, and Google Scholar databases. During the fieldwork phase, in-depth interviews were conducted with eight nurses who were purposely selected. Finally, following an analysis of the literature and the qualitative interviews, a theoretical description of the concept of holistic care was extracted. Results: Two main themes were extracted of analytical phase: “Holistic care for offering a comprehensive model for caring” and “holistic care for improving patients' and nurses' conditions.” Conclusion: By undertaking a conceptual analysis of holistic care, its meaning can be clarified which will encourage nursing educators to include holistic care in nursing syllabi, and consequently facilitate its provision in practice. PMID:28216867
Causality in Psychiatry: A Hybrid Symptom Network Construct Model
Young, Gerald
2015-01-01
Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved) that inform approaches to nosology, or classification, such as in the DSM-5 [Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; (1)]. However, network approaches to symptom interaction [i.e., symptoms are formative of the construct; e.g., (2), for posttraumatic stress disorder (PTSD)] are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth non-linear dynamical systems theory (NLDST). The article applies the concept of emergent circular causality (3) to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning) and universal (e.g., causal) processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments. PMID:26635639
Causality in Psychiatry: A Hybrid Symptom Network Construct Model.
Young, Gerald
2015-01-01
Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved) that inform approaches to nosology, or classification, such as in the DSM-5 [Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; (1)]. However, network approaches to symptom interaction [i.e., symptoms are formative of the construct; e.g., (2), for posttraumatic stress disorder (PTSD)] are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth non-linear dynamical systems theory (NLDST). The article applies the concept of emergent circular causality (3) to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning) and universal (e.g., causal) processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments.
Efficient Vaccine Distribution Based on a Hybrid Compartmental Model
Yu, Zhiwen; Liu, Jiming; Wang, Xiaowei; Zhu, Xianjun; Wang, Daxing; Han, Guoqiang
2016-01-01
To effectively and efficiently reduce the morbidity and mortality that may be caused by outbreaks of emerging infectious diseases, it is very important for public health agencies to make informed decisions for controlling the spread of the disease. Such decisions must incorporate various kinds of intervention strategies, such as vaccinations, school closures and border restrictions. Recently, researchers have paid increased attention to searching for effective vaccine distribution strategies for reducing the effects of pandemic outbreaks when resources are limited. Most of the existing research work has been focused on how to design an effective age-structured epidemic model and to select a suitable vaccine distribution strategy to prevent the propagation of an infectious virus. Models that evaluate age structure effects are common, but models that additionally evaluate geographical effects are less common. In this paper, we propose a new SEIR (susceptible—exposed—infectious šC recovered) model, named the hybrid SEIR-V model (HSEIR-V), which considers not only the dynamics of infection prevalence in several age-specific host populations, but also seeks to characterize the dynamics by which a virus spreads in various geographic districts. Several vaccination strategies such as different kinds of vaccine coverage, different vaccine releasing times and different vaccine deployment methods are incorporated into the HSEIR-V compartmental model. We also design four hybrid vaccination distribution strategies (based on population size, contact pattern matrix, infection rate and infectious risk) for controlling the spread of viral infections. Based on data from the 2009–2010 H1N1 influenza epidemic, we evaluate the effectiveness of our proposed HSEIR-V model and study the effects of different types of human behaviour in responding to epidemics. PMID:27233015
Efficient Vaccine Distribution Based on a Hybrid Compartmental Model.
Yu, Zhiwen; Liu, Jiming; Wang, Xiaowei; Zhu, Xianjun; Wang, Daxing; Han, Guoqiang
2016-01-01
To effectively and efficiently reduce the morbidity and mortality that may be caused by outbreaks of emerging infectious diseases, it is very important for public health agencies to make informed decisions for controlling the spread of the disease. Such decisions must incorporate various kinds of intervention strategies, such as vaccinations, school closures and border restrictions. Recently, researchers have paid increased attention to searching for effective vaccine distribution strategies for reducing the effects of pandemic outbreaks when resources are limited. Most of the existing research work has been focused on how to design an effective age-structured epidemic model and to select a suitable vaccine distribution strategy to prevent the propagation of an infectious virus. Models that evaluate age structure effects are common, but models that additionally evaluate geographical effects are less common. In this paper, we propose a new SEIR (susceptible-exposed-infectious šC recovered) model, named the hybrid SEIR-V model (HSEIR-V), which considers not only the dynamics of infection prevalence in several age-specific host populations, but also seeks to characterize the dynamics by which a virus spreads in various geographic districts. Several vaccination strategies such as different kinds of vaccine coverage, different vaccine releasing times and different vaccine deployment methods are incorporated into the HSEIR-V compartmental model. We also design four hybrid vaccination distribution strategies (based on population size, contact pattern matrix, infection rate and infectious risk) for controlling the spread of viral infections. Based on data from the 2009-2010 H1N1 influenza epidemic, we evaluate the effectiveness of our proposed HSEIR-V model and study the effects of different types of human behaviour in responding to epidemics.
Coarse-grained DNA modeling: Hybridization and ionic effects
NASA Astrophysics Data System (ADS)
Hinckley, Daniel M.
Deoxyribonucleic acid (DNA) is a biopolymer of enormous significance in living systems. The utility of DNA in such systems is derived from the programmable nature of DNA and its unique mechanical properties. Recently, material scientists have harnessed these properties in order to create systems that spontaneous self-assemble on the nanoscale. Both biologists and material scientists are hindered by an incomplete understanding of the physical interactions that together govern DNA's behavior. Computer simulations, especially those at the coarse-grained (CG) level, can potentially complete this understanding by resolving details indiscernible with current experimental techniques. In this thesis, we advance the state-of-the-art of DNA CG simulations by first reviewing the relevant theory and the evolution of CG DNA models since their inception. Then we present 3SPN.2, an improved CG model for DNA that should provide new insights into biological and nanotechnological systems which incorporate DNA. We perform forward flux sampling simulations in order to examine the effect of sequence, oligomer length, and ionic strength on DNA oligomer hybridization. Due to the limitations inherent in continuum treatments of electrostatic interactions in biological systems, we generate a CG model of biological ions for use with 3SPN.2 and other CG models. Lastly, we illustrate the potential of 3SPN.2 and CG ions by using the models in simulations of viral capsid packaging experiments. The models and results described in this thesis will be useful in future modeling efforts that seek to identify the fundamental physics that govern behavior such as nucleosome positioning, DNA hybridization, and DNA nanoassembly.
NASA Astrophysics Data System (ADS)
Fahimi, Farzad; Yaseen, Zaher Mundher; El-shafie, Ahmed
2017-05-01
Since the middle of the twentieth century, artificial intelligence (AI) models have been used widely in engineering and science problems. Water resource variable modeling and prediction are the most challenging issues in water engineering. Artificial neural network (ANN) is a common approach used to tackle this problem by using viable and efficient models. Numerous ANN models have been successfully developed to achieve more accurate results. In the current review, different ANN models in water resource applications and hydrological variable predictions are reviewed and outlined. In addition, recent hybrid models and their structures, input preprocessing, and optimization techniques are discussed and the results are compared with similar previous studies. Moreover, to achieve a comprehensive view of the literature, many articles that applied ANN models together with other techniques are included. Consequently, coupling procedure, model evaluation, and performance comparison of hybrid models with conventional ANN models are assessed, as well as, taxonomy and hybrid ANN models structures. Finally, current challenges and recommendations for future researches are indicated and new hybrid approaches are proposed.
NASA Astrophysics Data System (ADS)
Fahimi, Farzad; Yaseen, Zaher Mundher; El-shafie, Ahmed
2016-02-01
Since the middle of the twentieth century, artificial intelligence (AI) models have been used widely in engineering and science problems. Water resource variable modeling and prediction are the most challenging issues in water engineering. Artificial neural network (ANN) is a common approach used to tackle this problem by using viable and efficient models. Numerous ANN models have been successfully developed to achieve more accurate results. In the current review, different ANN models in water resource applications and hydrological variable predictions are reviewed and outlined. In addition, recent hybrid models and their structures, input preprocessing, and optimization techniques are discussed and the results are compared with similar previous studies. Moreover, to achieve a comprehensive view of the literature, many articles that applied ANN models together with other techniques are included. Consequently, coupling procedure, model evaluation, and performance comparison of hybrid models with conventional ANN models are assessed, as well as, taxonomy and hybrid ANN models structures. Finally, current challenges and recommendations for future researches are indicated and new hybrid approaches are proposed.
Hybrid Modeling Method for a DEP Based Particle Manipulation
Miled, Mohamed Amine; Gagne, Antoine; Sawan, Mohamad
2013-01-01
In this paper, a new modeling approach for Dielectrophoresis (DEP) based particle manipulation is presented. The proposed method fulfills missing links in finite element modeling between the multiphysic simulation and the biological behavior. This technique is amongst the first steps to develop a more complex platform covering several types of manipulations such as magnetophoresis and optics. The modeling approach is based on a hybrid interface using both ANSYS and MATLAB to link the propagation of the electrical field in the micro-channel to the particle motion. ANSYS is used to simulate the electrical propagation while MATLAB interprets the results to calculate cell displacement and send the new information to ANSYS for another turn. The beta version of the proposed technique takes into account particle shape, weight and its electrical properties. First obtained results are coherent with experimental results. PMID:23364197
Proposal: A Hybrid Dictionary Modelling Approach for Malay Tweet Normalization
NASA Astrophysics Data System (ADS)
Muhamad, Nor Azlizawati Binti; Idris, Norisma; Arshi Saloot, Mohammad
2017-02-01
Malay Twitter message presents a special deviation from the original language. Malay Tweet widely used currently by Twitter users, especially at Malaya archipelago. Thus, it is important to make a normalization system which can translated Malay Tweet language into the standard Malay language. Some researchers have conducted in natural language processing which mainly focuses on normalizing English Twitter messages, while few studies have been done for normalize Malay Tweets. This paper proposes an approach to normalize Malay Twitter messages based on hybrid dictionary modelling methods. This approach normalizes noisy Malay twitter messages such as colloquially language, novel words, and interjections into standard Malay language. This research will be used Language Model and N-grams model.
Building Hybrid Rover Models for NASA: Lessons Learned
NASA Technical Reports Server (NTRS)
Willeke, Thomas; Dearden, Richard
2004-01-01
Particle filters have recently become popular for diagnosis and monitoring of hybrid systems. In this paper we describe our experiences using particle filters on a real diagnosis problem, the NASA Ames Research Center's K-9 rover. As well as the challenge of modelling the dynamics of the system, there are two major issues in applying a particle filter to such a model. The first is the asynchronous nature of the system-observations from different subsystems arrive at different rates, and occasionally out of order, leading to large amounts of uncertainty in the state of the system. The second issue is data interpretation. The particle filter produces a probability distribution over the state of the system, from which summary statistics that can be used for control or higher-level diagnosis must be extracted. We describe our approaches to both these problems, as well as other modelling issues that arose in this domain.
COSMIC RAY MODULATION BEYOND THE HELIOPAUSE: A HYBRID MODELING APPROACH
Strauss, R. D.; Potgieter, M. S.; Ferreira, S. E. S.; Fichtner, H.; Scherer, K.
2013-03-01
Results from a newly developed hybrid cosmic ray (CR) modulation model are presented. In this approach, the transport of CRs is computed by incorporating the plasma flow from a magnetohydrodynamic model for the heliospheric environment, resulting in representative CR transport. The model is applied to the modulation of CRs beyond the heliopause (HP) and we show that (1) CR modulation persists beyond the HP, so it is unlikely that the Voyager spacecraft will measure the pristine local interstellar spectra of galactic CRs when crossing the HP. (2) CR modulation in the outer heliosheath could maintain solar-cycle-related changes. (3) The modulation of CRs in the outer heliosheath is primarily determined by the ratio of perpendicular to parallel diffusion, so that the value of the individual diffusion coefficients cannot be determined uniquely using this approach. (4) CRs can efficiently diffuse between the nose and tail regions of the heliosphere.
Cosmic Ray Modulation Beyond the Heliopause: A Hybrid Modeling Approach
NASA Astrophysics Data System (ADS)
Strauss, R. D.; Potgieter, M. S.; Ferreira, S. E. S.; Fichtner, H.; Scherer, K.
2013-03-01
Results from a newly developed hybrid cosmic ray (CR) modulation model are presented. In this approach, the transport of CRs is computed by incorporating the plasma flow from a magnetohydrodynamic model for the heliospheric environment, resulting in representative CR transport. The model is applied to the modulation of CRs beyond the heliopause (HP) and we show that (1) CR modulation persists beyond the HP, so it is unlikely that the Voyager spacecraft will measure the pristine local interstellar spectra of galactic CRs when crossing the HP. (2) CR modulation in the outer heliosheath could maintain solar-cycle-related changes. (3) The modulation of CRs in the outer heliosheath is primarily determined by the ratio of perpendicular to parallel diffusion, so that the value of the individual diffusion coefficients cannot be determined uniquely using this approach. (4) CRs can efficiently diffuse between the nose and tail regions of the heliosphere.
Hybrid modeling method for a DEP based particle manipulation.
Miled, Mohamed Amine; Gagne, Antoine; Sawan, Mohamad
2013-01-30
In this paper, a new modeling approach for Dielectrophoresis (DEP) based particle manipulation is presented. The proposed method fulfills missing links in finite element modeling between the multiphysic simulation and the biological behavior. This technique is amongst the first steps to develop a more complex platform covering several types of manipulations such as magnetophoresis and optics. The modeling approach is based on a hybrid interface using both ANSYS and MATLAB to link the propagation of the electrical field in the micro-channel to the particle motion. ANSYS is used to simulate the electrical propagation while MATLAB interprets the results to calculate cell displacement and send the new information to ANSYS for another turn. The beta version of the proposed technique takes into account particle shape, weight and its electrical properties. First obtained results are coherent with experimental results.
Krasnopolsky, Vladimir M; Fox-Rabinovitz, Michael S
2006-03-01
A new practical application of neural network (NN) techniques to environmental numerical modeling has been developed. Namely, a new type of numerical model, a complex hybrid environmental model based on a synergetic combination of deterministic and machine learning model components, has been introduced. Conceptual and practical possibilities of developing hybrid models are discussed in this paper for applications to climate modeling and weather prediction. The approach presented here uses NN as a statistical or machine learning technique to develop highly accurate and fast emulations for time consuming model physics components (model physics parameterizations). The NN emulations of the most time consuming model physics components, short and long wave radiation parameterizations or full model radiation, presented in this paper are combined with the remaining deterministic components (like model dynamics) of the original complex environmental model--a general circulation model or global climate model (GCM)--to constitute a hybrid GCM (HGCM). The parallel GCM and HGCM simulations produce very similar results but HGCM is significantly faster. The speed-up of model calculations opens the opportunity for model improvement. Examples of developed HGCMs illustrate the feasibility and efficiency of the new approach for modeling complex multidimensional interdisciplinary systems.
Axelrod Model of Social Influence with Cultural Hybridization
NASA Astrophysics Data System (ADS)
Radillo-Díaz, Alejandro; Pérez, Luis A.; Del Castillo-Mussot, Marcelo
2012-10-01
Since cultural interactions between a pair of social agents involve changes in both individuals, we present simulations of a new model based on Axelrod's homogenization mechanism that includes hybridization or mixture of the agents' features. In this new hybridization model, once a cultural feature of a pair of agents has been chosen for the interaction, the average of the values for this feature is reassigned as the new value for both agents after interaction. Moreover, a parameter representing social tolerance is implemented in order to quantify whether agents are similar enough to engage in interaction, as well as to determine whether they belong to the same cluster of similar agents after the system has reached the frozen state. The transitions from a homogeneous state to a fragmented one decrease in abruptness as tolerance is increased. Additionally, the entropy associated to the system presents a maximum within the transition, the width of which increases as tolerance does. Moreover, a plateau was found inside the transition for a low-tolerance system of agents with only two cultural features.
Design, modeling and performance of a hybrid portable gamma camera
NASA Astrophysics Data System (ADS)
Smith, Leon Eric
The combination of a mechanically-collimated gamma-ray camera with an electronically-collimated gamma camera offers both the high efficiency and good angular resolution typical in a mechanically-collimated camera for lower photon energies and the uncoupling of spatial resolution and efficiency provided by an electronically-collimated camera at higher energies. The design, construction, performance modeling and measured performance of the Hybrid Portable Gamma Camera (HPGC) are presented here. Intended for industrial use, the HPGC offers good angular resolution and efficiency over a broad energy range (50 keV to 2 MeV) by combining a MURA coded aperture camera with a Compton scatter camera in a single system. The HPGC consists of two detector modules: (1) a NaI(Tl) scintillator with Anger logic readout and (2) a CsI(Na) pixellated crystal viewed by a position-sensitive photomultiplier tube. Analytical calculations of angular resolution components and efficiency for the HPGC were compared to Monte Carlo calculations of the same quantities. The predicted angular resolution performance for on-axis point sources, a central scattering angle of 45sp° and a detector separation distance of 35 cm ranges from 3.5-6sp° FWHM over the sensitive energy range. The mechanical collimation intrinsic efficiency for energies up to 800 keV varies from 0.50 to 0.05 while the electronic collimation intrinsic efficiency for energies above 400 keV is 7.0×10sp{-4} to 5×10sp{-5}. The experimentally measured angular resolution and efficiency values show good agreement with the modeling predictions for incident energies of 412 keV and 662 keV. Although work has been done on mechanical collimation cameras and electronic collimation cameras operating independently, no truly hybrid imaging system has been constructed that uses the same gamma ray for both mechanical collimation and electronic collimation information. This dissertation compares the relative information per photon for three
Titan's Midrange Magnetotail from Cassini Observations and Hybrid Modeling
NASA Astrophysics Data System (ADS)
Feyerabend, M.; Simon, S.; Saur, J.; Motschmann, U. M.
2014-12-01
We study Titan's plasma interaction during Cassini's midrange tail encounters T9, T63 and T75. During each of these flybys, the Cassini Plasma Spectrometer detected a peculiar split signature in Titan's pickup tail, i.e. the spacecraft passed through two spatially separated cold ion populations of different masses. Whether this might be an omnipresent feature of Titan's plasma interacting or a result of non-stationary upstream conditions is still unclear. To explain these features, we apply the hybrid simulation code AIKEF. The latest version of this code includes chemical reactions, a realistic and statistically consistent photoionization model and recombination for a sophisticated description of the ionospheric composition of Titan. To validate the ionosphere model, we compare its output against measurements of the magnetic field and the electron density from the T70 flyby, which reached the lowest altitude of all flybys and had nearly ideal upstream conditions with the magnetic field pointing southward.
Hybrid Air Quality Modeling Approach For Use in the Near ...
The Near-road EXposures to Urban air pollutant Study (NEXUS) investigated whether children with asthma living in close proximity to major roadways in Detroit, MI, (particularly near roadways with high diesel traffic) have greater health impacts associated with exposure to air pollutants than those living farther away. A major challenge in such health and exposure studies is the lack of information regarding pollutant exposure characterization. Air quality modeling can provide spatially and temporally varying exposure estimates for examining relationships between traffic-related air pollutants and adverse health outcomes. This paper presents a hybrid air quality modeling approach and its application in NEXUS in order to provide spatial and temporally varying exposure estimates and identification of the mobile source contribution to the total pollutant exposure. Model-based exposure metrics, associated with local variations of emissions and meteorology, were estimated using a combination of the AERMOD and R-LINE dispersion models, local emission source information from the National Emissions Inventory, detailed road network locations and traffic activity, and meteorological data from the Detroit City Airport. The regional background contribution was estimated using a combination of the Community Multiscale Air Quality (CMAQ) model and the Space/Time Ordinary Kriging (STOK) model. To capture the near-road pollutant gradients, refined “mini-grids” of model recep
Estimation of Flood Inundation Extent Using Hybrid Models (Invited)
NASA Astrophysics Data System (ADS)
Chang, L.; Wang, Y.; Shen, H.
2009-12-01
We present a two-stage procedure underlying CHIM (clustering-based hybrid inundation model), which is composed of the linear regression models and ANNs to build the regional flood inundation estimation model. The two-stage procedure includes data preprocessing and model building stages. In the data preprocessing stage, the K-means clustering is used to categorize the data points of the different flooding characteristics and to identify the control point(s) from individual flooding cluster(s). In the model building stage, three classes of flood depth estimation models are built in each cluster: the back-propagation neural network (BPNN) for each control point, the linear regression models for the grids those have highly linear correlation with the control point, and a multi-grid BPNN for the grids those do not exist highly linear correlation with the control point. The effectiveness of the proposed approach is tested in the Dacun township in Taiwan. The results show that the CHIM can continuously and adequately provide one-hour-ahead flood inundation maps and effectively reduce 99% CPU time.
Garcia-Lopez, Alicia C; Garcia-Rubio, Luis H
2008-03-31
Rayleigh-Debye-Gans and Mie theory were previously shown to disagree for spherical particles under ideal conditions4. A Hybrid model for spheres was developed by the authors combining Mie theory and Rayleigh- Debye-Gans. The hybrid model was tested against Mie and Rayleigh- Debye-Gans for different refractive indices and diameter sizes across the UV-Vis spectrum. The results of this study show that the hybrid model represents a considerable improvement over Rayleigh-Debye-Gans for submicron particles and is computationally more effective compared to Mie model. The development of the spherical hybrid model establishes a platform for the analysis of non-spherical particles.
Modeling Integrated Cellular Machinery Using Hybrid Petri-Boolean Networks
Berestovsky, Natalie; Zhou, Wanding; Nagrath, Deepak; Nakhleh, Luay
2013-01-01
The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM) that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them using such more
Modeling integrated cellular machinery using hybrid Petri-Boolean networks.
Berestovsky, Natalie; Zhou, Wanding; Nagrath, Deepak; Nakhleh, Luay
2013-01-01
The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM) that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them using such more
A new approach to flow simulation using hybrid models
NASA Astrophysics Data System (ADS)
Solgi, Abazar; Zarei, Heidar; Nourani, Vahid; Bahmani, Ramin
2017-01-01
The necessity of flow prediction in rivers, for proper management of water resource, and the need for determining the inflow to the dam reservoir, designing efficient flood warning systems and so forth, have always led water researchers to think about models with high-speed response and low error. In the recent years, the development of Artificial Neural Networks and Wavelet theory and using the combination of models help researchers to estimate the river flow better and better. In this study, daily and monthly scales were used for simulating the flow of Gamasiyab River, Nahavand, Iran. The first simulation was done using two types of ANN and ANFIS models. Then, using wavelet theory and decomposing input signals of the used parameters, sub-signals were obtained and were fed into the ANN and ANFIS to obtain hybrid models of WANN and WANFIS. In this study, in addition to the parameters of precipitation and flow, parameters of temperature and evaporation were used to analyze their effects on the simulation. The results showed that using wavelet transform improved the performance of the models in both monthly and daily scale. However, it had a better effect on the monthly scale and the WANFIS was the best model.
A numerical oil spill model based on a hybrid method.
Guo, W J; Wang, Y X
2009-05-01
The purpose of this paper is the development of a hybrid particle tracking/Eulerian-Lagrangian approach for the simulation of spilled oil in coastal areas. Oil discharge from the source is modeled by the release of particles. When the oil slick thickness or the oil concentration reaches a critical value, particles are mapped on slick thickness or node concentrations, and the calculations proceed in the Eulerian-Lagrangian mode. To acquire accurate environment information, the model is coupled with the 3-D free-surface hydrodynamics model (POM) and the third-generation wave model (SWAN). By simulating the oil processes of spreading, advection, turbulent diffusion, evaporation, emulsification, dissolution and shoreline deposition, it has the ability to predict the horizontal movement of surface oil slick, the vertical distribution of oil particles, the concentration in the water column and the mass balance of spilled oil. An accidental oil release near Dalian coastal waters is simulated to validate the developed model. Compared with the satellite images of oil slicks on the surface, the numerical results indicate that the model has a reasonable accuracy.
Hybrid Transureteral Nephrectomy in a Survival Porcine Model
Anderson, Kirk M.; Alsyouf, Muhannad; Richards, Gideon; Agarwal, Gautum; Heldt, Jonathan P.; Schlaifer, Amy E.
2014-01-01
Background and Objectives: Natural-orifice approaches for nephrectomy have included access via the stomach, vagina, bladder, and rectum. Recently, the feasibility of using the ureter as a natural orifice for natural-orifice transluminal endoscopic surgery nephrectomy has been demonstrated in a nonsurvival porcine model. The purpose of this study was to assess the outcomes of transureteral laparoscopic natural-orifice transluminal endoscopic surgery nephrectomy in a survival porcine model. Methods: Three pigs underwent hybrid transureteral natural-orifice transluminal endoscopic surgery nephrectomy. An experimental balloon/dilating sheath was inserted over a wire to dilate the urethra, ureteral orifice, and ureter. Through a bariatric 12-mm laparoscopic port, the ureter was opened medially and the hilar dissection was performed. Next, 2 needlescopic ports were placed transabdominally to facilitate hilar transection. The kidney was morcellated using a bipolar sealing device and extracted via the ureter using the housing of a bariatric stapling device. The ureteral orifice was closed with a laparoscopic suturing device. The bladder was drained by a catheter for 10 to 14 days postoperatively. Pigs were euthanized on postoperative day 21. Results: All surgical procedures were successfully completed, with no intraoperative complications. One pig had an episode of postoperative clot retention that resolved with catheter irrigation. Each pig was healthy and eating a normal diet prior to euthanasia. Conclusions: This study demonstrates the feasibility of a hybrid transureteral approach to nephrectomy in a survival porcine model. This technique avoids the intentional violation of a second organ system and the risk for peritoneal contamination. Improved instrumentation is needed prior to implementation in the human population. PMID:25489210
A hybrid HDRF model of GOMS and SAIL: GOSAIL
NASA Astrophysics Data System (ADS)
Dou, B.; Wu, S.; Wen, J.
2016-12-01
Understanding the surface reflectance anisotropy is the key facet in interpreting the features of land surface from remotely sensed information, which describes the property of land surface to reflect the solar radiation directionally. Most reflectance anisotropy models assumed the nature surface was illuminated only by the direct solar radiation, while the diffuse skylight becomes dominant especially for the over cast sky conditions and high rugged terrain. Correcting the effect of diffuse skylight on the reflectance anisotropy to obtain the intrinsic directional reflectance of land surface is highly desirable for remote sensing applications. This paper developed a hybrid HDRF model of GOMS and SAIL called GOSAIL model for discrete canopies. The accurate area proportions of four scene components are calculated by the GOMS model and the spectral signatures of scene components are provided by the SAIL model. Both the single scattering contribution and the multiple scattering contributions within and between the canopy and background under the clear and diffuse illumination conditions are considered in the GOSAIL model. The HDRF simulated by the 3-D Discrete Anisotropic Radiative Transfer (DART) model and the HDRF measurements over the 100m×100m mature pine stand at the Järvselja, Estonia are used for validating and evaluating the performance of proposed GOSAIL model. The comparison results indicate the GOSAIL model can accurately reproducing the angular feature of discrete canopy for both the clear and overcast atmospheric conditions. The GOSAIL model is promising for the land surface biophysical parameters retrieval (e.g. albedo, leaf area index) over the heterogeneous terrain.
NASA Astrophysics Data System (ADS)
Tang, Xiaolin; Yang, Wei; Hu, Xiaosong; Zhang, Dejiu
2017-02-01
In this study, based on our previous work, a novel simplified torsional vibration dynamic model is established to study the torsional vibration characteristics of a compound planetary hybrid propulsion system. The main frequencies of the hybrid driveline are determined. In contrast to vibration characteristics of the previous 16-degree of freedom model, the simplified model can be used to accurately describe the low-frequency vibration property of this hybrid powertrain. This study provides a basis for further vibration control of the hybrid powertrain during the process of engine start/stop.
Quasicycles in the stochastic hybrid Morris-Lecar neural model
NASA Astrophysics Data System (ADS)
Brooks, Heather A.; Bressloff, Paul C.
2015-07-01
Intrinsic noise arising from the stochastic opening and closing of voltage-gated ion channels has been shown experimentally and mathematically to have important effects on a neuron's function. Study of classical neuron models with stochastic ion channels is becoming increasingly important, especially in understanding a cell's ability to produce subthreshold oscillations and to respond to weak periodic stimuli. While it is known that stochastic models can produce oscillations (quasicycles) in parameter regimes where the corresponding deterministic model has only a stable fixed point, little analytical work has been done to explore these connections within the context of channel noise. Using a stochastic hybrid Morris-Lecar (ML) model, we combine a system-size expansion in K+ and a quasi-steady-state (QSS) approximation in persistent Na+ in order to derive an effective Langevin equation that preserves the low-dimensional (planar) structure of the underlying deterministic ML model. (The QSS analysis exploits the fact that persistent Na+ channels are fast.) By calculating the corresponding power spectrum, we determine analytically how noise significantly extends the parameter regime in which subthreshold oscillations occur.
Modeling and simulation of a hybrid ship power system
NASA Astrophysics Data System (ADS)
Doktorcik, Christopher J.
2011-12-01
Optimizing the performance of naval ship power systems requires integrated design and coordination of the respective subsystems (sources, converters, and loads). A significant challenge in the system-level integration is solving the Power Management Control Problem (PMCP). The PMCP entails deciding on subsystem power usages for achieving a trade-off between the error in tracking a desired position/velocity profile, minimizing fuel consumption, and ensuring stable system operation, while at the same time meeting performance limitations of each subsystem. As such, the PMCP naturally arises at a supervisory level of a ship's operation. In this research, several critical steps toward the solution of the PMCP for surface ships have been undertaken. First, new behavioral models have been developed for gas turbine engines, wound rotor synchronous machines, DC super-capacitors, induction machines, and ship propulsion systems. Conventional models describe system inputs and outputs in terms of physical variables such as voltage, current, torque, and force. In contrast, the behavioral models developed herein express system inputs and outputs in terms of power whenever possible. Additionally, the models have been configured to form a hybrid system-level power model (HSPM) of a proposed ship electrical architecture. Lastly, several simulation studies have been completed to expose the capabilities and limitations of the HSPM.
HALM: A Hybrid Asperity Likelihood Model for Italy
NASA Astrophysics Data System (ADS)
Gulia, L.; Wiemer, S.
2009-04-01
The Asperity Likelihood Model (ALM), first developed and currently tested for California, hypothesizes that small-scale spatial variations in the b-value of the Gutenberg and Richter relationship play a central role in forecasting future seismicity (Wiemer and Schorlemmer, SRL, 2007). The physical basis of the model is the concept that the local b-value is inversely dependent on applied shear stress. Thus low b-values (b < 0.7) characterize the locked paches of faults -asperities- from which future mainshocks are more likely to be generated, whereas the high b-values (b > 1.1) found for example in creeping section of faults suggest a lower seismic hazard. To test this model in a reproducible and prospective way suitable for the requirements of the CSEP initiative (www.cseptesting.org), the b-value variability is mapped on a grid. First, using the entire dataset above the overall magnitude of completeness, the regional b-value is estimated. This value is then compared to the one locally estimated at each grid-node for a number of radii, we use the local value if its likelihood score, corrected for the degrees of freedom using the Akaike Information Criterion, suggest to do so. We are currently calibrating the ALM model for implementation in the Italian testing region, the first region within the CSEP EU testing Center (eu.cseptesting.org) for which fully prospective tests of earthquake likelihood models will commence in Europe. We are also developing a modified approach, ‘hybrid' between a grid-based and a zoning one: the HALM (Hybrid Asperity Likelihood Model). According to HALM, the Italian territory is divided in three distinct regions depending on the main tectonic elements, combined with knowledge derived from GPS networks, seismic profile interpretation, borehole breakouts and the focal mechanisms of the event. The local b-value variability was thus mapped using three independent overall b-values. We evaluate the performance of the two models in
Hybrid Eulerian-Lagrangian Vortex Model for Turbulent Reacting Flows
NASA Astrophysics Data System (ADS)
Royero, John; Ahmed, Kareem
2016-11-01
A hybrid Eulerian-Lagrangian model for three dimensional large eddy simulations of turbulent reacting flows is presented. The method utilizes a Eulerian grid to resolve large scale flow features and the Lagrangian vortex element method to capture smaller subgrid scale effects and carry out reactions which are then communicated back to the Eulerian grid after a set number of Lagrangian time steps. Lagrangian influences are localized in order to reduce computational cost. The Lagrangian vortex method which utilizes the Helmholtz decomposition of the velocity into potential, expansive, and solenoidal components allows the separation of the various mechanisms contributing to vorticity including gas expansion, diffusion, external body forces and baroclinic torque and is coupled with the Eulerian solver allowing easier implementation in arbitrary reacting flows at a reduced computational cost compared to a pure Lagrangian solver.
Inhomogeneous loop quantum cosmology: Hybrid quantization of the Gowdy model
NASA Astrophysics Data System (ADS)
Garay, L. J.; Martín-Benito, M.; Mena Marugán, G. A.
2010-08-01
The Gowdy cosmologies provide a suitable arena to further develop loop quantum cosmology, allowing the presence of inhomogeneities. For the particular case of Gowdy spacetimes with the spatial topology of a three-torus and a content of linearly polarized gravitational waves, we detail a hybrid quantum theory in which we combine a loop quantization of the degrees of freedom that parametrize the subfamily of homogeneous solutions, which represent Bianchi I spacetimes, and a Fock quantization of the inhomogeneities. Two different theories are constructed and compared, corresponding to two different schemes for the quantization of the Bianchi I model within the improved dynamics formalism of loop quantum cosmology. One of these schemes has been recently put forward by Ashtekar and Wilson-Ewing. We address several issues, including the quantum resolution of the cosmological singularity, the structure of the superselection sectors in the quantum system, or the construction of the Hilbert space of physical states.
Recent developments on the UrQMD hybrid model
Steinheimer, J. Nahrgang, M. Gerhard, J. Schramm, S. Bleicher, M.
2012-06-15
We present recent results from the UrQMD hybrid approach investigating the influence of a deconfinement phase transition on the dynamics of hot and dense nuclear matter. In the hydrodynamic stage an equation of state that incorporates a critical end-point (CEP) in line with lattice data is used. The equation of state describes chiral restoration as well as the deconfinement phase transition. We compare the results from this new equation of state to results obtained by applying a hadron resonance gas equation of state, focusing on bulk observables. Furthermore we will discuss future improvements of the hydrodynamic model. This includes the formulation of chiral fluid dynamics to be able to study the effects of a chiral critical point as well as considerable improvements in terms of computational time which would open up possibilities for observables that require high statistics.
Development of a hybrid cloud parameterization for general circulation models
Kao, C.Y.J.; Kristjansson, J.E.; Langley, D.L.
1995-04-01
We have developed a cloud package with state-of-the-art physical schemes that can parameterize low-level stratus or stratocumulus, penetrative cumulus, and high-level cirrus. Such parameterizations will improve cloud simulations in general circulation models (GCMs). The principal tool in this development comprises the physically based Arakawa-Schubert scheme for convective clouds and the Sundqvist scheme for layered, nonconvective clouds. The term {open_quotes}hybrid{close_quotes} addresses the fact that the generation of high-attitude layered clouds can be associated with preexisting convective clouds. Overall, the cloud parameterization package developed should better determine cloud heating and drying effects in the thermodynamic budget, realistic precipitation patterns, cloud coverage and liquid/ice water content for radiation purposes, and the cloud-induced transport and turbulent diffusion for atmospheric trace gases.
NASA Astrophysics Data System (ADS)
Mekonnen, B.; Nazemi, A.; Elshorbagy, A.; Mazurek, K.; Putz, G.
2012-04-01
Modeling the hydrological response in prairie regions, characterized by flat and undulating terrain, and thus, large non-contributing areas, is a known challenge. The hydrological response (runoff) is the combination of the traditional runoff from the hydrologically contributing area and the occasional overflow from the non-contributing area. This study provides a unique opportunity to analyze the issue of fusing the Soil and Water Assessment Tool (SWAT) and Artificial Neural Networks (ANNs) in a hybrid structure to model the hydrological response in prairie regions. A hybrid SWAT-ANN model is proposed, where the SWAT component and the ANN module deal with the effective (contributing) area and the non-contributing area, respectively. The hybrid model is applied to the case study of Moose Jaw watershed, located in southern Saskatchewan, Canada. As an initial exploration, a comparison between ANN and SWAT models is established based on addressing the daily runoff (streamflow) prediction accuracy using multiple error measures. This is done to identify the merits and drawbacks of each modeling approach. It has been found out that the SWAT model has better performance during the low flow periods but with degraded efficiency during periods of high flows. The case is different for the ANN model as ANNs exhibit improved simulation during high flow periods but with biased estimates during low flow periods. The modelling results show that the new hybrid SWAT-ANN model is capable of exploiting the strengths of both SWAT and ANN models in an integrated framrwork. The new hybrid SWAT-ANN model simulates daily runoff quite satisfactorily with NSE measures of 0.80 and 0.83 during calibration and validation periods, respectively. Furthermore, an experimental assessment was performed to identify the effects of the ANN training method on the performance of the hybrid model as well as the parametric identifiability. Overall, the results obtained in this study suggest that the fusion
Modeling and optimization of a hybrid solar combined cycle (HYCS)
NASA Astrophysics Data System (ADS)
Eter, Ahmad Adel
2011-12-01
The main objective of this thesis is to investigate the feasibility of integrating concentrated solar power (CSP) technology with the conventional combined cycle technology for electric generation in Saudi Arabia. The generated electricity can be used locally to meet the annual increasing demand. Specifically, it can be utilized to meet the demand during the hours 10 am-3 pm and prevent blackout hours, of some industrial sectors. The proposed CSP design gives flexibility in the operation system. Since, it works as a conventional combined cycle during night time and it switches to work as a hybrid solar combined cycle during day time. The first objective of the thesis is to develop a thermo-economical mathematical model that can simulate the performance of a hybrid solar-fossil fuel combined cycle. The second objective is to develop a computer simulation code that can solve the thermo-economical mathematical model using available software such as E.E.S. The developed simulation code is used to analyze the thermo-economic performance of different configurations of integrating the CSP with the conventional fossil fuel combined cycle to achieve the optimal integration configuration. This optimal integration configuration has been investigated further to achieve the optimal design of the solar field that gives the optimal solar share. Thermo-economical performance metrics which are available in the literature have been used in the present work to assess the thermo-economic performance of the investigated configurations. The economical and environmental impact of integration CSP with the conventional fossil fuel combined cycle are estimated and discussed. Finally, the optimal integration configuration is found to be solarization steam side in conventional combined cycle with solar multiple 0.38 which needs 29 hectare and LEC of HYCS is 63.17 $/MWh under Dhahran weather conditions.
Hybrid Models for Trajectory Error Modelling in Urban Environments
NASA Astrophysics Data System (ADS)
Angelatsa, E.; Parés, M. E.; Colomina, I.
2016-06-01
This paper tackles the first step of any strategy aiming to improve the trajectory of terrestrial mobile mapping systems in urban environments. We present an approach to model the error of terrestrial mobile mapping trajectories, combining deterministic and stochastic models. Due to urban specific environment, the deterministic component will be modelled with non-continuous functions composed by linear shifts, drifts or polynomial functions. In addition, we will introduce a stochastic error component for modelling residual noise of the trajectory error function. First step for error modelling requires to know the actual trajectory error values for several representative environments. In order to determine as accurately as possible the trajectories error, (almost) error less trajectories should be estimated using extracted nonsemantic features from a sequence of images collected with the terrestrial mobile mapping system and from a full set of ground control points. Once the references are estimated, they will be used to determine the actual errors in terrestrial mobile mapping trajectory. The rigorous analysis of these data sets will allow us to characterize the errors of a terrestrial mobile mapping system for a wide range of environments. This information will be of great use in future campaigns to improve the results of the 3D points cloud generation. The proposed approach has been evaluated using real data. The data originate from a mobile mapping campaign over an urban and controlled area of Dortmund (Germany), with harmful GNSS conditions. The mobile mapping system, that includes two laser scanner and two cameras, was mounted on a van and it was driven over a controlled area around three hours. The results show the suitability to decompose trajectory error with non-continuous deterministic and stochastic components.
Modelling human perception processes in pedestrian dynamics: a hybrid approach
Colombi, A.
2017-01-01
In this paper, we present a hybrid mathematical model describing crowd dynamics. More specifically, our approach is based on the well-established Helbing-like discrete model, where each pedestrian is individually represented as a dimensionless point and set to move in order to reach a target destination, with deviations deriving from both physical and social forces. In particular, physical forces account for interpersonal collisions, whereas social components include the individual desire to remain sufficiently far from other walkers (the so-called territorial effect). In this respect, the repulsive behaviour of pedestrians is here set to be different from traditional Helbing-like methods, as it is assumed to be largely determined by how they perceive the presence and the position of neighbouring individuals, i.e. either objectively as pointwise/localized entities or subjectively as spatially distributed masses. The resulting modelling environment is then applied to specific scenarios, that first reproduce a real-world experiment, specifically designed to derive our model hypothesis. Sets of numerical realizations are also run to analyse in more details the pedestrian paths resulting from different types of perception of small groups of static individuals. Finally, analytical investigations formalize and validate from a mathematical point of view selected simulation outcomes. PMID:28405352
3D hybrid modelling of vascular network formation.
Perfahl, Holger; Hughes, Barry D; Alarcón, Tomás; Maini, Philip K; Lloyd, Mark C; Reuss, Matthias; Byrne, Helen M
2017-02-07
We develop an off-lattice, agent-based model to describe vasculogenesis, the de novo formation of blood vessels from endothelial progenitor cells during development. The endothelial cells that comprise our vessel network are viewed as linearly elastic spheres that move in response to the forces they experience. We distinguish two types of endothelial cells: vessel elements are contained within the network and tip cells are located at the ends of vessels. Tip cells move in response to mechanical forces caused by interactions with neighbouring vessel elements and the local tissue environment, chemotactic forces and a persistence force which accounts for their tendency to continue moving in the same direction. Vessel elements are subject to similar mechanical forces but are insensitive to chemotaxis. An angular persistence force representing interactions with the local tissue is introduced to stabilise buckling instabilities caused by cell proliferation. Only vessel elements proliferate, at rates which depend on their degree of stretch: elongated elements have increased rates of proliferation, and compressed elements have reduced rates. Following division, the fate of the new cell depends on the local mechanical environment: the probability of forming a new sprout is increased if the parent vessel is highly compressed and the probability of being incorporated into the parent vessel increased if the parent is stretched. Simulation results reveal that our hybrid model can reproduce the key qualitative features of vasculogenesis. Extensive parameter sensitivity analyses show that significant changes in network size and morphology are induced by varying the chemotactic sensitivity of tip cells, and the sensitivities of the proliferation rate and the sprouting probability to mechanical stretch. Varying the chemotactic sensitivity directly influences the directionality of the networks. The degree of branching, and thereby the density of the networks, is influenced by the
Status and modeling improvements of hybrid wind/PV/diesel power systems for Brazilian applications
McGowan, J.G.; Manwell, J.F.; Avelar, C.; Taylor, R.
1997-12-31
This paper present a summary of the ongoing work on the modeling and system design of hybrid wind/PV/diesel systems for two different sites in the Amazonia region of Brazil. The work incorporates the latest resource data and is based on the use of the Hybrid2 simulation code developed by the University of Massachusetts and NREL. Details of the baseline operating hybrid systems are reviewed, and the results of the latest detailed hybrid system evaluation for each site are summarized. Based on the system modeling results, separate recommendations for system modification and improvements are made.
An Efficient Model-based Diagnosis Engine for Hybrid Systems Using Structural Model Decomposition
NASA Technical Reports Server (NTRS)
Bregon, Anibal; Narasimhan, Sriram; Roychoudhury, Indranil; Daigle, Matthew; Pulido, Belarmino
2013-01-01
Complex hybrid systems are present in a large range of engineering applications, like mechanical systems, electrical circuits, or embedded computation systems. The behavior of these systems is made up of continuous and discrete event dynamics that increase the difficulties for accurate and timely online fault diagnosis. The Hybrid Diagnosis Engine (HyDE) offers flexibility to the diagnosis application designer to choose the modeling paradigm and the reasoning algorithms. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. However, HyDE faces some problems regarding performance in terms of complexity and time. Our focus in this paper is on developing efficient model-based methodologies for online fault diagnosis in complex hybrid systems. To do this, we propose a diagnosis framework where structural model decomposition is integrated within the HyDE diagnosis framework to reduce the computational complexity associated with the fault diagnosis of hybrid systems. As a case study, we apply our approach to a diagnostic testbed, the Advanced Diagnostics and Prognostics Testbed (ADAPT), using real data.
Comprehensive thermal modeling of a power-split hybrid powertrain using battery cell model
NASA Astrophysics Data System (ADS)
Mayyas, Abdel Raouf; Omar, Mohammed; Pisu, Pierluigi; Al-Ahmer, Ali; Mayyas, Ahmad; Montes, Carlos; Dongri, Shan
2011-08-01
This manuscript discusses the development of a 3D thermal model for a power-split hybrid powertrain, including its battery modules and power electronics. The 3D model utilizes a finite differencing (FD) heat transfer algorithm, complemented with experimental boundary conditions. The experimental setup is configured to acquire the battery current, voltage, and its inner and surface temperatures in discrete and in full-field scans. The power-split hybrid configuration is tested using a standard and artificial driving cycles. A battery resistance model is then used to couple the experimental boundary conditions with the finite differencing code, which employed a cell-based internal heat generation model to describe the pack chemical reaction mechanism. This study presents a complete analysis based on battery current and voltage in relation to vehicle speed. The proposed model also predicts the powertrain spatial and temporal temperature profiles in agreement with the vehicle actual conditions as indicated by the On-Board Diagnosis (OBD) module.
Tracer Modeling with the Hybrid Coordinates Ocean Model (hycom)
NASA Astrophysics Data System (ADS)
Garraffo, Z. D.; Kim, H.; Li, B.; Mehra, A.; Rivin, I.; Spindler, T.; Tolman, H. L.
2012-12-01
A series of tracer simulations have been started at NCEP/NWS aiming to a variety of applications, from dispersion of contaminants in estimations motivated by the Japanese nuclear accident near Fukushima, to nutrient estimations. The tracer capabilities of HYCOM are used, in regional domains, nested to daily nowcast/forecast fields from 1/12 HYCOM (RTOFS-Global) model output. A Fukushima Cs-137 simulation is now run in operational mode (RTOFS_ET). The simulation was initialized at the time of the Fukushima nuclear accident, and includes atmospheric deposition of Cs-137 and coastal discharge from a high resolution coastal model (ROMS done at NOAA/NOS). Almost all tracer moved offshore before the end of the first year after the accident. The tracer initially deposited in the Pacific ocean through the atmosphere slowly moves eastward and to deeper waters following the 3D ocean circulation. A series of simulations were started for nutrient estimations in the Gulf Stream and Mid Atlantic Bight region. Initially the capabilities implemented in HYCOM are used. The work aims to monitoring nutrients in the chosen region. Work is done in collaboration with Victoria Coles of U. Maryland.
Multi-level and hybrid modelling approaches for systems biology.
Bardini, R; Politano, G; Benso, A; Di Carlo, S
2017-01-01
During the last decades, high-throughput techniques allowed for the extraction of a huge amount of data from biological systems, unveiling more of their underling complexity. Biological systems encompass a wide range of space and time scales, functioning according to flexible hierarchies of mechanisms making an intertwined and dynamic interplay of regulations. This becomes particularly evident in processes such as ontogenesis, where regulative assets change according to process context and timing, making structural phenotype and architectural complexities emerge from a single cell, through local interactions. The information collected from biological systems are naturally organized according to the functional levels composing the system itself. In systems biology, biological information often comes from overlapping but different scientific domains, each one having its own way of representing phenomena under study. That is, the different parts of the system to be modelled may be described with different formalisms. For a model to have improved accuracy and capability for making a good knowledge base, it is good to comprise different system levels, suitably handling the relative formalisms. Models which are both multi-level and hybrid satisfy both these requirements, making a very useful tool in computational systems biology. This paper reviews some of the main contributions in this field.
Mobile phone use while driving: a hybrid modeling approach.
Márquez, Luis; Cantillo, Víctor; Arellana, Julián
2015-05-01
The analysis of the effects that mobile phone use produces while driving is a topic of great interest for the scientific community. There is consensus that using a mobile phone while driving increases the risk of exposure to traffic accidents. The purpose of this research is to evaluate the drivers' behavior when they decide whether or not to use a mobile phone while driving. For that, a hybrid modeling approach that integrates a choice model with the latent variable "risk perception" was used. It was found that workers and individuals with the highest education level are more prone to use a mobile phone while driving than others. Also, "risk perception" is higher among individuals who have been previously fined and people who have been in an accident or almost been in an accident. It was also found that the tendency to use mobile phones while driving increases when the traffic speed reduces, but it decreases when the fine increases. Even though the urgency of the phone call is the most important explanatory variable in the choice model, the cost of the fine is an important attribute in order to control mobile phone use while driving. Copyright © 2015 Elsevier Ltd. All rights reserved.
Weighted Hybrid Decision Tree Model for Random Forest Classifier
NASA Astrophysics Data System (ADS)
Kulkarni, Vrushali Y.; Sinha, Pradeep K.; Petare, Manisha C.
2016-06-01
Random Forest is an ensemble, supervised machine learning algorithm. An ensemble generates many classifiers and combines their results by majority voting. Random forest uses decision tree as base classifier. In decision tree induction, an attribute split/evaluation measure is used to decide the best split at each node of the decision tree. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation among them. The work presented in this paper is related to attribute split measures and is a two step process: first theoretical study of the five selected split measures is done and a comparison matrix is generated to understand pros and cons of each measure. These theoretical results are verified by performing empirical analysis. For empirical analysis, random forest is generated using each of the five selected split measures, chosen one at a time. i.e. random forest using information gain, random forest using gain ratio, etc. The next step is, based on this theoretical and empirical analysis, a new approach of hybrid decision tree model for random forest classifier is proposed. In this model, individual decision tree in Random Forest is generated using different split measures. This model is augmented by weighted voting based on the strength of individual tree. The new approach has shown notable increase in the accuracy of random forest.
A. Weiskittel; D. Maguire; R. Monserud
2007-01-01
Hybrid models offer the opportunity to improve future growth projections by combining advantages of both empirical and process-based modeling approaches. Hybrid models have been constructed in several regions and their performance relative to a purely empirical approach has varied. A hybrid model was constructed for intensively managed Douglas-fir plantations in the...
Modeling, hybridization, and optimal charging of electrical energy storage systems
NASA Astrophysics Data System (ADS)
Parvini, Yasha
The rising rate of global energy demand alongside the dwindling fossil fuel resources has motivated research for alternative and sustainable solutions. Within this area of research, electrical energy storage systems are pivotal in applications including electrified vehicles, renewable power generation, and electronic devices. The approach of this dissertation is to elucidate the bottlenecks of integrating supercapacitors and batteries in energy systems and propose solutions by the means of modeling, control, and experimental techniques. In the first step, the supercapacitor cell is modeled in order to gain fundamental understanding of its electrical and thermal dynamics. The dependence of electrical parameters on state of charge (SOC), current direction and magnitude (20-200 A), and temperatures ranging from -40°C to 60°C was embedded in this computationally efficient model. The coupled electro-thermal model was parameterized using specifically designed temporal experiments and then validated by the application of real world duty cycles. Driving range is one of the major challenges of electric vehicles compared to combustion vehicles. In order to shed light on the benefits of hybridizing a lead-acid driven electric vehicle via supercapacitors, a model was parameterized for the lead-acid battery and combined with the model already developed for the supercapacitor, to build the hybrid battery-supercapacitor model. A hardware in the loop (HIL) setup consisting of a custom built DC/DC converter, micro-controller (muC) to implement the power management strategy, 12V lead-acid battery, and a 16.2V supercapacitor module was built to perform the validation experiments. Charging electrical energy storage systems in an efficient and quick manner, motivated to solve an optimal control problem with the objective of maximizing the charging efficiency for supercapacitors, lead-acid, and lithium ion batteries. Pontryagins minimum principle was used to solve the problems
Kalman Filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry.
Zhang, Yuxin; Chen, Shuo; Deng, Kexin; Chen, Bingyao; Wei, Xing; Yang, Jiafei; Wang, Shi; Ying, Kui
2017-01-01
To develop a self-adaptive and fast thermometry method by combining the original hybrid magnetic resonance thermometry method and the bio heat transfer equation (BHTE) model. The proposed Kalman filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry, abbreviated as KalBHT hybrid method, introduced the BHTE model to synthesize a window on the regularization term of the hybrid algorithm, which leads to a self-adaptive regularization both spatially and temporally with change of temperature. Further, to decrease the sensitivity to accuracy of the BHTE model, Kalman filter is utilized to update the window at each iteration time. To investigate the effect of the proposed model, computer heating simulation, phantom microwave heating experiment and dynamic in-vivo model validation of liver and thoracic tumor were conducted in this study. The heating simulation indicates that the KalBHT hybrid algorithm achieves more accurate results without adjusting λ to a proper value in comparison to the hybrid algorithm. The results of the phantom heating experiment illustrate that the proposed model is able to follow temperature changes in the presence of motion and the temperature estimated also shows less noise in the background and surrounding the hot spot. The dynamic in-vivo model validation with heating simulation demonstrates that the proposed model has a higher convergence rate, more robustness to susceptibility problem surrounding the hot spot and more accuracy of temperature estimation. In the healthy liver experiment with heating simulation, the RMSE of the hot spot of the proposed model is reduced to about 50% compared to the RMSE of the original hybrid model and the convergence time becomes only about one fifth of the hybrid model. The proposed model is able to improve the accuracy of the original hybrid algorithm and accelerate the convergence rate of MR temperature estimation.
Local identification of scalar hybrid models with tree structure
NASA Astrophysics Data System (ADS)
Fiedler, Bernold; Schuppert, Andreas
2008-06-01
Standard modelling approaches, e.g. in chemical engineering, suffer from two principal difficulties: the curse of dimension and a lack of extrapolability. We propose an approach via structured hybrid models (SHMs) to resolve both issues. For simplicity, we consider reactor models which can be written as a tree-like composition of scalar input-output (i/o) functions uj. The vertices j of the finite tree structure represent known or unknown subprocesses of the overall process. Known processes are modelled by white-box functions uj; unknown processes are represented by black boxes uj. Oriented edges of the tree indicate composition of the i/o relations uj in a feedforward structure. The tree structure of a mixture of black and white boxes constitutes what we call an SHM. Under certain assumptions on differentiability, genericity and monotonicity, we provide an inductive algorithm which uniquely identifies all black boxes in the SHM up to a trivial scaling calibration between adjacent black boxes. Our result does not require any extra measurements interior to the SHM. Instead, we only require global, overall i/o data clustered along a d-dimensional database of inputs. More precisely, information on partial derivatives of order up to 5 is required, in all directions, but only at base points within the d-dimensional database. The dimension d need not exceed the maximal input dimension of any individual black box in the SHM. Compared to the total input dimension n of the reactor, which may be much larger than d, this dimension reduction effectively avoids the curse of dimension: the complexity of our approach is polynomial in n and exponential in d, only, rather than exponential in n. Moreover, our unique identification of all black boxes accommodates a reliable global extrapolation, far beyond the original database, to input regions of full dimension. We illustrate our results with a model of an industrial continuous polymerization plant.
Forecasting Stock Exchange Movements Using Artificial Neural Network Models and Hybrid Models
NASA Astrophysics Data System (ADS)
Güreşen, Erkam; Kayakutlu, Gülgün
Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models; recurrent neural network (RNN), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) to extract new input variables. The comparison for each model is done in two view points: MSE and MAD using real exchange daily rate values of Istanbul Stock Exchange (ISE) index XU10).
Hybrid E-Learning Acceptance Model: Learner Perceptions
ERIC Educational Resources Information Center
Ahmed, Hassan M. Selim
2010-01-01
E-learning tools and technologies have been used to supplement conventional courses in higher education institutions creating a "hybrid" e-learning module that aims to enhance the learning experiences of students. Few studies have addressed the acceptance of hybrid e-learning by learners and the factors affecting the learners'…
Hybrid E-Learning Acceptance Model: Learner Perceptions
ERIC Educational Resources Information Center
Ahmed, Hassan M. Selim
2010-01-01
E-learning tools and technologies have been used to supplement conventional courses in higher education institutions creating a "hybrid" e-learning module that aims to enhance the learning experiences of students. Few studies have addressed the acceptance of hybrid e-learning by learners and the factors affecting the learners'…
Vector/dyad notation in computer symbolic modeling of hybrid parameter mechanical systems
Barhorst, A.A.
1996-11-01
In this paper, computer symbolic algebra based algorithms written to take advantage of engineering vector notation, as applied to hybrid parameter mechanical systems, are demonstrated. The symbolic manipulation tools are utilized to implement a hybrid parameter system modeling algorithm previously developed by the author. The modeling algorithm produces minimal holonomic and nonholonomic equations of motion for hybrid systems of any continuum dimension and kinematic topology. Boundary conditions are rigorously supplied by the modeling method. The system model presented as an example is a hybrid parameter planar two link model of a robot manipulator. A complete analysis from model to simulation and animation in a Mathematica notebook is presented. The modeling tools presented herein are applicable for researchers, practicing engineers, and students in advanced dynamic system modeling and control courses.
Hybrid Network Defense Model Based on Fuzzy Evaluation
2014-01-01
With sustained and rapid developments in the field of information technology, the issue of network security has become increasingly prominent. The theme of this study is network data security, with the test subject being a classified and sensitive network laboratory that belongs to the academic network. The analysis is based on the deficiencies and potential risks of the network's existing defense technology, characteristics of cyber attacks, and network security technologies. Subsequently, a distributed network security architecture using the technology of an intrusion prevention system is designed and implemented. In this paper, first, the overall design approach is presented. This design is used as the basis to establish a network defense model, an improvement over the traditional single-technology model that addresses the latter's inadequacies. Next, a distributed network security architecture is implemented, comprising a hybrid firewall, intrusion detection, virtual honeynet projects, and connectivity and interactivity between these three components. Finally, the proposed security system is tested. A statistical analysis of the test results verifies the feasibility and reliability of the proposed architecture. The findings of this study will potentially provide new ideas and stimuli for future designs of network security architecture. PMID:24574870
A Hybrid Fuzzy Model for Lean Product Development Performance Measurement
NASA Astrophysics Data System (ADS)
Osezua Aikhuele, Daniel; Mohd Turan, Faiz
2016-02-01
In the effort for manufacturing companies to meet up with the emerging consumer demands for mass customized products, many are turning to the application of lean in their product development process, and this is gradually moving from being a competitive advantage to a necessity. However, due to lack of clear understanding of the lean performance measurements, many of these companies are unable to implement and fully integrated the lean principle into their product development process. Extensive literature shows that only few studies have focus systematically on the lean product development performance (LPDP) evaluation. In order to fill this gap, the study therefore proposed a novel hybrid model based on Fuzzy Reasoning Approach (FRA), and the extension of Fuzzy-AHP and Fuzzy-TOPSIS methods for the assessment of the LPDP. Unlike the existing methods, the model considers the importance weight of each of the decision makers (Experts) since the performance criteria/attributes are required to be rated, and these experts have different level of expertise. The rating is done using a new fuzzy Likert rating scale (membership-scale) which is designed such that it can address problems resulting from information lost/distortion due to closed-form scaling and the ordinal nature of the existing Likert scale.
Tracking Inter-Regional Carbon Flows: A Hybrid Network Model.
Chen, Shaoqing; Chen, Bin
2016-05-03
The mitigation of anthropogenic carbon emissions has moved beyond the local scale because they diffuse across boundaries, and the consumption that triggers emissions has become regional and global. A precondition of effective mitigation is to explicitly assess inter-regional transfer of emissions. This study presents a hybrid network model to track inter-regional carbon flows by combining network analysis and input-output analysis. The direct, embodied, and controlled emissions associated with regions are quantified for assessing various types of carbon flow. The network-oriented metrics called "controlled emissions" is proposed to cover the amount of carbon emissions that can be mitigated within a region by adjusting its consumption. The case study of the Jing-Jin-Ji Area suggests that CO2 emissions embodied in products are only partially controlled by a region from a network perspective. Controlled carbon accounted for about 70% of the total embodied carbon flows, while household consumption only controlled about 25% of Beijing's emissions, much lower than its proportion of total embodied carbon. In addition to quantifying emissions, the model can pinpoint the dominant processes and sectors of emissions transfer across regions. This technique is promising for searching efficient pathways of coordinated emissions control across various regions connected by trade.
Hybrid CMS methods with model reduction for assembly of structures
NASA Technical Reports Server (NTRS)
Farhat, Charbel
1991-01-01
Future on-orbit structures will be designed and built in several stages, each with specific control requirements. Therefore there must be a methodology which can predict the dynamic characteristics of the assembled structure, based on the dynamic characteristics of the subassemblies and their interfaces. The methodology developed by CSC to address this issue is Hybrid Component Mode Synthesis (HCMS). HCMS distinguishes itself from standard component mode synthesis algorithms in the following features: (1) it does not require the subcomponents to have displacement compatible models, which makes it ideal for analyzing the deployment of heterogeneous flexible multibody systems, (2) it incorporates a second-level model reduction scheme at the interface, which makes it much faster than other algorithms and therefore suitable for control purposes, and (3) it does answer specific questions such as 'how does the global fundamental frequency vary if I change the physical parameters of substructure k by a specified amount?'. Because it is based on an energy principle rather than displacement compatibility, this methodology can also help the designer to define an assembly process. Current and future efforts are devoted to applying the HCMS method to design and analyze docking and berthing procedures in orbital construction.
Hybrid network defense model based on fuzzy evaluation.
Cho, Ying-Chiang; Pan, Jen-Yi
2014-01-01
With sustained and rapid developments in the field of information technology, the issue of network security has become increasingly prominent. The theme of this study is network data security, with the test subject being a classified and sensitive network laboratory that belongs to the academic network. The analysis is based on the deficiencies and potential risks of the network's existing defense technology, characteristics of cyber attacks, and network security technologies. Subsequently, a distributed network security architecture using the technology of an intrusion prevention system is designed and implemented. In this paper, first, the overall design approach is presented. This design is used as the basis to establish a network defense model, an improvement over the traditional single-technology model that addresses the latter's inadequacies. Next, a distributed network security architecture is implemented, comprising a hybrid firewall, intrusion detection, virtual honeynet projects, and connectivity and interactivity between these three components. Finally, the proposed security system is tested. A statistical analysis of the test results verifies the feasibility and reliability of the proposed architecture. The findings of this study will potentially provide new ideas and stimuli for future designs of network security architecture.
Development of hybrid 3-D hydrological modeling for the NCAR Community Earth System Model (CESM)
Zeng, Xubin; Troch, Peter; Pelletier, Jon; Niu, Guo-Yue; Gochis, David
2015-11-15
This is the Final Report of our four-year (3-year plus one-year no cost extension) collaborative project between the University of Arizona (UA) and the National Center for Atmospheric Research (NCAR). The overall objective of our project is to develop and evaluate the first hybrid 3-D hydrological model with a horizontal grid spacing of 1 km for the NCAR Community Earth System Model (CESM).
A formal hybrid modeling scheme for handling discontinuities in physical system models
Mosterman, P.J.; Biswas, G.
1996-12-31
Physical systems are by nature continuous, but often exhibit nonlinearities that make behavior generation complex and hard to analyze. Complexity is often reduced by linearizing model constraints and by abstracting the time scale for behavior generation. In either case, the physical components are modeled to operate in multiple modes, with abrupt changes between modes. This paper discusses a hybrid modeling methodology and analysis algorithms that combine continuous energy flow modeling and localized discrete signal flow modeling to generate complex, multi-mode behavior in a consistent and correct manner. Energy phase space analysis is employed to demonstrate the correctness of the algorithm, and the reachability of a continuous mode.
Hybrid modelling approach for effective simulation of reactive pollutants like Ozone
NASA Astrophysics Data System (ADS)
Sharma, Sumit; Sharma, Prateek; Khare, Mukesh
2013-12-01
Prediction of air quality is an important component of any air quality management programme. Broadly, two approaches are used to predict the ambient air quality - the deterministic and the statistical approach, with each approach having its own merits and demerits. While the models based on the deterministic approach accurately predict the concentrations of air pollutants in the middle percentile range, the statistical models provide a better estimate of concentrations in the extreme percentile ranges. However, the statistical models are site specific and are not able generate ‘what-if' scenarios; the deterministic models on the other hand are general in character and useful in creating alternative scenarios. An alternative approach - hybrid modelling is a technique which aggregates the benefits of the two techniques and predicts the ‘entire range' of the distribution. While in the past there were attempts to predict the concentrations of inert pollutants using hybrid modelling approach, this paper shows the hybrid model applications for reactive secondary pollutants like ground level Ozone (GLO). This study presents the development of a hybrid model that concatenates the results of CMAQ (community multi-scale air quality model) as its deterministic component with statistical distribution model (based on the specific area category and timeframe) to predict the entire range of GLO concentrations. Predictions have been made using both purely deterministic and hybrid approaches at a receptor location near a major traffic intersection. The performance of the model has been found to improve from an index of agreement from 0.77 (deterministic model) to 0.91 (hybrid model). In order to assess the predictive capability of the hybrid approach, the model has been tested at an entirely different location for different set of temporal data. The results show an improvement in the predictions using the hybrid model over the deterministic model.
Hybrid Simulation Modeling to Estimate U.S. Energy Elasticities
NASA Astrophysics Data System (ADS)
Baylin-Stern, Adam C.
This paper demonstrates how an U.S. application of CIMS, a technologically explicit and behaviourally realistic energy-economy simulation model which includes macro-economic feedbacks, can be used to derive estimates of elasticity of substitution (ESUB) and autonomous energy efficiency index (AEEI) parameters. The ability of economies to reduce greenhouse gas emissions depends on the potential for households and industry to decrease overall energy usage, and move from higher to lower emissions fuels. Energy economists commonly refer to ESUB estimates to understand the degree of responsiveness of various sectors of an economy, and use estimates to inform computable general equilibrium models used to study climate policies. Using CIMS, I have generated a set of future, 'pseudo-data' based on a series of simulations in which I vary energy and capital input prices over a wide range. I then used this data set to estimate the parameters for transcendental logarithmic production functions using regression techniques. From the production function parameter estimates, I calculated an array of elasticity of substitution values between input pairs. Additionally, this paper demonstrates how CIMS can be used to calculate price-independent changes in energy-efficiency in the form of the AEEI, by comparing energy consumption between technologically frozen and 'business as usual' simulations. The paper concludes with some ideas for model and methodological improvement, and how these might figure into future work in the estimation of ESUBs from CIMS. Keywords: Elasticity of substitution; hybrid energy-economy model; translog; autonomous energy efficiency index; rebound effect; fuel switching.
Modeling and design of a high-performance hybrid actuator
NASA Astrophysics Data System (ADS)
Aloufi, Badr; Behdinan, Kamran; Zu, Jean
2016-12-01
This paper presents the model and design of a novel hybrid piezoelectric actuator which provides high active and passive performances for smart structural systems. The actuator is composed of a pair of curved pre-stressed piezoelectric actuators, so-called commercially THUNDER actuators, installed opposite each other using two clamping mechanisms constructed of in-plane fixable hinges, grippers and solid links. A fully mathematical model is developed to describe the active and passive dynamics of the actuator and investigate the effects of its geometrical parameters on the dynamic stiffness, free displacement and blocked force properties. Among the literature that deals with piezoelectric actuators in which THUNDER elements are used as a source of electromechanical power, the proposed study is unique in that it presents a mathematical model that has the ability to predict the actuator characteristics and achieve other phenomena, such as resonances, mode shapes, phase shifts, dips, etc. For model validation, the measurements of the free dynamic response per unit voltage and passive acceleration transmissibility of a particular actuator design are used to check the accuracy of the results predicted by the model. The results reveal that there is a good agreement between the model and experiment. Another experiment is performed to teste the linearity of the actuator system by examining the variation of the output dynamic responses with varying forces and voltages at different frequencies. From the results, it can be concluded that the actuator acts approximately as a linear system at frequencies up to 1000 Hz. A parametric study is achieved here by applying the developed model to analyze the influence of the geometrical parameters of the fixable hinges on the active and passive actuator properties. The model predictions in the frequency range of 0-1000 Hz show that the hinge thickness, radius, and opening angle parameters have great effects on the frequency dynamic
Three hybridization models based on local search scheme for job shop scheduling problem
NASA Astrophysics Data System (ADS)
Balbi Fraga, Tatiana
2015-05-01
This work presents three different hybridization models based on the general schema of Local Search Heuristics, named Hybrid Successive Application, Hybrid Neighborhood, and Hybrid Improved Neighborhood. Despite similar approaches might have already been presented in the literature in other contexts, in this work these models are applied to analyzes the solution of the job shop scheduling problem, with the heuristics Taboo Search and Particle Swarm Optimization. Besides, we investigate some aspects that must be considered in order to achieve better solutions than those obtained by the original heuristics. The results demonstrate that the algorithms derived from these three hybrid models are more robust than the original algorithms and able to get better results than those found by the single Taboo Search.
Advances in modeling of lower hybrid current drive
NASA Astrophysics Data System (ADS)
Peysson, Y.; Decker, J.; Nilsson, E.; Artaud, J.-F.; Ekedahl, A.; Goniche, M.; Hillairet, J.; Ding, B.; Li, M.; Bonoli, P. T.; Shiraiwa, S.; Madi, M.
2016-04-01
First principle modeling of the lower hybrid (LH) current drive in tokamak plasmas is a longstanding activity, which is gradually gaining in accuracy thanks to quantitative comparisons with experimental observations. The ability to reproduce simulatenously the plasma current and the non-thermal bremsstrahlung radial profiles in the hard x-ray (HXR) photon energy range represents in this context a significant achievement. Though subject to limitations, ray tracing calculations are commonly used for describing wave propagation in conjunction with Fokker-Planck codes, as it can capture prominent features of the LH wave dynamics in a tokamak plasma-like toroidal refraction. This tool has been validated on several machines when the full absorption of the LH wave requires the transfer of a small fraction of power from the main lobes of the launched power spectrum to a tail at a higher parallel refractive index. Conversely, standard modeling based on toroidal refraction only becomes more challenging when the spectral gap is large, except if other physical mechanisms may dominate to bridge it, like parametric instabilities, as suggested for JET LH discharges (Cesario et al 2004 Phys. Rev. Lett. 92 175002), or fast fluctuations of the launched power spectrum or ‘tail’ LH model, as shown for Tore Supra (Decker et al 2014 Phys. Plasma 21 092504). The applicability of the heuristic ‘tail’ LH model is investigated for a broader range of plasma parameters as compared to the Tore Supra study and with different LH wave characteristics. Discrepancies and agreements between simulations and experiments depending upon the different models used are discussed. The existence of a ‘tail’ in the launched power spectrum significantly improves the agreement between modeling and experiments in plasma conditions for which the spectral gap is large in EAST and Alcator C-Mod tokamaks. For the Alcator C-Mod tokamak, the experimental evolution of the HXR profiles with density suggests
Thermal equilibrium solution to new model of bipolar hybrid quantum hydrodynamics
NASA Astrophysics Data System (ADS)
Di Michele, Federica; Mei, Ming; Rubino, Bruno; Sampalmieri, Rosella
2017-08-01
In this paper we study the hybrid quantum hydrodynamic model for nano-sized bipolar semiconductor devices in thermal equilibrium. By introducing a hybrid version of the Bhom potential, we derive a bipolar hybrid quantum hydrodynamic model, which is able to account for quantum effects in a localized region of the device for both electrons and holes. Coupled with Poisson equation for the electric potential, the steady-state system is regionally degenerate in its ellipticity, due to the quantum effect only in part of the device. This regional degeneracy of ellipticity makes the study more challenging. The main purpose of the paper is to investigate the existence and uniqueness of the weak solutions to this new type of equations. We first establish the uniform boundedness of the smooth solutions to the modified bipolar quantum hydrodynamic model by the variational method, then we use the compactness technique to prove the existence of weak solutions to the original hybrid system by taking hybrid limit. In particular, we account for two different kinds of hybrid behaviour. We perform the first hybrid limit when both electrons and holes behave quantum in a given region of the device, and the second one when only one carrier exhibits hybrid behaviour, whereas the other one is presented classically in the whole domain. The semi-classical limit results are also obtained. Finally, the theoretical results are tested numerically on a simple toy model.
Concept analysis of moral courage in nursing: A hybrid model.
Sadooghiasl, Afsaneh; Parvizy, Soroor; Ebadi, Abbas
2016-04-20
Moral courage is one of the most fundamental virtues in nursing profession; however, little attention has been paid to it; as a result, no exact and clear definition of moral courage has ever been accessible. This study is carried out for the purposes of defining and clarifying its concept in nursing profession. This study used a hybrid model of concept analysis comprising three phases, namely, a theoretical phase, field work phase, and a final analysis phase. To find relevant literature, electronic search of valid databases was utilized using keywords related to the concept of courage. Field work data were collected over an 11 months' time period from 2013 to 2014. In the field work phase, in-depth interviews were performed with 10 nurses. The conventional content analysis was used in two theoretical and field work phases using Graneheim and Lundman stages, and the results were combined in the final analysis phase. Permission for this study was obtained from the ethics committee of Tehran University of Medical Sciences. Oral and written informed consent was received from the participants. From the sum of 750 gained titles in theoretical phase, 26 texts were analyzed. The analysis resulted in 494 codes in text analysis and 226 codes in interview analysis. The literature review in the theoretical phase revealed two features of inherent-transcendental characteristics, two of which possessed a difficult nature. Working in the field phase added moral self-actualization characteristic, rationalism, spiritual beliefs, and scientific-professional qualifications to the feature of the concept. Moral courage is a pure and prominent characteristic of human beings. The antecedents of moral courage include model orientation, model acceptance, rationalism, individual excellence, acquiring academic and professional qualification, spiritual beliefs, organizational support, organizational repression, and internal and external personal barriers. Professional excellence resulting
Hybrid model for forecasting time series with trend, seasonal and salendar variation patterns
NASA Astrophysics Data System (ADS)
Suhartono; Rahayu, S. P.; Prastyo, D. D.; Wijayanti, D. G. P.; Juliyanto
2017-09-01
Most of the monthly time series data in economics and business in Indonesia and other Moslem countries not only contain trend and seasonal, but also affected by two types of calendar variation effects, i.e. the effect of the number of working days or trading and holiday effects. The purpose of this research is to develop a hybrid model or a combination of several forecasting models to predict time series that contain trend, seasonal and calendar variation patterns. This hybrid model is a combination of classical models (namely time series regression and ARIMA model) and/or modern methods (artificial intelligence method, i.e. Artificial Neural Networks). A simulation study was used to show that the proposed procedure for building the hybrid model could work well for forecasting time series with trend, seasonal and calendar variation patterns. Furthermore, the proposed hybrid model is applied for forecasting real data, i.e. monthly data about inflow and outflow of currency at Bank Indonesia. The results show that the hybrid model tend to provide more accurate forecasts than individual forecasting models. Moreover, this result is also in line with the third results of the M3 competition, i.e. the hybrid model on average provides a more accurate forecast than the individual model.
Predicting System Accidents with Model Analysis During Hybrid Simulation
NASA Technical Reports Server (NTRS)
Malin, Jane T.; Fleming, Land D.; Throop, David R.
2002-01-01
Standard discrete event simulation is commonly used to identify system bottlenecks and starving and blocking conditions in resources and services. The CONFIG hybrid discrete/continuous simulation tool can simulate such conditions in combination with inputs external to the simulation. This provides a means for evaluating the vulnerability to system accidents of a system's design, operating procedures, and control software. System accidents are brought about by complex unexpected interactions among multiple system failures , faulty or misleading sensor data, and inappropriate responses of human operators or software. The flows of resource and product materials play a central role in the hazardous situations that may arise in fluid transport and processing systems. We describe the capabilities of CONFIG for simulation-time linear circuit analysis of fluid flows in the context of model-based hazard analysis. We focus on how CONFIG simulates the static stresses in systems of flow. Unlike other flow-related properties, static stresses (or static potentials) cannot be represented by a set of state equations. The distribution of static stresses is dependent on the specific history of operations performed on a system. We discuss the use of this type of information in hazard analysis of system designs.
Singular Vector and ENSO Predictability in a Hybrid Coupled Model
NASA Astrophysics Data System (ADS)
Zhou, Xiaobing; Tang, Youmin
2010-05-01
In this study, singular vector (SV) and retrospective ENSO (El Niño and Southern Oscillation) predictions were performed respectively for the period from 1876 to 2000 using a hybrid coupled model. Emphasis was placed on exploring the relationship between SV and ENSO predictability. It is found that a defined Niño3 index from the first singular vector of sea surface temperature anomaly (SSTA) is highly correlated with the predicted Niño3 SSTA index of 6-month leads and that the first singular value (FSV) is positively correlated with the predictive skill. These results and findings improve our knowledge and understanding to the relationship between SV and predictability. It was thought that the fastest growth rate of errors to be inversely related to the prediction skill. The reasons why there is such a relationship between SV and realistic predictability include: (1) the strong signals of ENSO variability that favour the growth of initial uncertainties also have significant contributions to the predictability; (2) the averaged climate state of the tropical Pacific Ocean simultaneously effects both SV and predictability.
Rezvani, Alireza; Khalili, Abbas; Mazareie, Alireza; Gandomkar, Majid
2016-07-01
Nowadays, photovoltaic (PV) generation is growing increasingly fast as a renewable energy source. Nevertheless, the drawback of the PV system is its dependence on weather conditions. Therefore, battery energy storage (BES) can be considered to assist for a stable and reliable output from PV generation system for loads and improve the dynamic performance of the whole generation system in grid connected mode. In this paper, a novel topology of intelligent hybrid generation systems with PV and BES in a DC-coupled structure is presented. Each photovoltaic cell has a specific point named maximum power point on its operational curve (i.e. current-voltage or power-voltage curve) in which it can generate maximum power. Irradiance and temperature changes affect these operational curves. Therefore, the nonlinear characteristic of maximum power point to environment has caused to development of different maximum power point tracking techniques. In order to capture the maximum power point (MPP), a hybrid fuzzy-neural maximum power point tracking (MPPT) method is applied in the PV system. Obtained results represent the effectiveness and superiority of the proposed method, and the average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison to the conventional methods. It has the advantages of robustness, fast response and good performance. A detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have been proposed using Matlab/Simulink. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Field Test of a Hybrid Finite-Difference and Analytic Element Regional Model.
Abrams, D B; Haitjema, H M; Feinstein, D T; Hunt, R J
2016-01-01
Regional finite-difference models often have cell sizes that are too large to sufficiently model well-stream interactions. Here, a steady-state hybrid model is applied whereby the upper layer or layers of a coarse MODFLOW model are replaced by the analytic element model GFLOW, which represents surface waters and wells as line and point sinks. The two models are coupled by transferring cell-by-cell leakage obtained from the original MODFLOW model to the bottom of the GFLOW model. A real-world test of the hybrid model approach is applied on a subdomain of an existing model of the Lake Michigan Basin. The original (coarse) MODFLOW model consists of six layers, the top four of which are aggregated into GFLOW as a single layer, while the bottom two layers remain part of MODFLOW in the hybrid model. The hybrid model and a refined "benchmark" MODFLOW model simulate similar baseflows. The hybrid and benchmark models also simulate similar baseflow reductions due to nearby pumping when the well is located within the layers represented by GFLOW. However, the benchmark model requires refinement of the model grid in the local area of interest, while the hybrid approach uses a gridless top layer and is thus unaffected by grid discretization errors. The hybrid approach is well suited to facilitate cost-effective retrofitting of existing coarse grid MODFLOW models commonly used for regional studies because it leverages the strengths of both finite-difference and analytic element methods for predictions in mildly heterogeneous systems that can be simulated with steady-state conditions.
Data driven components in a model of inner shelf sorted bedforms: a new hybrid model
NASA Astrophysics Data System (ADS)
Goldstein, E. B.; Coco, G.; Murray, A. B.; Green, M. O.
2013-10-01
Numerical models rely on the parameterization of processes that often lack a deterministic description. In this contribution we demonstrate the applicability of using machine learning, optimization tools from the discipline of computer science, to develop parameterizations when extensive data sets exist. We develop a new predictor for near bed suspended sediment reference concentration under unbroken waves using genetic programming, a machine learning technique. This newly developed parameterization performs better than existing empirical predictors. We add this new predictor into an established model for inner shelf sorted bedforms. Additionally we incorporate a previously reported machine learning derived predictor for oscillatory flow ripples into the sorted bedform model. This new "hybrid" sorted bedform model, whereby machine learning components are integrated into a numerical model, demonstrates a method of incorporating observational data (filtered through a machine learning algorithm) directly into a numerical model. Results suggest that the new hybrid model is able to capture dynamics previously absent from the model, specifically, the two observed pattern modes of sorted bedforms. However, caveats exist when data driven components do not have parity with traditional theoretical components of morphodynamic models, and we discuss the challenges of integrating these disparate pieces and the future of this type of modeling.
Clifton, Shari; Jo, Phill
2016-01-01
To facilitate systematic learning and to complement the limitations of conventional one-shot library instruction sessions, a hybrid embedded instruction model was designed and implemented for undergraduate students and residents in three disciplines at the University of Oklahoma Health Sciences Center. Variations of hybrid instruction are explored, including models that combine face-to-face interactions, online content delivery, flipped instruction techniques, and individual consultations. The hybrid model highlights benefits of collaborative teaching between course faculty members and librarians and enhances the relevance of library instruction for users.
Stochastic hybrid model of spontaneous dendritic NMDA spikes
NASA Astrophysics Data System (ADS)
Bressloff, Paul C.; Newby, Jay M.
2014-02-01
Following recent advances in imaging techniques and methods of dendritic stimulation, active voltage spikes have been observed in thin dendritic branches of excitatory pyramidal neurons, where the majority of synapses occur. The generation of these dendritic spikes involves both Na+ ion channels and M-methyl-D-aspartate receptor (NMDAR) channels. During strong stimulation of a thin dendrite, the resulting high levels of glutamate, the main excitatory neurotransmitter in the central nervous system and an NMDA agonist, modify the current-voltage (I-V) characteristics of an NMDAR so that it behaves like a voltage-gated Na+ channel. Hence, the NMDARs can fire a regenerative dendritic spike, just as Na+ channels support the initiation of an action potential following membrane depolarization. However, the duration of the dendritic spike is of the order 100 ms rather than 1 ms, since it involves slow unbinding of glutamate from NMDARs rather than activation of hyperpolarizing K+ channels. It has been suggested that dendritic NMDA spikes may play an important role in dendritic computations and provide a cellular substrate for short-term memory. In this paper, we consider a stochastic, conductance-based model of dendritic NMDA spikes, in which the noise originates from the stochastic opening and closing of a finite number of Na+ and NMDA receptor ion channels. The resulting model takes the form of a stochastic hybrid system, in which membrane voltage evolves according to a piecewise deterministic dynamics that is coupled to a jump Markov process describing the opening and closing of the ion channels. We formulate the noise-induced initiation and termination of a dendritic spike in terms of a first-passage time problem, under the assumption that glutamate unbinding is negligible, which we then solve using a combination of WKB methods and singular perturbation theory. Using a stochastic phase-plane analysis we then extend our analysis to take proper account of the combined
A hybrid model to simulate the annual runoff of the Kaidu River in northwest China
NASA Astrophysics Data System (ADS)
Xu, Jianhua; Chen, Yaning; Bai, Ling; Xu, Yiwen
2016-04-01
Fluctuant and complicated hydrological processes can result in the uncertainty of runoff forecasting. Thus, it is necessary to apply the multi-method integrated modeling approaches to simulate runoff. Integrating the ensemble empirical mode decomposition (EEMD), the back-propagation artificial neural network (BPANN) and the nonlinear regression equation, we put forward a hybrid model to simulate the annual runoff (AR) of the Kaidu River in northwest China. We also validate the simulated effects by using the coefficient of determination (R2) and the Akaike information criterion (AIC) based on the observed data from 1960 to 2012 at the Dashankou hydrological station. The average absolute and relative errors show the high simulation accuracy of the hybrid model. R2 and AIC both illustrate that the hybrid model has a much better performance than the single BPANN. The hybrid model and integrated approach elicited by this study can be applied to simulate the annual runoff of similar rivers in northwest China.
Santos, A O; Nuvunga, J J; Silva, C P; Pires, L P M; Von Pinho, R G; Guimarães, L J M; Balestre, M
2017-06-29
In several crops, the water deficit is perhaps the main limiting factor in the search for high yields. The objective of this study was to evaluate the phenotypic stability of maize hybrids in environments with and without water restriction using the analytical factor (AF) approach. We evaluated 171 maize hybrids in 14 environments, divided into environments with (A1, A2, A3, A4, A5, A6, and A7) and without (A8, A9, A10, A11, A12, A13, and A14) water restriction, over a period of 7 years. Each year, 36 hybrids were evaluated. A square lattice design (6 x 6) was used, with common treatments between years. The characteristics of grain yield (GY), male flowering (MF) and female flowering (FF), plant height (PH), and ear height (EH) were evaluated. Phenotypic adaptability and stability of the hybrids were also verified. Hybrids G66, G99, G86, and G26 were the most stable and showed potential for use in environments with and without water restriction. The AF models showed to be useful for evaluating hybrids over many years, allowing selection of better hybrids with adaptability, specific and general stability, and correlation of hybrids with their production components, in addition to allowing identification of mega-environments that permit stability in the response of the adapted hybrids.
A transient hybrid model for karst aquifer characterization
NASA Astrophysics Data System (ADS)
Reimann, T.; Liedl, R.; Lankenau, L.; Geyer, T.; Sauter, M.; Dörfliger, N.
2009-12-01
between both approaches and reflects the importance of transient storage in karst conduits. To demonstrate the abilities of ModBraC with respect to karst characterization, we performed a parameter study in a synthetic catchment stressed by pumping. Parameters as well as pumping data were based on a field experiment conducted by Maréchal et al. (2008). Using ModBraC, the influence of several parameters on the observed discharge data was investigated, demonstrating their relative importance. In sum, the St. Venant equations are appropriate to describe flow in fully as well as in partly saturated karst conduits. ModBraC, a modified version of MODBRNCH, is an existing hybrid model, which can be used to describe the transient behavior of karst aquifers.
A Hybrid Sensitivity Analysis Approach for Agent-based Disease Spread Models
Pullum, Laura L; Cui, Xiaohui
2012-01-01
Agent-based models (ABM) have been widely deployed in different fields for studying the collective behavior of large numbers of interacting agents. Of particular interest lately is the application of agent-based and hybrid models to epidemiology, specifically Agent-based Disease Spread Models (ABDSM). Validation (one aspect of the means to achieve dependability) of ABDSM simulation models is extremely important. It ensures that the right model has been built and lends confidence to the use of that model to inform critical decisions. In this report, we describe our preliminary efforts in ABDSM validation by using hybrid model fusion technology.
Modeling Hybrid Nuclear Systems With Chilled-Water Storage
Misenheimer, Corey T.; Terry, Stephen D.
2016-06-27
Air-conditioning loads during the warmer months of the year are large contributors to an increase in the daily peak electrical demand. Traditionally, utility companies boost output to meet daily cooling load spikes, often using expensive and polluting fossil fuel plants to match the demand. Likewise, heating, ventilation, and air conditioning (HVAC) system components must be sized to meet these peak cooling loads. However, the use of a properly sized stratified chilled-water storage system in conjunction with conventional HVAC system components can shift daily energy peaks from cooling loads to off-peak hours. This process is examined in light of the recentmore » development of small modular nuclear reactors (SMRs). In this paper, primary components of an air-conditioning system with a stratified chilled-water storage tank were modeled in FORTRAN 95. A basic chiller operation criterion was employed. Simulation results confirmed earlier work that the air-conditioning system with thermal energy storage (TES) capabilities not only reduced daily peaks in energy demand due to facility cooling loads but also shifted the energy demand from on-peak to off-peak hours, thereby creating a more flattened total electricity demand profile. Thus, coupling chilled-water storage-supplemented HVAC systems to SMRs is appealing because of the decrease in necessary reactor power cycling, and subsequently reduced associated thermal stresses in reactor system materials, to meet daily fluctuations in cooling demand. Finally and also, such a system can be used as a thermal sink during reactor transients or a buffer due to renewable intermittency in a nuclear hybrid energy system (NHES).« less
Modeling Hybrid Nuclear Systems With Chilled-Water Storage
Misenheimer, Corey T.; Terry, Stephen D.
2016-06-27
Air-conditioning loads during the warmer months of the year are large contributors to an increase in the daily peak electrical demand. Traditionally, utility companies boost output to meet daily cooling load spikes, often using expensive and polluting fossil fuel plants to match the demand. Likewise, heating, ventilation, and air conditioning (HVAC) system components must be sized to meet these peak cooling loads. However, the use of a properly sized stratified chilled-water storage system in conjunction with conventional HVAC system components can shift daily energy peaks from cooling loads to off-peak hours. This process is examined in light of the recent development of small modular nuclear reactors (SMRs). In this paper, primary components of an air-conditioning system with a stratified chilled-water storage tank were modeled in FORTRAN 95. A basic chiller operation criterion was employed. Simulation results confirmed earlier work that the air-conditioning system with thermal energy storage (TES) capabilities not only reduced daily peaks in energy demand due to facility cooling loads but also shifted the energy demand from on-peak to off-peak hours, thereby creating a more flattened total electricity demand profile. Thus, coupling chilled-water storage-supplemented HVAC systems to SMRs is appealing because of the decrease in necessary reactor power cycling, and subsequently reduced associated thermal stresses in reactor system materials, to meet daily fluctuations in cooling demand. Finally and also, such a system can be used as a thermal sink during reactor transients or a buffer due to renewable intermittency in a nuclear hybrid energy system (NHES).
Ordóñez, Fco. Javier; de Toledo, Paula; Sanchis, Araceli
2013-01-01
Activities of daily living are good indicators of elderly health status, and activity recognition in smart environments is a well-known problem that has been previously addressed by several studies. In this paper, we describe the use of two powerful machine learning schemes, ANN (Artificial Neural Network) and SVM (Support Vector Machines), within the framework of HMM (Hidden Markov Model) in order to tackle the task of activity recognition in a home setting. The output scores of the discriminative models, after processing, are used as observation probabilities of the hybrid approach. We evaluate our approach by comparing these hybrid models with other classical activity recognition methods using five real datasets. We show how the hybrid models achieve significantly better recognition performance, with significance level p < 0.05, proving that the hybrid approach is better suited for the addressed domain. PMID:23615583
An integrated hybrid spatial-compartmental modeling approach is presented for analyzing the dynamic distribution of chemicals in the multimedia environment. Information obtained from such analysis, which includes temporal chemical concentration profiles in various media, mass ...
An integrated hybrid spatial-compartmental modeling approach is presented for analyzing the dynamic distribution of chemicals in the multimedia environment. Information obtained from such analysis, which includes temporal chemical concentration profiles in various media, mass ...
Ordóñez, Fco Javier; de Toledo, Paula; Sanchis, Araceli
2013-04-24
Activities of daily living are good indicators of elderly health status, and activity recognition in smart environments is a well-known problem that has been previously addressed by several studies. In this paper, we describe the use of two powerful machine learning schemes, ANN (Artificial Neural Network) and SVM (Support Vector Machines), within the framework of HMM (Hidden Markov Model) in order to tackle the task of activity recognition in a home setting. The output scores of the discriminative models, after processing, are used as observation probabilities of the hybrid approach. We evaluate our approach by comparing these hybrid models with other classical activity recognition methods using five real datasets. We show how the hybrid models achieve significantly better recognition performance, with significance level p < 0.05, proving that the hybrid approach is better suited for the addressed domain.
Validation of a hybrid two-site gamma model for naphthalene desorption kinetics
Ahn, I.S.; Lion, L.W.; Shuler, M.L.
1999-09-15
Three models for sorption/desorption of polycyclic aromatic hydrocarbon (PAH) contaminants from soil were compared for their ability to predict the transport of PAH in soil: a gamma model, a two-site/two-region nonequilibrium model, and a hybrid model. In the hybrid model, soil organic matter was conceptually divided into two compartments; a fraction with rapid sorption/desorption kinetics and a compartment with mass-transfer-limited kinetics. Contaminant sorbed in the rapid compartment was assumed to be in instantaneous equilibrium with the aqueous phase, while the release of contaminant from the slow fraction was assumed to be governed by a gamma distribution of rate coefficients. The hybrid model successfully described the initial rapid release of a model PAH contaminant, naphthalene, from a sieved soil sample of moderate organic content ({approx_equal} 2.3%) as well as the following slow release observed over 25 days in batch desorption experiments. Other necessary model parameters, such as the hydrodynamic dispersion coefficient of naphthalene and the macropore porosity, were evaluated in separate experiments. A transport model incorporating the hybrid model for naphthalene sorption/desorption successfully predicted the elution profile of naphthalene in independent soil-column experiments with no adjustable parameters. The success of the hybrid model suggests that a wide array of rate controls govern PAH desorption. This conclusion is consistent with the view of soils as consisting of a mix of different sorptive constituents and heterogeneous physical constraints on PAH release.
Formal methods for modeling and analysis of hybrid systems
NASA Technical Reports Server (NTRS)
Tiwari, Ashish (Inventor); Lincoln, Patrick D. (Inventor)
2009-01-01
A technique based on the use of a quantifier elimination decision procedure for real closed fields and simple theorem proving to construct a series of successively finer qualitative abstractions of hybrid automata is taught. The resulting abstractions are always discrete transition systems which can then be used by any traditional analysis tool. The constructed abstractions are conservative and can be used to establish safety properties of the original system. The technique works on linear and non-linear polynomial hybrid systems: the guards on discrete transitions and the continuous flows in all modes can be specified using arbitrary polynomial expressions over the continuous variables. An exemplar tool in the SAL environment built over the theorem prover PVS is detailed. The technique scales well to large and complex hybrid systems.
Assar, Rodrigo; Montecino, Martín A; Maass, Alejandro; Sherman, David J
2014-07-01
In order to describe the dynamic behavior of a complex biological system, it is useful to combine models integrating processes at different levels and with temporal dependencies. Such combinations are necessary for modeling acclimatization, a phenomenon where changes in environmental conditions can induce drastic changes in the behavior of a biological system. In this article we formalize the use of hybrid systems as a tool to model this kind of biological behavior. A modeling scheme called strong switches is proposed. It allows one to take into account both minor adjustments to the coefficients of a continuous model, and, more interestingly, large-scale changes to the structure of the model. We illustrate the proposed methodology with two applications: acclimatization in wine fermentation kinetics, and acclimatization of osteo-adipo differentiation system linking stimulus signals to bone mass. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Nandola, Naresh N.; Rivera, Daniel E.
2011-01-01
This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem because model parameters depend on the mode or operating point of the system. The proposed algorithm applies Model-on-Demand (MoD) estimation to generate a local linear approximation of the nonlinear hybrid system at each time step, using a small subset of data selected by an adaptive bandwidth selector. The appeal of the MoD approach lies in the fact that model parameters are estimated based on a current operating point; hence estimation of locations or modes governed by autonomous discrete events is achieved automatically. The local MoD model is then converted into a mixed logical dynamical (MLD) system representation which can be used directly in a model predictive control (MPC) law for hybrid systems using multiple-degree-of-freedom tuning. The effectiveness of the proposed MoD predictive control algorithm for nonlinear hybrid systems is demonstrated on a hypothetical adaptive behavioral intervention problem inspired by Fast Track, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results demonstrate that the proposed algorithm can be useful for adaptive intervention problems exhibiting both nonlinear and hybrid character. PMID:21874087
Modern constraints on F-term SUSY hybrid inflation models
NASA Astrophysics Data System (ADS)
Civiletti, Matthew
We study modifications of supersymmetric hybrid inflation, which continues to be one of the most popular inflationary models. The seminal formulation considered the VG+Delta V potential, in which one can show that [special characters ommitted], which indicates that the breaking scale M ˜ 1016 GeV. This is a non-trivial fact, and provides a clue the group may be a Grand Unified Theory (GUT). Inspired by this, we consider inflating while constraining the breaking scale M at the MSSM gauge coupling unification scale, 2.86 x 1016 GeV. We show that one can inflate successfully; in particular, we use non-minimal Kahler to achieve, for the recent Planck bounds 0.945 < ns < 0.975, r ≃ 3 x 10--4 in the case where V is bounded from below, and r ≃ 1 x 10--2 where this condition is relaxed. Unfortunately, GUTs tend to predict topological defects. To ameliorate this problem, we consider the addition of a Planck-suppressed term which gives rise to shifted inflation, where one inflates in a similar way except that ∥φ∥ ≠ 0. We show that one can inflate successfully; one achieves similar results as in the standard case, including the large r solutions particular to non-minimal Kahler contributions. We achieve r ≃ 0.02, which is similar to the non-minimal standard case. Finally, we consider a generalization of the model to include Planck-suppressed R-symmetry violation, parametrized by alpha. One can generate masses more naturally in MSSM by treating R-symmetry as approximate, and we discover that, keeping to the standard inflationary track ∥φ∥ = 0, the effect is to raise r in the preferred ns range by about four orders of magnitude as compared with the standard case, for alpha ≃ 10--9. By considering alpha ≃ 10--7, one can achieve r ≃ 10 --4. This is fairly remarkable in that it is done with only minimal Kahler.
Modeling effective FRW cosmologies with perfect fluids from states of the hybrid quantum Gowdy model
NASA Astrophysics Data System (ADS)
Elizaga Navascués, Beatriz; Martín-Benito, Mercedes; Mena Marugán, Guillermo A.
2015-01-01
We employ recently developed approximation methods in the hybrid quantization of the Gowdy T3 model with linear polarization and a massless scalar field to obtain physically interesting solutions of this inhomogeneous cosmology. More specifically, we propose some particular approximate solutions of the quantum Gowdy model constructed in such a way that, for the Hamiltonian constraint, they effectively behave as those corresponding to a flat homogeneous and isotropic universe filled with a perfect fluid, even though these quantum states are far from being homogeneous and isotropic. We analyze how one can get different perfect fluid effective behaviors, including the cases of dust, radiation, and a cosmological constant.
NASA Astrophysics Data System (ADS)
Kobayashi, Koichi; Hiraishi, Kunihiko
The model predictive/optimal control problem for hybrid systems is reduced to a mixed integer quadratic programming (MIQP) problem. However, the MIQP problem has one serious weakness, i.e., the computation time to solve the MIQP problem is too long for practical plants. For overcoming this technical issue, there are several approaches. In this paper, a modeling of mode transition constraints, which are expressed by a directed graph, is focused, and a new method to represent a directed graph is proposed. The effectiveness of the proposed method is shown by numerical examples on linear switched systems and piecewise linear systems.
Synthesis of a hybrid model of the VSC FACTS devices and HVDC technologies
NASA Astrophysics Data System (ADS)
Borovikov, Yu S.; Gusev, A. S.; Sulaymanov, A. O.; Ufa, R. A.
2014-10-01
The motivation of the presented research is based on the need for development of new methods and tools for adequate simulation of FACTS devices and HVDC systems as part of real electric power systems (EPS). The Research object: An alternative hybrid approach for synthesizing VSC-FACTS and -HVDC hybrid model is proposed. The results: the VSC- FACTS and -HVDC hybrid model is designed in accordance with the presented concepts of hybrid simulation. The developed model allows us to carry out adequate simulation in real time of all the processes in HVDC, FACTS devices and EPS as a whole without any decomposition and limitation on their duration, and also use the developed tool for effective solution of a design, operational and research tasks of EPS containing such devices.
Applying a Hybrid Model: Can It Enhance Student Learning Outcomes?
ERIC Educational Resources Information Center
Potter, Jodi
2015-01-01
There has been a marked increase in the use of online learning over the past decade. There remains conflict in the current body of research on the efficacy of online versus face to face learning in these environments. One resolution of these issues is the hybrid learning option which is a combination of face-to-face classroom instruction with…
FISH-ing for Genes: Modeling Fluorescence "in situ" Hybridization
ERIC Educational Resources Information Center
Baker, William P.; Jones, Carleton Buck
2006-01-01
Teaching methods of genetic analysis such as fluorescence in situ hybridization (FISH) can be an important part of instructional units in biology, microbiology, and biotechnology. Experience, however, indicates that these topics are difficult for many students. The authors of this article describe how they created an activity that effectively…
FISH-ing for Genes: Modeling Fluorescence "in situ" Hybridization
ERIC Educational Resources Information Center
Baker, William P.; Jones, Carleton Buck
2006-01-01
Teaching methods of genetic analysis such as fluorescence in situ hybridization (FISH) can be an important part of instructional units in biology, microbiology, and biotechnology. Experience, however, indicates that these topics are difficult for many students. The authors of this article describe how they created an activity that effectively…
Cao, J.; Bharathan, D.; Emadi, A.
2007-01-01
Isolated gate bipolar transistors (IGBTs) are widely used in power electronic applications including electric, hybrid electric, and plug-in hybrid electric vehicles (EVs, HEVs, and PHEVs). The trend towards more electric vehicles (MEVs) has demanded the need for power electronic devices capable of handling power in the range of 10-100 kW. However, the converter losses in this power range are of critical importance. Therefore, thermal management of the power electronic devices/converters is crucial for the reliability and longevity of the advanced vehicles. To aid the design of heat exchangers for the IGBT modules used in propulsion motor drives, a loss model for the IGBTs is necessary. The loss model of the IGBTs will help in the process of developing new heat exchangers and advanced thermal interface materials by reducing cost and time. This paper deals with the detailed loss modeling of IGBTs for advanced electrical propulsion systems. An experimental based loss model is proposed. The proposed loss calculation method utilizes the experimental data to reconstruct the loss surface of the power electronic devices by means of curve fitting and linear extrapolating. This enables the calculation of thermal losses in different voltage, current, and temperature conditions of operation. To verify the calculation method, an experimental test set-up was designed and built. The experimental set-up is an IGBT based bi-directional DC/DC converter. In addition, simulation results are presented to verify the proposed calculation method.
Hybrid resist model to enhance continuous process window model for OPC
NASA Astrophysics Data System (ADS)
Zhang, Qiaolin; Lucas, Kevin
2008-05-01
As the semiconductor industry enters the 45nm node and beyond, the tolerable lithography process window significantly shrinks due to the decreasing k1 factor and increasing lens NA required to meet product shrink goals. The usable depth of focus at the 45nm node for critical layer is less than 200nm and for the 32nm node it will approach 100nm. Consequently, process window aware Optical Proximity Correction (OPC) and Lithography Rule Check (LRC) become crucial to ensure the robustness of OPC to focus and dose variation. An accurately calibrated continuous process window model is the corner stone for successful process variation aware OPC and LRC. For ease of use, this calibrated model should be a continuous function of defocus and dose and able to interpolate and extrapolate in the usable process window. Lithographic proximity effects have an optical component and a resist component. As state of the art OPC simulation tool is capable of precise and fast optical simulation, however its treatment of chemical amplified resist effects is relatively crude and does not capture the complex behavior during acid & quencher reaction, diffusion and development. This in turn causes difficulties for a continuous process window model where the resist component plays an important role. We proposed a hybrid resist model, which is a superposition of a traditional OPC chemical amplified resist model and a first order resist bias model. Using Synopsys' OPC modeling software package-ProGen, we incorporated this hybrid resist model into the continuous process window (PW) modeling module, and very good model calibration performance was achieved.
AMITIS: A 3D GPU-Based Hybrid-PIC Model for Space and Plasma Physics
NASA Astrophysics Data System (ADS)
Fatemi, Shahab; Poppe, Andrew R.; Delory, Gregory T.; Farrell, William M.
2017-05-01
We have developed, for the first time, an advanced modeling infrastructure in space simulations (AMITIS) with an embedded three-dimensional self-consistent grid-based hybrid model of plasma (kinetic ions and fluid electrons) that runs entirely on graphics processing units (GPUs). The model uses NVIDIA GPUs and their associated parallel computing platform, CUDA, developed for general purpose processing on GPUs. The model uses a single CPU-GPU pair, where the CPU transfers data between the system and GPU memory, executes CUDA kernels, and writes simulation outputs on the disk. All computations, including moving particles, calculating macroscopic properties of particles on a grid, and solving hybrid model equations are processed on a single GPU. We explain various computing kernels within AMITIS and compare their performance with an already existing well-tested hybrid model of plasma that runs in parallel using multi-CPU platforms. We show that AMITIS runs ∼10 times faster than the parallel CPU-based hybrid model. We also introduce an implicit solver for computation of Faraday’s Equation, resulting in an explicit-implicit scheme for the hybrid model equation. We show that the proposed scheme is stable and accurate. We examine the AMITIS energy conservation and show that the energy is conserved with an error < 0.2% after 500,000 timesteps, even when a very low number of particles per cell is used.
Effect of Nonlinearity in Hybrid Kinetic Monte Carlo-Continuum Models
Balter, Ariel I.; Lin, Guang; Tartakovsky, Alexandre M.
2012-04-23
Recently there has been interest in developing efficient ways to model heterogeneous surface reactions with hybrid computational models that couple a KMC model for a surface to a finite difference model for bulk diffusion in a continuous domain. We consider two representative problems that validate a hybrid method and also show that this method captures the combined effects of nonlinearity and stochasticity. We first validate a simple deposition/dissolution model with a linear rate showing that the KMC-continuum hybrid agrees with both a fully deterministic model and its analytical solution. We then study a deposition/dissolution model including competitive adsorption, which leads to a nonlinear rate, and show that, in this case, the KMC-continuum hybrid and fully deterministic simulations do not agree. However, we are able to identify the difference as a natural result of the stochasticity coming from the KMC surface process. Because KMC captures inherent fluctuations, we consider it to be more realistic than a purely deterministic model. Therefore, we consider the KMC-continuum hybrid to be more representative of a real system.
NASA Astrophysics Data System (ADS)
Rath, S.; Sengupta, P. P.; Singh, A. P.; Marik, A. K.; Talukdar, P.
2013-07-01
Accurate prediction of roll force during hot strip rolling is essential for model based operation of hot strip mills. Traditionally, mathematical models based on theory of plastic deformation have been used for prediction of roll force. In the last decade, data driven models like artificial neural network have been tried for prediction of roll force. Pure mathematical models have accuracy limitations whereas data driven models have difficulty in convergence when applied to industrial conditions. Hybrid models by integrating the traditional mathematical formulations and data driven methods are being developed in different parts of world. This paper discusses the methodology of development of an innovative hybrid mathematical-artificial neural network model. In mathematical model, the most important factor influencing accuracy is flow stress of steel. Coefficients of standard flow stress equation, calculated by parameter estimation technique, have been used in the model. The hybrid model has been trained and validated with input and output data collected from finishing stands of Hot Strip Mill, Bokaro Steel Plant, India. It has been found that the model accuracy has been improved with use of hybrid model, over the traditional mathematical model.
Hybrid Model for Cascading Outage in a Power System: A Numerical Study
NASA Astrophysics Data System (ADS)
Susuki, Yoshihiko; Takatsuji, Yu; Hikihara, Takashi
Analysis of cascading outages in power systems is important for understanding why large blackouts emerge and how to prevent them. Cascading outages are complex dynamics of power systems, and one cause of them is the interaction between swing dynamics of synchronous machines and protection operation of relays and circuit breakers. This paper uses hybrid dynamical systems as a mathematical model for cascading outages caused by the interaction. Hybrid dynamical systems can combine families of flows describing swing dynamics with switching rules that are based on protection operation. This paper refers to data on a cascading outage in the September 2003 blackout in Italy and shows a hybrid dynamical system by which propagation of outages reproduced is consistent with the data. This result suggests that hybrid dynamical systems can provide an effective model for the analysis of cascading outages in power systems.
Rajagopalan, A.; Washington, G.; Rizzoni, G.; Guezennec, Y.
2003-12-01
This report describes the development of new control strategies and models for Hybrid Electric Vehicles (HEV) by the Ohio State University. The report indicates results from models created in NREL's ADvanced VehIcle SimulatOR (ADVISOR 3.2), and results of a scalable IC Engine model, called in Willan's Line technique, implemented in ADVISOR 3.2.
A hybridization model for the plasmon response of complex nanostructures
NASA Astrophysics Data System (ADS)
Prodan, Emil; Radloff, Corey; Halas, Naomi; Nordlander, Peter
2004-03-01
We discuss a simple and intuitive method, an electromagnetic analog of molecular orbital theory, to describe the plasmon response of complex nanostructures of arbitrary shape, (Science 302(2003)419-422). The method expresses the plasmon response of complex or composite nanoparticles as resulting from the interaction or "hybridization" of elementary plasmons supported by nanostructures of elementary geometries. As an example, the approach is applied to the important cases of metallic nanoshells and concentric multishell structures, nanomatryushkas. For the nanoshell, the plasmons can be described as resulting from the interaction between the cavity plasmon localized around the inner surface of the shell and a solid sphere plasmon localized around the outer surface of the shell. For the multishell structure, the plasmons can be viewed as resulting from the hybridization of the individual nanoshell plasmons on the different metallic shells. Work supported by ARO, TATP and the Robert A. Welch Foundation
Hybrid experimental/analytical models of structural dynamics - Creation and use for predictions
NASA Technical Reports Server (NTRS)
Balmes, Etienne
1993-01-01
An original complete methodology for the construction of predictive models of damped structural vibrations is introduced. A consistent definition of normal and complex modes is given which leads to an original method to accurately identify non-proportionally damped normal mode models. A new method to create predictive hybrid experimental/analytical models of damped structures is introduced, and the ability of hybrid models to predict the response to system configuration changes is discussed. Finally a critical review of the overall methodology is made by application to the case of the MIT/SERC interferometer testbed.
Hybrid experimental/analytical models of structural dynamics - Creation and use for predictions
NASA Technical Reports Server (NTRS)
Balmes, Etienne
1993-01-01
An original complete methodology for the construction of predictive models of damped structural vibrations is introduced. A consistent definition of normal and complex modes is given which leads to an original method to accurately identify non-proportionally damped normal mode models. A new method to create predictive hybrid experimental/analytical models of damped structures is introduced, and the ability of hybrid models to predict the response to system configuration changes is discussed. Finally a critical review of the overall methodology is made by application to the case of the MIT/SERC interferometer testbed.
Validation of biological models with temporal logic and Timed Hybrid Petri Nets.
Troncale, Sylvie; Comet, Jean-Paul; Bernot, Gilles
2007-01-01
The Hybrid Functional Petri Nets (HFPN) formalism has shown its convenience for modelling biological systems. This class of models has been fruitfully applied in biology but the remarkable expressiveness of HFPN often leads to incomplete validations. In this paper, we propose a logical framework for Timed Hybrid Petri Nets (THPN), a sub-class of HFPN. We propose an extension of Event Clock Logic dedicated to THPN and a procedure to convert a THPN into a real-time automaton. A small biological model shows that our framework allows us to formally prove properties by a well suited model-checking procedure.
Hybrid [sigma]-p Coordinate Choices for a Global Model
2009-01-01
Arakawa–Lamb hybrid, which remains intrinsically less stable than the others. Impacts of different coordinates on forecast skill are neutral or...unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 mons and Strüfing (1981) and Simmons and Burridge (1981) tested different ...0. This coordinate is defined by vertical profiles of the two coefficients, A and B, which control, respectively, its isobaric and terrain
Charging and hybridization in the finite cluster model
NASA Technical Reports Server (NTRS)
Bauschlicher, C. W., Jr.; Bagus, P. S.; Nelin, C. J.
1984-01-01
Cluster wavefunctions which have appropriate hybridization and polarization lead to reasonable properties for the interaction of an adsorbate with a solid surface. However, for Al clusters, it was found that the atomic change distribution is not uniform. The finite cluster size leads to changes not representative for an extended system. This effect appears to be dependent on the particular materials being studied; it does not occur in all cases.
Ocean Prediction with the Hybrid Coordinate Ocean Model (HYCOM)
2006-01-01
into hybrid approaches. Isopycnal (density tracking) layers are best in the deep stratified ocean, z-levels (constant fixed depths) are best used to...when this would lead to excessive crowding of coordinate surfaces. Thus, vertical grid points can be geometrically constrained to remain at a fixed depth...isopycnal in the open stratified ocean, but smoothly reverts to a terrain-following (a) coordinate in shallow coastal regions and to fixed pressure
Higher Order Modeling In the BEM/FEM Hybrid Formulation
NASA Technical Reports Server (NTRS)
Fink, Patrick W.; Wilton, Don R.
2000-01-01
Hybrid formulations using low order curl-conforming bases to represent the total electric field within a finite element region and low order divergence-conforming bases to represent equivalent electric and magnetic currents on the boundary are well known. However, higher-order divergence and curl-conforming bases have been shown to provide significant benefits in convergence rates and accuracy when employed in strictly integral equation and strictly finite element formulations. In this paper, a hybrid electric field formulation employing higher order bases is presented. The paper addresses benefits and issues associated with using higher order divergence-and curl-conforming bases in the hybrid finite element/boundary element electric field formulation. The method of singularity subtraction may be used to compute the self terms of the boundary integral when the bases are of low order. But this method becomes laborious and requires great care when the divergence conforming bases are of higher order. In order to handle these singularities simply and accurately, a generalized Gaussian quadrature method is employed in which the expansion functions account for the singularity. In preliminary tests of the higher order hybrid formulation, the equivalent electric current induced by scattering of a plane wave from a square dielectric cylinder is examined. Accurate results are obtained using only a two-triangle mesh when the current basis is of order 4 or 5. Additional results are presented comparing the error obtained using higher order bases to that obtained using lower order bases when the number of unknowns is approximately equal. Also, convergence rates obtained with higher order bases are compared to those obtained with lower order bases for selected sample problems.
Ishishita, Satoshi; Matsuda, Yoichi
2016-10-13
Hybrid incompatibility is important in speciation as it prevents gene flow between closely related populations. Reduced fitness from hybrid incompatibility may also reinforce prezygotic reproductive isolation between sympatric populations. However, the genetic and developmental basis of hybrid incompatibility in higher vertebrates remains poorly understood. Mammals and birds, both amniotes, have similar developmental processes, but marked differences in development such as the XY/ZW sex determination systems and the presence or absence of genomic imprinting. Here, we review the sterile phenotype of hybrids between the Phodopus dwarf hamsters P. campbelli and P. sungorus, and the inviable phenotype of hybrids between two birds of the family Phasianidae, chicken (Gallus gallus domesticus) and Japanese quail (Coturnix japonica). We propose hypotheses for developmental defects that are associated with these hybrid incompatibilities. In addition, we discuss the genetic and developmental basis for these defects in conjunction with recent findings from mouse and avian models of genetics, reproductive biology and genomics. We suggest that these hybrids are ideal animal models for studying the genetic and developmental basis of hybrid incompatibility in amniotes.
Optimization of ultrasonic array inspections using an efficient hybrid model and real crack shapes
NASA Astrophysics Data System (ADS)
Felice, Maria V.; Velichko, Alexander; Wilcox, Paul D.; Barden, Tim; Dunhill, Tony
2015-03-01
Models which simulate the interaction of ultrasound with cracks can be used to optimize ultrasonic array inspections, but this approach can be time-consuming. To overcome this issue an efficient hybrid model is implemented which includes a finite element method that requires only a single layer of elements around the crack shape. Scattering Matrices are used to capture the scattering behavior of the individual cracks and a discussion on the angular degrees of freedom of elastodynamic scatterers is included. Real crack shapes are obtained from X-ray Computed Tomography images of cracked parts and these shapes are inputted into the hybrid model. The effect of using real crack shapes instead of straight notch shapes is demonstrated. An array optimization methodology which incorporates the hybrid model, an approximate single-scattering relative noise model and the real crack shapes is then described.
NASA Astrophysics Data System (ADS)
Kuo, K. A.; Verbraken, H.; Degrande, G.; Lombaert, G.
2016-07-01
Along with the rapid expansion of urban rail networks comes the need for accurate predictions of railway induced vibration levels at grade and in buildings. Current computational methods for making predictions of railway induced ground vibration rely on simplifying modelling assumptions and require detailed parameter inputs, which lead to high levels of uncertainty. It is possible to mitigate against these issues using a combination of field measurements and state-of-the-art numerical methods, known as a hybrid model. In this paper, two hybrid models are developed, based on the use of separate source and propagation terms that are quantified using in situ measurements or modelling results. These models are implemented using term definitions proposed by the Federal Railroad Administration and assessed using the specific illustration of a surface railway. It is shown that the limitations of numerical and empirical methods can be addressed in a hybrid procedure without compromising prediction accuracy.
The need for hybrid modeling in analysis of cardiovascular and respiratory support.
Zielinski, Krzysztof; Darowski, Marek; Kozarski, Maciej; Ferrari, Gianfranco
2016-08-19
The analysis of the efficiency and optimum use of cardiovascular and respiratory support systems is of great importance in research and development as well as in clinical practice. To understand the complex interaction between human cardiovascular or respiratory systems and the mechanical assist devices, a number of physical, computational or hybrid (physical-electrical or physical-computational) models/simulators have been developed and used in recent years. The hybrid models combine the advantages of both the physical models (interaction with assist devices) and of the computational/electrical models (accuracy, flexibility). This paper reviews the existing solutions and briefly describes their characteristics, advantages and disadvantages, chiefly emphasizing the features of the hybrid models that are most promising for future development.
Optimization of ultrasonic array inspections using an efficient hybrid model and real crack shapes
Felice, Maria V.; Velichko, Alexander Wilcox, Paul D.; Barden, Tim; Dunhill, Tony
2015-03-31
Models which simulate the interaction of ultrasound with cracks can be used to optimize ultrasonic array inspections, but this approach can be time-consuming. To overcome this issue an efficient hybrid model is implemented which includes a finite element method that requires only a single layer of elements around the crack shape. Scattering Matrices are used to capture the scattering behavior of the individual cracks and a discussion on the angular degrees of freedom of elastodynamic scatterers is included. Real crack shapes are obtained from X-ray Computed Tomography images of cracked parts and these shapes are inputted into the hybrid model. The effect of using real crack shapes instead of straight notch shapes is demonstrated. An array optimization methodology which incorporates the hybrid model, an approximate single-scattering relative noise model and the real crack shapes is then described.
Almeida Filho, J E; Tardin, F D; Guimarães, J F R; Resende, M D V; Silva, F F; Simeone, M L; Menezes, C B; Queiroz, V A V
2016-02-26
The breeding of sorghum, Sorghum bicolor (L.) Moench, aimed at improving its nutritional quality, is of great interest, since it can be used as a highly nutritive alternative food source and can possibly be cultivated in regions with low rainfall. The objective of the present study was to evaluate the potential and genetic diversity of grain-sorghum hybrids for traits of agronomic and nutritional interest. To this end, the traits grain yield and flowering, and concentrations of protein, potassium, calcium, magnesium, sulfur, iron, manganese, and zinc in the grain were evaluated in 25 grain-sorghum hybrids, comprising 18 experimental hybrids of Embrapa Milho e Sorgo and seven commercial hybrids. The genetic potential was analyzed by a multi-trait best linear unbiased prediction (BLUP) model, and cluster analysis was accomplished by squared Mahalanobis distance using the predicted genotypic values. Hybrids 0306037 and 0306034 stood out in the agronomic evaluation. The hybrids with agronomic prominence, however, did not stand out for the traits related to the nutritional quality of the grain. Three clusters were formed from the dendrogram obtained with the unweighted pair group method with arithmetic mean method. From the results of the genotypic BLUP and the analysis of the dendrogram, hybrids 0577337, 0441347, 0307651, and 0306037 were identified as having the potential to establish a population that can aggregate alleles for all the evaluated traits of interest.
Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R
2012-08-01
A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three
A Hybrid Acoustic and Pronunciation Model Adaptation Approach for Non-native Speech Recognition
NASA Astrophysics Data System (ADS)
Oh, Yoo Rhee; Kim, Hong Kook
In this paper, we propose a hybrid model adaptation approach in which pronunciation and acoustic models are adapted by incorporating the pronunciation and acoustic variabilities of non-native speech in order to improve the performance of non-native automatic speech recognition (ASR). Specifically, the proposed hybrid model adaptation can be performed at either the state-tying or triphone-modeling level, depending at which acoustic model adaptation is performed. In both methods, we first analyze the pronunciation variant rules of non-native speakers and then classify each rule as either a pronunciation variant or an acoustic variant. The state-tying level hybrid method then adapts pronunciation models and acoustic models by accommodating the pronunciation variants in the pronunciation dictionary and by clustering the states of triphone acoustic models using the acoustic variants, respectively. On the other hand, the triphone-modeling level hybrid method initially adapts pronunciation models in the same way as in the state-tying level hybrid method; however, for the acoustic model adaptation, the triphone acoustic models are then re-estimated based on the adapted pronunciation models and the states of the re-estimated triphone acoustic models are clustered using the acoustic variants. From the Korean-spoken English speech recognition experiments, it is shown that ASR systems employing the state-tying and triphone-modeling level adaptation methods can relatively reduce the average word error rates (WERs) by 17.1% and 22.1% for non-native speech, respectively, when compared to a baseline ASR system.
NASA Astrophysics Data System (ADS)
Szolgayova, Elena
2010-05-01
Hybrid modelling, used for simulation and forecasting of hydrological time series, involving both process-based and data-driven types of models combines the available domain knowledge and process physics with the recent advances in data driven tools. In this way, complex hydrological processes can be modelled and forecasted by decomposing the problem into several smaller sub - problems and using process physics based models where these are most appropriate, and data dictated tools (such as ANN, time series models or traditional statistics) for the residual data, when necessary. The fitting and forecasting performance of such models have to be explored case based. So far, only a few attempts to apply various nonlinear time series models within such a framework were reported in the hydrological modelling literature. This contribution presents results concerning the possibility to use GARCH type of models for such purposes. More specifically, error time series from two hydrological conceptual models were analyzed (applied on time series measured from the Hron and Morava Rivers in Slovakia), concentrating on the improvement of the modelling and forecasting performance of these models. The goal of investigation was to try to expand the knowledge in the time series modelling of hydrological model error time series with the aim to test and develop appropriate methods for various time steps from the GARCH family of models. In order to achieve this, following steps were taken: 1. The presence of heteroscedasticity was verified in time series. 2. A model from the GARCH family was fitted on the data, comparing the fit with a fit of an ARMA model. 3. One - step - ahead forecasts from the fitted models were produced, performing comparisons. The investigation of model properties and performances was thoroughly tested under various conditions of their future practical applications. In general, heteroscedasticity was present in the majority of the error time series of the
A hybrid model for mapping simplified seismic response via a GIS-metamodel approach
NASA Astrophysics Data System (ADS)
Grelle, G.; Bonito, L.; Revellino, P.; Guerriero, L.; Guadagno, F. M.
2014-07-01
In earthquake-prone areas, site seismic response due to lithostratigraphic sequence plays a key role in seismic hazard assessment. A hybrid model, consisting of GIS and metamodel (model of model) procedures, was introduced aimed at estimating the 1-D spatial seismic site response in accordance with spatial variability of sediment parameters. Inputs and outputs are provided and processed by means of an appropriate GIS model, named GIS Cubic Model (GCM). This consists of a block-layered parametric structure aimed at resolving a predicted metamodel by means of pixel to pixel vertical computing. The metamodel, opportunely calibrated, is able to emulate the classic shape of the spectral acceleration response in relation to the main physical parameters that characterize the spectrum itself. Therefore, via the GCM structure and the metamodel, the hybrid model provides maps of normalized acceleration response spectra. The hybrid model was applied and tested on the built-up area of the San Giorgio del Sannio village, located in a high-risk seismic zone of southern Italy. Efficiency tests showed a good correspondence between the spectral values resulting from the proposed approach and the 1-D physical computational models. Supported by lithology and geophysical data and corresponding accurate interpretation regarding modelling, the hybrid model can be an efficient tool in assessing urban planning seismic hazard/risk.
NASA Astrophysics Data System (ADS)
Ismail, S.; Samsudin, R.; Shabri, A.
2010-10-01
Successful river flow time series forecasting is a major goal and an essential procedure that is necessary in water resources planning and management. This study introduced a new hybrid model based on a combination of two familiar non-linear method of mathematical modeling: Self Organizing Map (SOM) and Least Square Support Vector Machine (LSSVM) model referred as SOM-LSSVM model. The hybrid model uses the SOM algorithm to cluster the training data into several disjointed clusters and the individual LSSVM is used to forecast the river flow. The feasibility of this proposed model is evaluated to actual river flow data from Bernam River located in Selangor, Malaysia. Their results have been compared to those obtained using LSSVM and artificial neural networks (ANN) models. The experiment results show that the SOM-LSSVM model outperforms other models for forecasting river flow. It also indicates that the proposed model can forecast more precisely and provides a promising alternative technique in river flow forecasting.
A Hybrid Approach for Efficient Modeling of Medium-Frequency Propagation in Coal Mines
Brocker, Donovan E.; Sieber, Peter E.; Waynert, Joseph A.; Li, Jingcheng; Werner, Pingjuan L.; Werner, Douglas H.
2015-01-01
An efficient procedure for modeling medium frequency (MF) communications in coal mines is introduced. In particular, a hybrid approach is formulated and demonstrated utilizing ideal transmission line equations to model MF propagation in combination with full-wave sections used for accurate simulation of local antenna-line coupling and other near-field effects. This work confirms that the hybrid method accurately models signal propagation from a source to a load for various system geometries and material compositions, while significantly reducing computation time. With such dramatic improvement to solution times, it becomes feasible to perform large-scale optimizations with the primary motivation of improving communications in coal mines both for daily operations and emergency response. Furthermore, it is demonstrated that the hybrid approach is suitable for modeling and optimizing large communication networks in coal mines that may otherwise be intractable to simulate using traditional full-wave techniques such as moment methods or finite-element analysis. PMID:26478686
NASA Technical Reports Server (NTRS)
Hadden, C. M.; Klimek-McDonald, D. R.; Pineda, E. J.; King, J. A.; Reichanadter, A. M.; Miskioglu, I.; Gowtham, S.; Odegard, G. M.
2015-01-01
Because of the relatively high specific mechanical properties of carbon fiber/epoxy composite materials, they are often used as structural components in aerospace applications. Graphene nanoplatelets (GNPs) can be added to the epoxy matrix to improve the overall mechanical properties of the composite. The resulting GNP/carbon fiber/epoxy hybrid composites have been studied using multiscale modeling to determine the influence of GNP volume fraction, epoxy crosslink density, and GNP dispersion on the mechanical performance. The hierarchical multiscale modeling approach developed herein includes Molecular Dynamics (MD) and micromechanical modeling, and it is validated with experimental testing of the same hybrid composite material system. The results indicate that the multiscale modeling approach is accurate and provides physical insight into the composite mechanical behavior. Also, the results quantify the substantial impact of GNP volume fraction and dispersion on the transverse mechanical properties of the hybrid composite while the effect on the axial properties is shown to be insignificant.
NASA Technical Reports Server (NTRS)
Hadden, C. M.; Klimek-McDonald, D. R.; Pineda, E. J.; King, J. A.; Reichanadter, A. M.; Miskioglu, I.; Gowtham, S.; Odegard, G. M.
2015-01-01
Because of the relatively high specific mechanical properties of carbon fiber/epoxy composite materials, they are often used as structural components in aerospace applications. Graphene nanoplatelets (GNPs) can be added to the epoxy matrix to improve the overall mechanical properties of the composite. The resulting GNP/carbon fiber/epoxy hybrid composites have been studied using multiscale modeling to determine the influence of GNP volume fraction, epoxy crosslink density, and GNP dispersion on the mechanical performance. The hierarchical multiscale modeling approach developed herein includes Molecular Dynamics (MD) and micromechanical modeling, and it is validated with experimental testing of the same hybrid composite material system. The results indicate that the multiscale modeling approach is accurate and provides physical insight into the composite mechanical behavior. Also, the results quantify the substantial impact of GNP volume fraction and dispersion on the transverse mechanical properties of the hybrid composite, while the effect on the axial properties is shown to be insignificant.
NASA Technical Reports Server (NTRS)
Hadden, Cameron M.; Klimek-McDonald, Danielle R.; Pineda, Evan J.; King, Julie A.; Reichanadter, Alex M.; Miskioglu, Ibrahim; Gowtham, S.; Odegard, Gregory M.
2015-01-01
Because of the relatively high specific mechanical properties of carbon fiber/epoxy composite materials, they are often used as structural components in aerospace applications. Graphene nanoplatelets (GNPs) can be added to the epoxy matrix to improve the overall mechanical properties of the composite. The resulting GNP/carbon fiber/epoxy hybrid composites have been studied using multiscale modeling to determine the influence of GNP volume fraction, epoxy crosslink density, and GNP dispersion on the mechanical performance. The hierarchical multiscale modeling approach developed herein includes Molecular Dynamics (MD) and micromechanical modeling, and it is validated with experimental testing of the same hybrid composite material system. The results indicate that the multiscale modeling approach is accurate and provides physical insight into the composite mechanical behavior. Also, the results quantify the substantial impact of GNP volume fraction and dispersion on the transverse mechanical properties of the hybrid composite, while the effect on the axial properties is shown to be insignificant.
A research using hybrid RBF/Elman neural networks for intrusion detection system secure model
NASA Astrophysics Data System (ADS)
Tong, Xiaojun; Wang, Zhu; Yu, Haining
2009-10-01
A hybrid RBF/Elman neural network model that can be employed for both anomaly detection and misuse detection is presented in this paper. The IDSs using the hybrid neural network can detect temporally dispersed and collaborative attacks effectively because of its memory of past events. The RBF network is employed as a real-time pattern classification and the Elman network is employed to restore the memory of past events. The IDSs using the hybrid neural network are evaluated against the intrusion detection evaluation data sponsored by U.S. Defense Advanced Research Projects Agency (DARPA). Experimental results are presented in ROC curves. Experiments show that the IDSs using this hybrid neural network improve the detection rate and decrease the false positive rate effectively.
Modeling hydraulic regenerative hybrid vehicles using AMESim and Matlab/Simulink
NASA Astrophysics Data System (ADS)
Lynn, Alfred; Smid, Edzko; Eshraghi, Moji; Caldwell, Niall; Woody, Dan
2005-05-01
This paper presents the overview of the simulation modeling of a hydraulic system with regenerative braking used to improve vehicle emissions and fuel economy. Two simulation software packages were used together to enhance the simulation capability for fuel economy results and development of vehicle and hybrid control strategy. AMESim, a hydraulic simulation software package modeled the complex hydraulic circuit and component hardware and was interlinked with a Matlab/Simulink model of the vehicle, engine and the control strategy required to operate the vehicle and the hydraulic hybrid system through various North American and European drive cycles.
Optimized Treatment of Fibromyalgia Using System Identification and Hybrid Model Predictive Control
Deshpande, Sunil; Nandola, Naresh N.; Rivera, Daniel E.; Younger, Jarred W.
2014-01-01
The term adaptive intervention is used in behavioral health to describe individually-tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health. PMID:25506132
Optimized Treatment of Fibromyalgia Using System Identification and Hybrid Model Predictive Control.
Deshpande, Sunil; Nandola, Naresh N; Rivera, Daniel E; Younger, Jarred W
2014-12-01
The term adaptive intervention is used in behavioral health to describe individually-tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health.
A Hybrid Model for the Computationally-Efficient Simulation of the Cerebellar Granular Layer
Cattani, Anna; Solinas, Sergio; Canuto, Claudio
2016-01-01
The aim of the present paper is to efficiently describe the membrane potential dynamics of neural populations formed by species having a high density difference in specific brain areas. We propose a hybrid model whose main ingredients are a conductance-based model (ODE system) and its continuous counterpart (PDE system) obtained through a limit process in which the number of neurons confined in a bounded region of the brain tissue is sent to infinity. Specifically, in the discrete model, each cell is described by a set of time-dependent variables, whereas in the continuum model, cells are grouped into populations that are described by a set of continuous variables. Communications between populations, which translate into interactions among the discrete and the continuous models, are the essence of the hybrid model we present here. The cerebellum and cerebellum-like structures show in their granular layer a large difference in the relative density of neuronal species making them a natural testing ground for our hybrid model. By reconstructing the ensemble activity of the cerebellar granular layer network and by comparing our results to a more realistic computational network, we demonstrate that our description of the network activity, even though it is not biophysically detailed, is still capable of reproducing salient features of neural network dynamics. Our modeling approach yields a significant computational cost reduction by increasing the simulation speed at least 270 times. The hybrid model reproduces interesting dynamics such as local microcircuit synchronization, traveling waves, center-surround, and time-windowing. PMID:27148027
A model updating method for hybrid composite/aluminum bolted joints using modal test data
NASA Astrophysics Data System (ADS)
Adel, Farhad; Shokrollahi, Saeed; Jamal-Omidi, Majid; Ahmadian, Hamid
2017-05-01
The aim of this paper is to present a simple and applicable model for predicting the dynamic behavior of bolted joints in hybrid aluminum/composite structures and its model updating using modal test data. In this regards, after investigations on bolted joints in metallic structures which led to a new concept called joint affected region (JAR) published in Shokrollahi and Adel (2016), now, a doubly connective layer is established in order to simulate the bolted joint interfaces in hybrid structures. Using the proposed model, the natural frequencies of the hybrid bolted joint structure are computed and compared to the modal test results in order to evaluate and verify the new model predictions. Because of differences in the results of two approaches, the finite element (FE) model is updated based on the genetic algorithm (GA) by minimizing the differences between analytical model and test results. This is done by identifying the parameters at the JAR including isotropic Young's modulus in metallic substructure and that of anisotropic composite substructure. The updated model compared to the initial model simulates experimental results more properly. Therefore, the proposed model can be used for modal analysis of the hybrid joint interfaces in complex and large structures.
Multiple model predictive control for a hybrid proton exchange membrane fuel cell system
NASA Astrophysics Data System (ADS)
Chen, Qihong; Gao, Lijun; Dougal, Roger A.; Quan, Shuhai
This paper presents a hierarchical predictive control strategy to optimize both power utilization and oxygen control simultaneously for a hybrid proton exchange membrane fuel cell/ultracapacitor system. The control employs fuzzy clustering-based modeling, constrained model predictive control, and adaptive switching among multiple models. The strategy has three major advantages. First, by employing multiple piecewise linear models of the nonlinear system, we are able to use linear models in the model predictive control, which significantly simplifies implementation and can handle multiple constraints. Second, the control algorithm is able to perform global optimization for both the power allocation and oxygen control. As a result, we can achieve the optimization from the entire system viewpoint, and a good tradeoff between transient performance of the fuel cell and the ultracapacitor can be obtained. Third, models of the hybrid system are identified using real-world data from the hybrid fuel cell system, and models are updated online. Therefore, the modeling mismatch is minimized and high control accuracy is achieved. Study results demonstrate that the control strategy is able to appropriately split power between fuel cell and ultracapacitor, avoid oxygen starvation, and so enhance the transient performance and extend the operating life of the hybrid system.
Learning fuzzy information in a hybrid connectionist, symbolic model
NASA Technical Reports Server (NTRS)
Romaniuk, Steve G.; Hall, Lawrence O.
1993-01-01
An instance-based learning system is presented. SC-net is a fuzzy hybrid connectionist, symbolic learning system. It remembers some examples and makes groups of examples into exemplars. All real-valued attributes are represented as fuzzy sets. The network representation and learning method is described. To illustrate this approach to learning in fuzzy domains, an example of segmenting magnetic resonance images of the brain is discussed. Clearly, the boundaries between human tissues are ill-defined or fuzzy. Example fuzzy rules for recognition are generated. Segmentations are presented that provide results that radiologists find useful.
A new hybrid model to simulate interaction between DNA and carbon nanostructure
NASA Astrophysics Data System (ADS)
Glukhova, O. E.; Savostyanov, G. V.; Slepchenkov, M. M.; Zyktin, A. A.
2017-02-01
A new hybrid mathematical model allowing us to investigate the interaction between the components of the DNA + carbon nanostructure molecular complex on the atomic and molecular levels are developed. Within the developed model we proposed to describe the carbon nanostructures by means of the methods and approaches of atomistic modeling, and to describe the DNA molecule using the methods and approaches of coarse-grained modeling. A coarse-grained structure of DNA is built based on 3-Site-Per-Nucleotide model. The proposed hybrid model has been implemented in the original software complex for molecular modeling KVAZAR using modern IT-solutions. The novelty of the model is concluded to a finding the weight coefficients for the interaction of large particles, simulating DNA, and conventional particle, simulating carbon nanostructure, and also for the intermolecular interactions. On the basis of established regularities for interaction between DNA and carbon nanostructures we will develop the model of the sensor device.
A four-stage hybrid model for hydrological time series forecasting.
Di, Chongli; Yang, Xiaohua; Wang, Xiaochao
2014-01-01
Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of 'denoising, decomposition and ensemble'. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.
A Four-Stage Hybrid Model for Hydrological Time Series Forecasting
Di, Chongli; Yang, Xiaohua; Wang, Xiaochao
2014-01-01
Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models. PMID:25111782
Design and Implementation of “Many Parallel Task” Hybrid Subsurface Model
Agarwal, Khushbu; Chase, Jared M.; Schuchardt, Karen L.; Scheibe, Timothy D.; Palmer, Bruce J.; Elsethagen, Todd O.
2011-11-01
Continuum scale models have been used to study subsurface flow, transport, and reactions for many years. Recently, pore scale models, which operate at scales of individual soil grains, have been developed to more accurately model pore scale phenomena, such as precipitation, that may not be well represented at the continuum scale. However, particle-based models become prohibitively expensive for modeling realistic domains. Instead, we are developing a hybrid model that simulates the full domain at continuum scale and applies the pore model only to areas of high reactivity. The hybrid model uses a dimension reduction approach to formulate the mathematical exchange of information across scales. Since the location, size, and number of pore regions in the model varies, an adaptive Pore Generator is being implemented to define pore regions at each iteration. A fourth code will provide data transformation from the pore scale back to the continuum scale. These components are coupled into a single hybrid model using the SWIFT workflow system. Our hybrid model workflow simulates a kinetic controlled mixing reaction in which multiple pore-scale simulations occur for every continuum scale timestep. Each pore-scale simulation is itself parallel, thus exhibiting multi-level parallelism. Our workflow manages these multiple parallel tasks simultaneously, with the number of tasks changing across iterations. It also supports dynamic allocation of job resources and visualization processing at each iteration. We discuss the design, implementation and challenges associated with building a scalable, Many Parallel Task, hybrid model to run efficiently on thousands to tens of thousands of processors.
Holmqvist, Kristian; Davidsson, Johan; Mendoza-Vazquez, Manuel; Rundberget, Peter; Svensson, Mats Y; Thorn, Stefan; Törnvall, Fredrik
2014-01-01
The main aim of this study was to improve the quality of injury risk assessments in steering wheel rim to chest impacts when using the Hybrid III crash test dummy in frontal heavy goods vehicle (HGV) collision tests. Correction factors for chest injury criteria were calculated as the model chest injury parameter ratios between finite element (FE) Hybrid III, evaluated in relevant load cases, and the Total Human Model for Safety (THUMS). This is proposed to be used to compensate Hybrid III measurements in crash tests where steering wheel rim to chest impacts occur. The study was conducted in an FE environment using an FE-Hybrid III model and the THUMS. Two impactor shapes were used, a circular hub and a long, thin horizontal bar. Chest impacts at velocities ranging from 3.0 to 6.0 m/s were simulated at 3 impact height levels. A ratio between FE-Hybrid III and THUMS chest injury parameters, maximum chest compression C max, and maximum viscous criterion VC max, were calculated for the different chest impact conditions to form a set of correction factors. The definition of the correction factor is based on the assumption that the response from a circular hub impact to the middle of the chest is well characterized and that injury risk measures are independent of impact height. The current limits for these chest injury criteria were used as a basis to develop correction factors that compensate for the limitations in biofidelity of the Hybrid III in steering wheel rim to chest impacts. The hub and bar impactors produced considerably higher C max and VC max responses in the THUMS compared to the FE-Hybrid III. The correction factor for the responses of the FE-Hybrid III showed that the criteria responses for the bar impactor were consistently overestimated. Ratios based on Hybrid III and THUMS responses provided correction factors for the Hybrid III responses ranging from 0.84 to 0.93. These factors can be used to estimate C max and VC max values when the Hybrid III is
Hybrid modeling in biochemical systems theory by means of functional petri nets.
Wu, Jialiang; Voit, Eberhard
2009-02-01
Many biological systems are genuinely hybrids consisting of interacting discrete and continuous components and processes that often operate at different time scales. It is therefore desirable to create modeling frameworks capable of combining differently structured processes and permitting their analysis over multiple time horizons. During the past 40 years, Biochemical Systems Theory (BST) has been a very successful approach to elucidating metabolic, gene regulatory, and signaling systems. However, its foundation in ordinary differential equations has precluded BST from directly addressing problems containing switches, delays, and stochastic effects. In this study, we extend BST to hybrid modeling within the framework of Hybrid Functional Petri Nets (HFPN). First, we show how the canonical GMA and S-system models in BST can be directly implemented in a standard Petri Net framework. In a second step we demonstrate how to account for different types of time delays as well as for discrete, stochastic, and switching effects. Using representative test cases, we validate the hybrid modeling approach through comparative analyses and simulations with other approaches and highlight the feasibility, quality, and efficiency of the hybrid method.
Applying TSOI Hybrid Learning Model to Enhance Blended Learning Experience in Science Education
ERIC Educational Resources Information Center
Tsoi, Mun Fie
2009-01-01
Purpose: Research on the nature of blended learning and its features has led to a variety of approaches to the practice of blended learning. The purpose of this paper is to provide an alternative practice model, the TSOI hybrid learning model (HLM) to enhance the blended learning experiences in science education. Design/methodology/approach: The…
Assessing the Therapeutic Environment in Hybrid Models of Treatment: Prisoner Perceptions of Staff
ERIC Educational Resources Information Center
Kubiak, Sheryl Pimlott
2009-01-01
Hybrid treatment models within prisons are staffed by both criminal justice and treatment professionals. Because these models may be indicative of future trends, examining the perceptions of prisoners/participants may provide important information. This study examines the perceptions of male and female inmates in three prisons, comparing those in…
Modeling and Analysis of Facility Systems for A Hybrid Materials Test Program
NASA Technical Reports Server (NTRS)
Congiardo, Jared F.
2007-01-01
Analytic modeling and analysis processes employed at NASA-SSC in rocket propulsion systems testing are discussed in this paper with application to test facility propellant supply system design, activation and test of a hybrid rocket motor provided. This paper discusses the analytic model employed, its utilization across project phases and reviews performance results.
Creating a Learning Flow: A Hybrid Course Model for High-Failure-Rate Math Classes
ERIC Educational Resources Information Center
Stevenson, Katherine; Zweier, Louis
2011-01-01
Higher education in the United States is facing a failure-rate crisis in entry-level mathematics courses. In this article, the authors describe an innovative, technology-enhanced hybrid course model that has significantly improved course completion and content mastery outcomes in general education (GE) mathematics courses. The model relies on five…
Hybrid modelling framework by using mathematics-based and information-based methods
NASA Astrophysics Data System (ADS)
Ghaboussi, J.; Kim, J.; Elnashai, A.
2010-06-01
Mathematics-based computational mechanics involves idealization in going from the observed behaviour of a system into mathematical equations representing the underlying mechanics of that behaviour. Idealization may lead mathematical models that exclude certain aspects of the complex behaviour that may be significant. An alternative approach is data-centric modelling that constitutes a fundamental shift from mathematical equations to data that contain the required information about the underlying mechanics. However, purely data-centric methods often fail for infrequent events and large state changes. In this article, a new hybrid modelling framework is proposed to improve accuracy in simulation of real-world systems. In the hybrid framework, a mathematical model is complemented by information-based components. The role of informational components is to model aspects which the mathematical model leaves out. The missing aspects are extracted and identified through Autoprogressive Algorithms. The proposed hybrid modelling framework has a wide range of potential applications for natural and engineered systems. The potential of the hybrid methodology is illustrated through modelling highly pinched hysteretic behaviour of beam-to-column connections in steel frames.
Applying TSOI Hybrid Learning Model to Enhance Blended Learning Experience in Science Education
ERIC Educational Resources Information Center
Tsoi, Mun Fie
2009-01-01
Purpose: Research on the nature of blended learning and its features has led to a variety of approaches to the practice of blended learning. The purpose of this paper is to provide an alternative practice model, the TSOI hybrid learning model (HLM) to enhance the blended learning experiences in science education. Design/methodology/approach: The…
Creating a Learning Flow: A Hybrid Course Model for High-Failure-Rate Math Classes
ERIC Educational Resources Information Center
Stevenson, Katherine; Zweier, Louis
2011-01-01
Higher education in the United States is facing a failure-rate crisis in entry-level mathematics courses. In this article, the authors describe an innovative, technology-enhanced hybrid course model that has significantly improved course completion and content mastery outcomes in general education (GE) mathematics courses. The model relies on five…
Reentrant excitation in an analog-digital hybrid circuit model of cardiac tissue
NASA Astrophysics Data System (ADS)
Mahmud, Farhanahani; Shiozawa, Naruhiro; Makikawa, Masaaki; Nomura, Taishin
2011-06-01
We propose an analog-digital hybrid circuit model of one-dimensional cardiac tissue with hardware implementation that allows us to perform real-time simulations of spatially conducting cardiac action potentials. Each active nodal compartment of the tissue model is designed using analog circuits and a dsPIC microcontroller, by which the time-dependent and time-independent nonlinear current-voltage relationships of six types of ion channel currents employed in the Luo-Rudy phase I (LR-I) model for a single mammalian cardiac ventricular cell can be reproduced quantitatively. Here, we perform real-time simulations of reentrant excitation conduction in a ring-shaped tissue model that includes eighty nodal compartments. In particular, we show that the hybrid tissue model can exhibit real-time dynamics for initiation of reentries induced by uni-directional block, as well as those for phase resetting that leads to annihilation of the reentry in response to impulsive current stimulations at appropriate nodes and timings. The dynamics of the hybrid model are comparable to those of a spatially distributed tissue model with LR-I compartments. Thus, it is conceivable that the hybrid model might be a useful tool for large scale simulations of cardiac tissue dynamics, as an alternative to numerical simulations, leading toward further understanding of the reentrant mechanisms.
Modeling of Divertor Plates in the Compact Toroidal Hybrid
NASA Astrophysics Data System (ADS)
Hartwell, G. J.; Small, C. M.; Ennis, D. A.; Hanson, J. D.; Knowlton, S. F.; Maurer, D. A.
2014-10-01
In long pulse length stellarator experiments, edge island divertors can be used as a method of plasma particle and heat exhaust. Knowledge of the detailed power loading on these structures and its relationship to the long connection length scrape off layer physics is a new Compact Toroidal Hybrid research thrust. We report the results of connection length studies for divertor plates to be installed in the Compact Toroidal Hybrid (CTH), a five field period torsatron with R0 = 0 . 75 m, ap ~ 0 . 2 m, and B <= 0 . 7 T. For these studies, CTH will be operated as a pure stellarator with no ohmically generated plasma current. The CTH edge rotational transform can be varied from tvac (a) = 0.02-0.35 by adjusting the ratio of currents in the helical and toroidal field coils. A poloidal field coil is used to adjust the shear of the rotational transform profile, and hence the size of edge islands, while the phase of the island is rotated with a set of five error coils producing an n = 1 perturbation. For the studies conducted, a magnetic configuration with a large n = 1, m = 3 magnetic island at the edge is generated. Results from multiple possible divertor plate locations relative to the island structure will be presented. This work is supported by U.S. Department of Energy Grant No. DE-FG02-00ER54610.
Hybrid Continuum and Molecular Modeling of Nano-scale Flows
NASA Astrophysics Data System (ADS)
Povitsky, Alex; Zhao, Shunliu
2010-11-01
A novel hybrid method combining the continuum approach based on boundary singularity method (BSM) and the molecular approach based on the direct simulation Monte Carlo (DSMC) is developed and then used to study viscous fibrous filtration flows in the transition flow regime, Kn>0.25. The DSMC is applied to a Knudsen layer enclosing the fiber and the BSM is employed to the entire flow domain. The parameters used in the DSMC and the coupling procedure, such as the number of simulated particles, the cell size and the size of the coupling zone are determined. Results are compared to the experiments measuring pressure drop and flowfield in filters. The optimal location of singularities outside of flow domain was determined and results are compared to those obtained by regularized Stokeslets. The developed hybrid method is parallelized by using MPI and extended to multi-fiber filtration flows. The multi-fiber filter flows considered are in the partial-slip and transition regimes. For Kn˜1, the computed velocity near fibers changes significantly that confirms the need of molecular methods in evaluation of the flow slip in transitional regime.
Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control †
Reinhart, René Felix; Shareef, Zeeshan; Steil, Jochen Jakob
2017-01-01
Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant’s intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms. PMID:28208697
Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control †.
Reinhart, René Felix; Shareef, Zeeshan; Steil, Jochen Jakob
2017-02-08
Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant's intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms.
Analysis of a model of fuel cell - gas turbine hybrid power system for enhanced energy efficiency
NASA Astrophysics Data System (ADS)
Calay, Rajnish K.; Mustafa, Mohamad Y.; Virk, Mohammad S.; Mustafa, Mahmoud F.
2012-11-01
A simple mathematical model to evaluate the performance of FC-GT hybrid system is presented in this paper. The model is used to analyse the influence of various parameters on the performance of a typical hybrid system, where excess heat rejected from the solid-oxide fuel cell stack is utilised to generate additional power through a gas turbine system and to provide heat energy for space heating. The model is based on thermodynamic analysis of various components of the plant and can be adapted for various configurations of the plant components. Because there are many parameters defining the efficiency and work output of the hybrid system, the technique is based on mathematical and graphical optimisation of various parameters; to obtain the maximum efficiency for a given plant configuration.
Hybrid neural network modeling of a full-scale industrial wastewater treatment process.
Lee, Dae Sung; Jeon, Che Ok; Park, Jong Moon; Chang, Kun Soo
2002-06-20
In recent years, hybrid neural network approaches, which combine mechanistic and neural network models, have received considerable attention. These approaches are potentially very efficient for obtaining more accurate predictions of process dynamics by combining mechanistic and neural network models in such a way that the neural network model properly accounts for unknown and nonlinear parts of the mechanistic model. In this work, a full-scale coke-plant wastewater treatment process was chosen as a model system. Initially, a process data analysis was performed on the actual operational data by using principal component analysis. Next, a simplified mechanistic model and a neural network model were developed based on the specific process knowledge and the operational data of the coke-plant wastewater treatment process, respectively. Finally, the neural network was incorporated into the mechanistic model in both parallel and serial configurations. Simulation results showed that the parallel hybrid modeling approach achieved much more accurate predictions with good extrapolation properties as compared with the other modeling approaches even in the case of process upset caused by, for example, shock loading of toxic compounds. These results indicate that the parallel hybrid neural modeling approach is a useful tool for accurate and cost-effective modeling of biochemical processes, in the absence of other reasonably accurate process models.
Support for the 7-factor hybrid model of PTSD in a community sample.
Seligowski, Antonia V; Orcutt, Holly K
2016-03-01
Research suggests that 4-factor models of posttraumatic stress disorder (PTSD) may be improved upon by the addition of novel factors, such as Dysphoric Arousal, Externalizing Behaviors, and Anhedonia. However, a novel 7-factor hybrid model has demonstrated superior fit in veteran and undergraduate samples. The current study sought to replicate this finding in a trauma-exposed community sample and examined relations with positive (PA) and negative affect (NA). Participants included 403 adults (M(age) = 37.75) recruited through Amazon's MTurk. PTSD was measured using the PTSD Checklist-5 (PCL-5). Confirmatory factor analyses were conducted in Mplus. The 7-factor hybrid model demonstrated good fit: CFI = .96, TLI = .95, RMSEA = .06 (90% CI [.05, .07]), SRMR = .03. This model was superior to the 5- and 6-factor models. All factors demonstrated significant relations with PA and NA, the largest of which were the Externalizing Behaviors (with NA) and Anhedonia (with PA) factors. Results provide support for the 7-factor hybrid model of PTSD using the PCL-5 in a community sample. Findings replicate previous research suggesting that PTSD is highly related to NA, which has been purported as an underlying dimension of PTSD. It is recommended that future research use clinical measures to further examine the hybrid model. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
Modeling, analysis and control of fuel cell hybrid power systems
NASA Astrophysics Data System (ADS)
Suh, Kyung Won
Transient performance is a key characteristic of fuel cells, that is sometimes more critical than efficiency, due to the importance of accepting unpredictable electric loads. To fulfill the transient requirement in vehicle propulsion and portable fuel cell applications, a fuel cell stack is typically coupled with a battery through a DC/DC converter to form a hybrid power system. Although many power management strategies already exist, they all rely on low level controllers that realize the power split. In this dissertation we design controllers that realize various power split strategies by directly manipulating physical actuators (low level commands). We maintain the causality of the electric dynamics (voltage and current) and investigate how the electric architecture affects the hybridization level and the power management. We first establish the performance limitations associated with a stand-alone and power-autonomous fuel cell system that is not supplemented by an additional energy storage and powers all its auxiliary components by itself. Specifically, we examine the transient performance in fuel cell power delivery as it is limited by the air supplied by a compressor driven by the fuel cell itself. The performance limitations arise from the intrinsic coupling in the fluid and electrical domain between the compressor and the fuel cell stack. Feedforward and feedback control strategies are used to demonstrate these limitations analytically and with simulations. Experimental tests on a small commercial fuel cell auxiliary power unit (APU) confirm the dynamics and the identified limitations. The dynamics associated with the integration of a fuel cell system and a DC/DC converter is then investigated. Decentralized and fully centralized (using linear quadratic techniques) controllers are designed to regulate the power system voltage and to prevent fuel cell oxygen starvation. Regulating these two performance variables is a difficult task and requires a compromise
HyDE Framework for Stochastic and Hybrid Model-Based Diagnosis
NASA Technical Reports Server (NTRS)
Narasimhan, Sriram; Brownston, Lee
2012-01-01
Hybrid Diagnosis Engine (HyDE) is a general framework for stochastic and hybrid model-based diagnosis that offers flexibility to the diagnosis application designer. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. Several alternative algorithms are available for the various steps in diagnostic reasoning. This approach is extensible, with support for the addition of new modeling paradigms as well as diagnostic reasoning algorithms for existing or new modeling paradigms. HyDE is a general framework for stochastic hybrid model-based diagnosis of discrete faults; that is, spontaneous changes in operating modes of components. HyDE combines ideas from consistency-based and stochastic approaches to model- based diagnosis using discrete and continuous models to create a flexible and extensible architecture for stochastic and hybrid diagnosis. HyDE supports the use of multiple paradigms and is extensible to support new paradigms. HyDE generates candidate diagnoses and checks them for consistency with the observations. It uses hybrid models built by the users and sensor data from the system to deduce the state of the system over time, including changes in state indicative of faults. At each time step when observations are available, HyDE checks each existing candidate for continued consistency with the new observations. If the candidate is consistent, it continues to remain in the candidate set. If it is not consistent, then the information about the inconsistency is used to generate successor candidates while discarding the candidate that was inconsistent. The models used by HyDE are similar to simulation models. They describe the expected behavior of the system under nominal and fault conditions. The model can be constructed in modular and hierarchical fashion by building component/subsystem models (which may themselves contain component/ subsystem models) and linking them through shared variables/parameters. The
Active control of high-frequency vibration: Optimisation using the hybrid modelling method
NASA Astrophysics Data System (ADS)
Muthalif, Asan G. A.; Langley, Robin S.
2012-06-01
This work presents active control of high-frequency vibration using skyhook dampers. The choice of the damper gain and its optimal location is crucial for the effective implementation of active vibration control. In vibration control, certain sensor/actuator locations are preferable for reducing structural vibration while using minimum control effort. In order to perform optimisation on a general built-up structure to control vibration, it is necessary to have a good modelling technique to predict the performance of the controller. The present work exploits the hybrid modelling approach, which combines the finite element method (FEM) and statistical energy analysis (SEA) to provide efficient response predictions at medium to high frequencies. The hybrid method is implemented here for a general network of plates, coupled via springs, to allow study of a variety of generic control design problems. By combining the hybrid method with numerical optimisation using a genetic algorithm, optimal skyhook damper gains and locations are obtained. The optimal controller gain and location found from the hybrid method are compared with results from a deterministic modelling method. Good agreement between the results is observed, whereas results from the hybrid method are found in a significantly reduced amount of time.
Daily air quality index forecasting with hybrid models: A case in China.
Zhu, Suling; Lian, Xiuyuan; Liu, Haixia; Hu, Jianming; Wang, Yuanyuan; Che, Jinxing
2017-09-19
Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the
Modeling of Nonacoustic Combustion Instability in Simulations of Hybrid Motor Tests
NASA Technical Reports Server (NTRS)
Rocker, M.
2000-01-01
A transient model of a hybrid motor was formulated to study the cause and elimination of nonacoustic combustion instability. The transient model was used to simulate four key tests out of a series of seventeen hybrid motor tests conducted by Thiokol, Rocketdyne, and Martin Marietta at NASA Marshall Space Flight Center (MSFC). These tests were performed under the Hybrid Propulsion Technology for Launch Vehicle Boosters (HPTLVB) program. The first test resulted in stable combustion. The second test resulted in large-amplitude, 6.5-Hz chamber pressure oscillations that gradually damped away by the end of the test. The third test resulted in large-amplitude, 7.5-Hz chamber pressure oscillations that were sustained throughout the test. The seventh test resulted in elimination of combustion instability with the installation of an orifice immediately upstream of the injector. Formulation and implementation of the model are the scope of this presentation. The current model is an independent continuation of modeling presented previously by joint Thiokol-Rocketdyne collaborators Boardman, Hawkins, Wassom. and Claflin. The previous model simulated an unstable independent research and development (IR&D) hybrid motor test performed by Thiokol. There was very good agreement between the model and test data. Like the previous model, the current model was developed using Matrix-x simulation software. However, tests performed at MSFC under the HPTLVB program were actually simulated. ln the current model, the hybrid motor, consisting of the liquid oxygen (lox) injector, the multiport solid fuel grain, and nozzle, was simulated. The lox feedsystem, consisting of the tank, venturi. valve, and feed lines, was also simulated in the model. All components of the hybrid motor and lox feedsystem are treated by a lumped-parameter approach. Agreement between the results of the transient model and actual test data was very good. This agreement between simulated and actual test data indicated
Numerical modeling of lower hybrid heating and current drive
Valeo, E.J.; Eder, D.C.
1986-03-01
The generation of currents in toroidal plasma by application of waves in the lower hybrid frequency range involves the interplay of several physical phenomena which include: wave propagation in toroidal geometry, absorption via wave-particle resonances, the quasilinear generation of strongly nonequilibrium electron and ion distribution functions, and the self-consistent evolution of the current density in such a nonequilibrium plasma. We describe a code, LHMOD, which we have developed to treat these aspects of current drive and heating in tokamaks. We present results obtained by applying the code to a computation of current ramp-up and to an investigation of the possible importance of minority hydrogen absorption in a deuterium plasma as the ''density limit'' to current drive is approached.
Robust dynamics in minimal hybrid models of genetic networks.
Perkins, Theodore J; Wilds, Roy; Glass, Leon
2010-11-13
Many gene-regulatory networks necessarily display robust dynamics that are insensitive to noise and stable under evolution. We propose that a class of hybrid systems can be used to relate the structure of these networks to their dynamics and provide insight into the origin of robustness. In these systems, the genes are represented by logical functions, and the controlling transcription factor protein molecules are real variables, which are produced and destroyed. As the transcription factor concentrations cross thresholds, they control the production of other transcription factors. We discuss mathematical analysis of these systems and show how the concepts of robustness and minimality can be used to generate putative logical organizations based on observed symbolic sequences. We apply the methods to control of the cell cycle in yeast.
A hybrid model for predicting carbon monoxide from vehicular exhausts in urban environments
NASA Astrophysics Data System (ADS)
Gokhale, Sharad; Khare, Mukesh
Several deterministic-based air quality models evaluate and predict the frequently occurring pollutant concentration well but, in general, are incapable of predicting the 'extreme' concentrations. In contrast, the statistical distribution models overcome the above limitation of the deterministic models and predict the 'extreme' concentrations. However, the environmental damages are caused by both extremes as well as by the sustained average concentration of pollutants. Hence, the model should predict not only 'extreme' ranges but also the 'middle' ranges of pollutant concentrations, i.e. the entire range. Hybrid modelling is one of the techniques that estimates/predicts the 'entire range' of the distribution of pollutant concentrations by combining the deterministic based models with suitable statistical distribution models ( Jakeman, et al., 1988). In the present paper, a hybrid model has been developed to predict the carbon monoxide (CO) concentration distributions at one of the traffic intersections, Income Tax Office (ITO), in the Delhi city, where the traffic is heterogeneous in nature and meteorology is 'tropical'. The model combines the general finite line source model (GFLSM) as its deterministic, and log logistic distribution (LLD) model, as its statistical components. The hybrid (GFLSM-LLD) model is then applied at the ITO intersection. The results show that the hybrid model predictions match with that of the observed CO concentration data within the 5-99 percentiles range. The model is further validated at different street location, i.e. Sirifort roadway. The validation results show that the model predicts CO concentrations fairly well ( d=0.91) in 10-95 percentiles range. The regulatory compliance is also developed to estimate the probability of exceedance of hourly CO concentration beyond the National Ambient Air Quality Standards (NAAQS) of India. It consists of light vehicles, heavy vehicles, three- wheelers (auto rickshaws) and two
Bifurcation analysis on a hybrid systems model of intermittent hormonal therapy for prostate cancer
NASA Astrophysics Data System (ADS)
Tanaka, Gouhei; Tsumoto, Kunichika; Tsuji, Shigeki; Aihara, Kazuyuki
2008-10-01
Hybrid systems are widely used to model dynamical phenomena that are characterized by interplay between continuous dynamics and discrete events. An example of biomedical application is modeling of disease progression of prostate cancer under intermittent hormonal therapy, where continuous tumor dynamics is switched by interruption and reinstitution of medication. In the present paper, we study a hybrid systems model representing intermittent androgen suppression (IAS) therapy for advanced prostate cancer. Intermittent medication with switching between on-treatment and off-treatment periods is intended to possibly prevent a prostatic tumor from developing into a hormone-refractory state and is anticipated as a possible strategy for delaying or hopefully averting a cancer relapse which most patients undergo as a result of long-term hormonal suppression. Clinical efficacy of IAS therapy for prostate cancer is still under investigation but at least worth considering in terms of reduction of side effects and economic costs during off-treatment periods. In the model of IAS therapy, it depends on some clinically controllable parameters whether a relapse of prostate cancer occurs or not. Therefore, we examine nonlinear dynamics and bifurcation structure of the model by exploiting a numerical method to clarify bifurcation sets in the hybrid system. Our results suggest that adjustment of the normal androgen level in combination with appropriate medication scheduling could enhance the possibility of relapse prevention. Moreover, a two-dimensional piecewise-linear system reduced from the original model highlights the origin of nonlinear phenomena specific to the hybrid system.
A hybrid MC-FEM model for analysis of light propagation in highly scattering medium
NASA Astrophysics Data System (ADS)
Kurihara, Kazuki; Wu, Xue; Okada, Eiji; Dehghani, Hamid
2013-06-01
The hemodynamic change related to the brain activation can be located by the diffuse optical tomography (DOT) using the near-infrared spectroscopy (NIRS) signals and the spatial sensitivity profiles (SSP). Monte Carlo (MC) method and finite element method (FEM) have been used to predict the SSPs. The computation time for MC method is much longer than that for the FEM, however, the accurate solution in the region close to the light source cannot be obtained by FEM solutions of the diffusion equation. In this study, a hybrid MC-FEM model is proposed for fast and accurate simulation of light propagation in a highly scattering medium. In the hybrid model, the solution in the region close to the light source is calculated by the MC method whereas that in the region far from the light source is calculated by the FEM. The solutions by the FEM in hemispherical models were compared with thoseby the MC method to determine the region in which diffusion approximation does not hold and the number of photons for the MC method for the hybrid model. The results demonstratethat theproposed hybrid model can calculatethe accurate solutionswithin reasonable computation time for a multi-layered model.
NASA Astrophysics Data System (ADS)
Hu, Y.; Balachandran, S.; Pachon, J. E.; Baek, J.; Ivey, C.; Holmes, H.; Odman, M. T.; Mulholland, J. A.; Russell, A. G.
2013-10-01
A hybrid fine particulate matter (PM2.5) source apportionment approach based on a receptor-model (RM) species balance and species specific source impacts from a chemical transport model (CTM) equipped with a sensitivity analysis tool is developed to provide physically- and chemically-consistent relationships between source emissions and receptor impacts. This hybrid approach enhances RM results by providing initial estimates of source impacts from a much larger number of sources than are typically used in RMs, and provides source-receptor relationships for secondary species. Further, the method addresses issues of source collinearities, and accounts for emissions uncertainties. Hybrid method results also provide information on the resulting source impact uncertainties. We apply this hybrid approach to conduct PM2.5 source apportionment at Chemical Speciation Network (CSN) sites across the US. Ambient PM2.5 concentrations at these receptor sites were apportioned to 33 separate sources. Hybrid method results led to large changes of impacts from CTM estimates for sources such as dust, woodstove, and other biomass burning sources, but limited changes to others. The refinements reduced the differences between CTM-simulated and observed concentrations of individual PM2.5 species by over 98% when using a weighted least squared error minimization. The rankings of source impacts changed from the initial estimates, revealing that CTM-only results should be evaluated with observations. Assessment with RM results at six US locations showed that the hybrid results differ somewhat from commonly resolved sources. The hybrid method also resolved sources that typical RM methods do not capture without extra measurement information on unique tracers. The method can be readily applied to large domains and long (such as multi-annual) time periods to provide source impact estimates for management- and health-related studies.
Modeling of plasma in a hybrid electric propulsion for small satellites
NASA Astrophysics Data System (ADS)
Jugroot, Manish; Christou, Alex
2016-09-01
As space flight becomes more available and reliable, space-based technology is allowing for smaller and more cost-effective satellites to be produced. Working in large swarms, many small satellites can provide additional capabilities while reducing risk. These satellites require efficient, long term propulsion for manoeuvres, orbit maintenance and de-orbiting. The high exhaust velocity and propellant efficiency of electric propulsion makes it ideally suited for low thrust missions. The two dominant types of electric propulsion, namely ion thrusters and Hall thrusters, excel in different mission types. In this work, a novel electric hybrid propulsion design is modelled to enhance understanding of key phenomena and evaluate performance. Specifically, the modelled hybrid thruster seeks to overcome issues with existing Ion and Hall thruster designs. Scaling issues and optimization of the design will be discussed and will investigate a conceptual design of a hybrid spacecraft plasma engine.
Physical and JIT Model Based Hybrid Modeling Approach for Building Thermal Load Prediction
NASA Astrophysics Data System (ADS)
Iino, Yutaka; Murai, Masahiko; Murayama, Dai; Motoyama, Ichiro
Energy conservation in building fields is one of the key issues in environmental point of view as well as that of industrial, transportation and residential fields. The half of the total energy consumption in a building is occupied by HVAC (Heating, Ventilating and Air Conditioning) systems. In order to realize energy conservation of HVAC system, a thermal load prediction model for building is required. This paper propose a hybrid modeling approach with physical and Just-in-Time (JIT) model for building thermal load prediction. The proposed method has features and benefits such as, (1) it is applicable to the case in which past operation data for load prediction model learning is poor, (2) it has a self checking function, which always supervises if the data driven load prediction and the physical based one are consistent or not, so it can find if something is wrong in load prediction procedure, (3) it has ability to adjust load prediction in real-time against sudden change of model parameters and environmental conditions. The proposed method is evaluated with real operation data of an existing building, and the improvement of load prediction performance is illustrated.
Hybrid OPC modeling with SEM contour technique for 10nm node process
NASA Astrophysics Data System (ADS)
Hitomi, Keiichiro; Halle, Scott; Miller, Marshal; Graur, Ioana; Saulnier, Nicole; Dunn, Derren; Okai, Nobuhiro; Hotta, Shoji; Yamaguchi, Atsuko; Komuro, Hitoshi; Ishimoto, Toru; Koshihara, Shunsuke; Hojo, Yutaka
2014-03-01
Hybrid OPC modeling is investigated using both CDs from 1D and simple 2D structures and contours extracted from complex 2D structures, which are obtained by a Critical Dimension-Scanning Electron Microscope (CD-SEM). Recent studies have addressed some of key issues needed for the implementation of contour extraction, including an edge detection algorithm consistent with conventional CD measurements, contour averaging and contour alignment. Firstly, pattern contours obtained from CD-SEM images were used to complement traditional site driven CD metrology for the calibration of OPC models for both metal and contact layers of 10 nm-node logic device, developed in Albany Nano-Tech. The accuracy of hybrid OPC model was compared with that of conventional OPC model, which was created with only CD data. Accuracy of the model, defined as total error root-mean-square (RMS), was improved by 23% with the use of hybrid OPC modeling for contact layer and 18% for metal layer, respectively. Pattern specific benefit of hybrid modeling was also examined. Resist shrink correction was applied to contours extracted from CD-SEM images in order to improve accuracy of the contours, and shrink corrected contours were used for OPC modeling. The accuracy of OPC model with shrink correction was compared with that without shrink correction, and total error RMS was decreased by 0.2nm (12%) with shrink correction technique. Variation of model accuracy among 8 modeling runs with different model calibration patterns was reduced by applying shrink correction. The shrink correction of contours can improve accuracy and stability of OPC model.
A Comparison of Different Hybrid Methods on the Lorenz 1963 Model
NASA Astrophysics Data System (ADS)
Goodliff, Michael; van Leeuwen, Peter Jan; Amezcua, Javier
2014-05-01
Hybrid data assimilation schemes are becoming more widely used in Numerical Weather Prediction (NWP). These methods combine ideas from successful schemes such as 4DVAR and the ensemble transform Kalman filter (ETKF). The motivation behind hybrid schemes is to make use of a flow-dependent background error covariance matrix (Pb) in a variational setting. Although some of these hybrid schemes are being used operationally now, several basic questions on the reasons behind their performance are still open. Hybrid methods mainly differ in their use of Pb. Here we study 3 formulations. The first scheme, ETKF-4DVAR, uses Pb from the ETKF and combines it (weighted) with the climatological background error covariance matrix in 4DVAR (Bclim), at the start of each assimilation window. The second scheme, 4DVAR-BEN, is similar to ETKF-4DVAR but has zero weighting on Bclim. The third scheme, 4DENVAR, uses a the 4-dimensional covariance from the ensemble that alleviates the need for the tangent-linear and adjoint model in the 4DVar. We systematically compare the performance of ETKF-4DVAR, 4DVAR-BEN and 4DENVAR with respect to two traditional schemes (4DVAR and ETKF) on the Lorenz 1963 model. Using the analysis root mean square error (RMSE) as a metric, these schemes have been compared considering (1) assimilation window length and observation interval size, (2) ensemble size and (3) inflation of the climatological background error covariance matrix. For short assimilation windows, hybrid schemes are shown to outperform traditional methods. As the assimilation window length increases, sequential schemes become more accurate over both traditional variational and hybrid schemes which use an adjoint model. The 4DENVAR scheme performs slightly better in most cases than the ETKF over longer assimilation windows, which suggests that replacing the adjoint model by 4D-covariances from sequential schemes can increase the accuracy of variational schemes.
Ultra-Short-Term Wind Power Prediction Using a Hybrid Model
NASA Astrophysics Data System (ADS)
Mohammed, E.; Wang, S.; Yu, J.
2017-05-01
This paper aims to develop and apply a hybrid model of two data analytical methods, multiple linear regressions and least square (MLR&LS), for ultra-short-term wind power prediction (WPP), for example taking, Northeast China electricity demand. The data was obtained from the historical records of wind power from an offshore region, and from a wind farm of the wind power plant in the areas. The WPP achieved in two stages: first, the ratios of wind power were forecasted using the proposed hybrid method, and then the transformation of these ratios of wind power to obtain forecasted values. The hybrid model combines the persistence methods, MLR and LS. The proposed method included two prediction types, multi-point prediction and single-point prediction. WPP is tested by applying different models such as autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN). By comparing results of the above models, the validity of the proposed hybrid model is confirmed in terms of error and correlation coefficient. Comparison of results confirmed that the proposed method works effectively. Additional, forecasting errors were also computed and compared, to improve understanding of how to depict highly variable WPP and the correlations between actual and predicted wind power.
Trapped gyro-Landau-fluid transport modeling of DIII-D hybrid discharges
NASA Astrophysics Data System (ADS)
Kinsey, J. E.; Staebler, G. M.; Petty, C. C.
2010-12-01
Previous work has summarized the physics and first results of benchmarking the trapped gyro-Landau-fluid (TGLF) model for turbulent transport driven by trapped ion and electron modes, ion and electron temperature gradient (ETG) modes, and electromagnetic kinetic ballooning modes including the effects of shaped geometry. Recently, an improved collision model was implemented which provides a more accurate fit to a transport database of nonlinear collisional GYRO [J. Candy and R. E. Waltz, J. Comput. Phys. 186, 545 (2003)] simulations of long wavelength driftwave turbulence. The impact of the new collision model on TGLF modeling results was unknown. Using the improved TGLF model we obtain excellent agreement with the ion and electron temperature profiles from 30 DIII-D [A. Mahdavi and J. L. Luxon, Fusion Sci. Technol. 48, 2 (2005)] hybrid discharges. The transport results show that the electron energy transport tends to be dominated by short wavelength ETG modes in cases where the ion energy transport approaches neoclassical levels. The hybrid regime has significant energy confinement improvement from E ×B velocity shear which is well predicted by TGLF. Weak magnetic shear and low safety factor are also shown to enhance the hybrid regime energy confinement. In high normalized β hybrids, we find that finite β effects noticably reduce the predicted electron energy transport and improve agreement with the measured electron temperature profiles.
Trapped gyro-Landau-fluid transport modeling of DIII-D hybrid discharges
Kinsey, J. E.; Staebler, G. M.; Petty, C. C.
2010-12-15
Previous work has summarized the physics and first results of benchmarking the trapped gyro-Landau-fluid (TGLF) model for turbulent transport driven by trapped ion and electron modes, ion and electron temperature gradient (ETG) modes, and electromagnetic kinetic ballooning modes including the effects of shaped geometry. Recently, an improved collision model was implemented which provides a more accurate fit to a transport database of nonlinear collisional GYRO[J. Candy and R. E. Waltz, J. Comput. Phys. 186, 545 (2003)] simulations of long wavelength driftwave turbulence. The impact of the new collision model on TGLF modeling results was unknown. Using the improved TGLF model we obtain excellent agreement with the ion and electron temperature profiles from 30 DIII-D [A. Mahdavi and J. L. Luxon, Fusion Sci. Technol. 48, 2 (2005)] hybrid discharges. The transport results show that the electron energy transport tends to be dominated by short wavelength ETG modes in cases where the ion energy transport approaches neoclassical levels. The hybrid regime has significant energy confinement improvement from ExB velocity shear which is well predicted by TGLF. Weak magnetic shear and low safety factor are also shown to enhance the hybrid regime energy confinement. In high normalized {beta} hybrids, we find that finite {beta} effects noticably reduce the predicted electron energy transport and improve agreement with the measured electron temperature profiles.
Winkelmann, Stefanie; Schütte, Christof
2017-09-21
Well-mixed stochastic chemical kinetics are properly modeled by the chemical master equation (CME) and associated Markov jump processes in molecule number space. If the reactants are present in large amounts, however, corresponding simulations of the stochastic dynamics become computationally expensive and model reductions are demanded. The classical model reduction approach uniformly rescales the overall dynamics to obtain deterministic systems characterized by ordinary differential equations, the well-known mass action reaction rate equations. For systems with multiple scales, there exist hybrid approaches that keep parts of the system discrete while another part is approximated either using Langevin dynamics or deterministically. This paper aims at giving a coherent overview of the different hybrid approaches, focusing on their basic concepts and the relation between them. We derive a novel general description of such hybrid models that allows expressing various forms by one type of equation. We also check in how far the approaches apply to model extensions of the CME for dynamics which do not comply with the central well-mixed condition and require some spatial resolution. A simple but meaningful gene expression system with negative self-regulation is analysed to illustrate the different approximation qualities of some of the hybrid approaches discussed. Especially, we reveal the cause of error in the case of small volume approximations.
NASA Astrophysics Data System (ADS)
Winkelmann, Stefanie; Schütte, Christof
2017-09-01
Well-mixed stochastic chemical kinetics are properly modeled by the chemical master equation (CME) and associated Markov jump processes in molecule number space. If the reactants are present in large amounts, however, corresponding simulations of the stochastic dynamics become computationally expensive and model reductions are demanded. The classical model reduction approach uniformly rescales the overall dynamics to obtain deterministic systems characterized by ordinary differential equations, the well-known mass action reaction rate equations. For systems with multiple scales, there exist hybrid approaches that keep parts of the system discrete while another part is approximated either using Langevin dynamics or deterministically. This paper aims at giving a coherent overview of the different hybrid approaches, focusing on their basic concepts and the relation between them. We derive a novel general description of such hybrid models that allows expressing various forms by one type of equation. We also check in how far the approaches apply to model extensions of the CME for dynamics which do not comply with the central well-mixed condition and require some spatial resolution. A simple but meaningful gene expression system with negative self-regulation is analysed to illustrate the different approximation qualities of some of the hybrid approaches discussed. Especially, we reveal the cause of error in the case of small volume approximations.
Hybrid Incompatibility Arises in a Sequence-Based Bioenergetic Model of Transcription Factor Binding
Tulchinsky, Alexander Y.; Johnson, Norman A.; Watt, Ward B.; Porter, Adam H.
2014-01-01
Postzygotic isolation between incipient species results from the accumulation of incompatibilities that arise as a consequence of genetic divergence. When phenotypes are determined by regulatory interactions, hybrid incompatibility can evolve even as a consequence of parallel adaptation in parental populations because interacting genes can produce the same phenotype through incompatible allelic combinations. We explore the evolutionary conditions that promote and constrain hybrid incompatibility in regulatory networks using a bioenergetic model (combining thermodynamics and kinetics) of transcriptional regulation, considering the bioenergetic basis of molecular interactions between transcription factors (TFs) and their binding sites. The bioenergetic parameters consider the free energy of formation of the bond between the TF and its binding site and the availability of TFs in the intracellular environment. Together these determine fractional occupancy of the TF on the promoter site, the degree of subsequent gene expression and in diploids, and the degree of dominance among allelic interactions. This results in a sigmoid genotype–phenotype map and fitness landscape, with the details of the shape determining the degree of bioenergetic evolutionary constraint on hybrid incompatibility. Using individual-based simulations, we subjected two allopatric populations to parallel directional or stabilizing selection. Misregulation of hybrid gene expression occurred under either type of selection, although it evolved faster under directional selection. Under directional selection, the extent of hybrid incompatibility increased with the slope of the genotype–phenotype map near the derived parental expression level. Under stabilizing selection, hybrid incompatibility arose from compensatory mutations and was greater when the bioenergetic properties of the interaction caused the space of nearly neutral genotypes around the stable expression level to be wide. F2’s showed
Modeling of plasma and thermo-fluid transport in hybrid welding
NASA Astrophysics Data System (ADS)
Ribic, Brandon D.
Hybrid welding combines a laser beam and electrical arc in order to join metals within a single pass at welding speeds on the order of 1 m min -1. Neither autonomous laser nor arc welding can achieve the weld geometry obtained from hybrid welding for the same process parameters. Depending upon the process parameters, hybrid weld depth and width can each be on the order of 5 mm. The ability to produce a wide weld bead increases gap tolerance for square joints which can reduce machining costs and joint fitting difficulty. The weld geometry and fast welding speed of hybrid welding make it a good choice for application in ship, pipeline, and aerospace welding. Heat transfer and fluid flow influence weld metal mixing, cooling rates, and weld bead geometry. Cooling rate affects weld microstructure and subsequent weld mechanical properties. Fluid flow and heat transfer in the liquid weld pool are affected by laser and arc energy absorption. The laser and arc generate plasmas which can influence arc and laser energy absorption. Metal vapors introduced from the keyhole, a vapor filled cavity formed near the laser focal point, influence arc plasma light emission and energy absorption. However, hybrid welding plasma properties near the opening of the keyhole are not known nor is the influence of arc power and heat source separation understood. A sound understanding of these processes is important to consistently achieving sound weldments. By varying process parameters during welding, it is possible to better understand their influence on temperature profiles, weld metal mixing, cooling rates, and plasma properties. The current literature has shown that important process parameters for hybrid welding include: arc power, laser power, and heat source separation distance. However, their influence on weld temperatures, fluid flow, cooling rates, and plasma properties are not well understood. Modeling has shown to be a successful means of better understanding the influence of
NASA Astrophysics Data System (ADS)
Curci, Vita; Dassisti, Michele; Josefa, Mula Bru; Manuel, Díaz Madroñero
2014-10-01
Supply chain model (SCM) are potentially capable to integrate different aspects in supporting decision making for enterprise management tasks. The aim of the paper is to propose an hybrid mathematical programming model for optimization of production requirements resources planning. The preliminary model was conceived bottom-up from a real industrial case analysed oriented to maximize cash flow. Despite the intense computational effort required to converge to a solution, optimisation done brought good result in solving the objective function.
Development of hybrid 3-D hydrological modeling for the NCAR Community Earth System Model (CESM)
Zeng, Xubin; Troch, Peter; Pelletier, Jon; Niu, Guo-Yue; Gochis, David
2015-11-15
This is the Final Report of our four-year (3-year plus one-year no cost extension) collaborative project between the University of Arizona (UA) and the National Center for Atmospheric Research (NCAR). The overall objective of our project is to develop and evaluate the first hybrid 3-D hydrological model with a horizontal grid spacing of 1 km for the NCAR Community Earth System Model (CESM). We have made substantial progress in model development and evaluation, computational efficiencies and software engineering, and data development and evaluation, as discussed in Sections 2-4. Section 5 presents our success in data dissemination, while Section 6 discusses the scientific impacts of our work. Section 7 discusses education and mentoring success of our project, while Section 8 lists our relevant DOE services. All peer-reviewed papers that acknowledged this project are listed in Section 9. Highlights of our achievements include: • We have finished 20 papers (most published already) on model development and evaluation, computational efficiencies and software engineering, and data development and evaluation • The global datasets developed under this project have been permanently archived and publicly available • Some of our research results have already been implemented in WRF and CLM • Patrick Broxton and Michael Brunke have received their Ph.D. • PI Zeng has served on DOE proposal review panels and DOE lab scientific focus area (SFA) review panels
NASA Astrophysics Data System (ADS)
Johnson, Erik A.; Elhaddad, Wael M.; Wojtkiewicz, Steven F.
2016-04-01
A variety of strategies have been developed over the past few decades to determine controllable damping device forces to mitigate the response of structures and mechanical systems to natural hazards and other excitations. These "smart" damping devices produce forces through passive means but have properties that can be controlled in real time, based on sensor measurements of response across the structure, to dramatically reduce structural motion by exploiting more than the local "information" that is available to purely passive devices. A common strategy is to design optimal damping forces using active control approaches and then try to reproduce those forces with the smart damper. However, these design forces, for some structures and performance objectives, may achieve high performance by selectively adding energy, which cannot be replicated by a controllable damping device, causing the smart damper performance to fall far short of what an active system would provide. The authors have recently demonstrated that a model predictive control strategy using hybrid system models, which utilize both continuous and binary states (the latter to capture the switching behavior between dissipative and non-dissipative forces), can provide reductions in structural response on the order of 50% relative to the conventional clipped-optimal design strategy. This paper explores the robustness of this newly proposed control strategy through evaluating controllable damper performance when the structure model differs from the nominal one used to design the damping strategy. Results from the application to a two-degree-of-freedom structure model confirms the robustness of the proposed strategy.
Modeling and control of a hybrid-electric vehicle for drivability and fuel economy improvements
NASA Astrophysics Data System (ADS)
Koprubasi, Kerem
The gradual decline of oil reserves and the increasing demand for energy over the past decades has resulted in automotive manufacturers seeking alternative solutions to reduce the dependency on fossil-based fuels for transportation. A viable technology that enables significant improvements in the overall tank-to-wheel vehicle energy conversion efficiencies is the hybridization of electrical and conventional drive systems. Sophisticated hybrid powertrain configurations require careful coordination of the actuators and the onboard energy sources for optimum use of the energy saving benefits. The term optimality is often associated with fuel economy, although other measures such as drivability and exhaust emissions are also equally important. This dissertation focuses on the design of hybrid-electric vehicle (HEV) control strategies that aim to minimize fuel consumption while maintaining good vehicle drivability. In order to facilitate the design of controllers based on mathematical models of the HEV system, a dynamic model that is capable of predicting longitudinal vehicle responses in the low-to-mid frequency region (up to 10 Hz) is developed for a parallel HEV configuration. The model is validated using experimental data from various driving modes including electric only, engine only and hybrid. The high fidelity of the model makes it possible to accurately identify critical drivability issues such as time lags, shunt, shuffle, torque holes and hesitation. Using the information derived from the vehicle model, an energy management strategy is developed and implemented on a test vehicle. The resulting control strategy has a hybrid structure in the sense that the main mode of operation (the hybrid mode) is occasionally interrupted by event-based rules to enable the use of the engine start-stop function. The changes in the driveline dynamics during this transition further contribute to the hybrid nature of the system. To address the unique characteristics of the HEV
Hybrid continuum-molecular modelling of multiscale internal gas flows
NASA Astrophysics Data System (ADS)
Patronis, Alexander; Lockerby, Duncan A.; Borg, Matthew K.; Reese, Jason M.
2013-12-01
We develop and apply an efficient multiscale method for simulating a large class of low-speed internal rarefied gas flows. The method is an extension of the hybrid atomistic-continuum approach proposed by Borg et al. (2013) [28] for the simulation of micro/nano flows of high-aspect ratio. The major new extensions are: (1) incorporation of fluid compressibility; (2) implementation using the direct simulation Monte Carlo (DSMC) method for dilute rarefied gas flows, and (3) application to a broader range of geometries, including periodic, non-periodic, pressure-driven, gravity-driven and shear-driven internal flows. The multiscale method is applied to micro-scale gas flows through a periodic converging-diverging channel (driven by an external acceleration) and a non-periodic channel with a bend (driven by a pressure difference), as well as the flow between two eccentric cylinders (with the inner rotating relative to the outer). In all these cases there exists a wide variation of Knudsen number within the geometries, as well as substantial compressibility despite the Mach number being very low. For validation purposes, our multiscale simulation results are compared to those obtained from full-scale DSMC simulations: very close agreement is obtained in all cases for all flow variables considered. Our multiscale simulation is an order of magnitude more computationally efficient than the full-scale DSMC for the first and second test cases, and two orders of magnitude more efficient for the third case.
An Optimization Model for Plug-In Hybrid Electric Vehicles
Malikopoulos, Andreas; Smith, David E
2011-01-01
The necessity for environmentally conscious vehicle designs in conjunction with increasing concerns regarding U.S. dependency on foreign oil and climate change have induced significant investment towards enhancing the propulsion portfolio with new technologies. More recently, plug-in hybrid electric vehicles (PHEVs) have held great intuitive appeal and have attracted considerable attention. PHEVs have the potential to reduce petroleum consumption and greenhouse gas (GHG) emissions in the commercial transportation sector. They are especially appealing in situations where daily commuting is within a small amount of miles with excessive stop-and-go driving. The research effort outlined in this paper aims to investigate the implications of motor/generator and battery size on fuel economy and GHG emissions in a medium-duty PHEV. An optimization framework is developed and applied to two different parallel powertrain configurations, e.g., pre-transmission and post-transmission, to derive the optimal design with respect to motor/generator and battery size. A comparison between the conventional and PHEV configurations with equivalent size and performance under the same driving conditions is conducted, thus allowing an assessment of the fuel economy and GHG emissions potential improvement. The post-transmission parallel configuration yields higher fuel economy and less GHG emissions compared to pre-transmission configuration partly attributable to the enhanced regenerative braking efficiency.
Photonic states mixing beyond the plasmon hybridization model
Suryadharma, Radius N. S.; Iskandar, Alexander A. Tjia, May-On
2016-07-28
A study is performed on a photonic-state mixing-pattern in an insulator-metal-insulator cylindrical silver nanoshell and its rich variations induced by changes in the geometry and dielectric media of the system, representing the combined influences of plasmon coupling strength and cavity effects. This study is performed in terms of the photonic local density of states (LDOS) calculated using the Green tensor method, in order to elucidate those combined effects. The energy profiles of LDOS inside the dielectric core are shown to exhibit consistently growing number of redshifted photonic states due to an enhanced plasmon coupling induced state mixing arising from decreased shell thickness, increased cavity size effect, and larger symmetry breaking effect induced by increased permittivity difference between the core and the background media. Further, an increase in cavity size leads to increased additional peaks that spread out toward the lower energy regime. A systematic analysis of those variations for a silver nanoshell with a fixed inner radius in vacuum background reveals a certain pattern of those growing number of redshifted states with an analytic expression for the corresponding energy downshifts, signifying a photonic state mixing scheme beyond the commonly adopted plasmon hybridization scheme. Finally, a remarkable correlation is demonstrated between the LDOS energy profiles outside the shell and the corresponding scattering efficiencies.
Photonic states mixing beyond the plasmon hybridization model
NASA Astrophysics Data System (ADS)
Suryadharma, Radius N. S.; Iskandar, Alexander A.; Tjia, May-On
2016-07-01
A study is performed on a photonic-state mixing-pattern in an insulator-metal-insulator cylindrical silver nanoshell and its rich variations induced by changes in the geometry and dielectric media of the system, representing the combined influences of plasmon coupling strength and cavity effects. This study is performed in terms of the photonic local density of states (LDOS) calculated using the Green tensor method, in order to elucidate those combined effects. The energy profiles of LDOS inside the dielectric core are shown to exhibit consistently growing number of redshifted photonic states due to an enhanced plasmon coupling induced state mixing arising from decreased shell thickness, increased cavity size effect, and larger symmetry breaking effect induced by increased permittivity difference between the core and the background media. Further, an increase in cavity size leads to increased additional peaks that spread out toward the lower energy regime. A systematic analysis of those variations for a silver nanoshell with a fixed inner radius in vacuum background reveals a certain pattern of those growing number of redshifted states with an analytic expression for the corresponding energy downshifts, signifying a photonic state mixing scheme beyond the commonly adopted plasmon hybridization scheme. Finally, a remarkable correlation is demonstrated between the LDOS energy profiles outside the shell and the corresponding scattering efficiencies.
Exploring key factors in online shopping with a hybrid model.
Chen, Hsiao-Ming; Wu, Chia-Huei; Tsai, Sang-Bing; Yu, Jian; Wang, Jiangtao; Zheng, Yuxiang
2016-01-01
Nowadays, the web increasingly influences retail sales. An in-depth analysis of consumer decision-making in the context of e-business has become an important issue for internet vendors. However, factors affecting e-business are complicated and intertwined. To stimulate online sales, understanding key influential factors and causal relationships among the factors is important. To gain more insights into this issue, this paper introduces a hybrid method, which combines the Decision Making Trial and Evaluation Laboratory (DEMATEL) with the analytic network process, called DANP method, to find out the driving factors that influence the online business mostly. By DEMATEL approach the causal graph showed that "online service" dimension has the highest degree of direct impact on other dimensions; thus, the internet vendor is suggested to made strong efforts on service quality throughout the online shopping process. In addition, the study adopted DANP to measure the importance of key factors, among which "transaction security" proves to be the most important criterion. Hence, transaction security should be treated with top priority to boost the online businesses. From our study with DANP approach, the comprehensive information can be visually detected so that the decision makers can spotlight on the root causes to develop effectual actions.
Strategy and gaps for modeling, simulation, and control of hybrid systems
Rabiti, Cristian; Garcia, Humberto E.; Hovsapian, Rob; Kinoshita, Robert; Mesina, George L.; Bragg-Sitton, Shannon M.; Boardman, Richard D.
2015-04-01
The purpose of this report is to establish a strategy for modeling and simulation of candidate hybrid energy systems. Modeling and simulation is necessary to design, evaluate, and optimize the system technical and economic performance. Accordingly, this report first establishes the simulation requirements to analysis candidate hybrid systems. Simulation fidelity levels are established based on the temporal scale, real and synthetic data availability or needs, solution accuracy, and output parameters needed to evaluate case-specific figures of merit. Accordingly, the associated computational and co-simulation resources needed are established; including physical models when needed, code assembly and integrated solutions platforms, mathematical solvers, and data processing. This report first attempts to describe the figures of merit, systems requirements, and constraints that are necessary and sufficient to characterize the grid and hybrid systems behavior and market interactions. Loss of Load Probability (LOLP) and effective cost of Effective Cost of Energy (ECE), as opposed to the standard Levelized Cost of Electricty (LCOE), are introduced as technical and economical indices for integrated energy system evaluations. Financial assessment methods are subsequently introduced for evaluation of non-traditional, hybrid energy systems. Algorithms for coupled and iterative evaluation of the technical and economic performance are subsequently discussed. This report further defines modeling objectives, computational tools, solution approaches, and real-time data collection and processing (in some cases using real test units) that will be required to model, co-simulate, and optimize; (a) an energy system components (e.g., power generation unit, chemical process, electricity management unit), (b) system domains (e.g., thermal, electrical or chemical energy generation, conversion, and transport), and (c) systems control modules. Co-simulation of complex, tightly coupled
Comparative study of hybrid RANS-LES models for separated flows
NASA Astrophysics Data System (ADS)
Kumar, G.; Lakshmanan, S. K.; Gopalan, H.; De, A.
2016-06-01
Hybrid RANS-LES models are proven to be capable of predicting massively separated flows with reasonable computation cost. In this paper, Spalart-Allmaras (S-A) based detached eddy simulation (DES) model and three SST based hybrid models with different RANS to LES switching criteriaare investigated. The flow over periodic hill at Re = 10,595 is chosen as the benchmark for comparing the performance of the different models due to the complex flow physics and reasonablecomputational cost. The model performances are evaluated based on their prediction capabilities of velocity and stress profiles, and separation and reattachment point. The simulated results are validatedagainst experimental and numerical results available in literature. The S-A DES model predicted separation bubble accurately at the top of the hill, as reported earlier in experiments and other numerical results. This model also correctly predicted velocity and stress profiles in recirculation region. However, the performance of this model was poor in the post reattachment region. On the other hand, the k-ω SST based hybrid models performed poorly in recirculation region, but it fairly predicted stress profiles in post reattachment region.
Ares 1X Hybrid Modeling with Comparisons to Flight Data
NASA Technical Reports Server (NTRS)
Niedermaier, Dan; Kaouk, Mo
2010-01-01
This slide presentation reviews the Ares 1X test flight and compares the resultant flight data with the results of modeled data from siumulations of the flight. It includes: (1) Ares 1X Flight Summary, (2) Ares 1X Data Summary (3) Model Descriptions (4) Model Comparisons to Flight Data in three areas: (a) Liftoff, (b) Transonic and (c) Roll Control Firings (RCS) Firings.
Fuzzy logic-based analogue forecasting and hybrid modelling of horizontal visibility
NASA Astrophysics Data System (ADS)
Tuba, Zoltán; Bottyán, Zsolt
2017-02-01
Forecasting visibility is one of the greatest challenges in aviation meteorology. At the same time, high accuracy visibility forecasts can significantly reduce or make avoidable weather-related risk in aviation as well. To improve forecasting visibility, this research links fuzzy logic-based analogue forecasting and post-processed numerical weather prediction model outputs in hybrid forecast. Performance of analogue forecasting model was improved by the application of Analytic Hierarchy Process. Then, linear combination of the mentioned outputs was applied to create ultra-short term hybrid visibility prediction which gradually shifts the focus from statistical to numerical products taking their advantages during the forecast period. It gives the opportunity to bring closer the numerical visibility forecast to the observations even it is wrong initially. Complete verification of categorical forecasts was carried out; results are available for persistence and terminal aerodrome forecasts (TAF) as well in order to compare. The average value of Heidke Skill Score (HSS) of examined airports of analogue and hybrid forecasts shows very similar results even at the end of forecast period where the rate of analogue prediction in the final hybrid output is 0.1-0.2 only. However, in case of poor visibility (1000-2500 m), hybrid (0.65) and analogue forecasts (0.64) have similar average of HSS in the first 6 h of forecast period, and have better performance than persistence (0.60) or TAF (0.56). Important achievement that hybrid model takes into consideration physics and dynamics of the atmosphere due to the increasing part of the numerical weather prediction. In spite of this, its performance is similar to the most effective visibility forecasting methods and does not follow the poor verification results of clearly numerical outputs.
Analysis and Modeling of DIII-D Hybrid Discharges and their Extrapolation to ITER
Makowski, M A; Casper, T A; Jayakumar, R J; Pearlstein, L D; Petty, C C; Wade, M R
2006-06-16
Recent experiments on tokamaks around the world [1-5] have demonstrated discharges with moderately high performance in which the q-profile remains stationary, as measured by the motional Stark effect diagnostic, for periods up to several {tau}{sub R}. Hybrid discharges are characterize by q{sub min} {approx} 1, high {beta}{sub N}, and good confinement. These discharges have been termed hybrid because of their intermediate nature between that of an ordinary H-mode and advanced tokamak discharges. They form an attractive scenario for ITER as the normalized fusion performance ({beta}{sub N}H{sub 89P}/q{sub 95}{sup 2}) is at or above that for the ITER baseline Q{sub fus} = 10 scenario, even for q{sub 95} as high as 4.6. The startup phase is thought to be crucial to the ultimate evolution of the hybrid discharge. An open question is how hybrid discharges achieve and maintain their stationary state during the initial startup phase. To investigate this aspect of hybrid discharges, we have used the CORSICA code to model the early stages of a discharge. Results clearly indicate that neoclassical current evolution alone is insufficient to account for the time evolution of the q-profile and that an addition of non-inductive current source must be incorporated into the model to reproduce the experimental time history. We include non-inductive neutral beam and bootstrap current sources in the model, and investigate the difference between simulations with these sources and the experimentally inferred q-profile. Further, we have made preliminary estimates of the spatial structure of the current needed to bring the simulation and experiment into agreement. This additional non-inductive source has not been tied to any physical mechanism as yet. We present these results and discuss the implications for hybrid startup on ITER.
HYBRID SNCR-SCR TECHNOLOGIES FOR NOX CONTROL: MODELING AND EXPERIMENT
The hybrid process of homogeneous gas-phase selective non-catalytic reduction (SNCR) followed by selective catalytic reduction (SCR) of nitric oxide (NO) was investigated through experimentation and modeling. Measurements, using NO-doped flue gas from a gas-fired 29 kW test combu...
A Design Perspective on the School-Work Boundary: A Hybrid Curriculum Model
ERIC Educational Resources Information Center
Zitter, Ilya; Hoeve, Aimée; de Bruijn, Elly
2016-01-01
This article proposes a model for the design of a hybrid VET curriculum across the school-work boundary. VET-curricula are designed on the basis of two main types of learning arrangements, namely, the plan for learning in school and for learning at the workplace. A challenge for curriculum development is creating consistency between different…
Hybrid Model of Inhomogeneous Solar Wind Plasma Heating by Alfven Wave Spectrum: Parametric Studies
NASA Technical Reports Server (NTRS)
Ofman, L.
2010-01-01
Observations of the solar wind plasma at 0.3 AU and beyond show that a turbulent spectrum of magnetic fluctuations is present. Remote sensing observations of the corona indicate that heavy ions are hotter than protons and their temperature is anisotropic (T(sub perpindicular / T(sub parallel) >> 1). We study the heating and the acceleration of multi-ion plasma in the solar wind by a turbulent spectrum of Alfvenic fluctuations using a 2-D hybrid numerical model. In the hybrid model the protons and heavy ions are treated kinetically as particles, while the electrons are included as neutralizing background fluid. This is the first two-dimensional hybrid parametric study of the solar wind plasma that includes an input turbulent wave spectrum guided by observation with inhomogeneous background density. We also investigate the effects of He++ ion beams in the inhomogeneous background plasma density on the heating of the solar wind plasma. The 2-D hybrid model treats parallel and oblique waves, together with cross-field inhomogeneity, self-consistently. We investigate the parametric dependence of the perpendicular heating, and the temperature anisotropy in the H+-He++ solar wind plasma. It was found that the scaling of the magnetic fluctuations power spectrum steepens in the higher-density regions, and the heating is channeled to these regions from the surrounding lower-density plasma due to wave refraction. The model parameters are applicable to the expected solar wind conditions at about 10 solar radii.
NASA Astrophysics Data System (ADS)
Shen, Yanfeng; Cesnik, Carlos E. S.
2016-09-01
This paper presents a new hybrid modeling technique for the efficient simulation of guided wave generation, propagation, and interaction with damage in complex composite structures. A local finite element model is deployed to capture the piezoelectric effects and actuation dynamics of the transmitter, while the global domain wave propagation and interaction with structural complexity (structure features and damage) are solved utilizing a local interaction simulation approach (LISA). This hybrid approach allows the accurate modeling of the local dynamics of the transducers and keeping the LISA formulation in an explicit format, which facilitates its readiness for parallel computing. The global LISA framework was extended through the 3D Kelvin-Voigt viscoelasticity theory to include anisotropic damping effects for composite structures, as an improvement over the existing LISA formulation. The global LISA framework was implemented using the compute unified device architecture running on graphic processing units. A commercial preprocessor is integrated seamlessly with the computational framework for grid generation and material property allocation to handle complex structures. The excitability and damping effects are successfully captured by this hybrid model, with experimental validation using the scanning laser doppler vibrometry. To demonstrate the capability of our hybrid approach for complex structures, guided wave propagation and interaction with a delamination in a composite panel with stiffeners is presented.
ERIC Educational Resources Information Center
Grable, John E.
2011-01-01
Innovation in doctoral degree program development and delivery provides an effective counterpoint to the expert-apprentice model established in the Middle Ages. The author outlines the importance of innovation in reaching adult learners and describes an innovative hybrid PhD program designed to allow aspiring doctoral adult-age students to pursue…
ERIC Educational Resources Information Center
Hannah, David R.; Venkatachary, Ranga
2010-01-01
In this article, the authors present a retrospective analysis of an instructor's multiyear redesign of a course on organization theory into what is called a hybrid Classroom-as-Organization model. It is suggested that this new course design served to apprentice students to function in quasi-real organizational structures. The authors further argue…
ERIC Educational Resources Information Center
Grable, John E.
2011-01-01
Innovation in doctoral degree program development and delivery provides an effective counterpoint to the expert-apprentice model established in the Middle Ages. The author outlines the importance of innovation in reaching adult learners and describes an innovative hybrid PhD program designed to allow aspiring doctoral adult-age students to pursue…
Ocean U.S. GODAE: Global Ocean Prediction with the HYbrid Coordinate Ocean Model (HYCOM)
2008-10-01
Ocean U.S. GODAE: Global Ocean Prediction with the HYbrid Coordinate Ocean Model (HYCOM) By Eric P. Chassignet1 and Harley E. Hurlburt2 1 COAPS ...UAcademia:U Florida State University/Center for Ocean-Atmospheric Prediction Studies ( COAPS ); University of Miami/Rosenstiel School of Marine and
A Design Perspective on the School-Work Boundary: A Hybrid Curriculum Model
ERIC Educational Resources Information Center
Zitter, Ilya; Hoeve, Aimée; de Bruijn, Elly
2016-01-01
This article proposes a model for the design of a hybrid VET curriculum across the school-work boundary. VET-curricula are designed on the basis of two main types of learning arrangements, namely, the plan for learning in school and for learning at the workplace. A challenge for curriculum development is creating consistency between different…
ERIC Educational Resources Information Center
Hannah, David R.; Venkatachary, Ranga
2010-01-01
In this article, the authors present a retrospective analysis of an instructor's multiyear redesign of a course on organization theory into what is called a hybrid Classroom-as-Organization model. It is suggested that this new course design served to apprentice students to function in quasi-real organizational structures. The authors further argue…
Investigation of the hybrid model with the Killingbeck potential in a variational approach
NASA Astrophysics Data System (ADS)
Alimohammadi, M.; Hassanabadi, H.
2017-10-01
The energy spectrum and transition rates have been obtained for some nuclei in critical point by employing the hybrid collective model. For this purpose, the corresponding Hamiltonian with β-dependent Killingbeck potential has been solved by the variational method. Our results have been compared with other references and experimental data.
HYBRID SNCR-SCR TECHNOLOGIES FOR NOX CONTROL: MODELING AND EXPERIMENT
The hybrid process of homogeneous gas-phase selective non-catalytic reduction (SNCR) followed by selective catalytic reduction (SCR) of nitric oxide (NO) was investigated through experimentation and modeling. Measurements, using NO-doped flue gas from a gas-fired 29 kW test combu...
Hybrid Model of Inhomogeneous Solar Wind Plasma Heating by Alfven Wave Spectrum: Parametric Studies
NASA Technical Reports Server (NTRS)
Ofman, L.
2010-01-01
Observations of the solar wind plasma at 0.3 AU and beyond show that a turbulent spectrum of magnetic fluctuations is present. Remote sensing observations of the corona indicate that heavy ions are hotter than protons and their temperature is anisotropic (T(sub perpindicular / T(sub parallel) >> 1). We study the heating and the acceleration of multi-ion plasma in the solar wind by a turbulent spectrum of Alfvenic fluctuations using a 2-D hybrid numerical model. In the hybrid model the protons and heavy ions are treated kinetically as particles, while the electrons are included as neutralizing background fluid. This is the first two-dimensional hybrid parametric study of the solar wind plasma that includes an input turbulent wave spectrum guided by observation with inhomogeneous background density. We also investigate the effects of He++ ion beams in the inhomogeneous background plasma density on the heating of the solar wind plasma. The 2-D hybrid model treats parallel and oblique waves, together with cross-field inhomogeneity, self-consistently. We investigate the parametric dependence of the perpendicular heating, and the temperature anisotropy in the H+-He++ solar wind plasma. It was found that the scaling of the magnetic fluctuations power spectrum steepens in the higher-density regions, and the heating is channeled to these regions from the surrounding lower-density plasma due to wave refraction. The model parameters are applicable to the expected solar wind conditions at about 10 solar radii.
Separable Transition Density in the Hybrid Model for Tumor-Immune System Competition
Cattani, Carlo; Ciancio, Armando
2012-01-01
A hybrid model, on the competition tumor cells immune system, is studied under suitable hypotheses. The explicit form for the equations is obtained in the case where the density function of transition is expressed as the product of separable functions. A concrete application is given starting from a modified Lotka-Volterra system of equations. PMID:22291853
Hybrid ensemble 4DVar assimilation of stratospheric ozone using a global shallow water model
NASA Astrophysics Data System (ADS)
Allen, Douglas R.; Hoppel, Karl W.; Kuhl, David D.
2016-07-01
Wind extraction from stratospheric ozone (O3) assimilation is examined using a hybrid ensemble 4-D variational assimilation (4DVar) shallow water model (SWM) system coupled to the tracer advection equation. Stratospheric radiance observations are simulated using global observations of the SWM fluid height (Z), while O3 observations represent sampling by a typical polar-orbiting satellite. Four ensemble sizes were examined (25, 50, 100, and 1518 members), with the largest ensemble equal to the number of dynamical state variables. The optimal length scale for ensemble localization was found by tuning an ensemble Kalman filter (EnKF). This scale was then used for localizing the ensemble covariances that were blended with conventional covariances in the hybrid 4DVar experiments. Both optimal length scale and optimal blending coefficient increase with ensemble size, with optimal blending coefficients varying from 0.2-0.5 for small ensembles to 0.5-1.0 for large ensembles. The hybrid system outperforms conventional 4DVar for all ensemble sizes, while for large ensembles the hybrid produces similar results to the offline EnKF. Assimilating O3 in addition to Z benefits the winds in the hybrid system, with the fractional improvement in global vector wind increasing from ˜ 35 % with 25 and 50 members to ˜ 50 % with 1518 members. For the smallest ensembles (25 and 50 members), the hybrid 4DVar assimilation improves the zonal wind analysis over conventional 4DVar in the Northern Hemisphere (winter-like) region and also at the Equator, where Z observations alone have difficulty constraining winds due to lack of geostrophy. For larger ensembles (100 and 1518 members), the hybrid system results in both zonal and meridional wind error reductions, relative to 4DVar, across the globe.
Jackson, Chris J; Izadikah, Zahra; Oei, Tian P S
2012-06-01
Jackson's (2005, 2008a) hybrid model of learning identifies a number of learning mechanisms that lead to the emergence and maintenance of the balance between rationality and irrationality. We test a general hypothesis that Jackson's model will predict depressive symptoms, such that poor learning is related to depression. We draw comparisons between Jackson's model and Ellis' (2004) Rational Emotive Behavior Therapy and Theory (REBT) and thereby provide a set of testable learning mechanisms potentially underlying REBT. Results from 80 patients diagnosed with depression completed the learning styles profiler (LSP; Jackson, 2005) and two measures of depression. Results provide support for the proposed model of learning and further evidence that low rationality is a key predictor of depression. We conclude that the hybrid model of learning has the potential to explain some of the learning and cognitive processes related to the development and maintenance of irrational beliefs and depression. Copyright © 2011. Published by Elsevier B.V.
Rhombic micro-displacement amplifier for piezoelectric actuator and its linear and hybrid model
NASA Astrophysics Data System (ADS)
Chen, Jinglong; Zhang, Chunlin; Xu, Minglong; Zi, Yanyang; Zhang, Xinong
2015-01-01
This paper proposes rhombic micro-displacement amplifier (RMDA) for piezoelectric actuator (PA). First, the geometric amplification relations are analyzed and linear model is built to analyze the mechanical and electrical properties of this amplifier. Next, the accurate modeling method of amplifier is studied for important application of precise servo control. The classical Preisach model (CPM) is generally implemented using a numerical technique based on the first-order reversal curves (FORCs). The accuracy of CPM mainly depends on the number of FORCs. However, it is generally difficult to achieve enough number of FORCs in practice. So, Support Vector Machine (SVM) is employed in the work to circumvent the deficiency of the CPM. Then the hybrid model, which is based on discrete CPM and SVM is developed to account for hysteresis and dynamic effects. Finally, experimental validation is carried out. The analyzed result shows that this amplifier with the hybrid model is suitable for control application.
A zero-equation turbulence model for two-dimensional hybrid Hall thruster simulations
Cappelli, Mark A. Young, Christopher V.; Cha, Eunsun; Fernandez, Eduardo
2015-11-15
We present a model for electron transport across the magnetic field of a Hall thruster and integrate this model into 2-D hybrid particle-in-cell simulations. The model is based on a simple scaling of the turbulent electron energy dissipation rate and the assumption that this dissipation results in Ohmic heating. Implementing the model into 2-D hybrid simulations is straightforward and leverages the existing framework for solving the electron fluid equations. The model recovers the axial variation in the mobility seen in experiments, predicting the generation of a transport barrier which anchors the region of plasma acceleration. The predicted xenon neutral and ion velocities are found to be in good agreement with laser-induced fluorescence measurements.
Elsheikh, Ahmed H.; Wheeler, Mary F.; Hoteit, Ibrahim
2014-02-01
A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems.
Modeling and Simulation of Metallurgical Process Based on Hybrid Petri Net
NASA Astrophysics Data System (ADS)
Ren, Yujuan; Bao, Hong
2016-11-01
In order to achieve the goals of energy saving and emission reduction of iron and steel enterprises, an increasing number of modeling and simulation technologies are used to research and analyse metallurgical production process. In this paper, the basic principle of Hybrid Petri net is used to model and analyse the Metallurgical Process. Firstly, the definition of Hybrid Petri Net System of Metallurgical Process (MPHPNS) and its modeling theory are proposed. Secondly, the model of MPHPNS based on material flow is constructed. The dynamic flow of materials and the real-time change of each technological state in metallurgical process are simulated vividly by using this model. The simulation process can implement interaction between the continuous event dynamic system and the discrete event dynamic system at the same level, and play a positive role in the production decision.
One hybrid model combining singular spectrum analysis and LS + ARMA for polar motion prediction
NASA Astrophysics Data System (ADS)
Shen, Yi; Guo, Jinyun; Liu, Xin; Wei, Xiaobei; Li, Wudong
2017-01-01
Accurate real-time polar motion parameters play an important role in satellite navigation and positioning and spacecraft tracking. To meet the needs for real-time and high-accuracy polar motion prediction, a hybrid model that integrated singular spectrum analysis (SSA), least-squares (LS) extrapolation and an autoregressive moving average (ARMA) model was proposed. SSA was applied to separate the trend, the annual and the Chandler components from a given polar motion time series. LS extrapolation models were constructed for the separated trend, annual and Chandler components. An ARMA model was established for a synthetic sequence that contained the remaining SSA component and the residual series of LS fitting. In applying this hybrid model, multiple sets of polar motion predictions with lead times of 360 days were made based on an IERS 08 C04 series. The results showed that the proposed method could effectively predict the polar motion parameters.
NASA Astrophysics Data System (ADS)
Sun, Xiaoqiang; Yuan, Chaochun; Cai, Yingfeng; Wang, Shaohua; Chen, Long
2017-09-01
This paper presents the hybrid modeling and the model predictive control of an air suspension system with damping multi-mode switching damper. Unlike traditional damper with continuously adjustable damping, in this study, a new damper with four discrete damping modes is applied to vehicle semi-active air suspension. The new damper can achieve different damping modes by just controlling the on-off statuses of two solenoid valves, which makes its damping adjustment more efficient and more reliable. However, since the damping mode switching induces different modes of operation, the air suspension system with the new damper poses challenging hybrid control problem. To model both the continuous/discrete dynamics and the switching between different damping modes, the framework of mixed logical dynamical (MLD) systems is used to establish the system hybrid model. Based on the resulting hybrid dynamical model, the system control problem is recast as a model predictive control (MPC) problem, which allows us to optimize the switching sequences of the damping modes by taking into account the suspension performance requirements. Numerical simulations results demonstrate the efficacy of the proposed control method finally.
Simulation-optimization framework for multi-site multi-season hybrid stochastic streamflow modeling
NASA Astrophysics Data System (ADS)
Srivastav, Roshan; Srinivasan, K.; Sudheer, K. P.
2016-11-01
A simulation-optimization (S-O) framework is developed for the hybrid stochastic modeling of multi-site multi-season streamflows. The multi-objective optimization model formulated is the driver and the multi-site, multi-season hybrid matched block bootstrap model (MHMABB) is the simulation engine within this framework. The multi-site multi-season simulation model is the extension of the existing single-site multi-season simulation model. A robust and efficient evolutionary search based technique, namely, non-dominated sorting based genetic algorithm (NSGA - II) is employed as the solution technique for the multi-objective optimization within the S-O framework. The objective functions employed are related to the preservation of the multi-site critical deficit run sum and the constraints introduced are concerned with the hybrid model parameter space, and the preservation of certain statistics (such as inter-annual dependence and/or skewness of aggregated annual flows). The efficacy of the proposed S-O framework is brought out through a case example from the Colorado River basin. The proposed multi-site multi-season model AMHMABB (whose parameters are obtained from the proposed S-O framework) preserves the temporal as well as the spatial statistics of the historical flows. Also, the other multi-site deficit run characteristics namely, the number of runs, the maximum run length, the mean run sum and the mean run length are well preserved by the AMHMABB model. Overall, the proposed AMHMABB model is able to show better streamflow modeling performance when compared with the simulation based SMHMABB model, plausibly due to the significant role played by: (i) the objective functions related to the preservation of multi-site critical deficit run sum; (ii) the huge hybrid model parameter space available for the evolutionary search and (iii) the constraint on the preservation of the inter-annual dependence. Split-sample validation results indicate that the AMHMABB model is
A hybrid finite-difference and analytic element groundwater model.
Haitjema, H M; Feinstein, D T; Hunt, R J; Gusyev, M A
2010-01-01
Regional finite-difference models tend to have large cell sizes, often on the order of 1-2 km on a side. Although the regional flow patterns in deeper formations may be adequately represented by such a model, the intricate surface water and groundwater interactions in the shallower layers are not. Several stream reaches and nearby wells may occur in a single cell, precluding any meaningful modeling of the surface water and groundwater interactions between the individual features. We propose to replace the upper MODFLOW layer or layers, in which the surface water and groundwater interactions occur, by an analytic element model (GFLOW) that does not employ a model grid; instead, it represents wells and surface waters directly by the use of point-sinks and line-sinks. For many practical cases it suffices to provide GFLOW with the vertical leakage rates calculated in the original coarse MODFLOW model in order to obtain a good representation of surface water and groundwater interactions. However, when the combined transmissivities in the deeper (MODFLOW) layers dominate, the accuracy of the GFLOW solution diminishes. For those cases, an iterative coupling procedure, whereby the leakages between the GFLOW and MODFLOW model are updated, appreciably improves the overall solution, albeit at considerable computational cost. The coupled GFLOW-MODFLOW model is applicable to relatively large areas, in many cases to the entire model domain, thus forming an attractive alternative to local grid refinement or inset models.
A hybrid model for short-term bacillary dysentery prediction in Yichang City, China.
Yan, Weirong; Xu, Yong; Yang, Xiaobing; Zhou, Yikai
2010-07-01
Bacillary dysentery is still a common and serious public health problem in China. This paper is aimed at developing and evaluating an innovative hybrid model, which combines the seasonal autoregressive integrated moving average (SARIMA) and the generalized regression neural network (GRNN) models, for bacillary dysentery forecasting. Data of monthly bacillary dysentery incidence in Yichang City from 2000-2007 was obtained from Yichang Disease Control and Prevention Center. The SARIMA and SARIMA-GRNN model were developed and validated by dividing the data file into two data sets: data from the past 5 years was used to construct the models, and data from January to June of the 6th year was used to validate them. Simulation and forecasting performance was evaluated and compared between the two models. The hybrid SARIMA-GRNN model was found to outperform the SARIMA model with the lower mean square error, mean absolute error, and mean absolute percentage error in simulation and prediction results. Developing and applying the SARIMA-GRNN hybrid model is an effective decision supportive method for producing reliable forecasts of bacillary dysentery for the study area.
Quilodrán, Claudio S.; Currat, Mathias; Montoya-Burgos, Juan I.
2014-01-01
Interspecific hybridization is common in nature but can be increased in frequency or even originated by human actions, such as species introduction or habitat modification, which may threaten species persistence. When hybridization occurs between distantly related species, referred to as “distant hybridization,” the resulting hybrids are generally infertile or fertile but do not undergo chromosomal recombination during gametogenesis. Here, we present a model describing this frequent but poorly studied interspecific hybridization to assess its consequences on parental species and to anticipate the conditions under which they can reach extinction. Our general model fully incorporates three important processes: density-dependent competition, dominance/recessivity inheritance of traits and assortative mating. We demonstrate its use and flexibility by assessing population extinction risk between Atlantic salmon and brown trout in Norway, whose interbreeding has recently increased due to farmed fish releases into the wild. We identified the set of conditions under which hybridization may threaten salmonid species. Thanks to the flexibility of our model, we evaluated the effect of an additional risk factor, a parasitic disease, and showed that the cumulative effects dramatically increase the extinction risk. The consequences of distant hybridization are not genetically, but demographically mediated. Our general model is useful to better comprehend the evolution of such hybrid systems and we demonstrated its importance in the field of conservation biology to set up management recommendations when this increasingly frequent type of hybridization is in action. PMID:25003336
Quilodrán, Claudio S; Currat, Mathias; Montoya-Burgos, Juan I
2014-01-01
Interspecific hybridization is common in nature but can be increased in frequency or even originated by human actions, such as species introduction or habitat modification, which may threaten species persistence. When hybridization occurs between distantly related species, referred to as "distant hybridization," the resulting hybrids are generally infertile or fertile but do not undergo chromosomal recombination during gametogenesis. Here, we present a model describing this frequent but poorly studied interspecific hybridization to assess its consequences on parental species and to anticipate the conditions under which they can reach extinction. Our general model fully incorporates three important processes: density-dependent competition, dominance/recessivity inheritance of traits and assortative mating. We demonstrate its use and flexibility by assessing population extinction risk between Atlantic salmon and brown trout in Norway, whose interbreeding has recently increased due to farmed fish releases into the wild. We identified the set of conditions under which hybridization may threaten salmonid species. Thanks to the flexibility of our model, we evaluated the effect of an additional risk factor, a parasitic disease, and showed that the cumulative effects dramatically increase the extinction risk. The consequences of distant hybridization are not genetically, but demographically mediated. Our general model is useful to better comprehend the evolution of such hybrid systems and we demonstrated its importance in the field of conservation biology to set up management recommendations when this increasingly frequent type of hybridization is in action.
Modelling grain-scattered ultrasound in austenitic stainless-steel welds: A hybrid model
Nowers, O.; Duxbury, D. J.; Velichko, A.; Drinkwater, B. W.
2015-03-31
The ultrasonic inspection of austenitic stainless steel welds can be challenging due to their coarse grain structure, charaterised by preferentially oriented, elongated grains. The anisotropy of the weld is manifested as both a ‘steering’ of the beam and the back-scatter of energy due to the macroscopic granular structure of the weld. However, the influence of weld properties, such as mean grain size and orientation distribution, on the magnitude of scattered ultrasound is not well understood. A hybrid model has been developed to allow the study of grain-scatter effects in austenitic welds. An efficient 2D Finite Element (FE) method is used to calculate the complete scattering response from a single elliptical austenitic grain of arbitrary length and width as a function of the specific inspection frequency. A grain allocation model of the weld is presented to approximate the characteristic structures observed in austenitic welds and the complete scattering behaviour of each grain calculated. This model is incorporated into a semi-analytical framework for a single-element inspection of a typical weld in immersion. Experimental validation evidence is demonstrated indicating excellent qualitative agreement of SNR as a function of frequency and a minimum SNR difference of 2 dB at a centre frequency of 2.25 MHz. Additionally, an example Monte-Carlo study is presented detailing the variation of SNR as a function of the anisotropy distribution of the weld, and the application of confidence analysis to inform inspection development.
Modelling grain-scattered ultrasound in austenitic stainless-steel welds: A hybrid model
NASA Astrophysics Data System (ADS)
Nowers, O.; Duxbury, D. J.; Velichko, A.; Drinkwater, B. W.
2015-03-01
The ultrasonic inspection of austenitic stainless steel welds can be challenging due to their coarse grain structure, charaterised by preferentially oriented, elongated grains. The anisotropy of the weld is manifested as both a `steering' of the beam and the back-scatter of energy due to the macroscopic granular structure of the weld. However, the influence of weld properties, such as mean grain size and orientation distribution, on the magnitude of scattered ultrasound is not well understood. A hybrid model has been developed to allow the study of grain-scatter effects in austenitic welds. An efficient 2D Finite Element (FE) method is used to calculate the complete scattering response from a single elliptical austenitic grain of arbitrary length and width as a function of the specific inspection frequency. A grain allocation model of the weld is presented to approximate the characteristic structures observed in austenitic welds and the complete scattering behaviour of each grain calculated. This model is incorporated into a semi-analytical framework for a single-element inspection of a typical weld in immersion. Experimental validation evidence is demonstrated indicating excellent qualitative agreement of SNR as a function of frequency and a minimum SNR difference of 2 dB at a centre frequency of 2.25 MHz. Additionally, an example Monte-Carlo study is presented detailing the variation of SNR as a function of the anisotropy distribution of the weld, and the application of confidence analysis to inform inspection development.
Early experiments with the OpenMP/MPI hybrid programming model.
Lusk, E.; Chan, A.; Mathematics and Computer Science; Univ. of Chicago
2008-01-01
The paper describes some very early experiments on new architectures that support the hybrid programming model. The results are promising in that OpenMP threads interact with MPI as desired, allowing OpenMP-agnostic tools to be used. They explore three environments: a 'typical' Linux cluster, a new large-scale machine from SiCortex, and the new IBM BG/P, which have quite different compilers and runtime systems for both OpenMP and MPI. They look at a few simple, diagnostic programs, and one 'application-like' test program. They demonstrate the use of a tool that can examine the detailed sequence of events in a hybrid program and illustrate that a hybrid computation might not always proceed as expected.
Bootstrap data methodology for sequential hybrid model building
NASA Technical Reports Server (NTRS)
Volponi, Allan J. (Inventor); Brotherton, Thomas (Inventor)
2007-01-01
A method for modeling engine operation comprising the steps of: 1. collecting a first plurality of sensory data, 2. partitioning a flight envelope into a plurality of sub-regions, 3. assigning the first plurality of sensory data into the plurality of sub-regions, 4. generating an empirical model of at least one of the plurality of sub-regions, 5. generating a statistical summary model for at least one of the plurality of sub-regions, 6. collecting an additional plurality of sensory data, 7. partitioning the second plurality of sensory data into the plurality of sub-regions, 8. generating a plurality of pseudo-data using the empirical model, and 9. concatenating the plurality of pseudo-data and the additional plurality of sensory data to generate an updated empirical model and an updated statistical summary model for at least one of the plurality of sub-regions.
The HYCOM (HYbrid Coordinate Ocean Model) Data Assimilative System
2007-06-01
predictability of coastal and regional subsystems, and z-levels (constant fixed depths) are best used to initial conditions for climate forecast models...dynamically smooth transition to a coordinates Lagrangian layer model in the sense that the MICOM in shallow coastal regions and to fixed pressure-level...models with fixed z- and a-coordinates greatest extent possible while enforcing the minimum that use the continuity equation to diagnose vertical
Simulating physiological interactions in a hybrid system of mathematical models.
Kretschmer, Jörn; Haunsberger, Thomas; Drost, Erick; Koch, Edmund; Möller, Knut
2014-12-01
Mathematical models can be deployed to simulate physiological processes of the human organism. Exploiting these simulations, reactions of a patient to changes in the therapy regime can be predicted. Based on these predictions, medical decision support systems (MDSS) can help in optimizing medical therapy. An MDSS designed to support mechanical ventilation in critically ill patients should not only consider respiratory mechanics but should also consider other systems of the human organism such as gas exchange or blood circulation. A specially designed framework allows combining three model families (respiratory mechanics, cardiovascular dynamics and gas exchange) to predict the outcome of a therapy setting. Elements of the three model families are dynamically combined to form a complex model system with interacting submodels. Tests revealed that complex model combinations are not computationally feasible. In most patients, cardiovascular physiology could be simulated by simplified models decreasing computational costs. Thus, a simplified cardiovascular model that is able to reproduce basic physiological behavior is introduced. This model purely consists of difference equations and does not require special algorithms to be solved numerically. The model is based on a beat-to-beat model which has been extended to react to intrathoracic pressure levels that are present during mechanical ventilation. The introduced reaction to intrathoracic pressure levels as found during mechanical ventilation has been tuned to mimic the behavior of a complex 19-compartment model. Tests revealed that the model is able to represent general system behavior comparable to the 19-compartment model closely. Blood pressures were calculated with a maximum deviation of 1.8 % in systolic pressure and 3.5 % in diastolic pressure, leading to a simulation error of 0.3 % in cardiac output. The gas exchange submodel being reactive to changes in cardiac output showed a resulting deviation of less
Rainfall-runoff modeling through hybrid intelligent system
NASA Astrophysics Data System (ADS)
Nayak, P. C.; Sudheer, K. P.; Jain, S. K.
2007-07-01
This study explores the potential of integrating two different artificial intelligence techniques, namely neural network and fuzzy logic, effectively to model the rainfall-runoff process from rainfall and runoff information. The integration is achieved through representing fuzzy system computations in a generic artificial neural network (ANN) architecture, which is functionally equivalent to a fuzzy inference system. The model is initialized by a hyperellipsoidal fuzzy clustering (HEC) procedure, which identifies suitable numbers of fuzzy if-then rules through proper partition of the input space. The parameters of the membership functions are optimized using a nonlinear optimization procedure. The consequent functions are chosen to be linear in their parameters, and a standard least squares error method is employed for parameter estimation. The proposed model is tested on two case studies: Narmada basin in India and Kentucky basin in the United States. The results are highly encouraging as the model is able to explain more than 92% of the variance. The performance of the proposed model is found to be comparable to that of an adaptive neural based fuzzy inference system (ANFIS) developed for both the basins. The number of parameters in the proposed model is fewer compared to ANFIS, and the former can be trained in lesser time. It is also observed that the proposed model simulates the peak flow better than ANFIS. Overall, the study suggests that the proposed model can potentially be a viable alternative to ANFIS for use as an operational tool for rainfall runoff modeling purposes.
NASA Astrophysics Data System (ADS)
Hu, Y.; Balachandran, S.; Pachon, J. E.; Baek, J.; Ivey, C.; Holmes, H.; Odman, M. T.; Mulholland, J. A.; Russell, A. G.
2014-06-01
A hybrid fine particulate matter (PM2.5) source apportionment approach based on a receptor model (RM) species balance and species specific source impacts from a chemical transport model (CTM) equipped with a sensitivity analysis tool is developed to provide physically and chemically consistent relationships between source emissions and receptor impacts. This hybrid approach enhances RM results by providing initial estimates of source impacts from a much larger number of sources than are typically used in RMs, and provides source-receptor relationships for secondary species. Further, the method addresses issues of source collinearities and accounts for emissions uncertainties. We apply this hybrid approach to conduct PM2.5 source apportionment at Chemical Speciation Network (CSN) sites across the US. Ambient PM2.5 concentrations at these receptor sites were apportioned to 33 separate sources. Hybrid method results led to large changes of impacts from CTM estimates for sources such as dust, woodstoves, and other biomass-burning sources, but limited changes to others. The refinements reduced the differences between CTM-simulated and observed concentrations of individual PM2.5 species by over 98% when using a weighted least-squares error minimization. The rankings of source impacts changed from the initial estimates, further demonstrating that CTM-only results should be evaluated with observations. Assessment with RM results at six US locations showed that the hybrid results differ somewhat from commonly resolved sources. The hybrid method also resolved sources that typical RM methods do not capture without extra measurement information for unique tracers. The method can be readily applied to large domains and long (such as multi-annual) time periods to provide source impact estimates for management- and health-related studies.
A hybrid deep neural network and physically based distributed model for river stage prediction
NASA Astrophysics Data System (ADS)
hitokoto, Masayuki; sakuraba, Masaaki
2016-04-01
We developed the real-time river stage prediction model, using the hybrid deep neural network and physically based distributed model. As the basic model, 4 layer feed-forward artificial neural network (ANN) was used. As a network training method, the deep learning technique was applied. To optimize the network weight, the stochastic gradient descent method based on the back propagation method was used. As a pre-training method, the denoising autoencoder was used. Input of the ANN model is hourly change of water level and hourly rainfall, output data is water level of downstream station. In general, the desirable input of the ANN has strong correlation with the output. In conceptual hydrological model such as tank model and storage-function model, river discharge is governed by the catchment storage. Therefore, the change of the catchment storage, downstream discharge subtracted from rainfall, can be the potent input candidate of the ANN model instead of rainfall. From this point of view, the hybrid deep neural network and physically based distributed model was developed. The prediction procedure of the hybrid model is as follows; first, downstream discharge was calculated by the distributed model, and then estimates the hourly change of catchment storage form rainfall and calculated discharge as the input of the ANN model, and finally the ANN model was calculated. In the training phase, hourly change of catchment storage can be calculated by the observed rainfall and discharge data. The developed model was applied to the one catchment of the OOYODO River, one of the first-grade river in Japan. The modeled catchment is 695 square km. For the training data, 5 water level gauging station and 14 rain-gauge station in the catchment was used. The training floods, superior 24 events, were selected during the period of 2005-2014. Prediction was made up to 6 hours, and 6 models were developed for each prediction time. To set the proper learning parameters and network
A hybrid, coupled approach for modeling charged fluids from the nano to the mesoscale
Cheung, James; Frischknecht, Amalie L.; Perego, Mauro; ...
2017-07-20
Here, we develop and demonstrate a new, hybrid simulation approach for charged fluids, which combines the accuracy of the nonlocal, classical density functional theory (cDFT) with the efficiency of the Poisson–Nernst–Planck (PNP) equations. The approach is motivated by the fact that the more accurate description of the physics in the cDFT model is required only near the charged surfaces, while away from these regions the PNP equations provide an acceptable representation of the ionic system. We formulate the hybrid approach in two stages. The first stage defines a coupled hybrid model in which the PNP and cDFT equations act independentlymore » on two overlapping domains, subject to suitable interface coupling conditions. At the second stage we apply the principles of the alternating Schwarz method to the hybrid model by using the interface conditions to define the appropriate boundary conditions and volume constraints exchanged between the PNP and the cDFT subdomains. Numerical examples with two representative examples of ionic systems demonstrate the numerical properties of the method and its potential to reduce the computational cost of a full cDFT calculation, while retaining the accuracy of the latter near the charged surfaces.« less
Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model.
Zhou, Changjun; Hou, Caixia; Zhang, Qiang; Wei, Xiaopeng
2013-09-01
The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences.
Large tensor spectrum of BICEP2 in the natural supersymmetric hybrid model
NASA Astrophysics Data System (ADS)
Choi, Ki-Young; Kyae, Bumseok
2014-07-01
The large tensor spectrum recently observed by the BICEP2 Collaboration requires a super-Planckian field variation of the inflaton in the single-field inflationary scenario. The required slow-roll parameter ɛ ≈0.01 would restrict the e-folding number to around 7 in (sub-)Planckian inflationary models. To overcome such problems, we consider a two-field scenario based on the natural assisted supersymmetric (SUSY) hybrid model {"natural SUSY hybrid inflation" [K.-Y. Choi and B. Kyae, Phys. Lett. B 706, 243 (2012)]}, which combines the SUSY hybrid and the natural inflation models. The axionic inflaton field from the natural inflation sector can admit the right values for the tensor spectrum as well as a spectral index of 0.96 with a decay constant smaller than the Planck scale, f ≲MP. On the other hand, the vacuum energy of 2×1016 GeV with 50 e-folds is provided by the inflaton coming from the SUSY hybrid sector, avoiding the eta problem. These are achieved by introducing both the U(1)R and a shift symmetry, and employing the minimal Kähler potential.
NASA Astrophysics Data System (ADS)
Cardelli, E.; Faba, A.; Laudani, A.; Lozito, G. M.; Riganti Fulginei, F.; Salvini, A.
2016-04-01
This paper presents a hybrid neural network approach to model magnetic hysteresis at macro-magnetic scale. That approach aims to be coupled together with numerical treatments of magnetic hysteresis such as FEM numerical solvers of the Maxwell's equations in time domain, as in case of the non-linear dynamic analysis of electrical machines, and other similar devices, allowing a complete computer simulation with acceptable run times. The proposed Hybrid Neural System consists of four inputs representing the magnetic induction and magnetic field components at each time step and it is trained by 2D and scalar measurements performed on the magnetic material to be modeled. The magnetic induction B is assumed as entry point and the output of the Hybrid Neural System returns the predicted value of the field H at the same time step. Within the Hybrid Neural System, a suitably trained neural network is used for predicting the hysteretic behavior of the material to be modeled. Validations with experimental tests and simulations for symmetric, non-symmetric and minor loops are presented.
Modeling Hybridization Kinetics of Gene Probes in a DNA Biochip Using FEMLAB.
Munir, Ahsan; Waseem, Hassan; Williams, Maggie R; Stedtfeld, Robert D; Gulari, Erdogan; Tiedje, James M; Hashsham, Syed A
2017-05-29
Microfluidic DNA biochips capable of detecting specific DNA sequences are useful in medical diagnostics, drug discovery, food safety monitoring and agriculture. They are used as miniaturized platforms for analysis of nucleic acids-based biomarkers. Binding kinetics between immobilized single stranded DNA on the surface and its complementary strand present in the sample are of interest. To achieve optimal sensitivity with minimum sample size and rapid hybridization, ability to predict the kinetics of hybridization based on the thermodynamic characteristics of the probe is crucial. In this study, a computer aided numerical model for the design and optimization of a flow-through biochip was developed using a finite element technique packaged software tool (FEMLAB; package included in COMSOL Multiphysics) to simulate the transport of DNA through a microfluidic chamber to the reaction surface. The model accounts for fluid flow, convection and diffusion in the channel and on the reaction surface. Concentration, association rate constant, dissociation rate constant, recirculation flow rate, and temperature were key parameters affecting the rate of hybridization. The model predicted the kinetic profile and signal intensities of eighteen 20-mer probes targeting vancomycin resistance genes (VRGs). Predicted signal intensities and hybridization kinetics strongly correlated with experimental data in the biochip (R² = 0.8131).
Application of Equilibrium Models of Solution Hybridization to Microarray Design and Analysis
Gharaibeh, Raad Z.; Newton, Joshua M.; Weller, Jennifer W.; Gibas, Cynthia J.
2010-01-01
Background The probe percent bound value, calculated using multi-state equilibrium models of solution hybridization, is shown to be useful in understanding the hybridization behavior of microarray probes having 50 nucleotides, with and without mismatches. These longer oligonucleotides are in widespread use on microarrays, but there are few controlled studies of their interactions with mismatched targets compared to 25-mer based platforms. Principal Findings 50-mer oligonucleotides with centrally placed single, double and triple mismatches were spotted on an array. Over a range of target concentrations it was possible to discriminate binding to perfect matches and mismatches, and the type of mismatch could be predicted accurately in the concentration midrange (100 pM to 200 pM) using solution hybridization modeling methods. These results have implications for microarray design, optimization and analysis methods. Conclusions Our results highlight the importance of incorporating biophysical factors in both the design and the analysis of microarrays. Use of the probe “percent bound” value predicted by equilibrium models of hybridization is confirmed to be important for predicting and interpreting the behavior of long oligonucleotide arrays, as has been shown for short oligonucleotide arrays. PMID:20548788
Hybrid modeling and empirical analysis of automobile supply chain network
NASA Astrophysics Data System (ADS)
Sun, Jun-yan; Tang, Jian-ming; Fu, Wei-ping; Wu, Bing-ying
2017-05-01
Based on the connection mechanism of nodes which automatically select upstream and downstream agents, a simulation model for dynamic evolutionary process of consumer-driven automobile supply chain is established by integrating ABM and discrete modeling in the GIS-based map. Firstly, the rationality is proved by analyzing the consistency of sales and changes in various agent parameters between the simulation model and a real automobile supply chain. Second, through complex network theory, hierarchical structures of the model and relationships of networks at different levels are analyzed to calculate various characteristic parameters such as mean distance, mean clustering coefficients, and degree distributions. By doing so, it verifies that the model is a typical scale-free network and small-world network. Finally, the motion law of this model is analyzed from the perspective of complex self-adaptive systems. The chaotic state of the simulation system is verified, which suggests that this system has typical nonlinear characteristics. This model not only macroscopically illustrates the dynamic evolution of complex networks of automobile supply chain but also microcosmically reflects the business process of each agent. Moreover, the model construction and simulation of the system by means of combining CAS theory and complex networks supplies a novel method for supply chain analysis, as well as theory bases and experience for supply chain analysis of auto companies.
APT: Costs and Benefits of a Hybrid Model
ERIC Educational Resources Information Center
Dijkstra, Ton; Haverkort, Marco
2004-01-01
In their keynote contribution, Truscott and Sharwood Smith offer a general model of language development from a processing perspective. As they state, their model is very ambitious: Their "acquisition by processing" theory (APT) aims not only at explaining both first and second language acquisition but also real-time processing in language…
A Toolkit for Building Hybrid, Multi-Resolution PMESII Models
2007-11-01
Ptolemy Integration 23 4. Support for Interoperable PMESII Modeling 31 5. Support for PMESII Model Verification and Validation 34 5.1 Verification 34...terrorist leader behavior .............................................. 20 Figure 3-2: Editors for Two Ptolemy Components in the GRADE Edit Workspace...27 Figure 3-3: Selecting the Customize→Ports Context Menu Item in the Ptolemy Component Editor
Annual updating of plantation inventory estimates using hybrid models
Peter Snowdon
2000-01-01
Data for Pinus radiata D. Don grown in the Australian Capital Territory (ACT) are used to show that annual indices of growth potential can be successfully incorporated into Schumacher projection models of stand basal area growth. Significant reductions in the error mean squares of the models can be obtained by including an annual growth index derived...
Data-driven components in a model of inner-shelf sorted bedforms: a new hybrid model
NASA Astrophysics Data System (ADS)
Goldstein, E. B.; Coco, G.; Murray, A. B.; Green, M. O.
2014-01-01
Numerical models rely on the parameterization of processes that often lack a deterministic description. In this contribution we demonstrate the applicability of using machine learning, a class of optimization tools from the discipline of computer science, to develop parameterizations when extensive data sets exist. We develop a new predictor for near-bed suspended sediment reference concentration under unbroken waves using genetic programming, a machine learning technique. We demonstrate that this newly developed parameterization performs as well or better than existing empirical predictors, depending on the chosen error metric. We add this new predictor into an established model for inner-shelf sorted bedforms. Additionally we incorporate a previously reported machine-learning-derived predictor for oscillatory flow ripples into the sorted bedform model. This new "hybrid" sorted bedform model, whereby machine learning components are integrated into a numerical model, demonstrates a method of incorporating observational data (filtered through a machine learning algorithm) directly into a numerical model. Results suggest that the new hybrid model is able to capture dynamics previously absent from the model - specifically, two observed pattern modes of sorted bedforms. Lastly we discuss the challenge of integrating data-driven components into morphodynamic models and the future of hybrid modeling.
Photoionization models of the CALIFA H II regions. I. Hybrid models
NASA Astrophysics Data System (ADS)
Morisset, C.; Delgado-Inglada, G.; Sánchez, S. F.; Galbany, L.; García-Benito, R.; Husemann, B.; Marino, R. A.; Mast, D.; Roth, M. M.
2016-10-01
Photoionization models of H ii regions require as input a description of the ionizing spectral energy distribution (SED) and of the gas distribution, in terms of ionization parameter U and chemical abundances (e.g., O/H and N/O).A strong degeneracy exists between the hardness of the SED and U, which in turn leads to high uncertainties in the determination of the other parameters, including abundances. One way to resolve the degeneracy is to fix one of the parameters using additional information. For each of the ~20 000 sources of the CALIFA H ii regions catalog, a grid of photoionization models is computed assuming the ionizing SED to be described by the underlying stellar population obtained from spectral synthesis modeling. The ionizing SED is then defined as the sum of various stellar bursts of different ages and metallicities. This solves the degeneracy between the shape of the ionizing SED and U. The nebular metallicity (associated with O/H) is defined using the classical strong line method O3N2 (which gives our models the status of "hybrids"). The remaining free parameters are the abundance ratio N/O and the ionization parameter U, which are determined by looking for the model fitting [N ii]/Hα and [O iii]/Hβ. The models are also selected to fit [O ii]/Hβ. This process leads to a set of ~3200 models that reproduce the three observations simultaneously. We find that the regions associated with young stellar bursts (i.e., ionized by OB stars) are affected by leaking of ionizing photons,the proportion of escaping photons having a median of 80%. The set of photoionization models satisfactorily reproduces the electron temperature derived from the [O iii]λ4363/5007 line ratio. We determine new relations between the nebular parameters, like the ionization parameter U and the [O ii]/[O iii] or [S ii]/[S iii] line ratios. A new relation between N/O and O/H is obtained, mostly compatible with previous empirical determinations (and not with previous results obtained
Hybrid model for wireless mobility management using IPv6
NASA Astrophysics Data System (ADS)
Howie, Douglas P.; Sun, Junzhao; Koivisto, Antti T.
2001-07-01
Within the coming decade, there will be a dramatic increase in the availability of inexpensive, computationally powerful mobile devices running applications which use the Internet Protocol (IP) to access multimedia services over broad-band wireless connections. To this end, there has been extensive research and standardization in the areas of Mobile IP and IPv6. The purpose of this paper is to apply this work to the issues involved in designing a mobility model able to adapt to different wireless mobile IP scenarios. We describe the usefulness of this model in the 4th generation mobile multimedia systems to come. This new model has been synthesized through a comparative analysis of current mobile IP models where particular attention has been given to the problems of mobile IP handoff and mobility management and their impact on QoS. By applying a unique perspective to these problems, our model is used to set a roadmap for future mobile IPv6 testbed construction.
Teixeira, A; Cunha, A E; Clemente, J J; Moreira, J L; Cruz, H J; Alves, P M; Carrondo, M J T; Oliveira, R
2005-08-22
In this work a model-based optimization study of fed-batch BHK-21 cultures expressing the human fusion glycoprotein IgG1-IL2 was performed. It was concluded that due to the complexity of the BHK metabolism it is rather difficult to develop a kinetic model with sufficient accuracy for optimization studies. Many kinetic expressions and a large number of parameters are involved resulting in a complex identification problem. For this reason, an alternative more cost-effective methodology based on hybrid grey-box models was adopted. Several model structures combining the a priori reliable first principles knowledge with black-box models were investigated using data from batch and fed-batch experiments. It has been reported in previous studies that the BHK metabolism exhibits modulation particularities when compared to other mammalian cell lines. It was concluded that these mechanisms were effectively captured by the hybrid model, this being of crucial importance for the successful optimization of the process operation. A method was proposed to monitor the risk of hybrid model unreliability and to constraint the optimization results to acceptable risk levels. From the optimization study it was concluded that the process productivity may be considerably increased if the glutamine and glucose concentrations are maintained at low levels during the growth phase and then glutamine feeding is increased.
NASA Astrophysics Data System (ADS)
Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong
2017-01-01
A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model.
Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong
2017-01-01
A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model. PMID:28120889
NASA Astrophysics Data System (ADS)
Chiadamrong, N.; Piyathanavong, V.
2017-04-01
Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The proposed approach is based on iterative procedures until the difference between subsequent solutions satisfies the pre-determined termination criteria. The effectiveness of proposed approach is illustrated by an example, which shows closer to optimal results with much faster solving time than the results obtained from the conventional simulation-based optimization model. The efficacy of this proposed hybrid approach is promising and can be applied as a powerful tool in designing a real supply chain network. It also provides the possibility to model and solve more realistic problems, which incorporate dynamism and uncertainty.
A hybrid model of the CO2 geochemical cycle and its application to large impact events
NASA Technical Reports Server (NTRS)
Kasting, J. F.; Pollack, J. B.; Toon, O. B.; Richardson, S. M.
1986-01-01
The effects of a large asteriod or comet impact on modern and ancient marine biospheres are analyzed. A hybrid model of the carbonate-silicate geochemical cycle, which is capable of calculating the concentrations of carbon dioxide in the atmosphere, ocean, and sedimentary rocks, is described. The differences between the Keir and Berger (1983) model and the hybrid model are discussed. Equilibrium solutions are derived for the preindustrial atmosphere/ocean system and for a system similar to that of the late Cretaceous Period. The model data reveal that globl darkening caused by a stratospheric dust veil could destroy the existing phytoplankton within a period of several weeks or months, nd the dissolution of atmospheric NO(x) compounds would lower the pH of ocean surface waters and release CO2 into the atmosphere. It is noted that the surface temperatures could be increased by several degrees and surface oceans would be uninhabitable for calcaerous organisms for approximately 20 years.
A linear dispersion relation for the hybrid kinetic-ion/fluid-electron model of plasma physics
NASA Astrophysics Data System (ADS)
Told, D.; Cookmeyer, J.; Astfalk, P.; Jenko, F.
2016-07-01
A dispersion relation for a commonly used hybrid model of plasma physics is developed, which combines fully kinetic ions and a massless-electron fluid description. Although this model and variations of it have been used to describe plasma phenomena for about 40 years, to date there exists no general dispersion relation to describe the linear wave physics contained in the model. Previous efforts along these lines are extended here to retain arbitrary wave propagation angles, temperature anisotropy effects, as well as additional terms in the generalized Ohm’s law which determines the electric field. A numerical solver for the dispersion relation is developed, and linear wave physics is benchmarked against solutions of a full Vlasov-Maxwell dispersion relation solver. This work opens the door to a more accurate interpretation of existing and future wave and turbulence simulations using this type of hybrid model.
Accurate modeling of switched reluctance machine based on hybrid trained WNN
NASA Astrophysics Data System (ADS)
Song, Shoujun; Ge, Lefei; Ma, Shaojie; Zhang, Man
2014-04-01
According to the strong nonlinear electromagnetic characteristics of switched reluctance machine (SRM), a novel accurate modeling method is proposed based on hybrid trained wavelet neural network (WNN) which combines improved genetic algorithm (GA) with gradient descent (GD) method to train the network. In the novel method, WNN is trained by GD method based on the initial weights obtained per improved GA optimization, and the global parallel searching capability of stochastic algorithm and local convergence speed of deterministic algorithm are combined to enhance the training accuracy, stability and speed. Based on the measured electromagnetic characteristics of a 3-phase 12/8-pole SRM, the nonlinear simulation model is built by hybrid trained WNN in Matlab. The phase current and mechanical characteristics from simulation under different working conditions meet well with those from experiments, which indicates the accuracy of the model for dynamic and static performance evaluation of SRM and verifies the effectiveness of the proposed modeling method.
Accurate modeling of switched reluctance machine based on hybrid trained WNN
Song, Shoujun Ge, Lefei; Ma, Shaojie; Zhang, Man
2014-04-15
According to the strong nonlinear electromagnetic characteristics of switched reluctance machine (SRM), a novel accurate modeling method is proposed based on hybrid trained wavelet neural network (WNN) which combines improved genetic algorithm (GA) with gradient descent (GD) method to train the network. In the novel method, WNN is trained by GD method based on the initial weights obtained per improved GA optimization, and the global parallel searching capability of stochastic algorithm and local convergence speed of deterministic algorithm are combined to enhance the training accuracy, stability and speed. Based on the measured electromagnetic characteristics of a 3-phase 12/8-pole SRM, the nonlinear simulation model is built by hybrid trained WNN in Matlab. The phase current and mechanical characteristics from simulation under different working conditions meet well with those from experiments, which indicates the accuracy of the model for dynamic and static performance evaluation of SRM and verifies the effectiveness of the proposed modeling method.
Two-field axion-monodromy hybrid inflation model: Dante's Waterfall
NASA Astrophysics Data System (ADS)
Carone, Christopher D.; Erlich, Joshua; Sensharma, Anuraag; Wang, Zhen
2015-02-01
We describe a hybrid axion-monodromy inflation model motivated by the Dante's Inferno scenario. In Dante's Inferno, a two-field potential features a stable trench along which a linear combination of the two fields slowly rolls, rendering the dynamics essentially identical to that of single-field chaotic inflation. A shift symmetry allows for the Lyth bound to be effectively evaded as in other axion-monodromy models. In our proposal, the potential is concave downward near the origin and the inflaton trajectory is a gradual downward spiral, ending at a point where the trench becomes unstable. There, the fields begin falling rapidly towards the minimum of the potential and inflation terminates as in a hybrid model. We find parameter choices that reproduce observed features of the cosmic microwave background, and discuss our model in light of recent results from the BICEP2 and Planck experiments.
Suited and Unsuited Hybrid III Impact Testing and Finite Element Model Characterization
NASA Technical Reports Server (NTRS)
Lawrence, C.; Somers, J. T.; Baldwin, M. A.; Wells, J. A.; Newby, N.; Currie, N. J.
2016-01-01
NASA spacecraft design requirements for occupant protection are a combination of the Brinkley Dynamic Response Criteria and injury assessment reference values (IARV) extracted from anthropomorphic test devices (ATD). For the ATD IARVs, the requirements specify the use of the 5th percentile female Hybrid III and the 95th percentile male Hybrid III. Each of these ATDs is required to be fitted with an articulating pelvis (also known as the aerospace pelvis) and a straight spine. The articulating pelvis is necessary for the ATD to fit into spacecraft seats, while the straight spine is required as injury metrics for vertical accelerations are better defined for this configuration. Sled testing of the Hybrid III 5th Percentile Female Anthropomorphic Test Device (ATD) was performed at Wright-Patterson Air Force Base (WAPFB). Two 5th Percentile ATDs were tested, the Air Force Research Lab (AFRL) and NASA owned Hybrid III ATDs with aerospace pelvises. Testing was also conducted with a NASA-owned 95th Percentile Male Hybrid III with aerospace pelvis at WPAFB. Testing was performed using an Orion seat prototype provided by Johnson Space Center (JSC). A 5-point harness comprised of 2 inch webbing was also provided by JSC. For suited runs, a small and extra-large Advanced Crew Escape System (ACES) suit and helmet were also provided by JSC. Impact vectors were combined frontal/spinal and rear/lateral. Some pure spinal and rear axis testing was also performed for model validation. Peak accelerations ranged between 15 and 20-g. This range was targeted because the ATD responses fell close to the IARV defined in the Human-Systems Integration Requirements (HSIR) document. Rise times varied between 70 and 110 ms to assess differences in ATD responses and model correlation for different impact energies. The purpose of the test series was to evaluate the Hybrid III ATD models in Orion-specific landing orientations both with and without a spacesuit. The results of these tests were used
Provencher-Mandeville, Josée; Debnath, Chhanda; Mandal, Sanat K; Leblanc, Valérie; Parent, Sophie; Asselin, Eric; Bérubé, Gervais
2011-01-01
The synthesis of a series of 17β-estradiol-platinum(II) hybrid molecules is reported. The hybrids are made of a PEG linking chain of various length and a 2-(2'-aminoethyl)pyridine ligand. They are prepared from estrone in only 5 chemical steps with an overall yield of 22%. The length of the PEG chain does not influence the solubility of the compounds as it remains relatively constant throughout the series. MTT assays showed that the derivative with the longest PEG chain showed the best activity against two human breast cancer cell lines (MCF-7 and MDA-MB-231). The novel PEG-hybrids are also compared in terms of activities with two other families of 17β-estradiol-platinum(II) hybrids that we reported in previous studies. Molecular modeling study performed on a representative member of each family of hybrids reveals distinct molecular interactions with the estrogen receptor α which further corroborates their notably contrasting cytocidal activities on breast cancer cell lines. This study also shows that lipophilicity and the orientation of the tether chain between the estrogenic portion and the platinum(II) core contribute markedly to the biological activity of the various families of hybrids. The most active hybrids are those possessing an alkyl tether chain at position 16β of the steroid nucleus. For example, derivative 3 (p=6) is about 16 times more potent on MCF-7 breast cancer cells than the corresponding 16α-PEG-hybrids (2b) made in this study. Copyright © 2010 Elsevier Inc. All rights reserved.
THYME: Toolkit for Hybrid Modeling of Electric Power Systems
Nutaro Kalyan Perumalla, James Joseph
2011-01-01
THYME is an object oriented library for building models of wide area control and communications in electric power systems. This software is designed as a module to be used with existing open source simulators for discrete event systems in general and communication systems in particular. THYME consists of a typical model for simulating electro-mechanical transients (e.g., as are used in dynamic stability studies), data handling objects to work with CDF and PTI formatted power flow data, and sample models of discrete sensors and controllers.
Chen, Yumiao; Yang, Zhongliang
2017-01-01
Recently, several researchers have considered the problem of reconstruction of handwriting and other meaningful arm and hand movements from surface electromyography (sEMG). Although much progress has been made, several practical limitations may still affect the clinical applicability of sEMG-based techniques. In this paper, a novel three-step hybrid model of coordinate state transition, sEMG feature extraction and gene expression programming (GEP) prediction is proposed for reconstructing drawing traces of 12 basic one-stroke shapes from multichannel surface electromyography. Using a specially designed coordinate data acquisition system, we recorded the coordinate data of drawing traces collected in accordance with the time series while 7-channel EMG signals were recorded. As a widely-used time domain feature, Root Mean Square (RMS) was extracted with the analysis window. The preliminary reconstruction models can be established by GEP. Then, the original drawing traces can be approximated by a constructed prediction model. Applying the three-step hybrid model, we were able to convert seven channels of EMG activity recorded from the arm muscles into smooth reconstructions of drawing traces. The hybrid model can yield a mean accuracy of 74% in within-group design (one set of prediction models for all shapes) and 86% in between-group design (one separate set of prediction models for each shape), averaged for the reconstructed x and y coordinates. It can be concluded that it is feasible for the proposed three-step hybrid model to improve the reconstruction ability of drawing traces from sEMG. PMID:28261041
Chen, Yumiao; Yang, Zhongliang
2017-01-01
Recently, several researchers have considered the problem of reconstruction of handwriting and other meaningful arm and hand movements from surface electromyography (sEMG). Although much progress has been made, several practical limitations may still affect the clinical applicability of sEMG-based techniques. In this paper, a novel three-step hybrid model of coordinate state transition, sEMG feature extraction and gene expression programming (GEP) prediction is proposed for reconstructing drawing traces of 12 basic one-stroke shapes from multichannel surface electromyography. Using a specially designed coordinate data acquisition system, we recorded the coordinate data of drawing traces collected in accordance with the time series while 7-channel EMG signals were recorded. As a widely-used time domain feature, Root Mean Square (RMS) was extracted with the analysis window. The preliminary reconstruction models can be established by GEP. Then, the original drawing traces can be approximated by a constructed prediction model. Applying the three-step hybrid model, we were able to convert seven channels of EMG activity recorded from the arm muscles into smooth reconstructions of drawing traces. The hybrid model can yield a mean accuracy of 74% in within-group design (one set of prediction models for all shapes) and 86% in between-group design (one separate set of prediction models for each shape), averaged for the reconstructed x and y coordinates. It can be concluded that it is feasible for the proposed three-step hybrid model to improve the reconstruction ability of drawing traces from sEMG.
Valuation and Modeling of EQ-5D-5L Health States Using a Hybrid Approach.
Ramos-Goñi, Juan M; Pinto-Prades, Jose L; Oppe, Mark; Cabasés, Juan M; Serrano-Aguilar, Pedro; Rivero-Arias, Oliver
2017-07-01
The EQ-5D instrument is the most widely used preference-based health-related quality of life questionnaire in cost-effectiveness analysis of health care technologies. Recently, a version called EQ-5D-5L with 5 levels on each dimension was developed. This manuscript explores the performance of a hybrid approach for the modeling of EQ-5D-5L valuation data. Two elicitation techniques, the composite time trade-off, and discrete choice experiments, were applied to a sample of the Spanish population (n=1000) using a computer-based questionnaire. The sampling process consisted of 2 stages: stratified sampling of geographic area, followed by systematic sampling in each area. A hybrid regression model combining composite time trade-off and discrete choice data was used to estimate the potential value sets using main effects as starting point. The comparison between the models was performed using the criteria of logical consistency, goodness of fit, and parsimony. Twenty-seven participants from the 1000 were removed following the exclusion criteria. The best-fitted model included 2 significant interaction terms but resulted in marginal improvements in model fit compared to the main effects model. We therefore selected the model results with main effects as a potential value set for this methodological study, based on the parsimony criteria. The results showed that the main effects hybrid model was consistent, with a range of utility values between 1 and -0.224. This paper shows the feasibility of using a hybrid approach to estimate a value set for EQ-5D-5L valuation data.
NASA Astrophysics Data System (ADS)
Geli, H. M. E.
2015-12-01
Estimates of actual crop evapotranspiration (ETa) at field scale over the growing season are required for improving agricultural water management, particularly in water limited and drought prone regions. Remote sensing data from multiple platforms such as airborne and Landsat-based sensors can be used to provide these estimates. Combining these data with surface energy balance models can provide ETa estimates at sub- field scale as well as information on vegetation stress and soil moisture conditions. However, the temporal resolution of airborne and Landsat data does not allow for a continuous ETa monitoring over the course of the growing season. This study presents the application of a hybrid ETa modeling approach developed for monitoring daily ETa and root zone available water at high spatial resolutions. The hybrid ETa modeling approach couples a thermal-based energy balance model with a water balance-based scheme using data assimilation. The two source energy balance (TSEB) model is used to estimate instantaneous ETa which can be extrapolated to daily ETa using a water balance model modified to use the reflectance-based basal crop coefficient for interpolating ETa in between airborne and/or Landsat overpass dates. Moreover, since it is a water balance model, the soil moisture profile is also estimated. The hybrid ETa approach is applied over vineyard fields in central California. High resolution airborne and Landsat imagery were used to drive the hybrid model. These images were collected during periods that represented different vine phonological stages in 2013 growing season. Estimates of daily ETa and surface energy balance fluxes will be compared with ground-based eddy covariance tower measurements. Estimates of soil moisture at multiple depths will be compared with measurements.
Hybrid Modeling and Diagnosis in the Real World: A Case Study
2002-05-04
mapped to system components case study of an aircraft fuel system, and discuss and parameters. The relations in the model are employed to methodologies for...UNCLASSIFIED Defense Technical Information Center Compilation Part Notice ADP012687 TITLE: Hybrid Modeling and Diagnosis in the Real World : A Case...Study DISTRIBUTION: Approved for public release, distribution unlimited This paper is part of the following report: TITLE: Thirteenth International
Temperature analysis of induction motors using a hybrid thermal model with distributed heat sources
NASA Astrophysics Data System (ADS)
Mukhopadhyay, S. C.; Pal, S. K.
1998-06-01
The article presents a hybrid thermal model for the accurate estimation of temperature distribution of induction motors. The developed model is a combination of lumped and distributed thermal parameters which are obtained from motor dimensions and other constants such as material density, specific heats, thermal conductivity, etc. The model is especially suited for the derating of induction motors operating under distorted and unbalanced supply condition. The model have been applied to a small (2hp, 415 V, 3-phase) cage rotor induction motor. The performance of the model is confirmed by experimental temperature data from the body and the conductor inside the slots of the motor.
Pope, Bernard J; Fitch, Blake G; Pitman, Michael C; Rice, John J; Reumann, Matthias
2011-10-01
Future multiscale and multiphysics models that support research into human disease, translational medical science, and treatment can utilize the power of high-performance computing (HPC) systems. We anticipate that computationally efficient multiscale models will require the use of sophisticated hybrid programming models, mixing distributed message-passing processes [e.g., the message-passing interface (MPI)] with multithreading (e.g., OpenMP, Pthreads). The objective of this study is to compare the performance of such hybrid programming models when applied to the simulation of a realistic physiological multiscale model of the heart. Our results show that the hybrid models perform favorably when compared to an implementation using only the MPI and, furthermore, that OpenMP in combination with the MPI provides a satisfactory compromise between performance and code complexity. Having the ability to use threads within MPI processes enables the sophisticated use of all processor cores for both computation and communication phases. Considering that HPC systems in 2012 will have two orders of magnitude more cores than what was used in this study, we believe that faster than real-time multiscale cardiac simulations can be achieved on these systems.
Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.
Hagen, Espen; Dahmen, David; Stavrinou, Maria L; Lindén, Henrik; Tetzlaff, Tom; van Albada, Sacha J; Grün, Sonja; Diesmann, Markus; Einevoll, Gaute T
2016-12-01
With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm(2) patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail.
Hybrid modeling of microbial exopolysaccharide (EPS) production: The case of Enterobacter A47.
Marques, Rodolfo; von Stosch, Moritz; Portela, Rui M C; Torres, Cristiana A V; Antunes, Sílvia; Freitas, Filomena; Reis, Maria A M; Oliveira, Rui
2017-03-20
Enterobacter A47 is a bacterium that produces high amounts of a fucose-rich exopolysaccharide (EPS) from glycerol residue of the biodiesel industry. The fed-batch process is characterized by complex non-linear dynamics with highly viscous pseudo-plastic rheology due to the accumulation of EPS in the culture medium. In this paper, we study hybrid modeling as a methodology to increase the predictive power of models for EPS production optimization. We compare six hybrid structures that explore different levels of knowledge-based and machine-learning model components. Knowledge-based components consist of macroscopic material balances, Monod type kinetics, cardinal temperature and pH (CTP) dependency and power-law viscosity models. Unknown dependencies are set to be identified by a feedforward artificial neural network (ANN). A semiparametric identification schema is applied resorting to a data set of 13 independent fed-batch experiments. A parsimonious hybrid model was identified that describes the dynamics of the 13 experiments with the same parameterization. The final model is specific to Enterobacter A47 but can be easily extended to other microbial EPS processes. Copyright © 2017 Elsevier B.V. All rights reserved.
Plasma Simulation Using Gyrokinetic-Gyrofluid Hybrid Models
Scott Parker
2009-04-09
We are developing kinetic ion models for the simulation of extended MHD phenomena. The model they have developed uses full Lorentz force ions, and either drift-kinetic or gyro-kinetic electrons. Quasi-neutrality is assumed and the displacement current is neglected. They are also studying alpha particle driven Toroidal Alfven Eigenmodes (TAE) in the GEM gyrokinetic code [Chen 07]. The basic kinetic ion MHD model was recently reported in an invited talk given by Dan Barnes at the 2007 American Physical Society - Division of Plasma Physics (APS-DPP) and it has been published [Jones 04, Barnes 08]. The model uses an Ohm's law that includes the Hall term, pressure term and the electron inertia [Jones 04]. These results focused on the ion physics and assumed an isothermal electron closure. It is found in conventional gyrokinetic turbulence simulations that the timestep cannot be made much greater than the ion cyclotron period. However, the kinetic ion MHD model has the compressional mode, which further limits the timestep. They have developed an implicit scheme to avoid this timestep constraint. They have also added drift kinetic electrons. This model has been benchmarked linearly. Waves investigated where shear and compressional Alfven, whisterl, ion acoustic, and drift waves, including the kinetic damping rates. This work is ongoing and was first reported at the 2008 Sherwood Fusion Theory Conference [Chen 08] and they are working on a publication. They have also formulated an integrated gyrokinetic electron model, which is of interest for studying electron gradient instabilities and weak guide-field magnetic reconnection.
NASA Astrophysics Data System (ADS)
Ho, Kuan-Ying; Lu, I.-Hsin; Wu, Yuh-Renn
2016-03-01
A numerical model for PEDOT:PSS/SiNW hybrid solar cell has been developed and the structure has been simulated and analyzed. The limiting factor leading to low open circuit voltage (Voc) in PEDOT:PSS/SiNW hybrid solar cell is investigated. By adding a p-type silicon layer into the device to create an electric field in the silicon layer, the recombination at interface is improved and the Voc increases. The efficiency is improved to over 15% and more optimized work can be done in the future.
NASA Astrophysics Data System (ADS)
Sun, Xiaoqiang; Cai, Yingfeng; Wang, Shaohua; Liu, Yanling; Chen, Long
2016-01-01
The control problems associated with vehicle height adjustment of electronically controlled air suspension (ECAS) still pose theoretical challenges for researchers, which manifest themselves in the publications on this subject over the last years. This paper deals with modeling and control of a vehicle height adjustment system for ECAS, which is an example of a hybrid dynamical system due to the coexistence and coupling of continuous variables and discrete events. A mixed logical dynamical (MLD) modeling approach is chosen for capturing enough details of the vehicle height adjustment process. The hybrid dynamic model is constructed on the basis of some assumptions and piecewise linear approximation for components nonlinearities. Then, the on-off statuses of solenoid valves and the piecewise approximation process are described by propositional logic, and the hybrid system is transformed into the set of linear mixed-integer equalities and inequalities, denoted as MLD model, automatically by HYSDEL. Using this model, a hybrid model predictive controller (HMPC) is tuned based on online mixed-integer quadratic optimization (MIQP). Two different scenarios are considered in the simulation, whose results verify the height adjustment effectiveness of the proposed approach. Explicit solutions of the controller are computed to control the vehicle height adjustment system in realtime using an offline multi-parametric programming technology (MPT), thus convert the controller into an equivalent explicit piecewise affine form. Finally, bench experiments for vehicle height lifting, holding and lowering procedures are conducted, which demonstrate that the HMPC can adjust the vehicle height by controlling the on-off statuses of solenoid valves directly. This research proposes a new modeling and control method for vehicle height adjustment of ECAS, which leads to a closed-loop system with favorable dynamical properties.
Qualitative Fault Isolation of Hybrid Systems: A Structural Model Decomposition-Based Approach
NASA Technical Reports Server (NTRS)
Bregon, Anibal; Daigle, Matthew; Roychoudhury, Indranil
2016-01-01
Quick and robust fault diagnosis is critical to ensuring safe operation of complex engineering systems. A large number of techniques are available to provide fault diagnosis in systems with continuous dynamics. However, many systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete behavioral modes, each with its own continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task computationally more complex due to the large number of possible system modes and the existence of autonomous mode transitions. This paper presents a qualitative fault isolation framework for hybrid systems based on structural model decomposition. The fault isolation is performed by analyzing the qualitative information of the residual deviations. However, in hybrid systems this process becomes complex due to possible existence of observation delays, which can cause observed deviations to be inconsistent with the expected deviations for the current mode in the system. The great advantage of structural model decomposition is that (i) it allows to design residuals that respond to only a subset of the faults, and (ii) every time a mode change occurs, only a subset of the residuals will need to be reconfigured, thus reducing the complexity of the reasoning process for isolation purposes. To demonstrate and test the validity of our approach, we use an electric circuit simulation as the case study.
NASA Astrophysics Data System (ADS)
Maclay, James D.; Brouwer, Jacob; Samuelsen, G. Scott
A model of a photovoltaic (PV) powered residence in stand-alone configuration was developed and evaluated. The model assesses the sizing, capital costs, control strategies, and efficiencies of reversible fuel cells (RFC), batteries, and ultra-capacitors (UC) both individually, and in combination, as hybrid energy storage devices. The choice of control strategy for a hybrid energy storage system is found to have a significant impact on system efficiency, hydrogen production and component utilization. A hybrid energy storage system comprised of batteries and RFC has the advantage of reduced cost (compared to using a RFC as the sole energy storage device), high system efficiency and hydrogen energy production capacity. A control strategy that preferentially used the RFC before the battery in meeting load demand allows both grid independent operation and better RFC utilization compared to a system that preferentially used the battery before the RFC. Ultra-capacitors coupled with a RFC in a hybrid energy storage system contain insufficient energy density to meet dynamic power demands typical of residential applications.
Modelling and Optimising the Value of a Hybrid Solar-Wind System
NASA Astrophysics Data System (ADS)
Nair, Arjun; Murali, Kartik; Anbuudayasankar, S. P.; Arjunan, C. V.
2017-05-01
In this paper, a net present value (NPV) approach for a solar hybrid system has been presented. The system, in question aims at supporting an investor by assessing an investment in solar-wind hybrid system in a given area. The approach follow a combined process of modelling the system, with optimization of major investment-related variables to maximize the financial yield of the investment. The consideration of solar wind hybrid supply presents significant potential for cost reduction. The investment variables concern the location of solar wind plant, and its sizing. The system demand driven, meaning that its primary aim is to fully satisfy the energy demand of the customers. Therefore, the model is a practical tool in the hands of investor to assess and optimize in financial terms an investment aiming at covering real energy demand. Optimization is performed by taking various technical, logical constraints. The relation between the maximum power obtained between individual system and the hybrid system as a whole in par with the net present value of the system has been highlighted.
Application of explicit model predictive control to a hybrid battery-ultracapacitor power source
NASA Astrophysics Data System (ADS)
Hredzak, Branislav; Agelidis, Vassilios G.; Demetriades, Georgios
2015-03-01
An explicit model predictive control (EMPC) system for a hybrid battery-ultracapacitor power source is proposed and experimentally verified in this paper. The main advantage of using the EMPC system is that the control law computation is reduced to evaluation of an explicitly defined piecewise linear function of the states. Separate EMPC systems for the total output current loop, the battery loop and the ultracapacitor loop are designed. This modular design approach allows evaluation of the performance of each individual EMPC system separately and also improves the convergence of the EMPC system design algorithm as the models used to design each loop are smaller. In order to protect the hybrid power source, the designed EMPC systems maintain operation of the hybrid power source within specified constraints, namely, battery and ultracapacitor current constraints, battery state of charge constraints and ultracapacitor voltage constraints. At the same time, the total output current EMPC system allocates high frequency current changes to the ultracapacitor and the low frequency current changes to the battery thus extending the battery lifetime. Presented experimental results verify that the hybrid power source operates within the specified constraints while allocating high and low frequency current changes to the ultracapacitor and battery respectively.
An hybrid multiscale model for immersed granular flows
NASA Astrophysics Data System (ADS)
Constant, Matthieu; Dubois, Frédéric; Lambrechts, Jonathan; Legat, Vincent
2017-06-01
The intensive use of immersed granular flows has motivated a large amount of work in this field of research. We present here a new numerical model to represent accurately and effciently mixtures of fluid with grains. On one hand, the motion of the grains is solved at the grain scale by a contact dynamics method that consider grains as discrete elements. On the other hand, the fluid flow is computed from a continuous model of the mixture at a greater scale. The link between the two scales is provided by an interaction force based on the Darcy's law for porous media and used as a closure relation in the equations of the model. Some results of simulations in a two dimensional space are provided to prove the effciency of the implementation.
Spatial self-organization in hybrid models of multicellular adhesion
NASA Astrophysics Data System (ADS)
Bonforti, Adriano; Duran-Nebreda, Salva; Montañez, Raúl; Solé, Ricard
2016-10-01
Spatial self-organization emerges in distributed systems exhibiting local interactions when nonlinearities and the appropriate propagation of signals are at work. These kinds of phenomena can be modeled with different frameworks, typically cellular automata or reaction-diffusion systems. A different class of dynamical processes involves the correlated movement of agents over space, which can be mediated through chemotactic movement or minimization of cell-cell interaction energy. A classic example of the latter is given by the formation of spatially segregated assemblies when cells display differential adhesion. Here, we consider a new class of dynamical models, involving cell adhesion among two stochastically exchangeable cell states as a minimal model capable of exhibiting well-defined, ordered spatial patterns. Our results suggest that a whole space of pattern-forming rules is hosted by the combination of physical differential adhesion and the value of probabilities modulating cell phenotypic switching, showing that Turing-like patterns can be obtained without resorting to reaction-diffusion processes. If the model is expanded allowing cells to proliferate and die in an environment where diffusible nutrient and toxic waste are at play, different phases are observed, characterized by regularly spaced patterns. The analysis of the parameter space reveals that certain phases reach higher population levels than other modes of organization. A detailed exploration of the mean-field theory is also presented. Finally, we let populations of cells with different adhesion matrices compete for reproduction, showing that, in our model, structural organization can improve the fitness of a given cell population. The implications of these results for ecological and evolutionary models of pattern formation and the emergence of multicellularity are outlined.
Spatial self-organization in hybrid models of multicellular adhesion.
Bonforti, Adriano; Duran-Nebreda, Salva; Montañez, Raúl; Solé, Ricard
2016-10-01
Spatial self-organization emerges in distributed systems exhibiting local interactions when nonlinearities and the appropriate propagation of signals are at work. These kinds of phenomena can be modeled with different frameworks, typically cellular automata or reaction-diffusion systems. A different class of dynamical processes involves the correlated movement of agents over space, which can be mediated through chemotactic movement or minimization of cell-cell interaction energy. A classic example of the latter is given by the formation of spatially segregated assemblies when cells display differential adhesion. Here, we consider a new class of dynamical models, involving cell adhesion among two stochastically exchangeable cell states as a minimal model capable of exhibiting well-defined, ordered spatial patterns. Our results suggest that a whole space of pattern-forming rules is hosted by the combination of physical differential adhesion and the value of probabilities modulating cell phenotypic switching, showing that Turing-like patterns can be obtained without resorting to reaction-diffusion processes. If the model is expanded allowing cells to proliferate and die in an environment where diffusible nutrient and toxic waste are at play, different phases are observed, characterized by regularly spaced patterns. The analysis of the parameter space reveals that certain phases reach higher population levels than other modes of organization. A detailed exploration of the mean-field theory is also presented. Finally, we let populations of cells with different adhesion matrices compete for reproduction, showing that, in our model, structural organization can improve the fitness of a given cell population. The implications of these results for ecological and evolutionary models of pattern formation and the emergence of multicellularity are outlined.
Hybrid stars in an SU(3) parity doublet model
NASA Astrophysics Data System (ADS)
Dexheimer, V.; Steinheimer, J.; Negreiros, R.; Schramm, S.
2013-01-01
We apply an extended version of the SU(3) parity model, containing quark degrees of freedom, to study neutron stars. The model successfully reproduces the main thermodynamic features of QCD which allows us to describe the composition of dense matter. Chiral symmetry restoration is realized inside the star and the chiral partners of the baryons appear, their masses becoming degenerate. Furthermore, quark degrees of freedom appear in a transition to a deconfined state. Performing an investigation of the macroscopic properties of neutron stars, we show that observational constraints, such as mass and thermal evolution, are satisfied and new predictions can be made.
Coarse-grained modeling of hybrid block copolymer system
NASA Astrophysics Data System (ADS)
Su, Yongrui
This thesis is comprised of three major projects of my research. In the first project, I proposed a nanoparticle model and combined it with the Theoretically Informed Coarse Grained (TICG) model for pure polymer systems and the grand canonical slip springs model developed in our group to build a new model for entangled nanocomposites. With Molecule Dynamics(MD) simulation, I studied the mechanic properties of the nanocomposites, for example the influence of nanoparticles size and volume fraction on entanglements, the diffusion of polymers and nanoparticles, and the influence of nanoparticles size and volume fraction on viscosity et al.. We found that the addition of small-size nanoparticles reduces the viscosity of the nanocomposites, which is in contrary to what Einstein predicted a century ago. However, when particle increases its size to micrometers the Einstein predictions is recovered. From our simulation, we believe that small-size nanoparticles can more effectively decrease the entanglements of nanocomposites than larger particles. The free volume effect introduced by small-size nanoparticles also helps decrease the viscosity of the whole system. In the second project, I combined the Ohta-Kawasaki (OK) model [3] and the Covariance Matrix Adaptation Evolutionary Strategy(CMA-ES) to optimize the block copolymer blends self-assembly in the hole-shrink process. The aim is to predict the optimal composition and the optimal surface energy to direct the block copolymer blends self-assembly process in the confined hole. After optimization in the OK model, we calibrated the optimal results by the more reliable TICG model and got the same morphology. By comparing different optimization process, we found that the homopolymers which are comprised of the same monomers as either block of the block copolymer can form a perfect perforated hole and might have better performance than the pure block copolymer. While homopolymers which are comprised of a third-party monomers
Preliminary analysis on hybrid Box-Jenkins - GARCH modeling in forecasting gold price
NASA Astrophysics Data System (ADS)
Yaziz, Siti Roslindar; Azizan, Noor Azlinna; Ahmad, Maizah Hura; Zakaria, Roslinazairimah; Agrawal, Manju; Boland, John
2015-02-01
Gold has been regarded as a valuable precious metal and the most popular commodity as a healthy return investment. Hence, the analysis and prediction of gold price become very significant to investors. This study is a preliminary analysis on gold price and its volatility that focuses on the performance of hybrid Box-Jenkins models together with GARCH in analyzing and forecasting gold price. The Box-Cox formula is used as the data transformation method due to its potential best practice in normalizing data, stabilizing variance and reduces heteroscedasticity using 41-year daily gold price data series starting 2nd January 1973. Our study indicates that the proposed hybrid model ARIMA-GARCH with t-innovation can be a new potential approach in forecasting gold price. This finding proves the strength of GARCH in handling volatility in the gold price as well as overcomes the non-linear limitation in the Box-Jenkins modeling.
Model-Invariant Hybrid Computations of Separated Flows for RCA Standard Test Cases
NASA Technical Reports Server (NTRS)
Woodruff, Stephen
2016-01-01
NASA's Revolutionary Computational Aerosciences (RCA) subproject has identified several smooth-body separated flows as standard test cases to emphasize the challenge these flows present for computational methods and their importance to the aerospace community. Results of computations of two of these test cases, the NASA hump and the FAITH experiment, are presented. The computations were performed with the model-invariant hybrid LES-RANS formulation, implemented in the NASA code VULCAN-CFD. The model- invariant formulation employs gradual LES-RANS transitions and compensation for model variation to provide more accurate and efficient hybrid computations. Comparisons revealed that the LES-RANS transitions employed in these computations were sufficiently gradual that the compensating terms were unnecessary. Agreement with experiment was achieved only after reducing the turbulent viscosity to mitigate the effect of numerical dissipation. The stream-wise evolution of peak Reynolds shear stress was employed as a measure of turbulence dynamics in separated flows useful for evaluating computations.
Online motor fault detection and diagnosis using a hybrid FMM-CART model.
Seera, Manjeevan; Lim, Chee Peng
2014-04-01
In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks.
New hybrid turbulence modelling approach, with application to dynamic stall control
NASA Astrophysics Data System (ADS)
Haering, Sigfried; Moser, Robert
2014-11-01
We present numerical studies of a stalled airfoil experiencing transitory flow control using a new hybrid RANS/LES modeling approach developed specifically for such challenging flow scenarios. Traditional hybrid approaches exhibit deficiencies when used for fluctuating smooth-wall separation and reattachment necessitating ad-hoc delaying functions and model tuning making them no longer useful as a predictive tool. Additionally, complex geometries and flows often require high cell aspect-ratios and large grid gradients as a compromise between resolution and cost. Such transitions and inconsistencies in resolution detrimentally effect the fidelity of the simulation. Our approach more naturally transitions between RANS to LES obviating the need for tuning and directly accounts for anisotropy and inhomogeneity in the flow and grid. The results of these simulations not only provide fundamental insight into experimentally observed stall control mechanisms but also display the versatility and accuracy of the new modeling method in simulating complex flow phenomena.
Hybrid modelling the Venus-solar wind interaction with non-constant IMF clock angle
NASA Astrophysics Data System (ADS)
Jarvinen, R.; Kallio, E.; Zhang, T. L.; Barabash, S.; Fedorov, A.; Liu, K.; Sillanpää, I.; Jahunen, P.
2008-09-01
ABSTRACT When the unmagnetized planet Venus with a dense atmosphere interacts with the solar wind, an induced magnetosphere is formed and the interplanetary magnetic field (IMF) is enhanced and draped near the planet. Hybrid modelling is a semi-kinetic method to study the plasma interactions of Venus-like objects in a global planetary scale. HYB simulation code solves numerically the hybrid model equations and provides, for example, a three dimensional structure of the magnetic field in the objectss near-space. It is proposed that the changes in the clock angle of the upstream IMF can be modelled by using a stationary simulation solution. In this method, the solution is rotated around the Venus-Sun axis along the spacecraft trajectory. In the study the magnetic fields produced by the HYB-Venus runs are compared to the Venus Express MAG magnetometer observations.
Modelling Nonlinear Dynamic Textures using Hybrid DWT-DCT and Kernel PCA with GPU
NASA Astrophysics Data System (ADS)
Ghadekar, Premanand Pralhad; Chopade, Nilkanth Bhikaji
2016-12-01
Most of the real-world dynamic textures are nonlinear, non-stationary, and irregular. Nonlinear motion also has some repetition of motion, but it exhibits high variation, stochasticity, and randomness. Hybrid DWT-DCT and Kernel Principal Component Analysis (KPCA) with YCbCr/YIQ colour coding using the Dynamic Texture Unit (DTU) approach is proposed to model a nonlinear dynamic texture, which provides better results than state-of-art methods in terms of PSNR, compression ratio, model coefficients, and model size. Dynamic texture is decomposed into DTUs as they help to extract temporal self-similarity. Hybrid DWT-DCT is used to extract spatial redundancy. YCbCr/YIQ colour encoding is performed to capture chromatic correlation. KPCA is applied to capture nonlinear motion. Further, the proposed algorithm is implemented on Graphics Processing Unit (GPU), which comprise of hundreds of small processors to decrease time complexity and to achieve parallelism.
NASA Technical Reports Server (NTRS)
Baurle, R. A.
2015-01-01
Steady-state and scale-resolving simulations have been performed for flow in and around a model scramjet combustor flameholder. The cases simulated corresponded to those used to examine this flowfield experimentally using particle image velocimetry. A variety of turbulence models were used for the steady-state Reynolds-averaged simulations which included both linear and non-linear eddy viscosity models. The scale-resolving simulations used a hybrid Reynolds-averaged / large eddy simulation strategy that is designed to be a large eddy simulation everywhere except in the inner portion (log layer and below) of the boundary layer. Hence, this formulation can be regarded as a wall-modeled large eddy simulation. This effort was undertaken to formally assess the performance of the hybrid Reynolds-averaged / large eddy simulation modeling approach in a flowfield of interest to the scramjet research community. The numerical errors were quantified for both the steady-state and scale-resolving simulations prior to making any claims of predictive accuracy relative to the measurements. The steady-state Reynolds-averaged results showed a high degree of variability when comparing the predictions obtained from each turbulence model, with the non-linear eddy viscosity model (an explicit algebraic stress model) providing the most accurate prediction of the measured values. The hybrid Reynolds-averaged/large eddy simulation results were carefully scrutinized to ensure that even the coarsest grid had an acceptable level of resolution for large eddy simulation, and that the time-averaged statistics were acceptably accurate. The autocorrelation and its Fourier transform were the primary tools used for this assessment. The statistics extracted from the hybrid simulation strategy proved to be more accurate than the Reynolds-averaged results obtained using the linear eddy viscosity models. However, there was no predictive improvement noted over the results obtained from the explicit
Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models
NASA Astrophysics Data System (ADS)
Bai, Yun; Chen, Zhiqiang; Xie, Jingjing; Li, Chuan
2016-01-01
Inflow forecasting applies data supports for the operations and managements of reservoirs. A multiscale deep feature learning (MDFL) method with hybrid models is proposed in this paper to deal with the daily reservoir inflow forecasting. Ensemble empirical mode decomposition and Fourier spectrum are first employed to extract multiscale (trend, period and random) features, which are then represented by three deep belief networks (DBNs), respectively. The weights of each DBN are subsequently applied to initialize a neural network (D-NN). The outputs of the three-scale D-NNs are finally reconstructed using a sum-up strategy toward the forecasting results. A historical daily inflow series (from 1/1/2000 to 31/12/2012) of the Three Gorges reservoir, China, is investigated by the proposed MDFL with hybrid models. For comparison, four peer models are adopted for the same task. The results show that, the present model overwhelms all the peer models in terms of mean absolute percentage error (MAPE = 11.2896%), normalized root-mean-square error (NRMSE = 0.2292), determination coefficient criteria (R2 = 0.8905), and peak percent threshold statistics (PPTS(5) = 10.0229%). The addressed method integrates the deep framework with multiscale and hybrid observations, and therefore being good at exploring sophisticated natures in the reservoir inflow forecasting.
ERIC Educational Resources Information Center
Delialioglu, Omer; Yildirim, Zahide
2008-01-01
Using the model for learning and teaching activities (MOLTA), a new technology enhanced hybrid instruction was designed, developed and implemented. The effectiveness of the hybrid instruction in regard to students' achievement, knowledge retention, attitudes towards the subject, and course satisfaction was evaluated in comparison to traditional…
ERIC Educational Resources Information Center
Delialioglu, Omer; Yildirim, Zahide
2008-01-01
Using the model for learning and teaching activities (MOLTA), a new technology enhanced hybrid instruction was designed, developed and implemented. The effectiveness of the hybrid instruction in regard to students' achievement, knowledge retention, attitudes towards the subject, and course satisfaction was evaluated in comparison to traditional…
Real time hybrid simulation with online model updating: An analysis of accuracy
NASA Astrophysics Data System (ADS)
Ou, Ge; Dyke, Shirley J.; Prakash, Arun
2017-02-01
In conventional hybrid simulation (HS) and real time hybrid simulation (RTHS) applications, the information exchanged between the experimental substructure and numerical substructure is typically restricted to the interface boundary conditions (force, displacement, acceleration, etc.). With additional demands being placed on RTHS and recent advances in recursive system identification techniques, an opportunity arises to improve the fidelity by extracting information from the experimental substructure. Online model updating algorithms enable the numerical model of components (herein named the target model), that are similar to the physical specimen to be modified accordingly. This manuscript demonstrates the power of integrating a model updating algorithm into RTHS (RTHSMU) and explores the possible challenges of this approach through a practical simulation. Two Bouc-Wen models with varying levels of complexity are used as target models to validate the concept and evaluate the performance of this approach. The constrained unscented Kalman filter (CUKF) is selected for using in the model updating algorithm. The accuracy of RTHSMU is evaluated through an estimation output error indicator, a model updating output error indicator, and a system identification error indicator. The results illustrate that, under applicable constraints, by integrating model updating into RTHS, the global response accuracy can be improved when the target model is unknown. A discussion on model updating parameter sensitivity to updating accuracy is also presented to provide guidance for potential users.
Using a hybrid genetic algorithm and fuzzy logic for metabolic modeling
Yen, J.; Lee, B.; Liao, J.C.
1996-12-31
The identification of metabolic systems is a complex task due to the complexity of the system and limited knowledge about the model. Mathematical equations and ODE`s have been used to capture the structure of the model, and the conventional optimization techniques have been used to identify the parameters of the model. In general, however, a pure mathematical formulation of the model is difficult due to parametric uncertainty and incomplete knowledge of mechanisms. In this paper, we propose a modeling approach that (1) uses fuzzy rule-based model to augment algebraic enzyme models that are incomplete, and (2) uses a hybrid genetic algorithm to identify uncertain parameters in the model. The hybrid genetic algorithm (GA) integrates a GA with the simplex method in functional optimization to improve the GA`s convergence rate. We have applied this approach to modeling the rate of three enzyme reactions in E. coli central metabolism. The proposed modeling strategy allows (1) easy incorporation of qualitative insights into a pure mathematical model and (2) adaptive identification and optimization of key parameters to fit system behaviors observed in biochemical experiments.
The characteristic analysis of a hybrid multifluid turbulent-mix model
Cheng, B.; Cranfill, C.W.
1998-07-13
A thorough analysis of the characteristics of a multifluid turbulent mix model in the case of one-dimensional two phase flows is presented under various physical circumstances. It has been found that the new hybrid multifluid turbulent mix model has all real characteristics if either real or turbulent viscosity is present. When real viscosity vanishes, the model still has all real characteristics for zero relative motion between fluids. For nonzero relative motions between fluids, the model will have all real characteristics if the disordered motions and turbulent viscosity together are generated with the nonzero relative motions simultaneously. The implications of the results are further discussed.
NASA Astrophysics Data System (ADS)
Mantovanelli, Ivana C. C.; Rivera, Elmer Ccopa; da Costa, Aline C.; Filho, Rubens Maciel
In this work a procedure for the development of a robust mathematical model for an industrial alcoholic fermentation process was evaluated. The proposed model is a hybrid neural model, which combines mass and energy balance equations with functional link networks to describe the kinetics. These networks have been shown to have a good nonlinear approximation capability, although the estimation of its weights is linear. The proposed model considers the effect of temperature on the kinetics and has the neural network weights reestimated always so that a change in operational conditions occurs. This allow to follow the system behavior when changes in operating conditions occur.
Neutrino masses, leptogenesis, and dark matter in a hybrid seesaw model
Gu Peihong; Hirsch, M.; Valle, J. W. F.
2009-02-01
We suggest a hybrid seesaw model where relatively light right-handed neutrinos give no contribution to neutrino mass matrix due to a special symmetry. This allows their Yukawa couplings to the standard model particles to be relatively strong, so that the standard model Higgs boson can decay dominantly to a left- and a right-handed neutrino, leaving another stable right-handed neutrino as cold dark matter. In our model neutrino masses arise via the type-II seesaw mechanism, the Higgs triplet scalars being also responsible for the generation of the matter-antimatter asymmetry via the leptogenesis mechanism.
Theoretical analysis of a hybrid traffic model accounting for safe velocity
NASA Astrophysics Data System (ADS)
Wang, Yu-Qing; Zhou, Chao-Fan; Yan, Bo-Wen; Zhang, De-Chen; Wang, Ji-Xin; Jia, Bin; Gao, Zi-You; Wu, Qing-Song
2017-04-01
A hybrid traffic-flow model [Wang-Zhou-Yan (WZY) model] is brought out in this paper. In WZY model, the global equilibrium velocity is replaced by the local equilibrium one, which emphasizes that the modification of vehicle velocity is based on the view of safe-driving rather than the global deployment. In the view of safe-driving, the effect of drivers’ estimation is taken into account. Moreover, the linear stability of the traffic model has been performed. Furthermore, in order to test the robustness of the system, the evolvement of the density wave and the velocity wave of the traffic flow has been numerically calculated.
Generalized gradient approximation model exchange holes for range-separated hybrids
Henderson, Thomas M.; Janesko, Benjamin G.; Scuseria, Gustavo E.
2008-01-01
We propose a general model for the spherically averaged exchange hole corresponding to a generalized gradient approximation (GGA) exchange functional. Parameters are reported for several common GGAs. Our model is based upon that of Ernzerhof and Perdew [J. Chem. Phys. 109, 3313 (1998)]. It improves upon the former by precisely reproducing the energy of the parent GGA, and by enabling fully analytic evaluation of range-separated hybrid density functionals. Analytic results and preliminary thermochemical tests indicate that our model also improves upon the simple, local-density-based exchange hole model of Iikura et al. [J. Chem. Phys. 115, 3540 (2001)]. PMID:18500854
Bottom friction optimization for barotropic tide modelling using the HYbrid Coordinate Ocean Model
NASA Astrophysics Data System (ADS)
Boutet, Martial; Lathuilière, Cyril; Baraille, Rémy; Son Hoang, Hong; Morel, Yves
2014-05-01
tested and validated with the HYbrid Coordinate Ocean Model (HYCOM) in barotropic mode (one isopycnal layer), using twin experiments (the observations are obtained with the direct model, prescribing the reference parameter distribution). The modeled area is the Bay of Biscay and the English Channel and the estimated parameter is the bottom roughness (z0).
An Advanced Hierarchical Hybrid Environment for Reliability and Performance Modeling
NASA Technical Reports Server (NTRS)
Ciardo, Gianfranco
2003-01-01
The key issue we intended to address in our proposed research project was the ability to model and study logical and probabilistic aspects of large computer systems. In particular, we wanted to focus mostly on automatic solution algorithms based on a state-space exploration as their first step, in addition to the more traditional discrete-event simulation approaches commonly employed in industry. One explicitly-stated goal was to extend by several orders of magnitude the size of models that can be solved exactly, using a combination of techniques: 1) Efficient exploration and storage of the state space using new data structures that require an amount of memory sublinear in the number states; and 2) Exploitation of the existing symmetries in the matrices describing the system behavior using Kronecker operators. Not only we have been successful in achieving the above goals, but we exceeded them in many respects.
Dynamical analysis of Parkinsonian state emulated by hybrid Izhikevich neuron models
NASA Astrophysics Data System (ADS)
Liu, Chen; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Li, Huiyan; Loparo, Kenneth A.; Fietkiewicz, Chris
2015-11-01
Computational models play a significant role in exploring novel theories to complement the findings of physiological experiments. Various computational models have been developed to reveal the mechanisms underlying brain functions. Particularly, in the development of therapies to modulate behavioral and pathological abnormalities, computational models provide the basic foundations to exhibit transitions between physiological and pathological conditions. Considering the significant roles of the intrinsic properties of the globus pallidus and the coupling connections between neurons in determining the firing patterns and the dynamical activities of the basal ganglia neuronal network, we propose a hypothesis that pathological behaviors under the Parkinsonian state may originate from combined effects of intrinsic properties of globus pallidus neurons and synaptic conductances in the whole neuronal network. In order to establish a computational efficient network model, hybrid Izhikevich neuron model is used due to its capacity of capturing the dynamical characteristics of the biological neuronal activities. Detailed analysis of the individual Izhikevich neuron model can assist in understanding the roles of model parameters, which then facilitates the establishment of the basal ganglia-thalamic network model, and contributes to a further exploration of the underlying mechanisms of the Parkinsonian state. Simulation results show that the hybrid Izhikevich neuron model is capable of capturing many of the dynamical properties of the basal ganglia-thalamic neuronal network, such as variations of the firing rates and emergence of synchronous oscillations under the Parkinsonian condition, despite the simplicity of the two-dimensional neuronal model. It may suggest that the computational efficient hybrid Izhikevich neuron model can be used to explore basal ganglia normal and abnormal functions. Especially it provides an efficient way of emulating the large-scale neuron network
Comets in the Young Solar System: Hybrid Plasma Model Results
NASA Astrophysics Data System (ADS)
Alho, Markku; Kallio, Esa; Wedlund, Cyril Simon; Lammer, Helmut; Güdel, Manuel; Johnstone, Colin
2017-04-01
The observations of the Rosetta mission and, in particular, of the Rosetta Plasma Consortium (RPC) have provided lengthy in-situ observations of a cometary plasma environment. Building on results of the Rosetta mission, we have taken recent astronomical findings on the evolution of the Sun and the solar wind and employed them to provide the first iteration of an early solar system cometary plasma model. We investigate a 67P-like comet at three heliocentric distances (corresponding to the orbital distances of Venus, Earth and Mars) and at solar system age of approximately 100 My, using EK Draconis as a solar proxy. The strong inferred EUV flux, along with strong solar wind and low solar constant provide harsh conditions for the coma, creating plasma environments considerably smaller than with contemporary conditions. We discuss the differences between modern and young solar system cometary plasma environments, present the first results of the modelling and provide discussion on the planned developments to modelling the young solar system comets.
Hybrid Model for Homogenization of the Elastoplastic Properties of Isotropic Matrix Composites
NASA Astrophysics Data System (ADS)
Fedotov, A. F.
2017-07-01
A hybrid homogenization model for calculating the effective elastoplastic properties of isotropic matrix composites is suggested. The hybrid model combines the continuous deformation models of heterogeneous solid and porous materials. A distinctive feature of the model is the calculation of concentration coefficients of the average Hill strains in terms of the effective volumes of strain averaging. The effective volumes of averaging are determined by solving the boundary-value problem on plastic deformation of a simplified structural model of a two-phase composite considering the porous state of matrix. A comparison of calculation results with experimental data upon constructing deformation diagrams for polymer-matrix and metal-matrix composites is carried out. The possibility of changing the properties of the metal matrix in producing composites is mentioned. Therefore, the adequacy of analytical models greatly depends on the accuracy of identification of material constants of the matrix. On the whole, the new model described the plastic deformation of matrix composites more accurately than the Mori-Tanaka model. The analytical model proposed has a simpler sampling scheme, a simple computation algorithm, and ensured the same calculation accuracy for the deformation diagram of an aluminum-matrix composite as the numerical finite-element model created by the ABAQUS software.
Human-model hybrid Korean air quality forecasting system.
Chang, Lim-Seok; Cho, Ara; Park, Hyunju; Nam, Kipyo; Kim, Deokrae; Hong, Ji-Hyoung; Song, Chang-Keun
2016-09-01
The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters' capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters' error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m(3) and the R(2) of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the
Hybrid fluid/kinetic model for parallel heat conduction
Callen, J.D.; Hegna, C.C.; Held, E.D.
1998-12-31
It is argued that in order to use fluid-like equations to model low frequency ({omega} < {nu}) phenomena such as neoclassical tearing modes in low collisionality ({nu} < {omega}{sub b}) tokamak plasmas, a Chapman-Enskog-like approach is most appropriate for developing an equation for the kinetic distortion (F) of the distribution function whose velocity-space moments lead to the needed fluid moment closure relations. Further, parallel heat conduction in a long collision mean free path regime can be described through a combination of a reduced phase space Chapman-Enskog-like approach for the kinetics and a multiple-time-scale analysis for the fluid and kinetic equations.
A Hybrid Causal Search Algorithm for Latent Variable Models
Ogarrio, Juan Miguel; Spirtes, Peter; Ramsey, Joe
2017-01-01
Existing score-based causal model search algorithms such as GES (and a speeded up version, FGS) are asymptotically correct, fast, and reliable, but make the unrealistic assumption that the true causal graph does not contain any unmeasured confounders. There are several constraint-based causal search algorithms (e.g RFCI, FCI, or FCI+) that are asymptotically correct without assuming that there are no unmeasured confounders, but often perform poorly on small samples. We describe a combined score and constraint-based algorithm, GFCI, that we prove is asymptotically correct. On synthetic data, GFCI is only slightly slower than RFCI but more accurate than FCI, RFCI and FCI+. PMID:28239434
A Hybrid Latent Class Analysis Modeling Approach to Analyze Urban Expressway Crash Risk.
Yu, Rongjie; Wang, Xuesong; Abdel-Aty, Mohamed
2017-04-01
Crash risk analysis is rising as a hot research topic as it could reveal the relationships between traffic flow characteristics and crash occurrence risk, which is beneficial to understand crash mechanisms which would further refine the design of Active Traffic Management System (ATMS). However, the majority of the current crash risk analysis studies have ignored the impact of geometric characteristics on crash risk estimation while recent studies proved that crash occurrence risk was affected by the various alignment features. In this study, a hybrid Latent Class Analysis (LCA) modeling approach was proposed to account for the heterogeneous effects of geometric characteristics. Crashes were first segmented into homogenous subgroups, where the optimal number of latent classes was identified based on bootstrap likelihood ratio tests. Then, separate crash risk analysis models were developed using Bayesian random parameter logistic regression technique; data from Shanghai urban expressway system were employed to conduct the empirical study. Different crash risk contributing factors were unveiled by the hybrid LCA approach and better model goodness-of-fit was obtained while comparing to an overall total crash model. Finally, benefits of the proposed hybrid LCA approach were discussed.
Models to estimate the minimum ignition temperature of dusts and hybrid mixtures.
Addai, Emmanuel Kwasi; Gabel, Dieter; Krause, Ulrich
2016-03-05
The minimum ignition temperatures (MIT) of hybrid mixtures have been investigated by performing several series of tests in a modified Godbert-Greenwald furnace. Five dusts as well as three perfect gases and three real were used in different combinations as test samples. Further, seven mathematical models for prediction of the MIT of dust/air mixtures were presented of which three were chosen for deeper study and comparison with the experimental results based on the availability of the input quantities needed and their applicability. Additionally, two alternative models were proposed to calculate the MIT of hybrid mixtures and were validated against the experimental results. A significant decrease of the minimum ignition temperature of either the gas or the vapor as well as an increase in the explosion likelihood could be observed when a small amount of dust which was either below its minimum explosible concentration or not ignitable itself at that particular temperature was mixed with the gas. The various models developed by Cassel, Krishma and Mitsui to predict the MIT of dust were in good agreement with the experimental results as well as the two models proposed to predict the MIT of hybrid mixtures were also in agreement with the experimental value.
A hybrid DEM-SPH model for deformable landslide and its generated surge waves
NASA Astrophysics Data System (ADS)
Tan, Hai; Chen, Shenghong
2017-10-01
Reservoir bank landslide and its generated surge waves are catastrophic hazards which may give rise to additional sedimentation, destroy hydraulic structures, and even cause fatalities. Since this process is very complex involving landslide impact, wave generation and propagation, it cannot be well captured with traditional numerical approaches. In this paper, a hybrid DEM-SPH model is presented to simulate landslide and to reproduce its generated surge waves. This model consists of discrete element method (DEM) for solid phase and smoothed particle hydrodynamics (SPH) for fluid phase as well as drag force and buoyancy for solid-fluid interaction. Meanwhile, the δ-SPH algorithm is employed to eliminate spurious numerical noise on the pressure field. Submarine rigid block slide is numerically tested to validate the proposed hybrid model, and the computed wave profiles exhibit a satisfactory agreement with the experiment. The hybrid model is further extended towards the submarine granular deformable slide which generates smaller and less violent surge waves. Kinetic and potential energy of both solid and fluid particle system are extracted to throw a light upon the process of landslide water interaction from an energy perspective. Finally, a sensitivity analysis on particle friction coefficient indicates that the lubrication of the solid particles is another important effect influencing the underwater landslide movement in addition to the drag effect.
Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata
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.
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.
NASA Astrophysics Data System (ADS)
Jian, Weilin; He, Daohang; Song, Shaoyun
2016-08-01
Natural stilbenes (especially resveratrol) play important roles in plant protection by acting as both constitutive and inducible defenses. However, their exogenous applications on crops as fungicidal agents are challenged by their oxidative degradation and limited availability. In this study, a new class of resveratrol-inspired oxadiazole-stilbene hybrids was synthesized via Wittig-Horner reaction. Bioassay results indicated that some of the compounds exhibited potent fungicidal activity against Botrytis cinerea in vitro. Among these stilbene hybrids, compounds 11 showed promising inhibitory activity with the EC50 value of 144.6 μg/mL, which was superior to that of resveratrol (315.6 μg/mL). Remarkably, the considerably abnormal mycelial morphology was observed in the presence of compound 11. The inhibitory profile was further proposed by homology modeling and molecular docking studies, which showed the possible interaction of resveratrol and oxadiazole-stilbene hybrids with the cytochrome P450-dependent sterol 14α-demethylase from B. cinerea (BcCYP51) for the first time. Taken together, these results would provide new insights into the fungicidal mechanism of stilbenes, as well as an important clue for biology-oriented synthesis of stilbene hybrids with improved bioactivity against plant pathogenic fungi in crop protection.
Jian, Weilin; He, Daohang; Song, Shaoyun
2016-01-01
Natural stilbenes (especially resveratrol) play important roles in plant protection by acting as both constitutive and inducible defenses. However, their exogenous applications on crops as fungicidal agents are challenged by their oxidative degradation and limited availability. In this study, a new class of resveratrol-inspired oxadiazole-stilbene hybrids was synthesized via Wittig-Horner reaction. Bioassay results indicated that some of the compounds exhibited potent fungicidal activity against Botrytis cinerea in vitro. Among these stilbene hybrids, compounds 11 showed promising inhibitory activity with the EC50 value of 144.6 μg/mL, which was superior to that of resveratrol (315.6 μg/mL). Remarkably, the considerably abnormal mycelial morphology was observed in the presence of compound 11. The inhibitory profile was further proposed by homology modeling and molecular docking studies, which showed the possible interaction of resveratrol and oxadiazole-stilbene hybrids with the cytochrome P450-dependent sterol 14α-demethylase from B. cinerea (BcCYP51) for the first time. Taken together, these results would provide new insights into the fungicidal mechanism of stilbenes, as well as an important clue for biology-oriented synthesis of stilbene hybrids with improved bioactivity against plant pathogenic fungi in crop protection. PMID:27530962
Extensive heterosis in growth of yeast hybrids is explained by a combination of genetic models.
Shapira, R; Levy, T; Shaked, S; Fridman, E; David, L
2014-10-01
Heterosis, also known as hybrid vigor, is the superior performance of a heterozygous hybrid relative to its homozygous parents. Despite the scientific curiosity of this phenotypic phenomenon and its significance for food production in agriculture, its genetic basis is insufficiently understood. Studying heterosis in yeast can potentially yield insights into its genetic basis, can allow one to test the different hypotheses that have been proposed to explain the phenomenon and allows better understanding of how to take advantage of this phenomenon to enhance food production. We therefore crossed 16 parental yeast strains to form 120 yeast hybrids, and measured their growth rates under five environmental conditions. A considerable amount of dominant genetic variation was found in growth performance, and heterosis was measured in 35% of the hybrid-condition combinations. Despite previous reports of correlations between heterosis and measures of sequence divergence between parents, we detected no such relationship. We used several analyses to examine which genetic model might explain heterosis. We found that dominance complementation of recessive alleles, overdominant interactions within loci and epistatic interactions among loci each contribute to heterosis. We concluded that in yeast heterosis is a complex phenotype created by the combined contribution of different genetic interactions.
Hybrid modeling of CO2 sequestration processes using the lattice-Boltzmann method and PFLOTRAN
NASA Astrophysics Data System (ADS)
Porter, M. L.; Coon, E. T.; Kang, Q.; Lichtner, P. C.; Carey, J. W.
2011-12-01
Successful CO2 injection and sequestration requires fundamental understanding of many complex processes encountered in multiphase flow and reactive transport through porous media. Although these processes are inherently governed by microscopic interfacial phenomena, they must be described at much larger scales for many practical engineering applications. In this work we present a parallel hybrid modeling scheme that couples a lattice-Boltzmann (LB) simulator for porescale multiphase flow to PFLOTRAN for continuum (Darcy) scale multiphase flow and both continuum and porescale reactive transport. We discuss details regarding the LB method, PFLOTRAN, and the coupling of the two simulators. In addition, we present a number of simulations that validate and highlight both the porescale and hybrid modeling capabilities for applications involving CO2 sequestration.
Hybrid method for numerical modelling of LWR coolant chemistry
NASA Astrophysics Data System (ADS)
Swiatla-Wojcik, Dorota
2016-10-01
A comprehensive approach is proposed to model radiation chemistry of the cooling water under exposure to neutron and gamma radiation at 300 °C. It covers diffusion-kinetic processes in radiation tracks and secondary reactions in the bulk coolant. Steady-state concentrations of the radiolytic products have been assessed based on the simulated time dependent concentration profiles. The principal reactions contributing to the formation of H2, O2 and H2O2 were indicated. Simulation was carried out depending on the amount of extra hydrogen dissolved in the coolant to reduce concentration of corrosive agents. High sensitivity to the rate of reaction H+H2O=OH+H2 is shown and discussed.
An advanced environment for hybrid modeling of biological systems based on modelica.
Pross, Sabrina; Bachmann, Bernhard
2011-01-20
Biological systems are often very complex so that an appropriate formalism is needed for modeling their behavior. Hybrid Petri Nets, consisting of time-discrete Petri Net elements as well as continuous ones, have proven to be ideal for this task. Therefore, a new Petri Net library was implemented based on the object-oriented modeling language Modelica which allows the modeling of discrete, stochastic and continuous Petri Net elements by differential, algebraic and discrete equations. An appropriate Modelica-tool performs the hybrid simulation with discrete events and the solution of continuous differential equations. A special sub-library contains so-called wrappers for specific reactions to simplify the modeling process. The Modelica-models can be connected to Simulink-models for parameter optimization, sensitivity analysis and stochastic simulation in Matlab. The present paper illustrates the implementation of the Petri Net component models, their usage within the modeling process and the coupling between the Modelica-tool Dymola and Matlab/Simulink. The application is demonstrated by modeling the metabolism of Chinese Hamster Ovary Cells.
Li, Bing Keong; Liu, Feng; Weber, Ewald; Padhi, Shantanu; Crozier, Stuart
2007-01-01
In this work, an improved hybrid MoM/FDTD algorithm for modeling low to ultra high field MRI RF coil/sample interactions has been proposed. In our previous hybrid MoM/FDTD method, the accuracy of modeling MRI RF coils is generally hindered by two major issues, staircasing errors and rough approximation of the coil current distortions by electromagnetic reflections from sample. In view of this, a Huygen's equivalent surface method has been proposed to effectively bridge MoM and FDTD. In the improved hybrid MoM/FDTD algorithm, staircasing errors are eliminated, and most importantly the complex coil/tissue interactions are explicitly accounted for. The accuracy of the improved hybrid MoM/FDTD method is numerically verified with a well established hybrid Green function/MoM solution and also experimentally underpinned with MR images obtained using a prototype rotary phased array head coil.
Andalam, Sidharta; Ramanna, Harshavardhan; Malik, Avinash; Roop, Parthasarathi; Patel, Nitish; Trew, Mark L
2016-08-01
Virtual heart models have been proposed for closed loop validation of safety-critical embedded medical devices, such as pacemakers. These models must react in real-time to off-the-shelf medical devices. Real-time performance can be obtained by implementing models in computer hardware, and methods of compiling classes of Hybrid Automata (HA) onto FPGA have been developed. Models of ventricular cardiac cell electrophysiology have been described using HA which capture the complex nonlinear behavior of biological systems. However, many models that have been used for closed-loop validation of pacemakers are highly abstract and do not capture important characteristics of the dynamic rate response. We developed a new HA model of cardiac cells which captures dynamic behavior and we implemented the model in hardware. This potentially enables modeling the heart with over 1 million dynamic cells, making the approach ideal for closed loop testing of medical devices.
Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabuñal, J. R.; Dorado, J.; Pazos, A.
2013-01-01
Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected. PMID:24453933
Fernandez-Lozano, C; Canto, C; Gestal, M; Andrade-Garda, J M; Rabuñal, J R; Dorado, J; Pazos, A
2013-01-01
Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected.
A hybrid programming model for compressible gas dynamics using openCL
Bergen, Benjamin Karl; Daniels, Marcus G; Weber, Paul M
2010-01-01
The current trend towards multicore/manycore and accelerated architectures presents challenges, both in portability, and also in the choices that developers must make on how to use the resources that these architectures provide. This paper explores some of the possibilities that are enabled by the Open Computing Language (OpenCL), and proposes a programming model that allows developers and scientists to more fully subscribe hybrid compute nodes, while, at the same time, reducing the impact of system failure.
Modeling and Analysis of Asynchronous Systems Using SAL and Hybrid SAL
NASA Technical Reports Server (NTRS)
Tiwari, Ashish; Dutertre, Bruno
2013-01-01
We present formal models and results of formal analysis of two different asynchronous systems. We first examine a mid-value select module that merges the signals coming from three different sensors that are each asynchronously sampling the same input signal. We then consider the phase locking protocol proposed by Daly, Hopkins, and McKenna. This protocol is designed to keep a set of non-faulty (asynchronous) clocks phase locked even in the presence of Byzantine-faulty clocks on the network. All models and verifications have been developed using the SAL model checking tools and the Hybrid SAL abstractor.
Value-Driven Design and Sensitivity Analysis of Hybrid Energy Systems using Surrogate Modeling
Wenbo Du; Humberto E. Garcia; William R. Binder; Christiaan J. J. Paredis
2001-10-01
A surrogate modeling and analysis methodology is applied to study dynamic hybrid energy systems (HES). The effect of battery size on the smoothing of variability in renewable energy generation is investigated. Global sensitivity indices calculated using surrogate models show the relative sensitivity of system variability to dynamic properties of key components. A value maximization approach is used to consider the tradeoff between system variability and required battery size. Results are found to be highly sensitive to the renewable power profile considered, demonstrating the importance of accurate renewable resource modeling and prediction. The documented computational framework and preliminary results represent an important step towards a comprehensive methodology for HES evaluation, design, and optimization.
An Approach of Bio-inspired Hybrid Model for Financial Markets
NASA Astrophysics Data System (ADS)
Simić, Dragan; Gajić, Vladeta; Simić, Svetlana
Biological systems are inspiration for the design of optimisation and classification models. Applying various forms of bio-inspired algorithms may be a very high-complex system. Modelling of financial markets is challenging for several reasons, because many plausible factors impact on it. An automated trading on financial market is not a new phenomenon. The model of bio-inspired hybrid adaptive trading system based on technical indicators usage by grammatical evolution and moving window is presented in this paper. The proposed system is just one of possible bio-inspired system which can be used in financial forecast, corporate failure prediction or bond rating company.
Hybrid Structural Model of the Complete Human ESCRT-0 Complex
Ren, Xuefeng; Kloer, Daniel P.; Kim, Young C.; Ghirlando, Rodolfo; Saidi, Layla F.; Hummer, Gerhard; Hurley, James H.
2009-03-31
The human Hrs and STAM proteins comprise the ESCRT-0 complex, which sorts ubiquitinated cell surface receptors to lysosomes for degradation. Here we report a model for the complete ESCRT-0 complex based on the crystal structure of the Hrs-STAM core complex, previously solved domain structures, hydrodynamic measurements, and Monte Carlo simulations. ESCRT-0 expressed in insect cells has a hydrodynamic radius of R{sub H} = 7.9 nm and is a 1:1 heterodimer. The 2.3 {angstrom} crystal structure of the ESCRT-0 core complex reveals two domain-swapped GAT domains and an antiparallel two-stranded coiled-coil, similar to yeast ESCRT-0. ESCRT-0 typifies a class of biomolecular assemblies that combine structured and unstructured elements, and have dynamic and open conformations to ensure versatility in target recognition. Coarse-grained Monte Carlo simulations constrained by experimental R{sub H} values for ESCRT-0 reveal a dynamic ensemble of conformations well suited for diverse functions.
Advanced 3D electromagnetic and particle-in-cell modeling on structured/unstructured hybrid grids
Seidel, D.B.; Pasik, M.F.; Kiefer, M.L.; Riley, D.J.; Turner, C.D.
1998-01-01
New techniques have been recently developed that allow unstructured, free meshes to be embedded into standard 3-dimensional, rectilinear, finite-difference time-domain grids. The resulting hybrid-grid modeling capability allows the higher resolution and fidelity of modeling afforded by free meshes to be combined with the simplicity and efficiency of rectilinear techniques. Integration of these new methods into the full-featured, general-purpose QUICKSILVER electromagnetic, Particle-In-Cell (PIC) code provides new modeling capability for a wide variety of electromagnetic and plasma physics problems. To completely exploit the integration of this technology into QUICKSILVER for applications requiring the self-consistent treatment of charged particles, this project has extended existing PIC methods for operation on these hybrid unstructured/rectilinear meshes. Several technical issues had to be addressed in order to accomplish this goal, including the location of particles on the unstructured mesh, adequate conservation of charge, and the proper handling of particles in the transition region between structured and unstructured portions of the hybrid grid.
Numerical Modelling of Staged Combustion Aft-Injected Hybrid Rocket Motors
NASA Astrophysics Data System (ADS)
Nijsse, Jeff
The staged combustion aft-injected hybrid (SCAIH) rocket motor is a promising design for the future of hybrid rocket propulsion. Advances in computational fluid dynamics and scientific computing have made computational modelling an effective tool in hybrid rocket motor design and development. The focus of this thesis is the numerical modelling of the SCAIH rocket motor in a turbulent combustion, high-speed, reactive flow framework accounting for solid soot transport and radiative heat transfer. The SCAIH motor is modelled with a shear coaxial injector with liquid oxygen injected in the center at sub-critical conditions: 150 K and 150 m/s (Mach ≈ 0.9), and a gas-generator gas-solid mixture of one-third carbon soot by mass injected in the annual opening at 1175 K and 460 m/s (Mach ≈ 0.6). Flow conditions in the near injector region and the flame anchoring mechanism are of particular interest. Overall, the flow is shown to exhibit instabilities and the flame is shown to anchor directly on the injector faceplate with temperatures in excess of 2700 K.
Analysis of the PEDOT:PSS/Si nanowire hybrid solar cell with a tail state model
NASA Astrophysics Data System (ADS)
Ho, Kuan-Ying; Li, Chi-Kang; Syu, Hong-Jhang; Lai, Yi; Lin, Ching-Fuh; Wu, Yuh-Renn
2016-12-01
In this paper, the electrical properties of the poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS)/silicon nanowire hybrid solar cell have been analyzed and an optimized structure is proposed. In addition, the planar PEDOT:PSS/c-Si hybrid solar cell is also modeled for comparison. We first developed a simulation software which is capable of modeling organic/inorganic hybrid solar cells by including Gaussian shape density of states into Poisson and drift-diffusion solver to present the tail states and trap states in the organic material. Therefore, the model can handle carrier transport, generation, and recombination in both organic and inorganic materials. Our results show that at the applied voltage near open-circuit voltage (Voc), the recombination rate becomes much higher at the PEDOT:PSS/Si interface region, which limits the fill factor and Voc. Hence, a modified structure with a p-type amorphous silicon (a-Si) layer attached on the interface of Si layer and an n+-type Si layer inserted near the bottom contact are proposed. The highest conversion efficiency of 16.10% can be achieved if both structures are applied.
Fontenete, Sílvia; Guimarães, Nuno; Wengel, Jesper; Azevedo, Nuno Filipe
2016-01-01
The thermodynamics and kinetics of DNA hybridization, i.e. the process of self-assembly of one, two or more complementary nucleic acid strands, has been studied for many years. The appearance of the nearest-neighbor model led to several theoretical and experimental papers on DNA thermodynamics that provide reasonably accurate thermodynamic information on nucleic acid duplexes and allow estimation of the melting temperature. Because there are no thermodynamic models specifically developed to predict the hybridization temperature of a probe used in a fluorescence in situ hybridization (FISH) procedure, the melting temperature is used as a reference, together with corrections for certain compounds that are used during FISH. However, the quantitative relation between melting and experimental FISH temperatures is poorly described. In this review, various models used to predict the melting temperature for rRNA targets, for DNA oligonucleotides and for nucleic acid mimics (chemically modified oligonucleotides), will be addressed in detail, together with a critical assessment of how this information should be used in FISH.
Inferring methane fluxes at a larch forest using Lagrangian, Eulerian, and hybrid inverse models
NASA Astrophysics Data System (ADS)
Ueyama, Masahito; Takanashi, Satoru; Takahashi, Yoshiyuki
2014-10-01
Measuring methane (CH4) flux at upland forests is challenging due to high levels of heterogeneity in upscaling chamber measurements and the detection limits of currently available micrometeorological methods. We estimated CH4 fluxes in an upland forest from vertical concentration profiles using three different inverse multilayer models: the Lagrangian localized near field theory, Eulerian, and hybrid Lagrangian-Eulerian models. The approach could estimate spatially representative fluxes, and use of higher gradients within canopies than above them could minimize uncertainties associated with sensor noises. Comparing fluxes by the models and measurements by the micrometeorological hyperbolic relaxed eddy accumulation and chamber methods, daytime fluxes were reasonably reproduced, but nighttime fluxes were overestimated most likely due to an underestimation of stable conditions and storage effects. The models and measurements show that the forest acted as a CH4 sink during the study period, and the soil acted as the dominant sink. The estimated sink increased with increasing soil temperatures and decreasing soil water content. The CH4 sink estimated during the study period were 1.5 ± 0.2 nmol m-2 s-1 by the micrometeorological method, 2.4 ± 0.5 nmol m-2 s-1 by chambers, 2.8 ± 1.1 nmol m-2 s-1 by the Lagrangian model, 2.7 ± 1.0 nmol m-2 s-1 by the Eulerian model, and 3.7 ± 2.8 nmol m-2 s-1 by the hybrid model. The performance of the Lagrangian and hybrid models increased when the CH4 sink/source was assumed to only exist in the soil.
1D-3D hybrid modeling-from multi-compartment models to full resolution models in space and time.
Grein, Stephan; Stepniewski, Martin; Reiter, Sebastian; Knodel, Markus M; Queisser, Gillian
2014-01-01
Investigation of cellular and network dynamics in the brain by means of modeling and simulation has evolved into a highly interdisciplinary field, that uses sophisticated modeling and simulation approaches to understand distinct areas of brain function. Depending on the underlying complexity, these models vary in their level of detail, in order to cope with the attached computational cost. Hence for large network simulations, single neurons are typically reduced to time-dependent signal processors, dismissing the spatial aspect of each cell. For single cell or networks with relatively small numbers of neurons, general purpose simulators allow for space and time-dependent simulations of electrical signal processing, based on the cable equation theory. An emerging field in Computational Neuroscience encompasses a new level of detail by incorporating the full three-dimensional morphology of cells and organelles into three-dimensional, space and time-dependent, simulations. While every approach has its advantages and limitations, such as computational cost, integrated and methods-spanning simulation approaches, depending on the network size could establish new ways to investigate the brain. In this paper we present a hybrid simulation approach, that makes use of reduced 1D-models using e.g., the NEURON simulator-which couples to fully resolved models for simulating cellular and sub-cellular dynamics, including the detailed three-dimensional morphology of neurons and organelles. In order to couple 1D- and 3D-simulations, we present a geometry-, membrane potential- and intracellular concentration mapping framework, with which graph- based morphologies, e.g., in the swc- or hoc-format, are mapped to full surface and volume representations of the neuron and computational data from 1D-simulations can be used as boundary conditions for full 3D simulations and vice versa. Thus, established models and data, based on general purpose 1D-simulators, can be directly coupled to the
An exact stochastic hybrid model of excitable membranes including spatio-temporal evolution.
Buckwar, Evelyn; Riedler, Martin G
2011-12-01
In this paper, we present a mathematical description for excitable biological membranes, in particular neuronal membranes. We aim to model the (spatio-) temporal dynamics, e.g., the travelling of an action potential along the axon, subject to noise, such as ion channel noise. Using the framework of Piecewise Deterministic Processes (PDPs) we provide an exact mathematical description-in contrast to pseudo-exact algorithms considered in the literature-of the stochastic process one obtains coupling a continuous time Markov chain model with a deterministic dynamic model of a macroscopic variable, that is coupling Markovian channel dynamics to the time-evolution of the transmembrane potential. We extend the existing framework of PDPs in finite dimensional state space to include infinite-dimensional evolution equations and thus obtain a stochastic hybrid model suitable for modelling spatio-temporal dynamics. We derive analytic results for the infinite-dimensional process, such as existence, the strong Markov property and its extended generator. Further, we exemplify modelling of spatially extended excitable membranes with PDPs by a stochastic hybrid version of the Hodgkin-Huxley model of the squid giant axon. Finally, we discuss the advantages of the PDP formulation in view of analytical and numerical investigations as well as the application of PDPs to structurally more complex models of excitable membranes. © Springer-Verlag 2011
Hybrid Markov chain models of S-I-R disease dynamics.
Rebuli, Nicolas P; Bean, N G; Ross, J V
2017-09-01
Deterministic epidemic models are attractive due to their compact nature, allowing substantial complexity with computational efficiency. This partly explains their dominance in epidemic modelling. However, the small numbers of infectious individuals at early and late stages of an epidemic, in combination with the stochastic nature of transmission and recovery events, are critically important to understanding disease dynamics. This motivates the use of a stochastic model, with continuous-time Markov chains being a popular choice. Unfortunately, even the simplest Markovian S-I-R model-the so-called general stochastic epidemic-has a state space of order [Formula: see text], where N is the number of individuals in the population, and hence computational limits are quickly reached. Here we introduce a hybrid Markov chain epidemic model, which maintains the stochastic and discrete dynamics of the Markov chain in regions of the state space where they are of most importance, and uses an approximate model-namely a deterministic or a diffusion model-in the remainder of the state space. We discuss the evaluation, efficiency and accuracy of this hybrid model when approximating the distribution of the duration of the epidemic and the distribution of the final size of the epidemic. We demonstrate that the computational complexity is [Formula: see text] and that under suitable conditions our approximations are highly accurate.
Effects of Na+ and He+ pickup ions on the lunar plasma environment: 3D hybrid modeling
NASA Astrophysics Data System (ADS)
Lipatov, A. S.; Cooper, J. F.; Sittler, E. C.; Hartle, R. E.; Sarantos, M.
2011-12-01
The hybrid kinetic model used here supports comprehensive simulation of the interaction between different spatial and energetic elements of the moon-solar wind-magnetosphere of the Earth system. There is a set of MHD,kinetic, hybrid, drift kinetic, electrostatic and full kinetic modeling of the lunar plasma environment [1]. However, observations show the existence of several species of the neutrals and pickup ions like Na, He, K, O etc., (see e.g., [2,3,4]). The solar wind parameters are chosen from the ARTEMIS observations [5]. The Na+, He+ lunar exosphere's parameters are chosen from [6,7]. The hybrid kinetic model allows us to take into account the finite gyroradius effects of pickup ions and to correctly estimate the ions velocity distribution and the fluxes along the magnetic field, and on the lunar surface. Modeling shows the formation of the asymmetric Mach cone, the structuring of the pickup ion tails, and presents another type of lunar-solar wind interaction. We will compare the results of our modeling with observed distributions. References [1] Lipatov, A.S., and Cooper, J.F., Hybrid kinetic modeling of the Lunar plasma environment: Past, present and future. In: Lunar Dust, Plasma and Atmosphere: The Next Steps, January 27-29, 2010, Boulder, Colorado, Abstracts/lpa2010.colorado.edu/. [2] Potter, A.E., and Morgan, T.H., Discovery of sodium and potassium vapor in the atmosphere of the Moon, Science, 241, 675-680, doi:10.1126/science.241.4866.675, 1988. [3] Tyler, A.L., et al., Observations of sodium in the tenuous lunar atmosphere, Geophys. Res. Lett., 15(10), 1141-1144, doi:10.1029/GL015i010p01141, 1988. [4] Tanaka, T., et al., First in situ observation of the Moon-originating ions in the Earth's Magnetosphere by MAP-PACE on SELENE (KAGUYA), Geophys. Res. Lett., 36, L22106, doi:10.1029/2009GL040682, 2009. [5] Wiehle, S., et al., First Lunar Wake Passage of ARTEMIS: Discrimination of Wake Effects and Solar Wind Fluctuations by 3D Hybrid Simulations, Planet
Continuing Development of a Hybrid Model (VSH) of the Neutral Thermosphere
NASA Technical Reports Server (NTRS)
Burns, Alan
1996-01-01
We propose to continue the development of a new operational model of neutral thermospheric density, composition, temperatures and winds to improve current engineering environment definitions of the neutral thermosphere. This model will be based on simulations made with the National Center for Atmospheric Research (NCAR) Thermosphere-Ionosphere- Electrodynamic General Circulation Model (TIEGCM) and on empirical data. It will be capable of using real-time geophysical indices or data from ground-based and satellite inputs and provides neutral variables at specified locations and times. This "hybrid" model will be based on a Vector Spherical Harmonic (VSH) analysis technique developed (over the last 8 years) at the University of Michigan that permits the incorporation of the TIGCM outputs and data into the model. The VSH model will be a more accurate version of existing models of the neutral thermospheric, and will thus improve density specification for satellites flying in low Earth orbit (LEO).
A hybrid approach for modelling dynamic behaviours of a rotor-foundation system
NASA Astrophysics Data System (ADS)
Zhang, Z. G.; Zhang, Z. Y.; Jing, B.; Hua, H. X.
2016-09-01
A new hybrid approach is presented to study the dynamic behaviour of a rotor- foundation system, in which a shaft coupled with various discontinuities are connected to a flexible foundation via discrete spring subunits. By modelling the rotor with the modified transfer matrix method and describing the flexible foundation through the appropriate modal model, the proposed technique facilitates a computationally efficient modelling approach where a mixture of theoretical, numerical or experimental models can be incorporated into one overall numerical model. Particularly, the present model enables one to conveniently consider both the free and forced vibrations as well as effects of various combinations of discontinuities encountered in the rotor. Some results are compared with available results in previous publications and those from the finite element method to validate the model. Parametric studies are also performed to demonstrate the accuracy and versatility of the developed method for substructure coupling analysis.
Yilmaz, L. Safak; Loy, Alexander; Wright, Erik S.; Wagner, Michael; Noguera, Daniel R.
2012-01-01
Application of high-density microarrays to the diagnostic analysis of microbial communities is challenged by the optimization of oligonucleotide probe sensitivity and specificity, as it is generally unfeasible to experimentally test thousands of probes. This study investigated the adjustment of hybridization stringency using formamide with the idea that sensitivity and specificity can be optimized during probe design if the hybridization efficiency of oligonucleotides with target and non-target molecules can be predicted as a function of formamide concentration. Sigmoidal denaturation profiles were obtained using fluorescently labeled and fragmented 16S rRNA gene amplicon of Escherichia coli as the target with increasing concentrations of formamide in the hybridization buffer. A linear free energy model (LFEM) was developed and microarray-specific nearest neighbor rules were derived. The model simulated formamide melting with a denaturant m-value that increased hybridization free energy (ΔG°) by 0.173 kcal/mol per percent of formamide added (v/v). Using the LFEM and specific probe sets, free energy rules were systematically established to predict the stability of single and double mismatches, including bulged and tandem mismatches. The absolute error in predicting the position of experimental denaturation profiles was less than 5% formamide for more than 90 percent of probes, enabling a practical level of accuracy in probe design. The potential of the modeling approach for probe design and optimization is demonstrated using a dataset including the 16S rRNA gene of Rhodobacter sphaeroides as an additional target molecule. The LFEM and thermodynamic databases were incorporated into a computational tool (ProbeMelt) that is freely available at http://DECIPHER.cee.wisc.edu. PMID:22952791
Cilfone, Nicholas A.; Kirschner, Denise E.; Linderman, Jennifer J.
2015-01-01
Biologically related processes operate across multiple spatiotemporal scales. For computational modeling methodologies to mimic this biological complexity, individual scale models must be linked in ways that allow for dynamic exchange of information across scales. A powerful methodology is to combine a discrete modeling approach, agent-based models (ABMs), with continuum models to form hybrid models. Hybrid multi-scale ABMs have been used to simulate emergent responses of biological systems. Here, we review two aspects of hybrid multi-scale ABMs: linking individual scale models and efficiently solving the resulting model. We discuss the computational choices associated with aspects of linking individual scale models while simultaneously maintaining model tractability. We demonstrate implementations of existing numerical methods in the context of hybrid multi-scale ABMs. Using an example model describing Mycobacterium tuberculosis infection, we show relative computational speeds of various combinations of numerical methods. Efficient linking and solution of hybrid multi-scale ABMs is key to model portability, modularity, and their use in understanding biological phenomena at a systems level. PMID:26366228
Forecasting currency circulation data of Bank Indonesia by using hybrid ARIMAX-ANN model
NASA Astrophysics Data System (ADS)
Prayoga, I. Gede Surya Adi; Suhartono, Rahayu, Santi Puteri
2017-05-01
The purpose of this study is to forecast currency inflow and outflow data of Bank Indonesia. Currency circulation in Indonesia is highly influenced by the presence of Eid al-Fitr. One way to forecast the data with Eid al-Fitr effect is using autoregressive integrated moving average with exogenous input (ARIMAX) model. However, ARIMAX is a linear model, which cannot handle nonlinear correlation structures of the data. In the field of forecasting, inaccurate predictions can be considered caused by the existence of nonlinear components that are uncaptured by the model. In this paper, we propose a hybrid model of ARIMAX and artificial neural networks (ANN) that can handle both linear and nonlinear correlation. This method was applied for 46 series of currency inflow and 46 series of currency outflow. The results showed that based on out-of-sample root mean squared error (RMSE), the hybrid models are up to10.26 and 10.65 percent better than ARIMAX for inflow and outflow series, respectively. It means that ANN performs well in modeling nonlinear correlation of the data and can increase the accuracy of linear model.
NASA Astrophysics Data System (ADS)
Economou, J. T.; Knowles, K.; Tsourdos, A.; White, B. A.
2011-02-01
In this article, the fuzzy-hybrid modelling (FHM) approach is used and compared to the input-output system Takagi-Sugeno (TS) modelling approach which correlates the drivetrain power flow equations with the vehicle dynamics. The output power relations were related to the drivetrain bounded efficiencies and also to the wheel slips. The model relates also to the wheel and ground interactions via suitable friction coefficient models relative to the wheel slip profiles. The wheel slip had a significant efficiency contribution to the overall driveline system efficiency. The peak friction slip and peak coefficient of friction values are known a priori during the analysis. Lastly, the rigid body dynamical power has been verified through both simulation and experimental results. The mathematical analysis has been supported throughout the paper via experimental data for a specific electric robotic vehicle. The identification of the localised and input-output TS models for the fuzzy hybrid and the experimental data were obtained utilising the subtractive clustering (SC) methodology. These results were also compared to a real-time TS SC approach operating on periodic time windows. This article concludes with the benefits of the real-time FHM method for the vehicle electric driveline due to the advantage of both the analytical TS sub-model and the physical system modelling for the remaining process which can be clearly utilised for control purposes.
NASA Astrophysics Data System (ADS)
McLarty, Dustin Fogle
Distributed energy systems are a promising means by which to reduce both emissions and costs. Continuous generators must be responsive and highly efficiency to support building dynamics and intermittent on-site renewable power. Fuel cell -- gas turbine hybrids (FC/GT) are fuel-flexible generators capable of ultra-high efficiency, ultra-low emissions, and rapid power response. This work undertakes a detailed study of the electrochemistry, chemistry and mechanical dynamics governing the complex interaction between the individual systems in such a highly coupled hybrid arrangement. The mechanisms leading to the compressor stall/surge phenomena are studied for the increased risk posed to particular hybrid configurations. A novel fuel cell modeling method introduced captures various spatial resolutions, flow geometries, stack configurations and novel heat transfer pathways. Several promising hybrid configurations are analyzed throughout the work and a sensitivity analysis of seven design parameters is conducted. A simple estimating method is introduced for the combined system efficiency of a fuel cell and a turbine using component performance specifications. Existing solid oxide fuel cell technology is capable of hybrid efficiencies greater than 75% (LHV) operating on natural gas, and existing molten carbonate systems greater than 70% (LHV). A dynamic model is calibrated to accurately capture the physical coupling of a FC/GT demonstrator tested at UC Irvine. The 2900 hour experiment highlighted the sensitivity to small perturbations and a need for additional control development. Further sensitivity studies outlined the responsiveness and limits of different control approaches. The capability for substantial turn-down and load following through speed control and flow bypass with minimal impact on internal fuel cell thermal distribution is particularly promising to meet local demands or provide dispatchable support for renewable power. Advanced control and dispatch
Ozmutlu, H. Cenk
2014-01-01
We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204
A novel hybrid forecasting model for PM₁₀ and SO₂ daily concentrations.
Wang, Ping; Liu, Yong; Qin, Zuodong; Zhang, Guisheng
2015-02-01
Air-quality forecasting in urban areas is difficult because of the uncertainties in describing both the emission and meteorological fields. The use of incomplete information in the training phase restricts practical air-quality forecasting. In this paper, we propose a hybrid artificial neural network and a hybrid support vector machine, which effectively enhance the forecasting accuracy of an artificial neural network (ANN) and support vector machine (SVM) by revising the error term of the traditional methods. The hybrid methodology can be described in two stages. First, we applied the ANN or SVM forecasting system with historical data and exogenous parameters, such as meteorological variables. Then, the forecasting target was revised by the Taylor expansion forecasting model using the residual information of the error term in the previous stage. The innovation involved in this approach is that it sufficiently and validly utilizes the useful residual information on an incomplete input variable condition. The proposed method was evaluated by experiments using a 2-year dataset of daily PM₁₀ (particles with a diameter of 10 μm or less) concentrations and SO₂ (su