Continuum Modeling of Biological Network Formation

KAUST Repository

Albi, Giacomo

2017-04-10

We present an overview of recent analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transportation networks. The model describes the pressure field using a Darcy type equation and the dynamics of the conductance network under pressure force effects. Randomness in the material structure is represented by a linear diffusion term and conductance relaxation by an algebraic decay term. We first introduce micro- and mesoscopic models and show how they are connected to the macroscopic PDE system. Then, we provide an overview of analytical results for the PDE model, focusing mainly on the existence of weak and mild solutions and analysis of the steady states. The analytical part is complemented by extensive numerical simulations. We propose a discretization based on finite elements and study the qualitative properties of network structures for various parameter values.

Dynamic thermo-hydraulic model of district cooling networks

International Nuclear Information System (INIS)

Oppelt, Thomas; Urbaneck, Thorsten; Gross, Ulrich; Platzer, Bernd

2016-01-01

Highlights: • A dynamic thermo-hydraulic model for district cooling networks is presented. • The thermal modelling is based on water segment tracking (Lagrangian approach). • Thus, numerical errors and balance inaccuracies are avoided. • Verification and validation studies proved the reliability of the model. - Abstract: In the present paper, the dynamic thermo-hydraulic model ISENA is presented which can be applied for answering different questions occurring in design and operation of district cooling networks—e.g. related to economic and energy efficiency. The network model consists of a quasistatic hydraulic model and a transient thermal model based on tracking water segments through the whole network (Lagrangian method). Applying this approach, numerical errors and balance inaccuracies can be avoided which leads to a higher quality of results compared to other network models. Verification and validation calculations are presented in order to show that ISENA provides reliable results and is suitable for practical application.

Diffusion in random networks: Asymptotic properties, and numerical and engineering approximations

Science.gov (United States)

Padrino, Juan C.; Zhang, Duan Z.

2016-11-01

The ensemble phase averaging technique is applied to model mass transport by diffusion in random networks. The system consists of an ensemble of random networks, where each network is made of a set of pockets connected by tortuous channels. Inside a channel, we assume that fluid transport is governed by the one-dimensional diffusion equation. Mass balance leads to an integro-differential equation for the pores mass density. The so-called dual porosity model is found to be equivalent to the leading order approximation of the integration kernel when the diffusion time scale inside the channels is small compared to the macroscopic time scale. As a test problem, we consider the one-dimensional mass diffusion in a semi-infinite domain, whose solution is sought numerically. Because of the required time to establish the linear concentration profile inside a channel, for early times the similarity variable is xt- 1 / 4 rather than xt- 1 / 2 as in the traditional theory. This early time sub-diffusive similarity can be explained by random walk theory through the network. In addition, by applying concepts of fractional calculus, we show that, for small time, the governing equation reduces to a fractional diffusion equation with known solution. We recast this solution in terms of special functions easier to compute. Comparison of the numerical and exact solutions shows excellent agreement.

Continuum Modeling of Biological Network Formation

KAUST Repository

Albi, Giacomo; Burger, Martin; Haskovec, Jan; Markowich, Peter A.; Schlottbom, Matthias

2017-01-01

We present an overview of recent analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transportation networks. The model describes

Integration of numerical modeling and observations for the Gulf of Naples monitoring network

Science.gov (United States)

Iermano, I.; Uttieri, M.; Zambianchi, E.; Buonocore, B.; Cianelli, D.; Falco, P.; Zambardino, G.

2012-04-01

Lethal effects of mineral oils on fragile marine and coastal ecosystems are now well known. Risks and damages caused by a maritime accident can be reduced with the help of better forecasts and efficient monitoring systems. The MED project TOSCA (Tracking Oil Spills and Coastal Awareness Network), which gathers 13 partners from 4 Mediterranean countries, has been designed to help create a better response system to maritime accidents. Through the construction of an observational network, based on state of the art technology (HF radars and drifters), TOSCA provides real-time observations and forecasts of the Mediterranean coastal marine environmental conditions. The system is installed and assessed in five test sites on the coastal areas of oil spill outlets (Eastern Mediterranean) and on high traffic areas (Western Mediterranean). The Gulf of Naples, a small semi-closed basin opening to the Tyrrhenian Sea is one of the five test-sites. It is of particular interest from both the environmental point of view, due to peculiar ecosystem properties in the area, and because it sustains important touristic and commercial activities. Currently the Gulf of Naples monitoring network is represented by five automatic weather stations distributed along the coasts of the Gulf, one weather radar, two tide gauges, one waverider buoy, and moored physical, chemical and bio-optical instrumentation. In addition, a CODAR-SeaSonde HF coastal radar system composed of three antennas is located in Portici, Massa Lubrense and Castellammare. The system provides hourly data of surface currents over the entire Gulf with a 1km spatial resolution. A numerical modeling implementation based on Regional Ocean Modeling System (ROMS) is actually integrated in the Gulf of Naples monitoring network. ROMS is a 3-D, free-surface, hydrostatic, primitive equation, finite difference ocean model. In our configuration, the model has high horizontal resolution (250m), and 30 sigma levels in the vertical. Thanks

An evolving network model with modular growth

International Nuclear Information System (INIS)

Zou Zhi-Yun; Liu Peng; Lei Li; Gao Jian-Zhi

2012-01-01

In this paper, we propose an evolving network model growing fast in units of module, according to the analysis of the evolution characteristics in real complex networks. Each module is a small-world network containing several interconnected nodes and the nodes between the modules are linked by preferential attachment on degree of nodes. We study the modularity measure of the proposed model, which can be adjusted by changing the ratio of the number of inner-module edges and the number of inter-module edges. In view of the mean-field theory, we develop an analytical function of the degree distribution, which is verified by a numerical example and indicates that the degree distribution shows characteristics of the small-world network and the scale-free network distinctly at different segments. The clustering coefficient and the average path length of the network are simulated numerically, indicating that the network shows the small-world property and is affected little by the randomness of the new module. (interdisciplinary physics and related areas of science and technology)

Numerical simulations of a reduced model for blood coagulation

Science.gov (United States)

Pavlova, Jevgenija; Fasano, Antonio; Sequeira, Adélia

2016-04-01

In this work, the three-dimensional numerical resolution of a complex mathematical model for the blood coagulation process is presented. The model was illustrated in Fasano et al. (Clin Hemorheol Microcirc 51:1-14, 2012), Pavlova et al. (Theor Biol 380:367-379, 2015). It incorporates the action of the biochemical and cellular components of blood as well as the effects of the flow. The model is characterized by a reduction in the biochemical network and considers the impact of the blood slip at the vessel wall. Numerical results showing the capacity of the model to predict different perturbations in the hemostatic system are discussed.

Neural network modeling of associative memory: Beyond the Hopfield model

Science.gov (United States)

Dasgupta, Chandan

1992-07-01

A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying dynamics are used to store and associatively recall information, are described. In the first class of models, a hierarchical structure is used to store an exponentially large number of strongly correlated memories. The second class of models uses limit cycles to store and retrieve individual memories. A neurobiologically plausible network that generates low-amplitude periodic variations of activity, similar to the oscillations observed in electroencephalographic recordings, is also described. Results obtained from analytic and numerical studies of the properties of these networks are discussed.

A Numerical Model of Anisotropic Mass Transport Through Grain Boundary Networks

Science.gov (United States)

Wang, Yibo

Tin (Sn) thin films are commonly used in electronic circuit applications as coatings on contacts and solders for joining components. It is widely observed, for some such system, that whiskers---long, thin crystalline structures---emerge and grow from the film. The Sn whisker phenomenon has become a highly active research area since Sn whiskers have caused a large amount of damage and loss in manufacturing, military, medical and power industries. Though lead (Pb) addition to Sn has been used to solve this problem for over five decades, the adverse environmental and health effects of Pb have motivated legislation to severely constrain Pb use in society. People are researching and seeking the reasons which cause whiskers and corresponding methods to solve the problem. The contributing factors to cause a Sn whisker are potentially many and much still remains unknown. Better understanding of fundamental driving forces should point toward strategies to improve (a) the accuracy with which we can predict whisker formation, and (b) our ability to mitigate the phenomenon. This thesis summarizes recent important research achievements in understanding Sn whisker formation and growth, both experimentally and theoretically. Focus is then placed on examining the role that anisotropy in grain boundary diffusivity plays in determining whisker characteristics (specifically, whether they form and, if so, where on a surface). To study this aspect of the problem and to enable future studies on stress driven grain boundary diffusion, this thesis presents a numerical anisotropic mass transport model. In addition to presenting details of the model and implementation, model predictions for a set of increasingly complex grain boundary networks are discussed. Preliminary results from the model provide evidence that anisotropic grain boundary diffusion may be a primary driving mechanism in whisker formation.

Numerical modeling of the thermomechanical behavior of networks of underground galleries for the storage of the radioactive waste: approach by homogenization

International Nuclear Information System (INIS)

Zokimila, P.

2005-10-01

Deep geological disposal is one of the privileged options for the storage of High Level radioactive waste. A good knowledge of the behavior and properties of the potential geological formations as well as theirs evolution in time under the effect of the stress change induced by a possible installation of storage is required. The geological formation host will be subjected to mechanical and thermal solicitations due respectively to the excavation of the disposal tunnels and the release of heat of the canisters of radioactive waste. These thermomechanical solicitations will generate a stress relief in the host layer and disposal tunnels deformations as well as the extension of the damaged zones (EDZ) could cause local and global instabilities. This work aims to develop calculation methods to optimize numerical modeling of the thermoelastic behavior of the disposal at a large scale and to evaluate thermomechanical disturbance induced by storage on the geological formation host. Accordingly, after a presentation of the state of knowledge on the thermomechanical aspects of the rocks related to deep storage, of numerical modeling 2D and 3D of the thermoelastic behavior of individual disposal tunnel and a network of tunnels were carried out by a discrete approach. However, this classical approach is penalizing to study the global behavior of disposal storage. To mitigate that, an approach of numerical modeling, based on homogenization of periodic structures, was proposed. Formulations as numerical procedures were worked out to calculate the effective thermoelastic behavior of an equivalent heterogeneous structure. The model, obtained by this method, was validated with existing methods of homogenization such as the self-consistent model, as well as the Hashin-Shtrikman bounds. The comparison between the effective thermoelastic behavior and current thermoelastic behavior of reference showed a good coherence of the results. For an application to deep geological storage, the

Modeling and Analysis of New Products Diffusion on Heterogeneous Networks

Directory of Open Access Journals (Sweden)

Shuping Li

2014-01-01

Full Text Available We present a heterogeneous networks model with the awareness stage and the decision-making stage to explain the process of new products diffusion. If mass media is neglected in the decision-making stage, there is a threshold whether the innovation diffusion is successful or not, or else it is proved that the network model has at least one positive equilibrium. For networks with the power-law degree distribution, numerical simulations confirm analytical results, and also at the same time, by numerical analysis of the influence of the network structure and persuasive advertisements on the density of adopters, we give two different products propagation strategies for two classes of nodes in scale-free networks.

Multi-digit handwritten sindhi numerals recognition using som neural network

International Nuclear Information System (INIS)

Chandio, A.A.; Jalbani, A.H.; Awan, S.A.

2017-01-01

In this research paper a multi-digit Sindhi handwritten numerals recognition system using SOM Neural Network is presented. Handwritten digits recognition is one of the challenging tasks and a lot of research is being carried out since many years. A remarkable work has been done for recognition of isolated handwritten characters as well as digits in many languages like English, Arabic, Devanagari, Chinese, Urdu and Pashto. However, the literature reviewed does not show any remarkable work done for Sindhi numerals recognition. The recognition of Sindhi digits is a difficult task due to the various writing styles and different font sizes. Therefore, SOM (Self-Organizing Map), a NN (Neural Network) method is used which can recognize digits with various writing styles and different font sizes. Only one sample is required to train the network for each pair of multi-digit numerals. A database consisting of 4000 samples of multi-digits consisting only two digits from 10-50 and other matching numerals have been collected by 50 users and the experimental results of proposed method show that an accuracy of 86.89% is achieved. (author)

Advanced approach to numerical forecasting using artificial neural networks

Directory of Open Access Journals (Sweden)

Michael Štencl

2009-01-01

Full Text Available Current global market is driven by many factors, such as the information age, the time and amount of information distributed by many data channels it is practically impossible analyze all kinds of incoming information flows and transform them to data with classical methods. New requirements could be met by using other methods. Once trained on patterns artificial neural networks can be used for forecasting and they are able to work with extremely big data sets in reasonable time. The patterns used for learning process are samples of past data. This paper uses Radial Basis Functions neural network in comparison with Multi Layer Perceptron network with Back-propagation learning algorithm on prediction task. The task works with simplified numerical time series and includes forty observations with prediction for next five observations. The main topic of the article is the identification of the main differences between used neural networks architectures together with numerical forecasting. Detected differences then verify on practical comparative example.

Thermal conductivity model for nanofiber networks

Science.gov (United States)

Zhao, Xinpeng; Huang, Congliang; Liu, Qingkun; Smalyukh, Ivan I.; Yang, Ronggui

2018-02-01

Understanding thermal transport in nanofiber networks is essential for their applications in thermal management, which are used extensively as mechanically sturdy thermal insulation or high thermal conductivity materials. In this study, using the statistical theory and Fourier's law of heat conduction while accounting for both the inter-fiber contact thermal resistance and the intrinsic thermal resistance of nanofibers, an analytical model is developed to predict the thermal conductivity of nanofiber networks as a function of their geometric and thermal properties. A scaling relation between the thermal conductivity and the geometric properties including volume fraction and nanofiber length of the network is revealed. This model agrees well with both numerical simulations and experimental measurements found in the literature. This model may prove useful in analyzing the experimental results and designing nanofiber networks for both high and low thermal conductivity applications.

Thermal conductivity model for nanofiber networks

Energy Technology Data Exchange (ETDEWEB)

Zhao, Xinpeng [Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309, USA; Huang, Congliang [Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309, USA; School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China; Liu, Qingkun [Department of Physics, University of Colorado, Boulder, Colorado 80309, USA; Smalyukh, Ivan I. [Department of Physics, University of Colorado, Boulder, Colorado 80309, USA; Materials Science and Engineering Program, University of Colorado, Boulder, Colorado 80309, USA; Yang, Ronggui [Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309, USA; Materials Science and Engineering Program, University of Colorado, Boulder, Colorado 80309, USA; Buildings and Thermal Systems Center, National Renewable Energy Laboratory, Golden, Colorado 80401, USA

2018-02-28

Understanding thermal transport in nanofiber networks is essential for their applications in thermal management, which are used extensively as mechanically sturdy thermal insulation or high thermal conductivity materials. In this study, using the statistical theory and Fourier's law of heat conduction while accounting for both the inter-fiber contact thermal resistance and the intrinsic thermal resistance of nanofibers, an analytical model is developed to predict the thermal conductivity of nanofiber networks as a function of their geometric and thermal properties. A scaling relation between the thermal conductivity and the geometric properties including volume fraction and nanofiber length of the network is revealed. This model agrees well with both numerical simulations and experimental measurements found in the literature. This model may prove useful in analyzing the experimental results and designing nanofiber networks for both high and low thermal conductivity applications.

Analysis of organizational culture with social network models

OpenAIRE

Titov, S.

2015-01-01

Organizational culture is nowadays an object of numerous scientific papers. However, only marginal part of existing research attempts to use the formal models of organizational cultures. The lack of organizational culture models significantly limits the further research in this area and restricts the application of the theory to practice of organizational culture change projects. The article consists of general views on potential application of network models and social network analysis to th...

SPLAI: Computational Finite Element Model for Sensor Networks

Directory of Open Access Journals (Sweden)

Ruzana Ishak

2006-01-01

Full Text Available Wireless sensor network refers to a group of sensors, linked by a wireless medium to perform distributed sensing task. The primary interest is their capability in monitoring the physical environment through the deployment of numerous tiny, intelligent, wireless networked sensor nodes. Our interest consists of a sensor network, which includes a few specialized nodes called processing elements that can perform some limited computational capabilities. In this paper, we propose a model called SPLAI that allows the network to compute a finite element problem where the processing elements are modeled as the nodes in the linear triangular approximation problem. Our model also considers the case of some failures of the sensors. A simulation model to visualize this network has been developed using C++ on the Windows environment.

Creating, generating and comparing random network models with NetworkRandomizer.

Science.gov (United States)

Tosadori, Gabriele; Bestvina, Ivan; Spoto, Fausto; Laudanna, Carlo; Scardoni, Giovanni

2016-01-01

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. To fill the validation gap we present an app, for the Cytoscape platform, which aims at creating randomised networks and randomising existing, real networks. Since there is a lack of tools that allow performing such operations, our app aims at enabling researchers to exploit different, well known random network models that could be used as a benchmark for validating real, biological datasets. We also propose a novel methodology for creating random weighted networks, i.e. the multiplication algorithm, starting from real, quantitative data. Finally, the app provides a statistical tool that compares real versus randomly computed attributes, in order to validate the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

A Markov model for the temporal dynamics of balanced random networks of finite size

Science.gov (United States)

Lagzi, Fereshteh; Rotter, Stefan

2014-01-01

The balanced state of recurrent networks of excitatory and inhibitory spiking neurons is characterized by fluctuations of population activity about an attractive fixed point. Numerical simulations show that these dynamics are essentially nonlinear, and the intrinsic noise (self-generated fluctuations) in networks of finite size is state-dependent. Therefore, stochastic differential equations with additive noise of fixed amplitude cannot provide an adequate description of the stochastic dynamics. The noise model should, rather, result from a self-consistent description of the network dynamics. Here, we consider a two-state Markovian neuron model, where spikes correspond to transitions from the active state to the refractory state. Excitatory and inhibitory input to this neuron affects the transition rates between the two states. The corresponding nonlinear dependencies can be identified directly from numerical simulations of networks of leaky integrate-and-fire neurons, discretized at a time resolution in the sub-millisecond range. Deterministic mean-field equations, and a noise component that depends on the dynamic state of the network, are obtained from this model. The resulting stochastic model reflects the behavior observed in numerical simulations quite well, irrespective of the size of the network. In particular, a strong temporal correlation between the two populations, a hallmark of the balanced state in random recurrent networks, are well represented by our model. Numerical simulations of such networks show that a log-normal distribution of short-term spike counts is a property of balanced random networks with fixed in-degree that has not been considered before, and our model shares this statistical property. Furthermore, the reconstruction of the flow from simulated time series suggests that the mean-field dynamics of finite-size networks are essentially of Wilson-Cowan type. We expect that this novel nonlinear stochastic model of the interaction between

A general evolving model for growing bipartite networks

International Nuclear Information System (INIS)

Tian, Lixin; He, Yinghuan; Liu, Haijun; Du, Ruijin

2012-01-01

In this Letter, we propose and study an inner evolving bipartite network model. Significantly, we prove that the degree distribution of two different kinds of nodes both obey power-law form with adjustable exponents. Furthermore, the joint degree distribution of any two nodes for bipartite networks model is calculated analytically by the mean-field method. The result displays that such bipartite networks are nearly uncorrelated networks, which is different from one-mode networks. Numerical simulations and empirical results are given to verify the theoretical results. -- Highlights: ► We proposed a general evolving bipartite network model which was based on priority connection, reconnection and breaking edges. ► We prove that the degree distribution of two different kinds of nodes both obey power-law form with adjustable exponents. ► The joint degree distribution of any two nodes for bipartite networks model is calculated analytically by the mean-field method. ► The result displays that such bipartite networks are nearly uncorrelated networks, which is different from one-mode networks.

Numerical simulation of shower cooling tower based on artificial neural network

International Nuclear Information System (INIS)

Qi Xiaoni; Liu Zhenyan; Li Dandan

2008-01-01

This study was prompted by the need to design towers for applications in which, due to salt deposition on the packing and subsequent blockage, the use of tower packing is not practical. The cooling tower analyzed in this study is void of fill, named shower cooling tower (SCT). However, the present study focuses mostly on experimental investigation of the SCT, and no systematic numerical method is available. In this paper, we first developed a one dimensional model and analyzed the heat and mass transfer processes of the SCT; then we used the concept of artificial neural network (ANN) to propose a computer design tool that can help the designer evaluate the outlet water temperature from a given set of experimentally obtained data. For comparison purposes and accurate evaluation of the predictions, part of the experimental data was used to train the neural network and the remainder to test the model. The results predicted by the ANN model were compared with those of the standard model and the experimental data. The ANN model predicted the outlet water temperature with a MAE (mean absolute error) of 1.31%, whereas the standard one dimensional model showed a MAE of 9.42%

Model reduction for the dynamics and control of large structural systems via neutral network processing direct numerical optimization

Science.gov (United States)

Becus, Georges A.; Chan, Alistair K.

1993-01-01

Three neural network processing approaches in a direct numerical optimization model reduction scheme are proposed and investigated. Large structural systems, such as large space structures, offer new challenges to both structural dynamicists and control engineers. One such challenge is that of dimensionality. Indeed these distributed parameter systems can be modeled either by infinite dimensional mathematical models (typically partial differential equations) or by high dimensional discrete models (typically finite element models) often exhibiting thousands of vibrational modes usually closely spaced and with little, if any, damping. Clearly, some form of model reduction is in order, especially for the control engineer who can actively control but a few of the modes using system identification based on a limited number of sensors. Inasmuch as the amount of 'control spillover' (in which the control inputs excite the neglected dynamics) and/or 'observation spillover' (where neglected dynamics affect system identification) is to a large extent determined by the choice of particular reduced model (RM), the way in which this model reduction is carried out is often critical.

A growing social network model in geographical space

Science.gov (United States)

Antonioni, Alberto; Tomassini, Marco

2017-09-01

In this work we propose a new model for the generation of social networks that includes their often ignored spatial aspects. The model is a growing one and links are created either taking space into account, or disregarding space and only considering the degree of target nodes. These two effects can be mixed linearly in arbitrary proportions through a parameter. We numerically show that for a given range of the combination parameter, and for given mean degree, the generated network class shares many important statistical features with those observed in actual social networks, including the spatial dependence of connections. Moreover, we show that the model provides a good qualitative fit to some measured social networks.

Modeling the propagation of mobile malware on complex networks

Science.gov (United States)

Liu, Wanping; Liu, Chao; Yang, Zheng; Liu, Xiaoyang; Zhang, Yihao; Wei, Zuxue

2016-08-01

In this paper, the spreading behavior of malware across mobile devices is addressed. By introducing complex networks to model mobile networks, which follows the power-law degree distribution, a novel epidemic model for mobile malware propagation is proposed. The spreading threshold that guarantees the dynamics of the model is calculated. Theoretically, the asymptotic stability of the malware-free equilibrium is confirmed when the threshold is below the unity, and the global stability is further proved under some sufficient conditions. The influences of different model parameters as well as the network topology on malware propagation are also analyzed. Our theoretical studies and numerical simulations show that networks with higher heterogeneity conduce to the diffusion of malware, and complex networks with lower power-law exponents benefit malware spreading.

Climate Prediction for Brazil's Nordeste: Performance of Empirical and Numerical Modeling Methods.

Science.gov (United States)

Moura, Antonio Divino; Hastenrath, Stefan

2004-07-01

Comparisons of performance of climate forecast methods require consistency in the predictand and a long common reference period. For Brazil's Nordeste, empirical methods developed at the University of Wisconsin use preseason (October January) rainfall and January indices of the fields of meridional wind component and sea surface temperature (SST) in the tropical Atlantic and the equatorial Pacific as input to stepwise multiple regression and neural networking. These are used to predict the March June rainfall at a network of 27 stations. An experiment at the International Research Institute for Climate Prediction, Columbia University, with a numerical model (ECHAM4.5) used global SST information through February to predict the March June rainfall at three grid points in the Nordeste. The predictands for the empirical and numerical model forecasts are correlated at +0.96, and the period common to the independent portion of record of the empirical prediction and the numerical modeling is 1968 99. Over this period, predicted versus observed rainfall are evaluated in terms of correlation, root-mean-square error, absolute error, and bias. Performance is high for both approaches. Numerical modeling produces a correlation of +0.68, moderate errors, and strong negative bias. For the empirical methods, errors and bias are small, and correlations of +0.73 and +0.82 are reached between predicted and observed rainfall.

Malware Propagation and Prevention Model for Time-Varying Community Networks within Software Defined Networks

Directory of Open Access Journals (Sweden)

Lan Liu

2017-01-01

Full Text Available As the adoption of Software Defined Networks (SDNs grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the network as communities and links that are dense in subnets but sparse between subnets. Using numerical simulation and theoretical analysis, we find that the efficiency of network malware propagation in this model depends on the mobility rate q of the nodes between subnets. We also find that there exists a mobility rate threshold qc. The network malware will spread in the SDN when the mobility rate q>qc. The malware will survive when q>qc and perish when qmodel is effective, and the results may help to decide the SDN control strategy to defend against network malware and provide a theoretical basis to reduce and prevent network security incidents.

Unified model for vortex-string network evolution

International Nuclear Information System (INIS)

Martins, C.J.A.P.; Moore, J.N.; Shellard, E.P.S.

2004-01-01

We describe and numerically test the velocity-dependent one-scale string evolution model, a simple analytic approach describing a string network with the averaged correlation length and velocity. We show that it accurately reproduces the large-scale behavior (in particular the scaling laws) of numerical simulations of both Goto-Nambu and field theory string networks. We explicitly demonstrate the relation between the high-energy physics approach and the damped and nonrelativistic limits which are relevant for condensed matter physics. We also reproduce experimental results in this context and show that the vortex-string density is significantly reduced by loop production, an effect not included in the usual 'coarse-grained' approach

Modeling Broadband Microwave Structures by Artificial Neural Networks

Directory of Open Access Journals (Sweden)

V. Otevrel

2004-06-01

Full Text Available The paper describes the exploitation of feed-forward neural networksand recurrent neural networks for replacing full-wave numerical modelsof microwave structures in complex microwave design tools. Building aneural model, attention is turned to the modeling accuracy and to theefficiency of building a model. Dealing with the accuracy, we describea method of increasing it by successive completing a training set.Neural models are mutually compared in order to highlight theiradvantages and disadvantages. As a reference model for comparisons,approximations based on standard cubic splines are used. Neural modelsare used to replace both the time-domain numeric models and thefrequency-domain ones.

Constitutive modelling of composite biopolymer networks.

Science.gov (United States)

Fallqvist, B; Kroon, M

2016-04-21

The mechanical behaviour of biopolymer networks is to a large extent determined at a microstructural level where the characteristics of individual filaments and the interactions between them determine the response at a macroscopic level. Phenomena such as viscoelasticity and strain-hardening followed by strain-softening are observed experimentally in these networks, often due to microstructural changes (such as filament sliding, rupture and cross-link debonding). Further, composite structures can also be formed with vastly different mechanical properties as compared to the individual networks. In this present paper, we present a constitutive model presented in a continuum framework aimed at capturing these effects. Special care is taken to formulate thermodynamically consistent evolution laws for dissipative effects. This model, incorporating possible anisotropic network properties, is based on a strain energy function, split into an isochoric and a volumetric part. Generalisation to three dimensions is performed by numerical integration over the unit sphere. Model predictions indicate that the constitutive model is well able to predict the elastic and viscoelastic response of biological networks, and to an extent also composite structures. Copyright © 2016 Elsevier Ltd. All rights reserved.

Experimental and Numerical Investigation of Preferential Flow in Fractured Network with Clogging Process

Directory of Open Access Journals (Sweden)

Xiaobing Chen

2014-01-01

Full Text Available In this study, physical experiments and numerical simulations are combined to provide a detailed understanding of flow dynamics in fracture network. Hydraulic parameters such as pressure head, velocity field, Reynolds number on certain monitoring cross points, and total flux rate are examined under various clogging conditions. Applying the COMSOL Multiphysics code to solve the Navier-Stokes equation instead of Reynolds equation and using the measured data to validate the model, the fluid flow in the horizontal 2D cross-sections of the fracture network was simulated. Results show that local clogging leads to a significant reshaping of the flow velocity field and a reduction of the transport capacity of the entire system. The flow rate distribution is highly influenced by the fractures connected to the dominant flow channels, although local disturbances in velocity field are unlikely to spread over the whole network. Also, modeling results indicate that water flow in a fracture network, compared with that in a single fracture, is likely to transit into turbulence earlier under the same hydraulic gradient due to the influence of fracture intersections.

Modelling the permeability of polymers: a neural network approach

NARCIS (Netherlands)

Wessling, Matthias; Mulder, M.H.V.; Bos, A.; Bos, A.; van der Linden, M.K.T.; Bos, M.; van der Linden, W.E.

1994-01-01

In this short communication, the prediction of the permeability of carbon dioxide through different polymers using a neural network is studied. A neural network is a numeric-mathematical construction that can model complex non-linear relationships. Here it is used to correlate the IR spectrum of a

Strategy for a numerical Rock Mechanics Site Descriptive Model. Further development of the theoretical/numerical approach

International Nuclear Information System (INIS)

Olofsson, Isabelle; Fredriksson, Anders

2005-05-01

The Swedish Nuclear and Fuel Management Company (SKB) is conducting Preliminary Site Investigations at two different locations in Sweden in order to study the possibility of a Deep Repository for spent fuel. In the frame of these Site Investigations, Site Descriptive Models are achieved. These products are the result of an interaction of several disciplines such as geology, hydrogeology, and meteorology. The Rock Mechanics Site Descriptive Model constitutes one of these models. Before the start of the Site Investigations a numerical method using Discrete Fracture Network (DFN) models and the 2D numerical software UDEC was developed. Numerical simulations were the tool chosen for applying the theoretical approach for characterising the mechanical rock mass properties. Some shortcomings were identified when developing the methodology. Their impacts on the modelling (in term of time and quality assurance of results) were estimated to be so important that the improvement of the methodology with another numerical tool was investigated. The theoretical approach is still based on DFN models but the numerical software used is 3DEC. The main assets of the programme compared to UDEC are an optimised algorithm for the generation of fractures in the model and for the assignment of mechanical fracture properties. Due to some numerical constraints the test conditions were set-up in order to simulate 2D plane strain tests. Numerical simulations were conducted on the same data set as used previously for the UDEC modelling in order to estimate and validate the results from the new methodology. A real 3D simulation was also conducted in order to assess the effect of the '2D' conditions in the 3DEC model. Based on the quality of the results it was decided to update the theoretical model and introduce the new methodology based on DFN models and 3DEC simulations for the establishment of the Rock Mechanics Site Descriptive Model. By separating the spatial variability into two parts, one

Flood routing modelling with Artificial Neural Networks

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R. Peters

2006-01-01

Full Text Available For the modelling of the flood routing in the lower reaches of the Freiberger Mulde river and its tributaries the one-dimensional hydrodynamic modelling system HEC-RAS has been applied. Furthermore, this model was used to generate a database to train multilayer feedforward networks. To guarantee numerical stability for the hydrodynamic modelling of some 60 km of streamcourse an adequate resolution in space requires very small calculation time steps, which are some two orders of magnitude smaller than the input data resolution. This leads to quite high computation requirements seriously restricting the application – especially when dealing with real time operations such as online flood forecasting. In order to solve this problem we tested the application of Artificial Neural Networks (ANN. First studies show the ability of adequately trained multilayer feedforward networks (MLFN to reproduce the model performance.

Biochemical Network Stochastic Simulator (BioNetS: software for stochastic modeling of biochemical networks

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Elston Timothy C

2004-03-01

Full Text Available Abstract Background Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks. Results We have developed the software package Biochemical Network Stochastic Simulator (BioNetS for efficientlyand accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solvesthe appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS. Conclusions We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.

Discrete dynamic modeling of cellular signaling networks.

Science.gov (United States)

Albert, Réka; Wang, Rui-Sheng

2009-01-01

Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.

Neural network modeling for near wall turbulent flow

International Nuclear Information System (INIS)

Milano, Michele; Koumoutsakos, Petros

2002-01-01

A neural network methodology is developed in order to reconstruct the near wall field in a turbulent flow by exploiting flow fields provided by direct numerical simulations. The results obtained from the neural network methodology are compared with the results obtained from prediction and reconstruction using proper orthogonal decomposition (POD). Using the property that the POD is equivalent to a specific linear neural network, a nonlinear neural network extension is presented. It is shown that for a relatively small additional computational cost nonlinear neural networks provide us with improved reconstruction and prediction capabilities for the near wall velocity fields. Based on these results advantages and drawbacks of both approaches are discussed with an outlook toward the development of near wall models for turbulence modeling and control

Analytic models for the evolution of semilocal string networks

International Nuclear Information System (INIS)

Nunes, A. S.; Martins, C. J. A. P.; Avgoustidis, A.; Urrestilla, J.

2011-01-01

We revisit previously developed analytic models for defect evolution and adapt them appropriately for the study of semilocal string networks. We thus confirm the expectation (based on numerical simulations) that linear scaling evolution is the attractor solution for a broad range of model parameters. We discuss in detail the evolution of individual semilocal segments, focusing on the phenomenology of segment growth, and also provide a preliminary comparison with existing numerical simulations.

A Network Model of Credit Risk Contagion

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Ting-Qiang Chen

2012-01-01

Full Text Available A network model of credit risk contagion is presented, in which the effect of behaviors of credit risk holders and the financial market regulators and the network structure are considered. By introducing the stochastic dominance theory, we discussed, respectively, the effect mechanisms of the degree of individual relationship, individual attitude to credit risk contagion, the individual ability to resist credit risk contagion, the monitoring strength of the financial market regulators, and the network structure on credit risk contagion. Then some derived and proofed propositions were verified through numerical simulations.

A Simplified Network Model for Travel Time Reliability Analysis in a Road Network

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Kenetsu Uchida

2017-01-01

Full Text Available This paper proposes a simplified network model which analyzes travel time reliability in a road network. A risk-averse driver is assumed in the simplified model. The risk-averse driver chooses a path by taking into account both a path travel time variance and a mean path travel time. The uncertainty addressed in this model is that of traffic flows (i.e., stochastic demand flows. In the simplified network model, the path travel time variance is not calculated by considering all travel time covariance between two links in the network. The path travel time variance is calculated by considering all travel time covariance between two adjacent links in the network. Numerical experiments are carried out to illustrate the applicability and validity of the proposed model. The experiments introduce the path choice behavior of a risk-neutral driver and several types of risk-averse drivers. It is shown that the mean link flows calculated by introducing the risk-neutral driver differ as a whole from those calculated by introducing several types of risk-averse drivers. It is also shown that the mean link flows calculated by the simplified network model are almost the same as the flows calculated by using the exact path travel time variance.

Multilevel method for modeling large-scale networks.

Energy Technology Data Exchange (ETDEWEB)

Safro, I. M. (Mathematics and Computer Science)

2012-02-24

Understanding the behavior of real complex networks is of great theoretical and practical significance. It includes developing accurate artificial models whose topological properties are similar to the real networks, generating the artificial networks at different scales under special conditions, investigating a network dynamics, reconstructing missing data, predicting network response, detecting anomalies and other tasks. Network generation, reconstruction, and prediction of its future topology are central issues of this field. In this project, we address the questions related to the understanding of the network modeling, investigating its structure and properties, and generating artificial networks. Most of the modern network generation methods are based either on various random graph models (reinforced by a set of properties such as power law distribution of node degrees, graph diameter, and number of triangles) or on the principle of replicating an existing model with elements of randomization such as R-MAT generator and Kronecker product modeling. Hierarchical models operate at different levels of network hierarchy but with the same finest elements of the network. However, in many cases the methods that include randomization and replication elements on the finest relationships between network nodes and modeling that addresses the problem of preserving a set of simplified properties do not fit accurately enough the real networks. Among the unsatisfactory features are numerically inadequate results, non-stability of algorithms on real (artificial) data, that have been tested on artificial (real) data, and incorrect behavior at different scales. One reason is that randomization and replication of existing structures can create conflicts between fine and coarse scales of the real network geometry. Moreover, the randomization and satisfying of some attribute at the same time can abolish those topological attributes that have been undefined or hidden from

Copula-based modeling of degree-correlated networks

International Nuclear Information System (INIS)

Raschke, Mathias; Schläpfer, Markus; Trantopoulos, Konstantinos

2014-01-01

Dynamical processes on complex networks such as information exchange, innovation diffusion, cascades in financial networks or epidemic spreading are highly affected by their underlying topologies as characterized by, for instance, degree–degree correlations. Here, we introduce the concept of copulas in order to generate random networks with an arbitrary degree distribution and a rich a priori degree–degree correlation (or ‘association’) structure. The accuracy of the proposed formalism and corresponding algorithm is numerically confirmed, while the method is tested on a real-world network of yeast protein–protein interactions. The derived network ensembles can be systematically deployed as proper null models, in order to unfold the complex interplay between the topology of real-world networks and the dynamics on top of them. (paper)

Computational Modeling of Complex Protein Activity Networks

NARCIS (Netherlands)

Schivo, Stefano; Leijten, Jeroen; Karperien, Marcel; Post, Janine N.; Prignet, Claude

2017-01-01

Because of the numerous entities interacting, the complexity of the networks that regulate cell fate makes it impossible to analyze and understand them using the human brain alone. Computational modeling is a powerful method to unravel complex systems. We recently described the development of a

An Improved Walk Model for Train Movement on Railway Network

International Nuclear Information System (INIS)

Li Keping; Mao Bohua; Gao Ziyou

2009-01-01

In this paper, we propose an improved walk model for simulating the train movement on railway network. In the proposed method, walkers represent trains. The improved walk model is a kind of the network-based simulation analysis model. Using some management rules for walker movement, walker can dynamically determine its departure and arrival times at stations. In order to test the proposed method, we simulate the train movement on a part of railway network. The numerical simulation and analytical results demonstrate that the improved model is an effective tool for simulating the train movement on railway network. Moreover, it can well capture the characteristic behaviors of train scheduling in railway traffic. (general)

STEADY-STATE modeling and simulation of pipeline networks for compressible fluids

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A.L.H. Costa

1998-12-01

Full Text Available This paper presents a model and an algorithm for the simulation of pipeline networks with compressible fluids. The model can predict pressures, flow rates, temperatures and gas compositions at any point of the network. Any network configuration can be simulated; the existence of cycles is not an obstacle. Numerical results from simulated data on a proposed network are shown for illustration. The potential of the simulator is explored by the analysis of a pressure relief network, using a stochastic procedure for the evaluation of system performance.

Automated smoother for the numerical decoupling of dynamics models.

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Vilela, Marco; Borges, Carlos C H; Vinga, Susana; Vasconcelos, Ana Tereza R; Santos, Helena; Voit, Eberhard O; Almeida, Jonas S

2007-08-21

Structure identification of dynamic models for complex biological systems is the cornerstone of their reverse engineering. Biochemical Systems Theory (BST) offers a particularly convenient solution because its parameters are kinetic-order coefficients which directly identify the topology of the underlying network of processes. We have previously proposed a numerical decoupling procedure that allows the identification of multivariate dynamic models of complex biological processes. While described here within the context of BST, this procedure has a general applicability to signal extraction. Our original implementation relied on artificial neural networks (ANN), which caused slight, undesirable bias during the smoothing of the time courses. As an alternative, we propose here an adaptation of the Whittaker's smoother and demonstrate its role within a robust, fully automated structure identification procedure. In this report we propose a robust, fully automated solution for signal extraction from time series, which is the prerequisite for the efficient reverse engineering of biological systems models. The Whittaker's smoother is reformulated within the context of information theory and extended by the development of adaptive signal segmentation to account for heterogeneous noise structures. The resulting procedure can be used on arbitrary time series with a nonstationary noise process; it is illustrated here with metabolic profiles obtained from in-vivo NMR experiments. The smoothed solution that is free of parametric bias permits differentiation, which is crucial for the numerical decoupling of systems of differential equations. The method is applicable in signal extraction from time series with nonstationary noise structure and can be applied in the numerical decoupling of system of differential equations into algebraic equations, and thus constitutes a rather general tool for the reverse engineering of mechanistic model descriptions from multivariate experimental

Risk prediction model: Statistical and artificial neural network approach

Science.gov (United States)

Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

2017-04-01

Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

Different Epidemic Models on Complex Networks

International Nuclear Information System (INIS)

Zhang Haifeng; Small, Michael; Fu Xinchu

2009-01-01

Models for diseases spreading are not just limited to SIS or SIR. For instance, for the spreading of AIDS/HIV, the susceptible individuals can be classified into different cases according to their immunity, and similarly, the infected individuals can be sorted into different classes according to their infectivity. Moreover, some diseases may develop through several stages. Many authors have shown that the individuals' relation can be viewed as a complex network. So in this paper, in order to better explain the dynamical behavior of epidemics, we consider different epidemic models on complex networks, and obtain the epidemic threshold for each case. Finally, we present numerical simulations for each case to verify our results.

An information spreading model based on online social networks

Science.gov (United States)

Wang, Tao; He, Juanjuan; Wang, Xiaoxia

2018-01-01

Online social platforms are very popular in recent years. In addition to spreading information, users could review or collect information on online social platforms. According to the information spreading rules of online social network, a new information spreading model, namely IRCSS model, is proposed in this paper. It includes sharing mechanism, reviewing mechanism, collecting mechanism and stifling mechanism. Mean-field equations are derived to describe the dynamics of the IRCSS model. Moreover, the steady states of reviewers, collectors and stiflers and the effects of parameters on the peak values of reviewers, collectors and sharers are analyzed. Finally, numerical simulations are performed on different networks. Results show that collecting mechanism and reviewing mechanism, as well as the connectivity of the network, make information travel wider and faster, and compared to WS network and ER network, the speed of reviewing, sharing and collecting information is fastest on BA network.

Numerical Transducer Modeling

DEFF Research Database (Denmark)

Henriquez, Vicente Cutanda

This thesis describes the development of a numerical model of the propagation of sound waves in fluids with viscous and thermal losses, with application to the simulation of acoustic transducers, in particular condenser microphones for measurement. The theoretical basis is presented, numerical...... manipulations are developed to satisfy the more complicated boundary conditions, and a model of a condenser microphone with a coupled membrane is developed. The model is tested against measurements of ¼ inch condenser microphones and analytical calculations. A detailed discussion of the results is given....

A coupled 3D-1D numerical monodomain solver for cardiac electrical activation in the myocardium with detailed Purkinje network

Science.gov (United States)

Vergara, Christian; Lange, Matthias; Palamara, Simone; Lassila, Toni; Frangi, Alejandro F.; Quarteroni, Alfio

2016-03-01

We present a model for the electrophysiology in the heart to handle the electrical propagation through the Purkinje system and in the myocardium, with two-way coupling at the Purkinje-muscle junctions. In both the subproblems the monodomain model is considered, whereas at the junctions a resistor element is included that induces an orthodromic propagation delay from the Purkinje network towards the heart muscle. We prove a sufficient condition for convergence of a fixed-point iterative algorithm to the numerical solution of the coupled problem. Numerical comparison of activation patterns is made with two different combinations of models for the coupled Purkinje network/myocardium system, the eikonal/eikonal and the monodomain/monodomain models. Test cases are investigated for both physiological and pathological activation of a model left ventricle. Finally, we prove the reliability of the monodomain/monodomain coupling on a realistic scenario. Our results underlie the importance of using physiologically realistic Purkinje-trees with propagation solved using the monodomain model for simulating cardiac activation.

A mathematical model for optimization of an integrated network logistic design

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Lida Tafaghodi

2011-10-01

Full Text Available In this study, the integrated forward/reverse logistics network is investigated, and a capacitated multi-stage, multi-product logistics network design is proposed by formulating a generalized logistics network problem into a mixed-integer nonlinear programming model (MINLP for minimizing the total cost of the closed-loop supply chain network. Moreover, the proposed model is solved by using optimization solver, which provides the decisions related to the facility location problem, optimum quantity of shipped product, and facility capacity. Numerical results show the power of the proposed MINLP model to avoid th sub-optimality caused by separate design of forward and reverse logistics networks and to handle various transportation modes and periodic demand.

The Kuramoto model in complex networks

Science.gov (United States)

Rodrigues, Francisco A.; Peron, Thomas K. DM.; Ji, Peng; Kurths, Jürgen

2016-01-01

Synchronization of an ensemble of oscillators is an emergent phenomenon present in several complex systems, ranging from social and physical to biological and technological systems. The most successful approach to describe how coherent behavior emerges in these complex systems is given by the paradigmatic Kuramoto model. This model has been traditionally studied in complete graphs. However, besides being intrinsically dynamical, complex systems present very heterogeneous structure, which can be represented as complex networks. This report is dedicated to review main contributions in the field of synchronization in networks of Kuramoto oscillators. In particular, we provide an overview of the impact of network patterns on the local and global dynamics of coupled phase oscillators. We cover many relevant topics, which encompass a description of the most used analytical approaches and the analysis of several numerical results. Furthermore, we discuss recent developments on variations of the Kuramoto model in networks, including the presence of noise and inertia. The rich potential for applications is discussed for special fields in engineering, neuroscience, physics and Earth science. Finally, we conclude by discussing problems that remain open after the last decade of intensive research on the Kuramoto model and point out some promising directions for future research.

Mathematical modelling of complex contagion on clustered networks

Science.gov (United States)

O'sullivan, David J.; O'Keeffe, Gary; Fennell, Peter; Gleeson, James

2015-09-01

The spreading of behavior, such as the adoption of a new innovation, is influenced bythe structure of social networks that interconnect the population. In the experiments of Centola (Science, 2010), adoption of new behavior was shown to spread further and faster across clustered-lattice networks than across corresponding random networks. This implies that the “complex contagion” effects of social reinforcement are important in such diffusion, in contrast to “simple” contagion models of disease-spread which predict that epidemics would grow more efficiently on random networks than on clustered networks. To accurately model complex contagion on clustered networks remains a challenge because the usual assumptions (e.g. of mean-field theory) regarding tree-like networks are invalidated by the presence of triangles in the network; the triangles are, however, crucial to the social reinforcement mechanism, which posits an increased probability of a person adopting behavior that has been adopted by two or more neighbors. In this paper we modify the analytical approach that was introduced by Hebert-Dufresne et al. (Phys. Rev. E, 2010), to study disease-spread on clustered networks. We show how the approximation method can be adapted to a complex contagion model, and confirm the accuracy of the method with numerical simulations. The analytical results of the model enable us to quantify the level of social reinforcement that is required to observe—as in Centola’s experiments—faster diffusion on clustered topologies than on random networks.

Mathematical modelling of complex contagion on clustered networks

Directory of Open Access Journals (Sweden)

David J. P. O'Sullivan

2015-09-01

Full Text Available The spreading of behavior, such as the adoption of a new innovation, is influenced bythe structure of social networks that interconnect the population. In the experiments of Centola (Science, 2010, adoption of new behavior was shown to spread further and faster across clustered-lattice networks than across corresponding random networks. This implies that the complex contagion effects of social reinforcement are important in such diffusion, in contrast to simple contagion models of disease-spread which predict that epidemics would grow more efficiently on random networks than on clustered networks. To accurately model complex contagion on clustered networks remains a challenge because the usual assumptions (e.g. of mean-field theory regarding tree-like networks are invalidated by the presence of triangles in the network; the triangles are, however, crucial to the social reinforcement mechanism, which posits an increased probability of a person adopting behavior that has been adopted by two or more neighbors. In this paper we modify the analytical approach that was introduced by Hebert-Dufresne et al. (Phys. Rev. E, 2010, to study disease-spread on clustered networks. We show how the approximation method can be adapted to a complex contagion model, and confirm the accuracy of the method with numerical simulations. The analytical results of the model enable us to quantify the level of social reinforcement that is required to observe—as in Centola’s experiments—faster diffusion on clustered topologies than on random networks.

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

Science.gov (United States)

Balogh, Peter; Bagchi, Prosenjit

2017-11-01

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

Employing Tropospheric Numerical Weather Prediction Model for High-Precision GNSS Positioning

Science.gov (United States)

Alves, Daniele; Gouveia, Tayna; Abreu, Pedro; Magário, Jackes

2014-05-01

In the past few years is increasing the necessity of realizing high accuracy positioning. In this sense, the spatial technologies have being widely used. The GNSS (Global Navigation Satellite System) has revolutionized the geodetic positioning activities. Among the existent methods one can emphasize the Precise Point Positioning (PPP) and network-based positioning. But, to get high accuracy employing these methods, mainly in real time, is indispensable to realize the atmospheric modeling (ionosphere and troposphere) accordingly. Related to troposphere, there are the empirical models (for example Saastamoinen and Hopfield). But when highly accuracy results (error of few centimeters) are desired, maybe these models are not appropriated to the Brazilian reality. In order to minimize this limitation arises the NWP (Numerical Weather Prediction) models. In Brazil the CPTEC/INPE (Center for Weather Prediction and Climate Studies / Brazilian Institute for Spatial Researches) provides a regional NWP model, currently used to produce Zenithal Tropospheric Delay (ZTD) predictions (http://satelite.cptec.inpe.br/zenital/). The actual version, called eta15km model, has a spatial resolution of 15 km and temporal resolution of 3 hours. In this paper the main goal is to accomplish experiments and analysis concerning the use of troposphere NWP model (eta15km model) in PPP and network-based positioning. Concerning PPP it was used data from dozens of stations over the Brazilian territory, including Amazon forest. The results obtained with NWP model were compared with Hopfield one. NWP model presented the best results in all experiments. Related to network-based positioning it was used data from GNSS/SP Network in São Paulo State, Brazil. This network presents the best configuration in the country to realize this kind of positioning. Actually the network is composed by twenty stations (http://www.fct.unesp.br/#!/pesquisa/grupos-de-estudo-e-pesquisa/gege//gnss-sp-network2789/). The

Network modelling of fluid retention behaviour in unsaturated soils

Directory of Open Access Journals (Sweden)

Athanasiadis Ignatios

2016-01-01

Full Text Available The paper describes discrete modelling of the retention behaviour of unsaturated porous materials. A network approach is used within a statistical volume element (SVE, suitable for subsequent use in hydro-mechanical analysis and incorporation within multi-scale numerical modelling. The soil pore structure is modelled by a network of cylindrical pipes connecting spheres, with the spheres representing soil voids and the pipes representing inter-connecting throats. The locations of pipes and spheres are determined by a Voronoi tessellation of the domain. Original aspects of the modelling include a form of periodic boundary condition implementation applied for the first time to this type of network, a new pore volume scaling technique to provide more realistic modelling and a new procedure for initiating drying or wetting paths in a network model employing periodic boundary conditions. Model simulations, employing two linear cumulative probability distributions to represent the distributions of sphere and pipe radii, are presented for the retention behaviour reported from a mercury porosimetry test on a sandstone.

A new fuzzy mathematical model for green supply chain network design

Directory of Open Access Journals (Sweden)

Mohsen Sadegh Amalnick

2017-01-01

Full Text Available The environmental changes caused by industrial activities have spurred a significant interest in designing supply chain networks by considering environmental issues such as CO2 emission. The pivotal role of taking uncertainty and risk into account in closed-loop supply chain networks has induced numerous researchers and practitioners to develop appropriate decision making tools to cope with these issues in such networks. To design a supply chain regarding environmental impacts under uncertainty of the input data and to cope with the operational risks, this paper proposes a multi objective possibilistic optimization model. The proposed model minimizes traditional costs such as cost of products shipment, purchasing machines and so on, as well as minimizing the environmental impact, and as a results strikes a balance between the two objective functions. Furthermore, in order to solve the proposed multi objective fuzzy mathematical programming model, an interactive fuzzy solution approach is applied. Numerical experiments are used to prove the applicability and feasibility of the developed possibilistic programming model and the usefulness of the applied hybrid solution approach.

A Combined Remote Sensing-Numerical Modelling Approach to the Stability Analysis of Delabole Slate Quarry, Cornwall, UK

Science.gov (United States)

Havaej, Mohsen; Coggan, John; Stead, Doug; Elmo, Davide

2016-04-01

Rock slope geometry and discontinuity properties are among the most important factors in realistic rock slope analysis yet they are often oversimplified in numerical simulations. This is primarily due to the difficulties in obtaining accurate structural and geometrical data as well as the stochastic representation of discontinuities. Recent improvements in both digital data acquisition and incorporation of discrete fracture network data into numerical modelling software have provided better tools to capture rock mass characteristics, slope geometries and digital terrain models allowing more effective modelling of rock slopes. Advantages of using improved data acquisition technology include safer and faster data collection, greater areal coverage, and accurate data geo-referencing far exceed limitations due to orientation bias and occlusion. A key benefit of a detailed point cloud dataset is the ability to measure and evaluate discontinuity characteristics such as orientation, spacing/intensity and persistence. This data can be used to develop a discrete fracture network which can be imported into the numerical simulations to study the influence of the stochastic nature of the discontinuities on the failure mechanism. We demonstrate the application of digital terrestrial photogrammetry in discontinuity characterization and distinct element simulations within a slate quarry. An accurately geo-referenced photogrammetry model is used to derive the slope geometry and to characterize geological structures. We first show how a discontinuity dataset, obtained from a photogrammetry model can be used to characterize discontinuities and to develop discrete fracture networks. A deterministic three-dimensional distinct element model is then used to investigate the effect of some key input parameters (friction angle, spacing and persistence) on the stability of the quarry slope model. Finally, adopting a stochastic approach, discrete fracture networks are used as input for 3D

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

Science.gov (United States)

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

2017-10-01

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

Stylized facts in social networks: Community-based static modeling

Science.gov (United States)

Jo, Hang-Hyun; Murase, Yohsuke; Török, János; Kertész, János; Kaski, Kimmo

2018-06-01

The past analyses of datasets of social networks have enabled us to make empirical findings of a number of aspects of human society, which are commonly featured as stylized facts of social networks, such as broad distributions of network quantities, existence of communities, assortative mixing, and intensity-topology correlations. Since the understanding of the structure of these complex social networks is far from complete, for deeper insight into human society more comprehensive datasets and modeling of the stylized facts are needed. Although the existing dynamical and static models can generate some stylized facts, here we take an alternative approach by devising a community-based static model with heterogeneous community sizes and larger communities having smaller link density and weight. With these few assumptions we are able to generate realistic social networks that show most stylized facts for a wide range of parameters, as demonstrated numerically and analytically. Since our community-based static model is simple to implement and easily scalable, it can be used as a reference system, benchmark, or testbed for further applications.

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

Science.gov (United States)

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

2013-12-01

Modeling of flow and solute transport in discrete fracture networks is an important approach for understanding the migration of contaminants in impermeable hard rocks such as granite, where fractures provide dominant flow and transport pathways. The discrete fracture network (DFN) model attempts to mimic discrete pathways for fluid flow through a fractured low-permeable rock mass, and may be combined with particle tracking simulations to address solute transport. However, experience has shown that it is challenging to obtain accurate transport results in three-dimensional DFNs because of the high computational burden and difficulty in constructing a high-quality unstructured computational mesh on simulated fractures. An integrated DFN meshing [1], flow, and particle tracking [2] simulation capability that enables accurate flow and particle tracking simulation on large DFNs has recently been developed. The new capability has been used in numerical experiments on advective transport in large DFNs with tens of thousands of fractures and millions of computational cells. The modeling procedure starts from the fracture network generation using a stochastic model derived from site data. A high-quality computational mesh is then generated [1]. Flow is then solved using the highly parallel PFLOTRAN [3] code. PFLOTRAN uses the finite volume approach, which is locally mass conserving and thus eliminates mass balance problems during particle tracking. The flow solver provides the scalar fluxes on each control volume face. From the obtained fluxes the Darcy velocity is reconstructed for each node in the network [4]. Velocities can then be continuously interpolated to any point in the domain of interest, thus enabling random walk particle tracking. In order to describe the flow field on fractures intersections, the control volume cells on intersections are split into four planar polygons, where each polygon corresponds to a piece of a fracture near the intersection line. Thus

Modeling and simulation of different and representative engineering problems using Network Simulation Method.

Science.gov (United States)

Sánchez-Pérez, J F; Marín, F; Morales, J L; Cánovas, M; Alhama, F

2018-01-01

Mathematical models simulating different and representative engineering problem, atomic dry friction, the moving front problems and elastic and solid mechanics are presented in the form of a set of non-linear, coupled or not coupled differential equations. For different parameters values that influence the solution, the problem is numerically solved by the network method, which provides all the variables of the problems. Although the model is extremely sensitive to the above parameters, no assumptions are considered as regards the linearization of the variables. The design of the models, which are run on standard electrical circuit simulation software, is explained in detail. The network model results are compared with common numerical methods or experimental data, published in the scientific literature, to show the reliability of the model.

Design and implementation of new design of numerical experiments for non linear models

International Nuclear Information System (INIS)

Gazut, St.

2007-03-01

This thesis addresses the problem of the construction of surrogate models in numerical simulation. Whenever numerical experiments are costly, the simulation model is complex and difficult to use. It is important then to select the numerical experiments as efficiently as possible in order to minimize their number. In statistics, the selection of experiments is known as optimal experimental design. In the context of numerical simulation where no measurement uncertainty is present, we describe an alternative approach based on statistical learning theory and re-sampling techniques. The surrogate models are constructed using neural networks and the generalization error is estimated by leave-one-out, cross-validation and bootstrap. It is shown that the bootstrap can control the over-fitting and extend the concept of leverage for non linear in their parameters surrogate models. The thesis describes an iterative method called LDR for Learner Disagreement from experiment Re-sampling, based on active learning using several surrogate models constructed on bootstrap samples. The method consists in adding new experiments where the predictors constructed from bootstrap samples disagree most. We compare the LDR method with other methods of experimental design such as D-optimal selection. (author)

A delta-rule model of numerical and non-numerical order processing.

Science.gov (United States)

Verguts, Tom; Van Opstal, Filip

2014-06-01

Numerical and non-numerical order processing share empirical characteristics (distance effect and semantic congruity), but there are also important differences (in size effect and end effect). At the same time, models and theories of numerical and non-numerical order processing developed largely separately. Currently, we combine insights from 2 earlier models to integrate them in a common framework. We argue that the same learning principle underlies numerical and non-numerical orders, but that environmental features determine the empirical differences. Implications for current theories on order processing are pointed out. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Multilayer network modeling creates opportunities for novel network statistics. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

Science.gov (United States)

Muldoon, Sarah Feldt

2018-03-01

As described in the review by Gosak et al., the field of network science has had enormous success in providing new insights into the structure and function of biological systems [1]. In the complex networks framework, system elements are network nodes, and connections between nodes represent some form of interaction between system elements [2]. The flexibility to define network nodes and edges to represent different aspects of biological systems has been employed to model numerous diverse systems at multiple scales.

The theoretical strength of rubber: numerical simulations of polyisoprene networks at high tensile strains evidence the role of average chain tortuosity

International Nuclear Information System (INIS)

Hanson, David E; Barber, John L

2013-01-01

The ultimate stress and strain of polyisoprene rubber were studied by numerical simulations of three-dimensional random networks, subjected to tensile strains high enough to cause chain rupture. Previously published molecular chain force extension models and a numerical network construction procedure were used to perform the simulations for network crosslink densities between 2 × 10 19 and 1 × 10 20 cm −3 , corresponding to experimental dicumyl-peroxide concentrations of 1–5 parts per hundred. At tensile failure (defined as the point of maximum stress), we find that the fraction of network chains ruptured is between 0.1% and 1%, depending on the crosslink density. The fraction of network chains that are taut, i.e. their end-to-end distance is greater than their unstretched contour length, ranges between 10% and 15% at failure. Our model predicts that the theoretical (defect-free) failure stress should be about twice the highest experimental value reported. For extensions approaching failure, tensile stress is dominated by the network morphology and purely enthalpic bond distortion forces and, in this regime, the model has essentially no free parameters. The average initial chain tortuosity (τ) appears to be an important statistical property of rubber networks; if the stress is scaled by τ and the tensile strain is scaled by τ −1 , we obtain a master curve for stress versus strain, valid for all crosslink densities. We derive an analytic expression for the average tortuosity, which is in agreement with values calculated in the simulations. (paper)

A random walk evolution model of wireless sensor networks and virus spreading

International Nuclear Information System (INIS)

Wang Ya-Qi; Yang Xiao-Yuan

2013-01-01

In this paper, considering both cluster heads and sensor nodes, we propose a novel evolving a network model based on a random walk to study the fault tolerance decrease of wireless sensor networks (WSNs) due to node failure, and discuss the spreading dynamic behavior of viruses in the evolution model. A theoretical analysis shows that the WSN generated by such an evolution model not only has a strong fault tolerance, but also can dynamically balance the energy loss of the entire network. It is also found that although the increase of the density of cluster heads in the network reduces the network efficiency, it can effectively inhibit the spread of viruses. In addition, the heterogeneity of the network improves the network efficiency and enhances the virus prevalence. We confirm all the theoretical results with sufficient numerical simulations. (general)

Modeling and simulation of different and representative engineering problems using Network Simulation Method

Science.gov (United States)

2018-01-01

Mathematical models simulating different and representative engineering problem, atomic dry friction, the moving front problems and elastic and solid mechanics are presented in the form of a set of non-linear, coupled or not coupled differential equations. For different parameters values that influence the solution, the problem is numerically solved by the network method, which provides all the variables of the problems. Although the model is extremely sensitive to the above parameters, no assumptions are considered as regards the linearization of the variables. The design of the models, which are run on standard electrical circuit simulation software, is explained in detail. The network model results are compared with common numerical methods or experimental data, published in the scientific literature, to show the reliability of the model. PMID:29518121

Probability Model for Data Redundancy Detection in Sensor Networks

Directory of Open Access Journals (Sweden)

Suman Kumar

2009-01-01

Full Text Available Sensor networks are made of autonomous devices that are able to collect, store, process and share data with other devices. Large sensor networks are often redundant in the sense that the measurements of some nodes can be substituted by other nodes with a certain degree of confidence. This spatial correlation results in wastage of link bandwidth and energy. In this paper, a model for two associated Poisson processes, through which sensors are distributed in a plane, is derived. A probability condition is established for data redundancy among closely located sensor nodes. The model generates a spatial bivariate Poisson process whose parameters depend on the parameters of the two individual Poisson processes and on the distance between the associated points. The proposed model helps in building efficient algorithms for data dissemination in the sensor network. A numerical example is provided investigating the advantage of this model.

Connectivity, flow and transport in network models of fractured media

International Nuclear Information System (INIS)

Robinson, P.C.

1984-10-01

In order to evaluate the safety of radioactive waste disposal underground it is important to understand the way in which radioactive material is transported through the rock to the surface. If the rock is fractured the usual models may not be applicable. In this work we look at three aspects of fracture networks: connectivity, flow and transport. These are studied numerically by generating fracture networks in a computer and modelling the processes which occur. Connectivity relates to percolation theory, and critical densities for fracture systems are found in two and three dimensions. The permeability of two-dimensional networks is studied. The way that permeability depends on fracture density, network size and spread of fracture length can be predicted using a cut lattice model. Transport through the fracture network by convection through the fractures and mixing at the intersections is studied. The Fickian dispersion equation does not describe the resulting hydrodynamic dispersion. Extensions to the techniques to three dimensions and to include other processes are discussed. (author)

Modeling the citation network by network cosmology.

Science.gov (United States)

Xie, Zheng; Ouyang, Zhenzheng; Zhang, Pengyuan; Yi, Dongyun; Kong, Dexing

2015-01-01

Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well.

Numerical Modeling of Cavitating Venturi: A Flow Control Element of Propulsion System

Science.gov (United States)

Majumdar, Alok; Saxon, Jeff (Technical Monitor)

2002-01-01

In a propulsion system, the propellant flow and mixture ratio could be controlled either by variable area flow control valves or by passive flow control elements such as cavitating venturies. Cavitating venturies maintain constant propellant flowrate for fixed inlet conditions (pressure and temperature) and wide range of outlet pressures, thereby maintain constant, engine thrust and mixture ratio. The flowrate through the venturi reaches a constant value and becomes independent of outlet pressure when the pressure at throat becomes equal to vapor pressure. In order to develop a numerical model of propulsion system, it is necessary to model cavitating venturies in propellant feed systems. This paper presents a finite volume model of flow network of a cavitating venturi. The venturi was discretized into a number of control volumes and mass, momentum and energy conservation equations in each control volume are simultaneously solved to calculate one-dimensional pressure, density, and flowrate and temperature distribution. The numerical model predicts cavitations at the throat when outlet pressure was gradually reduced. Once cavitation starts, with further reduction of downstream pressure, no change in flowrate is found. The numerical predictions have been compared with test data and empirical equation based on Bernoulli's equation.

Developed hydraulic simulation model for water pipeline networks

Directory of Open Access Journals (Sweden)

A. Ayad

2013-03-01

Full Text Available A numerical method that uses linear graph theory is presented for both steady state, and extended period simulation in a pipe network including its hydraulic components (pumps, valves, junctions, etc.. The developed model is based on the Extended Linear Graph Theory (ELGT technique. This technique is modified to include new network components such as flow control valves and tanks. The technique also expanded for extended period simulation (EPS. A newly modified method for the calculation of updated flows improving the convergence rate is being introduced. Both benchmarks, ad Actual networks are analyzed to check the reliability of the proposed method. The results reveal the finer performance of the proposed method.

Highway Passenger Transport Based Express Parcel Service Network Design: Model and Algorithm

Directory of Open Access Journals (Sweden)

Yuan Jiang

2017-01-01

Full Text Available Highway passenger transport based express parcel service (HPTB-EPS is an emerging business that uses unutilised room of coach trunk to ship parcels between major cities. While it is reaping more and more express market, the managers are facing difficult decisions to design the service network. This paper investigates the HPTB-EPS network design problem and analyses the time-space characteristics of such network. A mixed-integer programming model is formulated integrating the service decision, frequency, and network flow distribution. To solve the model, a decomposition-based heuristic algorithm is designed by decomposing the problem as three steps: construction of service network, service path selection, and distribution of network flow. Numerical experiment using real data from our partner company demonstrates the effectiveness of our model and algorithm. We found that our solution could reduce the total cost by up to 16.3% compared to the carrier’s solution. The sensitivity analysis demonstrates the robustness and flexibility of the solutions of the model.

Simulation model for centrifugal pump in flow networks based on internal characteristics

International Nuclear Information System (INIS)

Sun, Ji-Lin; Xue, Ruo-Jun; Peng, Min-Jun

2018-01-01

For the simulation of centrifugal pump in flow network system, in general three approaches can be used, the fitting model, the numerical method and the internal characteristics model. The fitting model is simple and rapid thus widely used. The numerical method can provide more detailed information in comparison with the fitting model, but increases implementation complexity and computational cost. In real-time simulations of flow networks, to simulate the condition out of the rated condition, especially for the volume flow rate, which the accuracy of fitting model is incredible, a new method for simulating centrifugal pumps was proposed in this research. The method based on the theory head and hydraulic loss in centrifugal pumps, and cavitation is also to be considered. The simulation results are verified with experimental benchmark data from an actual pump. The comparison confirms that the proposed method could fit the flow-head curves well, and the responses of main parameters in dynamic-state operations are consistent with theoretical analyses.

Modelling computer networks

International Nuclear Information System (INIS)

Max, G

2011-01-01

Traffic models in computer networks can be described as a complicated system. These systems show non-linear features and to simulate behaviours of these systems are also difficult. Before implementing network equipments users wants to know capability of their computer network. They do not want the servers to be overloaded during temporary traffic peaks when more requests arrive than the server is designed for. As a starting point for our study a non-linear system model of network traffic is established to exam behaviour of the network planned. The paper presents setting up a non-linear simulation model that helps us to observe dataflow problems of the networks. This simple model captures the relationship between the competing traffic and the input and output dataflow. In this paper, we also focus on measuring the bottleneck of the network, which was defined as the difference between the link capacity and the competing traffic volume on the link that limits end-to-end throughput. We validate the model using measurements on a working network. The results show that the initial model estimates well main behaviours and critical parameters of the network. Based on this study, we propose to develop a new algorithm, which experimentally determines and predict the available parameters of the network modelled.

Bayesian networks modeling for thermal error of numerical control machine tools

Institute of Scientific and Technical Information of China (English)

Xin-hua YAO; Jian-zhong FU; Zi-chen CHEN

2008-01-01

The interaction between the heat source location,its intensity,thermal expansion coefficient,the machine system configuration and the running environment creates complex thermal behavior of a machine tool,and also makes thermal error prediction difficult.To address this issue,a novel prediction method for machine tool thermal error based on Bayesian networks (BNs) was presented.The method described causal relationships of factors inducing thermal deformation by graph theory and estimated the thermal error by Bayesian statistical techniques.Due to the effective combination of domain knowledge and sampled data,the BN method could adapt to the change of running state of machine,and obtain satisfactory prediction accuracy.Ex-periments on spindle thermal deformation were conducted to evaluate the modeling performance.Experimental results indicate that the BN method performs far better than the least squares(LS)analysis in terms of modeling estimation accuracy.

Riemannian multi-manifold modeling and clustering in brain networks

Science.gov (United States)

Slavakis, Konstantinos; Salsabilian, Shiva; Wack, David S.; Muldoon, Sarah F.; Baidoo-Williams, Henry E.; Vettel, Jean M.; Cieslak, Matthew; Grafton, Scott T.

2017-08-01

This paper introduces Riemannian multi-manifold modeling in the context of brain-network analytics: Brainnetwork time-series yield features which are modeled as points lying in or close to a union of a finite number of submanifolds within a known Riemannian manifold. Distinguishing disparate time series amounts thus to clustering multiple Riemannian submanifolds. To this end, two feature-generation schemes for brain-network time series are put forth. The first one is motivated by Granger-causality arguments and uses an auto-regressive moving average model to map low-rank linear vector subspaces, spanned by column vectors of appropriately defined observability matrices, to points into the Grassmann manifold. The second one utilizes (non-linear) dependencies among network nodes by introducing kernel-based partial correlations to generate points in the manifold of positivedefinite matrices. Based on recently developed research on clustering Riemannian submanifolds, an algorithm is provided for distinguishing time series based on their Riemannian-geometry properties. Numerical tests on time series, synthetically generated from real brain-network structural connectivity matrices, reveal that the proposed scheme outperforms classical and state-of-the-art techniques in clustering brain-network states/structures.

Treating network junctions in finite volume solution of transient gas flow models

Science.gov (United States)

Bermúdez, Alfredo; López, Xián; Vázquez-Cendón, M. Elena

2017-09-01

A finite volume scheme for the numerical solution of a non-isothermal non-adiabatic compressible flow model for gas transportation networks on non-flat topography is introduced. Unlike standard Euler equations, the model takes into account wall friction, variable height and heat transfer between the pipe and the environment which are source terms. The case of one single pipe was considered in a previous reference by the authors, [8], where a finite volume method with upwind discretization of the flux and source terms has been proposed in order to get a well-balanced scheme. The main goal of the present paper is to go a step further by considering a network of pipes. The main issue is the treatment of junctions for which container-like 2D finite volumes are introduced. The couplings between pipes (1D) and containers (2D) are carefully described and the conservation properties are analyzed. Numerical tests including real gas networks are solved showing the performance of the proposed methodology.

The General Evolving Model for Energy Supply-Demand Network with Local-World

Science.gov (United States)

Sun, Mei; Han, Dun; Li, Dandan; Fang, Cuicui

2013-10-01

In this paper, two general bipartite network evolving models for energy supply-demand network with local-world are proposed. The node weight distribution, the "shifting coefficient" and the scaling exponent of two different kinds of nodes are presented by the mean-field theory. The numerical results of the node weight distribution and the edge weight distribution are also investigated. The production's shifted power law (SPL) distribution of coal enterprises and the installed capacity's distribution of power plants in the US are obtained from the empirical analysis. Numerical simulations and empirical results are given to verify the theoretical results.

A Note on Some Numerical Approaches to Solve a θ˙ Neuron Networks Model

Directory of Open Access Journals (Sweden)

Samir Kumar Bhowmik

2014-01-01

Full Text Available Space time integration plays an important role in analyzing scientific and engineering models. In this paper, we consider an integrodifferential equation that comes from modeling θ˙ neuron networks. Here, we investigate various schemes for time discretization of a theta-neuron model. We use collocation and midpoint quadrature formula for space integration and then apply various time integration schemes to get a full discrete system. We present some computational results to demonstrate the schemes.

Model-based leakage localization in drinking water distribution networks using structured residuals

OpenAIRE

Puig Cayuela, Vicenç; Rosich, Albert

2013-01-01

In this paper, a new model based approach to leakage localization in drinking water networks is proposed based on generating a set of structured residuals. The residual evaluation is based on a numerical method based on an enhanced Newton-Raphson algorithm. The proposed method is suitable for water network systems because the non-linearities of the model make impossible to derive analytical residuals. Furthermore, the computed residuals are designed so that leaks are decoupled, which impro...

Recursive Bayesian recurrent neural networks for time-series modeling.

Science.gov (United States)

Mirikitani, Derrick T; Nikolaev, Nikolay

2010-02-01

This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.

Real time wave forecasting using wind time history and numerical model

Science.gov (United States)

Jain, Pooja; Deo, M. C.; Latha, G.; Rajendran, V.

Operational activities in the ocean like planning for structural repairs or fishing expeditions require real time prediction of waves over typical time duration of say a few hours. Such predictions can be made by using a numerical model or a time series model employing continuously recorded waves. This paper presents another option to do so and it is based on a different time series approach in which the input is in the form of preceding wind speed and wind direction observations. This would be useful for those stations where the costly wave buoys are not deployed and instead only meteorological buoys measuring wind are moored. The technique employs alternative artificial intelligence approaches of an artificial neural network (ANN), genetic programming (GP) and model tree (MT) to carry out the time series modeling of wind to obtain waves. Wind observations at four offshore sites along the east coast of India were used. For calibration purpose the wave data was generated using a numerical model. The predicted waves obtained using the proposed time series models when compared with the numerically generated waves showed good resemblance in terms of the selected error criteria. Large differences across the chosen techniques of ANN, GP, MT were not noticed. Wave hindcasting at the same time step and the predictions over shorter lead times were better than the predictions over longer lead times. The proposed method is a cost effective and convenient option when a site-specific information is desired.

A model-guided symbolic execution approach for network protocol implementations and vulnerability detection.

Science.gov (United States)

Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

2017-01-01

Formal techniques have been devoted to analyzing whether network protocol specifications violate security policies; however, these methods cannot detect vulnerabilities in the implementations of the network protocols themselves. Symbolic execution can be used to analyze the paths of the network protocol implementations, but for stateful network protocols, it is difficult to reach the deep states of the protocol. This paper proposes a novel model-guided approach to detect vulnerabilities in network protocol implementations. Our method first abstracts a finite state machine (FSM) model, then utilizes the model to guide the symbolic execution. This approach achieves high coverage of both the code and the protocol states. The proposed method is implemented and applied to test numerous real-world network protocol implementations. The experimental results indicate that the proposed method is more effective than traditional fuzzing methods such as SPIKE at detecting vulnerabilities in the deep states of network protocol implementations.

Modelling the guaranteed QoS for wireless sensor networks: a network calculus approach

Directory of Open Access Journals (Sweden)

Yu Jianping

2011-01-01

Full Text Available Abstract Wireless sensor networks (WSNs became one of the high technology domains during the last 10 years. Real-time applications for them make it necessary to provide the guaranteed quality of service (QoS. The main contributions of this article are a system skeleton and a guaranteed QoS model that are suitable for the WSNs. To do it, we develop a sensor node model based on virtual buffer sharing and present a two-layer scheduling model using the network calculus. With the system skeleton, we develop a guaranteed QoS model, such as the upper bounds on buffer queue length/delay/effective bandwidth, and single-hop/multi-hops delay/jitter/effective bandwidth. Numerical results show the system skeleton and the guaranteed QoS model are scalable for different types of flows, including the self-similar traffic flows, and the parameters of flow regulators and service curves of sensor nodes affect them. Our proposal leads to buffer dimensioning, guaranteed QoS support and control in the WSNs.

Optimization of recurrent neural networks for time series modeling

DEFF Research Database (Denmark)

Pedersen, Morten With

1997-01-01

The present thesis is about optimization of recurrent neural networks applied to time series modeling. In particular is considered fully recurrent networks working from only a single external input, one layer of nonlinear hidden units and a li near output unit applied to prediction of discrete time...... series. The overall objective s are to improve training by application of second-order methods and to improve generalization ability by architecture optimization accomplished by pruning. The major topics covered in the thesis are: 1. The problem of training recurrent networks is analyzed from a numerical...... of solution obtained as well as computation time required. 3. A theoretical definition of the generalization error for recurrent networks is provided. This definition justifies a commonly adopted approach for estimating generalization ability. 4. The viability of pruning recurrent networks by the Optimal...

The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.

Science.gov (United States)

Zhu, Zhenyu; Wang, Rubin; Zhu, Fengyun

2018-01-01

Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.

Limitations of demand- and pressure-driven modeling for large deficient networks

Science.gov (United States)

Braun, Mathias; Piller, Olivier; Deuerlein, Jochen; Mortazavi, Iraj

2017-10-01

The calculation of hydraulic state variables for a network is an important task in managing the distribution of potable water. Over the years the mathematical modeling process has been improved by numerous researchers for utilization in new computer applications and the more realistic modeling of water distribution networks. But, in spite of these continuous advances, there are still a number of physical phenomena that may not be tackled correctly by current models. This paper will take a closer look at the two modeling paradigms given by demand- and pressure-driven modeling. The basic equations are introduced and parallels are drawn with the optimization formulations from electrical engineering. These formulations guarantee the existence and uniqueness of the solution. One of the central questions of the French and German research project ResiWater is the investigation of the network resilience in the case of extreme events or disasters. Under such extraordinary conditions where models are pushed beyond their limits, we talk about deficient network models. Examples of deficient networks are given by highly regulated flow, leakage or pipe bursts and cases where pressure falls below the vapor pressure of water. These examples will be presented and analyzed on the solvability and physical correctness of the solution with respect to demand- and pressure-driven models.

Accelerated Monte Carlo system reliability analysis through machine-learning-based surrogate models of network connectivity

International Nuclear Information System (INIS)

Stern, R.E.; Song, J.; Work, D.B.

2017-01-01

The two-terminal reliability problem in system reliability analysis is known to be computationally intractable for large infrastructure graphs. Monte Carlo techniques can estimate the probability of a disconnection between two points in a network by selecting a representative sample of network component failure realizations and determining the source-terminal connectivity of each realization. To reduce the runtime required for the Monte Carlo approximation, this article proposes an approximate framework in which the connectivity check of each sample is estimated using a machine-learning-based classifier. The framework is implemented using both a support vector machine (SVM) and a logistic regression based surrogate model. Numerical experiments are performed on the California gas distribution network using the epicenter and magnitude of the 1989 Loma Prieta earthquake as well as randomly-generated earthquakes. It is shown that the SVM and logistic regression surrogate models are able to predict network connectivity with accuracies of 99% for both methods, and are 1–2 orders of magnitude faster than using a Monte Carlo method with an exact connectivity check. - Highlights: • Surrogate models of network connectivity are developed by machine-learning algorithms. • Developed surrogate models can reduce the runtime required for Monte Carlo simulations. • Support vector machine and logistic regressions are employed to develop surrogate models. • Numerical example of California gas distribution network demonstrate the proposed approach. • The developed models have accuracies 99%, and are 1–2 orders of magnitude faster than MCS.

Improved numerical modelling of heat transfer in human tissue exposed to RF

International Nuclear Information System (INIS)

Prishvin, Mikheil; Zaridze, Revaz; Bit-Babik, Georgi; Faraone, Antonio

2010-01-01

Full text: A novel numerical model to simulate thermal response of human body tissues exposed to RF energy is presented in this article. It is based on a new algorithm for the construction of a realistic blood vessel network, a new model of blood flow velocity distribution and an approach to solve the bio-heat equation in human tissue with variable and initially unknown blood temperature distribution. The algorithm generates a discrete 3D representation of both arterial and venous vascular networks and a continuous blood velocity vector field for arbitrary enclosed geome tries required to represent the complex anatomy of human body and blood flow. The results obtained in this article by applying the developed method to realistic exposure con ditions demonstrates relative difference in thermal response of the exposed tissue compared to results obtained by conventional bio-heat equation with constant blood perfusion and temperature. The developed technique may provide more accurate and realistic modelling in thermal dosimetry studies of human body RF exposure.

UAV Trajectory Modeling Using Neural Networks

Science.gov (United States)

Xue, Min

2017-01-01

Large amount of small Unmanned Aerial Vehicles (sUAVs) are projected to operate in the near future. Potential sUAV applications include, but not limited to, search and rescue, inspection and surveillance, aerial photography and video, precision agriculture, and parcel delivery. sUAVs are expected to operate in the uncontrolled Class G airspace, which is at or below 500 feet above ground level (AGL), where many static and dynamic constraints exist, such as ground properties and terrains, restricted areas, various winds, manned helicopters, and conflict avoidance among sUAVs. How to enable safe, efficient, and massive sUAV operations at the low altitude airspace remains a great challenge. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative works on establishing infrastructure and developing policies, requirement, and rules to enable safe and efficient sUAVs' operations. To achieve this goal, it is important to gain insights of future UTM traffic operations through simulations, where the accurate trajectory model plays an extremely important role. On the other hand, like what happens in current aviation development, trajectory modeling should also serve as the foundation for any advanced concepts and tools in UTM. Accurate models of sUAV dynamics and control systems are very important considering the requirement of the meter level precision in UTM operations. The vehicle dynamics are relatively easy to derive and model, however, vehicle control systems remain unknown as they are usually kept by manufactures as a part of intellectual properties. That brings challenges to trajectory modeling for sUAVs. How to model the vehicle's trajectories with unknown control system? This work proposes to use a neural network to model a vehicle's trajectory. The neural network is first trained to learn the vehicle's responses at numerous conditions. Once being fully trained, given current vehicle states, winds, and desired future trajectory, the neural

A model-guided symbolic execution approach for network protocol implementations and vulnerability detection.

Directory of Open Access Journals (Sweden)

Shameng Wen

Full Text Available Formal techniques have been devoted to analyzing whether network protocol specifications violate security policies; however, these methods cannot detect vulnerabilities in the implementations of the network protocols themselves. Symbolic execution can be used to analyze the paths of the network protocol implementations, but for stateful network protocols, it is difficult to reach the deep states of the protocol. This paper proposes a novel model-guided approach to detect vulnerabilities in network protocol implementations. Our method first abstracts a finite state machine (FSM model, then utilizes the model to guide the symbolic execution. This approach achieves high coverage of both the code and the protocol states. The proposed method is implemented and applied to test numerous real-world network protocol implementations. The experimental results indicate that the proposed method is more effective than traditional fuzzing methods such as SPIKE at detecting vulnerabilities in the deep states of network protocol implementations.

Rumor Spreading Model with Trust Mechanism in Complex Social Networks

Science.gov (United States)

Wang, Ya-Qi; Yang, Xiao-Yuan; Han, Yi-Liang; Wang, Xu-An

2013-04-01

In this paper, to study rumor spreading, we propose a novel susceptible-infected-removed (SIR) model by introducing the trust mechanism. We derive mean-field equations that describe the dynamics of the SIR model on homogeneous networks and inhomogeneous networks. Then a steady-state analysis is conducted to investigate the critical threshold and the final size of the rumor spreading. We show that the introduction of trust mechanism reduces the final rumor size and the velocity of rumor spreading, but increases the critical thresholds on both networks. Moreover, the trust mechanism not only greatly reduces the maximum rumor influence, but also postpones the rumor terminal time, which provides us with more time to take measures to control the rumor spreading. The theoretical results are confirmed by sufficient numerical simulations.

Modeling the Propagation of Mobile Phone Virus under Complex Network

Science.gov (United States)

Yang, Wei; Wei, Xi-liang; Guo, Hao; An, Gang; Guo, Lei

2014-01-01

Mobile phone virus is a rogue program written to propagate from one phone to another, which can take control of a mobile device by exploiting its vulnerabilities. In this paper the propagation model of mobile phone virus is tackled to understand how particular factors can affect its propagation and design effective containment strategies to suppress mobile phone virus. Two different propagation models of mobile phone viruses under the complex network are proposed in this paper. One is intended to describe the propagation of user-tricking virus, and the other is to describe the propagation of the vulnerability-exploiting virus. Based on the traditional epidemic models, the characteristics of mobile phone viruses and the network topology structure are incorporated into our models. A detailed analysis is conducted to analyze the propagation models. Through analysis, the stable infection-free equilibrium point and the stability condition are derived. Finally, considering the network topology, the numerical and simulation experiments are carried out. Results indicate that both models are correct and suitable for describing the spread of two different mobile phone viruses, respectively. PMID:25133209

Brain Network Modelling

DEFF Research Database (Denmark)

Andersen, Kasper Winther

Three main topics are presented in this thesis. The first and largest topic concerns network modelling of functional Magnetic Resonance Imaging (fMRI) and Diffusion Weighted Imaging (DWI). In particular nonparametric Bayesian methods are used to model brain networks derived from resting state f...... for their ability to reproduce node clustering and predict unseen data. Comparing the models on whole brain networks, BCD and IRM showed better reproducibility and predictability than IDM, suggesting that resting state networks exhibit community structure. This also points to the importance of using models, which...... allow for complex interactions between all pairs of clusters. In addition, it is demonstrated how the IRM can be used for segmenting brain structures into functionally coherent clusters. A new nonparametric Bayesian network model is presented. The model builds upon the IRM and can be used to infer...

Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control.

Science.gov (United States)

Yang, Shiju; Li, Chuandong; Huang, Tingwen

2016-03-01

The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Resource Allocation Optimization Model of Collaborative Logistics Network Based on Bilevel Programming

Directory of Open Access Journals (Sweden)

Xiao-feng Xu

2017-01-01

Full Text Available Collaborative logistics network resource allocation can effectively meet the needs of customers. It can realize the overall benefit maximization of the logistics network and ensure that collaborative logistics network runs orderly at the time of creating value. Therefore, this article is based on the relationship of collaborative logistics network supplier, the transit warehouse, and sellers, and we consider the uncertainty of time to establish a bilevel programming model with random constraints and propose a genetic simulated annealing hybrid intelligent algorithm to solve it. Numerical example shows that the method has stronger robustness and convergence; it can achieve collaborative logistics network resource allocation rationalization and optimization.

A Bio-Inspired QoS-Oriented Handover Model in Heterogeneous Wireless Networks

Directory of Open Access Journals (Sweden)

Daxin Tian

2014-01-01

Full Text Available We propose a bio-inspired model for making handover decision in heterogeneous wireless networks. It is based on an extended attractor selection model, which is biologically inspired by the self-adaptability and robustness of cellular response to the changes in dynamic environments. The goal of the proposed model is to guarantee multiple terminals’ satisfaction by meeting the QoS requirements of those terminals’ applications, and this model also attempts to ensure the fairness of network resources allocation, in the meanwhile, to enable the QoS-oriented handover decision adaptive to dynamic wireless environments. Some numerical simulations are preformed to validate our proposed bio-inspired model in terms of adaptive attractor selection in different noisy environments. And the results of some other simulations prove that the proposed handover scheme can adapt terminals’ network selection to the varying wireless environment and benefits the QoS of multiple terminal applications simultaneously and automatically. Furthermore, the comparative analysis also shows that the bio-inspired model outperforms the utility function based handover decision scheme in terms of ensuring a better QoS satisfaction and a better fairness of network resources allocation in dynamic heterogeneous wireless networks.

A Multiobjective Optimization Model in Automotive Supply Chain Networks

Directory of Open Access Journals (Sweden)

Abdolhossein Sadrnia

2013-01-01

Full Text Available In the new decade, green investment decisions are attracting more interest in design supply chains due to the hidden economic benefits and environmental legislative barriers. In this paper, a supply chain network design problem with both economic and environmental concerns is presented. Therefore, a multiobjective optimization model that captures the trade-off between the total logistics cost and CO2 emissions is proposed. With regard to the complexity of logistic networks, a new multiobjective swarm intelligence algorithm known as a multiobjective Gravitational search algorithm (MOGSA has been implemented for solving the proposed mathematical model. To evaluate the effectiveness of the model, a comprehensive set of numerical experiments is explained. The results obtained show that the proposed model can be applied as an effective tool in strategic planning for optimizing cost and CO2 emissions in an environmentally friendly automotive supply chain.

A rumor transmission model with incubation in social networks

Science.gov (United States)

Jia, Jianwen; Wu, Wenjiang

2018-02-01

In this paper, we propose a rumor transmission model with incubation period and constant recruitment in social networks. By carrying out an analysis of the model, we study the stability of rumor-free equilibrium and come to the local stable condition of the rumor equilibrium. We use the geometric approach for ordinary differential equations for showing the global stability of the rumor equilibrium. And when ℜ0 = 1, the new model occurs a transcritical bifurcation. Furthermore, numerical simulations are used to support the analysis. At last, some conclusions are presented.

Opinion formation and distribution in a bounded-confidence model on various networks

Science.gov (United States)

Meng, X. Flora; Van Gorder, Robert A.; Porter, Mason A.

2018-02-01

In the social, behavioral, and economic sciences, it is important to predict which individual opinions eventually dominate in a large population, whether there will be a consensus, and how long it takes for a consensus to form. Such ideas have been studied heavily both in physics and in other disciplines, and the answers depend strongly both on how one models opinions and on the network structure on which opinions evolve. One model that was created to study consensus formation quantitatively is the Deffuant model, in which the opinion distribution of a population evolves via sequential random pairwise encounters. To consider heterogeneity of interactions in a population along with social influence, we study the Deffuant model on various network structures (deterministic synthetic networks, random synthetic networks, and social networks constructed from Facebook data). We numerically simulate the Deffuant model and conduct regression analyses to investigate the dependence of the time to reach steady states on various model parameters, including a confidence bound for opinion updates, the number of participating entities, and their willingness to compromise. We find that network structure and parameter values both have important effects on the convergence time and the number of steady-state opinion groups. For some network architectures, we observe that the relationship between the convergence time and model parameters undergoes a transition at a critical value of the confidence bound. For some networks, the steady-state opinion distribution also changes from consensus to multiple opinion groups at this critical value.

The Channel Network model and field applications

International Nuclear Information System (INIS)

Khademi, B.; Moreno, L.; Neretnieks, I.

1999-01-01

The Channel Network model describes the fluid flow and solute transport in fractured media. The model is based on field observations, which indicate that flow and transport take place in a three-dimensional network of connected channels. The channels are generated in the model from observed stochastic distributions and solute transport is modeled taking into account advection and rock interactions, such as matrix diffusion and sorption within the rock. The most important site-specific data for the Channel Network model are the conductance distribution of the channels and the flow-wetted surface. The latter is the surface area of the rock in contact with the flowing water. These parameters may be estimated from hydraulic measurements. For the Aespoe site, several borehole data sets are available, where a packer distance of 3 meters was used. Numerical experiments were performed in order to study the uncertainties in the determination of the flow-wetted surface and conductance distribution. Synthetic data were generated along a borehole and hydraulic tests with different packer distances were simulated. The model has previously been used to study the Long-term Pumping and Tracer Test (LPT2) carried out in the Aespoe Hard Rock Laboratory (HRL) in Sweden, where the distance travelled by the tracers was of the order hundreds of meters. Recently, the model has been used to simulate the tracer tests performed in the TRUE experiment at HRL, with travel distance of the order of tens of meters. Several tracer tests with non-sorbing and sorbing species have been performed

Rumor Spreading Model with Trust Mechanism in Complex Social Networks

International Nuclear Information System (INIS)

Wang Ya-Qi; Yang Xiao-Yuan; Han Yi-Liang; Wang Xu-An

2013-01-01

In this paper, to study rumor spreading, we propose a novel susceptible-infected-removed (SIR) model by introducing the trust mechanism. We derive mean-field equations that describe the dynamics of the SIR model on homogeneous networks and inhomogeneous networks. Then a steady-state analysis is conducted to investigate the critical threshold and the final size of the rumor spreading. We show that the introduction of trust mechanism reduces the final rumor size and the velocity of rumor spreading, but increases the critical thresholds on both networks. Moreover, the trust mechanism not only greatly reduces the maximum rumor influence, but also postpones the rumor terminal time, which provides us with more time to take measures to control the rumor spreading. The theoretical results are confirmed by sufficient numerical simulations. (interdisciplinary physics and related areas of science and technology)

Numerical Modeling of Shoreline Undulations

DEFF Research Database (Denmark)

Kærgaard, Kasper Hauberg

model has been developed which describes the longshore sediment transport along arbitrarily shaped shorelines. The numerical model is based on a spectral wave model, a depth integrated flow model, a wave-phase resolving sediment transport description and a one-line shoreline model. First the theoretical...... of the feature and under predicts the migration speeds of the features. On the second shoreline, the shoreline model predicts undulations lengths which are longer than the observed undulations. Lastly the thesis considers field measurements of undulations of the bottom bathymetry along an otherwise straight...... length of the shoreline undulations is determined in the linear regime using a shoreline stability analysis based on the numerical model. The analysis shows that the length of the undulations in the linear regime depends on the incoming wave conditions and on the coastal profile. For larger waves...

Modeling information diffusion in time-varying community networks

Science.gov (United States)

Cui, Xuelian; Zhao, Narisa

2017-12-01

Social networks are rarely static, and they typically have time-varying network topologies. A great number of studies have modeled temporal networks and explored social contagion processes within these models; however, few of these studies have considered community structure variations. In this paper, we present a study of how the time-varying property of a modular structure influences the information dissemination. First, we propose a continuous-time Markov model of information diffusion where two parameters, mobility rate and community attractiveness, are introduced to address the time-varying nature of the community structure. The basic reproduction number is derived, and the accuracy of this model is evaluated by comparing the simulation and theoretical results. Furthermore, numerical results illustrate that generally both the mobility rate and community attractiveness significantly promote the information diffusion process, especially in the initial outbreak stage. Moreover, the strength of this promotion effect is much stronger when the modularity is higher. Counterintuitively, it is found that when all communities have the same attractiveness, social mobility no longer accelerates the diffusion process. In addition, we show that the local spreading in the advantage group has been greatly enhanced due to the agglomeration effect caused by the social mobility and community attractiveness difference, which thus increases the global spreading.

Phase models and clustering in networks of oscillators with delayed coupling

Science.gov (United States)

Campbell, Sue Ann; Wang, Zhen

2018-01-01

We consider a general model for a network of oscillators with time delayed coupling where the coupling matrix is circulant. We use the theory of weakly coupled oscillators to reduce the system of delay differential equations to a phase model where the time delay enters as a phase shift. We use the phase model to determine model independent existence and stability results for symmetric cluster solutions. Our results extend previous work to systems with time delay and a more general coupling matrix. We show that the presence of the time delay can lead to the coexistence of multiple stable clustering solutions. We apply our analytical results to a network of Morris Lecar neurons and compare these results with numerical continuation and simulation studies.

Numerical modelling of mine workings.

CSIR Research Space (South Africa)

Lightfoot, N

1999-03-01

Full Text Available to cover most of what is required for a practising rock mechanics engineer to be able to use any of these five programs to solve practical mining problems. The chapters on specific programs discuss their individual strengths and weaknesses and highlight... and applications of numerical modelling in the context of the South African gold and platinum mining industries. This includes an example that utilises a number of different numerical 3 modelling programs to solve a single problem. This particular example...

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

Science.gov (United States)

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

2015-11-01

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

Mathematical and Numerical Modeling in Maritime Geomechanics

Directory of Open Access Journals (Sweden)

Miguel Martín Stickle

2012-04-01

Full Text Available A theoretical and numerical framework to model the foundation of marine offshore structures is presented. The theoretical model is composed by a system of partial differential equations describing coupling between seabed solid skeleton and pore fluids (water, air, oil,... combined with a system of ordinary differential equations describing the specific constitutive relation of the seabed soil skeleton. Once the theoretical model is described, the finite element numerical procedure to achieve an approximate solution of the overning equations is outlined. In order to validate the proposed theoretical and numerical framework the seaward tilt mechanism induced by the action of breaking waves over a vertical breakwater is numerically reproduced. The results numerically attained are in agreement with the main conclusions drawn from the literature associated with this failure mechanism.

Scaling and percolation in the small-world network model

Energy Technology Data Exchange (ETDEWEB)

Newman, M. E. J. [Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501 (United States); Watts, D. J. [Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501 (United States)

1999-12-01

In this paper we study the small-world network model of Watts and Strogatz, which mimics some aspects of the structure of networks of social interactions. We argue that there is one nontrivial length-scale in the model, analogous to the correlation length in other systems, which is well-defined in the limit of infinite system size and which diverges continuously as the randomness in the network tends to zero, giving a normal critical point in this limit. This length-scale governs the crossover from large- to small-world behavior in the model, as well as the number of vertices in a neighborhood of given radius on the network. We derive the value of the single critical exponent controlling behavior in the critical region and the finite size scaling form for the average vertex-vertex distance on the network, and, using series expansion and Pade approximants, find an approximate analytic form for the scaling function. We calculate the effective dimension of small-world graphs and show that this dimension varies as a function of the length-scale on which it is measured, in a manner reminiscent of multifractals. We also study the problem of site percolation on small-world networks as a simple model of disease propagation, and derive an approximate expression for the percolation probability at which a giant component of connected vertices first forms (in epidemiological terms, the point at which an epidemic occurs). The typical cluster radius satisfies the expected finite size scaling form with a cluster size exponent close to that for a random graph. All our analytic results are confirmed by extensive numerical simulations of the model. (c) 1999 The American Physical Society.

Scaling and percolation in the small-world network model

International Nuclear Information System (INIS)

Newman, M. E. J.; Watts, D. J.

1999-01-01

In this paper we study the small-world network model of Watts and Strogatz, which mimics some aspects of the structure of networks of social interactions. We argue that there is one nontrivial length-scale in the model, analogous to the correlation length in other systems, which is well-defined in the limit of infinite system size and which diverges continuously as the randomness in the network tends to zero, giving a normal critical point in this limit. This length-scale governs the crossover from large- to small-world behavior in the model, as well as the number of vertices in a neighborhood of given radius on the network. We derive the value of the single critical exponent controlling behavior in the critical region and the finite size scaling form for the average vertex-vertex distance on the network, and, using series expansion and Pade approximants, find an approximate analytic form for the scaling function. We calculate the effective dimension of small-world graphs and show that this dimension varies as a function of the length-scale on which it is measured, in a manner reminiscent of multifractals. We also study the problem of site percolation on small-world networks as a simple model of disease propagation, and derive an approximate expression for the percolation probability at which a giant component of connected vertices first forms (in epidemiological terms, the point at which an epidemic occurs). The typical cluster radius satisfies the expected finite size scaling form with a cluster size exponent close to that for a random graph. All our analytic results are confirmed by extensive numerical simulations of the model. (c) 1999 The American Physical Society

Mean-field modeling approach for understanding epidemic dynamics in interconnected networks

International Nuclear Information System (INIS)

Zhu, Guanghu; Fu, Xinchu; Tang, Qinggan; Li, Kezan

2015-01-01

Modern systems (e.g., social, communicant, biological networks) are increasingly interconnected each other formed as ‘networks of networks’. Such complex systems usually possess inconsistent topologies and permit agents distributed in different subnetworks to interact directly/indirectly. Corresponding dynamics phenomena, such as the transmission of information, power, computer virus and disease, would exhibit complicated and heterogeneous tempo-spatial patterns. In this paper, we focus on the scenario of epidemic spreading in interconnected networks. We intend to provide a typical mean-field modeling framework to describe the time-evolution dynamics, and offer some mathematical skills to study the spreading threshold and the global stability of the model. Integrating the research with numerical analysis, we are able to quantify the effects of networks structure and epidemiology parameters on the transmission dynamics. Interestingly, we find that the diffusion transition in the whole network is governed by a unique threshold, which mainly depends on the most heterogenous connection patterns of network substructures. Further, the dynamics is highly sensitive to the critical values of cross infectivity with switchable phases.

Numerical simulation of coherent resonance in a model network of Rulkov neurons

Science.gov (United States)

Andreev, Andrey V.; Runnova, Anastasia E.; Pisarchik, Alexander N.

2018-04-01

In this paper we study the spiking behaviour of a neuronal network consisting of Rulkov elements. We find that the regularity of this behaviour maximizes at a certain level of environment noise. This effect referred to as coherence resonance is demonstrated in a random complex network of Rulkov neurons. An external stimulus added to some of neurons excites them, and then activates other neurons in the network. The network coherence is also maximized at the certain stimulus amplitude.

Inducing self-organized criticality in a network toy model by neighborhood assortativity.

Science.gov (United States)

Allen-Perkins, Alfonso; Galeano, Javier; Pastor, Juan Manuel

2016-11-01

Complex networks are a recent type of framework used to study complex systems with many interacting elements, such as self-organized criticality (SOC). The network nodes' tendency to link to other nodes of similar type is characterized by assortative mixing. Real networks exhibit assortative mixing by vertex degree, however, typical random network models, such as the Erdős-Rényi or the Barabási-Albert model, show no assortative arrangements. In this paper we introduce the notion of neighborhood assortativity as the tendency of a node to belong to a community (its neighborhood) showing an average property similar to its own. Imposing neighborhood assortative mixing by degree in a network toy model, SOC dynamics can be found. These dynamics are driven only by the network topology. The long-range correlations resulting from criticality have been characterized by means of fluctuation analysis and show an anticorrelation in the node's activity. The model contains only one parameter and its statistics plots for different values of the parameter can be collapsed into a single curve. The simplicity of the model allows us to perform numerical simulations and also to study analytically the statistics for a specific value of the parameter, making use of the Markov chains.

Modeling generator power plant portfolios and pollution taxes in electric power supply chain networks: a transportation network equilibrium transformation

International Nuclear Information System (INIS)

Kai Wu; Nagurney, A.; University of Massachusetts, Amherst, MA; Zugang Liu; Stranlund, J.K.

2006-01-01

Global climate change and fuel security risks have encouraged international and regional adoption of pollution/carbon taxes. A major portion of such policy interventions is directed at the electric power industry with taxes applied according to the type of fuel used by the power generators in their power plants. This paper proposes an electric power supply chain network model that captures the behavior of power generators faced with a portfolio of power plant options and subject to pollution taxes. We demonstrate that this general model can be reformulated as a transportation network equilibrium model with elastic demands and qualitatively analyzed and solved as such. The connections between these two different modeling schemas is done through finite-dimensional variational inequality theory. The numerical examples illustrate how changes in the pollution/carbon taxes affect the equilibrium electric power supply chain network production outputs, the transactions between the various decision-makers the demand market prices, as well as the total amount of carbon emissions generated. (author)

Design of analog networks in the control theory formulation. Part 2: Numerical results

OpenAIRE

Zemliak, A. M.

2005-01-01

The paper presents numerical results of design of nonlinear electronic networks based on the problem formulation in terms of the control theory. Several examples illustrate the prospects of the approach suggested in the first part of the work.

Numerical model CCC

International Nuclear Information System (INIS)

Bodvarsson, G.S.; Lippmann, M.J.

1980-01-01

The computer program CCC (conduction-convection-consolidation), developed at Lawrence Berkeley Laboratory, solves numerically the heat and mass flow equations for a fully saturated medium, and computes one-dimensional consolidation of the simulated systems. The model employs the Integrated Finite Difference Method (IFDM) in discretizing the saturated medium and formulating the governing equations. The sets of equations are solved either by an iterative solution technique (old version) or an efficient sparse solver (new version). The deformation of the medium is calculated using the one-dimensional consolidation theory of Terzaghi. In this paper, the numerical code is described, validation examples given and areas of application discussed. Several example problems involving flow through fractured media are also presented

Collaborative networks: Reference modeling

NARCIS (Netherlands)

Camarinha-Matos, L.M.; Afsarmanesh, H.

2008-01-01

Collaborative Networks: Reference Modeling works to establish a theoretical foundation for Collaborative Networks. Particular emphasis is put on modeling multiple facets of collaborative networks and establishing a comprehensive modeling framework that captures and structures diverse perspectives of

Models and synchronization of time-delayed complex dynamical networks with multi-links based on adaptive control

International Nuclear Information System (INIS)

Peng Haipeng; Wei Nan; Li Lixiang; Xie Weisheng; Yang Yixian

2010-01-01

In this Letter, time-delay has been introduced in to split the networks, upon which a model of complex dynamical networks with multi-links has been constructed. Moreover, based on Lyapunov stability theory and some hypotheses, we achieve synchronization between two complex networks with different structures by designing effective controllers. The validity of the results was proved through numerical simulations of this Letter.

CIME course on Modelling and Optimisation of Flows on Networks

CERN Document Server

Ambrosio, Luigi; Helbing, Dirk; Klar, Axel; Zuazua, Enrique

2013-01-01

In recent years flows in networks have attracted the interest of many researchers from different areas, e.g. applied mathematicians, engineers, physicists, economists. The main reason for this ubiquity is the wide and diverse range of applications, such as vehicular traffic, supply chains, blood flow, irrigation channels, data networks and others. This book presents an extensive set of notes by world leaders on the main mathematical techniques used to address such problems, together with investigations into specific applications. The main focus is on partial differential equations in networks, but ordinary differential equations and optimal transport are also included. Moreover, the modeling is completed by analysis, numerics, control and optimization of flows in networks. The book will be a valuable resource for every researcher or student interested in the subject.

Semi-empirical neural network models of controlled dynamical systems

Directory of Open Access Journals (Sweden)

Mihail V. Egorchev

2017-12-01

Full Text Available A simulation approach is discussed for maneuverable aircraft motion as nonlinear controlled dynamical system under multiple and diverse uncertainties including knowledge imperfection concerning simulated plant and its environment exposure. The suggested approach is based on a merging of theoretical knowledge for the plant with training tools of artificial neural network field. The efficiency of this approach is demonstrated using the example of motion modeling and the identification of the aerodynamic characteristics of a maneuverable aircraft. A semi-empirical recurrent neural network based model learning algorithm is proposed for multi-step ahead prediction problem. This algorithm sequentially states and solves numerical optimization subproblems of increasing complexity, using each solution as initial guess for subsequent subproblem. We also consider a procedure for representative training set acquisition that utilizes multisine control signals.

A turbulent transport network model in MULTIFLUX coupled with TOUGH2

International Nuclear Information System (INIS)

Danko, G.; Bahrami, D.; Birkholzer, J.T.

2011-01-01

A new numerical method is described for the fully iterated, conjugate solution of two discrete submodels, involving (a) a transport network model for heat, moisture, and airflows in a high-permeability, air-filled cavity; and (b) a variably saturated fractured porous medium. The transport network submodel is an integrated-parameter, computational fluid dynamics solver, describing the thermal-hydrologic transport processes in the flow channel system of the cavity with laminar or turbulent flow and convective heat and mass transport, using MULTIFLUX. The porous medium submodel, using TOUGH2, is a solver for the heat and mass transport in the fractured rock mass. The new model solution extends the application fields of TOUGH2 by integrating it with turbulent flow and transport in a discrete flow network system. We present demonstrational results for a nuclear waste repository application at Yucca Mountain with the most realistic model assumptions and input parameters including the geometrical layout of the nuclear spent fuel and waste with variable heat load for the individual containers. The MULTIFLUX and TOUGH2 model elements are fully iterated, applying a programmed reprocessing of the Numerical Transport Code Functionalization model-element in an automated Outside Balance Iteration loop. The natural, convective airflow field and the heat and mass transport in a representative emplacement drift during postclosure are explicitly solved in the new model. The results demonstrate that the direction and magnitude of the air circulation patterns and all transport modes are strongly affected by the heat and moisture transport processes in the surrounding rock, justifying the need for a coupled, fully iterated model solution such as the one presented in the paper.

Numerical Modeling of Large-Scale Rocky Coastline Evolution

Science.gov (United States)

Limber, P.; Murray, A. B.; Littlewood, R.; Valvo, L.

2008-12-01

, increases weathering and erosion around the headland, and eventually changes the headland into an embayment! Improvements to our modeling approach include refining the initial conditions. To create a fractal, immature rocky coastline, self-similar river networks with random side branches were drawn on the shoreline domain. River networks and side branches were scaled according to Horton's law and Tokunaga statistics, respectively, and each river pathway was assigned a simple exponential longitudinal profile. Topography was generated around the river networks to create drainage basins and, on a larger scale, represent a mountainous, fluvially-sculpted landscape. The resultant morphology was then flooded to a given elevation, leaving a fractal rocky coastline. In addition to the simulated terrain, actual digital elevation models will also be used to derive the initial conditions. Elevation data from different mountainous geomorphic settings such as the decaying Appalachian Mountains or actively uplifting Sierra Nevada can be effectively flooded to a given sea level, resulting in a fractal and immature coastline that can be input to the numerical model. This approach will offer insight into how rocky coastlines in different geomorphic settings evolve, and provide a useful complement to results using the simulated terrain.

Numerical Modeling of Piezoelectric Transducers Using Physical Parameters

NARCIS (Netherlands)

Cappon, H.; Keesman, K.J.

2012-01-01

Design of ultrasonic equipment is frequently facilitated with numerical models. These numerical models, however, need a calibration step, because usually not all characteristics of the materials used are known. Characterization of material properties combined with numerical simulations and

Ferrofluids: Modeling, numerical analysis, and scientific computation

Science.gov (United States)

Tomas, Ignacio

This dissertation presents some developments in the Numerical Analysis of Partial Differential Equations (PDEs) describing the behavior of ferrofluids. The most widely accepted PDE model for ferrofluids is the Micropolar model proposed by R.E. Rosensweig. The Micropolar Navier-Stokes Equations (MNSE) is a subsystem of PDEs within the Rosensweig model. Being a simplified version of the much bigger system of PDEs proposed by Rosensweig, the MNSE are a natural starting point of this thesis. The MNSE couple linear velocity u, angular velocity w, and pressure p. We propose and analyze a first-order semi-implicit fully-discrete scheme for the MNSE, which decouples the computation of the linear and angular velocities, is unconditionally stable and delivers optimal convergence rates under assumptions analogous to those used for the Navier-Stokes equations. Moving onto the much more complex Rosensweig's model, we provide a definition (approximation) for the effective magnetizing field h, and explain the assumptions behind this definition. Unlike previous definitions available in the literature, this new definition is able to accommodate the effect of external magnetic fields. Using this definition we setup the system of PDEs coupling linear velocity u, pressure p, angular velocity w, magnetization m, and magnetic potential ϕ We show that this system is energy-stable and devise a numerical scheme that mimics the same stability property. We prove that solutions of the numerical scheme always exist and, under certain simplifying assumptions, that the discrete solutions converge. A notable outcome of the analysis of the numerical scheme for the Rosensweig's model is the choice of finite element spaces that allow the construction of an energy-stable scheme. Finally, with the lessons learned from Rosensweig's model, we develop a diffuse-interface model describing the behavior of two-phase ferrofluid flows and present an energy-stable numerical scheme for this model. For a

Loss Performance Modeling for Hierarchical Heterogeneous Wireless Networks With Speed-Sensitive Call Admission Control

DEFF Research Database (Denmark)

Huang, Qian; Huang, Yue-Cai; Ko, King-Tim

2011-01-01

. This approach avoids unnecessary and frequent handoff between cells and reduces signaling overheads. An approximation model with guaranteed accuracy and low computational complexity is presented for the loss performance of multiservice traffic. The accuracy of numerical results is validated by comparing......A hierarchical overlay structure is an alternative solution that integrates existing and future heterogeneous wireless networks to provide subscribers with better mobile broadband services. Traffic loss performance in such integrated heterogeneous networks is necessary for an operator's network...

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

Science.gov (United States)

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

2016-12-01

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

Space evolution model and empirical analysis of an urban public transport network

Science.gov (United States)

Sui, Yi; Shao, Feng-jing; Sun, Ren-cheng; Li, Shu-jing

2012-07-01

This study explores the space evolution of an urban public transport network, using empirical evidence and a simulation model validated on that data. Public transport patterns primarily depend on traffic spatial-distribution, demands of passengers and expected utility of investors. Evolution is an iterative process of satisfying the needs of passengers and investors based on a given traffic spatial-distribution. The temporal change of urban public transport network is evaluated both using topological measures and spatial ones. The simulation model is validated using empirical data from nine big cities in China. Statistical analyses on topological and spatial attributes suggest that an evolution network with traffic demands characterized by power-law numerical values which distribute in a mode of concentric circles tallies well with these nine cities.

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

Science.gov (United States)

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

2018-04-01

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

Numerical Modeling of Ablation Heat Transfer

Science.gov (United States)

Ewing, Mark E.; Laker, Travis S.; Walker, David T.

2013-01-01

A unique numerical method has been developed for solving one-dimensional ablation heat transfer problems. This paper provides a comprehensive description of the method, along with detailed derivations of the governing equations. This methodology supports solutions for traditional ablation modeling including such effects as heat transfer, material decomposition, pyrolysis gas permeation and heat exchange, and thermochemical surface erosion. The numerical scheme utilizes a control-volume approach with a variable grid to account for surface movement. This method directly supports implementation of nontraditional models such as material swelling and mechanical erosion, extending capabilities for modeling complex ablation phenomena. Verifications of the numerical implementation are provided using analytical solutions, code comparisons, and the method of manufactured solutions. These verifications are used to demonstrate solution accuracy and proper error convergence rates. A simple demonstration of a mechanical erosion (spallation) model is also provided to illustrate the unique capabilities of the method.

A generic interference model for uplink OFDMA networks with fractional frequency reuse

KAUST Repository

Tabassum, Hina

2014-03-01

Fractional frequency reuse (FFR) has emerged as a viable solution to coordinate and mitigate cochannel interference (CCI) in orthogonal frequency-division multiple-access (OFDMA)-based wireless cellular networks. The incurred CCI in cellular networks with FFR is highly uncertain and varies as a function of various design parameters that include the user scheduling schemes, the transmit power distribution among multiple allocated subcarriers, the partitioning of the cellular region into cell-edge and cell-center zones, the allocation of spectrum within each zone, and the channel reuse factors. To this end, this paper derives a generic analytical model for uplink CCI in multicarrier OFDMA networks with FFR. The derived expressions capture several network design parameters and are applicable to any composite fading-channel models. The accuracy of the derivations is verified via Monte Carlo simulations. Moreover, their usefulness is demonstrated by obtaining closed-form expressions for the Rayleigh fading-channel model and by evaluating important network performance metrics such as ergodic capacity. Numerical results provide useful system design guidelines and highlight the trade-offs associated with the deployment of FFR schemes in OFDMA-based networks. © 2013 IEEE.

A generic interference model for uplink OFDMA networks with fractional frequency reuse

KAUST Repository

Tabassum, Hina; Dawy, Zaher; Alouini, Mohamed-Slim; Yilmaz, Ferkan

2014-01-01

Fractional frequency reuse (FFR) has emerged as a viable solution to coordinate and mitigate cochannel interference (CCI) in orthogonal frequency-division multiple-access (OFDMA)-based wireless cellular networks. The incurred CCI in cellular networks with FFR is highly uncertain and varies as a function of various design parameters that include the user scheduling schemes, the transmit power distribution among multiple allocated subcarriers, the partitioning of the cellular region into cell-edge and cell-center zones, the allocation of spectrum within each zone, and the channel reuse factors. To this end, this paper derives a generic analytical model for uplink CCI in multicarrier OFDMA networks with FFR. The derived expressions capture several network design parameters and are applicable to any composite fading-channel models. The accuracy of the derivations is verified via Monte Carlo simulations. Moreover, their usefulness is demonstrated by obtaining closed-form expressions for the Rayleigh fading-channel model and by evaluating important network performance metrics such as ergodic capacity. Numerical results provide useful system design guidelines and highlight the trade-offs associated with the deployment of FFR schemes in OFDMA-based networks. © 2013 IEEE.

A Hierarchical Modeling for Reactive Power Optimization With Joint Transmission and Distribution Networks by Curve Fitting

DEFF Research Database (Denmark)

Ding, Tao; Li, Cheng; Huang, Can

2018-01-01

–slave structure and improves traditional centralized modeling methods by alleviating the big data problem in a control center. Specifically, the transmission-distribution-network coordination issue of the hierarchical modeling method is investigated. First, a curve-fitting approach is developed to provide a cost......In order to solve the reactive power optimization with joint transmission and distribution networks, a hierarchical modeling method is proposed in this paper. It allows the reactive power optimization of transmission and distribution networks to be performed separately, leading to a master...... optimality. Numerical results on two test systems verify the effectiveness of the proposed hierarchical modeling and curve-fitting methods....

SISL and SIRL: Two knowledge dissemination models with leader nodes on cooperative learning networks

Science.gov (United States)

Li, Jingjing; Zhang, Yumei; Man, Jiayu; Zhou, Yun; Wu, Xiaojun

2017-02-01

Cooperative learning is one of the most effective teaching methods, which has been widely used. Students' mutual contact forms a cooperative learning network in this process. Our previous research demonstrated that the cooperative learning network has complex characteristics. This study aims to investigating the dynamic spreading process of the knowledge in the cooperative learning network and the inspiration of leaders in this process. To this end, complex network transmission dynamics theory is utilized to construct the knowledge dissemination model of a cooperative learning network. Based on the existing epidemic models, we propose a new susceptible-infected-susceptible-leader (SISL) model that considers both students' forgetting and leaders' inspiration, and a susceptible-infected-removed-leader (SIRL) model that considers students' interest in spreading and leaders' inspiration. The spreading threshold λcand its impact factors are analyzed. Then, numerical simulation and analysis are delivered to reveal the dynamic transmission mechanism of knowledge and leaders' role. This work is of great significance to cooperative learning theory and teaching practice. It also enriches the theory of complex network transmission dynamics.

Artificial Neural Network L* from different magnetospheric field models

Science.gov (United States)

Yu, Y.; Koller, J.; Zaharia, S. G.; Jordanova, V. K.

2011-12-01

The third adiabatic invariant L* plays an important role in modeling and understanding the radiation belt dynamics. The popular way to numerically obtain the L* value follows the recipe described by Roederer [1970], which is, however, slow and computational expensive. This work focuses on a new technique, which can compute the L* value in microseconds without losing much accuracy: artificial neural networks. Since L* is related to the magnetic flux enclosed by a particle drift shell, global magnetic field information needed to trace the drift shell is required. A series of currently popular empirical magnetic field models are applied to create the L* data pool using 1 million data samples which are randomly selected within a solar cycle and within the global magnetosphere. The networks, trained from the above L* data pool, can thereby be used for fairly efficient L* calculation given input parameters valid within the trained temporal and spatial range. Besides the empirical magnetospheric models, a physics-based self-consistent inner magnetosphere model (RAM-SCB) developed at LANL is also utilized to calculate L* values and then to train the L* neural network. This model better predicts the magnetospheric configuration and therefore can significantly improve the L*. The above neural network L* technique will enable, for the first time, comprehensive solar-cycle long studies of radiation belt processes. However, neural networks trained from different magnetic field models can result in different L* values, which could cause mis-interpretation of radiation belt dynamics, such as where the source of the radiation belt charged particle is and which mechanism is dominant in accelerating the particles. Such a fact calls for attention to cautiously choose a magnetospheric field model for the L* calculation.

Study on Maritime Logistics Warehousing Center Model and Precision Marketing Strategy Optimization Based on Fuzzy Method and Neural Network Model

Directory of Open Access Journals (Sweden)

Xiao Kefeng

2017-08-01

Full Text Available The bulk commodity, different with the retail goods, has a uniqueness in the location selection, the chosen of transportation program and the decision objectives. How to make optimal decisions in the facility location, requirement distribution, shipping methods and the route selection and establish an effective distribution system to reduce the cost has become a burning issue for the e-commerce logistics, which is worthy to be deeply and systematically solved. In this paper, Logistics warehousing center model and precision marketing strategy optimization based on fuzzy method and neural network model is proposed to solve this problem. In addition, we have designed principles of the fuzzy method and neural network model to solve the proposed model because of its complexity. Finally, we have solved numerous examples to compare the results of lingo and Matlab, we use Matlab and lingo just to check the result and to illustrate the numerical example, we can find from the result, the multi-objective model increases logistics costs and improves the efficiency of distribution time.

Stability of an SAIRS alcoholism model on scale-free networks

Science.gov (United States)

Xiang, Hong; Liu, Ying-Ping; Huo, Hai-Feng

2017-05-01

A new SAIRS alcoholism model with birth and death on complex heterogeneous networks is proposed. The total population of our model is partitioned into four compartments: the susceptible individual, the light problem alcoholic, the heavy problem alcoholic and the recovered individual. The spread of alcoholism threshold R0 is calculated by the next generation matrix method. When R0 alcohol free equilibrium is globally asymptotically stable, then the alcoholics will disappear. When R0 > 1, the alcoholism equilibrium is global attractivity, then the number of alcoholics will remain stable and alcoholism will become endemic. Furthermore, the modified SAIRS alcoholism model on weighted contact network is introduced. Dynamical behavior of the modified model is also studied. Numerical simulations are also presented to verify and extend theoretical results. Our results show that it is very important to treat alcoholics to control the spread of the alcoholism.

Resource allocation based uplink intercell interference model in multi-carrier networks

KAUST Repository

Tabassum, Hina; Yilmaz, Ferkan; Dawy, Zaher; Alouini, Mohamed-Slim

2013-01-01

Intercell interference (ICI) is a primary cause for performance limitation in emerging wireless cellular systems due to its highly indeterministic nature. In this paper, we derive an analytical statistical model for the uplink ICI in a multiuser multi-carrier cellular network considering the impact of various uncoordinated scheduling schemes on the locations and transmit powers of the interferers. The derived model applies to generic composite fading distributions and provides a useful computational tool to evaluate key performance metrics such as the network ergodic capacity. The derived model is extended to incorporate coordinated scheduling schemes. A study is then presented to quantify the potential performance gains of coordinated over uncoordinated scheduling schemes under various base station coordination scenarios. Numerical results demonstrate that different frequency allocation patterns significantly impact the network performance depending on the coordination among neighboring base stations. The accuracy of the derived analytical expressions is verified via Monte-Carlo simulations. © 2013 IEEE.

Resource allocation based uplink intercell interference model in multi-carrier networks

KAUST Repository

Tabassum, Hina

2013-06-01

Intercell interference (ICI) is a primary cause for performance limitation in emerging wireless cellular systems due to its highly indeterministic nature. In this paper, we derive an analytical statistical model for the uplink ICI in a multiuser multi-carrier cellular network considering the impact of various uncoordinated scheduling schemes on the locations and transmit powers of the interferers. The derived model applies to generic composite fading distributions and provides a useful computational tool to evaluate key performance metrics such as the network ergodic capacity. The derived model is extended to incorporate coordinated scheduling schemes. A study is then presented to quantify the potential performance gains of coordinated over uncoordinated scheduling schemes under various base station coordination scenarios. Numerical results demonstrate that different frequency allocation patterns significantly impact the network performance depending on the coordination among neighboring base stations. The accuracy of the derived analytical expressions is verified via Monte-Carlo simulations. © 2013 IEEE.

Numerical modelling of elastic space tethers

DEFF Research Database (Denmark)

Kristiansen, Kristian Uldall; Palmer, P. L.; Roberts, R. M.

2012-01-01

In this paper the importance of the ill-posedness of the classical, non-dissipative massive tether model on an orbiting tether system is studied numerically. The computations document that via the regularisation of bending resistance a more reliable numerical integrator can be produced. Furthermo....... It is also shown that on the slow manifold the dynamics of the satellites are well-approximated by the finite dimensional slack-spring model....

The SIS Model of Epidemic Spreading in a Hierarchical Social Network

International Nuclear Information System (INIS)

Grabowski, A.; Kosinski, R.A.

2005-01-01

The phenomenon of epidemic spreading in a population with a hierarchical structure of interpersonal interactions is described and investigated numerically. The SIS model with temporal immunity to a disease and a time of incubation is used. In our model spatial localization of individuals belonging to different social groups, effectiveness of different interpersonal interactions and the mobility of a contemporary community are taken into account. The structure of interpersonal connections is based on a scale-free network. The influence of the structure of the social network on typical relations characterizing the spreading process, like a range of epidemic and epidemic curves, is discussed. The probability that endemic state occurs is also calculated. Surprisingly it occurs, that less contagious diseases has greater chance to survive. The influence of preventive vaccinations on the spreading process is investigated and critical range of vaccinations that is sufficient for the suppression of an epidemic is calculated. Our results of numerical calculations are compared with the solutions of the master equation for the spreading process, and good agreement is found. (author)

Folk music style modelling by recurrent neural networks with long short term memory units

OpenAIRE

Sturm, Bob; Santos, João Felipe; Korshunova, Iryna

2015-01-01

We demonstrate two generative models created by training a recurrent neural network (RNN) with three hidden layers of long short-term memory (LSTM) units. This extends past work in numerous directions, including training deeper models with nearly 24,000 high-level transcriptions of folk tunes. We discuss our on-going work.

Dynamical properties of fractal networks: Scaling, numerical simulations, and physical realizations

International Nuclear Information System (INIS)

Nakayama, T.; Yakubo, K.; Orbach, R.L.

1994-01-01

This article describes the advances that have been made over the past ten years on the problem of fracton excitations in fractal structures. The relevant systems to this subject are so numerous that focus is limited to a specific structure, the percolating network. Recent progress has followed three directions: scaling, numerical simulations, and experiment. In a happy coincidence, large-scale computations, especially those involving array processors, have become possible in recent years. Experimental techniques such as light- and neutron-scattering experiments have also been developed. Together, they form the basis for a review article useful as a guide to understanding these developments and for charting future research directions. In addition, new numerical simulation results for the dynamical properties of diluted antiferromagnets are presented and interpreted in terms of scaling arguments. The authors hope this article will bring the major advances and future issues facing this field into clearer focus, and will stimulate further research on the dynamical properties of random systems

Numerical methods and modelling for engineering

CERN Document Server

Khoury, Richard

2016-01-01

This textbook provides a step-by-step approach to numerical methods in engineering modelling. The authors provide a consistent treatment of the topic, from the ground up, to reinforce for students that numerical methods are a set of mathematical modelling tools which allow engineers to represent real-world systems and compute features of these systems with a predictable error rate. Each method presented addresses a specific type of problem, namely root-finding, optimization, integral, derivative, initial value problem, or boundary value problem, and each one encompasses a set of algorithms to solve the problem given some information and to a known error bound. The authors demonstrate that after developing a proper model and understanding of the engineering situation they are working on, engineers can break down a model into a set of specific mathematical problems, and then implement the appropriate numerical methods to solve these problems. Uses a “building-block” approach, starting with simpler mathemati...

Modelling of cardiovascular system: development of a hybrid (numerical-physical) model.

Science.gov (United States)

Ferrari, G; Kozarski, M; De Lazzari, C; Górczyńska, K; Mimmo, R; Guaragno, M; Tosti, G; Darowski, M

2003-12-01

Physical models of the circulation are used for research, training and for testing of implantable active and passive circulatory prosthetic and assistance devices. However, in comparison with numerical models, they are rigid and expensive. To overcome these limitations, we have developed a model of the circulation based on the merging of a lumped parameter physical model into a numerical one (producing therefore a hybrid). The physical model is limited to the barest essentials and, in this application, developed to test the principle, it is a windkessel representing the systemic arterial tree. The lumped parameters numerical model was developed in LabVIEW environment and represents pulmonary and systemic circulation (except the systemic arterial tree). Based on the equivalence between hydraulic and electrical circuits, this prototype was developed connecting the numerical model to an electrical circuit--the physical model. This specific solution is valid mainly educationally but permits the development of software and the verification of preliminary results without using cumbersome hydraulic circuits. The interfaces between numerical and electrical circuits are set up by a voltage controlled current generator and a voltage controlled voltage generator. The behavior of the model is analyzed based on the ventricular pressure-volume loops and on the time course of arterial and ventricular pressures and flow in different circulatory conditions. The model can represent hemodynamic relationships in different ventricular and circulatory conditions.

Nonspinning numerical relativity waveform surrogates: assessing the model

Science.gov (United States)

Field, Scott; Blackman, Jonathan; Galley, Chad; Scheel, Mark; Szilagyi, Bela; Tiglio, Manuel

2015-04-01

Recently, multi-modal gravitational waveform surrogate models have been built directly from data numerically generated by the Spectral Einstein Code (SpEC). I will describe ways in which the surrogate model error can be quantified. This task, in turn, requires (i) characterizing differences between waveforms computed by SpEC with those predicted by the surrogate model and (ii) estimating errors associated with the SpEC waveforms from which the surrogate is built. Both pieces can have numerous sources of numerical and systematic errors. We make an attempt to study the most dominant error sources and, ultimately, the surrogate model's fidelity. These investigations yield information about the surrogate model's uncertainty as a function of time (or frequency) and parameter, and could be useful in parameter estimation studies which seek to incorporate model error. Finally, I will conclude by comparing the numerical relativity surrogate model to other inspiral-merger-ringdown models. A companion talk will cover the building of multi-modal surrogate models.

Advanced Models and Algorithms for Self-Similar IP Network Traffic Simulation and Performance Analysis

Science.gov (United States)

Radev, Dimitar; Lokshina, Izabella

2010-11-01

The paper examines self-similar (or fractal) properties of real communication network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Advanced fractal models of sequentional generators and fixed-length sequence generators, and efficient algorithms that are used to simulate self-similar behavior of IP network traffic data are developed and applied. Numerical examples are provided; and simulation results are obtained and analyzed.

Modeling fiber Bragg grating device networks in photomechanical polymer optical fibers

Science.gov (United States)

Lanska, Joseph T.; Kuzyk, Mark G.; Sullivan, Dennis M.

2015-09-01

We report on the modeling of fiber Bragg grating (FBG) networks in poly(methyl methacrylate) (PMMA) polymer fibers doped with azo dyes. Our target is the development of Photomechanical Optical Devices (PODs), comprised of two FBGs in series, separated by a Fabry-Perot cavity of photomechanical material. PODs exhibit photomechanical multi-stability, with the capacity to access multiple length states for a fixed input intensity when a mechanical shock is applied. Using finite-difference time-domain (FDTD) numerical methods, we modeled the photomechanical response of both Fabry-Perot and Bragg-type PODs in a single polymer optical fiber. The polymer fiber was modeled as an instantaneous Kerr-type nonlinear χ(3) material. Our model correctly predicts the essential optical features of FBGs as well as the photomechanical multi-stability of nonlinear Fabry-Perot cavity-based PODs. Networks of PODs may provide a framework for smart shape-shifting materials and fast optical computation where the decision process is distributed over the entire network. In addition, a POD can act as memory, and its response can depend on input history. Our models inform and will accelerate targeted development of novel Bragg grating-based polymer fiber device networks for a variety of applications in optical computing and smart materials.

Numerical modeling of economic uncertainty

DEFF Research Database (Denmark)

Schjær-Jacobsen, Hans

2007-01-01

Representation and modeling of economic uncertainty is addressed by different modeling methods, namely stochastic variables and probabilities, interval analysis, and fuzzy numbers, in particular triple estimates. Focusing on discounted cash flow analysis numerical results are presented, comparisons...... are made between alternative modeling methods, and characteristics of the methods are discussed....

A numerical approach for assessing effects of shear on equivalent permeability and nonlinear flow characteristics of 2-D fracture networks

Science.gov (United States)

Liu, Richeng; Li, Bo; Jiang, Yujing; Yu, Liyuan

2018-01-01

Hydro-mechanical properties of rock fractures are core issues for many geoscience and geo-engineering practices. Previous experimental and numerical studies have revealed that shear processes could greatly enhance the permeability of single rock fractures, yet the shear effects on hydraulic properties of fractured rock masses have received little attention. In most previous fracture network models, single fractures are typically presumed to be formed by parallel plates and flow is presumed to obey the cubic law. However, related studies have suggested that the parallel plate model cannot realistically represent the surface characters of natural rock fractures, and the relationship between flow rate and pressure drop will no longer be linear at sufficiently large Reynolds numbers. In the present study, a numerical approach was established to assess the effects of shear on the hydraulic properties of 2-D discrete fracture networks (DFNs) in both linear and nonlinear regimes. DFNs considering fracture surface roughness and variation of aperture in space were generated using an originally developed code DFNGEN. Numerical simulations by solving Navier-Stokes equations were performed to simulate the fluid flow through these DFNs. A fracture that cuts through each model was sheared and by varying the shear and normal displacements, effects of shear on equivalent permeability and nonlinear flow characteristics of DFNs were estimated. The results show that the critical condition of quantifying the transition from a linear flow regime to a nonlinear flow regime is: 10-4 〈 J hydraulic gradient. When the fluid flow is in a linear regime (i.e., J reduce the equivalent permeability significantly in the orientation perpendicular to the sheared fracture as much as 53.86% when J = 1, shear displacement Ds = 7 mm, and normal displacement Dn = 1 mm. By fitting the calculated results, the mathematical expression for δ2 is established to help choose proper governing equations when

Numeric Input Relations for Relational Learning with Applications to Community Structure Analysis

DEFF Research Database (Denmark)

Jiang, Jiuchuan; Jaeger, Manfred

2015-01-01

distribution is defined by the model from numerical input variables that are only used for conditioning the distribution of discrete response variables. We show how numerical input relations can very easily be used in the Relational Bayesian Network framework, and that existing inference and learning methods......Most work in the area of statistical relational learning (SRL) is focussed on discrete data, even though a few approaches for hybrid SRL models have been proposed that combine numerical and discrete variables. In this paper we distinguish numerical random variables for which a probability...... use the augmented RBN framework to define probabilistic models for multi-relational (social) networks in which the probability of a link between two nodes depends on numeric latent feature vectors associated with the nodes. A generic learning procedure can be used to obtain a maximum-likelihood fit...

Numerical model SMODERP

Science.gov (United States)

Kavka, P.; Jeřábek, J.; Strouhal, L.

2016-12-01

The contribution presents a numerical model SMODERP that is used for calculation and prediction of surface runoff and soil erosion from agricultural land. The physically based model includes the processes of infiltration (Phillips equation), surface runoff routing (kinematic wave based equation), surface retention, surface roughness and vegetation impact on runoff. The model is being developed at the Department of Irrigation, Drainage and Landscape Engineering, Civil Engineering Faculty, CTU in Prague. 2D version of the model was introduced in last years. The script uses ArcGIS system tools for data preparation. The physical relations are implemented through Python scripts. The main computing part is stand alone in numpy arrays. Flow direction is calculated by Steepest Descent algorithm and in multiple flow algorithm. Sheet flow is described by modified kinematic wave equation. Parameters for five different soil textures were calibrated on the set of hundred measurements performed on the laboratory and filed rainfall simulators. Spatially distributed models enable to estimate not only surface runoff but also flow in the rills. Development of the rills is based on critical shear stress and critical velocity. For modelling of the rills a specific sub model was created. This sub model uses Manning formula for flow estimation. Flow in the ditches and streams are also computed. Numerical stability of the model is controled by Courant criterion. Spatial scale is fixed. Time step is dynamic and depends on the actual discharge. The model is used in the framework of the project "Variability of Short-term Precipitation and Runoff in Small Czech Drainage Basins and its Influence on Water Resources Management". Main goal of the project is to elaborate a methodology and online utility for deriving short-term design precipitation series, which could be utilized by a broad community of scientists, state administration as well as design planners. The methodology will account for

Applying the Network Simulation Method for testing chaos in a resistively and capacitively shunted Josephson junction model

Directory of Open Access Journals (Sweden)

Fernando Gimeno Bellver

Full Text Available In this paper, we explore the chaotic behavior of resistively and capacitively shunted Josephson junctions via the so-called Network Simulation Method. Such a numerical approach establishes a formal equivalence among physical transport processes and electrical networks, and hence, it can be applied to efficiently deal with a wide range of differential systems.The generality underlying that electrical equivalence allows to apply the circuit theory to several scientific and technological problems. In this work, the Fast Fourier Transform has been applied for chaos detection purposes and the calculations have been carried out in PSpice, an electrical circuit software.Overall, it holds that such a numerical approach leads to quickly computationally solve Josephson differential models. An empirical application regarding the study of the Josephson model completes the paper. Keywords: Electrical analogy, Network Simulation Method, Josephson junction, Chaos indicator, Fast Fourier Transform

A network thermodynamic method for numerical solution of the Nernst-Planck and Poisson equation system with application to ionic transport through membranes.

Science.gov (United States)

Horno, J; González-Caballero, F; González-Fernández, C F

1990-01-01

Simple techniques of network thermodynamics are used to obtain the numerical solution of the Nernst-Planck and Poisson equation system. A network model for a particular physical situation, namely ionic transport through a thin membrane with simultaneous diffusion, convection and electric current, is proposed. Concentration and electric field profiles across the membrane, as well as diffusion potential, have been simulated using the electric circuit simulation program, SPICE. The method is quite general and extremely efficient, permitting treatments of multi-ion systems whatever the boundary and experimental conditions may be.

Telecommunications network modelling, planning and design

CERN Document Server

Evans, Sharon

2003-01-01

Telecommunication Network Modelling, Planning and Design addresses sophisticated modelling techniques from the perspective of the communications industry and covers some of the major issues facing telecommunications network engineers and managers today. Topics covered include network planning for transmission systems, modelling of SDH transport network structures and telecommunications network design and performance modelling, as well as network costs and ROI modelling and QoS in 3G networks.

Comparing Models GRM, Refraction Tomography and Neural Network to Analyze Shallow Landslide

Directory of Open Access Journals (Sweden)

Armstrong F. Sompotan

2011-11-01

Full Text Available Detailed investigations of landslides are essential to understand fundamental landslide mechanisms. Seismic refraction method has been proven as a useful geophysical tool for investigating shallow landslides. The objective of this study is to introduce a new workflow using neural network in analyzing seismic refraction data and to compare the result with some methods; that are general reciprocal method (GRM and refraction tomography. The GRM is effective when the velocity structure is relatively simple and refractors are gently dipping. Refraction tomography is capable of modeling the complex velocity structures of landslides. Neural network is found to be more potential in application especially in time consuming and complicated numerical methods. Neural network seem to have the ability to establish a relationship between an input and output space for mapping seismic velocity. Therefore, we made a preliminary attempt to evaluate the applicability of neural network to determine velocity and elevation of subsurface synthetic models corresponding to arrival times. The training and testing process of the neural network is successfully accomplished using the synthetic data. Furthermore, we evaluated the neural network using observed data. The result of the evaluation indicates that the neural network can compute velocity and elevation corresponding to arrival times. The similarity of those models shows the success of neural network as a new alternative in seismic refraction data interpretation.

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

Science.gov (United States)

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

2015-11-01

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

A neural approach for the numerical modeling of two-dimensional magnetic hysteresis

International Nuclear Information System (INIS)

Cardelli, E.; Faba, A.; Laudani, A.; Riganti Fulginei, F.; Salvini, A.

2015-01-01

This paper deals with a neural network approach to model magnetic hysteresis at macro-magnetic scale. Such approach to the problem seems promising in order to couple the numerical treatment of magnetic hysteresis to 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, making possible a full computer simulation in a reasonable time. The neural system proposed consists of four inputs representing the magnetic field and the magnetic inductions components at each time step and it is trained by 2-d measurements performed on the magnetic material to be modeled. The magnetic induction B is assumed as entry point and the output of the neural system returns the predicted value of the field H at the same time step. A suitable partitioning of the neural system, described in the paper, makes the computing process rather fast. Validations with experimental tests and simulations for non-symmetric and minor loops are presented

Numerical model updating technique for structures using firefly algorithm

Science.gov (United States)

Sai Kubair, K.; Mohan, S. C.

2018-03-01

Numerical model updating is a technique used for updating the existing experimental models for any structures related to civil, mechanical, automobiles, marine, aerospace engineering, etc. The basic concept behind this technique is updating the numerical models to closely match with experimental data obtained from real or prototype test structures. The present work involves the development of numerical model using MATLAB as a computational tool and with mathematical equations that define the experimental model. Firefly algorithm is used as an optimization tool in this study. In this updating process a response parameter of the structure has to be chosen, which helps to correlate the numerical model developed with the experimental results obtained. The variables for the updating can be either material or geometrical properties of the model or both. In this study, to verify the proposed technique, a cantilever beam is analyzed for its tip deflection and a space frame has been analyzed for its natural frequencies. Both the models are updated with their respective response values obtained from experimental results. The numerical results after updating show that there is a close relationship that can be brought between the experimental and the numerical models.

2-dimensional numerical modeling of active magnetic regeneration

DEFF Research Database (Denmark)

Nielsen, Kaspar Kirstein; Pryds, Nini; Smith, Anders

2009-01-01

Various aspects of numerical modeling of Active Magnetic Regeneration (AMR) are presented. Using a 2-dimensional numerical model for solving the unsteady heat transfer equations for the AMR system, a range of physical effects on both idealized and non-idealized AMR are investigated. The modeled...

Numerical modelling of steel arc welding

International Nuclear Information System (INIS)

Hamide, M.

2008-07-01

Welding is a highly used assembly technique. Welding simulation software would give access to residual stresses and information about the weld's microstructure, in order to evaluate the mechanical resistance of a weld. It would also permit to evaluate the process feasibility when complex geometrical components are to be made, and to optimize the welding sequences in order to minimize defects. This work deals with the numerical modelling of arc welding process of steels. After describing the industrial context and the state of art, the models implemented in TransWeld (software developed at CEMEF) are presented. The set of macroscopic equations is followed by a discussion on their numerical implementation. Then, the theory of re-meshing and our adaptive anisotropic re-meshing strategy are explained. Two welding metal addition techniques are investigated and are compared in terms of the joint size and transient temperature and stresses. The accuracy of the finite element model is evaluated based on experimental results and the results of the analytical solution. Comparative analysis between experimental and numerical results allows the assessment of the ability of the numerical code to predict the thermomechanical and metallurgical response of the welded structure. The models limitations and the phenomena identified during this study are finally discussed and permit to define interesting orientations for future developments. (author)

NEESROCK: A Physical and Numerical Modeling Investigation of Seismically Induced Rock-Slope Failure

Science.gov (United States)

Applegate, K. N.; Wartman, J.; Keefer, D. K.; Maclaughlin, M.; Adams, S.; Arnold, L.; Gibson, M.; Smith, S.

2013-12-01

Worldwide, seismically induced rock-slope failures have been responsible for approximately 30% of the most significant landslide catastrophes of the past century. They are among the most common, dangerous, and still today, least understood of all seismic hazards. Seismically Induced Rock-Slope Failure: Mechanisms and Prediction (NEESROCK) is a major research initiative that fully integrates physical modeling (geotechnical centrifuge) and advanced numerical simulations (discrete element modeling) to investigate the fundamental mechanisms governing the stability of rock slopes during earthquakes. The research is part of the National Science Foundation-supported Network for Earthquake Engineering Simulation Research (NEES) program. With its focus on fractures and rock materials, the project represents a significant departure from the traditional use of the geotechnical centrifuge for studying soil, and pushes the boundaries of physical modeling in new directions. In addition to advancing the fundamental understanding of the rock-slope failure process under seismic conditions, the project is developing improved rock-slope failure assessment guidelines, analysis procedures, and predictive tools. Here, we provide an overview of the project, present experimental and numerical modeling results, discuss special considerations for the use of synthetic rock materials in physical modeling, and address the suitability of discrete element modeling for simulating the dynamic rock-slope failure process.

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

Science.gov (United States)

Gao, Bo; Zhang, Ronghui; Lou, Xiaoming

2016-01-01

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

Numerical algebraic geometry for model selection and its application to the life sciences

KAUST Repository

Gross, Elizabeth

2016-10-12

Researchers working with mathematical models are often confronted by the related problems of parameter estimation, model validation and model selection. These are all optimization problems, well known to be challenging due to nonlinearity, non-convexity and multiple local optima. Furthermore, the challenges are compounded when only partial data are available. Here, we consider polynomial models (e.g. mass-action chemical reaction networks at steady state) and describe a framework for their analysis based on optimization using numerical algebraic geometry. Specifically, we use probability-one polynomial homotopy continuation methods to compute all critical points of the objective function, then filter to recover the global optima. Our approach exploits the geometrical structures relating models and data, and we demonstrate its utility on examples from cell signalling, synthetic biology and epidemiology.

Coevolutionary modeling in network formation

KAUST Repository

Al-Shyoukh, Ibrahim

2014-12-03

Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.

Coevolutionary modeling in network formation

KAUST Repository

Al-Shyoukh, Ibrahim; Chasparis, Georgios; Shamma, Jeff S.

2014-01-01

Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.

A network model for electrical transport in sea ice

International Nuclear Information System (INIS)

Zhu, J.; Golden, K.M.; Gully, A.; Sampson, C.

2010-01-01

Monitoring the thickness of sea ice is an important tool in assessing the impact of global warming on Earth's polar regions, and most methods of measuring ice thickness depend on detailed knowledge of its electrical properties. We develop a network model for the electrical conductivity of sea ice, which incorporates statistical measurements of the brine microstructure. The numerical simulations are in close agreement with direct measurements we made in Antarctica on the vertical conductivity of first year sea ice.

Modeling online social signed networks

Science.gov (United States)

Li, Le; Gu, Ke; Zeng, An; Fan, Ying; Di, Zengru

2018-04-01

People's online rating behavior can be modeled by user-object bipartite networks directly. However, few works have been devoted to reveal the hidden relations between users, especially from the perspective of signed networks. We analyze the signed monopartite networks projected by the signed user-object bipartite networks, finding that the networks are highly clustered with obvious community structure. Interestingly, the positive clustering coefficient is remarkably higher than the negative clustering coefficient. Then, a Signed Growing Network model (SGN) based on local preferential attachment is proposed to generate a user's signed network that has community structure and high positive clustering coefficient. Other structural properties of the modeled networks are also found to be similar to the empirical networks.

Numerical simulation of Higgs models

International Nuclear Information System (INIS)

Jaster, A.

1995-10-01

The SU(2) Higgs and the Schwinger model on the lattice were analysed. Numerical simulations of the SU(2) Higgs model were performed to study the finite temperature electroweak phase transition. With the help of the multicanonical method the distribution of an order parameter at the phase transition point was measured. This was used to obtain the order of the phase transition and the value of the interface tension with the histogram method. Numerical simulations were also performed at zero temperature to perform renormalization. The measured values for the Wilson loops were used to determine the static potential and from this the renormalized gauge coupling. The Schwinger model was simulated at different gauge couplings to analyse the properties of the Kaplan-Shamir fermions. The prediction that the mass parameter gets only multiplicative renormalization was tested and verified. (orig.)

Numerical Investigation of a Chip Printed Antenna Performances for Wireless Implantable Body Area Network Applications

Science.gov (United States)

Ramli, N. H.; Jaafar, H.; Lee, Y. S.

2018-03-01

Recently, wireless implantable body area network (WiBAN) system become an active area of research due to their various applications such as healthcare, support systems for specialized occupations and personal communications. Biomedical sensors networks mounted in the human body have drawn greater attention for health care monitoring systems. The implantable chip printed antenna for WiBAN applications is designed and the antenna performances is investigated in term of gain, efficiency, return loss, operating bandwidth and radiation pattern at different environments. This paper is presents the performances of implantable chip printed antenna in selected part of human body (hand, chest, leg, heart and skull). The numerical investigation is done by using human voxel model in built in the CST Microwave Studio Software. Results proved that the chip printed antenna is suitable to implant in the human hand model. The human hand model has less complex structure as it consists of skin, fat, muscle, blood and bone. Moreover, the antenna is implanted under the skin. Therefore the signal propagation path length to the base station at free space environment is considerably short. The antenna’s gain, efficiency and Specific Absorption Rate (SAR) are - 13.62dBi, 1.50 % and 0.12 W/kg respectively; which confirms the safety of the antenna usage. The results of the investigations can be used as guidance while designing chip implantable antenna in future.

Influence of Landscape Coverage on Measuring Spatial and Length Properties of Rock Fracture Networks: Insights from Numerical Simulation

Science.gov (United States)

Cao, Wenzhuo; Lei, Qinghua

2018-01-01

Natural fractures are ubiquitous in the Earth's crust and often deeply buried in the subsurface. Due to the difficulty in accessing to their three-dimensional structures, the study of fracture network geometry is usually achieved by sampling two-dimensional (2D) exposures at the Earth's surface through outcrop mapping or aerial photograph techniques. However, the measurement results can be considerably affected by the coverage of forests and other plant species over the exposed fracture patterns. We quantitatively study such effects using numerical simulation. We consider the scenario of nominally isotropic natural fracture systems and represent them using 2D discrete fracture network models governed by fractal and length scaling parameters. The groundcover is modelled as random patches superimposing onto the 2D fracture patterns. The effects of localisation and total coverage of landscape patches are further investigated. The fractal dimension and length exponent of the covered fracture networks are measured and compared with those of the original non-covered patterns. The results show that the measured length exponent increases with the reduced localisation and increased coverage of landscape patches, which is more evident for networks dominated by very large fractures (i.e. small underlying length exponent). However, the landscape coverage seems to have a minor impact on the fractal dimension measurement. The research findings of this paper have important implications for field survey and statistical analysis of geological systems.

The influence of numerical models on determining the drag coefficient

Directory of Open Access Journals (Sweden)

Dobeš Josef

2014-03-01

Full Text Available The paper deals with numerical modelling of body aerodynamic drag coefficient in the transition from laminar to turbulent flow regimes, where the selection of a suitable numerical model is problematic. On the basic problem of flow around a simple body – sphere selected computational models are tested. The values obtained by numerical simulations of drag coefficients of each model are compared with the graph of dependency of the drag coefficient vs. Reynolds number for a sphere. Next the dependency of Strouhal number vs. Reynolds number is evaluated, where the vortex shedding frequency values for given speed are obtained numerically and experimentally and then the values are compared for each numerical model and experiment. The aim is to specify trends for the selection of appropriate numerical model for flow around bodies problem in which the precise description of the flow field around the obstacle is used to define the acoustic noise source. Numerical modelling is performed by finite volume method using CFD code.

Novel recurrent neural network for modelling biological networks: oscillatory p53 interaction dynamics.

Science.gov (United States)

Ling, Hong; Samarasinghe, Sandhya; Kulasiri, Don

2013-12-01

Understanding the control of cellular networks consisting of gene and protein interactions and their emergent properties is a central activity of Systems Biology research. For this, continuous, discrete, hybrid, and stochastic methods have been proposed. Currently, the most common approach to modelling accurate temporal dynamics of networks is ordinary differential equations (ODE). However, critical limitations of ODE models are difficulty in kinetic parameter estimation and numerical solution of a large number of equations, making them more suited to smaller systems. In this article, we introduce a novel recurrent artificial neural network (RNN) that addresses above limitations and produces a continuous model that easily estimates parameters from data, can handle a large number of molecular interactions and quantifies temporal dynamics and emergent systems properties. This RNN is based on a system of ODEs representing molecular interactions in a signalling network. Each neuron represents concentration change of one molecule represented by an ODE. Weights of the RNN correspond to kinetic parameters in the system and can be adjusted incrementally during network training. The method is applied to the p53-Mdm2 oscillation system - a crucial component of the DNA damage response pathways activated by a damage signal. Simulation results indicate that the proposed RNN can successfully represent the behaviour of the p53-Mdm2 oscillation system and solve the parameter estimation problem with high accuracy. Furthermore, we presented a modified form of the RNN that estimates parameters and captures systems dynamics from sparse data collected over relatively large time steps. We also investigate the robustness of the p53-Mdm2 system using the trained RNN under various levels of parameter perturbation to gain a greater understanding of the control of the p53-Mdm2 system. Its outcomes on robustness are consistent with the current biological knowledge of this system. As more

Parallel numerical modeling of hybrid-dimensional compositional non-isothermal Darcy flows in fractured porous media

Science.gov (United States)

Xing, F.; Masson, R.; Lopez, S.

2017-09-01

This paper introduces a new discrete fracture model accounting for non-isothermal compositional multiphase Darcy flows and complex networks of fractures with intersecting, immersed and non-immersed fractures. The so called hybrid-dimensional model using a 2D model in the fractures coupled with a 3D model in the matrix is first derived rigorously starting from the equi-dimensional matrix fracture model. Then, it is discretized using a fully implicit time integration combined with the Vertex Approximate Gradient (VAG) finite volume scheme which is adapted to polyhedral meshes and anisotropic heterogeneous media. The fully coupled systems are assembled and solved in parallel using the Single Program Multiple Data (SPMD) paradigm with one layer of ghost cells. This strategy allows for a local assembly of the discrete systems. An efficient preconditioner is implemented to solve the linear systems at each time step and each Newton type iteration of the simulation. The numerical efficiency of our approach is assessed on different meshes, fracture networks, and physical settings in terms of parallel scalability, nonlinear convergence and linear convergence.

Numerical models of groundwater flow and transport

International Nuclear Information System (INIS)

Konikow, L.F.

1996-01-01

This chapter reviews the state-of-the-art in deterministic modeling of groundwater flow and transport processes, which can be used for interpretation of isotope data through groundwater flow analyses. Numerical models which are available for this purpose are described and their applications to complex field problems are discussed. The theoretical bases of deterministic modeling are summarized, and advantages and limitations of numerical models are described. The selection of models for specific applications and their calibration procedures are described, and results of a few illustrative case study type applications are provided. (author). 145 refs, 17 figs, 2 tabs

Numerical models of groundwater flow and transport

Energy Technology Data Exchange (ETDEWEB)

Konikow, L F [Geological Survey, Reston, VA (United States)

1996-10-01

This chapter reviews the state-of-the-art in deterministic modeling of groundwater flow and transport processes, which can be used for interpretation of isotope data through groundwater flow analyses. Numerical models which are available for this purpose are described and their applications to complex field problems are discussed. The theoretical bases of deterministic modeling are summarized, and advantages and limitations of numerical models are described. The selection of models for specific applications and their calibration procedures are described, and results of a few illustrative case study type applications are provided. (author). 145 refs, 17 figs, 2 tabs.

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

Science.gov (United States)

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

2008-02-19

The flooding of abandoned mines in the Lorraine Iron Basin (LIB) over the past 25 years has degraded the quality of the groundwater tapped for drinking water. High concentrations of dissolved sulphate have made the water unsuitable for human consumption. This problematic issue has led to the development of numerical tools to support water-resource management in mining contexts. Here we examine two modelling approaches using different numerical tools that we tested on the Saizerais flooded iron-ore mine (Lorraine, France). A first approach considers the Saizerais Mine as a network of two chemical reactors (NCR). The second approach is based on a physically distributed pipe network model (PNM) built with EPANET 2 software. This approach considers the mine as a network of pipes defined by their geometric and chemical parameters. Each reactor in the NCR model includes a detailed chemical model built to simulate quality evolution in the flooded mine water. However, in order to obtain a robust PNM, we simplified the detailed chemical model into a specific sulphate dissolution-precipitation model that is included as sulphate source/sink in both a NCR model and a pipe network model. Both the NCR model and the PNM, based on different numerical techniques, give good post-calibration agreement between the simulated and measured sulphate concentrations in the drinking-water well and overflow drift. The NCR model incorporating the detailed chemical model is useful when a detailed chemical behaviour at the overflow is needed. The PNM incorporating the simplified sulphate dissolution-precipitation model provides better information of the physics controlling the effect of flow and low flow zones, and the time of solid sulphate removal whereas the NCR model will underestimate clean-up time due to the complete mixing assumption. In conclusion, the detailed NCR model will give a first assessment of chemical processes at overflow, and in a second time, the PNM model will provide more

Statistical Models for Social Networks

NARCIS (Netherlands)

Snijders, Tom A. B.; Cook, KS; Massey, DS

2011-01-01

Statistical models for social networks as dependent variables must represent the typical network dependencies between tie variables such as reciprocity, homophily, transitivity, etc. This review first treats models for single (cross-sectionally observed) networks and then for network dynamics. For

Agent-based modeling and network dynamics

CERN Document Server

Namatame, Akira

2016-01-01

The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...

Modeling and numerical simulations of the influenced Sznajd model

Science.gov (United States)

Karan, Farshad Salimi Naneh; Srinivasan, Aravinda Ramakrishnan; Chakraborty, Subhadeep

2017-08-01

This paper investigates the effects of independent nonconformists or influencers on the behavioral dynamic of a population of agents interacting with each other based on the Sznajd model. The system is modeled on a complete graph using the master equation. The acquired equation has been numerically solved. Accuracy of the mathematical model and its corresponding assumptions have been validated by numerical simulations. Regions of initial magnetization have been found from where the system converges to one of two unique steady-state PDFs, depending on the distribution of influencers. The scaling property and entropy of the stationary system in presence of varying level of influence have been presented and discussed.

Modeling Epidemic Network Failures

DEFF Research Database (Denmark)

Ruepp, Sarah Renée; Fagertun, Anna Manolova

2013-01-01

This paper presents the implementation of a failure propagation model for transport networks when multiple failures occur resulting in an epidemic. We model the Susceptible Infected Disabled (SID) epidemic model and validate it by comparing it to analytical solutions. Furthermore, we evaluate...... the SID model’s behavior and impact on the network performance, as well as the severity of the infection spreading. The simulations are carried out in OPNET Modeler. The model provides an important input to epidemic connection recovery mechanisms, and can due to its flexibility and versatility be used...... to evaluate multiple epidemic scenarios in various network types....

Numerical Modelling Of Pumpkin Balloon Instability

Science.gov (United States)

Wakefield, D.

Tensys have been involved in the numerical formfinding and load analysis of architectural stressed membrane structures for 15 years. They have recently broadened this range of activities into the `lighter than air' field with significant involvement in aerostat and heavy-lift hybrid airship design. Since early 2004 they have been investigating pumpkin balloon instability on behalf of the NASA ULDB programme. These studies are undertaken using inTENS, an in-house finite element program suite based upon the Dynamic Relaxation solution method and developed especially for the non-linear analysis and patterning of membrane structures. The paper describes the current state of an investigation that started with a numerical simulation of the lobed cylinder problem first studied by Calladine. The influence of material properties and local geometric deformation on stability is demonstrated. A number of models of complete pumpkin balloons have then been established, including a 64-gore balloon with geometry based upon Julian Nott's Endeavour. This latter clefted dramatically upon initial inflation, a phenomenon that has been reproduced in the numerical model. Ongoing investigations include the introduction of membrane contact modelling into inTENS and correlation studies with the series of large-scale ULDB models currently in preparation.

Numerical Modeling and Mechanical Analysis of Flexible Risers

Directory of Open Access Journals (Sweden)

J. Y. Li

2015-01-01

Full Text Available ABAQUS is used to create a detailed finite element model for a 10-layer unbonded flexible riser to simulate the riser’s mechanical behavior under three load conditions: tension force and internal and external pressure. It presents a technique to create detailed finite element model and to analyze flexible risers. In FEM model, all layers are modeled separately with contact interfaces; interaction between steel trips in certain layers has been considered as well. FEM model considering contact interaction, geometric nonlinearity, and friction has been employed to accurately simulate the structural behavior of riser. The model includes the main features of the riser geometry with very little simplifying assumptions. The model was solved using a fully explicit time-integration scheme implemented in a parallel environment on an eight-processor cluster and 24 G memory computer. There is a very good agreement obtained from numerical and analytical comparisons, which validates the use of numerical model here. The results from the numerical simulation show that the numerical model takes into account various details of the riser. It has been shown that the detailed finite element model can be used to predict riser’s mechanics behavior under various load cases and bound conditions.

Numerical Modelling of Sediment Transport in Combined Sewer Systems

DEFF Research Database (Denmark)

Schlütter, Flemming

A conceptual sediment transport model has been developed. Through a case study a comparison with other numerical models is performed.......A conceptual sediment transport model has been developed. Through a case study a comparison with other numerical models is performed....

Some Remarks on Prediction of Drug-Target Interaction with Network Models.

Science.gov (United States)

Zhang, Shao-Wu; Yan, Xiao-Ying

2017-01-01

System-level understanding of the relationships between drugs and targets is very important for enhancing drug research, especially for drug function repositioning. The experimental methods used to determine drug-target interactions are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Thus, it is highly desired to develop computational methods for efficiently and effectively analyzing and detecting new drug-target interaction pairs. With the explosive growth of different types of omics data, such as genome, pharmacology, phenotypic, and other kinds of molecular networks, numerous computational approaches have been developed to predict Drug-Target Interactions (DTI). In this review, we make a survey on the recent advances in predicting drug-target interaction with network-based models from the following aspects: i) Available public data sources and benchmark datasets; ii) Drug/target similarity metrics; iii) Network construction; iv) Common network algorithms; v) Performance comparison of existing network-based DTI predictors. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Current approaches to gene regulatory network modelling

Directory of Open Access Journals (Sweden)

Brazma Alvis

2007-09-01

Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.

Network Bandwidth Utilization Forecast Model on High Bandwidth Network

Energy Technology Data Exchange (ETDEWEB)

Yoo, Wucherl; Sim, Alex

2014-07-07

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

Network bandwidth utilization forecast model on high bandwidth networks

Energy Technology Data Exchange (ETDEWEB)

Yoo, Wuchert (William) [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2015-03-30

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

Numerical modeling and optimization of machining duplex stainless steels

Directory of Open Access Journals (Sweden)

Rastee D. Koyee

2015-01-01

Full Text Available The shortcomings of the machining analytical and empirical models in combination with the industry demands have to be fulfilled. A three-dimensional finite element modeling (FEM introduces an attractive alternative to bridge the gap between pure empirical and fundamental scientific quantities, and fulfill the industry needs. However, the challenging aspects which hinder the successful adoption of FEM in the machining sector of manufacturing industry have to be solved first. One of the greatest challenges is the identification of the correct set of machining simulation input parameters. This study presents a new methodology to inversely calculate the input parameters when simulating the machining of standard duplex EN 1.4462 and super duplex EN 1.4410 stainless steels. JMatPro software is first used to model elastic–viscoplastic and physical work material behavior. In order to effectively obtain an optimum set of inversely identified friction coefficients, thermal contact conductance, Cockcroft–Latham critical damage value, percentage reduction in flow stress, and Taylor–Quinney coefficient, Taguchi-VIKOR coupled with Firefly Algorithm Neural Network System is applied. The optimization procedure effectively minimizes the overall differences between the experimentally measured performances such as cutting forces, tool nose temperature and chip thickness, and the numerically obtained ones at any specified cutting condition. The optimum set of input parameter is verified and used for the next step of 3D-FEM application. In the next stage of the study, design of experiments, numerical simulations, and fuzzy rule modeling approaches are employed to optimize types of chip breaker, insert shapes, process conditions, cutting parameters, and tool orientation angles based on many important performances. Through this study, not only a new methodology in defining the optimal set of controllable parameters for turning simulations is introduced, but also

Numerical Modelling of Flow and Settling in Secondary Settling Tanks

DEFF Research Database (Denmark)

Dahl, Claus Poulsen

This thesis discusses the development of a numerical model for the simulation of secondary settling tanks. In the first part, the status on the development of numerical models for settling tanks and a discussion of the current design practice are presented. A study of the existing numerical models...... and design practice proved a demand for further development to include numerical models in the design of settling tanks, thus improving the future settling tanks....

Numerical modeling of the thermoelectric cooler with a complementary equation for heat circulation in air gaps

Science.gov (United States)

Fang, En; Wu, Xiaojie; Yu, Yuesen; Xiu, Junrui

2017-03-01

In this paper, a numerical model is developed by combining thermodynamics with heat transfer theory. Taking inner and external multi-irreversibility into account, it is with a complementary equation for heat circulation in air gaps of a steady cooling system with commercial thermoelectric modules operating in refrigeration mode. With two modes concerned, the equation presents the heat flowing through air gaps which forms heat circulations between both sides of thermoelectric coolers (TECs). In numerical modelling, a TEC is separated as two temperature controlled constant heat flux reservoirs in a thermal resistance network. In order to obtain the parameter values, an experimental apparatus with a commercial thermoelectric cooler was built to characterize the performance of a TEC with heat source and sink assembly. At constant power dissipation, steady temperatures of heat source and both sides of the thermoelectric cooler were compared with those in a standard numerical model. The method displayed that the relationship between Φf and the ratio Φ_{c}'/Φ_{c} was linear as expected. Then, for verifying the accuracy of proposed numerical model, the data in another system were recorded. It is evident that the experimental results are in good agreement with simulation(proposed model) data at different heat transfer rates. The error is small and mainly results from the instabilities of thermal resistances with temperature change and heat flux, heat loss of the device vertical surfaces and measurements.

Numerical 3-D Modelling of Overflows

DEFF Research Database (Denmark)

Larsen, Torben; Nielsen, L.; Jensen, B.

2008-01-01

-dimensional so-called Volume of Fluid Models (VOF-models) based on the full Navier-Stokes equations (named NS3 and developed by DHI Water & Environment) As a general conclusion, the two numerical models show excellent results when compared with measurements. However, considerable errors occur when...

RMBNToolbox: random models for biochemical networks

Directory of Open Access Journals (Sweden)

Niemi Jari

2007-05-01

Full Text Available Abstract Background There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models. Results We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language. Conclusion While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.

Logic-based models in systems biology: a predictive and parameter-free network analysis method.

Science.gov (United States)

Wynn, Michelle L; Consul, Nikita; Merajver, Sofia D; Schnell, Santiago

2012-11-01

Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network's dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples.

Numerical simulation of groundwater flow in LILW Repository site:I. Groundwater flow modeling

Energy Technology Data Exchange (ETDEWEB)

Park, Koung Woo; Ji, Sung Hoon; Kim, Chun Soo; Kim, Kyoung Su [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Kim, Ji Yeon [Korea Hydro and Nuclear Power Co. Ltd., Seoul (Korea, Republic of)

2008-12-15

Based on the site characterization works in a low and intermediate level waste (LILW) repository site, the numerical simulations for groundwater flow were carried out in order to understand the groundwater flow system of repository site. To accomplish the groundwater flow modeling in the repository site, the discrete fracture network (DFN) model was constructed using the characteristics of fracture zones and background fractures. At result, the total 10 different hydraulic conductivity(K) fields were obtained from DFN model stochastically and K distributions of constructed mesh were inputted into the 10 cases of groundwater flow simulations in FEFLOW. From the total 10 numerical simulation results, the simulated groundwater levels were strongly governed by topography and the groundwater fluxes were governed by locally existed high permeable fracture zones in repository depth. Especially, the groundwater table was predicted to have several tens meters below the groundwater table compared with the undisturbed condition around disposal silo after construction of underground facilities. After closure of disposal facilities, the groundwater level would be almost recovered within 1 year and have a tendency to keep a steady state of groundwater level in 2 year.

Benchmarking Measures of Network Controllability on Canonical Graph Models

Science.gov (United States)

Wu-Yan, Elena; Betzel, Richard F.; Tang, Evelyn; Gu, Shi; Pasqualetti, Fabio; Bassett, Danielle S.

2018-03-01

The control of networked dynamical systems opens the possibility for new discoveries and therapies in systems biology and neuroscience. Recent theoretical advances provide candidate mechanisms by which a system can be driven from one pre-specified state to another, and computational approaches provide tools to test those mechanisms in real-world systems. Despite already having been applied to study network systems in biology and neuroscience, the practical performance of these tools and associated measures on simple networks with pre-specified structure has yet to be assessed. Here, we study the behavior of four control metrics (global, average, modal, and boundary controllability) on eight canonical graphs (including Erdős-Rényi, regular, small-world, random geometric, Barábasi-Albert preferential attachment, and several modular networks) with different edge weighting schemes (Gaussian, power-law, and two nonparametric distributions from brain networks, as examples of real-world systems). We observe that differences in global controllability across graph models are more salient when edge weight distributions are heavy-tailed as opposed to normal. In contrast, differences in average, modal, and boundary controllability across graph models (as well as across nodes in the graph) are more salient when edge weight distributions are less heavy-tailed. Across graph models and edge weighting schemes, average and modal controllability are negatively correlated with one another across nodes; yet, across graph instances, the relation between average and modal controllability can be positive, negative, or nonsignificant. Collectively, these findings demonstrate that controllability statistics (and their relations) differ across graphs with different topologies and that these differences can be muted or accentuated by differences in the edge weight distributions. More generally, our numerical studies motivate future analytical efforts to better understand the mathematical

Stochastic Wilson–Cowan models of neuronal network dynamics with memory and delay

International Nuclear Information System (INIS)

Goychuk, Igor; Goychuk, Andriy

2015-01-01

We consider a simple Markovian class of the stochastic Wilson–Cowan type models of neuronal network dynamics, which incorporates stochastic delay caused by the existence of a refractory period of neurons. From the point of view of the dynamics of the individual elements, we are dealing with a network of non-Markovian stochastic two-state oscillators with memory, which are coupled globally in a mean-field fashion. This interrelation of a higher-dimensional Markovian and lower-dimensional non-Markovian dynamics is discussed in its relevance to the general problem of the network dynamics of complex elements possessing memory. The simplest model of this class is provided by a three-state Markovian neuron with one refractory state, which causes firing delay with an exponentially decaying memory within the two-state reduced model. This basic model is used to study critical avalanche dynamics (the noise sustained criticality) in a balanced feedforward network consisting of the excitatory and inhibitory neurons. Such avalanches emerge due to the network size dependent noise (mesoscopic noise). Numerical simulations reveal an intermediate power law in the distribution of avalanche sizes with the critical exponent around −1.16. We show that this power law is robust upon a variation of the refractory time over several orders of magnitude. However, the avalanche time distribution is biexponential. It does not reflect any genuine power law dependence. (paper)

On the Hughes model and numerical aspects

KAUST Repository

Gomes, Diogo A.

2017-01-05

We study a crowd model proposed by R. Hughes in [11] and we describe a numerical approach to solve it. This model comprises a Fokker-Planck equation coupled with an eikonal equation with Dirichlet or Neumann data. First, we establish a priori estimates for the solutions. Second, we study radial solutions and identify a shock formation mechanism. Third, we illustrate the existence of congestion, the breakdown of the model, and the trend to the equilibrium. Finally, we propose a new numerical method and consider two examples.

Modeling and analysis of mobility management in mobile communication networks.

Science.gov (United States)

Baek, Woon Min; Yoon, Ji Hyun; Kim, Chesoong

2014-01-01

Many strategies have been proposed to reduce the mobility management cost in mobile communication networks. This paper studies the zone-based registration methods that have been adopted by most mobile communication networks. We focus on two special zone-based registration methods, called two-zone registration (2Z) and two-zone registration with implicit registration by outgoing calls (2Zi). We provide a new mathematical model to analyze the exact performance of 2Z and 2Zi. We also present various numerical results, to compare the performance of 2Zi with those of 2Z and one-zone registration (1Z), and show that 2Zi is superior to 2Z as well as 1Z in most cases.

Numerical Leak Detection in a Pipeline Network of Complex Structure with Unsteady Flow

Science.gov (United States)

Aida-zade, K. R.; Ashrafova, E. R.

2017-12-01

An inverse problem for a pipeline network of complex loopback structure is solved numerically. The problem is to determine the locations and amounts of leaks from unsteady flow characteristics measured at some pipeline points. The features of the problem include impulse functions involved in a system of hyperbolic differential equations, the absence of classical initial conditions, and boundary conditions specified as nonseparated relations between the states at the endpoints of adjacent pipeline segments. The problem is reduced to a parametric optimal control problem without initial conditions, but with nonseparated boundary conditions. The latter problem is solved by applying first-order optimization methods. Results of numerical experiments are presented.

Development and testing of a compartmentalized reaction network model for redox zones in contaminated aquifers

Science.gov (United States)

Abrams , Robert H.; Loague, Keith; Kent, Douglas B.

1998-01-01

The work reported here is the first part of a larger effort focused on efficient numerical simulation of redox zone development in contaminated aquifers. The sequential use of various electron acceptors, which is governed by the energy yield of each reaction, gives rise to redox zones. The large difference in energy yields between the various redox reactions leads to systems of equations that are extremely ill-conditioned. These equations are very difficult to solve, especially in the context of coupled fluid flow, solute transport, and geochemical simulations. We have developed a general, rational method to solve such systems where we focus on the dominant reactions, compartmentalizing them in a manner that is analogous to the redox zones that are often observed in the field. The compartmentalized approach allows us to easily solve a complex geochemical system as a function of time and energy yield, laying the foundation for our ongoing work in which we couple the reaction network, for the development of redox zones, to a model of subsurface fluid flow and solute transport. Our method (1) solves the numerical system without evoking a redox parameter, (2) improves the numerical stability of redox systems by choosing which compartment and thus which reaction network to use based upon the concentration ratios of key constituents, (3) simulates the development of redox zones as a function of time without the use of inhibition factors or switching functions, and (4) can reduce the number of transport equations that need to be solved in space and time. We show through the use of various model performance evaluation statistics that the appropriate compartment choice under different geochemical conditions leads to numerical solutions without significant error. The compartmentalized approach described here facilitates the next phase of this effort where we couple the redox zone reaction network to models of fluid flow and solute transport.

Modeling and Analysis of Cellular Networks using Stochastic Geometry: A Tutorial

KAUST Repository

Elsawy, Hesham; Salem, Ahmed Sultan; Alouini, Mohamed-Slim; Win, Moe Z.

2016-01-01

This paper presents a tutorial on stochastic geometry (SG) based analysis for cellular networks. This tutorial is distinguished by its depth with respect to wireless communication details and its focus on cellular networks. The paper starts by modeling and analyzing the baseband interference in a baseline single-tier downlink cellular network with single antenna base stations and universal frequency reuse. Then, it characterizes signal-to-interference-plus-noise-ratio (SINR) and its related performance metrics. In particular, a unified approach to conduct error probability, outage probability, and transmission rate analysis is presented. Although the main focus of the paper is on cellular networks, the presented unified approach applies for other types of wireless networks that impose interference protection around receivers. The paper then extends the unified approach to capture cellular network characteristics (e.g., frequency reuse, multiple antenna, power control, etc.). It also presents numerical examples associated with demonstrations and discussions. To this end, the paper highlights the state-of-the- art research and points out future research directions.

Modeling and Analysis of Cellular Networks using Stochastic Geometry: A Tutorial

KAUST Repository

Elsawy, Hesham

2016-11-03

This paper presents a tutorial on stochastic geometry (SG) based analysis for cellular networks. This tutorial is distinguished by its depth with respect to wireless communication details and its focus on cellular networks. The paper starts by modeling and analyzing the baseband interference in a baseline single-tier downlink cellular network with single antenna base stations and universal frequency reuse. Then, it characterizes signal-to-interference-plus-noise-ratio (SINR) and its related performance metrics. In particular, a unified approach to conduct error probability, outage probability, and transmission rate analysis is presented. Although the main focus of the paper is on cellular networks, the presented unified approach applies for other types of wireless networks that impose interference protection around receivers. The paper then extends the unified approach to capture cellular network characteristics (e.g., frequency reuse, multiple antenna, power control, etc.). It also presents numerical examples associated with demonstrations and discussions. To this end, the paper highlights the state-of-the- art research and points out future research directions.

Numerical modelling in material physics

International Nuclear Information System (INIS)

Proville, L.

2004-12-01

The author first briefly presents his past research activities: investigation of a dislocation sliding in solid solution by molecular dynamics, modelling of metal film growth by phase field and Monte Carlo kinetics, phase field model for surface self-organisation, phase field model for the Al 3 Zr alloy, calculation of anharmonic photons, mobility of bipolarons in superconductors. Then, he more precisely reports the mesoscopic modelling in phase field, and some atomistic modelling (dislocation sliding, Monte Carlo simulation of metal surface growth, anharmonic network optical spectrum modelling)

Overcoming the sign problem at finite temperature: Quantum tensor network for the orbital eg model on an infinite square lattice

Science.gov (United States)

Czarnik, Piotr; Dziarmaga, Jacek; Oleś, Andrzej M.

2017-07-01

The variational tensor network renormalization approach to two-dimensional (2D) quantum systems at finite temperature is applied to a model suffering the notorious quantum Monte Carlo sign problem—the orbital eg model with spatially highly anisotropic orbital interactions. Coarse graining of the tensor network along the inverse temperature β yields a numerically tractable 2D tensor network representing the Gibbs state. Its bond dimension D —limiting the amount of entanglement—is a natural refinement parameter. Increasing D we obtain a converged order parameter and its linear susceptibility close to the critical point. They confirm the existence of finite order parameter below the critical temperature Tc, provide a numerically exact estimate of Tc, and give the critical exponents within 1 % of the 2D Ising universality class.

In situ bioremediation: A network model of diffusion and flow in granular porous media

Energy Technology Data Exchange (ETDEWEB)

Griffiths, S.K.; Nilson, R.H.; Bradshaw, R.W.

1997-04-01

In situ bioremediation is a potentially expedient, permanent and cost- effective means of waste site decontamination. However, permeability reductions due to the transport and deposition of native fines or due to excessive microorganism populations may severely inhibit the injection of supplemental oxygen in the contamination zone. To help understand this phenomenon, we have developed a micro-mechanical network model of flow, diffusion and particle transport in granular porous materials. The model differs from most similar models in that the network is defined by particle positions in a numerically-generated particle array. The model is thus widely applicable to computing effective transport properties for both ordered and realistic random porous media. A laboratory-scale apparatus to measure permeability reductions has also been designed, built and tested.

A new fuzzy regression model based on interval-valued fuzzy neural network and its applications to management

Directory of Open Access Journals (Sweden)

Somaye Yeylaghi

2017-06-01

Full Text Available In this paper, a novel hybrid method based on interval-valued fuzzy neural network for approximate of interval-valued fuzzy regression models, is presented. The work of this paper is an expansion of the research of real fuzzy regression models. In this paper interval-valued fuzzy neural network (IVFNN can be trained with crisp and interval-valued fuzzy data. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples and compare this method with existing methods.

Advanced Numerical Model for Irradiated Concrete

Energy Technology Data Exchange (ETDEWEB)

Giorla, Alain B. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2015-03-01

In this report, we establish a numerical model for concrete exposed to irradiation to address these three critical points. The model accounts for creep in the cement paste and its coupling with damage, temperature and relative humidity. The shift in failure mode with the loading rate is also properly represented. The numerical model for creep has been validated and calibrated against different experiments in the literature [Wittmann, 1970, Le Roy, 1995]. Results from a simplified model are shown to showcase the ability of numerical homogenization to simulate irradiation effects in concrete. In future works, the complete model will be applied to the analysis of the irradiation experiments of Elleuch et al. [1972] and Kelly et al. [1969]. This requires a careful examination of the experimental environmental conditions as in both cases certain critical information are missing, including the relative humidity history. A sensitivity analysis will be conducted to provide lower and upper bounds of the concrete expansion under irradiation, and check if the scatter in the simulated results matches the one found in experiments. The numerical and experimental results will be compared in terms of expansion and loss of mechanical stiffness and strength. Both effects should be captured accordingly by the model to validate it. Once the model has been validated on these two experiments, it can be applied to simulate concrete from nuclear power plants. To do so, the materials used in these concrete must be as well characterized as possible. The main parameters required are the mechanical properties of each constituent in the concrete (aggregates, cement paste), namely the elastic modulus, the creep properties, the tensile and compressive strength, the thermal expansion coefficient, and the drying shrinkage. These can be either measured experimentally, estimated from the initial composition in the case of cement paste, or back-calculated from mechanical tests on concrete. If some

Mathematical model on malicious attacks in a mobile wireless network with clustering

International Nuclear Information System (INIS)

Haldar, Kaushik; Mishra, Bimal Kumar

2015-01-01

A mathematical model has been formulated for the analysis of a wireless epidemic on a clustered heterogeneous network. The model introduces mobility into the epidemic framework assuming that the component nodes have a tendency to be attached with a frequently visited home cluster. This underlines the inherent regularity in the mobility pattern of mobile nodes in a wireless network. The analysis focuses primarily on features that arise because of the mobility considerations compared in the larger scenario formed by the epidemic aspects. A result on the invariance of the home cluster populations with respect to time provides an important view-point of the long-term behavior of the system. The analysis also focuses on obtaining a basic threshold condition that guides the epidemic behavior of the system. Analytical as well as numerical results have also been obtained to establish the asymptotic behavior of the connected components of the network, and that of the whole network when the underlying graph turns out to be irreducible. Applications to proximity based attacks and to scenarios with high cluster density have also been outlined

Evaluation of wave runup predictions from numerical and parametric models

Science.gov (United States)

Stockdon, Hilary F.; Thompson, David M.; Plant, Nathaniel G.; Long, Joseph W.

2014-01-01

Wave runup during storms is a primary driver of coastal evolution, including shoreline and dune erosion and barrier island overwash. Runup and its components, setup and swash, can be predicted from a parameterized model that was developed by comparing runup observations to offshore wave height, wave period, and local beach slope. Because observations during extreme storms are often unavailable, a numerical model is used to simulate the storm-driven runup to compare to the parameterized model and then develop an approach to improve the accuracy of the parameterization. Numerically simulated and parameterized runup were compared to observations to evaluate model accuracies. The analysis demonstrated that setup was accurately predicted by both the parameterized model and numerical simulations. Infragravity swash heights were most accurately predicted by the parameterized model. The numerical model suffered from bias and gain errors that depended on whether a one-dimensional or two-dimensional spatial domain was used. Nonetheless, all of the predictions were significantly correlated to the observations, implying that the systematic errors can be corrected. The numerical simulations did not resolve the incident-band swash motions, as expected, and the parameterized model performed best at predicting incident-band swash heights. An assimilated prediction using a weighted average of the parameterized model and the numerical simulations resulted in a reduction in prediction error variance. Finally, the numerical simulations were extended to include storm conditions that have not been previously observed. These results indicated that the parameterized predictions of setup may need modification for extreme conditions; numerical simulations can be used to extend the validity of the parameterized predictions of infragravity swash; and numerical simulations systematically underpredict incident swash, which is relatively unimportant under extreme conditions.

Numerical modelling approach for mine backfill

Indian Academy of Sciences (India)

Muhammad Zaka Emad

2017-07-24

Jul 24, 2017 ... conditions. This paper discusses a numerical modelling strategy for modelling mine backfill material. The .... placed in an ore pass that leads the ore to the ore bin and crusher, from ... 1 year, depending on the mine plan.

System Identification, Prediction, Simulation and Control with Neural Networks

DEFF Research Database (Denmark)

Sørensen, O.

1997-01-01

a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...

Generalized Network Psychometrics : Combining Network and Latent Variable Models

NARCIS (Netherlands)

Epskamp, S.; Rhemtulla, M.; Borsboom, D.

2017-01-01

We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between

Dynamics of an epidemic model with quarantine on scale-free networks

Science.gov (United States)

Kang, Huiyan; Liu, Kaihui; Fu, Xinchu

2017-12-01

Quarantine strategies are frequently used to control or reduce the transmission risks of epidemic diseases such as SARS, tuberculosis and cholera. In this paper, we formulate a susceptible-exposed-infected-quarantined-recovered model on a scale-free network incorporating the births and deaths of individuals. Considering that the infectivity is related to the degrees of infectious nodes, we introduce quarantined rate as a function of degree into the model, and quantify the basic reproduction number, which is shown to be dependent on some parameters, such as quarantined rate, infectivity and network structures. A theoretical result further indicates the heterogeneity of networks and higher infectivity will raise the disease transmission risk while quarantine measure will contribute to the prevention of epidemic spreading. Meanwhile, the contact assumption between susceptibles and infectives may impact the disease transmission. Furthermore, we prove that the basic reproduction number serves as a threshold value for the global stability of the disease-free and endemic equilibria and the uniform persistence of the disease on the network by constructing appropriate Lyapunov functions. Finally, some numerical simulations are illustrated to perform and complement our analytical results.

Numerical Modeling of the Thomson Ring in Stationary Levitation Using FEM-Electrical Network and Newton-Raphson

Directory of Open Access Journals (Sweden)

Guzmán Juan

2015-07-01

Full Text Available There are a lot of applications of the Thomson ring: levitation of superconductor materials, power interrupters (used as actuator and elimination of electric arcs. Therefore, it is important the numerical modeling of Thomson ring. The aim of this work is to model the stationary levitation of the Thomson ring. This Thomson ring consists of a copper coil with ferromagnetic core and an aluminum ring threaded in the core. The coil is fed by a cosine voltage to ensure that the aluminum ring is in a stationary levitated position. In this situation, the state of the electromagnetic field is stable and can be used the phasor equations of the electromagnetic field. These equations are discretized using the Galerkin method in the Lagrange base space (finite element method, FEM. These equations are solved using the COMSOL software. A methodology is also described (which uses the Newton-Raphson method that obtains the separation between coil and aluminum ring. The numerical solutions of this separation are compared with experimental data. The conclusion is that the magnetic coupling of the aluminum ring on the coil can be neglected if the source voltage is high.

Distributed Sensor Network for meteorological observations and numerical weather Prediction Calculations

Directory of Open Access Journals (Sweden)

Á. Vas

2013-06-01

Full Text Available The prediction of weather generally means the solution of differential equations on the base of the measured initial conditions where the data of close and distant neighboring points are used for the calculations. It requires the maintenance of expensive weather stations and supercomputers. However, if weather stations are not only capable of measuring but can also communicate with each other, then these smart sensors can also be applied to run forecasting calculations. This applies the highest possible level of parallelization without the collection of measured data into one place. Furthermore, if more nodes are involved, the result becomes more accurate, but the computing power required from one node does not increase. Our Distributed Sensor Network for meteorological sensing and numerical weather Prediction Calculations (DSN-PC can be applied in several different areas where sensing and numerical calculations, even the solution of differential equations, are needed.

Numerical Modeling of Pressurization of Cryogenic Propellant Tank for Integrated Vehicle Fluid System

Science.gov (United States)

Majumdar, Alok K.; LeClair, Andre C.; Hedayat, Ali

2016-01-01

This paper presents a numerical model of pressurization of a cryogenic propellant tank for the Integrated Vehicle Fluid (IVF) system using the Generalized Fluid System Simulation Program (GFSSP). The IVF propulsion system, being developed by United Launch Alliance, uses boiloff propellants to drive thrusters for the reaction control system as well as to run internal combustion engines to develop power and drive compressors to pressurize propellant tanks. NASA Marshall Space Flight Center (MSFC) has been running tests to verify the functioning of the IVF system using a flight tank. GFSSP, a finite volume based flow network analysis software developed at MSFC, has been used to develop an integrated model of the tank and the pressurization system. This paper presents an iterative algorithm for converging the interface boundary conditions between different component models of a large system model. The model results have been compared with test data.

Bayesian Network Webserver: a comprehensive tool for biological network modeling.

Science.gov (United States)

Ziebarth, Jesse D; Bhattacharya, Anindya; Cui, Yan

2013-11-01

The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). ycui2@uthsc.edu. Supplementary data are available at Bioinformatics online.

Eight challenges for network epidemic models

Directory of Open Access Journals (Sweden)

Lorenzo Pellis

2015-03-01

Full Text Available Networks offer a fertile framework for studying the spread of infection in human and animal populations. However, owing to the inherent high-dimensionality of networks themselves, modelling transmission through networks is mathematically and computationally challenging. Even the simplest network epidemic models present unanswered questions. Attempts to improve the practical usefulness of network models by including realistic features of contact networks and of host–pathogen biology (e.g. waning immunity have made some progress, but robust analytical results remain scarce. A more general theory is needed to understand the impact of network structure on the dynamics and control of infection. Here we identify a set of challenges that provide scope for active research in the field of network epidemic models.

Conceptual and Numerical Models for UZ Flow and Transport

International Nuclear Information System (INIS)

Liu, H.

2000-01-01

The purpose of this Analysis/Model Report (AMR) is to document the conceptual and numerical models used for modeling of unsaturated zone (UZ) fluid (water and air) flow and solute transport processes. This is in accordance with ''AMR Development Plan for U0030 Conceptual and Numerical Models for Unsaturated Zone (UZ) Flow and Transport Processes, Rev 00''. The conceptual and numerical modeling approaches described in this AMR are used for models of UZ flow and transport in fractured, unsaturated rock under ambient and thermal conditions, which are documented in separate AMRs. This AMR supports the UZ Flow and Transport Process Model Report (PMR), the Near Field Environment PMR, and the following models: Calibrated Properties Model; UZ Flow Models and Submodels; Mountain-Scale Coupled Processes Model; Thermal-Hydrologic-Chemical (THC) Seepage Model; Drift Scale Test (DST) THC Model; Seepage Model for Performance Assessment (PA); and UZ Radionuclide Transport Models

Brand Marketing Model on Social Networks

Directory of Open Access Journals (Sweden)

Jolita Jezukevičiūtė

2014-04-01

Full Text Available The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalysis of a single case study revealed a brand marketingsocial networking tools that affect consumers the most. Basedon information analysis and methodological studies, develop abrand marketing model on social networks.

Numerical models for differential problems

CERN Document Server

Quarteroni, Alfio

2017-01-01

In this text, we introduce the basic concepts for the numerical modelling of partial differential equations. We consider the classical elliptic, parabolic and hyperbolic linear equations, but also the diffusion, transport, and Navier-Stokes equations, as well as equations representing conservation laws, saddle-point problems and optimal control problems. Furthermore, we provide numerous physical examples which underline such equations. We then analyze numerical solution methods based on finite elements, finite differences, finite volumes, spectral methods and domain decomposition methods, and reduced basis methods. In particular, we discuss the algorithmic and computer implementation aspects and provide a number of easy-to-use programs. The text does not require any previous advanced mathematical knowledge of partial differential equations: the absolutely essential concepts are reported in a preliminary chapter. It is therefore suitable for students of bachelor and master courses in scientific disciplines, an...

Econometric model as a regulatory tool in electricity distribution. Case network performance assessment model

International Nuclear Information System (INIS)

Honkapuro, S.; Lassila, J.; Viljainen, S.; Tahvanainen, K.; Partanen, J.

2004-01-01

Electricity distribution companies operate in the state of natural monopolies since building of parallel networks is not cost- effective. Monopoly companies do not have pressure from the open markets to keep their prices and costs at reasonable level. The regulation of these companies is needed to prevent the misuse of the monopoly position. Regulation is usually focused either on the profit of company or on the price of electricity. Regulation method which determines allowed income for each company with generic computation model can be seen as an econometric model. In this document, the usability of an econometric model in the regulation of electricity distribution companies is evaluated. As the special case of an econometric model, the method called Network Performance Assessment Model, NPAM (Naetnyttomodellen in Swedish), is analysed. NPAM is developed by Swedish Energy Agency (STEM) for the regulation of electricity distribution companies. Both theoretical analysis and calculations of an example network area are presented in this document to find the major directing effects of the model. The parameters of NPAM, which are used in the calculations of this research report, were dated on 30th of March 2004. These parameters were most recent ones available at the time when analysis was done. However, since NPAM have been under development, the parameters have been constantly changing. Therefore slight changes might occur in the numerical results of calculations if they were made with the latest set of parameters. However, main conclusions are same and do not depend on exact parameters

Modeling and Optimization of M/G/1-Type Queueing Networks: An Efficient Sensitivity Analysis Approach

Directory of Open Access Journals (Sweden)

Liang Tang

2010-01-01

Full Text Available A mathematical model for M/G/1-type queueing networks with multiple user applications and limited resources is established. The goal is to develop a dynamic distributed algorithm for this model, which supports all data traffic as efficiently as possible and makes optimally fair decisions about how to minimize the network performance cost. An online policy gradient optimization algorithm based on a single sample path is provided to avoid suffering from a “curse of dimensionality”. The asymptotic convergence properties of this algorithm are proved. Numerical examples provide valuable insights for bridging mathematical theory with engineering practice.

Graph Regularized Meta-path Based Transductive Regression in Heterogeneous Information Network.

Science.gov (United States)

Wan, Mengting; Ouyang, Yunbo; Kaplan, Lance; Han, Jiawei

2015-01-01

A number of real-world networks are heterogeneous information networks, which are composed of different types of nodes and links. Numerical prediction in heterogeneous information networks is a challenging but significant area because network based information for unlabeled objects is usually limited to make precise estimations. In this paper, we consider a graph regularized meta-path based transductive regression model ( Grempt ), which combines the principal philosophies of typical graph-based transductive classification methods and transductive regression models designed for homogeneous networks. The computation of our method is time and space efficient and the precision of our model can be verified by numerical experiments.

Dynamic behaviors in directed networks

International Nuclear Information System (INIS)

Park, Sung Min; Kim, Beom Jun

2006-01-01

Motivated by the abundance of directed synaptic couplings in a real biological neuronal network, we investigate the synchronization behavior of the Hodgkin-Huxley model in a directed network. We start from the standard model of the Watts-Strogatz undirected network and then change undirected edges to directed arcs with a given probability, still preserving the connectivity of the network. A generalized clustering coefficient for directed networks is defined and used to investigate the interplay between the synchronization behavior and underlying structural properties of directed networks. We observe that the directedness of complex networks plays an important role in emerging dynamical behaviors, which is also confirmed by a numerical study of the sociological game theoretic voter model on directed networks

Optimization of blanking process using neural network simulation

International Nuclear Information System (INIS)

Hambli, R.

2005-01-01

The present work describes a methodology using the finite element method and neural network simulation in order to predict the optimum punch-die clearance during sheet metal blanking processes. A damage model is used in order to describe crack initiation and propagation into the sheet. The proposed approach combines predictive finite element and neural network modeling of the leading blanking parameters. Numerical results obtained by finite element computation including damage and fracture modeling were utilized to train the developed simulation environment based on back propagation neural network modeling. The comparative study between the numerical results and the experimental ones shows the good agreement. (author)

Evolutionary neural network modeling for software cumulative failure time prediction

International Nuclear Information System (INIS)

Tian Liang; Noore, Afzel

2005-01-01

An evolutionary neural network modeling approach for software cumulative failure time prediction based on multiple-delayed-input single-output architecture is proposed. Genetic algorithm is used to globally optimize the number of the delayed input neurons and the number of neurons in the hidden layer of the neural network architecture. Modification of Levenberg-Marquardt algorithm with Bayesian regularization is used to improve the ability to predict software cumulative failure time. The performance of our proposed approach has been compared using real-time control and flight dynamic application data sets. Numerical results show that both the goodness-of-fit and the next-step-predictability of our proposed approach have greater accuracy in predicting software cumulative failure time compared to existing approaches

Dynamics of Symmetric Conserved Mass Aggregation Model on Complex Networks

Institute of Scientific and Technical Information of China (English)

HUA Da-Yin

2009-01-01

We investigate the dynamical behaviour of the aggregation process in the symmetric conserved mass aggregation model under three different topological structures. The dispersion σ(t, L) = (∑i(mi - ρ0)2/L)1/2 is defined to describe the dynamical behaviour where ρ0 is the density of particle and mi is the particle number on a site. It is found numerically that for a regular lattice and a scale-free network, σ(t, L) follows a power-law scaling σ(t, L) ～ tδ1 and σ(t, L) ～ tδ4 from a random initial condition to the stationary states, respectively. However, for a small-world network, there are two power-law scaling regimes, σ(t, L) ～ tδ2 when t＜T and a(t, L) ～ tδ3 when tT. Moreover, it is found numerically that δ2 is near to δ1 for small rewiring probability q, and δ3 hardly changes with varying q and it is almost the same as δ4. We speculate that the aggregation of the connection degree accelerates the mass aggregation in the initial relaxation stage and the existence of the long-distance interactions in the complex networks results in the acceleration of the mass aggregation when tT for the small-world networks. We also show that the relaxation time T follows a power-law scaling τ Lz and σ(t, L) in the stationary state follows a power-law σs(L) ～ Lσ for three different structures.

Energy-Aware Topology Evolution Model with Link and Node Deletion in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Xiaojuan Luo

2012-01-01

Full Text Available Based on the complex network theory, a new topological evolving model is proposed. In the evolution of the topology of sensor networks, the energy-aware mechanism is taken into account, and the phenomenon of change of the link and node in the network is discussed. Theoretical analysis and numerical simulation are conducted to explore the topology characteristics and network performance with different node energy distribution. We find that node energy distribution has the weak effect on the degree distribution P(k that evolves into the scale-free state, nodes with more energy carry more connections, and degree correlation is nontrivial disassortative. Moreover, the results show that, when nodes energy is more heterogeneous, the network is better clustered and enjoys higher performance in terms of the network efficiency and the average path length for transmitting data.

Stochastic dynamical model of a growing citation network based on a self-exciting point process.

Science.gov (United States)

Golosovsky, Michael; Solomon, Sorin

2012-08-31

We put under experimental scrutiny the preferential attachment model that is commonly accepted as a generating mechanism of the scale-free complex networks. To this end we chose a citation network of physics papers and traced the citation history of 40,195 papers published in one year. Contrary to common belief, we find that the citation dynamics of the individual papers follows the superlinear preferential attachment, with the exponent α=1.25-1.3. Moreover, we show that the citation process cannot be described as a memoryless Markov chain since there is a substantial correlation between the present and recent citation rates of a paper. Based on our findings we construct a stochastic growth model of the citation network, perform numerical simulations based on this model and achieve an excellent agreement with the measured citation distributions.

[Hardware Implementation of Numerical Simulation Function of Hodgkin-Huxley Model Neurons Action Potential Based on Field Programmable Gate Array].

Science.gov (United States)

Wang, Jinlong; Lu, Mai; Hu, Yanwen; Chen, Xiaoqiang; Pan, Qiangqiang

2015-12-01

Neuron is the basic unit of the biological neural system. The Hodgkin-Huxley (HH) model is one of the most realistic neuron models on the electrophysiological characteristic description of neuron. Hardware implementation of neuron could provide new research ideas to clinical treatment of spinal cord injury, bionics and artificial intelligence. Based on the HH model neuron and the DSP Builder technology, in the present study, a single HH model neuron hardware implementation was completed in Field Programmable Gate Array (FPGA). The neuron implemented in FPGA was stimulated by different types of current, the action potential response characteristics were analyzed, and the correlation coefficient between numerical simulation result and hardware implementation result were calculated. The results showed that neuronal action potential response of FPGA was highly consistent with numerical simulation result. This work lays the foundation for hardware implementation of neural network.

Modeling Network Interdiction Tasks

Science.gov (United States)

2015-09-17

118 xiii Table Page 36 Computation times for weighted, 100-node random networks for GAND Approach testing in Python ...in Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 38 Accuracy measures for weighted, 100-node random networks for GAND...networks [15:p. 1]. A common approach to modeling network interdiction is to formulate the problem in terms of a two-stage strategic game between two

Numerical modeling of turbulent combustion and flame spread

Energy Technology Data Exchange (ETDEWEB)

Yan Zhenghua

1999-01-01

Theoretical models have been developed to address several important aspects of numerical modeling of turbulent combustion and flame spread. The developed models include a pyrolysis model for charring and non-charring solid materials, a fast narrow band radiation property evaluation model (FASTNB) and a turbulence model for buoyant flow and flame. In the pyrolysis model, a completely new algorithm has been proposed, where a moving dual mesh concept was developed and implemented. With this new concept, it provides proper spatial resolution for both temperature and density and automatically considers the regression of the surface of the non-charring solid material during its pyrolysis. It is simple, very efficient and applicable to both charring and non-charring materials. FASTNB speeds up significantly the evaluation of narrow band spectral radiation properties and thus provides a potential of applying narrow band model in numerical simulations of practical turbulent combustion. The turbulence model was developed to improve the consideration of buoyancy effect on turbulence and turbulent transport. It was found to be simple, promising and numerically stable. It has been tested against both plane and axisymmetric thermal plumes and an axisymmetric buoyant diffusion flame. When compared with the widely used standard buoyancy-modified {kappa} - {epsilon} model, it gives significant improvement on numerical results. These developed models have been fully incorporated into CFD (Computational Fluid Dynamics) code and coupled with other CFD sub-models, including the DT (Discrete Transfer) radiation model, EDC (Eddy Dissipation Concept) combustion model, flamelet combustion model, various soot models and transpired wall function. Comprehensive numerical simulations have been carried out to study soot formation and oxidation in turbulent buoyant diffusion flames, flame heat transfer and flame spread in fires. The gas temperature and velocity, soot volume fraction, wall

Mobility Models for Next Generation Wireless Networks Ad Hoc, Vehicular and Mesh Networks

CERN Document Server

Santi, Paolo

2012-01-01

Mobility Models for Next Generation Wireless Networks: Ad Hoc, Vehicular and Mesh Networks provides the reader with an overview of mobility modelling, encompassing both theoretical and practical aspects related to the challenging mobility modelling task. It also: Provides up-to-date coverage of mobility models for next generation wireless networksOffers an in-depth discussion of the most representative mobility models for major next generation wireless network application scenarios, including WLAN/mesh networks, vehicular networks, wireless sensor networks, and

Explosion localization and characterization via infrasound using numerical modeling

Science.gov (United States)

Fee, D.; Kim, K.; Iezzi, A. M.; Matoza, R. S.; Jolly, A. D.; De Angelis, S.; Diaz Moreno, A.; Szuberla, C.

2017-12-01

Numerous methods have been applied to locate, detect, and characterize volcanic and anthropogenic explosions using infrasound. Far-field localization techniques typically use back-azimuths from multiple arrays (triangulation) or Reverse Time Migration (RTM, or back-projection). At closer ranges, networks surrounding a source may use Time Difference of Arrival (TDOA), semblance, station-pair double difference, etc. However, at volcanoes and regions with topography or obstructions that block the direct path of sound, recent studies have shown that numerical modeling is necessary to provide an accurate source location. A heterogeneous and moving atmosphere (winds) may also affect the location. The time reversal mirror (TRM) application of Kim et al. (2015) back-propagates the wavefield using a Finite Difference Time Domain (FDTD) algorithm, with the source corresponding to the location of peak convergence. Although it provides high-resolution source localization and can account for complex wave propagation, TRM is computationally expensive and limited to individual events. Here we present a new technique, termed RTM-FDTD, which integrates TRM and FDTD. Travel time and transmission loss information is computed from each station to the entire potential source grid from 3-D Green's functions derived via FDTD. The wave energy is then back-projected and stacked at each grid point, with the maximum corresponding to the likely source. We apply our method to detect and characterize thousands of explosions from Yasur Volcano, Vanuatu and Etna Volcano, Italy, which both provide complex wave propagation and multiple source locations. We compare our results with those from more traditional methods (e.g. semblance), and suggest our method is preferred as it is computationally less expensive than TRM but still integrates numerical modeling. RTM-FDTD could be applied to volcanic other anthropogenic sources at a wide variety of ranges and scenarios. Kim, K., Lees, J.M., 2015. Imaging

Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors.

Science.gov (United States)

Duardo-Sánchez, Aliuska; Munteanu, Cristian R; Riera-Fernández, Pablo; López-Díaz, Antonio; Pazos, Alejandro; González-Díaz, Humberto

2014-01-27

The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order k(th) (W(k)). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the W(k)(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated W

Effect of risk perception on epidemic spreading in temporal networks

Science.gov (United States)

Moinet, Antoine; Pastor-Satorras, Romualdo; Barrat, Alain

2018-01-01

Many progresses in the understanding of epidemic spreading models have been obtained thanks to numerous modeling efforts and analytical and numerical studies, considering host populations with very different structures and properties, including complex and temporal interaction networks. Moreover, a number of recent studies have started to go beyond the assumption of an absence of coupling between the spread of a disease and the structure of the contacts on which it unfolds. Models including awareness of the spread have been proposed, to mimic possible precautionary measures taken by individuals that decrease their risk of infection, but have mostly considered static networks. Here, we adapt such a framework to the more realistic case of temporal networks of interactions between individuals. We study the resulting model by analytical and numerical means on both simple models of temporal networks and empirical time-resolved contact data. Analytical results show that the epidemic threshold is not affected by the awareness but that the prevalence can be significantly decreased. Numerical studies on synthetic temporal networks highlight, however, the presence of very strong finite-size effects, resulting in a significant shift of the effective epidemic threshold in the presence of risk awareness. For empirical contact networks, the awareness mechanism leads as well to a shift in the effective threshold and to a strong reduction of the epidemic prevalence.

Complex Networks in Psychological Models

Science.gov (United States)

Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.

We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.

Large scale experiments as a tool for numerical model development

DEFF Research Database (Denmark)

Kirkegaard, Jens; Hansen, Erik Asp; Fuchs, Jesper

2003-01-01

Experimental modelling is an important tool for study of hydrodynamic phenomena. The applicability of experiments can be expanded by the use of numerical models and experiments are important for documentation of the validity of numerical tools. In other cases numerical tools can be applied...

Some Numerical Aspects on Crowd Motion - The Hughes Model

KAUST Repository

Gomes, Diogo A.

2016-01-06

Here, we study a crowd model proposed by R. Hughes in [5] and we describe a numerical approach to solve it. This model comprises a Fokker-Planck equation coupled with an Eikonal equation with Dirichlet or Neumann data. First, we establish a priori estimates for the solution. Second, we study radial solutions and identify a shock formation mechanism. Third, we illustrate the existence of congestion, the breakdown of the model, and the trend to the equilibrium. Finally, we propose a new numerical method and consider two numerical examples.

Modelling the effect of religion on human empathy based on an adaptive temporal-causal network model.

Science.gov (United States)

van Ments, Laila; Roelofsma, Peter; Treur, Jan

2018-01-01

Religion is a central aspect of many individuals' lives around the world, and its influence on human behaviour has been extensively studied from many different perspectives. The current study integrates a number of these perspectives into one adaptive temporal-causal network model describing the mental states involved, their mutual relations, and the adaptation of some of these relations over time due to learning. By first developing a conceptual representation of a network model based on the literature, and then formalizing this model into a numerical representation, simulations can be done for almost any kind of religion and person, showing different behaviours for persons with different religious backgrounds and characters. The focus was mainly on the influence of religion on human empathy and dis-empathy, a topic very relevant today. The developed model could be valuable for many uses, involving support for a better understanding, and even prediction, of the behaviour of religious individuals. It is illustrated for a number of different scenarios based on different characteristics of the persons and of the religion.

Generalized neurofuzzy network modeling algorithms using Bézier-Bernstein polynomial functions and additive decomposition.

Science.gov (United States)

Hong, X; Harris, C J

2000-01-01

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bézier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bézier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bézier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bézier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.

Minimum requirements for predictive pore-network modeling of solute transport in micromodels

Science.gov (United States)

Mehmani, Yashar; Tchelepi, Hamdi A.

2017-10-01

Pore-scale models are now an integral part of analyzing fluid dynamics in porous materials (e.g., rocks, soils, fuel cells). Pore network models (PNM) are particularly attractive due to their computational efficiency. However, quantitative predictions with PNM have not always been successful. We focus on single-phase transport of a passive tracer under advection-dominated regimes and compare PNM with high-fidelity direct numerical simulations (DNS) for a range of micromodel heterogeneities. We identify the minimum requirements for predictive PNM of transport. They are: (a) flow-based network extraction, i.e., discretizing the pore space based on the underlying velocity field, (b) a Lagrangian (particle tracking) simulation framework, and (c) accurate transfer of particles from one pore throat to the next. We develop novel network extraction and particle tracking PNM methods that meet these requirements. Moreover, we show that certain established PNM practices in the literature can result in first-order errors in modeling advection-dominated transport. They include: all Eulerian PNMs, networks extracted based on geometric metrics only, and flux-based nodal transfer probabilities. Preliminary results for a 3D sphere pack are also presented. The simulation inputs for this work are made public to serve as a benchmark for the research community.

Probabilistic modeling of crack networks in thermal fatigue

International Nuclear Information System (INIS)

Malesys, N.

2007-11-01

Thermal superficial crack networks have been detected in mixing zone of cooling system in nuclear power plants. Numerous experimental works have already been led to characterize initiation and propagation of these cracks. The random aspect of initiation led to propose a probabilistic model for the formation and propagation of crack networks in thermal fatigue. In a first part, uniaxial mechanical test were performed on smooth and slightly notched specimens in order to characterize the initiation of multiple cracks, their arrest due to obscuration and the coalescence phenomenon by recovery of amplification stress zones. In a second time, the probabilistic model was established under two assumptions: the continuous cracks initiation on surface, described by a Poisson point process law with threshold, and the shielding phenomenon which prohibits the initiation or the propagation of a crack if this one is in the relaxation stress zone of another existing crack. The crack propagation is assumed to follow a Paris' law based on the computation of stress intensity factors at the top and the bottom of crack. The evolution of multiaxial cracks on the surface can be followed thanks to three quantities: the shielding probability, comparable to a damage variable of the structure, the initiated crack density, representing the total number of cracks per unit surface which can be compared to experimental observations, and the propagating crack density, representing the number per unit surface of active cracks in the network. The crack sizes distribution is also computed by the model allowing an easier comparison with experimental results. (author)

a Multi Objective Model for Optimization of a Green Supply Chain Network

Science.gov (United States)

Paksoy, Turan; Özceylan, Eren; Weber, Gerhard-Wilhelm

2010-06-01

This study develops a model of a closed-loop supply chain (CLSC) network which starts with the suppliers and recycles with the decomposition centers. As a traditional network design, we consider minimizing the all transportation costs and the raw material purchasing costs. To pay attention for the green impacts, different transportation choices are presented between echelons according to their CO2 emissions. The plants can purchase different raw materials in respect of their recyclable ratios. The focuses of this paper are conducting the minimizing total CO2 emissions. Also we try to encourage the customers to use recyclable materials as an environmental performance viewpoint besides minimizing total costs. A multi objective linear programming model is developed via presenting a numerical example. We close the paper with recommendations for future researches.

Social power and opinion formation in complex networks

Science.gov (United States)

Jalili, Mahdi

2013-02-01

In this paper we investigate the effects of social power on the evolution of opinions in model networks as well as in a number of real social networks. A continuous opinion formation model is considered and the analysis is performed through numerical simulation. Social power is given to a proportion of agents selected either randomly or based on their degrees. As artificial network structures, we consider scale-free networks constructed through preferential attachment and Watts-Strogatz networks. Numerical simulations show that scale-free networks with degree-based social power on the hub nodes have an optimal case where the largest number of the nodes reaches a consensus. However, given power to a random selection of nodes could not improve consensus properties. Introducing social power in Watts-Strogatz networks could not significantly change the consensus profile.

Numerical modeling of rapidly varying flows using HEC-RAS and WSPG models.

Science.gov (United States)

Rao, Prasada; Hromadka, Theodore V

2016-01-01

The performance of two popular hydraulic models (HEC-RAS and WSPG) for modeling hydraulic jump in an open channel is investigated. The numerical solutions are compared with a new experimental data set obtained for varying channel bottom slopes and flow rates. Both the models satisfactorily predict the flow depths and location of the jump. The end results indicate that the numerical models output is sensitive to the value of chosen roughness coefficient. For this application, WSPG model is easier to implement with few input variables.

Influences of variables on ship collision probability in a Bayesian belief network model

International Nuclear Information System (INIS)

Hänninen, Maria; Kujala, Pentti

2012-01-01

The influences of the variables in a Bayesian belief network model for estimating the role of human factors on ship collision probability in the Gulf of Finland are studied for discovering the variables with the largest influences and for examining the validity of the network. The change in the so-called causation probability is examined while observing each state of the network variables and by utilizing sensitivity and mutual information analyses. Changing course in an encounter situation is the most influential variable in the model, followed by variables such as the Officer of the Watch's action, situation assessment, danger detection, personal condition and incapacitation. The least influential variables are the other distractions on bridge, the bridge view, maintenance routines and the officer's fatigue. In general, the methods are found to agree on the order of the model variables although some disagreements arise due to slightly dissimilar approaches to the concept of variable influence. The relative values and the ranking of variables based on the values are discovered to be more valuable than the actual numerical values themselves. Although the most influential variables seem to be plausible, there are some discrepancies between the indicated influences in the model and literature. Thus, improvements are suggested to the network.

Artificial neural network modelling

CERN Document Server

Samarasinghe, Sandhya

2016-01-01

This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

Global stability of an SIR model with differential infectivity on complex networks

Science.gov (United States)

Yuan, Xinpeng; Wang, Fang; Xue, Yakui; Liu, Maoxing

2018-06-01

In this paper, an SIR model with birth and death on complex networks is analyzed, where infected individuals are divided into m groups according to their infection and contact between human is treated as a scale-free social network. We obtain the basic reproduction number R0 as well as the effects of various immunization schemes. The results indicate that the disease-free equilibrium is locally and globally asymptotically stable in some conditions, otherwise disease-free equilibrium is unstable and exists an unique endemic equilibrium that is globally asymptotically stable. Our theoretical results are confirmed by numerical simulations and a promising way for infectious diseases control is suggested.

Gossip spread in social network Models

Science.gov (United States)

Johansson, Tobias

2017-04-01

Gossip almost inevitably arises in real social networks. In this article we investigate the relationship between the number of friends of a person and limits on how far gossip about that person can spread in the network. How far gossip travels in a network depends on two sets of factors: (a) factors determining gossip transmission from one person to the next and (b) factors determining network topology. For a simple model where gossip is spread among people who know the victim it is known that a standard scale-free network model produces a non-monotonic relationship between number of friends and expected relative spread of gossip, a pattern that is also observed in real networks (Lind et al., 2007). Here, we study gossip spread in two social network models (Toivonen et al., 2006; Vázquez, 2003) by exploring the parameter space of both models and fitting them to a real Facebook data set. Both models can produce the non-monotonic relationship of real networks more accurately than a standard scale-free model while also exhibiting more realistic variability in gossip spread. Of the two models, the one given in Vázquez (2003) best captures both the expected values and variability of gossip spread.

Numerical Modelling and Measurement in a Test Secondary Settling Tank

DEFF Research Database (Denmark)

Dahl, C.; Larsen, Torben; Petersen, O.

1994-01-01

sludge. Phenomena as free and hindered settling and the Bingham plastic characteristic of activated sludge suspensions are included in the numerical model. Further characterisation and test tank experiments are described. The characterisation experiments were designed to measure calibration parameters...... for model description of settling and density differences. In the test tank experiments, flow velocities and suspended sludge concentrations were measured with different tank inlet geomotry and hydraulic and sludge loads. The test tank experiments provided results for the calibration of the numerical model......A numerical model and measurements of flow and settling in activated sludge suspension is presented. The numerical model is an attempt to describe the complex and interrelated hydraulic and sedimentation phenomena by describing the turbulent flow field and the transport/dispersion of suspended...

Numerical modeling and the physical basis of seismic discriminants

International Nuclear Information System (INIS)

Denny, M.D.

1993-01-01

Accurate seismic event discrimination is critical to detection of nuclear explosions. Numerical modeling applied to seismic event discrimination can lead to increased reliability of proliferation detection. It is particularly applicable to error budgeting and to understanding explosion and earthquake phenomenologies. There also is a need for minimum requirements to validate the models used in numerical modeling

Mathematical and numerical foundations of turbulence models and applications

CERN Document Server

Chacón Rebollo, Tomás

2014-01-01

With applications to climate, technology, and industry, the modeling and numerical simulation of turbulent flows are rich with history and modern relevance. The complexity of the problems that arise in the study of turbulence requires tools from various scientific disciplines, including mathematics, physics, engineering, and computer science. Authored by two experts in the area with a long history of collaboration, this monograph provides a current, detailed look at several turbulence models from both the theoretical and numerical perspectives. The k-epsilon, large-eddy simulation, and other models are rigorously derived and their performance is analyzed using benchmark simulations for real-world turbulent flows. Mathematical and Numerical Foundations of Turbulence Models and Applications is an ideal reference for students in applied mathematics and engineering, as well as researchers in mathematical and numerical fluid dynamics. It is also a valuable resource for advanced graduate students in fluid dynamics,...

Numerical modelling of tidal circulation and studies on salinity distribution in Mandovi and Zuari estuaries

Digital Repository Service at National Institute of Oceanography (India)

Manoj, N.T.

the characteris- tics of tidal propagation and salinity distribution in the Mandovi and Zuari estuaries using a hybrid network numerical model. One of the main objective was to study the longi- tudinal variation of tides during dry and wet seasons and freshwater... of the Zuari and Mandovi estuaries in relation to tides. Proceedings of the Indian Academy of Sciences, 72:68–80, 1970. P. V. Dehadrai and R. M. S. Bhargava. Seasonal organic production in relation to en- vironmental features in Mandovi and Zuari estuaries, Goa...

New numerical approximation for solving fractional delay differential equations of variable order using artificial neural networks

Science.gov (United States)

Zúñiga-Aguilar, C. J.; Coronel-Escamilla, A.; Gómez-Aguilar, J. F.; Alvarado-Martínez, V. M.; Romero-Ugalde, H. M.

2018-02-01

In this paper, we approximate the solution of fractional differential equations with delay using a new approach based on artificial neural networks. We consider fractional differential equations of variable order with the Mittag-Leffler kernel in the Liouville-Caputo sense. With this new neural network approach, an approximate solution of the fractional delay differential equation is obtained. Synaptic weights are optimized using the Levenberg-Marquardt algorithm. The neural network effectiveness and applicability were validated by solving different types of fractional delay differential equations, linear systems with delay, nonlinear systems with delay and a system of differential equations, for instance, the Newton-Leipnik oscillator. The solution of the neural network was compared with the analytical solutions and the numerical simulations obtained through the Adams-Bashforth-Moulton method. To show the effectiveness of the proposed neural network, different performance indices were calculated.

Fundamentals of Numerical Modelling of Casting Processes

DEFF Research Database (Denmark)

Hattel, Jesper Henri; Pryds, Nini; Thorborg, Jesper

Fundamentals of Numerical Modelling of Casting Processes comprises a thorough presentation of the basic phenomena that need to be addressed in numerical simulation of casting processes. The main philosophy of the book is to present the topics in view of their physical meaning, whenever possible......, rather than relying strictly on mathematical formalism. The book, aimed both at the researcher and the practicing engineer, as well as the student, is naturally divided into four parts. Part I (Chapters 1-3) introduces the fundamentals of modelling in a 1-dimensional framework. Part II (Chapter 4...

Numerical modeling techniques for flood analysis

Science.gov (United States)

Anees, Mohd Talha; Abdullah, K.; Nawawi, M. N. M.; Ab Rahman, Nik Norulaini Nik; Piah, Abd. Rahni Mt.; Zakaria, Nor Azazi; Syakir, M. I.; Mohd. Omar, A. K.

2016-12-01

Topographic and climatic changes are the main causes of abrupt flooding in tropical areas. It is the need to find out exact causes and effects of these changes. Numerical modeling techniques plays a vital role for such studies due to their use of hydrological parameters which are strongly linked with topographic changes. In this review, some of the widely used models utilizing hydrological and river modeling parameters and their estimation in data sparse region are discussed. Shortcomings of 1D and 2D numerical models and the possible improvements over these models through 3D modeling are also discussed. It is found that the HEC-RAS and FLO 2D model are best in terms of economical and accurate flood analysis for river and floodplain modeling respectively. Limitations of FLO 2D in floodplain modeling mainly such as floodplain elevation differences and its vertical roughness in grids were found which can be improve through 3D model. Therefore, 3D model was found to be more suitable than 1D and 2D models in terms of vertical accuracy in grid cells. It was also found that 3D models for open channel flows already developed recently but not for floodplain. Hence, it was suggested that a 3D model for floodplain should be developed by considering all hydrological and high resolution topographic parameter's models, discussed in this review, to enhance the findings of causes and effects of flooding.

Piecewise Polynomial Aggregation as Preprocessing for Data Numerical Modeling

Science.gov (United States)

Dobronets, B. S.; Popova, O. A.

2018-05-01

Data aggregation issues for numerical modeling are reviewed in the present study. The authors discuss data aggregation procedures as preprocessing for subsequent numerical modeling. To calculate the data aggregation, the authors propose using numerical probabilistic analysis (NPA). An important feature of this study is how the authors represent the aggregated data. The study shows that the offered approach to data aggregation can be interpreted as the frequency distribution of a variable. To study its properties, the density function is used. For this purpose, the authors propose using the piecewise polynomial models. A suitable example of such approach is the spline. The authors show that their approach to data aggregation allows reducing the level of data uncertainty and significantly increasing the efficiency of numerical calculations. To demonstrate the degree of the correspondence of the proposed methods to reality, the authors developed a theoretical framework and considered numerical examples devoted to time series aggregation.

Analytical and numerical modeling of sandbanks dynamics

NARCIS (Netherlands)

Idier, Deborah; Astruc, Dominique

2003-01-01

Linear and nonlinear behavior of large-scale underwater bedform patterns like sandbanks are studied using linear stability analysis and numerical modeling. The model is based on depth-integrated hydrodynamics equations with a quadratic bottom friction law and a bed load sediment transport model

Stability and bifurcation of a discrete BAM neural network model with delays

International Nuclear Information System (INIS)

Zheng Baodong; Zhang Yang; Zhang Chunrui

2008-01-01

A map modelling a discrete bidirectional associative memory neural network with delays is investigated. Its dynamics is studied in terms of local analysis and Hopf bifurcation analysis. By analyzing the associated characteristic equation, its linear stability is investigated and Hopf bifurcations are demonstrated. It is found that there exist Hopf bifurcations when the delay passes a sequence of critical values. Numerical simulation is performed to verify the analytical results

Physical and numerical modeling of Joule-heated melters

Energy Technology Data Exchange (ETDEWEB)

Eyler, L.L.; Skarda, R.J.; Crowder, R.S. III; Trent, D.S.; Reid, C.R.; Lessor, D.L.

1985-10-01

The Joule-heated ceramic-lined melter is an integral part of the high level waste immobilization process under development by the US Department of Energy. Scaleup and design of this waste glass melting furnace requires an understanding of the relationships between melting cavity design parameters and the furnace performance characteristics such as mixing, heat transfer, and electrical requirements. Developing empirical models of these relationships through actual melter testing with numerous designs would be a very costly and time consuming task. Additionally, the Pacific Northwest Laboratory (PNL) has been developing numerical models that simulate a Joule-heated melter for analyzing melter performance. This report documents the method used and results of this modeling effort. Numerical modeling results are compared with the more conventional, physical modeling results to validate the approach. Also included are the results of numerically simulating an operating research melter at PNL. Physical Joule-heated melters modeling results used for qualiying the simulation capabilities of the melter code included: (1) a melter with a single pair of electrodes and (2) a melter with a dual pair (two pairs) of electrodes. The physical model of the melter having two electrode pairs utilized a configuration with primary and secondary electrodes. The principal melter parameters (the ratio of power applied to each electrode pair, modeling fluid depth, electrode spacing) were varied in nine tests of the physical model during FY85. Code predictions were made for five of these tests. Voltage drops, temperature field data, and electric field data varied in their agreement with the physical modeling results, but in general were judged acceptable. 14 refs., 79 figs., 17 tabs.

Physical and numerical modeling of Joule-heated melters

International Nuclear Information System (INIS)

Eyler, L.L.; Skarda, R.J.; Crowder, R.S. III; Trent, D.S.; Reid, C.R.; Lessor, D.L.

1985-10-01

The Joule-heated ceramic-lined melter is an integral part of the high level waste immobilization process under development by the US Department of Energy. Scaleup and design of this waste glass melting furnace requires an understanding of the relationships between melting cavity design parameters and the furnace performance characteristics such as mixing, heat transfer, and electrical requirements. Developing empirical models of these relationships through actual melter testing with numerous designs would be a very costly and time consuming task. Additionally, the Pacific Northwest Laboratory (PNL) has been developing numerical models that simulate a Joule-heated melter for analyzing melter performance. This report documents the method used and results of this modeling effort. Numerical modeling results are compared with the more conventional, physical modeling results to validate the approach. Also included are the results of numerically simulating an operating research melter at PNL. Physical Joule-heated melters modeling results used for qualiying the simulation capabilities of the melter code included: (1) a melter with a single pair of electrodes and (2) a melter with a dual pair (two pairs) of electrodes. The physical model of the melter having two electrode pairs utilized a configuration with primary and secondary electrodes. The principal melter parameters (the ratio of power applied to each electrode pair, modeling fluid depth, electrode spacing) were varied in nine tests of the physical model during FY85. Code predictions were made for five of these tests. Voltage drops, temperature field data, and electric field data varied in their agreement with the physical modeling results, but in general were judged acceptable. 14 refs., 79 figs., 17 tabs

Climate change and high-resolution whole-building numerical modelling

NARCIS (Netherlands)

Blocken, B.J.E.; Briggen, P.M.; Schellen, H.L.; Hensen, J.L.M.

2010-01-01

This paper briefly discusses the need of high-resolution whole-building numerical modelling in the context of climate change. High-resolution whole-building numerical modelling can be used for detailed analysis of the potential consequences of climate change on buildings and to evaluate remedial

Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models

Science.gov (United States)

Mills, Kyle; Tamblyn, Isaac

2018-03-01

We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbor energy of the 4 ×4 Ising model. Using its success at this task, we motivate the study of the larger 8 ×8 Ising model, showing that the deep neural network can learn the nearest-neighbor Ising Hamiltonian after only seeing a vanishingly small fraction of configuration space. Additionally, we show that the neural network has learned both the energy and magnetization operators with sufficient accuracy to replicate the low-temperature Ising phase transition. We then demonstrate the ability of the neural network to learn other spin models, teaching the convolutional deep neural network to accurately predict the long-range interaction of a screened Coulomb Hamiltonian, a sinusoidally attenuated screened Coulomb Hamiltonian, and a modified Potts model Hamiltonian. In the case of the long-range interaction, we demonstrate the ability of the neural network to recover the phase transition with equivalent accuracy to the numerically exact method. Furthermore, in the case of the long-range interaction, the benefits of the neural network become apparent; it is able to make predictions with a high degree of accuracy, and do so 1600 times faster than a CUDA-optimized exact calculation. Additionally, we demonstrate how the neural network succeeds at these tasks by looking at the weights learned in a simplified demonstration.

Models of Coupled Settlement and Habitat Networks for Biodiversity Conservation: Conceptual Framework, Implementation and Potential Applications

Directory of Open Access Journals (Sweden)

Maarten J. van Strien

2018-04-01

Full Text Available Worldwide, the expansion of settlement and transport infrastructure is one of the most important proximate as well as ultimate causes of biodiversity loss. As much as every modern human society depends on a network of settlements that is well-connected by transport infrastructure (i.e., settlement network, animal and plant species depend on networks of habitats between which they can move (i.e., habitat networks. However, changes to a settlement network in a region often threaten the integrity of the region's habitat networks. Determining plans and policy to prevent these threats is made difficult by the numerous interactions and feedbacks that exist between and within the settlement and habitat networks. Mathematical models of coupled settlement and habitat networks can help us understand the dynamics of this social-ecological system. Yet, few attempts have been made to develop such mathematical models. In this paper, we promote the development of models of coupled settlement and habitat networks for biodiversity conservation. First, we present a conceptual framework of key variables that are ideally considered when operationalizing the coupling of settlement and habitat networks. In this framework, we first describe important network-internal interactions by differentiating between the structural (i.e., relating to purely physical conditions determining the suitability of a location for living or movement and functional (i.e., relating to the actual presence, abundance or movement of people or other organisms properties of either network. We then describe the main one-way influences that a settlement network can exert on the habitat networks and vice versa. Second, we give several recommendations for the mathematical modeling of coupled settlement and habitat networks and present several existing modeling approaches (e.g., habitat network models and land-use transport interaction models that could be used for this purpose. Lastly, we elaborate

Brand Marketing Model on Social Networks

OpenAIRE

Jolita Jezukevičiūtė; Vida Davidavičienė

2014-01-01

The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalys...

Spreading dynamics of an e-commerce preferential information model on scale-free networks

Science.gov (United States)

Wan, Chen; Li, Tao; Guan, Zhi-Hong; Wang, Yuanmei; Liu, Xiongding

2017-02-01

In order to study the influence of the preferential degree and the heterogeneity of underlying networks on the spread of preferential e-commerce information, we propose a novel susceptible-infected-beneficial model based on scale-free networks. The spreading dynamics of the preferential information are analyzed in detail using the mean-field theory. We determine the basic reproductive number and equilibria. The theoretical analysis indicates that the basic reproductive number depends mainly on the preferential degree and the topology of the underlying networks. We prove the global stability of the information-elimination equilibrium. The permanence of preferential information and the global attractivity of the information-prevailing equilibrium are also studied in detail. Some numerical simulations are presented to verify the theoretical results.

A model of coauthorship networks

Science.gov (United States)

Zhou, Guochang; Li, Jianping; Xie, Zonglin

2017-10-01

A natural way of representing the coauthorship of authors is to use a generalization of graphs known as hypergraphs. A random geometric hypergraph model is proposed here to model coauthorship networks, which is generated by placing nodes on a region of Euclidean space randomly and uniformly, and connecting some nodes if the nodes satisfy particular geometric conditions. Two kinds of geometric conditions are designed to model the collaboration patterns of academic authorities and basic researches respectively. The conditions give geometric expressions of two causes of coauthorship: the authority and similarity of authors. By simulation and calculus, we show that the forepart of the degree distribution of the network generated by the model is mixture Poissonian, and the tail is power-law, which are similar to these of some coauthorship networks. Further, we show more similarities between the generated network and real coauthorship networks: the distribution of cardinalities of hyperedges, high clustering coefficient, assortativity, and small-world property

Numerical simulation of the heat extraction in EGS with thermal-hydraulic-mechanical coupling method based on discrete fractures model

International Nuclear Information System (INIS)

Sun, Zhi-xue; Zhang, Xu; Xu, Yi; Yao, Jun; Wang, Hao-xuan; Lv, Shuhuan; Sun, Zhi-lei; Huang, Yong; Cai, Ming-yu; Huang, Xiaoxue

2017-01-01

The Enhanced Geothermal System (EGS) creates an artificial geothermal reservoir by hydraulic fracturing which allows heat transmission through the fractures by the circulating fluids as they extract heat from Hot Dry Rock (HDR). The technique involves complex thermal–hydraulic–mechanical (THM) coupling process. A numerical approach is presented in this paper to simulate and analyze the heat extraction process in EGS. The reservoir is regarded as fractured porous media consisting of rock matrix blocks and discrete fracture networks. Based on thermal non-equilibrium theory, the mathematical model of THM coupling process in fractured rock mass is used. The proposed model is validated by comparing it with several analytical solutions. An EGS case from Cooper Basin, Australia is simulated with 2D stochastically generated fracture model to study the characteristics of fluid flow, heat transfer and mechanical response in geothermal reservoir. The main parameters controlling the outlet temperature of EGS are also studied by sensitivity analysis. The results shows the significance of taking into account the THM coupling effects when investigating the efficiency and performance of EGS. - Highlights: • EGS reservoir comprising discrete fracture networks and matrix rock is modeled. • A THM coupling model is proposed for simulating the heat extraction in EGS. • The numerical model is validated by comparing with several analytical solutions. • A case study is presented for understanding the main characteristics of EGS. • The THM coupling effects are shown to be significant factors to EGS's running performance.

NUMERICAL MODELLING OF THE SOIL BEHAVIOUR BY USING NEWLY DEVELOPED ADVANCED MATERIAL MODEL

Directory of Open Access Journals (Sweden)

Jan Veselý

2017-02-01

Full Text Available This paper describes a theoretical background, implementation and validation of the newly developed Jardine plastic hardening-softening model (JPHS model, which can be used for numerical modelling of the soils behaviour. Although the JPHS model is based on the elasto-plastic theory, like the Mohr-Coulomb model that is widely used in geotechnics, it contains some improvements, which removes the main disadvantages of the MC model. The presented model is coupled with an isotopically hardening and softening law, non-linear elastic stress-strain law, non-associated elasto-plastic material description and a cap yield surface. The validation of the model is done by comparing the numerical results with real measured data from the laboratory tests and by testing of the model on the real project of the tunnel excavation. The 3D numerical analysis is performed and the comparison between the JPHS, Mohr-Coulomb, Modified Cam-Clay, Hardening small strain model and monitoring in-situ data is done.

Ekofisk chalk: core measurements, stochastic reconstruction, network modeling and simulation

Energy Technology Data Exchange (ETDEWEB)

Talukdar, Saifullah

2002-07-01

This dissertation deals with (1) experimental measurements on petrophysical, reservoir engineering and morphological properties of Ekofisk chalk, (2) numerical simulation of core flood experiments to analyze and improve relative permeability data, (3) stochastic reconstruction of chalk samples from limited morphological information, (4) extraction of pore space parameters from the reconstructed samples, development of network model using pore space information, and computation of petrophysical and reservoir engineering properties from network model, and (5) development of 2D and 3D idealized fractured reservoir models and verification of the applicability of several widely used conventional up scaling techniques in fractured reservoir simulation. Experiments have been conducted on eight Ekofisk chalk samples and porosity, absolute permeability, formation factor, and oil-water relative permeability, capillary pressure and resistivity index are measured at laboratory conditions. Mercury porosimetry data and backscatter scanning electron microscope images have also been acquired for the samples. A numerical simulation technique involving history matching of the production profiles is employed to improve the relative permeability curves and to analyze hysteresis of the Ekofisk chalk samples. The technique was found to be a powerful tool to supplement the uncertainties in experimental measurements. Porosity and correlation statistics obtained from backscatter scanning electron microscope images are used to reconstruct microstructures of chalk and particulate media. The reconstruction technique involves a simulated annealing algorithm, which can be constrained by an arbitrary number of morphological parameters. This flexibility of the algorithm is exploited to successfully reconstruct particulate media and chalk samples using more than one correlation functions. A technique based on conditional simulated annealing has been introduced for exact reproduction of vuggy

An evolving model for the lodging-service network in a tourism destination

Science.gov (United States)

Hernández, Juan M.; González-Martel, Christian

2017-09-01

Tourism is a complex dynamic system including multiple actors which are related each other composing an evolving social network. This paper presents a growing model that explains how part of the supply components in a tourism system forms a social network. Specifically, the lodgings and services in a destination are the network nodes and a link between them appears if a representative tourist hosted in the lodging visits/consumes the service during his/her stay. The specific link between both categories are determined by a random and preferential attachment rule. The analytic results show that the long-term degree distribution of services follows a shifted power-law distribution. The numerical simulations show slight disagreements with the theoretical results in the case of the one-mode degree distribution of services, due to the low order of convergence to zero of X-motifs. The model predictions are compared with real data coming from a popular tourist destination in Gran Canaria, Spain, showing a good agreement between analytical and empirical data for the degree distribution of services. The theoretical model was validated assuming four type of perturbations in the real data.

Brand marketing model on social networks

OpenAIRE

Jezukevičiūtė, Jolita; Davidavičienė, Vida

2014-01-01

Paper analyzes the brand and its marketing solutions on social networks. This analysis led to the creation of improved brand marketing model on social networks, which will contribute to the rapid and cheap organization brand recognition, increase competitive advantage and enhance consumer loyalty. Therefore, the brand and a variety of social networks are becoming a hot research area for brand marketing model on social networks. The world‘s most successful brand marketing models exploratory an...

Global stability of a susceptible-infected-susceptible epidemic model on networks with individual awareness

International Nuclear Information System (INIS)

Li Ke-Zan; Xu Zhong-Pu; Zhu Guang-Hu; Ding Yong

2014-01-01

Recent research results indicate that individual awareness can play an important influence on epidemic spreading in networks. By local stability analysis, a significant conclusion is that the embedded awareness in an epidemic network can increase its epidemic threshold. In this paper, by using limit theory and dynamical system theory, we further give global stability analysis of a susceptible-infected-susceptible (SIS) epidemic model on networks with awareness. Results show that the obtained epidemic threshold is also a global stability condition for its endemic equilibrium, which implies the embedded awareness can enhance the epidemic threshold globally. Some numerical examples are presented to verify the theoretical results. (interdisciplinary physics and related areas of science and technology)

Numerical solution of conservation equations in the transient model for the system thermal - hydraulics in the Korsar computer code

International Nuclear Information System (INIS)

Yudov, Y.V.

2001-01-01

The functional part of the KORSAR computer code is based on the computational unit for the reactor system thermal-hydraulics and other thermal power systems with water cooling. The two-phase flow dynamics of the thermal-hydraulic network is modelled by KORSAR in one-dimensional two-fluid (non-equilibrium and nonhomogeneous) approximation with the same pressure of both phases. Each phase is characterized by parameters averaged over the channel sections, and described by the conservation equations for mass, energy and momentum. The KORSAR computer code relies upon a novel approach to mathematical modelling of two-phase dispersed-annular flows. This approach allows a two-fluid model to differentiate the effects of the liquid film and droplets in the gas core on the flow characteristics. A semi-implicit numerical scheme has been chosen for deriving discrete analogs the conservation equations in KORSAR. In the semi-implicit numerical scheme, solution of finite-difference equations is reduced to the problem of determining the pressure field at a new time level. For the one-channel case, the pressure field is found from the solution of a system of linear algebraic equations by using the tri-diagonal matrix method. In the branched network calculation, the matrix of coefficients in the equations describing the pressure field is no longer tri-diagonal but has a sparseness structure. In this case, the system of linear equations for the pressure field can be solved with any of the known classical methods. Such an approach is implemented in the existing best-estimate thermal-hydraulic computer codes (TRAC, RELAP5, etc.) For the KORSAR computer code, we have developed a new non-iterative method for calculating the pressure field in the network of any topology. This method is based on the tri-diagonal matrix method and performs well when solving the thermal-hydraulic network problems. (author)

Dynamic hydraulic models to study sedimentation in drinking water networks in detail

Directory of Open Access Journals (Sweden)

I. W. M. Pothof

2012-12-01

Full Text Available Sedimentation in drinking water networks can lead to discolouration complaints. A sufficient criterion to prevent sedimentation in the Dutch drinking water networks is a daily maximum velocity of 0.25 m s⁻¹. Flushing experiments have shown that this criterion is a sufficient condition for a clean network, but not a necessary condition. Drinking water networks include many locations with a maximum velocity well below 0.25 m s⁻¹ without accumulated sediments. Other criteria need to be developed to predict which locations are susceptible to sedimentation and to prevent sedimentation in future networks. More distinctive criteria are helpful to prioritise flushing operations and to prevent water quality complaints.

The authors use three different numerical modelling approaches – quasi-steady, rigid column and water hammer – with a temporal discretisation of 1 s in order to assess the influence of unsteady flows on the wall shear stress, causing resuspension of sediment particles. The model predictions are combined with results from flushing experiments in the drinking water distribution system of Purmerend, the Netherlands. The waterhammer model does not result in essentially different flow distribution patterns, compared to the rigid column and quasi-steady modelling approach. The extra information from the waterhammer model is a velocity oscillation of approximately 0.02 m s⁻¹ around the quasi-steady solution. The presence of stagnation zones and multiple flow direction reversals seem to be interesting new parameters to predict sediment accumulation, which are consistent with the observed turbidity data and theoretical considerations on critical shear stresses.

Control and Optimization of Network in Networked Control System

Directory of Open Access Journals (Sweden)

Wang Zhiwen

2014-01-01

Full Text Available In order to avoid quality of performance (QoP degradation resulting from quality of service (QoS, the solution to network congestion from the point of control theory, which marks departure of our results from the existing methods, is proposed in this paper. The congestion and bandwidth are regarded as state and control variables, respectively; then, the linear time-invariant (LTI model between congestion state and bandwidth of network is established. Consequently, linear quadratic method is used to eliminate the network congestion by allocating bandwidth dynamically. At last, numerical simulation results are given to illustrate the effectiveness of this modeling approach.

Random matrix approach to plasmon resonances in the random impedance network model of disordered nanocomposites

Science.gov (United States)

Olekhno, N. A.; Beltukov, Y. M.

2018-05-01

Random impedance networks are widely used as a model to describe plasmon resonances in disordered metal-dielectric and other two-component nanocomposites. In the present work, the spectral properties of resonances in random networks are studied within the framework of the random matrix theory. We have shown that the appropriate ensemble of random matrices for the considered problem is the Jacobi ensemble (the MANOVA ensemble). The obtained analytical expressions for the density of states in such resonant networks show a good agreement with the results of numerical simulations in a wide range of metal filling fractions 0

Prediction of residual stress for dissimilar metals welding at nuclear power plants using fuzzy neural network models

International Nuclear Information System (INIS)

Na, Man Gyun; Kim, Jin Weon; Lim, Dong Hyuk

2007-01-01

A fuzzy neural network model is presented to predict residual stress for dissimilar metal welding under various welding conditions. The fuzzy neural network model, which consists of a fuzzy inference system and a neuronal training system, is optimized by a hybrid learning method that combines a genetic algorithm to optimize the membership function parameters and a least squares method to solve the consequent parameters. The data of finite element analysis are divided into four data groups, which are split according to two end-section constraints and two prediction paths. Four fuzzy neural network models were therefore applied to the numerical data obtained from the finite element analysis for the two end-section constraints and the two prediction paths. The fuzzy neural network models were trained with the aid of a data set prepared for training (training data), optimized by means of an optimization data set and verified by means of a test data set that was different (independent) from the training data and the optimization data. The accuracy of fuzzy neural network models is known to be sufficiently accurate for use in an integrity evaluation by predicting the residual stress of dissimilar metal welding zones

Target-Centric Network Modeling

DEFF Research Database (Denmark)

Mitchell, Dr. William L.; Clark, Dr. Robert M.

In Target-Centric Network Modeling: Case Studies in Analyzing Complex Intelligence Issues, authors Robert Clark and William Mitchell take an entirely new approach to teaching intelligence analysis. Unlike any other book on the market, it offers case study scenarios using actual intelligence...... reporting formats, along with a tested process that facilitates the production of a wide range of analytical products for civilian, military, and hybrid intelligence environments. Readers will learn how to perform the specific actions of problem definition modeling, target network modeling......, and collaborative sharing in the process of creating a high-quality, actionable intelligence product. The case studies reflect the complexity of twenty-first century intelligence issues by dealing with multi-layered target networks that cut across political, economic, social, technological, and military issues...

Numerical modeling of fires on gas pipelines

International Nuclear Information System (INIS)

Zhao Yang; Jianbo Lai; Lu Liu

2011-01-01

When natural gas is released through a hole on a high-pressure pipeline, it disperses in the atmosphere as a jet. A jet fire will occur when the leaked gas meets an ignition source. To estimate the dangerous area, the shape and size of the fire must be known. The evolution of the jet fire in air is predicted by using a finite-volume procedure to solve the flow equations. The model is three-dimensional, elliptic and calculated by using a compressibility corrected version of the k - ξ turbulence model, and also includes a probability density function/laminar flamelet model of turbulent non-premixed combustion process. Radiation heat transfer is described using an adaptive version of the discrete transfer method. The model is compared with the experiments about a horizontal jet fire in a wind tunnel in the literature with success. The influence of wind and jet velocity on the fire shape has been investigated. And a correlation based on numerical results for predicting the stoichiometric flame length is proposed. - Research highlights: → We developed a model to predict the evolution of turbulent jet diffusion flames. → Measurements of temperature distributions match well with the numerical predictions. → A correlation has been proposed to predict the stoichiometric flame length. → Buoyancy effects are higher in the numerical results. → The radiative heat loss is bigger in the experimental results.

A Complex Network Approach to Distributional Semantic Models.

Directory of Open Access Journals (Sweden)

Akira Utsumi

Full Text Available A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models.

QSAR modelling using combined simple competitive learning networks and RBF neural networks.

Science.gov (United States)

Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

2018-04-01

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

NUMERICAL PREDICTION MODELS FOR AIR POLLUTION BY MOTOR VEHICLE EMISSIONS

Directory of Open Access Journals (Sweden)

M. M. Biliaiev

2016-12-01

Full Text Available Purpose. Scientific work involves: 1 development of 3D numerical models that allow calculating the process of air pollution by motor vehicles emissions; 2 creation of models which would allow predicting the air pollution level in urban areas. Methodology. To solve the problem upon assessing the level of air pollution by motor vehicles emissions fundamental equations of aerodynamics and mass transfer are used. For the solution of differential equations of aerodynamics and mass transfer finite-difference methods are used. For the numerical integration of the equation for the velocity potential the method of conditional approximations is applied. The equation for the velocity potential written in differential form, splits into two equations, where at each step of splitting an unknown value of the velocity potential is determined by an explicit scheme of running computation, while the difference scheme is implicit one. For the numerical integration of the emissions dispersion equation in the atmosphere applies the implicit alternating-triangular difference scheme of splitting. Emissions from the road are modeled by a series of point sources of given intensity. Developed numerical models form is the basis of the created software package. Findings. 3D numerical models were developed; they belong to the class of «diagnostic models». These models take into account main physical factors that influence the process of dispersion of harmful substances in the atmosphere when emissions from vehicles in the city occur. Based on the constructed numerical models the computational experiment was conducted to assess the level of air pollution in the street. Originality. Authors have developed numerical models that allow to calculate the 3D aerodynamics of the wind flow in urban areas and the process of mass transfer emissions from the highway. Calculations to determine the area of contamination, which is formed near the buildings, located along the highway were

Comparisons of complex network based models and real train flow model to analyze Chinese railway vulnerability

International Nuclear Information System (INIS)

Ouyang, Min; Zhao, Lijing; Hong, Liu; Pan, Zhezhe

2014-01-01

Recently numerous studies have applied complex network based models to study the performance and vulnerability of infrastructure systems under various types of attacks and hazards. But how effective are these models to capture their real performance response is still a question worthy of research. Taking the Chinese railway system as an example, this paper selects three typical complex network based models, including purely topological model (PTM), purely shortest path model (PSPM), and weight (link length) based shortest path model (WBSPM), to analyze railway accessibility and flow-based vulnerability and compare their results with those from the real train flow model (RTFM). The results show that the WBSPM can produce the train routines with 83% stations and 77% railway links identical to the real routines and can approach the RTFM the best for railway vulnerability under both single and multiple component failures. The correlation coefficient for accessibility vulnerability from WBSPM and RTFM under single station failures is 0.96 while it is 0.92 for flow-based vulnerability; under multiple station failures, where each station has the same failure probability fp, the WBSPM can produce almost identical vulnerability results with those from the RTFM under almost all failures scenarios when fp is larger than 0.62 for accessibility vulnerability and 0.86 for flow-based vulnerability

Lattice Boltzmann model for numerical relativity.

Science.gov (United States)

Ilseven, E; Mendoza, M

2016-02-01

In the Z4 formulation, Einstein equations are written as a set of flux conservative first-order hyperbolic equations that resemble fluid dynamics equations. Based on this formulation, we construct a lattice Boltzmann model for numerical relativity and validate it with well-established tests, also known as "apples with apples." Furthermore, we find that by increasing the relaxation time, we gain stability at the cost of losing accuracy, and by decreasing the lattice spacings while keeping a constant numerical diffusivity, the accuracy and stability of our simulations improve. Finally, in order to show the potential of our approach, a linear scaling law for parallelization with respect to number of CPU cores is demonstrated. Our model represents the first step in using lattice kinetic theory to solve gravitational problems.

Numerical modelling of river morphodynamics: Latest developments and remaining challenges

Science.gov (United States)

Siviglia, Annunziato; Crosato, Alessandra

2016-07-01

Numerical morphodynamic models provide scientific frameworks for advancing our understanding of river systems. The research on involved topics is an important and socially relevant undertaking regarding our environment. Nowadays numerical models are used for different purposes, from answering questions about basic morphodynamic research to managing complex river engineering problems. Due to increasing computer power and the development of advanced numerical techniques, morphodynamic models are now more and more used to predict the bed patterns evolution to a broad spectrum of spatial and temporal scales. The development and the success of application of such models are based upon a wide range of disciplines from applied mathematics for the numerical solution of the equations to geomorphology for the physical interpretation of the results. In this light we organized this special issue (SI) soliciting multidisciplinary contributions which encompass any aspect needed for the development and applications of such models. Most of the papers in the SI stem from contributions to session HS9.5/GM7.11 on numerical modelling and experiments in river morphodynamics at the European Geosciences Union (EGU) General Assembly held in Vienna, April 27th to May 2nd 2014.

Modeling and Analysis of Epidemic Diffusion within Small-World Network

Directory of Open Access Journals (Sweden)

Ming Liu

2012-01-01

Full Text Available To depict the rule of epidemic diffusion, two different models, the Susceptible-Exposure-Infected-Recovered-Susceptible (SEIRS model and the Susceptible-Exposure-Infected-Quarantine-Recovered-Susceptible (SEIQRS model, are proposed and analyzed within small-world network in this paper. Firstly, the epidemic diffusion models are constructed with mean-filed theory, and condition for the occurrence of disease diffusion is explored. Then, the existence and global stability of the disease-free equilibrium and the endemic equilibrium for these two complex epidemic systems are proved by differential equations knowledge and Routh-Hurwiz theory. At last, a numerical example which includes key parameters analysis and critical topic discussion is presented to test how well the proposed two models may be applied in practice. These works may provide some guidelines for decision makers when coping with epidemic diffusion controlling problems.

Modeling Renewable Penertration Using a Network Economic Model

Science.gov (United States)

Lamont, A.

2001-03-01

This paper evaluates the accuracy of a network economic modeling approach in designing energy systems having renewable and conventional generators. The network approach models the system as a network of processes such as demands, generators, markets, and resources. The model reaches a solution by exchanging prices and quantity information between the nodes of the system. This formulation is very flexible and takes very little time to build and modify models. This paper reports an experiment designing a system with photovoltaic and base and peak fossil generators. The level of PV penetration as a function of its price and the capacities of the fossil generators were determined using the network approach and using an exact, analytic approach. It is found that the two methods agree very closely in terms of the optimal capacities and are nearly identical in terms of annual system costs.

Numerical modelling in non linear fracture mechanics

Directory of Open Access Journals (Sweden)

Viggo Tvergaard

2007-07-01

Full Text Available Some numerical studies of crack propagation are based on using constitutive models that accountfor damage evolution in the material. When a critical damage value has been reached in a materialpoint, it is natural to assume that this point has no more carrying capacity, as is done numerically in the elementvanish technique. In the present review this procedure is illustrated for micromechanically based materialmodels, such as a ductile failure model that accounts for the nucleation and growth of voids to coalescence, and a model for intergranular creep failure with diffusive growth of grain boundary cavities leading to micro-crack formation. The procedure is also illustrated for low cycle fatigue, based on continuum damage mechanics. In addition, the possibility of crack growth predictions for elastic-plastic solids using cohesive zone models to represent the fracture process is discussed.

Flower, a Model for the Analysis of Hydraulic Networks and Processes

CERN Document Server

Bottura, L

2003-01-01

We have developed in the past years a model that describes hydraulic networks that are typical of the cryogenic interconnection of superconducting magnets. The original model, called Flower, was used mostly to provide consistent boundary conditions for the operation of a magnet. The main limitations were associated with the number and nature of modelling elements available, and to the maximum size of the model that could be solved. Here we present an improvement of the model largely relaxing the above limitations by the addition of new modelling elements, such as parallel flow heat exchangers, and by a significant improvement in the numerics of the solver, using sparse matrix storage and solution techniques. We finally show a typical application to the case of a magnet quench in the LHC string.

Numerical modeling of the thermomechanical behavior of networks of underground galleries for the storage of the radioactive waste: approach by homogenization; Modelisation numerique du comportement thermomecanique de reseaux de galeries souterraines pour le stockage des dechets radioactifs: Approche par homogeneisation

Energy Technology Data Exchange (ETDEWEB)

Zokimila, P

2005-10-15

Deep geological disposal is one of the privileged options for the storage of High Level radioactive waste. A good knowledge of the behavior and properties of the potential geological formations as well as theirs evolution in time under the effect of the stress change induced by a possible installation of storage is required. The geological formation host will be subjected to mechanical and thermal solicitations due respectively to the excavation of the disposal tunnels and the release of heat of the canisters of radioactive waste. These thermomechanical solicitations will generate a stress relief in the host layer and disposal tunnels deformations as well as the extension of the damaged zones (EDZ) could cause local and global instabilities. This work aims to develop calculation methods to optimize numerical modeling of the thermoelastic behavior of the disposal at a large scale and to evaluate thermomechanical disturbance induced by storage on the geological formation host. Accordingly, after a presentation of the state of knowledge on the thermomechanical aspects of the rocks related to deep storage, of numerical modeling 2D and 3D of the thermoelastic behavior of individual disposal tunnel and a network of tunnels were carried out by a discrete approach. However, this classical approach is penalizing to study the global behavior of disposal storage. To mitigate that, an approach of numerical modeling, based on homogenization of periodic structures, was proposed. Formulations as numerical procedures were worked out to calculate the effective thermoelastic behavior of an equivalent heterogeneous structure. The model, obtained by this method, was validated with existing methods of homogenization such as the self-consistent model, as well as the Hashin-Shtrikman bounds. The comparison between the effective thermoelastic behavior and current thermoelastic behavior of reference showed a good coherence of the results. For an application to deep geological storage, the

Impact of mussel bioengineering on fine-grained sediment dynamics in a coastal lagoon: a numerical modelling investigation

DEFF Research Database (Denmark)

Forsberg, Pernille Louise; Lumborg, Ulrik; Bundgaard, Klavs

2017-01-01

Rødsand lagoon in southeast Denmark is a non-tidal coastal lagoon. It is home to a wide range of marine flora and fauna and part of the Natura 2000 network. An increase in turbidity through elevated levels of suspended sediment concentration (SSC) within the lagoon may affect the ecosystem health...... due to reduced light penetration. Increasing SSC levels within Rødsand lagoon could be caused by increasing storm intensity or by a sediment spill from dredging activities west of the lagoon in relation to the planned construction of the Fehmarnbelt fixed link between Denmark and Germany. The aim...... of the study was to investigate the impact of a mussel reef on sediment import and SSC in a semi-enclosed lagoon through the development of a bioengineering modelling application that makes it possible to include the filtrating effect of mussels in a numerical model of the lagoonal system. The numerical...

Stochastic geometry model for multi-channel fog radio access networks

KAUST Repository

Emara, Mostafa

2017-06-29

Cache-enabled base station (BS) densification, denoted as a fog radio access network (F-RAN), is foreseen as a key component of 5G cellular networks. F-RAN enables storing popular files at the network edge (i.e., BS caches), which empowers local communication and alleviates traffic congestions at the core/backhaul network. The hitting probability, which is the probability of successfully transmitting popular files request from the network edge, is a fundamental key performance indicator (KPI) for F-RAN. This paper develops a scheduling aware mathematical framework, based on stochastic geometry, to characterize the hitting probability of F-RAN in a multi-channel environment. To this end, we assess and compare the performance of two caching distribution schemes, namely, uniform caching and Zipf caching. The numerical results show that the commonly used single channel environment leads to pessimistic assessment for the hitting probability of F-RAN. Furthermore, the numerical results manifest the superiority of the Zipf caching scheme and quantify the hitting probability gains in terms of the number of channels and cache size.

The Bass diffusion model on networks with correlations and inhomogeneous advertising

Science.gov (United States)

Bertotti, M. L.; Brunner, J.; Modanese, G.

2016-09-01

The Bass model, which is an effective forecasting tool for innovation diffusion based on large collections of empirical data, assumes an homogeneous diffusion process. We introduce a network structure into this model and we investigate numerically the dynamics in the case of networks with link density $P(k)=c/k^\\gamma$, where $k=1, \\ldots , N$. The resulting curve of the total adoptions in time is qualitatively similar to the homogeneous Bass curve corresponding to a case with the same average number of connections. The peak of the adoptions, however, tends to occur earlier, particularly when $\\gamma$ and $N$ are large (i.e., when there are few hubs with a large maximum number of connections). Most interestingly, the adoption curve of the hubs anticipates the total adoption curve in a predictable way, with peak times which can be, for instance when $N=100$, between 10% and 60% of the total adoptions peak. This may allow to monitor the hubs for forecasting purposes. We also consider the case of networks with assortative and disassortative correlations and a case of inhomogeneous advertising where the publicity terms are "targeted" on the hubs while maintaining their total cost constant.

Dynamics of epidemic spreading model with drug-resistant variation on scale-free networks

Science.gov (United States)

Wan, Chen; Li, Tao; Zhang, Wu; Dong, Jing

2018-03-01

Considering the influence of the virus' drug-resistant variation, a novel SIVRS (susceptible-infected-variant-recovered-susceptible) epidemic spreading model with variation characteristic on scale-free networks is proposed in this paper. By using the mean-field theory, the spreading dynamics of the model is analyzed in detail. Then, the basic reproductive number R0 and equilibriums are derived. Studies show that the existence of disease-free equilibrium is determined by the basic reproductive number R0. The relationships between the basic reproductive number R0, the variation characteristic and the topology of the underlying networks are studied in detail. Furthermore, our studies prove the global stability of the disease-free equilibrium, the permanence of epidemic and the global attractivity of endemic equilibrium. Numerical simulations are performed to confirm the analytical results.

Hydrologic behavior of fracture networks

International Nuclear Information System (INIS)

Long, J.C.S.; Endo, H.K.; Karasaki, K.; Pyrak, L.; MacLean, P.; Witherspoon, P.A.

1985-01-01

This paper reviews recent research on the nature of flow and transport in discontinuous fracture networks. The hydrologic behavior of these networks has been examined using two- and three-dimensional numerical models. The numerical models represent random realizations of fracture networks based on statistical field measurements of fracture geometry and equivalent hydraulic aperture. The authors have compared the flux and mechanical transported behavior of these networks to the behavior of equivalent continua. In this way they were able to determine whether a given fracture network could be modeled as an equivalent porous media in both flux and advective transport studies. They have examined departures from porous media behavior both as a function of interconnectivity and heterogeneity. Parameter studies have revealed behavior patterns such as: given a fracture frequency that can be measured in the field, porous media like behavior and the magnitude of permeability are both enhanced if the fractures are longer and the standard deviation of fracture permeabilities is smaller. The behavior of well tests in fractured networks has been modeled and compared to a new analytical well test solution which accounts for the early time dominance of the fractures intersecting the well. Finally, a three-dimensional fracture flow model has been constructed which assumes fractures are randomly located discs. This model has been constructed which assumes fractures are randomly located discs. This model uses a semi-analytical solution for flow such that it is relatively easy to use the model as a tool for stochastic analysis. 13 references, 12 figures

Numerical modeling of foam flows

International Nuclear Information System (INIS)

Cheddadi, Ibrahim

2010-01-01

Liquid foam flows are involved in numerous applications, e.g. food and cosmetics industries, oil extraction, nuclear decontamination. Moreover, their study leads to fundamental knowledge: as it is easier to manipulate and analyse, foam is used as a model material to understand the flow of emulsions, polymers, pastes, or cell aggregates, all of which display both solid and liquid behaviour. Systematic experiments performed by Francois Graner et al. provide precise data that emphasize the non Newtonian properties of the foam. Meanwhile, Pierre Saramito proposed a visco-elasto-plastic continuous tensorial model, akin to predict the behaviour of the foam. The goal of this thesis is to understand this complex behaviour, using these two elements. We have built and validated a resolution algorithm based on a bidimensional finite elements methods. The numerical solutions are in excellent agreement with the spatial distribution of all measured quantities, and confirm the predictive capabilities of the model. The dominant parameters have been identified and we evidenced the fact that the viscous, elastic, and plastic contributions to the flow have to be treated simultaneously in a tensorial formalism. We provide a substantial contribution to the understanding of foams and open the path to realistic simulations of complex VEP flows for industrial applications. (author)

Summary of Numerical Modeling for Underground Nuclear Test Monitoring Symposium

International Nuclear Information System (INIS)

Taylor, S.R.; Kamm, J.R.

1993-01-01

This document contains the Proceedings of the Numerical Modeling for Underground Nuclear Test Monitoring Symposium held in Durango, Colorado on March 23-25, 1993. The symposium was sponsored by the Office of Arms Control and Nonproliferation of the United States Department of Energy and hosted by the Source Region Program of Los Alamos National Laboratory. The purpose of the meeting was to discuss state-of-the-art advances in numerical simulations of nuclear explosion phenomenology for the purpose of test ban monitoring. Another goal of the symposium was to promote discussion between seismologists and explosion source-code calculators. Presentation topics include the following: numerical model fits to data, measurement and characterization of material response models, applications of modeling to monitoring problems, explosion source phenomenology, numerical simulations and seismic sources

Research on the model of home networking

Science.gov (United States)

Yun, Xiang; Feng, Xiancheng

2007-11-01

It is the research hotspot of current broadband network to combine voice service, data service and broadband audio-video service by IP protocol to transport various real time and mutual services to terminal users (home). Home Networking is a new kind of network and application technology which can provide various services. Home networking is called as Digital Home Network. It means that PC, home entertainment equipment, home appliances, Home wirings, security, illumination system were communicated with each other by some composing network technology, constitute a networking internal home, and connect with WAN by home gateway. It is a new network technology and application technology, and can provide many kinds of services inside home or between homes. Currently, home networking can be divided into three kinds: Information equipment, Home appliances, Communication equipment. Equipment inside home networking can exchange information with outer networking by home gateway, this information communication is bidirectional, user can get information and service which provided by public networking by using home networking internal equipment through home gateway connecting public network, meantime, also can get information and resource to control the internal equipment which provided by home networking internal equipment. Based on the general network model of home networking, there are four functional entities inside home networking: HA, HB, HC, and HD. (1) HA (Home Access) - home networking connects function entity; (2) HB (Home Bridge) Home networking bridge connects function entity; (3) HC (Home Client) - Home networking client function entity; (4) HD (Home Device) - decoder function entity. There are many physical ways to implement four function entities. Based on theses four functional entities, there are reference model of physical layer, reference model of link layer, reference model of IP layer and application reference model of high layer. In the future home network

Artificial Neural Networks for Reducing Computational Effort in Active Truncated Model Testing of Mooring Lines

DEFF Research Database (Denmark)

Christiansen, Niels Hørbye; Voie, Per Erlend Torbergsen; Høgsberg, Jan Becker

2015-01-01

simultaneously, this method is very demanding in terms of numerical efficiency and computational power. Therefore, this method has not yet proved to be feasible. It has recently been shown how a hybrid method combining classical numerical models and artificial neural networks (ANN) can provide a dramatic...... prior to the experiment and with a properly trained ANN it is no problem to obtain accurate simulations much faster than real time-without any need for large computational capacity. The present study demonstrates how this hybrid method can be applied to the active truncated experiments yielding a system...

Network models in economics and finance

CERN Document Server

Pardalos, Panos; Rassias, Themistocles

2014-01-01

Using network models to investigate the interconnectivity in modern economic systems allows researchers to better understand and explain some economic phenomena. This volume presents contributions by known experts and active researchers in economic and financial network modeling. Readers are provided with an understanding of the latest advances in network analysis as applied to economics, finance, corporate governance, and investments. Moreover, recent advances in market network analysis  that focus on influential techniques for market graph analysis are also examined. Young researchers will find this volume particularly useful in facilitating their introduction to this new and fascinating field. Professionals in economics, financial management, various technologies, and network analysis, will find the network models presented in this book beneficial in analyzing the interconnectivity in modern economic systems.

Numerical modelling of the HAB Energy Buoy: Stage 1

DEFF Research Database (Denmark)

Kurniawan, Adi

This report presents the results of the first stage of the project "Numerical modelling of the HAB Energy Buoy". The objectives of this stage are to develop a numerical model of the HAB Energy Buoy, a self-reacting wave energy device consisting of two heaving bodies, and to investigate a number...... and a summary of the main findings is presented. A numerical model of the HAB Energy Buoy has been developed in the frequency domain using two alternative formulations of the equations of motion. The model is capable of predicting the power capture, motion response, and power take-off loads of the device...... configuration are imposed to give a more realistic prediction of the power capture and help ensure a fair comparison. Recommendations with regard to the HAB design are finally suggested....

Numerical modelling of the jet nozzle enrichment process

International Nuclear Information System (INIS)

Vercelli, P.

1983-01-01

A numerical model was developed for the simulation of the isotopic enrichment produced by the jet nozzle process. The flow was considered stationary and under ideal gas conditions. The model calculates, for any position of the skimmer piece: (a) values of radial mass concentration profiles for each isotopic species and (b) values of elementary separation effect (Σ sub(A)) and uranium cut (theta). The comparison of the numerical results obtained with the experimental values given in the literature proves the validity of the present work as an initial step in the modelling of the process. (Author) [pt

A simple model for the evolution of a non-Abelian cosmic string network

Energy Technology Data Exchange (ETDEWEB)

Cella, G. [Istituto Nazionale di Fisica Nucleare, sez. Pisa, Largo Bruno Pontecorvo 3, 56126 Pisa (Italy); Pieroni, M., E-mail: giancarlo.cella@pi.infn.it, E-mail: mauro.pieroni@apc.univ-paris7.fr [AstroParticule et Cosmologie, Université Paris Diderot, CNRS, CEA, Observatoire de Paris, Sorbonne Paris Cité, F-75205 Paris Cedex 13 (France)

2016-06-01

In this paper we present the results of numerical simulations intended to study the behavior of non-Abelian cosmic strings networks. In particular we are interested in discussing the variations in the asymptotic behavior of the system as we variate the number of generators for the topological defects. A simple model which allows for cosmic strings is presented and its lattice discretization is discussed. The evolution of the generated cosmic string networks is then studied for different values for the number of generators for the topological defects. Scaling solution appears to be approached in most cases and we present an argument to justify the lack of scaling for the residual cases.

On the Hughes model and numerical aspects

KAUST Repository

Gomes, Diogo A.; Machado Velho, Roberto

2017-01-01

We study a crowd model proposed by R. Hughes in [11] and we describe a numerical approach to solve it. This model comprises a Fokker-Planck equation coupled with an eikonal equation with Dirichlet or Neumann data. First, we establish a priori

Complex networks under dynamic repair model

Science.gov (United States)

Chaoqi, Fu; Ying, Wang; Kun, Zhao; Yangjun, Gao

2018-01-01

Invulnerability is not the only factor of importance when considering complex networks' security. It is also critical to have an effective and reasonable repair strategy. Existing research on network repair is confined to the static model. The dynamic model makes better use of the redundant capacity of repaired nodes and repairs the damaged network more efficiently than the static model; however, the dynamic repair model is complex and polytropic. In this paper, we construct a dynamic repair model and systematically describe the energy-transfer relationships between nodes in the repair process of the failure network. Nodes are divided into three types, corresponding to three structures. We find that the strong coupling structure is responsible for secondary failure of the repaired nodes and propose an algorithm that can select the most suitable targets (nodes or links) to repair the failure network with minimal cost. Two types of repair strategies are identified, with different effects under the two energy-transfer rules. The research results enable a more flexible approach to network repair.

The stability of the extended model of hypothalamic-pituitary-adrenal axis examined by stoichiometric network analysis

Science.gov (United States)

Marković, V. M.; Čupić, Ž.; Ivanović, A.; Kolar-Anić, Lj.

2011-12-01

Stoichiometric network analysis (SNA) represents a powerful mathematical tool for stability analysis of complex stoichiometric networks. Recently, the important improvement of the method has been made, according to which instability relations can be entirely expressed via reaction rates, instead of thus far used, in general case undefined, current rates. Such an improved SNA methodology was applied to the determination of exact instability conditions of the extended model of the hypothalamic-pituitary-adrenal (HPA) axis, a neuroendocrinological system, whose hormone concentrations exert complex oscillatory evolution. For emergence of oscillations, the Hopf bifurcation condition was utilized. Instability relations predicted by SNA showed good correlation with numerical simulation data of the HPA axis model.

Adaptive-network models of collective dynamics

Science.gov (United States)

Zschaler, G.

2012-09-01

Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom. In this work, which is essentially my PhD thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system's collective and long-term behaviour by applying tools from dynamical systems theory. We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge

Linear approximation model network and its formation via ...

Indian Academy of Sciences (India)

To overcome the deficiency of `local model network' (LMN) techniques, an alternative `linear approximation model' (LAM) network approach is proposed. Such a network models a nonlinear or practical system with multiple linear models fitted along operating trajectories, where individual models are simply networked ...

Comparing numerically exact and modelled static friction

Directory of Open Access Journals (Sweden)

Krengel Dominik

2017-01-01

Full Text Available Currently there exists no mechanically consistent “numerically exact” implementation of static and dynamic Coulomb friction for general soft particle simulations with arbitrary contact situations in two or three dimension, but only along one dimension. We outline a differential-algebraic equation approach for a “numerically exact” computation of friction in two dimensions and compare its application to the Cundall-Strack model in some test cases.

Modelling the structure of complex networks

DEFF Research Database (Denmark)

Herlau, Tue

networks has been independently studied as mathematical objects in their own right. As such, there has been both an increased demand for statistical methods for complex networks as well as a quickly growing mathematical literature on the subject. In this dissertation we explore aspects of modelling complex....... The next chapters will treat some of the various symmetries, representer theorems and probabilistic structures often deployed in the modelling complex networks, the construction of sampling methods and various network models. The introductory chapters will serve to provide context for the included written...

Concave switching in single-hop and multihop networks

NARCIS (Netherlands)

Walton, N.

2015-01-01

Switched queueing networks model wireless networks, input-queued switches, and numerous other networked communications systems. We consider an (\\(\\alpha ,g\\))-switch policy; these policies provide a generalization of the MaxWeight policies of Tassiulas and Ephremides (IEEE Trans Autom Control

The Liquid Film Flow with Evaporation: Numerical Modelling

Directory of Open Access Journals (Sweden)

Rezanova Ekaterina

2016-01-01

Full Text Available The flow of thin liquid layer on an inclined substrate is investigated numerically. The mathematical modelling is based on the Oberbeck-Boussinesq equations and the generalized conditions on the thermocapillary boundary simplified during the parametrical analysis. In the framework of the long-wave approximation the evolution equation which determines the thickness of the liquid layer in the case of moderate Reynolds numbers is derived. The results of numerical modelling of the liquid flow with evaporation at the interface are obtained.

Polynomial model inversion control: numerical tests and applications

OpenAIRE

Novara, Carlo

2015-01-01

A novel control design approach for general nonlinear systems is described in this paper. The approach is based on the identification of a polynomial model of the system to control and on the on-line inversion of this model. Extensive simulations are carried out to test the numerical efficiency of the approach. Numerical examples of applicative interest are presented, concerned with control of the Duffing oscillator, control of a robot manipulator and insulin regulation in a type 1 diabetic p...

Numerical model for mapping of complex hydrogeological conditions: the Chmielnik area (South Poland) case study

Science.gov (United States)

Buszta, Kamila; Szklarczyk, Tadeusz; Malina, Grzegorz

2017-04-01

Detailed analysis of hydrogeological conditions at a study area is the basis for characterising adjacent groundwater circulation systems. It is also an essential element during executing hydrogeological documentations. The goal of this work was to reconstruct on a numerical model natural groundwater circulation systems of the studied area located within the municipality of Chmielnik in the region of Kielce (South Poland). The area is characterized by a complex geological structure, which along with the existing hydrographic network, makes the scheme of groundwater circulation complicated and difficult to map on a numerical model. The studied area is situated at the border of three geological units: on the North - the extended portion of the Palaeozoic Swietokrzyskie Mountains (mainly Devonian and Permian), in the center - the S-W part of the Mesozoic Margin of the Swietokrzyskie Mountains, and on the South - a marginal zone of the Carpathian Foredeep. The whole area belongs to the Vistula river basin, and it includes catchments of its left tributaries: the Nida and Czarna Staszowska rivers. Based on the collected field and archival hydrogeological, hydrological and sozological data a conceptual model was built, under which a numerical model of groundwater flow was developed using the specialized software - Visual MODFLOW. The numerical model maps the five-layer groundwater circulation system in conjunction with surface watercourses. Such division reflects appropriately the variability of hydrogeological parameters within the geological structures. Two principal and exploited aquifers comprise: a fractured-porous Neogene and fractured Upper Jurassic formations. The external model borders are based primarily on surface watercourses and locally on watersheds. The modelled area of 130 km2 was divided into square grids of 50 m. The model consists of 275 rows and 277 columns. Each of five layers was simulated with the same number of active blocks. In the construction of

Spatial Epidemic Modelling in Social Networks

Science.gov (United States)

Simoes, Joana Margarida

2005-06-01

The spread of infectious diseases is highly influenced by the structure of the underlying social network. The target of this study is not the network of acquaintances, but the social mobility network: the daily movement of people between locations, in regions. It was already shown that this kind of network exhibits small world characteristics. The model developed is agent based (ABM) and comprehends a movement model and a infection model. In the movement model, some assumptions are made about its structure and the daily movement is decomposed into four types: neighborhood, intra region, inter region and random. The model is Geographical Information Systems (GIS) based, and uses real data to define its geometry. Because it is a vector model, some optimization techniques were used to increase its efficiency.

Modeling of fluctuating reaction networks

International Nuclear Information System (INIS)

Lipshtat, A.; Biham, O.

2004-01-01

Full Text:Various dynamical systems are organized as reaction networks, where the population size of one component affects the populations of all its neighbors. Such networks can be found in interstellar surface chemistry, cell biology, thin film growth and other systems. I cases where the populations of reactive species are large, the network can be modeled by rate equations which provide all reaction rates within mean field approximation. However, in small systems that are partitioned into sub-micron size, these populations strongly fluctuate. Under these conditions rate equations fail and the master equation is needed for modeling these reactions. However, the number of equations in the master equation grows exponentially with the number of reactive species, severely limiting its feasibility for complex networks. Here we present a method which dramatically reduces the number of equations, thus enabling the incorporation of the master equation in complex reaction networks. The method is examplified in the context of reaction network on dust grains. Its applicability for genetic networks will be discussed. 1. Efficient simulations of gas-grain chemistry in interstellar clouds. Azi Lipshtat and Ofer Biham, Phys. Rev. Lett. 93 (2004), 170601. 2. Modeling of negative autoregulated genetic networks in single cells. Azi Lipshtat, Hagai B. Perets, Nathalie Q. Balaban and Ofer Biham, Gene: evolutionary genomics (2004), In press

Numerical Modelling and Prediction of Erosion Induced by Hydrodynamic Cavitation

Science.gov (United States)

Peters, A.; Lantermann, U.; el Moctar, O.

2015-12-01

The present work aims to predict cavitation erosion using a numerical flow solver together with a new developed erosion model. The erosion model is based on the hypothesis that collapses of single cavitation bubbles near solid boundaries form high velocity microjets, which cause sonic impacts with high pressure amplitudes damaging the surface. The erosion model uses information from a numerical Euler-Euler flow simulation to predict erosion sensitive areas and assess the erosion aggressiveness of the flow. The obtained numerical results were compared to experimental results from tests of an axisymmetric nozzle.

An Energy-Efficient Cluster-Based Vehicle Detection on Road Network Using Intention Numeration Method

Directory of Open Access Journals (Sweden)

Deepa Devasenapathy

2015-01-01

Full Text Available The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

An energy-efficient cluster-based vehicle detection on road network using intention numeration method.

Science.gov (United States)

Devasenapathy, Deepa; Kannan, Kathiravan

2015-01-01

The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN) is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

Deterministic ripple-spreading model for complex networks.

Science.gov (United States)

Hu, Xiao-Bing; Wang, Ming; Leeson, Mark S; Hines, Evor L; Di Paolo, Ezequiel

2011-04-01

This paper proposes a deterministic complex network model, which is inspired by the natural ripple-spreading phenomenon. The motivations and main advantages of the model are the following: (i) The establishment of many real-world networks is a dynamic process, where it is often observed that the influence of a few local events spreads out through nodes, and then largely determines the final network topology. Obviously, this dynamic process involves many spatial and temporal factors. By simulating the natural ripple-spreading process, this paper reports a very natural way to set up a spatial and temporal model for such complex networks. (ii) Existing relevant network models are all stochastic models, i.e., with a given input, they cannot output a unique topology. Differently, the proposed ripple-spreading model can uniquely determine the final network topology, and at the same time, the stochastic feature of complex networks is captured by randomly initializing ripple-spreading related parameters. (iii) The proposed model can use an easily manageable number of ripple-spreading related parameters to precisely describe a network topology, which is more memory efficient when compared with traditional adjacency matrix or similar memory-expensive data structures. (iv) The ripple-spreading model has a very good potential for both extensions and applications.

Network model of security system

Directory of Open Access Journals (Sweden)

Adamczyk Piotr

2016-01-01

Full Text Available The article presents the concept of building a network security model and its application in the process of risk analysis. It indicates the possibility of a new definition of the role of the network models in the safety analysis. Special attention was paid to the development of the use of an algorithm describing the process of identifying the assets, vulnerability and threats in a given context. The aim of the article is to present how this algorithm reduced the complexity of the problem by eliminating from the base model these components that have no links with others component and as a result and it was possible to build a real network model corresponding to reality.

Neural network modeling of emotion

Science.gov (United States)

Levine, Daniel S.

2007-03-01

This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.

Numerical Modeling of an Integrated Vehicle Fluids System Loop for Pressurizing a Cryogenic Tank

Science.gov (United States)

LeClair, A. C.; Hedayat, A.; Majumdar, A. K.

2017-01-01

This paper presents a numerical model of the pressurization loop of the Integrated Vehicle Fluids (IVF) system using the Generalized Fluid System Simulation Program (GFSSP). The IVF propulsion system, being developed by United Launch Alliance to reduce system weight and enhance reliability, uses boiloff propellants to drive thrusters for the reaction control system as well as to run internal combustion engines to develop power and drive compressors to pressurize propellant tanks. NASA Marshall Space Flight Center (MSFC) conducted tests to verify the functioning of the IVF system using a flight-like tank. GFSSP, a finite volume based flow network analysis software developed at MSFC, has been used to support the test program. This paper presents the simulation of three different test series, comparison of numerical prediction and test data and a novel method of presenting data in a dimensionless form. The paper also presents a methodology of implementing a compressor map in a system level code.

An acoustical model based monitoring network

NARCIS (Netherlands)

Wessels, P.W.; Basten, T.G.H.; Eerden, F.J.M. van der

2010-01-01

In this paper the approach for an acoustical model based monitoring network is demonstrated. This network is capable of reconstructing a noise map, based on the combination of measured sound levels and an acoustic model of the area. By pre-calculating the sound attenuation within the network the

Numerical Models of Sewage Dispersion and Statistica Bathing Water Standards

DEFF Research Database (Denmark)

Petersen, Ole; Larsen, Torben

1991-01-01

As bathing water standards usually are founded in statistical methods, the numerical models used in outfall design should reflect this. A statistical approach, where stochastic variations in source strength and bacterial disappearance is incorporated into a numerical dilution model is presented. ...

Plasma modelling and numerical simulation

International Nuclear Information System (INIS)

Van Dijk, J; Kroesen, G M W; Bogaerts, A

2009-01-01

Plasma modelling is an exciting subject in which virtually all physical disciplines are represented. Plasma models combine the electromagnetic, statistical and fluid dynamical theories that have their roots in the 19th century with the modern insights concerning the structure of matter that were developed throughout the 20th century. The present cluster issue consists of 20 invited contributions, which are representative of the state of the art in plasma modelling and numerical simulation. These contributions provide an in-depth discussion of the major theories and modelling and simulation strategies, and their applications to contemporary plasma-based technologies. In this editorial review, we introduce and complement those papers by providing a bird's eye perspective on plasma modelling and discussing the historical context in which it has surfaced. (editorial review)

Multilevel Modeling of Distributed Denial of Service Attacks in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Katarzyna Mazur

2016-01-01

Full Text Available The growing popularity of wireless sensor networks increases the risk of security attacks. One of the most common and dangerous types of attack that takes place these days in any electronic society is a distributed denial of service attack. Due to the resource constraint nature of mobile sensors, DDoS attacks have become a major threat to its stability. In this paper, we established a model of a structural health monitoring network, being disturbed by one of the most common types of DDoS attacks, the flooding attack. Through a set of simulations, we explore the scope of flood-based DDoS attack problem, assessing the performance and the lifetime of the network under the attack condition. To conduct our research, we utilized the Quality of Protection Modeling Language. With the proposed approach, it was possible to examine numerous network configurations, parameters, attack options, and scenarios. The results of the carefully performed multilevel analysis allowed us to identify a new kind of DDoS attack, the delayed distributed denial of service, by the authors, referred to as DDDoS attack. Multilevel approach to DDoS attack analysis confirmed that, examining endangered environments, it is significant to take into account many characteristics at once, just to not overlook any important aspect.

Modeling transmission parameters of polymer microstructured fibers for applications in FTTH networks

Science.gov (United States)

Gdula, P.; Welikow, K.; Szczepański, P.; Buczyński, R.; Piramidowicz, R.

2011-10-01

This paper is focused on selected aspects of designing and modeling of transmission parameters of plastic optical fibers (POFs), considered in the context of their potential applications in optical access networks and, specifically, in Fiber-To- The-Home (FTTH) systems. The survey of state-of-the-art solutions is presented and possibility of improving transmission properties of POFs by microstructurization is discussed on the basis of the first results of numerical modeling. In particular, the microstructured POF was designed supporting propagation of limited number of modes while keeping relatively large mode area and, simultaneously, significantly lowered bending losses.

How to model wireless mesh networks topology

International Nuclear Information System (INIS)

Sanni, M L; Hashim, A A; Anwar, F; Ali, S; Ahmed, G S M

2013-01-01

The specification of network connectivity model or topology is the beginning of design and analysis in Computer Network researches. Wireless Mesh Networks is an autonomic network that is dynamically self-organised, self-configured while the mesh nodes establish automatic connectivity with the adjacent nodes in the relay network of wireless backbone routers. Researches in Wireless Mesh Networks range from node deployment to internetworking issues with sensor, Internet and cellular networks. These researches require modelling of relationships and interactions among nodes including technical characteristics of the links while satisfying the architectural requirements of the physical network. However, the existing topology generators model geographic topologies which constitute different architectures, thus may not be suitable in Wireless Mesh Networks scenarios. The existing methods of topology generation are explored, analysed and parameters for their characterisation are identified. Furthermore, an algorithm for the design of Wireless Mesh Networks topology based on square grid model is proposed in this paper. The performance of the topology generated is also evaluated. This research is particularly important in the generation of a close-to-real topology for ensuring relevance of design to the intended network and validity of results obtained in Wireless Mesh Networks researches

Numerical investigation of closed-loop control for Hall accelerators

International Nuclear Information System (INIS)

Barral, S.; Miedzik, J.

2011-01-01

Low frequency discharge current oscillations in Hall accelerators are conventionally damped with external inductor-capacitor (LC) or resistor-inductor-capacitor (RLC) networks. The role of such network in the stabilization of the plasma discharge is investigated with a numerical model and the potential advantages of proportional-integral-derivative (PID) closed-loop control over RLC networks are subsequently assessed using either discharge voltage or magnetic field modulation. Simulations confirm the reduction of current oscillations in the presence of a RLC network, but suggest that PID control could ensure nearly oscillation-free operation with little sensitivity toward the PID settings.

Condensation front modelling in a moisture separator reheater by application of SICLE numerical model

International Nuclear Information System (INIS)

Grange, J.L.; Caremoli, C.; Eddi, M.

1988-01-01

This paper presents improvements performed on SICLE numerical model in order to analyse the condensation front that occurs in the moisture separator reheaters (MSR) of nuclear power plants. Modifications of SICLE numerical model architecture and a fine modelling of reheater have allowed to correctly simulate the MSR thermohydraulic behaviour during a severe transient (plant islanding) [fr

Mathematical modelling and numerical simulation of casting processes

DEFF Research Database (Denmark)

Hattel, Jesper Henri

1998-01-01

The control volume method applied to numerical modelling of castning. Analytical solutions based on the error function.Riemann-temperature. Modelling of release of latent heat with the enthalpy method....

Vulnerability of networks of interacting Markov chains.

Science.gov (United States)

Kocarev, L; Zlatanov, N; Trajanov, D

2010-05-13

The concept of vulnerability is introduced for a model of random, dynamical interactions on networks. In this model, known as the influence model, the nodes are arranged in an arbitrary network, while the evolution of the status at a node is according to an internal Markov chain, but with transition probabilities that depend not only on the current status of that node but also on the statuses of the neighbouring nodes. Vulnerability is treated analytically and numerically for several networks with different topological structures, as well as for two real networks--the network of infrastructures and the EU power grid--identifying the most vulnerable nodes of these networks.

Modeling and numerical simulation of multi-component flow in porous media

International Nuclear Information System (INIS)

Saad, B.

2011-01-01

This work deals with the modelization and numerical simulation of two phase multi-component flow in porous media. The study is divided into two parts. First we study and prove the mathematical existence in a weak sense of two degenerate parabolic systems modeling two phase (liquid and gas) two component (water and hydrogen) flow in porous media. In the first model, we assume that there is a local thermodynamic equilibrium between both phases of hydrogen by using the Henry's law. The second model consists of a relaxation of the previous model: the kinetic of the mass exchange between dissolved hydrogen and hydrogen in the gas phase is no longer instantaneous. The second part is devoted to the numerical analysis of those models. Firstly, we propose a numerical scheme to compare numerical solutions obtained with the first model and numerical solutions obtained with the second model where the characteristic time to recover the thermodynamic equilibrium goes to zero. Secondly, we present a finite volume scheme with a phase-by-phase upstream weighting scheme without simplified assumptions on the state law of gas densities. We also validate this scheme on a 2D test cases. (author)

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

Energy Technology Data Exchange (ETDEWEB)

Sossoe, K.S., E-mail: kwami.sossoe@irt-systemx.fr [TECHNOLOGICAL RESEARCH INSTITUTE SYSTEMX (France); Lebacque, J-P., E-mail: jean-patrick.lebacque@ifsttar.fr [UPE/IFSTTAR-COSYS-GRETTIA (France)

2015-03-10

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

Adaptive numerical modeling of dynamic crack propagation

International Nuclear Information System (INIS)

Adouani, H.; Tie, B.; Berdin, C.; Aubry, D.

2006-01-01

We propose an adaptive numerical strategy that aims at developing reliable and efficient numerical tools to model dynamic crack propagation and crack arrest. We use the cohesive zone theory as behavior of interface-type elements to model crack. Since the crack path is generally unknown beforehand, adaptive meshing is proposed to model the dynamic crack propagation. The dynamic study requires the development of specific solvers for time integration. As both geometry and finite element mesh of the studied structure evolve in time during transient analysis, the stability behavior of dynamic solver becomes a major concern. For this purpose, we use the space-time discontinuous Galerkin finite element method, well-known to provide a natural framework to manage meshes that evolve in time. As an important result, we prove that the space-time discontinuous Galerkin solver is unconditionally stable, when the dynamic crack propagation is modeled by the cohesive zone theory, which is highly non-linear. (authors)

Numerical modeling of magma-repository interactions

NARCIS (Netherlands)

Bokhove, Onno

2001-01-01

This report explains the numerical programs behind a comprehensive modeling effort of magma-repository interactions. Magma-repository interactions occur when a magma dike with high-volatile content magma ascends through surrounding rock and encounters a tunnel or drift filled with either a magmatic

Modeling the interdependent network based on two-mode networks

Science.gov (United States)

An, Feng; Gao, Xiangyun; Guan, Jianhe; Huang, Shupei; Liu, Qian

2017-10-01

Among heterogeneous networks, there exist obviously and closely interdependent linkages. Unlike existing research primarily focus on the theoretical research of physical interdependent network model. We propose a two-layer interdependent network model based on two-mode networks to explore the interdependent features in the reality. Specifically, we construct a two-layer interdependent loan network and develop several dependent features indices. The model is verified to enable us to capture the loan dependent features of listed companies based on loan behaviors and shared shareholders. Taking Chinese debit and credit market as case study, the main conclusions are: (1) only few listed companies shoulder the main capital transmission (20% listed companies occupy almost 70% dependent degree). (2) The control of these key listed companies will be more effective of avoiding the spreading of financial risks. (3) Identifying the companies with high betweenness centrality and controlling them could be helpful to monitor the financial risk spreading. (4) The capital transmission channel among Chinese financial listed companies and Chinese non-financial listed companies are relatively strong. However, under greater pressure of demand of capital transmission (70% edges failed), the transmission channel, which constructed by debit and credit behavior, will eventually collapse.

Behavioral modeling of SRIM tables for numerical simulation

Energy Technology Data Exchange (ETDEWEB)

Martinie, S., E-mail: sebastien.martinie@cea.fr; Saad-Saoud, T.; Moindjie, S.; Munteanu, D.; Autran, J.L., E-mail: jean-luc.autran@univ-amu.fr

2014-03-01

Highlights: • Behavioral modeling of SRIM data is performed on the basis of power polynomial fitting functions. • Fast and continuous numerical functions are proposed for the stopping power and projected range. • Functions have been successfully tested for a wide variety of ions and targets. • Typical accuracies below the percent have been obtained in the range 1 keV–1 GeV. - Abstract: This work describes a simple way to implement SRIM stopping power and range tabulated data in the form of fast and continuous numerical functions for intensive simulation. We provide here the methodology of this behavioral modeling as well as the details of the implementation and some numerical examples for ions in silicon target. Developed functions have been successfully tested and used for the simulation of soft errors in microelectronics circuits.

Behavioral modeling of SRIM tables for numerical simulation

International Nuclear Information System (INIS)

Martinie, S.; Saad-Saoud, T.; Moindjie, S.; Munteanu, D.; Autran, J.L.

2014-01-01

Highlights: • Behavioral modeling of SRIM data is performed on the basis of power polynomial fitting functions. • Fast and continuous numerical functions are proposed for the stopping power and projected range. • Functions have been successfully tested for a wide variety of ions and targets. • Typical accuracies below the percent have been obtained in the range 1 keV–1 GeV. - Abstract: This work describes a simple way to implement SRIM stopping power and range tabulated data in the form of fast and continuous numerical functions for intensive simulation. We provide here the methodology of this behavioral modeling as well as the details of the implementation and some numerical examples for ions in silicon target. Developed functions have been successfully tested and used for the simulation of soft errors in microelectronics circuits