Shadow networks: Discovering hidden nodes with models of information flow
Bagrow, James P; Frank, Morgan R; Manukyan, Narine; Mitchell, Lewis; Reagan, Andrew; Bloedorn, Eric E; Booker, Lashon B; Branting, Luther K; Smith, Michael J; Tivnan, Brian F; Danforth, Christopher M; Dodds, Peter S; Bongard, Joshua C
2013-01-01
Complex, dynamic networks underlie many systems, and understanding these networks is the concern of a great span of important scientific and engineering problems. Quantitative description is crucial for this understanding yet, due to a range of measurement problems, many real network datasets are incomplete. Here we explore how accidentally missing or deliberately hidden nodes may be detected in networks by the effect of their absence on predictions of the speed with which information flows through the network. We use Symbolic Regression (SR) to learn models relating information flow to network topology. These models show localized, systematic, and non-random discrepancies when applied to test networks with intentionally masked nodes, demonstrating the ability to detect the presence of missing nodes and where in the network those nodes are likely to reside.
CIME course on Modelling and Optimisation of Flows on Networks
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.
Enhancing debris flow modeling parameters integrating Bayesian networks
Graf, C.; Stoffel, M.; Grêt-Regamey, A.
2009-04-01
Applied debris-flow modeling requires suitably constraint input parameter sets. Depending on the used model, there is a series of parameters to define before running the model. Normally, the data base describing the event, the initiation conditions, the flow behavior, the deposition process and mainly the potential range of possible debris flow events in a certain torrent is limited. There are only some scarce places in the world, where we fortunately can find valuable data sets describing event history of debris flow channels delivering information on spatial and temporal distribution of former flow paths and deposition zones. Tree-ring records in combination with detailed geomorphic mapping for instance provide such data sets over a long time span. Considering the significant loss potential associated with debris-flow disasters, it is crucial that decisions made in regard to hazard mitigation are based on a consistent assessment of the risks. This in turn necessitates a proper assessment of the uncertainties involved in the modeling of the debris-flow frequencies and intensities, the possible run out extent, as well as the estimations of the damage potential. In this study, we link a Bayesian network to a Geographic Information System in order to assess debris-flow risk. We identify the major sources of uncertainty and show the potential of Bayesian inference techniques to improve the debris-flow model. We model the flow paths and deposition zones of a highly active debris-flow channel in the Swiss Alps using the numerical 2-D model RAMMS. Because uncertainties in run-out areas cause large changes in risk estimations, we use the data of flow path and deposition zone information of reconstructed debris-flow events derived from dendrogeomorphological analysis covering more than 400 years to update the input parameters of the RAMMS model. The probabilistic model, which consistently incorporates this available information, can serve as a basis for spatial risk
Network-Theoretic Modeling of Fluid Flow
2015-07-29
connections. Identifying such locations is especially critical when containment 3 measures are designed to control outbreaks of HIV [5], SARS [6...intuitive explanation that turbulent flows will be resilient against small-scale forcing while the global behavior can be easily modified by large-scale
1988-12-01
Researchers have suggested other solution strategies, using ideas from nonlinear progamming for solving this general separable convex cost flow problems. Some...plane methods and branch and bound procedures of integer programming, primal-dual methods of linear and nonlinear programming, and polyhedral methods...Combinatorial Optimization: Networks and Matroids), Bazaraa and Jarvis [1978] (Linear Programming and Network Flows), Minieka [1978] (Optimization Algorithms for
Abghari, H.; van de Giesen, N.; Mahdavi, M.; Salajegheh, A.
2009-04-01
Artificial intelligence modeling of nonstationary rainfall-runoff has some restrictions in simulation accuracy due to the complexity and nonlinearity of training patterns. Preprocessing of trainings dataset could determine homogeneity of rainfall-runoff patterns before modeling. In this presentation, a new hybrid model of Artificial Intelligence in conjunction with clustering is introduced and applied to flow prediction. Simulation of Nazloochaei river flow in North-West Iran was the case used for development of a PNN-RBF model. PNN classify a training dataset in two groups based on Parezen theory using unsupervised classification. Subsequently each data group is used to train and test two RBF networks and the results are compared to the application of all data in a RBF network without classification. Results show that classification of rainfall-runoff patterns using PNN and prediction of runoff with RBF increase prediction precise of networks. Keywords: Probabilistic Neural Network, Radial Base Function Neural Network, Parezen theory, River Flow Prediction
Queueing network model for obstetric patient flow in a hospital.
Takagi, Hideaki; Kanai, Yuta; Misue, Kazuo
2016-03-03
A queueing network is used to model the flow of patients in a hospital using the observed admission rate of patients and the histogram for the length of stay for patients in each ward. A complete log of orders for every movement of all patients from room to room covering two years was provided to us by the Medical Information Department of the University of Tsukuba Hospital in Japan. We focused on obstetric patients, who are generally hospitalized at random times throughout the year, and we analyzed the patient flow probabilistically. On admission, each obstetric patient is assigned to a bed in one of the two wards: one for normal delivery and the other for high-risk delivery. Then, the patient may be transferred between the two wards before discharge. We confirm Little's law of queueing theory for the patient flow in each ward. Next, we propose a new network model of M/G/ ∞ and M/M/ m queues to represent the flow of these patients, which is used to predict the probability distribution for the number of patients staying in each ward at the nightly census time. Although our model is a very rough and simplistic approximation of the real patient flow, the predicted probability distribution shows good agreement with the observed data. The proposed method can be used for capacity planning of hospital wards to predict future patient load in each ward.
Analogue models of melt-flow networks in folding migmatites
Barraud, Joseph; Gardien, Véronique; Allemand, Pascal; Grandjean, Philippe
2004-02-01
We have modelled the formation and the layer-parallel shortening of layered (stromatic) migmatites. The model consists of thin superposed layers of partially molten microcrystalline wax. The melt (30 vol.%) has a negative buoyancy and a high viscosity contrast with its solid matrix. As soon as the shortening begins, melt-filled veins with high aspect ratios open along foliation. The melt is segregated into the veins, forming a stromatic layering. During incipient folding, crescent-shaped saddle reefs open at the hinges of open sinusoidal folds. Further shortening and melt-enhanced shear displacements on interlayer interfaces cause chevron folds to develop and the saddle reefs to become triangular. In comparison, a melt-free experiment shows only a few layer-parallel openings and no saddle reefs in chevron folds. On the basis of our experimental results, we propose that in migmatites: (1) mesoscale melt migration is a combination of flow in immobile veins and movements of veins as a whole; (2) the changes in the geometry of the mesoscale melt-flow network create the pressure gradients that drive melt migration; (3) the melt-flow network does not need to be fully interconnected to allow local expulsion; (4) melt expulsion is episodic because the temporal evolution of the network combines with the spatial heterogeneity of the deformation.
de la Mata, Tamara; Llano, Carlos
2013-07-01
Recent literature on border effect has fostered research on informal barriers to trade and the role played by network dependencies. In relation to social networks, it has been shown that intensity of trade in goods is positively correlated with migration flows between pairs of countries/regions. In this article, we investigate whether such a relation also holds for interregional trade of services. We also consider whether interregional trade flows in services linked with tourism exhibit spatial and/or social network dependence. Conventional empirical gravity models assume the magnitude of bilateral flows between regions is independent of flows to/from regions located nearby in space, or flows to/from regions related through social/cultural/ethic network connections. With this aim, we provide estimates from a set of gravity models showing evidence of statistically significant spatial and network (demographic) dependence in the bilateral flows of the trade of services considered. The analysis has been applied to the Spanish intra- and interregional monetary flows of services from the accommodation, restaurants and travel agencies for the period 2000-2009, using alternative datasets for the migration stocks and definitions of network effects.
STUDY AND APPLICATION OF STEADY FLOW AND UNSTEADY FLOW MATHEMATICAL MODEL FOR CHANNEL NETWORKS
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Based on the Preissmann implicit scheme for the one-dimensional Saint-Venant equation, the mathematical model for one-dimensional river networks and canal networks was developed and the key issues on the model were expatiated particularly in this article. This model applies the method of three-steps solution for channel-junction-channel to simulate the river networks, and the Gauss elimination method was used to calculate the sparse matrix. This model was applied to simulate the tree-type irrigation canal networks, complex looped channel networks and the Lower Columbia Slough networks. The results of water level and discharge agree with the data from the Adlul and field data. The model is proved to be robust for simulating unsteady flows in river networks with various degrees of complex structure. The calculated results show that this model is useful for engineering applications in complicated river networks. Future research was recommended to focus on setting up ecological numerical model of water quality in river networks and canal networks.
Thermal power system analysis using a generalized network flow model
Energy Technology Data Exchange (ETDEWEB)
Kumar, John Arun [Former Senior Design Engineer, Power System Analysis and Control Group, Bharat Heavy Electricals Limited, New Delhi (India); Chebiyam, Radhakrishna [Former Director, Academic Staff College, JNT University, Hyderabad-72 (India)
2012-07-01
This paper analyzes an Integrated Thermal Power System using a Multiperiod Generalized Network Flow Model. The thermal system analysis is carried out by taking into account the complex dynamics involved in utilizing multiple energy carriers (coal, diesel and natural gas). The model comprises energy source nodes, energy transformation nodes, energy storage nodes, energy demand nodes and their interconnections. The solution to the integrated energy system problem involves the evaluation of energy flows that meet the electricity demand at minimum total cost, while satisfying system constraints. This is illustrated through the India case study using a minimum time-step of one hour. MATLAB based software was developed for carrying out this study. TOMLAB/CPLEX software was utilized for obtaining the optimal solution. The model and the methodology utilized for conducting the study would be of interest to those involved in integrated energy system planning for a country or a region.
International Trade Modelling Using Open Flow Networks: A Flow-Distance Based Analysis.
Shen, Bin; Zhang, Jiang; Li, Yixiao; Zheng, Qiuhua; Li, Xingsen
2015-01-01
This paper models and analyzes international trade flows using open flow networks (OFNs) with the approaches of flow distances, which provide a novel perspective and effective tools for the study of international trade. We discuss the establishment of OFNs of international trade from two coupled viewpoints: the viewpoint of trading commodity flow and that of money flow. Based on the novel model with flow distance approaches, meaningful insights are gained. First, by introducing the concepts of trade trophic levels and niches, countries' roles and positions in the global supply chains (or value-added chains) can be evaluated quantitatively. We find that the distributions of trading "trophic levels" have the similar clustering pattern for different types of commodities, and summarize some regularities between money flow and commodity flow viewpoints. Second, we find that active and competitive countries trade a wide spectrum of products, while inactive and underdeveloped countries trade a limited variety of products. Besides, some abnormal countries import many types of goods, which the vast majority of countries do not need to import. Third, harmonic node centrality is proposed and we find the phenomenon of centrality stratification. All the results illustrate the usefulness of the model of OFNs with its network approaches for investigating international trade flows.
Improving peak flow estimates in artificial neural network river flow models
Sudheer, K. P.; Nayak, P. C.; Ramasastri, K. S.
2003-02-01
In this paper, the concern of accuracy in peak estimation by the artificial neural network (ANN) river flow models is discussed and a suitable statistical procedure to get better estimates from these models is presented. The possible cause for underestimation of peak flow values has been attributed to the local variations in the function being mapped due to varying skewness in the data series, and theoretical considerations of the network functioning confirm this. It is envisaged that an appropriate data transformation will reduce the local variations in the function being mapped, and thus any ANN model built on the transformed series should perform better. This heuristic is illustrated and confirmed by many case studies and the results suggest that the model performance is significantly improved by data transformation. The model built on transformed data outperforms the model built on raw data in terms of various statistical performance indices. The peak estimates are improved significantly by data transformation.
A RIVER FLOW ROUTING MODEL BASED ON DIGITAL DRAINAGE NETWORK
Institute of Scientific and Technical Information of China (English)
YUAN Fei; REN Li-liang; YU Zhong-bo; XU Jing
2005-01-01
On the basis of Digital Elevation Model (DEM) data, watershed delineation and spatial topological relationship were proposed by the Digital Elevation Drainage Network Model (DEDNM) for the area upstream of the Hanzhong Hydrological Station in the Hanjiang River in China. Then, the Muskingum-Cunge method considering lateral flow into the river was applied to flood routing on the platform of digital basin derived from DEDNM. Because of considering lateral flow into the river, the Muskingum-Cunge method performs better than the Muskingum method in terms of the Nash-Sutcliffe model efficiency coefficient and the relative error of flood discharge peak value. With a routing-after-superposition algorithm, the Muskingum-Cunge method performs better than the Muskingum method in terms of the Nash-Sutcliffe model efficiency coefficient and the relative error of flood discharge peak value. As a result, the digital basin coupled with the Muskingum-Cunge method provides a better platform for water resources management and flood control.
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The continuum approach in fluid flow modeling is generally applied to porous geological media,but has limitel applicability to fractured rocks. With the presence of a discrete fracture network relatively sparsely distributed in the matrix, it may be difficult or erroneous to use a porous medium fluid flow model with continuum assumptions to describe the fluid flow in fractured rocks at small or even large field scales. A discrete fracture fluid flow approach incorporating a stochastic fracture network with numerical fluid flow simulations could have the capability of capturing fluid flow behaviors such as inhomogeneity and anisotropy while reflecting the changes of hydraulic features at different scales.Moreover, this approach can be implemented to estimate the size of the representative elementary volume (REV) in order to find out the scales at which a porous medium flow model could be applied, and then to determine the hydraulic conductivity tensor for fractured rocks. The following topics are focused on in this study: (a) conceptual discrete fracture fluid flow modeling incorporating a stochastic fracture network with numerical flow simulations; (b) estimation of REVand hydraulic conductivity tensor for fractured rocks utilizing a stochastic fracture network with numerical fluid flow simulations; (c) investigation of the effect of fracture orientation and density on the hydraulic conductivity and REV by implementing a stochastic fracture network with numerical fluid flow simulations, and (d) fluid flow conceptual models accounting for major and minor fractures in the 2-D or 3-D flow fields incorporating a stochastic fracture network with numerical fluid flow simulations.``
Olaizola Ortega, María Norma; Valenciano Llovera, Federico
2012-01-01
This paper provides a new model of network formation that bridges the gap between the two benchmark models by Bala and Goyal, the one-way flow model, and the two-way flow model, and includes both as particular extreme cases. As in both benchmark models, in what we call an "asymmetric flow" network a link can be initiated unilaterally by any player with any other, and the flow through a link towards the player who supports it is perfect. Unlike those models, in the opposite direction there is ...
Water flow based geometric active deformable model for road network
Leninisha, Shanmugam; Vani, Kaliaperumal
2015-04-01
A width and color based geometric active deformable model is proposed for road network extraction from remote sensing images with minimal human interception. Orientation and width of road are computed from a single manual seed point, from which the propagation starts both right and left hand directions of the starting point, which extracts the interconnected road network from the aerial or high spatial resolution satellite image automatically. Here the propagation (like water flow in canal with defined boundary) is restricted with color and width of the road. Road extraction is done for linear, curvilinear (U shape and S shape) roads first, irrespective of width and color. Then, this algorithm is improved to extract road with junctions in a shape of L, T and X along with center line. Roads with small break or disconnected roads are also extracts by a modified version of this same algorithm. This methodology is tested and evaluated with various remote sensing images. The experimental results show that the proposed method is efficient and extracting roads accurately with less computation time. However, in complex urban areas, the identification accuracy declines due to the various sizes of obstacles, over bridges, multilane etc.
Leitão, J P; Boonya-Aroonnet, S; Prodanović, D; Maksimović, C
2009-01-01
This paper presents the developments towards the next generation of overland flow modelling of urban pluvial flooding. Using a detailed analysis of the Digital Elevation Model (DEM) the developed GIS tools can automatically generate surface drainage networks which consist of temporary ponds (floodable areas) and flow paths and link them with the underground network through inlets. For different commercially-available Rainfall-Runoff simulation models, the tool will generate the overland flow network needed to model the surface runoff and pluvial flooding accurately. In this paper the emphasis is placed on a sensitivity analysis of ponds and preferential overland flow paths creation. Different DEMs for three areas were considered in order to compare the results obtained. The DEMs considered were generated using different acquisition techniques and hence represent terrain with varying levels of resolution and accuracy. The results show that DEMs can be used to generate surface flow networks reliably. As expected, the quality of the surface network generated is highly dependent on the quality and resolution of the DEMs and successful representation of buildings and streets.
Directory of Open Access Journals (Sweden)
Cheng Xu
2015-01-01
Full Text Available Free flow speed is a fundamental measure of traffic performance and has been found to affect the severity of crash risk. However, the previous studies lack analysis and modelling of impact factors on bicycles’ free flow speed. The main focus of this study is to develop multilayer back propagation artificial neural network (BPANN models for the prediction of free flow speed and crash risk on the separated bicycle path. Four different models with considering different combinations of input variables (e.g., path width, traffic condition, bicycle type, and cyclists’ characteristics were developed. 459 field data samples were collected from eleven bicycle paths in Hangzhou, China, and 70% of total samples were used for training, 15% for validation, and 15% for testing. The results show that considering the input variables of bicycle types and characteristics of cyclists will effectively improve the accuracy of the prediction models. Meanwhile, the parameters of bicycle types have more significant effect on predicting free flow speed of bicycle compared to those of cyclists’ characteristics. The findings could contribute for evaluation, planning, and management of bicycle safety.
Directory of Open Access Journals (Sweden)
M Soltani
Full Text Available Modeling of interstitial fluid flow involves processes such as fluid diffusion, convective transport in extracellular matrix, and extravasation from blood vessels. To date, majority of microvascular flow modeling has been done at different levels and scales mostly on simple tumor shapes with their capillaries. However, with our proposed numerical model, more complex and realistic tumor shapes and capillary networks can be studied. Both blood flow through a capillary network, which is induced by a solid tumor, and fluid flow in tumor's surrounding tissue are formulated. First, governing equations of angiogenesis are implemented to specify the different domains for the network and interstitium. Then, governing equations for flow modeling are introduced for different domains. The conservation laws for mass and momentum (including continuity equation, Darcy's law for tissue, and simplified Navier-Stokes equation for blood flow through capillaries are used for simulating interstitial and intravascular flows and Starling's law is used for closing this system of equations and coupling the intravascular and extravascular flows. This is the first study of flow modeling in solid tumors to naturalistically couple intravascular and extravascular flow through a network. This network is generated by sprouting angiogenesis and consisting of one parent vessel connected to the network while taking into account the non-continuous behavior of blood, adaptability of capillary diameter to hemodynamics and metabolic stimuli, non-Newtonian blood flow, and phase separation of blood flow in capillary bifurcation. The incorporation of the outlined components beyond the previous models provides a more realistic prediction of interstitial fluid flow pattern in solid tumors and surrounding tissues. Results predict higher interstitial pressure, almost two times, for realistic model compared to the simplified model.
Hua, Xiao Jia; Zhong, Guo Fang
1988-11-01
The flow and thermal characteristics of rapid cyclic flow cryogenic regenerators and their interrelationship have been analysed using the linear network theory and perturbation method. The effect of flow resistance, gas storage in void volume, temperature distribution, interaction of pressure wave and cyclic mass flow, and the factor of real gas were considered. A computer simulation program, CFCRX, was developed and the numerical results obtained are presented. This theory provides a better understanding of the working mechanism of cryogenic regenerators.
Modelling lava flows by Cellular Nonlinear Networks (CNN: preliminary results
Directory of Open Access Journals (Sweden)
C. Del Negro
2005-01-01
Full Text Available The forecasting of lava flow paths is a complex problem in which temperature, rheology and flux-rate all vary with space and time. The problem is more difficult to solve when lava runs down a real topography, considering that the relations between characteristic parameters of flow are typically nonlinear. An alternative approach to this problem that does not use standard differential equation methods is Cellular Nonlinear Networks (CNNs. The CNN paradigm is a natural and flexible framework for describing locally interconnected, simple, dynamic systems that have a lattice-like structure. They consist of arrays of essentially simple, nonlinearly coupled dynamic circuits containing linear and non-linear elements able to process large amounts of information in real time. Two different approaches have been implemented in simulating some lava flows. Firstly, a typical technique of the CNNs to analyze spatio-temporal phenomena (as Autowaves in 2-D and in 3-D has been utilized. Secondly, the CNNs have been used as solvers of partial differential equations of the Navier-Stokes treatment of Newtonian flow.
International Organization for Standardization. Geneva
2003-01-01
Information technology - Telecommunications and information exchange between systems - Private integrated services network - Specification, functional model and information flows - Call interception additional network feature
Linear Power-Flow Models in Multiphase Distribution Networks: Preprint
Energy Technology Data Exchange (ETDEWEB)
Bernstein, Andrey; Dall' Anese, Emiliano
2017-05-26
This paper considers multiphase unbalanced distribution systems and develops approximate power-flow models where bus-voltages, line-currents, and powers at the point of common coupling are linearly related to the nodal net power injections. The linearization approach is grounded on a fixed-point interpretation of the AC power-flow equations, and it is applicable to distribution systems featuring (i) wye connections; (ii) ungrounded delta connections; (iii) a combination of wye-connected and delta-connected sources/loads; and, (iv) a combination of line-to-line and line-to-grounded-neutral devices at the secondary of distribution transformers. The proposed linear models can facilitate the development of computationally-affordable optimization and control applications -- from advanced distribution management systems settings to online and distributed optimization routines. Performance of the proposed models is evaluated on different test feeders.
A neural network model for short term river flow prediction
2006-01-01
International audience; This paper presents a model using rain gauge and weather radar data to predict the runoff of a small alpine catchment in Austria. The gapless spatial coverage of the radar is important to detect small convective shower cells, but managing such a huge amount of data is a demanding task for an artificial neural network. The method described here uses statistical analysis to reduce the amount of data and find an appropriate input vector. Based on this analysis, radar meas...
Scalable Bayesian modeling, monitoring and analysis of dynamic network flow data
2016-01-01
Traffic flow count data in networks arise in many applications, such as automobile or aviation transportation, certain directed social network contexts, and Internet studies. Using an example of Internet browser traffic flow through site-segments of an international news website, we present Bayesian analyses of two linked classes of models which, in tandem, allow fast, scalable and interpretable Bayesian inference. We first develop flexible state-space models for streaming count data, able to...
Directory of Open Access Journals (Sweden)
Hongying Jin
2013-10-01
Full Text Available This paper aims at effectively predicting the dynamic network traffic flow based on quantum-behaved particle swarm optimization algorithm. Firstly, the dynamic network traffic flow prediction problem is analyzed through formal description. Secondly, the structure of the network traffic flow prediction model is given. In this structure, Users can used a computer to start the traffic flow prediction process, and data collecting module can collect and return the data through the destination device. Thirdly, the dynamic network traffic flow prediction model is implemented based on BP Neural Network. Particularly, in this paper, the BP Neural Network is trained by a modified quantum-behaved particle swarm optimization(QPSO. We modified the QPSO by utilizing chaos signals to implement typical logistic mapping and pursuing the fitness function of a particle by a set of optimal parameters. Afterwards, based on the above process, dynamic network traffic flow prediction model is illustrated. Finally, a series of experiments are conduct to make performance evaluation, and related analyses for experimental results are also given
A neural network model for short term river flow prediction
Teschl, R.; Randeu, W. L.
2006-07-01
This paper presents a model using rain gauge and weather radar data to predict the runoff of a small alpine catchment in Austria. The gapless spatial coverage of the radar is important to detect small convective shower cells, but managing such a huge amount of data is a demanding task for an artificial neural network. The method described here uses statistical analysis to reduce the amount of data and find an appropriate input vector. Based on this analysis, radar measurements (pixels) representing areas requiring approximately the same time to dewater are grouped.
A neural network model for short term river flow prediction
Directory of Open Access Journals (Sweden)
R. Teschl
2006-01-01
Full Text Available This paper presents a model using rain gauge and weather radar data to predict the runoff of a small alpine catchment in Austria. The gapless spatial coverage of the radar is important to detect small convective shower cells, but managing such a huge amount of data is a demanding task for an artificial neural network. The method described here uses statistical analysis to reduce the amount of data and find an appropriate input vector. Based on this analysis, radar measurements (pixels representing areas requiring approximately the same time to dewater are grouped.
Chen, Miawjane; Yan, Shangyao; Wang, Sin-Siang; Liu, Chiu-Lan
2015-02-01
An effective project schedule is essential for enterprises to increase their efficiency of project execution, to maximize profit, and to minimize wastage of resources. Heuristic algorithms have been developed to efficiently solve the complicated multi-mode resource-constrained project scheduling problem with discounted cash flows (MRCPSPDCF) that characterize real problems. However, the solutions obtained in past studies have been approximate and are difficult to evaluate in terms of optimality. In this study, a generalized network flow model, embedded in a time-precedence network, is proposed to formulate the MRCPSPDCF with the payment at activity completion times. Mathematically, the model is formulated as an integer network flow problem with side constraints, which can be efficiently solved for optimality, using existing mathematical programming software. To evaluate the model performance, numerical tests are performed. The test results indicate that the model could be a useful planning tool for project scheduling in the real world.
Perrin, Christian L; Tardy, Philippe M J; Sorbie, Ken S; Crawshaw, John C
2006-03-15
The in situ rheology of polymeric solutions has been studied experimentally in etched silicon micromodels which are idealizations of porous media. The rectangular channels in these etched networks have dimensions typical of pore sizes in sandstone rocks. Pressure drop/flow rate relations have been measured for water and non-Newtonian hydrolyzed-polyacrylamide (HPAM) solutions in both individual straight rectangular capillaries and in networks of such capillaries. Results from these experiments have been analyzed using pore-scale network modeling incorporating the non-Newtonian fluid mechanics of a Carreau fluid. Quantitative agreement is seen between the experiments and the network calculations in the Newtonian and shear-thinning flow regions demonstrating that the 'shift factor,'alpha, can be calculated a priori. Shear-thickening behavior was observed at higher flow rates in the micromodel experiments as a result of elastic effects becoming important and this remains to be incorporated in the network model.
Flow Distances on Open Flow Networks
Guo, Liangzhu; Shi, Peiteng; Wang, Jun; Huang, Xiaohan; Zhang, Jiang
2015-01-01
Open flow network is a weighted directed graph with a source and a sink, depicting flux distributions on networks in the steady state of an open flow system. Energetic food webs, economic input-output networks, and international trade networks, are open flow network models of energy flows between species, money or value flows between industrial sectors, and goods flows between countries, respectively. Flow distances (first-passage or total) between any given two nodes $i$ and $j$ are defined as the average number of transition steps of a random walker along the network from $i$ to $j$ under some conditions. They apparently deviate from the conventional random walk distance on a closed directed graph because they consider the openness of the flow network. Flow distances are explicitly expressed by underlying Markov matrix of a flow system in this paper. With this novel theoretical conception, we can visualize open flow networks, calculating centrality of each node, and clustering nodes into groups. We apply fl...
Investigation of Shannon and PolyWog Wavelet Neural Networks In Monthly River Flow Modeling
Abghari, H.; van de Giesen, N.; Noury, M.
2009-04-01
Intelligence models consist of distributed parallel processors that learn to reproduce the relationship between input and output signals and present the best topology of patterns simulation. Due to nonlinearity of hydrological events the learning process has restrictions . In this study, using a combination of Wavelet theory and a Multi Layer Perceptron Network, two Wavelet Neural Network models for monthly flow of Nazloochaei River basin in Iran were developed. Instead of using common sigmoid activation functions in the MLP network a wavelet function was used, The hybrid wavelet neural network (WNNs) employing a nonlinear wavelet basis was developed as an alternative approach to nonlinear fitting. Result of MLP base model show the 86% in training and 79% in model testing. Results of the MLP base model show a goodness of fit of 86% in training and 79% in model testing. Results shows that the Polywog neural network with the best topology has a 94% accuracy in the training phase and 89% in testing of model. The Shannon neural network with the best topology produces 79% accuracy in training phase and 61% in testing of model. Comparison of WNN and MLP shows that Polywog wavelet could have better accuracy in time series modeling. Classic sigmoid activation functions in the MLP network show better results than the Shannon wavelet. Keywords: Shannon and PolyWog Wavelet, Wavelet Neural Networks, Nazloochaei River Basin, River Flow Modeling.
Semi-automatic simulation model generation of virtual dynamic networks for production flow planning
Krenczyk, D.; Skolud, B.; Olender, M.
2016-08-01
Computer modelling, simulation and visualization of production flow allowing to increase the efficiency of production planning process in dynamic manufacturing networks. The use of the semi-automatic model generation concept based on parametric approach supporting processes of production planning is presented. The presented approach allows the use of simulation and visualization for verification of production plans and alternative topologies of manufacturing network configurations as well as with automatic generation of a series of production flow scenarios. Computational examples with the application of Enterprise Dynamics simulation software comprising the steps of production planning and control for manufacturing network have been also presented.
Modelling flow dynamics in water distribution networks using ...
African Journals Online (AJOL)
DR OKE
Keywords: Artificial neural network; Leakage detection technique; Water distribution; Leakages ... Leakage is a function of pipe age, pipe material type, pressure, soil type as well as pipe .... In order to train a neural network to perform some.
Numerical modeling and verification of gas flow through a network of crossed narrow v-grooves
Bejhed, Johan; Nguyen, Hugo; Åstrand, Peter; Eriksson, Anders; Köhler, Johan
2006-10-01
The gas flow through a network of crossing thin micro-machined channels has been successfully modeled and simulated. The crossings are formed by two sets of v-grooves that intersect as two silicon wafers are bonded together. The gas is distributed from inlets via a manifold of channels to the narrow v-grooves. The narrow v-grooves could work as a particle filter. The fluidic model is derived from the Navier-Stokes equation and assumes laminar isothermal flow and incorporates small Knudsen number corrections and Poiseuille number calculations. The simulations use the finite element method. Several elements of the full crossing network model are treated separately before lumping them together: the straight v-grooves, a single crossing in an infinite set and a set of exactly four crossings along the flow path. The introduction of a crossing effectively corresponds to a virtual reduction of the length of the flow path, thereby defining a new effective length. The first and last crossings of each flow path together contribute to a pressure drop equal to that from three ordinary crossings. The derived full network model has been compared to previous experimental results on several differently shaped crossed v-groove networks. Within the experimental errors, the model corresponds to the mass flow and pressure drop measurements. The main error source is the uncertainty in v-groove width which has a profound impact on the fluidic behavior.
Predicting commuter flows in spatial networks using a radiation model based on temporal ranges
Ren, Yihui; Wang, Pu; González, Marta C; Toroczkai, Zoltán
2014-01-01
Understanding network flows such as commuter traffic in large transportation networks is an ongoing challenge due to the complex nature of the transportation infrastructure and of human mobility. Here we show a first-principles based method for traffic prediction using a cost based generalization of the radiation model for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. Using US census and highway traffic data we show that traffic can efficiently and accurately be computed from a range-limited, network betweenness type calculation. The model based on travel time costs captures the lognormal distribution of the traffic and attains a high Pearson correlation coefficient (0.75) when compared to real traffic. Due to its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic e...
Predicting commuter flows in spatial networks using a radiation model based on temporal ranges
Ren, Yihui; Ercsey-Ravasz, Mária; Wang, Pu; González, Marta C.; Toroczkai, Zoltán
2014-11-01
Understanding network flows such as commuter traffic in large transportation networks is an ongoing challenge due to the complex nature of the transportation infrastructure and human mobility. Here we show a first-principles based method for traffic prediction using a cost-based generalization of the radiation model for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. Using US census and highway traffic data, we show that traffic can efficiently and accurately be computed from a range-limited, network betweenness type calculation. The model based on travel time costs captures the log-normal distribution of the traffic and attains a high Pearson correlation coefficient (0.75) when compared with real traffic. Because of its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events.
A power flow based model for the analysis of vulnerability in power networks
Wang, Zhuoyang; Chen, Guo; Hill, David J.; Dong, Zhao Yang
2016-10-01
An innovative model which considers power flow, one of the most important characteristics in a power system, is proposed for the analysis of power grid vulnerability. Moreover, based on the complex network theory and the Max-Flow theorem, a new vulnerability index is presented to identify the vulnerable lines in a power grid. In addition, comparative simulations between the power flow based model and existing models are investigated on the IEEE 118-bus system. The simulation results demonstrate that the proposed model and the index are more effective in power grid vulnerability analysis.
Global migration topology analysis and modeling of bilateral flow network 2006-2010
Porat, I.; Benguigui, L.
2016-07-01
Migration is one of the most dramatic and vast human processes in modern times. Migration is defined as people that leave their home and home-land and move to a new country. In this research we address the pattern of this massive human movement with the tools of network theory. The undirected global flow migration network (2006-2010) was identified as an exclusive disassortative network which combines two types of defined groups of large- and small-degree (D) countries with betweeness (Be) of Be˜D 3. This structure was modeled and simulated with synthetic networks of similar characteristics as the global flow migration network, and the results suggest that small-degree nodes have the topology of random networks, but the dominant part of the large-degree hubs controls this topology and shapes the network into an ultra-small world. This exclusive topology and the difference of the global flow migration network from scale-free and from Erdös-Rényi networks may be a result of two defined and different topologies of large- and small-degree countries.
Analysis and modelling of non-steady flow in pipe and channel networks
Jovic, Vinko
2013-01-01
Analysis and Modelling of Non-Steady Flow in Pipe and Channel Networks deals with flows in pipes and channel networks from the standpoints of hydraulics and modelling techniques and methods. These engineering problems occur in the course of the design and construction of hydroenergy plants, water-supply and other systems. In this book, the author presents his experience in solving these problems from the early 1970s to the present day. During this period new methods of solving hydraulic problems have evolved, due to the development of computers and numerical methods. This book
Pore-Scale Modeling of Navier-Stokes Flow in Distensible Networks and Porous Media
Sochi, Taha
2013-01-01
In this paper, a pore-scale network modeling method, based on the flow continuity residual in conjunction with a Newton-Raphson non-linear iterative solving technique, is proposed and used to obtain the pressure and flow fields in a network of interconnected distensible ducts representing, for instance, blood vasculature or deformable porous media. A previously derived analytical expression correlating boundary pressures to volumetric flow rate in compliant tubes for a pressure-area constitutive elastic relation has been used to represent the underlying flow model. Comparison to a preceding equivalent method, the one-dimensional Navier-Stokes finite element, was made and the results were analyzed. The advantages of the new method have been highlighted and practical computational issues, related mainly to the rate and speed of convergence, have been discussed.
Ren, Yihui
As real-world complex networks are heterogeneous structures, not all their components such as nodes, edges and subgraphs carry the same role or importance in the functions performed by the networks: some elements are more critical than others. Understanding the roles of the components of a network is crucial for understanding the behavior of the network as a whole. One the most basic function of networks is transport; transport of vehicles/people, information, materials, forces, etc., and these quantities are transported along edges between source and destination nodes. For this reason, network path-based importance measures, also called centralities, play a crucial role in the understanding of the transport functions of the network and the network's structural and dynamical behavior in general. In this thesis we study the notion of betweenness centrality, which measures the fraction of lowest-cost (or shortest) paths running through a network component, in particular through a node or an edge. High betweenness centrality nodes/edges are those that will be frequently used by the entities transported through the network and thus they play a key role in the overall transport properties of the network. In the first part of the thesis we present a first-principles based method for traffic prediction using a cost-based generalization of the radiation model (emission/absorbtion model) for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. Using US census and highway traffic data, we show that traffic can efficiently and accurately be computed from a range-limited, network betweenness type calculation. The model based on travel time costs captures the log-normal distribution of the traffic and attains a high Pearson correlation coefficient (0.75) when compared with real traffic. We then focus on studying the extent of changes in traffic flows in the wake of a localized damage or alteration to the
Spring-network-based model of a red blood cell for simulating mesoscopic blood flow.
Nakamura, Masanori; Bessho, Sadao; Wada, Shigeo
2013-01-01
We developed a mechanical model of a red blood cell (RBC) that is capable of expressing its characteristic behaviors in shear flows. The RBC was modeled as a closed shell membrane consisting of spring networks in the framework of the energy minimum concept. The fluid forces acting on RBCs were modeled from Newton's viscosity law and the conservation of momentum. In a steady shear flow, the RBC model exhibited various behaviors, depending on the shear rate; it tumbled, tank-treaded, or both. The transition from tumbling to tank-treading occurred at a shear rate of 20 s( - 1). The simulation of an RBC in steady and unsteady parallel shear flows (Couette flows) showed that the deformation parameters of the RBC were consistent with experimental results. The RBC in Poiseuille flow migrated radially towards the central axis of the flow channel. Axial migration became faster with an increase in the viscosity of the media, qualitatively consistent with experimental results. These results demonstrate that the proposed model satisfies the essential conditions for simulating RBC behavior in blood flow. Finally, a large-scale RBC flow simulation was implemented to show the capability of the proposed model for analyzing the mesoscopic nature of blood flow.
Explaining the internal behaviour of artificial neural network river flow models
Sudheer, K. P.; Jain, Ashu
2004-03-01
A novel method of visualizing and understanding the internal functional behaviour of an artificial neural network (ANN) river flow model is presented. The method hypothesizes that an ANN is able to map a function similar to the flow duration curve while modelling the river flow. A mathematical analysis of the hypothesis is presented, and a case study of an ANN river flow model confirms its significance. The proposed approach is also useful within other models that improve the performance of an ANN. The reasons why these models improve a raw ANN can be clearly understood using this approach. While the field of ANN knowledge-extraction is one that continues to attract considerable interest, it is anticipated that the current approach will initiate further research and make ANNs more useful to the hydrologic community.
Helbing, D; Lämmer, S; Helbing, Dirk; Siegmeier, Jan; L\\"{a}mmer, Stefan
2007-01-01
A model for traffic flow in street networks or material flows in supply networks is presented, that takes into account the conservation of cars or materials and other significant features of traffic flows such as jam formation, spillovers, and load-dependent transportation times. Furthermore, conflicts or coordination problems of intersecting or merging flows are considered as well. Making assumptions regarding the permeability of the intersection as a function of the conflicting flows and the queue lengths, we find self-organized oscillations in the flows similar to the operation of traffic lights.
Generic 2-D River Network Modeling of Flow and Sediment Transports
Guo, W.; Wang, C.; Xiang, X.; Ma, T.
2012-04-01
A generic 2D river network model of flow and sediment transports is proposed for the flow and sediment simulation in the complex river network. The paper expands the three-step method adopted in the 1D river network to the 2D river network simulation. A 2D river network model is divided into several cells, including single river cell, "tree-like" river cell, "ring-like" river cell and "cross-like" river cell, which can reflect the interactive influence of flow field in the bifurcated channel and applies to generic 2D simulation. Based on equation of the 2D shallow water and unsteady non-uniform suspended sediment, the relationship between the variables (water level, discharge and sediment concentration) of each section and those of the boundaries are obtained through the full implicit matrix chase-after method. Through the conservation of water and sediment on the boundaries, the water level and sediment concentration on the nodes can be got by solving the irregular sparse matrix of conservation equation, so as to implement the coupled simulation of flow and sediment in the whole river network. The paper take the Chengtong River Reach located in the low reaches of Yangtze River as the example of "cross-like" river to verify the algorithm. The model is calibrated using the measured data. A comparison of calculated water level, discharge and sediment concentration shows that the generic model can reflex the interactive influence of flow field, with reasonable accuracy, especially in the bifurcated channel.
A service flow model for the liner shipping network design problem
DEFF Research Database (Denmark)
Plum, Christian Edinger Munk; Pisinger, David; Sigurd, Mikkel M.
2014-01-01
. The model ensures strictly weekly frequencies of services, ensures that port-vessel draft capabilities are not violated, respects vessel capacities and the number of vessels available. The profit of the generated network is maximized, i.e. the revenue of flowed cargo subtracted operational costs...
Cortical information flow in Parkinson's disease: a composite network/field model
Directory of Open Access Journals (Sweden)
Cliff C. Kerr
2013-04-01
Full Text Available The basal ganglia play a crucial role in the execution of movements, as demonstrated by the severe motor deficits that accompany Parkinson's disease (PD. Since motor commands originate in the cortex, an important question is how the basal ganglia influence cortical information flow, and how this influence becomes pathological in PD. To explore this, we developed a composite neuronal network/neural field model. The network model consisted of 4950 spiking neurons, divided into 15 excitatory and inhibitory cell populations in the thalamus and cortex. The field model consisted of the cortex, thalamus, striatum, subthalamic nucleus, and globus pallidus. Both models have been separately validated in previous work. Three field models were used: one with basal ganglia parameters based on data from healthy individuals, one based on data from individuals with PD, and one purely thalamocortical model. Spikes generated by these field models were then used to drive the network model. Compared to the network driven by the healthy model, the PD-driven network had lower firing rates, a shift in spectral power towards lower frequencies, and higher probability of bursting; each of these findings is consistent with empirical data on PD. In the healthy model, we found strong Granger causality in the beta and low gamma bands between cortical layers, but this was largely absent in the PD model. In particular, the reduction in Granger causality from the main "input" layer of the cortex (layer 4 to the main "output" layer (layer 5 was pronounced. This may account for symptoms of PD that seem to reflect deficits in information flow, such as bradykinesia. In general, these results demonstrate that the brain's large-scale oscillatory environment, represented here by the field model, strongly influences the information processing that occurs within its subnetworks. Hence, it may be preferable to drive spiking network models with physiologically realistic inputs rather than
Discrete fracture network modeling of hydraulic stimulation coupling flow and geomechanics
McClure, Mark
2013-01-01
Discrete Fracture Network Modeling of Hydraulic Stimulation describes the development and testing of a model that couples fluid-flow, deformation, friction weakening, and permeability evolution in large, complex two-dimensional discrete fracture networks. The model can be used to explore the behavior of hydraulic stimulation in settings where matrix permeability is low and preexisting fractures play an important role, such as Enhanced Geothermal Systems and gas shale. Used also to describe pure shear stimulation, mixed-mechanism stimulation, or pure opening-mode stimulation. A variety of nov
Optimization model and algorithm for mixed traffic of urban road network with flow interference
Institute of Scientific and Technical Information of China (English)
SI BingFeng; LONG JianCeng; GAO ZiYou
2008-01-01
In this paper, the problem of interferences between motors and non-motors in ur-ban road mixed traffic network is considered and the corresponding link imped-ance function is presented based on travel demand, On the base of this, the main factors that influence travelers' traffic choices are all considered and a combined model including flow-split and assignment problem is proposed, Then a bi-level model with its algorithm for system optimization of urban road mixed traffic net-work is proposed. Finally the application of the model and its algorithm is illus-trated with a numerical example.
Liu, Zengkai; Liu, Yonghong; Wu, Xinlei; Yang, Dongwei; Cai, Baoping; Zheng, Chao
2016-09-01
Bayesian network (BN) is a widely used formalism for representing uncertainty in probabilistic systems and it has become a popular tool in reliability engineering. The GO-FLOW method is a success-oriented system analysis technique and capable of evaluating system reliability and risk. To overcome the limitations of GO-FLOW method and add new method for BN model development, this paper presents a novel approach on constructing a BN from GO-FLOW model. GO-FLOW model involves with several discrete time points and some signals change at different time points. But it is a static system at one time point, which can be described with BN. Therefore, the developed BN with the proposed method in this paper is equivalent to GO-FLOW model at one time point. The equivalent BNs of the fourteen basic operators in the GO-FLOW methodology are developed. Then, the existing GO-FLOW models can be mapped into equivalent BNs on basis of the developed BNs of operators. A case of auxiliary feedwater system of a pressurized water reactor is used to illustrate the method. The results demonstrate that the GO-FLOW chart can be successfully mapped into equivalent BNs.
Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing
Directory of Open Access Journals (Sweden)
Zhaosheng Yang
2014-01-01
Full Text Available To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing interface. The results demonstrate that the parallel GA-SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.
On the micro-to-macro limit for first-order traffic flow models on networks
Cristiani, Emiliano
2015-01-01
Connections between microscopic follow-the-leader and macroscopic fluid-dynamics traffic flow models are already well understood in the case of vehicles moving on a single road. Analogous connections in the case of road networks are instead lacking. This is probably due to the fact that macroscopic traffic models on networks are in general ill-posed, since the conservation of the mass is not sufficient alone to characterize a unique solution at junctions. This ambiguity makes more difficult to find the right limit of the microscopic model, which, in turn, can be defined in different ways near the junctions. In this paper we show that a natural extension of the first-order follow-the-leader model on networks corresponds, as the number of vehicles tends to infinity, to the LWR-based multi-path model introduced in [Bretti et al., Discrete Contin. Dyn. Syst. Ser. S, 7 (2014)] and [Briani and Cristiani, Netw. Heterog. Media, 9 (2014)].
Jia, Pin; Cheng, Linsong; Huang, Shijun; Wu, Yonghui
2016-06-01
This paper presents a semi-analytical model for the flow behavior of naturally fractured formations with multi-scale fracture networks. The model dynamically couples an analytical dual-porosity model with a numerical discrete fracture model. The small-scale fractures with the matrix are idealized as a dual-porosity continuum and an analytical flow solution is derived based on source functions in Laplace domain. The large-scale fractures are represented explicitly as the major fluid conduits and the flow is numerically modeled, also in Laplace domain. This approach allows us to include finer details of the fracture network characteristics while keeping the computational work manageable. For example, the large-scale fracture network may have complex geometry and varying conductivity, and the computations can be done at predetermined, discrete times, without any grids in the dual-porosity continuum. The validation of the semi-analytical model is demonstrated in comparison to the solution of ECLIPSE reservoir simulator. The simulation is fast, gridless and enables rapid model setup. On the basis of the model, we provide detailed analysis of the flow behavior of a horizontal production well in fractured reservoir with multi-scale fracture networks. The study has shown that the system may exhibit six flow regimes: large-scale fracture network linear flow, bilinear flow, small-scale fracture network linear flow, pseudosteady-state flow, interporosity flow and pseudoradial flow. During the first four flow periods, the large-scale fracture network behaves as if it only drains in the small-scale fracture network; that is, the effect of the matrix is negligibly small. The characteristics of the bilinear flow and the small-scale fracture network linear flow are predominantly determined by the dimensionless large-scale fracture conductivity. And low dimensionless fracture conductivity will generate large pressure drops in the large-scale fractures surrounding the wellbore. With
Modeling dynamic functional information flows on large-scale brain networks.
Lv, Peili; Guo, Lei; Hu, Xintao; Li, Xiang; Jin, Changfeng; Han, Junwei; Li, Lingjiang; Liu, Tianming
2013-01-01
Growing evidence from the functional neuroimaging field suggests that human brain functions are realized via dynamic functional interactions on large-scale structural networks. Even in resting state, functional brain networks exhibit remarkable temporal dynamics. However, it has been rarely explored to computationally model such dynamic functional information flows on large-scale brain networks. In this paper, we present a novel computational framework to explore this problem using multimodal resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) data. Basically, recent literature reports including our own studies have demonstrated that the resting state brain networks dynamically undergo a set of distinct brain states. Within each quasi-stable state, functional information flows from one set of structural brain nodes to other sets of nodes, which is analogous to the message package routing on the Internet from the source node to the destination. Therefore, based on the large-scale structural brain networks constructed from DTI data, we employ a dynamic programming strategy to infer functional information transition routines on structural networks, based on which hub routers that most frequently participate in these routines are identified. It is interesting that a majority of those hub routers are located within the default mode network (DMN), revealing a possible mechanism of the critical functional hub roles played by the DMN in resting state. Also, application of this framework on a post trauma stress disorder (PTSD) dataset demonstrated interesting difference in hub router distributions between PTSD patients and healthy controls.
Linear Programming and Network Flows
Bazaraa, Mokhtar S; Sherali, Hanif D
2011-01-01
The authoritative guide to modeling and solving complex problems with linear programming-extensively revised, expanded, and updated The only book to treat both linear programming techniques and network flows under one cover, Linear Programming and Network Flows, Fourth Edition has been completely updated with the latest developments on the topic. This new edition continues to successfully emphasize modeling concepts, the design and analysis of algorithms, and implementation strategies for problems in a variety of fields, including industrial engineering, management science, operations research
A pipe network simulation model with dynamic transition between free surface and pressurized flow
Directory of Open Access Journals (Sweden)
J. Fernández-Pato
2014-01-01
Full Text Available Water flow numerical simulation in urban pipe systems is one of the topics that shows the need for surface flows and pressurized flows in steady and transient situations. The governing equations for both flow types are different and this must be taken into account in order to get a complete numerical model for solving transients. A numerical simulation model is developed in this work, capable of solving pipe networks mainly unpressurized, with isolated peaks of pressurization. For this purpose, a reformulation of the mathematical model through the Preissmann slot method is proposed. By means of this technique, a reasonable estimation of the water pressure is calculated in cases of pressurization. The numerical model is based on the first order Roe's scheme, in the frame of finite volume methods. It is adapted to abrupt transient situations, with subcritial and supercritical flows. The validation has been done by means of several cases with analytic solutions or empirical laboratory data. It has also been applied to some more complex and realistic cases, like junctions or pipe networks.
End-to-end Information Flow Security Model for Software-Defined Networks
Directory of Open Access Journals (Sweden)
D. Ju. Chaly
2015-01-01
Full Text Available Software-defined networks (SDN are a novel paradigm of networking which became an enabler technology for many modern applications such as network virtualization, policy-based access control and many others. Software can provide flexibility and fast-paced innovations in the networking; however, it has a complex nature. In this connection there is an increasing necessity of means for assuring its correctness and security. Abstract models for SDN can tackle these challenges. This paper addresses to confidentiality and some integrity properties of SDNs. These are critical properties for multi-tenant SDN environments, since the network management software must ensure that no confidential data of one tenant are leaked to other tenants in spite of using the same physical infrastructure. We define a notion of end-to-end security in context of software-defined networks and propose a semantic model where the reasoning is possible about confidentiality, and we can check that confidential information flows do not interfere with non-confidential ones. We show that the model can be extended in order to reason about networks with secure and insecure links which can arise, for example, in wireless environments.The article is published in the authors’ wording.
Zakermoshfegh, M.; Ghodsian, M.; Salehi Neishabouri, S. A. A.; Shakiba, M.
River flow forecasting is required to provide important information on a wide range of cases related to design and operation of river systems. Since there are a lot of parameters with uncertainties and non-linear relationships, the calibration of conceptual or physically-based models is often a difficult and time consuming procedure. So it is preferred to implement a heuristic black box model to perform a non-linear mapping between the input and output spaces without detailed consideration of the internal structure of the physical process. In this study, the capability of artificial neural networks for stream flow forecasting in Kashkan River in West of Iran is investigated and compared to a NAM model which is a lumped conceptual model with shuffled complex evolution algorithm for auto calibration. Multi Layer Perceptron and Radial Basis Function neural networks are introduced and implemented. The results show that the discharge can be more adequately forecasted by Multi Layer Perceptron neural network, compared to other implemented models, in case of both peak discharge and base flow forecasting.
Todinov, Michael T
2013-01-01
Repairable flow networks are a new area of research, which analyzes the repair and flow disruption caused by failures of components in static flow networks. This book addresses a gap in current network research by developing the theory, algorithms and applications related to repairable flow networks and networks with disturbed flows. The theoretical results presented in the book lay the foundations of a new generation of ultra-fast algorithms for optimizing the flow in networks after failures or congestion, and the high computational speed creates the powerful possibility of optimal control
Information flow in a network model and the law of diminishing marginal returns
Marinazzo, Daniele; Wu, Guorong; Angelini, Leonardo; Stramaglia, Sebastiano
2012-01-01
We analyze a simple dynamical network model which describes the limited capacity of nodes to process the input information. For a suitable choice of the parameters, the information flow pattern is characterized by exponential distribution of the incoming information and a fat-tailed distribution of the outgoing information, as a signature of the law of diminishing marginal returns. The analysis of a real EEG data-set shows that similar phenomena may be relevant for brain signals.
A simple and accurate numerical network flow model for bionic micro heat exchangers
Energy Technology Data Exchange (ETDEWEB)
Pieper, M.; Klein, P. [Fraunhofer Institute (ITWM), Kaiserslautern (Germany)
2011-05-15
Heat exchangers are often associated with drawbacks like a large pressure drop or a non-uniform flow distribution. Recent research shows that bionic structures can provide possible improvements. We considered a set of such structures that were designed with M. Hermann's FracTherm {sup registered} algorithm. In order to optimize and compare them with conventional heat exchangers, we developed a numerical method to determine their performance. We simulated the flow in the heat exchanger applying a network model and coupled these results with a finite volume method to determine the heat distribution in the heat exchanger. (orig.)
Ye, Sheng; Covino, Timothy P.; Sivapalan, Murugesu; Basu, Nandita B.; Li, Hong-Yi; Wang, Shao-Wen
2012-06-01
We have used a dynamic hydrologic network model, coupled with a transient storage zone solute transport model, to simulate dissolved nutrient retention processes during transient flow events at the channel network scale. We explored several scenarios with a combination of rainfall variability, and biological and geomorphic characteristics of the catchment, to understand the dominant factors that control the transport of dissolved nutrients (e.g., nitrate) along channel networks. While much experimental work has focused on studying nutrient retention during base flow periods in headwater streams, our model-based theoretical analyses, for the given parameter combinations used, suggest that high-flow periods can contribute substantially to overall nutrient retention, and that bulk nutrient retention is greater in larger rivers compared to headwaters. The relative efficiencies of nutrient retention during high- and low-flow periods vary due to changes in the relative sizes of the main channel and transient storage zones, as well as due to differences in the relative strengths of the various nutrient retention mechanisms operating in both zones. Our results also indicate that nutrient retention efficiency at all spatial scales of observation has strong dependence on within-year variability of streamflow (e.g., frequency and duration of high and low flows), as well as on the relative magnitudes of the coefficients that govern biogeochemical uptake processes: the more variable the streamflow, the greater the export of nutrients. Despite limitations of the model parameterizations, our results suggest that increased attention must be paid to field observations of the interactions between process hydrology and nutrient transport and reaction processes at a range of scales to assist with extrapolation of understandings and estimates gained from site-specific studies to ungauged basins across gradients in climate, human impacts, and landscape characteristics.
Chaos in a dynamic model of traffic flows in an origin-destination network
Zhang, Xiaoyan; Jarrett, David F.
1998-06-01
In this paper we investigate the dynamic behavior of road traffic flows in an area represented by an origin-destination (O-D) network. Probably the most widely used model for estimating the distribution of O-D flows is the gravity model, [J. de D. Ortuzar and L. G. Willumsen, Modelling Transport (Wiley, New York, 1990)] which originated from an analogy with Newton's gravitational law. The conventional gravity model, however, is static. The investigation in this paper is based on a dynamic version of the gravity model proposed by Dendrinos and Sonis by modifying the conventional gravity model [D. S. Dendrinos and M. Sonis, Chaos and Social-Spatial Dynamics (Springer-Verlag, Berlin, 1990)]. The dynamic model describes the variations of O-D flows over discrete-time periods, such as each day, each week, and so on. It is shown that when the dimension of the system is one or two, the O-D flow pattern either approaches an equilibrium or oscillates. When the dimension is higher, the behavior found in the model includes equilibria, oscillations, periodic doubling, and chaos. Chaotic attractors are characterized by (positive) Liapunov exponents and fractal dimensions.
Dynamic Baysesian state-space model with a neural network for an online river flow prediction
Ham, Jonghwa; Hong, Yoon-Seok
2013-04-01
-space model formulation. The nonlinear Monte Carlo filtering algorithm is based on recursively constructing the posterior probability density (distribution) of the state variable of neural network's weight, with respect to measured data (in our case, river flow), through a random trajectory of the state by entities called 'particles' in the dynamic state-space model formulation. A weight, which is the probability of the trajectory of the state, is assigned to each particle by a Bayesian correction term based on measurement. The algorithms differ in the way that the swarm of particles evolves and adapts to incoming online measurement data. In order to demonstrate the efficiency and usefulness of the proposed MLP-SMC, a practical application of hydrological modeling is carried out to predict the river flow sequentially in advance on the arrival of each new item of river flow data at intervals of 10 minutes. The performance of the proposed MLP-SMC is compared with the performance of a multi-layer perceptron (MLP) model trained using the back-propagation learning algorithm (MLP-BP) in which a batch off-line learning algorithm is implemented. The results show that the proposed MLP-SMC shows superiority in terms of model accuracy and computational cost compared with MLP-BP. The sequential Monte Carlo learning algorithm implemented in MLP-SMC is shown to have less sensitivity to noisy and sparsely distributed data compared to the batch off-line learning algorithm used in MLP-BP.
A Generalized Fluid System Simulation Program to Model Flow Distribution in Fluid Networks
Majumdar, Alok; Bailey, John W.; Schallhorn, Paul; Steadman, Todd
1998-01-01
This paper describes a general purpose computer program for analyzing steady state and transient flow in a complex network. The program is capable of modeling phase changes, compressibility, mixture thermodynamics and external body forces such as gravity and centrifugal. The program's preprocessor allows the user to interactively develop a fluid network simulation consisting of nodes and branches. Mass, energy and specie conservation equations are solved at the nodes; the momentum conservation equations are solved in the branches. The program contains subroutines for computing "real fluid" thermodynamic and thermophysical properties for 33 fluids. The fluids are: helium, methane, neon, nitrogen, carbon monoxide, oxygen, argon, carbon dioxide, fluorine, hydrogen, parahydrogen, water, kerosene (RP-1), isobutane, butane, deuterium, ethane, ethylene, hydrogen sulfide, krypton, propane, xenon, R-11, R-12, R-22, R-32, R-123, R-124, R-125, R-134A, R-152A, nitrogen trifluoride and ammonia. The program also provides the options of using any incompressible fluid with constant density and viscosity or ideal gas. Seventeen different resistance/source options are provided for modeling momentum sources or sinks in the branches. These options include: pipe flow, flow through a restriction, non-circular duct, pipe flow with entrance and/or exit losses, thin sharp orifice, thick orifice, square edge reduction, square edge expansion, rotating annular duct, rotating radial duct, labyrinth seal, parallel plates, common fittings and valves, pump characteristics, pump power, valve with a given loss coefficient, and a Joule-Thompson device. The system of equations describing the fluid network is solved by a hybrid numerical method that is a combination of the Newton-Raphson and successive substitution methods. This paper also illustrates the application and verification of the code by comparison with Hardy Cross method for steady state flow and analytical solution for unsteady flow.
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.
Energy Technology Data Exchange (ETDEWEB)
Chen, D.J.
1988-01-01
The literature is abundant with combinatorial reliability analysis of communication networks and fault-tolerant computer systems. However, it is very difficult to formulate reliability indexes using combinatorial methods. These limitations have led to the development of time-dependent reliability analysis using stochastic processes. In this research, time-dependent reliability-analysis techniques using Dataflow Graphs (DGF) are developed. The chief advantages of DFG models over other models are their compactness, structural correspondence with the systems, and general amenability to direct interpretation. This makes the verification of the correspondence of the data-flow graph representation to the actual system possible. Several DGF models are developed and used to analyze the reliability of communication networks and computer systems. Specifically, Stochastic Dataflow graphs (SDFG), both the discrete-time and the continuous time models are developed and used to compute time-dependent reliability of communication networks and computer systems. The repair and coverage phenomenon of communication networks is also analyzed using SDFG models.
Directory of Open Access Journals (Sweden)
Ryo Hase
2016-01-01
Full Text Available This paper presents a sequential solution method to discover efficient trades in an electricity market model. The market model represents deregulated electricity market consisting of four types of participants: independent power producers, retailers, public utilities, and consumers. Our model is based on graph theory, and the market participants are denoted by a network composes of three types of agents including sellers, buyers, and traders. The market participants have different capacity and demand of electricity from each other, and each electricity trade should satisfy the capacity and demand. Our sequential solution method can discover efficient electricity trades satisfying the constraints regarding capacity and demand by utilizing network flow. Simulation results demonstrate the efficiency of electricity trades determined by our method by examining social welfare, which is the total of payoffs of all market participants. Furthermore, the simulation results also indicate the allocation of payoff to each market participant.
Flow Simulation of Urban Sewer Networks
Institute of Scientific and Technical Information of China (English)
佘云童; 茅泽育
2003-01-01
Flow in an urban drainage network is usually unsteady with backwater near junctions. The routing of hydrographs through a network is an important aspect of the design and analysis of urban drainage networks. Various numerical methods to analyze flow in urban drainage networks were compared and a new hybrid interpolation scheme was developed which combined time-line reachback interpolation, implicit interpolation and space-line interpolation. Numerical simulations show that the improved method more accurately models flows in urban drainage networks.
Berejnov, Viatcheslav; Sinton, David; Djilali, Ned
2009-01-01
Experimental two-phase invasion percolation flow patterns were observed in hydrophobic micro-porous networks designed to model fuel cell specific porous media. In order to mimic the operational conditions encountered in the porous electrodes of polymer electrolyte membrane fuel cells (PEMFCs), micro-porous networks were fabricated with corresponding microchannel size distributions. The inlet channels were invaded homogeneously with flow rates corresponding to fuel cell current densities of 1.0 to 0.1 A/cm2 (Ca 10e-7-10e-8). A variety of fractal breakthrough patterns were observed and analyzed to quantify flooding density and geometrical diversity in terms of the total saturation, St, local saturations, s, and fractal dimension, D. It was found that St increases monotonically during the invasion process until the breakthrough point is reached, and s profiles indicate the dynamic distribution of the liquid phase during the process. Fractal analysis confirmed that the experiments fall within the flow regime of i...
Donado-Garzon, L. D.; Pardo, Y.
2013-12-01
Fractured media are very heterogeneous systems where occur complex physical and chemical processes to model. One of the possible approaches to conceptualize this type of massifs is the Discrete Fracture Network (DFN). Donado et al., modeled flow and transport in a granitic batholith based on this approach and found good fitting with hydraulic and tracer tests, but the computational cost was excessive due to a gigantic amount of elements to model. We present in this work a methodology based on percolation theory for reducing the number of elements and in consequence, to reduce the bandwidth of the conductance matrix and the execution time of each network. DFN poses as an excellent representation of all the set of fractures of the media, but not all the fractures of the media are part of the conductive network. Percolation theory is used to identify which nodes or fractures are not conductive, based on the occupation probability or percolation threshold. In a fractured system, connectivity determines the flow pattern in the fractured rock mass. This volume of fluid is driven through connection paths formed by the fractures, when the permeability of the rock is negligible compared to the fractures. In a population of distributed fractures, each of this that has no intersection with any connected fracture do not contribute to generate a flow field. This algorithm also permits us to erase these elements however they are water conducting and hence, refine even more the backbone of the network. We used 100 different generations of DFN that were optimized in this study using percolation theory. In each of the networks calibrate hydrodynamic parameters as hydraulic conductivity and specific storage coefficient, for each of the five families of fractures, yielding a total of 10 parameters to estimate, at each generation. Since the effects of the distribution of fault orientation changes the value of the percolation threshold, but not the universal laws of classical
Xue, Jie; Gui, Dongwei; Zhao, Ying; Lei, Jiaqiang; Zeng, Fanjiang; Feng, Xinlong; Mao, Donglei; Shareef, Muhammad
2016-09-01
The competition for water resources between agricultural and natural oasis ecosystems has become an increasingly serious problem in oasis areas worldwide. Recently, the intensive extension of oasis farmland has led to excessive exploitation of water discharge, and consequently has resulted in a lack of water supply in natural oasis. To coordinate the conflicts, this paper provides a decision-making framework for modeling environmental flows in oasis areas using Bayesian networks (BNs). Three components are included in the framework: (1) assessment of agricultural economic loss due to meeting environmental flow requirements; (2) decision-making analysis using BNs; and (3) environmental flow decision-making under different water management scenarios. The decision-making criterion is determined based on intersection point analysis between the probability of large-level total agro-economic loss and the ratio of total to maximum agro-economic output by satisfying environmental flows. An application in the Qira oasis area of the Tarim Basin, Northwest China indicates that BNs can model environmental flow decision-making associated with agricultural economic loss effectively, as a powerful tool to coordinate water-use conflicts. In the case study, the environmental flow requirement is determined as 50.24%, 49.71% and 48.73% of the natural river flow in wet, normal and dry years, respectively. Without further agricultural economic loss, 1.93%, 0.66% and 0.43% of more river discharge can be allocated to eco-environmental water demands under the combined strategy in wet, normal and dry years, respectively. This work provides a valuable reference for environmental flow decision-making in any oasis area worldwide.
Modeling Liner Shipping Service Selection and Container Flows using a Multi-layer Network
DEFF Research Database (Denmark)
Karsten, Christian Vad; Balakrishnan, Anant
We introduce a new formulation for the tactical planning problem facing container shipping companies of selecting the best subset of sailing routes from a given pool of candidate routes so as to maximize profit. Since most containers are sent directly or transshipped at most twice in current liner...... shipping networks, we impose limits on the number of transshipments for each container (which most previous models do not incorporate). Our multi-layer multi-commodity model associates one commodity with each container origin port, and decides the route for each commodity on a logical network layer whose...... arcs represent segments (pairs of ports between which a container can use a single service). This approach, combined with commodity flow variables that are indexed by segment sequence permits us to incorporate the transshipment limits while also tracking the commodity’s outflow from the system...
Energy Technology Data Exchange (ETDEWEB)
Ye, Sheng; Covino, Timothy P.; Sivapalan, Murugesu; Basu, Nandita; Li, Hongyi; Wang, Shaowen
2012-06-30
In this paper, we use a dynamic network flow model, coupled with a transient storage zone biogeochemical model, to simulate dissolved nutrient removal processes at the channel network scale. We have explored several scenarios in respect of the combination of rainfall variability, and the biological and geomorphic characteristics of the catchment, to understand the dominant controls on removal and delivery of dissolved nutrients (e.g., nitrate). These model-based theoretical analyses suggested that while nutrient removal efficiency is lower during flood events compared to during baseflow periods, flood events contribute significantly to bulk nutrient removal, whereas bulk removal during baseflow periods is less. This is due to the fact that nutrient supply is larger during flood events; this trend is even stronger in large rivers. However, the efficiency of removal during both periods decreases in larger rivers, however, due to (i) increasing flow velocities and thus decreasing residence time, and (ii) increasing flow depth, and thus decreasing nutrient uptake rates. Besides nutrient removal processes can be divided into two parts: in the main channel and in the hyporheic transient storage zone. When assessing their relative contributions the size of the transient storage zone is a dominant control, followed by uptake rates in the main channel and in the transient storage zone. Increasing size of the transient storage zone with downstream distance affects the relative contributions to nutrient removal of the water column and the transient storage zone, which also impacts the way nutrient removal rates scale with increasing size of rivers. Intra-annual hydrologic variability has a significant impact on removal rates at all scales: the more variable the streamflow is, compared to mean discharge, the less nutrient is removed in the channel network. A scale-independent first order uptake coefficient, ke, estimated from model simulations, is highly dependent on the
Calibrating a flow model in an irrigation network: Case study in Alicante, Spain
Directory of Open Access Journals (Sweden)
Modesto Pérez-Sánchez
2017-04-01
Full Text Available The usefulness of models depends on their validation in a calibration process, ensuring that simulated flows and pressure values in any line are really occurring and, therefore, becoming a powerful decision tool for many aspects in the network management (i.e., selection of hydraulic machines in pumped systems, reduction of the installed power in operation, analysis of theoretical energy recovery. A new proposed method to assign consumptions patterns and to determine flows over time in irrigation networks is calibrated in the present research. As novelty, the present paper proposes a robust calibration strategy for flow assignment in lines, based on some key performance indicators (KPIF coming from traditional hydrological models: Nash-Sutcliffe coefficient (non-dimensional index, root relative square error (error index and percent bias (tendency index. The proposed strategy for calibration was applied to a real case in Alicante (Spain, with a goodness of fit considered as “very good” in many indicators. KPIF parameters observed present a satisfactory goodness of fit of the series, considering their repeatability. Average Nash-Sutcliffe coefficient value oscillated between 0.30 and 0.63, average percent bias values were below 10% in all the range, and average root relative square error values varied between 0.65 and 0.80.
Nandi, Anjan K; Sumana, Annagiri; Bhattacharya, Kunal
2014-12-06
Social insects provide an excellent platform to investigate flow of information in regulatory systems since their successful social organization is essentially achieved by effective information transfer through complex connectivity patterns among the colony members. Network representation of such behavioural interactions offers a powerful tool for structural as well as dynamical analysis of the underlying regulatory systems. In this paper, we focus on the dominance interaction networks in the tropical social wasp Ropalidia marginata-a species where behavioural observations indicate that such interactions are principally responsible for the transfer of information between individuals about their colony needs, resulting in a regulation of their own activities. Our research reveals that the dominance networks of R. marginata are structurally similar to a class of naturally evolved information processing networks, a fact confirmed also by the predominance of a specific substructure-the 'feed-forward loop'-a key functional component in many other information transfer networks. The dynamical analysis through Boolean modelling confirms that the networks are sufficiently stable under small fluctuations and yet capable of more efficient information transfer compared to their randomized counterparts. Our results suggest the involvement of a common structural design principle in different biological regulatory systems and a possible similarity with respect to the effect of selection on the organization levels of such systems. The findings are also consistent with the hypothesis that dominance behaviour has been shaped by natural selection to co-opt the information transfer process in such social insect species, in addition to its primal function of mediation of reproductive competition in the colony.
Treating network junctions in finite volume solution of transient gas flow models
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.
Stochastic cycle selection in active flow networks
Woodhouse, Francis; Forrow, Aden; Fawcett, Joanna; Dunkel, Jorn
2016-11-01
Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such non-equilibrium networks. By connecting concepts from lattice field theory, graph theory and transition rate theory, we show how topology controls dynamics in a generic model for actively driven flow on a network. Through theoretical and numerical analysis we identify symmetry-based rules to classify and predict the selection statistics of complex flow cycles from the network topology. Our conceptual framework is applicable to a broad class of biological and non-biological far-from-equilibrium networks, including actively controlled information flows, and establishes a new correspondence between active flow networks and generalized ice-type models.
The value of "black-box" neural network modeling in subsurface flow prediction
Paleologos, E.; Skitzi, I.; Katsifarakis, K.
2012-04-01
In several hydrologic cases the complexity of the processes involved tied in with the uncertainty in the subsurface geologic environment, geometries, and boundary conditions cannot be addressed by constitutive relationships, either in a deterministic or a stochastic framework. "Black-box" models are used routinely in surface hydrologic predictions, but in subsurface hydrology there is still a tendency to rely on physical descriptions, even in problems where the geometry, the medium, the processes, the boundary conditions are largely unknown. Subsurface flow in karstic environments exemplifies all the above complexities and uncertainties rendering the use of physical models impractical. The current study uses neural networks to exemplify that "black-box" models can provide useful predictions even in the absence of physical process descriptions. Daily discharges of two springs lying in a karstic environment were simulated for a period of two and a half years with the use of a multi-layer perceptron back-propagation neural network. Missing discharge values were supplemented by assuming linear relationships during base flow conditions, thus extending the length of the data record during the network's training phase and improving its performance. The time lag between precipitation and spring discharge differed significantly for the two springs indicating that in karstic environments hydraulic behavior is dominated, even within a few hundred meters, by local conditions. Optimum training results were attained with a Levenberg-Marquardt algorithm resulting in a network architecture consisting of two input layer neurons, four hidden layer neurons, and one output layer neuron, the spring's discharge. The neural network's predictions captured the behavior for both springs and followed very closely the discontinuities in the discharge time series. Under/over-estimation of observed discharges for the two springs remained below 3%, with the exception of a few local maxima where
Shah, Virag; Manjunath, D
2010-01-01
We consider in-network computation of an arbitrary function over an arbitrary communication network. A network with capacity constraints on the links is given. Some nodes in the network generate data, e.g., like sensor nodes in a sensor network. An arbitrary function of this distributed data is to be obtained at a terminal node. The structure of the function is described by a given computation schema, which in turn is represented by a directed tree. We design computing and communicating schemes to obtain the function at the terminal at the maximum rate. For this, we formulate linear programs to determine network flows that maximize the computation rate. We then develop fast combinatorial primal-dual algorithm to obtain $\\epsilon$-approximate solutions to these linear programs. We then briefly describe extensions of our techniques to the cases of multiple terminals wanting different functions, multiple computation schemas for a function, computation with a given desired precision, and to networks with energy c...
Institute of Scientific and Technical Information of China (English)
Chen Yu-Dong; Li Li; Zhang Yi; Hu Jian-Ming
2009-01-01
In the study of complex networks(systems),the scaling phenomenon of flow fluctuations rears to a certain power law between the mean flux(activity)(Fi)of the i-th node and its variance σi as σi ∝((Fi)α.Such scaling laws are found to be prevalent both in natural and man-made network systems,but the understanding of their origins still remains limited.This paper proposes a non-stationary Poisson process model to give an analytical explanation of the non-universal scaling phenomenon:the exponent α varies between 1/2 and 1 depending on the size of sampling time window and the relative strength of the external/internal driven forces of the systems.The crossover behaviour and the relation of fluctuation scaling with pseudo long range dependence are also accounted for by the model.Numerical experiments show that the proposed model can recover the multi-scaling phenomenon.
Power Laws and Fragility in Flow Networks
Shore, Jesse; Bianchi, Matt T
2013-01-01
What makes economic and ecological networks so unlike other highly skewed networks in their tendency toward turbulence and collapse? Here, we explore the consequences of a defining feature of these networks: their nodes are tied together by flow. We show that flow networks tend to the power law degree distribution (PLDD) due to a self-reinforcing process involving position within the global network structure, and thus present the first random graph model for PLDDs that does not depend on a rich-get-richer function of nodal degree. We also show that in contrast to non-flow networks, PLDD flow networks are dramatically more vulnerable to catastrophic failure than non-PLDD flow networks, a finding with potential explanatory power in our age of resource- and financial-interdependence and turbulence.
Stochastic cycle selection in active flow networks
Woodhouse, Francis G; Fawcett, Joanna B; Dunkel, Jörn
2016-01-01
Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such non-equilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of non-biological far-from-equilibrium networks, including actively controlled information flows, and establishes a new correspondence between active flow networks and generalized ...
Punnen, Abraham P
2007-01-01
The bottleneck network flow problem (BNFP) is a generalization of several well-studied bottleneck problems such as the bottleneck transportation problem (BTP), bottleneck assignment problem (BAP), bottleneck path problem (BPP), and so on. In this paper we provide a review of important results on this topic and its various special cases. We observe that the BNFP can be solved as a sequence of $O(\\log n)$ maximum flow problems. However, special augmenting path based algorithms for the maximum flow problem can be modified to obtain algorithms for the BNFP with the property that these variations and the corresponding maximum flow algorithms have identical worst case time complexity. On unit capacity network we show that BNFP can be solved in $O(\\min \\{{m(n\\log n)}^{{2/3}}, m^{{3/2}}\\sqrt{\\log n}\\})$. This improves the best available algorithm by a factor of $\\sqrt{\\log n}$. On unit capacity simple graphs, we show that BNFP can be solved in $O(m \\sqrt {n \\log n})$ time. As a consequence we have an $O(m \\sqrt {n \\l...
International Organization for Standardization. Geneva
2003-01-01
Information technology - Telecommunications and information exchange between systems - Private integrated services network - Specification, functional model and information flows - Call priority interruption and call priority interruption protection supplementary services
International Organization for Standardization. Geneva
2003-01-01
Information technology - Telecommunications and information exchange between systems - Private Integrated Services Network - Specification, functional model and information flows - Call transfer supplementary service
International Organization for Standardization. Geneva
2003-01-01
Information technology - Telecommunications and information exchange between systems - Private integrated services network - Specification, functional model and information flows - Recall supplementary service
Network structure of subway passenger flows
Xu, Qi; Bai, Yun
2016-01-01
The results of transportation infrastructure network analyses have been used to analyze complex networks in a topological context. However, most modeling approaches, including those based on complex network theory, do not fully account for real-life traffic patterns and may provide an incomplete view of network functions. This study utilizes trip data obtained from the Beijing Subway System to characterize individual passenger movement patterns. A directed weighted passenger flow network was constructed from the subway infrastructure network topology by incorporating trip data. The passenger flow networks exhibit several properties that can be characterized by power-law distributions based on flow size, and log-logistic distributions based on the fraction of boarding and departing passengers. The study also characterizes the temporal patterns of in-transit and waiting passengers and provides a hierarchical clustering structure for passenger flows. This hierarchical flow organization varies in the spatial doma...
Kalteh, Aman Mohammad
2013-04-01
Reliable and accurate forecasts of river flow is needed in many water resources planning, design development, operation and maintenance activities. In this study, the relative accuracy of artificial neural network (ANN) and support vector regression (SVR) models coupled with wavelet transform in monthly river flow forecasting is investigated, and compared to regular ANN and SVR models, respectively. The relative performance of regular ANN and SVR models is also compared to each other. For this, monthly river flow data of Kharjegil and Ponel stations in Northern Iran are used. The comparison of the results reveals that both ANN and SVR models coupled with wavelet transform, are able to provide more accurate forecasting results than the regular ANN and SVR models. However, it is found that SVR models coupled with wavelet transform provide better forecasting results than ANN models coupled with wavelet transform. The results also indicate that regular SVR models perform slightly better than regular ANN models.
Directory of Open Access Journals (Sweden)
Louis Gagnon
2016-08-01
Full Text Available Oxygen is delivered to brain tissue by a dense network of microvessels, which actively control cerebral blood flow (CBF through vasodilation and contraction in response to changing levels of neural activity. Understanding these network-level processes is immediately relevant for (1 interpretation of functional Magnetic Resonance Imaging (fMRI signals, and (2 investigation of neurological diseases in which a deterioration of neurovascular and neuro-metabolic physiology contributes to motor and cognitive decline. Experimental data on the structure, flow and oxygen levels of microvascular networks are needed, together with theoretical methods to integrate this information and predict physiologically relevant properties that are not directly measurable. Recent progress in optical imaging technologies for high-resolution in vivo measurement of the cerebral microvascular architecture, blood flow, and oxygenation enables construction of detailed computational models of cerebral hemodynamics and oxygen transport based on realistic three-dimensional microvascular networks. In this article, we review state-of-the-art optical microscopy technologies for quantitative in vivo imaging of cerebral microvascular structure, blood flow and oxygenation, and theoretical methods that utilize such data to generate spatially resolved models for blood flow and oxygen transport. These bottom-up models are essential for the understanding of the processes governing brain oxygenation in normal and disease states and for eventual translation of the lessons learned from animal studies to humans.
A generation-attraction model for renewable energy flows in Italy: A complex network approach
Valori, Luca; Giannuzzi, Giovanni Luca; Facchini, Angelo; Squartini, Tiziano; Garlaschelli, Diego; Basosi, Riccardo
2016-10-01
In recent years, in Italy, the trend of the electricity demand and the need to connect a large number of renewable energy power generators to the power-grid, developed a novel type of energy transmission/distribution infrastructure. The Italian Transmission System Operator (TSO) and the Distribution System Operator (DSO), worked on a new infrastructural model, based on electronic meters and information technology. In pursuing this objective it is crucial importance to understand how even more larger shares of renewable energy can be fully integrated, providing a constant and reliable energy background over space and time. This is particularly true for intermittent sources as photovoltaic installations due to the fine-grained distribution of them across the Country. In this work we use an over-simplified model to characterize the Italian power grid as a graph whose nodes are Italian municipalities and the edges cross the administrative boundaries between a selected municipality and its first neighbours, following a Delaunay triangulation. Our aim is to describe the power flow as a diffusion process over a network, and using open data on the solar irradiation at the ground level, we estimate the production of photovoltaic energy in each node. An attraction index was also defined using demographic data, in accordance with average per capita energy consumption data. The available energy on each node was calculated by finding the stationary state of a generation-attraction model.
Energy Technology Data Exchange (ETDEWEB)
Chung, Ji Bum [Institute for Advanced Engineering, Yongin (Korea, Republic of); Park, Jong Woon [Korea Electric Power Research Institute, Taejon (Korea, Republic of)
1998-12-31
In order to enhance the dynamic and interactive simulation capability of a system thermal hydraulic code for nuclear power plant, applicability of flow network models in SINDA/FLUINT{sup TM} has been tested by modeling feedwater system and coupling to DSNP which is one of a system thermal hydraulic simulation code for a pressurized heavy water reactor. The feedwater system is selected since it is one of the most important balance of plant systems with a potential to greatly affect the behavior of nuclear steam supply system. The flow network model of this feedwater system consists of condenser, condensate pumps, low and high pressure heaters, deaerator, feedwater pumps, and control valves. This complicated flow network is modeled and coupled to DSNP and it is tested for several normal and abnormal transient conditions such turbine load maneuvering, turbine trip, and loss of class IV power. The results show reasonable behavior of the coupled code and also gives a good dynamic and interactive simulation capabilities for the several mild transient conditions. It has been found that coupling system thermal hydraulic code with a flow network code is a proper way of upgrading simulation capability of DSNP to mature nuclear plant analyzer (NPA). 5 refs., 10 figs. (Author)
Huang, Ailing; Zang, Guangzhi; He, Zhengbing; Guan, Wei
2017-05-01
Urban public transit system is a typical mixed complex network with dynamic flow, and its evolution should be a process coupling topological structure with flow dynamics, which has received little attention. This paper presents the R-space to make a comparative empirical analysis on Beijing’s flow-weighted transit route network (TRN) and we found that both the Beijing’s TRNs in the year of 2011 and 2015 exhibit the scale-free properties. As such, we propose an evolution model driven by flow to simulate the development of TRNs with consideration of the passengers’ dynamical behaviors triggered by topological change. The model simulates that the evolution of TRN is an iterative process. At each time step, a certain number of new routes are generated driven by travel demands, which leads to dynamical evolution of new routes’ flow and triggers perturbation in nearby routes that will further impact the next round of opening new routes. We present the theoretical analysis based on the mean-field theory, as well as the numerical simulation for this model. The results obtained agree well with our empirical analysis results, which indicate that our model can simulate the TRN evolution with scale-free properties for distributions of node’s strength and degree. The purpose of this paper is to illustrate the global evolutional mechanism of transit network that will be used to exploit planning and design strategies for real TRNs.
DEFF Research Database (Denmark)
Kamel, S.; Jurado, F.; Chen, Zhe
2015-01-01
This paper presents an implicit modeling of Static Synchronous Series Compensator (SSSC) in Newton–Raphson load flow method. The algorithm of load flow is based on the revised current injection formulation. The developed model of SSSC is depended on the current injection approach. In this model......, the voltage source representation of SSSC is transformed to current source, and then this current is injected at the sending and auxiliary buses. These injected currents at the terminals of SSSC are a function of the required line flow and voltage of buses. These currents can be included easily...... to the original mismatches at the terminal buses of SSSC. The developed model can be used to control active and reactive line flow together or individually. The implicit modeling of SSSC device decreases the complexity of load flow code, the modification of Jacobian matrix is avoided, the change only...
Ryo, Masahiro; Iwasaki, Yuichi; Yoshimura, Chihiro; Saavedra V., Oliver C.
2015-01-01
Alteration of the spatial variability of natural flow regimes has been less studied than that of the temporal variability, despite its ecological importance for river ecosystems. Here, we aimed to quantify the spatial patterns of flow regime alterations along a river network in the Sagami River, Japan, by estimating river discharge under natural and altered flow conditions. We used a distributed hydrological model, which simulates hydrological processes spatiotemporally, to estimate 20-year daily river discharge along the river network. Then, 33 hydrologic indices (i.e., Indicators of Hydrologic Alteration) were calculated from the simulated discharge to estimate the spatial patterns of their alterations. Some hydrologic indices were relatively well estimated such as the magnitude and timing of maximum flows, monthly median flows, and the frequency of low and high flow pulses. The accuracy was evaluated with correlation analysis (r > 0.4) and the Kolmogorov–Smirnov test (α = 0.05) by comparing these indices calculated from both observed and simulated discharge. The spatial patterns of the flow regime alterations varied depending on the hydrologic indices. For example, both the median flow in August and the frequency of high flow pulses were reduced by the maximum of approximately 70%, but these strongest alterations were detected at different locations (i.e., on the mainstream and the tributary, respectively). These results are likely caused by different operational purposes of multiple water control facilities. The results imply that the evaluation only at discharge gauges is insufficient to capture the alteration of the flow regime. Our findings clearly emphasize the importance of evaluating the spatial pattern of flow regime alteration on a river network where its discharge is affected by multiple water control facilities. PMID:26207997
Directory of Open Access Journals (Sweden)
Masahiro Ryo
Full Text Available Alteration of the spatial variability of natural flow regimes has been less studied than that of the temporal variability, despite its ecological importance for river ecosystems. Here, we aimed to quantify the spatial patterns of flow regime alterations along a river network in the Sagami River, Japan, by estimating river discharge under natural and altered flow conditions. We used a distributed hydrological model, which simulates hydrological processes spatiotemporally, to estimate 20-year daily river discharge along the river network. Then, 33 hydrologic indices (i.e., Indicators of Hydrologic Alteration were calculated from the simulated discharge to estimate the spatial patterns of their alterations. Some hydrologic indices were relatively well estimated such as the magnitude and timing of maximum flows, monthly median flows, and the frequency of low and high flow pulses. The accuracy was evaluated with correlation analysis (r > 0.4 and the Kolmogorov-Smirnov test (α = 0.05 by comparing these indices calculated from both observed and simulated discharge. The spatial patterns of the flow regime alterations varied depending on the hydrologic indices. For example, both the median flow in August and the frequency of high flow pulses were reduced by the maximum of approximately 70%, but these strongest alterations were detected at different locations (i.e., on the mainstream and the tributary, respectively. These results are likely caused by different operational purposes of multiple water control facilities. The results imply that the evaluation only at discharge gauges is insufficient to capture the alteration of the flow regime. Our findings clearly emphasize the importance of evaluating the spatial pattern of flow regime alteration on a river network where its discharge is affected by multiple water control facilities.
Ryo, Masahiro; Iwasaki, Yuichi; Yoshimura, Chihiro; Saavedra V, Oliver C
2015-01-01
Alteration of the spatial variability of natural flow regimes has been less studied than that of the temporal variability, despite its ecological importance for river ecosystems. Here, we aimed to quantify the spatial patterns of flow regime alterations along a river network in the Sagami River, Japan, by estimating river discharge under natural and altered flow conditions. We used a distributed hydrological model, which simulates hydrological processes spatiotemporally, to estimate 20-year daily river discharge along the river network. Then, 33 hydrologic indices (i.e., Indicators of Hydrologic Alteration) were calculated from the simulated discharge to estimate the spatial patterns of their alterations. Some hydrologic indices were relatively well estimated such as the magnitude and timing of maximum flows, monthly median flows, and the frequency of low and high flow pulses. The accuracy was evaluated with correlation analysis (r > 0.4) and the Kolmogorov-Smirnov test (α = 0.05) by comparing these indices calculated from both observed and simulated discharge. The spatial patterns of the flow regime alterations varied depending on the hydrologic indices. For example, both the median flow in August and the frequency of high flow pulses were reduced by the maximum of approximately 70%, but these strongest alterations were detected at different locations (i.e., on the mainstream and the tributary, respectively). These results are likely caused by different operational purposes of multiple water control facilities. The results imply that the evaluation only at discharge gauges is insufficient to capture the alteration of the flow regime. Our findings clearly emphasize the importance of evaluating the spatial pattern of flow regime alteration on a river network where its discharge is affected by multiple water control facilities.
Institute of Scientific and Technical Information of China (English)
Ravindranadh BOBBILI; V. MADHU; A.K. GOGIA
2014-01-01
An artificial neural network (ANN) constitutive model is developed for high strength armor steel tempered at 500 ?C, 600 ?C and 650 ?C based on high strain rate data generated from split Hopkinson pressure bar (SHPB) experiments. A new neural network configuration consisting of both training and validation is effectively employed to predict flow stress. Tempering temperature, strain rate and strain are considered as inputs, whereas flow stress is taken as output of the neural network. A comparative study on JohnsoneCook (JeC) model and neural network model is performed. It was observed that the developed neural network model could predict flow stress under various strain rates and tempering temperatures. The experimental stressestrain data obtained from high strain rate compression tests using SHPB, over a range of tempering temperatures (500e650 ?C), strains (0.05e0.2) and strain rates (1000e5500/s) are employed to formulate JeC model to predict the high strain rate deformation behavior of high strength armor steels. The J-C model and the back-propagation ANN model were developed to predict the high strain rate deformation behavior of high strength armor steel and their predictability is evaluated in terms of correlation coefficient (R) and average absolute relative error (AARE). R and AARE for the JeC model are found to be 0.7461 and 27.624%, respectively, while R and AARE for the ANN model are 0.9995 and 2.58%, respectively. It was observed that the predictions by ANN model are in consistence with the experimental data for all tempering temperatures.
Exploring multi-layer flow network of international trade based on flow distances
Shen, Bin; Zheng, Qiuhua
2015-01-01
Based on the approach of flow distances, the international trade flow system is studied from the perspective of multi-layer flow network. A model of multi-layer flow network is proposed for modelling and analyzing multiple types of flows in flow systems. Then, flow distances are introduced, and symmetric minimum flow distance is presented. Subsequently, we discuss the establishment of the multi-layer flow networks of international trade from two coupled viewpoints, i.e., the viewpoint of commodity flow and that of money flow. Thus, the multi-layer flow networks of international trade is explored. First, trading "trophic levels" are adopted to depict positions that economies occupied in the flow network. We find that the distributions of trading "trophic levels" have the similar clustering pattern for different types of commodity, and there are some regularities between money flow network and commodity flow network. Second, we find that active and competitive countries trade a wide spectrum of products, while ...
Using higher-order Markov models to reveal flow-based communities in networks
Salnikov, Vsevolod; Lambiotte, Renaud
2016-01-01
Complex systems made of interacting elements are commonly abstracted as networks, in which nodes are associated with dynamic state variables, whose evolution is driven by interactions mediated by the edges. Markov processes have been the prevailing paradigm to model such a network-based dynamics, for instance in the form of random walks or other types of diffusions. Despite the success of this modelling perspective for numerous applications, it represents an over-simplification of several real-world systems. Importantly, simple Markov models lack memory in their dynamics, an assumption often not realistic in practice. Here, we explore possibilities to enrich the system description by means of second-order Markov models, exploiting empirical pathway information. We focus on the problem of community detection and show that standard network algorithms can be generalized in order to extract novel temporal information about the system under investigation. We also apply our methodology to temporal networks, where w...
Deterministic Chaos Model for Self-Organized Adaptive Networks in Atmospheric Flows
Selvam, A M
2003-01-01
The complex spatiotemporal patterns of atmospheric flows resulting from the cooperative existence of fluctuations ranging in size from millimeters to thousands of kilometers are found to exhibit long-range spatial and temporal correlations manifested as the selfsimilar fractal geometry to the global cloud cover pattern and the inverse power law form for the atmospheric eddy energy spectrum. Such long-range spatial and temporal correlations are ubiquitous to extended natural dynamical systems and is a signature of the strange attractor design characterizing deterministic chaos or self-organized criticality. The unified network of global atmospheric circulations is analogous to the neural networks of the human brain.
Flow whitelisting in SCADA networks
DEFF Research Database (Denmark)
Barbosa, Rafael Ramos Regis; Sadre, Ramin; Pras, Aiko
2013-01-01
Supervisory control and data acquisition (SCADA) networks are commonly deployed in large industrial facilities. Modern SCADA networks are becoming more vulnerable to cyber attacks due to the common use of standard communications protocols and increased interconnections with corporate networks...... and the Internet. This paper describes an approach for improving the security of SCADA networks using flow whitelisting. A flow whitelist describes legitimate traffic based on four properties of network packets: client address, server address, server-side port and transport protocol. The proposed approach...
Stress dependent fluid flow in porous rock: experiments and network modelling
Energy Technology Data Exchange (ETDEWEB)
Flornes, Olav
2005-07-01
permeability change was the same in the two directions. When a rock is subjected to sufficiently high stresses, permanent damage, or failure, occurs and we expect a more drastic change in its properties. In a series of experiments on three different materials, we study how permeability is changed by different failure modes. We observed that permeability followed the volumetric strain independent of failure mode in two of the materials, Saltwash and Tuffeau de Maastricht, while for Red Wildmoor, the permeability changed in a more complex manner. We present two numerical models that study how deformation and fluid flow are related. While they are both network models, one is focused on the effect of changed porosity on permeability, and tries to give some insight to the experimental results described above. The other one is more theoretical, and investigates some universal properties associated with hydraulic fracturing using a Biot-Cosserat model (author) (ml)
Directory of Open Access Journals (Sweden)
Mi Gan
2014-01-01
Full Text Available The multiproduct two-layer supply chain is very common in various industries. In this paper, we introduce a possible modeling and algorithms to solve a multiproduct two-layer supply chain network design problem. The decisions involved are the DCs location and capacity design decision and the initial distribution planning decision. First we describe the problem and give a mixed integer programming (MIP model; such problem is NP-hard and it is not easy to reduce the complexity. Inspired by it, we develop a transformation mechanism of relaxing the fixed cost and adding some virtual nodes and arcs to the original network. Thus, a network flow problem (NFP corresponding to the original problem has been formulated. Given that we could solve the NFP as a minimal cost flow problem. The solution procedures and network simplex algorithm (INS are discussed. To verify the effectiveness and efficiency of the model and algorithms, the performance measure experimental has been conducted. The experiments and result showed that comparing with MIP model solved by genetic algorithm (GA and Benders, decomposition algorithm (BD the NFP model and INS are also effective and even more efficient for both small-scale and large-scale problems.
Tzevelekos; Kikkinides; Kainourgiakis; Stubos; Kanellopoulos; Kaselouri
2000-03-01
Flow of condensable vapors in mesoporous media is investigated theoretically and experimentally during adsorption and desorption processes. A typical permeability curve of a condensable vapor is strongly enhanced in the capillary condensation region. This is because additional capillary pressure gradients are imposed on the capillary-condensed pores, which act as "good" conductors compared to the noncondensed pores, which are considered "poor" conductors. The percolation scaling properties that hold for a system of "good" and "poor" conductors are confirmed for the cases examined. As the ratio of gas flow/capillary-enhanced flow decreases, the rise of permeability with pressure becomes sharper. The network connectivity has a strong impact on the maximum permeability value and on the width of the scaling law regions. The contribution of surface flow does not affect the permeability in the peak region, but results in a shrinkage of the scaling law regions. During desorption, a marked hysteresis in the permeability curves is found and it is attributed only to thermodynamic hysteresis. The maximum permeability values in this case are higher and shifted to lower relative pressures. Copyright 2000 Academic Press.
Despagne, Wilfried; Frenod, Emmanuel
2014-01-01
Purpose: The purpose of this paper is to investigate the road freight haulage activity. Using the physical and data flow information from a freight forwarder, we intend to model the flow of inbound and outbound goods in a freight transport hub. Approach: This paper presents the operation of a road haulage group. To deliver goods within two days to any location in France, a haulage contractor needs to be part of a network. This network handles the processing of both physical goods and data. We...
Energy Technology Data Exchange (ETDEWEB)
Kim, Hyun Young; Hwang, Cheol Sang; Kang, Seok Man; Lee, Kwang Ya [Rural Development Corp., Seoul (Korea)
1998-08-31
When various elements of water balance are displayed at several points of a river network, the runoff amounts at an estuary especially tidal influenced are affected from the elements. This problem can be solved by a model that can generalize and formulate the elements and simulate daily runoff and water requirement. The WWASS model was built using DIROM for the simulation of daily runoff and water requirement, and the water balance elements were modeled to be balanced at the each control point of river network. The model was calibrated, verified and applied to the watershed for the Saemankeum tidal land reclamation development project. It showed that the results from the stream flow simulation at the Mankyung and Dongjin estuary were acceptable for the design of the Saemankeum estuary reservoir. (author). 7 refs., 3 tabs., 8 figs.
Energy Technology Data Exchange (ETDEWEB)
Yeh, G.T.
1982-01-01
A description is given of the development of a channel hydrodynamic model for simulating the behavior of flows and water surface elevations in a river network that may consist of any number of joined and branched rivers/streams, including both tidal and nontidal rivers. The model employs a numerical method, an integrated compartment method (ICM). The basic procedures of the ICM are first to discretize the river/stream system into compartments of various sizes, then to apply three integral theorems of vectors to transform the n-dimensional volume integral into an (n - 1)-dimensional surface integral, and finally to close the system by using simple interpolation to relate the interfacial values in terms of the compartment values. Thus, the method greatly facilitates the setup of algebraic equations for the discrete field approximating the corresponding continuous field. Most of the possible boundary conditions that may be anticipated in real-world problems are considered. These include junctions, prescribed flow, prescribed water surface elevation (or cross-sectional area), and rating curve boundaries. The use of ICM makes the implementation of these four types of boundary conditions relatively easy. The model is applied to two case studies: first to a single river and then to a network of five river channels in a watershed. Results indicate that the model can definitely simulate the behavior of the hydrodynamic variables that are required to compute chemical transport in a river/stream network.
Describing the Neuron Axons Network of the Human Brain by Continuous Flow Models
Hizanidis, J.; Katsaloulis, P.; Verganelakis, D. A.; Provata, A.
2014-12-01
The multifractal spectrum Dq (Rényi dimensions) is used for the analysis and comparison between the Neuron Axons Network (NAN) of healthy and pathological human brains because it conveys information about the statistics in many scales, from the very rare to the most frequent network configurations. Comparison of the Fractional Anisotropy Magnetic Resonance Images between healthy and pathological brains is performed with and without noise reduction. Modelling the complex structure of the NAN in the human brain is undertaken using the dynamics of the Lorenz model in the chaotic regime. The Lorenz multifractal spectra capture well the human brain characteristics in the large negative q's which represent the rare network configurations. In order to achieve a closer approximation in the positive part of the spectrum (q > 0) two independent modifications are considered: a) redistribution of the dense parts of the Lorenz model's phase space into their neighbouring areas and b) inclusion of additive uniform noise in the Lorenz model. Both modifications, independently, drive the Lorenz spectrum closer to the human NAN one in the positive q region without destroying the already good correspondence of the negative spectra. The modelling process shows that the unmodified Lorenz model in its full chaotic regime has a phase space distribution with high fluctuations in its dense parts, while the fluctuations in the human brain NAN are smoother. The induced modifications (phase space redistribution or additive noise) moderate the fluctuations only in the positive part of the Lorenz spectrum leading to a faithful representation of the human brain axons network in all scales.
Rezania, Vahid; Coombe, Dennis; Tuszynski, Jack A
2011-01-01
We develop a physiologically-based lattice model for the transport and metabolism of drugs in the functional unit of the liver, called the lobule. In contrast to earlier studies, we have emphasized the dominant role of convection in well-vascularized tissue with a given structure. Estimates of convective, diffusive and reaction contributions are given. We have compared drug concentration levels observed exiting the lobule with their predicted detailed distribution inside the lobule, assuming that most often the former is accessible information while the latter is not.
Non-blind watermarking of network flows
Houmansadr, Amir; Borisov, Nikita
2012-01-01
Linking network flows is an important problem in intrusion detection as well as anonymity. Passive traffic analysis can link flows but requires long periods of observation to reduce errors. Active traffic analysis, also known as flow watermarking, allows for better precision and is more scalable. Previous flow watermarks introduce significant delays to the traffic flow as a side effect of using a blind detection scheme; this enables attacks that detect and remove the watermark, while at the same time slowing down legitimate traffic. We propose the first non-blind approach for flow watermarking, called RAINBOW, that improves watermark invisibility by inserting delays hundreds of times smaller than previous blind watermarks, hence reduces the watermark interference on network flows. We derive and analyze the optimum detectors for RAINBOW as well as the passive traffic analysis under different traffic models by using hypothesis testing. Comparing the detection performance of RAINBOW and the passive approach we o...
Mode Selection in Compressible Active Flow Networks
Forrow, Aden; Woodhouse, Francis G.; Dunkel, Jörn
2017-07-01
Coherent, large-scale dynamics in many nonequilibrium physical, biological, or information transport networks are driven by small-scale local energy input. Here, we introduce and explore an analytically tractable nonlinear model for compressible active flow networks. In contrast to thermally driven systems, we find that active friction selects discrete states with a limited number of oscillation modes activated at distinct fixed amplitudes. Using perturbation theory, we systematically predict the stationary states of noisy networks and find good agreement with a Bayesian state estimation based on a hidden Markov model applied to simulated time series data. Our results suggest that the macroscopic response of active network structures, from actomyosin force networks to cytoplasmic flows, can be dominated by a significantly reduced number of modes, in contrast to energy equipartition in thermal equilibrium. The model is also well suited to study topological sound modes and spectral band gaps in active matter.
Parkin, G.; Birkinshaw, S. J.; Younger, P. L.; Rao, Z.; Kirk, S.
2007-06-01
SummaryEvaluation of the impacts of groundwater abstractions on surface water systems is a necessary task in integrated water resources management. A range of hydrological, hydrogeological, and geomorphological factors influence the complex processes of interaction between groundwater and rivers. This paper presents an approach which uses numerical modeling of generic river-aquifer systems to represent the interaction processes, and neural networks to capture the impacts of the different controlling factors. The generic models describe hydrogeological settings representing most river-aquifer systems in England and Wales: high diffusivity (e.g. Chalk) and low diffusivity (e.g. Triassic Sandstone) aquifers with flow to rivers mediated by alluvial gravels; the same aquifers where they are in direct connection with the river; and shallow alluvial aquifers which are disconnected from regional aquifers. Numerical model simulations using the SHETRAN integrated catchment modeling system provided outputs including time-series and spatial variations in river flow depletion, and spatially distributed groundwater levels. Artificial neural network models were trained using input parameters describing the controlling factors and the outputs from the numerical model simulations, providing an efficient tool for representing the impacts of groundwater abstractions across a wide range of conditions. There are very few field data sets of accurately quantified river flow depletion as a result of groundwater abstraction under controlled conditions. One such data set from an experimental study carried out in 1967 on the Winterbourne stream in the Lambourne catchment over a Chalk aquifer was used successfully to test the modeling tool. This modeling approach provides a general methodology for rapid simulations of complex hydrogeological systems which preserves the physical consistency between multiple and diverse model outputs.
Information flow analysis of interactome networks.
Directory of Open Access Journals (Sweden)
Patrycja Vasilyev Missiuro
2009-04-01
Full Text Available Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein-protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we
Directory of Open Access Journals (Sweden)
Peng Yang
2013-02-01
Full Text Available With the constant changes of the market demand trends and modern information technology which greatly influence supply chain operation and management, postponement has been developed to be an important strategy in some of the supply chain processes. This paper studies the multi-period and multi-commodity flow supply chain network equilibrium problem with postponement strategy under a type of supply chain management pattern. Taking the influences of the relationship between orders and inventory management of supply chain into consideration, and employing variational inequality to establish multi-period equilibrium model at each level and network equilibrium model , this paper eventually works out the conditions to make the system achieve equilibrium, and also offers economic interpretation with concrete examples to verify them.
Information Flows in Networked Engineering Design Projects
DEFF Research Database (Denmark)
Parraguez, Pedro; Maier, Anja
Complex engineering design projects need to manage simultaneously multiple information flows across design activities associated with different areas of the design process. Previous research on this area has mostly focused on either analysing the “required information flows” through activity...... networks at the project level or in studying the social networks that deliver the “actual information flow”. In this paper we propose and empirically test a model and method that integrates both social and activity networks into one compact representation, allowing to compare actual and required...... information flows between design spaces, and to assess the influence that these misalignments could have on the performance of engineering design projects....
Directory of Open Access Journals (Sweden)
Daniele Marinazzo
Full Text Available We analyze simple dynamical network models which describe the limited capacity of nodes to process the input information. For a proper range of their parameters, the information flow pattern in these models is characterized by exponential distribution of the incoming information and a fat-tailed distribution of the outgoing information, as a signature of the law of diminishing marginal returns. We apply this analysis to effective connectivity networks from human EEG signals, obtained by Granger Causality, which has recently been given an interpretation in the framework of information theory. From the distributions of the incoming versus the outgoing values of the information flow it is evident that the incoming information is exponentially distributed whilst the outgoing information shows a fat tail. This suggests that overall brain effective connectivity networks may also be considered in the light of the law of diminishing marginal returns. Interestingly, this pattern is reproduced locally but with a clear modulation: a topographic analysis has also been made considering the distribution of incoming and outgoing values at each electrode, suggesting a functional role for this phenomenon.
Energy Technology Data Exchange (ETDEWEB)
Amanifard, N.; Nariman-Zadeh, N.; Borji, M.; Khalkhali, A.; Habibdoust, A. [Department of Mechanical Engineering, The University of Guilan, P.O. Box 3756, Rasht (Iran)
2008-02-15
Three-dimensional heat transfer characteristics and pressure drop of water flow in a set of rectangular microchannels are numerically investigated using Fluent and compared with those of experimental results. Two metamodels based on the evolved group method of data handling (GMDH) type neural networks are then obtained for modelling of both pressure drop ({delta}P) and Nusselt number (Nu) with respect to design variables such as geometrical parameters of microchannels, the amount of heat flux and the Reynolds number. Using such obtained polynomial neural networks, multi-objective genetic algorithms (GAs) (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity preserving mechanism is then used for Pareto based optimization of microchannels considering two conflicting objectives such as ({delta}P) and (Nu). It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of microchannels can be discovered by Pareto based multi-objective optimization of the obtained polynomial metamodels representing their heat transfer and flow characteristics. Such important optimal principles would not have been obtained without the use of both GMDH type neural network modelling and the Pareto optimization approach. (author)
Cascades in interdependent flow networks
Scala, Antonio; Caldarelli, Guido; D'Agostino, Gregorio
2015-01-01
We investigate the abrupt breakdown behavior of coupled distribution grids under load growth. This scenario mimics the ever-increasing customer demand and the foreseen introduction of energy hubs interconnecting the different energy vectors. We extend an analytical model of cascading behavior due to line overloads to the case of interdependent networks and find evidence of first order transitions due to the long-range nature of the flows. Our results indicate that the foreseen increase in the couplings between the grids has two competing effects: on the one hand, it increases the safety region where grids can operate without withstanding systemic failures; on the other hand, it increases the possibility of a joint systems' failure.
Directory of Open Access Journals (Sweden)
Yun Seok Choi
2014-01-01
Full Text Available This work develops a grid based rainfall-runoff model (GRM, which is a physically based and spatially distributed model. Surface flow was analyzed using a kinematic wave model with the governing equations discretized using the finite volume method (FVM. This paper suggests a grid network flow analysis technique using variable rainfall intensity according to the flow directions to analyze one-dimensional flows between the grids. The model was evaluated by applying it to the Wuicheon watershed, a tributary of the Nakdonggang (Riv., in Korea. The results showed that the grid-based, one-dimensional kinematic wave model adopted the FVM and the grid network flow analysis technique well. The simulation results showed good agreement with the observed hydrographs and the initial soil saturation ratio was most sensitive to the modeling results.
Directory of Open Access Journals (Sweden)
Jiuping Xu
2012-01-01
Full Text Available The aim of this study is to deal with a minimum cost network flow problem (MCNFP in a large-scale construction project using a nonlinear multiobjective bilevel model with birandom variables. The main target of the upper level is to minimize both direct and transportation time costs. The target of the lower level is to minimize transportation costs. After an analysis of the birandom variables, an expectation multiobjective bilevel programming model with chance constraints is formulated to incorporate decision makers’ preferences. To solve the identified special conditions, an equivalent crisp model is proposed with an additional multiobjective bilevel particle swarm optimization (MOBLPSO developed to solve the model. The Shuibuya Hydropower Project is used as a real-world example to verify the proposed approach. Results and analysis are presented to highlight the performances of the MOBLPSO, which is very effective and efficient compared to a genetic algorithm and a simulated annealing algorithm.
Bashtani, Farzad; Maini, Brij; Kantzas, Apostolos
2016-08-01
3D random networks are constructed in order to represent the tight Mesaverde formation which is located in north Wyoming, USA. The porous-space is represented by pore bodies of different shapes and sizes which are connected to each other by pore throats of varying length and diameter. Pore bodies are randomly distributed in space and their connectivity varies based on the connectivity number distribution which is used in order to generate the network. Network representations are then validated using publicly available mercury porosimetry experiments. The network modeling software solves the fundamental equations of two-phase immiscible flow incorporating wettability and contact angle variability. Quasi-static displacement is assumed. Single phase macroscopic properties (porosity, permeability) are calculated and whenever possible are compared to experimental data. Using this information drainage and imbibition capillary pressure, and relative permeability curves are predicted and (whenever possible) compared to experimental data. The calculated information is grouped and compared to available literature information on typical behavior of tight formations. Capillary pressure curve for primary drainage process is predicted and compared to experimental mercury porosimetry in order to validate the virtual porous media by history matching. Relative permeability curves are also calculated and presented.
Directory of Open Access Journals (Sweden)
Jia Guo
2017-01-01
Full Text Available Conventional power systems are developing into cyber-physical power systems (CPPS with wide applications of communication, computer and control technologies. However, multiple practical cases show that the failure of cyber layers is a major factor leading to blackouts. Therefore, it is necessary to discuss the cascading failure process considering cyber layer failures and analyze the vulnerability of CPPS. In this paper, a CPPS model, which consists of cyber layer, physical layer and cyber-physical interface, is presented using complex network theory. Considering power flow properties, the impacts of cyber node failures on the cascading failure propagation process are studied. Moreover, two vulnerability indices are established from the perspective of both network structure and power flow properties. A vulnerability analysis method is proposed, and the CPPS performance before and after cascading failures is analyzed by the proposed method to calculate vulnerability indices. In the case study, three typical scenarios are analyzed to illustrate the method, and vulnerabilities under different interface strategies and attack strategies are compared. Two thresholds are proposed to value the CPPS vulnerability roughly. The results show that CPPS is more vulnerable under malicious attacks and cyber nodes with high indices are vulnerable points which should be reinforced.
Leandro, Jorge; Martins, Ricardo
2016-01-01
Pluvial flooding in urban areas is characterized by a gradually varying inundation process caused by surcharge of the sewer manholes. Therefore urban flood models need to simulate the interaction between the sewer network and the overland flow in order to accurately predict the flood inundation extents. In this work we present a methodology for linking 2D overland flow models with the storm sewer model SWMM 5. SWMM 5 is a well-known free open-source code originally developed in 1971. The latest major release saw its structure re-written in C ++ allowing it to be compiled as a command line executable or through a series of calls made to function inside a dynamic link library (DLL). The methodology developed herein is written inside the same DLL in C + +, and is able to simulate the bi-directional interaction between both models during simulation. Validation is done in a real case study with an existing urban flood coupled model. The novelty herein is that the new methodology can be added to SWMM without the need for editing SWMM's original code. Furthermore, it is directly applicable to other coupled overland flow models aiming to use SWMM 5 as the sewer network model.
Directory of Open Access Journals (Sweden)
Margareta RACOVITA
2011-11-01
Full Text Available This work proposes to solve a problem related to maximizing hotels’ revenues through two methods established in operational research domain. In the first part of the paper, the approach involves formulating the objective function and problem’s constraints, as well as the expansion of the model, taking into consideration clients’ preferences and the opportunities of group reservations. In the second part of the paper, the problem is solved with the help of network flows model, which allows optimum allocation of the rooms in real time. At the end of the paper, there are highlighted the advantages of applying those two mathematic methods within the strategies of performances development within hotel industry.
Hierarchical social networks and information flow
López, Luis; F. F. Mendes, Jose; Sanjuán, Miguel A. F.
2002-12-01
Using a simple model for the information flow on social networks, we show that the traditional hierarchical topologies frequently used by companies and organizations, are poorly designed in terms of efficiency. Moreover, we prove that this type of structures are the result of the individual aim of monopolizing as much information as possible within the network. As the information is an appropriate measurement of centrality, we conclude that this kind of topology is so attractive for leaders, because the global influence each actor has within the network is completely determined by the hierarchical level occupied.
Parameter estimation in channel network flow simulation
Institute of Scientific and Technical Information of China (English)
Han Longxi
2008-01-01
Simulations of water flow in channel networks require estimated values of roughness for all the individual channel segments that make up a network. When the number of individual channel segments is large, the parameter calibration workload is substantial and a high level of uncertainty in estimated roughness cannot be avoided. In this study, all the individual channel segments are graded according to the factors determining the value of roughness. It is assumed that channel segments with the same grade have the same value of roughness. Based on observed hydrological data, an optimal model for roughness estimation is built. The procedure of solving the optimal problem using the optimal model is described. In a test of its efficacy, this estimation method was applied successfully in the simulation of tidal water flow in a large complicated channel network in the lower reach of the Yangtze River in China.
Narimen Aouzellag LAHAÇANI; Boubekeur MENDIL
2008-01-01
The goal of any Flexible AC Transmission Systems (FACTS) devices study is to measure their impact on the state of the electrical networks into which they are introduced. Their principal function is to improve the static and dynamic properties of the electrical networks and that by increasing the margins of static and dynamic stability and to allow the power transit to the thermal limits of the lines.To study this impact, it is necessary to establish the state of the network (bus voltages and ...
Hodge Decomposition of Information Flow on Small-World Networks
Haruna, Taichi; Fujiki, Yuuya
2016-01-01
We investigate the influence of the small-world topology on the composition of information flow on networks. By appealing to the combinatorial Hodge theory, we decompose information flow generated by random threshold networks on the Watts-Strogatz model into three components: gradient, harmonic and curl flows. The harmonic and curl flows represent globally circular and locally circular components, respectively. The Watts-Strogatz model bridges the two extreme network topologies, a lattice network and a random network, by a single parameter that is the probability of random rewiring. The small-world topology is realized within a certain range between them. By numerical simulation we found that as networks become more random the ratio of harmonic flow to the total magnitude of information flow increases whereas the ratio of curl flow decreases. Furthermore, both quantities are significantly enhanced from the level when only network structure is considered for the network close to a random network and a lattice network, respectively. Finally, the sum of these two ratios takes its maximum value within the small-world region. These findings suggest that the dynamical information counterpart of global integration and that of local segregation are the harmonic flow and the curl flow, respectively, and that a part of the small-world region is dominated by internal circulation of information flow. PMID:27733817
Finding Elephant Flows for Optical Networks
Fioreze, Tiago; Oude Wolbers, Mattijs; Meent, van de Remco; Pras, Aiko
2007-01-01
Optical networks are fast and reliable networks that enable, amongst others, dedicated light paths to be established for elephant IP flows. Elephant IP flows are characterized by being small in number, but long in time and high in traffic volume. Moving these flows from the general IP network to ded
Coevolutionary modeling in network formation
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.
Piri, Mohammad; Blunt, Martin J
2005-02-01
We present a three-dimensional network model to simulate two- and three-phase capillary dominated processes at the pore level. The displacement mechanisms incorporated in the model are based on the physics of multiphase flow observed in micromodel experiments. All the important features of immiscible fluid flow at the pore scale, such as wetting layers, spreading layers of the intermediate-wet phase, hysteresis, and wettability alteration are implemented in the model. Wettability alteration allows any values for the advancing and receding oil-water, gas-water, and gas-oil contact angles to be assigned. Multiple phases can be present in each pore or throat (element), in wetting and spreading layers, as well as occupying the center of the pore space. In all, some 30 different generic fluid configurations for two- and three-phase flow are analyzed. Double displacement and layer formation are implemented as well as direct two-phase displacement and layer collapse events. Every element has a circular, square, or triangular cross section. A random network that represents the pore space in Berea sandstone is used in this study. The model computes relative permeabilities, saturation paths, and capillary pressures for any displacement sequence. A methodology to track a given three-phase saturation path is presented that enables us to compare predicted and measured relative permeabilities on a point-by-point basis. A robust displacement-based clustering algorithm is also presented.
Gable, C. W.; Hyman, J.; Karra, S.; Makedonska, N.; Painter, S. L.; Viswanathan, H. S.
2015-12-01
dfnWorks generates discrete fracture networks (DFN) of planar polygons, creates a high quality conforming Delaunay triangulation of the intersecting DFN polygons, assigns properties (aperture, permeability) using geostatistics, sets boundary and initial conditions, solves pressure/flow in single or multi-phase fluids (water, air, CO2) using the parallel PFLOTRAN or serial FEHM, and solves for transport using Lagrangian particle tracking. We outline the dfnWorks workflow and present applications from a range of fractured rock systems. dfnWorks (http://www.lanl.gov/expertise/teams/view/dfnworks) is composed of three main components, all of which are freely available. dfnGen generates a distribution of fracture polygons from site characterization data (statistics or deterministic fractures) and utilizes the FRAM (Feature Rejection Algorithm for Meshing) to guarantee the mesh generation package LaGriT (lagrit.lanl.gov) will generate a high quality conforming Delaunay triangular mesh. dfnWorks links the mesh to either PFLOTRAN (pflotran.org) or FEHM (fehm.lanl.gov) for solving flow and transport. The various physics options available in FEHM and PFLOTRAN such as single and multi-phase flow and reactive transport are all available with appropriate initial and boundary conditions and material property models. dfnTrans utilizes explicit Lagrangian particle tracking on the DFN using a velocity field reconstructed from the steady state pressure/flow field solution obtained in PFLOTRAN or FEHM. Applications are demonstrated for nuclear waste repository in fractured granite, CO2 sequestration and extraction of unconventional hydrocarbon resources.
Integrating lv network models and load-flow calculations into smart grid planning
Hoogsteen, Gerwin; Molderink, Albert; Bakker, Vincent; Smit, Gerard J.M.
2013-01-01
Increasing energy prices and the greenhouse effect demand a more efficient supply of energy. More residents start to install their own energy generation sources such as photovoltaic cells. The introduction of distributed generation in the low-voltage network can have effects that were unexpected whe
Directory of Open Access Journals (Sweden)
Narimen Aouzellag LAHAÇANI
2008-12-01
Full Text Available The goal of any Flexible AC Transmission Systems (FACTS devices study is to measure their impact on the state of the electrical networks into which they are introduced. Their principal function is to improve the static and dynamic properties of the electrical networks and that by increasing the margins of static and dynamic stability and to allow the power transit to the thermal limits of the lines.To study this impact, it is necessary to establish the state of the network (bus voltages and angles, powers injected and forwarded in the lines before and after the introduction of FACTS devices. This brings to calculate the powers transit by using an iterative method such as Newton-Raphson. Undertaking a calculation without the introduction of FACTS devices followed by a calculation with the modifications induced by the integration of FACTS devices into the network, makes it possible to compare the results obtained in both cases and thus assess the interest of the use of devices FACTS.
基于Petri网的网络攻击流模型研究%Research on Network Attack Flow Model Based on Petri Net
Institute of Scientific and Technical Information of China (English)
赵博夫; 殷肖川
2011-01-01
针对网络攻击的智能组织实施问题,提出一种攻击流的概念,选用Petri网作为工具,对网络攻击流进行建模.在此基础上,对3种基本网络攻击流模型进行分析,并结合IP欺骗攻击实例,分析其在IP欺骗攻击中的具体应用及其实现方式.实验结果表明,该模型既利于攻击者构建网络攻击方案,又能被计算机解析并组织实施网络攻击.%Aiming at the issue of organization and implementation of intelligent network attack problem, this paper presents the concept of attack flow and uses Petri nets as a tool to model the flow of network attacks.On the basis, this paper analyses three kinds of basic network attack flow model.Based on the model, this paper gives an example of IP spoofing and analyses the specific application of the model and its implementation method in the IP spoofing attacks to show that the model is not only beneficial for the attacker to build network attack programs, but also helpful for the computer to analyse model and organize network attacks.
A perturbative approach to Lagrangian flow networks
Fujiwara, Naoya; Donges, Jonathan F; Donner, Reik V
2016-01-01
Complex network approaches have been successfully applied for studying transport processes in complex systems ranging from road, railway or airline infrastructure over industrial manufacturing to fluid dynamics. Here, we utilize a generic framework for describing the dynamics of geophysical flows such as ocean currents or atmospheric wind fields in terms of Lagrangian flow networks. In this approach, information on the passive advection of particles is transformed into a Markov chain based on transition probabilities of particles between the volume elements of a given partition of space for a fixed time step. We employ perturbation-theoretic methods to investigate the effects of modifications of transport processes in the underlying flow for three different problem classes: efficient absorption (corresponding to particle trapping or leaking), constant input of particles (with additional source terms modeling, e.g., localized contamination), and shifts of the steady state under probability mass conservation (a...
Energy Technology Data Exchange (ETDEWEB)
Makedonska, Nataliia [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Kwicklis, Edward Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Birdsell, Kay Hanson [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Harrod, Jeremy Ashcraft [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Karra, Satish [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-10-18
This progress report for fiscal year 2015 (FY15) describes the development of discrete fracture network (DFN) models for Pahute Mesa. DFN models will be used to upscale parameters for simulations of subsurface flow and transport in fractured media in Pahute Mesa. The research focuses on modeling of groundwater flow and contaminant transport using DFNs generated according to fracture characteristics observed in the Topopah Spring Aquifer (TSA) and the Lava Flow Aquifer (LFA). This work will improve the representation of radionuclide transport processes in large-scale, regulatory-focused models with a view to reduce pessimistic bounding approximations and provide more realistic contaminant boundary calculations that can be used to describe the future extent of contaminated groundwater. Our goal is to refine a modeling approach that can translate parameters to larger-scale models that account for local-scale flow and transport processes, which tend to attenuate migration.
DEFF Research Database (Denmark)
Bisdom, Kevin; Nick, Hamid; Bertotti, Giovanni
2017-01-01
stresssensitive fracture permeability and matrix flow to determine the full permeability tensor. The applicability of this workflow is illustrated using an outcropping carbonate pavement in the Potiguar basin in Brazil, from which 1082 fractures are digitised. The permeability tensor for a range of matrix...
Energy Technology Data Exchange (ETDEWEB)
Zhou, Jing [Idaho National Lab. (INL), Idaho Falls, ID (United States); Huang, Hai [Idaho National Lab. (INL), Idaho Falls, ID (United States); Mattson, Earl [Idaho National Lab. (INL), Idaho Falls, ID (United States); Wang, Herb F. [Univ. of Wisconsin, Madison, WI (United States); Haimson, Bezalel C. [Univ. of Wisconsin, Madison, WI (United States); Doe, Thomas W. [Golder Associates Inc., Redmond, VA (United States); Oldenburg, Curtis M. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Dobson, Patrick F. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2017-02-01
Aimed at supporting the design of hydraulic fracturing experiments at the kISMET site, ~1500 m below ground in a deep mine, we performed pre-experimental hydraulic fracturing simulations in order to estimate the breakdown pressure, propagation pressure, fracture geometry, and the magnitude of induced seismicity using a newly developed fully coupled three-dimensional (3D) network flow and quasi-static discrete element model (DEM). The quasi-static DEM model, which is constructed by Delaunay tessellation of the rock volume, considers rock fabric heterogeneities by using the “disordered” DEM mesh and adding random perturbations to the stiffness and tensile/shear strengths of individual DEM elements and the elastic beams between them. A conjugate 3D flow network based on the DEM lattice is constructed to calculate the fluid flow in both the fracture and porous matrix. One distinctive advantage of the model is that fracturing is naturally described by the breakage of elastic beams between DEM elements. It is also extremely convenient to introduce mechanical anisotropy into the model by simply assigning orientation-dependent tensile/shear strengths to the elastic beams. In this paper, the 3D hydraulic fracturing model was verified against the analytic solution for a penny-shaped crack model. We applied the model to simulate fracture propagation from a vertical open borehole based on initial estimates of rock mechanical properties and in-situ stress conditions. The breakdown pressure and propagation pressure are directly obtained from the simulation. In addition, the released elastic strain energies of individual fracturing events were calculated and used as a conservative estimate for the magnitudes of the potential induced seismic activities associated with fracturing. The comparisons between model predictions and experimental results are still ongoing.
D'Angelo, M V; Allain, C; Hulin, J P; Angelo, Maria Veronica D'; Auradou, Harold; Allain, Catherine; Hulin, Jean-Pierre
2006-01-01
A change of solute dispersion regime with the flow velocity has been studied both at the macroscopic and pore scales in a transparent array of capillary channels using an optical technique allowing for simultaneous local and global concentration mappings. Two solutions of different polymer concentrations (500 and 1000 ppm) have been used at different P\\'eclet numbers. At the macroscopic scale, the displacement front displays a diffusive spreading: for $Pe \\leq 10$, the dispersivity $l\\_d$ is constant with $Pe$ and increases with the polymer concentration; for $Pe > 10$, $l\\_d$ increases as $Pe^{1.35}$ and is similar for the two concentrations. At the local scale, a time lag between the saturations of channels parallel and perpendicular to the mean flow has been observed and studied as a function of the flow rate. These local measurements suggest that the change of dispersion regime is related to variations of the degree of mixing at the junctions. For $Pe \\leq 10$, complete mixing leads to pure geometrical di...
Model predictive control for power flows in networks with limited capacity
DEFF Research Database (Denmark)
Biegel, Benjamin; Stoustrup, Jakob; Bendtsen, Jan Dimon
2012-01-01
We consider an interconnected network of consumers powered through an electrical grid of limited capacity. A subset of the consumers are intelligent consumers and have the ability to store energy in a controllable fashion; they can be filled and emptied as desired under power and capacity...... limitations. We address the problem of maintaining power balance between production and consumption using the intelligent consumers to ensure smooth power consumption from the grid. Further, certain capacity limitations to the links interconnecting the consumers must be honored. In this paper, we show how...
A new transient network model for associative polymer networks
Wientjes, R.H.W.; Jongschaap, R.J.J.; Duits, M.H.G.; Mellema, J.
1999-01-01
A new model for the linear viscoelastic behavior of polymer networks is developed. In this model the polymer system is described as a network of spring segments connected via sticky points (as in the Lodge model). [Lodge, A. S., “A network theory of flow birefringence and stress in concentrated poly
Structure and function in flow networks
Rubido, Nicolás; Baptista, Murilo S
2013-01-01
This Letter presents a unified approach for the fundamental relationship between structure and function in flow networks by solving analytically the voltages in a resistor network, transforming the network structure to an effective all-to-all topology, and then measuring the resultant flows. Moreover, it defines a way to study the structural resilience of the graph and to detect possible communities.
Wireless Network Information Flow: A Deterministic Approach
Avestimehr, Salman; Tse, David
2009-01-01
In contrast to wireline networks, not much is known about the flow of information over wireless networks. The main barrier is the complexity of the signal interaction in wireless channels in addition to the noise in the channel. A widely accepted model is the the additive Gaussian channel model, and for this model, the capacity of even a network with a single relay node is open for 30 years. In this paper, we present a deterministic approach to this problem by focusing on the signal interaction rather than the noise. To this end, we propose a deterministic channel model which is analytically simpler than the Gaussian model but still captures two key wireless channel properties of broadcast and superposition. We consider a model for a wireless relay network with nodes connected by such deterministic channels, and present an exact characterization of the end-to-end capacity when there is a single source and one or more destinations (all interested in the same information) and an arbitrary number of relay nodes....
Complex networks renormalization: flows and fixed points.
Radicchi, Filippo; Ramasco, José J; Barrat, Alain; Fortunato, Santo
2008-10-03
Recently, it has been claimed that some complex networks are self-similar under a convenient renormalization procedure. We present a general method to study renormalization flows in graphs. We find that the behavior of some variables under renormalization, such as the maximum number of connections of a node, obeys simple scaling laws, characterized by critical exponents. This is true for any class of graphs, from random to scale-free networks, from lattices to hierarchical graphs. Therefore, renormalization flows for graphs are similar as in the renormalization of spin systems. An analysis of classic renormalization for percolation and the Ising model on the lattice confirms this analogy. Critical exponents and scaling functions can be used to classify graphs in universality classes, and to uncover similarities between graphs that are inaccessible to a standard analysis.
Low-complexity wireless communication modeling for information flow control in sensor networks
Foeken, E. van; Kwakkernaat, M.R.J.A.E.
2011-01-01
The increasing demand for shared awareness in multi-platform sensor systems requires advanced wireless information sharing techniques. The analysis of these techniques requires information about communication resources and latency to be available in models. The work presented here introduces generic
Low-complexity wireless communication modeling for information flow control in sensor networks
Foeken, E. van; Kwakkernaat, M.R.J.A.E.
2011-01-01
The increasing demand for shared awareness in multi-platform sensor systems requires advanced wireless information sharing techniques. The analysis of these techniques requires information about communication resources and latency to be available in models. The work presented here introduces generic
Institute of Scientific and Technical Information of China (English)
HAN Dong; FANG Hong-wei; BAI Jing; HE Guo-jian
2011-01-01
A coupled one-dimensional(1-D)and two-dimensional(2-D)channel network mathematical model is proposed for flow calculations at nodes in a channel network system in this article.For the 1-D model,the finite difference method is used to discretize the Saint-Venant equations in all channels of a looped network.The Alternating Direction Implicit(ADI)method is adopted for the 2-D model at the nodes.In the coupled model,the 1-D model provides a good approximation with small computational effort,while the 2-D model is applied for complex topography to achieve a high accuracy.An Artificial Neural Network(ANN)method is used for the data exchange and the connectivity between the 1-D and 2-D models.The coupled model is applied to the Jingjiang-Dongting Lake region,to simulate the tremendous looped channel network system,and the results are compared with field data.The good agreement shows that the coupled hydraulic model is more effective than the conventional 1-D model.
Peng Yang; Jin Qin; Wenliang Zhou
2013-01-01
With the constant changes of the market demand trends and modern information technology which greatly influence supply chain operation and management, postponement has been developed to be an important strategy in some of the supply chain processes. This paper studies the multi-period and multi-commodity flow supply chain network equilibrium problem with postponement strategy under a type of supply chain management pattern. Taking the influences of the relationship between orders and inventor...
Stochastic power flow modeling
Energy Technology Data Exchange (ETDEWEB)
1980-06-01
The stochastic nature of customer demand and equipment failure on large interconnected electric power networks has produced a keen interest in the accurate modeling and analysis of the effects of probabilistic behavior on steady state power system operation. The principle avenue of approach has been to obtain a solution to the steady state network flow equations which adhere both to Kirchhoff's Laws and probabilistic laws, using either combinatorial or functional approximation techniques. Clearly the need of the present is to develop sound techniques for producing meaningful data to serve as input. This research has addressed this end and serves to bridge the gap between electric demand modeling, equipment failure analysis, etc., and the area of algorithm development. Therefore, the scope of this work lies squarely on developing an efficient means of producing sensible input information in the form of probability distributions for the many types of solution algorithms that have been developed. Two major areas of development are described in detail: a decomposition of stochastic processes which gives hope of stationarity, ergodicity, and perhaps even normality; and a powerful surrogate probability approach using proportions of time which allows the calculation of joint events from one dimensional probability spaces.
Transport on Complex Networks: Flow, Jamming and Optimization
Tadic, B; Thurner, S; Tadic, Bosiljka; Thurner, Stefan
2006-01-01
Many transport processes on networks depend crucially on the underlying network geometry, although the exact relationship between the structure of the network and the properties of transport processes remain elusive. In this paper we address this question by using numerical models in which both structure and dynamics are controlled systematically. We consider the traffic of information packets that include driving, searching and queuing. We present the results of extensive simulations on two classes of networks; a correlated cyclic scale-free network and an uncorrelated homogeneous weakly clustered network. By measuring different dynamical variables in the free flow regime we show how the global statistical properties of the transport are related to the temporal fluctuations at individual nodes (the traffic noise) and the links (the traffic flow). We then demonstrate that these two network classes appear as representative topologies for optimal traffic flow in the regimes of low density and high density traff...
Shakiba, Mohammad; Parson, Nick; Chen, X-Grant
2016-06-30
The hot deformation behavior of Al-0.12Fe-0.1Si alloys with varied amounts of Cu (0.002-0.31 wt %) was investigated by uniaxial compression tests conducted at different temperatures (400 °C-550 °C) and strain rates (0.01-10 s(-1)). The results demonstrated that flow stress decreased with increasing deformation temperature and decreasing strain rate, while flow stress increased with increasing Cu content for all deformation conditions studied due to the solute drag effect. Based on the experimental data, an artificial neural network (ANN) model was developed to study the relationship between chemical composition, deformation variables and high-temperature flow behavior. A three-layer feed-forward back-propagation artificial neural network with 20 neurons in a hidden layer was established in this study. The input parameters were Cu content, temperature, strain rate and strain, while the flow stress was the output. The performance of the proposed model was evaluated using the K-fold cross-validation method. The results showed excellent generalization capability of the developed model. Sensitivity analysis indicated that the strain rate is the most important parameter, while the Cu content exhibited a modest but significant influence on the flow stress.
Directory of Open Access Journals (Sweden)
Mohammad Shakiba
2016-06-01
Full Text Available The hot deformation behavior of Al-0.12Fe-0.1Si alloys with varied amounts of Cu (0.002–0.31 wt % was investigated by uniaxial compression tests conducted at different temperatures (400 °C–550 °C and strain rates (0.01–10 s−1. The results demonstrated that flow stress decreased with increasing deformation temperature and decreasing strain rate, while flow stress increased with increasing Cu content for all deformation conditions studied due to the solute drag effect. Based on the experimental data, an artificial neural network (ANN model was developed to study the relationship between chemical composition, deformation variables and high-temperature flow behavior. A three-layer feed-forward back-propagation artificial neural network with 20 neurons in a hidden layer was established in this study. The input parameters were Cu content, temperature, strain rate and strain, while the flow stress was the output. The performance of the proposed model was evaluated using the K-fold cross-validation method. The results showed excellent generalization capability of the developed model. Sensitivity analysis indicated that the strain rate is the most important parameter, while the Cu content exhibited a modest but significant influence on the flow stress.
Flows in networks under fuzzy conditions
Bozhenyuk, Alexander Vitalievich; Kacprzyk, Janusz; Rozenberg, Igor Naymovich
2017-01-01
This book offers a comprehensive introduction to fuzzy methods for solving flow tasks in both transportation and networks. It analyzes the problems of minimum cost and maximum flow finding with fuzzy nonzero lower flow bounds, and describes solutions to minimum cost flow finding in a network with fuzzy arc capacities and transmission costs. After a concise introduction to flow theory and tasks, the book analyzes two important problems. The first is related to determining the maximum volume for cargo transportation in the presence of uncertain network parameters, such as environmental changes, measurement errors and repair work on the roads. These parameters are represented here as fuzzy triangular, trapezoidal numbers and intervals. The second problem concerns static and dynamic flow finding in networks under fuzzy conditions, and an effective method that takes into account the network’s transit parameters is presented here. All in all, the book provides readers with a practical reference guide to state-of-...
Allometry and Dissipation of Ecological Flow Networks
Zhang, Jiang; Wu, Lingfei
2013-01-01
Background An ecological flow network is a weighted directed graph in which the nodes are species, the edges are “who eats whom” relationships and the weights are rates of energy or nutrient transferred between species. Allometric scaling is a ubiquitous feature for flow systems such as river basins, vascular networks and food webs. Methodology The “ecological network analysis” can serve to reveal hidden allometries, the power law relationship between the throughflux and the indirect impact of node , directly from the original flow networks without any need to cut edges in the network. The dissipation law, which is another significant scaling relationship between the energy dissipation (respiration) and the throughflow of any species, is also obtained from an analysis of the empirical flow networks. Interestingly, the exponents of the allometric law () and the dissipation law () show a strong relationship for both empirical and simulated flow networks. The dissipation law exponent , rather than the topology of the network, is the most important factors that affect the allometric exponent . Conclusions The exponent can be interpreted as the degree of centralization of the network, i.e., the concentration of impacts (direct and indirect influences on the entire network along all energy flow pathways) on hubs (the nodes with large throughflows). As a result, we find that as increases, the relative energy loss of large nodes increases, decreases, i.e., the relative importance of large species decreases. Moreover, the entire flow network is more decentralized. Therefore, network flow structure (allometry) and thermodynamic constraints (dissipation) are linked. PMID:24019871
Directory of Open Access Journals (Sweden)
Bibhab Kumar Lodh
2015-02-01
Full Text Available This paper will present the large eddy simulation of turbulence modeling for wind flow over a wall mounted 3D cubical model. The LES Smagorinsky scheme is employed for the numerical simulation. The domain for this study is of the size of 60 cm x 30 cm x 30 cm. The 3D cube model is taken of the size of 6 cm x 6 cm x 4 cm. The Reynolds number for the flow in respect of the height of the cube i.e, 4 cm is 5.3x104 . The hexahedral grids are used for the meshing of the flow domain. The results are discussed in terms of various parameters such as velocity profile around the cube and the computational domain, the pressure distribution over the cube, near wall velocity profile and the shear stress distribution and also the result of drag coefficient is verified by neural network time series analysis using MATLAB. In this present study we have used the OpenFoam platform for the computational and numerical analysis. The numerical scheme employed is the combination of the steady state incompressible Newtonian flow model using SIMPLE algorithm followed by the transient model of incompressible Newtonian flow using PISO algorithm. We have observed that there is a constant positive drag coefficient in case of steady state simulation where as there is a negative lift coefficient in the initial run and a very low lift coefficient at the end of the steady state simulation.
Biological transportation networks: Modeling and simulation
Albi, Giacomo
2015-09-15
We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.
Energy Technology Data Exchange (ETDEWEB)
Nordhaug, Hans Fredrik
2001-07-01
In reservoir problems we consider some or all of the following phases: Oil, gas, water and solid. The solid phase is normally assumed to be immobile and non-deforming, but in general this does not need to be the case. By multi phase flow we will mean the flow of oil, gas and water. The phases are categorized according to their different physical quantities. A hydrocarbon phase, may consist of different hydrocarbon components, e.g., the oil phase can contain several oil and gas types. In this work the components are neglected and only the phases are considered. A porous medium is any solid phase, e.g. sand stone, that is permeable. The flow in a porous medium takes place through connected pores in the rock. Regions on a larger scale that contain oil or gas are called reservoirs. The typical size of a reservoir is kilometers in each direction while the pore scale size is millimeters or less. Solving the Navier-Stokes equation at the pore scale to obtain the transport on a larger scale is not numerically feasible because of the huge difference in scales. Therefore, some averaging is necessary to go from the pore scale (micro scale) to the reservoir scale (macro scale). In this process the Navier-Stokes equations are replaced by macro scale equations that are solved for macro scale variables. The papers presented herein cover several topics in multi phase flow in porous media, and they address some central problems both on the micro scale as well as on the macro scale. In addition, operator splitting techniques have been developed for convection dominated non-linear transport equations.
P2P Network Characteristic Model Based on Flow%基于流的P2P网络特性模型
Institute of Scientific and Technical Information of China (English)
陈宝钢; 许勇; 胡金龙
2012-01-01
利用从实际网络获得的数据,提出基于流的P2P网络特性模型.采用图形方法和概述统计识别样本所服从的分布族,使用可视化图形方法和假设检验方法对统计分布模型进行拟合优度检验.分析结果表明,流持续时间的分布模型可以用对数正态分布精确表示,混合对数正态分布可以有效拟合流长和流传输速率分布的分布模型,且P2P应用的流长和流持续时间没有高度相关的关系.%This paper proposes a P2P network characteristic model based on flow obtained from the actual network. It uses diagram and summary statistics process to recognize the distribution of sample, designs the statistical distribution model, and uses visual graphic method and the hypothesis testing method to do goodness of fit test for statistical distribution model. Analysis results show that the lognormal distribution can accurately express the flow duration, while flow size and flow transmission rate can be reasonably modeled by lognormal mixture distribution, however P2P application flow size and flow duration are not highly related to each other.
Flow whitelisting in SCADA networks
Barbosa, Rafael Ramos Regis; Sadre, Ramin; Pras, Aiko
2013-01-01
Supervisory control and data acquisition (SCADA) networks are commonly deployed in large industrial facilities. Modern SCADA networks are becoming more vulnerable to cyber attacks due to the common use of standard communications protocols and increased interconnections with corporate networks and th
DEFF Research Database (Denmark)
Knudsen, Torben
2011-01-01
The purpose with this deliverable 2.5 is to use fresh experimental data for validation and selection of a flow model to be used for control design in WP3-4. Initially the idea was to investigate the models developed in WP2. However, in the project it was agreed to include and focus on a additive...... model turns out not to be useful for prediction of the flow. Moreover, standard Box Jenkins model structures and multiple output auto regressive models proves to be superior as they can give useful predictions of the flow....
Flow distributions and spatial correlations in human brain capillary networks
Lorthois, Sylvie; Peyrounette, Myriam; Larue, Anne; Le Borgne, Tanguy
2015-11-01
The vascular system of the human brain cortex is composed of a space filling mesh-like capillary network connected upstream and downstream to branched quasi-fractal arterioles and venules. The distribution of blood flow rates in these networks may affect the efficiency of oxygen transfer processes. Here, we investigate the distribution and correlation properties of blood flow velocities from numerical simulations in large 3D human intra-cortical vascular network (10000 segments) obtained from an anatomical database. In each segment, flow is solved from a 1D non-linear model taking account of the complex rheological properties of blood flow in microcirculation to deduce blood pressure, blood flow and red blood cell volume fraction distributions throughout the network. The network structural complexity is found to impart broad and spatially correlated Lagrangian velocity distributions, leading to power law transit time distributions. The origins of this behavior (existence of velocity correlations in capillary networks, influence of the coupling with the feeding arterioles and draining veins, topological disorder, complex blood rheology) are studied by comparison with results obtained in various model capillary networks of controlled disorder. ERC BrainMicroFlow GA615102, ERC ReactiveFronts GA648377.
Constructing minimum-cost flow-dependent networks
Thomas, Doreen A.; Weng, Jia F.
2002-09-01
In the construction of a communication network, the length of the network is an important but not unique factor determining the cost of the network. Among many possible network models, Gilbert proposed a flow-dependent model in which flow demands are assigned between each pair of points in a given point set A, and the cost per unit length of a link in the network is a function of the flow through the link. In this paper we first investigate the properties of this Gilbert model: the concavity of the cost function, decomposition, local minimality, the number of Steiner points and the maximum degree of Steiner points. Then we propose three heuristics for constructing minimum cost Gilbert networks. Two of them come from the fact that generally a minimum cost Gilbert network stands between two extremes: the complete network G(A) on A and the edge-weighted Steiner minimal tree W(A) on A. The first heuristic starts with G(A) and reduces the cost by splitting angles; the second one starts with both G(A) and W(A), and reduces the cost by selecting low cost paths. As a generalisation of the second heuristic, the third heuristic constructs a new Gilbert network of less cost by hybridising known Gilbert networks. Finally we discuss some considerations in practical applications.
OpenFlow Extensions for Programmable Quantum Networks
2017-06-19
protocol to be used for detecting and correcting errors. 3.2 Switch In an SDN, switches function as fast-forwarding devices with no internal ...release; distribution is unlimited. 8 6. References 1. Dasari VR, Humble TS. OpenFlow arbitrated programmable network channels for managing quantum...Dasari VR, Humble TS. OpenFlow arbitrated programmable network channels for managing quantum metadata. Journal of Defense Modeling and Simulations
Carluer, Nadia; Marsily, Ghislain De
2004-01-01
Up to now, most watershed models have been focused on the representation of 'natural' flow and transport processes. In this paper, we discuss the role of man-made networks, such as ditches, roads, hedge rows and hedges, underground drainage by buried pipes, etc. The influence of such features on the hydrology of a watershed may be of particular importance if the aim of the modelling is to predict the effect of landscape management or the fate of contaminants, e.g. pesticides, when a rain event occurs very soon after their spreading on the soil surface. It is likely that such artificial networks may act as conduits or short-circuits for the transport of contaminants, either dissolved or sorbed on soil particles, by-passing some of the retardation mechanisms such as sorption in the soil, retention of surface runoff by grass verges, biodegradation in the unsaturated zone, etc. We first present a small watershed on which the study was conducted, the Kervidy, which is a 5 km 2 'bocage ' catchment in Brittany, France. The man-made networks were observed and their extent and functioning described. We then included the potential hydraulic role of these networks in a distributed watershed model (TOPOG, [J. Hydrol. 150 (1993) 665]). This modified model, ANTHROPOG, was run, for comparison, with and without the man-made network; sensitivity tests were also made to assess the hydrologic importance of these networks. It was shown that they can have a very significant effect on the functioning of a watershed. We conclude on the relevance of the improved distributed model for the management of rural landscapes, and on the type of additional data needed to calibrate the model with parameters representative of the true processes. Bocage is a landscape with grassland, hedges, and occasional trees—often apple trees—typical of Brittany and Normandy.
Kavi, K. M.
1984-01-01
There have been a number of simulation packages developed for the purpose of designing, testing and validating computer systems, digital systems and software systems. Complex analytical tools based on Markov and semi-Markov processes have been designed to estimate the reliability and performance of simulated systems. Petri nets have received wide acceptance for modeling complex and highly parallel computers. In this research data flow models for computer systems are investigated. Data flow models can be used to simulate both software and hardware in a uniform manner. Data flow simulation techniques provide the computer systems designer with a CAD environment which enables highly parallel complex systems to be defined, evaluated at all levels and finally implemented in either hardware or software. Inherent in data flow concept is the hierarchical handling of complex systems. In this paper we will describe how data flow can be used to model computer system.
Three statistical approaches to sessionizing network flow data
Rubin-Delanchy, Patrick T G; Lawson, Daniel John; Turcotte, Melissa J.; Heard, Nicholas A.; Adams, Niall M.
2014-01-01
The network traffic generated by a computer, or a pair of computers, is often well modelled as a series of sessions. These are, roughly speaking, intervals of time during which a computer is engaging in the same, continued, activity. This article explores a variety of statistical approaches to re-discovering sessions from network flow data using timing alone. Solutions to this problem are essential for network monitoring and cyber-security. For example overlapping sessions on a computer netwo...
Multilayer perceptron neural network for flow prediction.
Araujo, P; Astray, G; Ferrerio-Lage, J A; Mejuto, J C; Rodriguez-Suarez, J A; Soto, B
2011-01-01
Artificial neural networks (ANNs) have proven to be a tool for characterizing, modeling and predicting many of the non-linear hydrological processes such as rainfall-runoff, groundwater evaluation or simulation of water quality. After proper training they are able to generate satisfactory predictive results for many of these processes. In this paper they have been used to predict 1 or 2 days ahead the average and maximum daily flow of a river in a small forest headwaters in northwestern Spain. The inputs used were the flow and climate data (precipitation, temperature, relative humidity, solar radiation and wind speed) as recorded in the basin between 2003 and 2008. Climatic data have been utilized in a disaggregated form by considering each one as an input variable in ANN(1), or in an aggregated form by its use in the calculation of evapotranspiration and using this as input variable in ANN(2). Both ANN(1) and ANN(2), after being trained with the data for the period 2003-2007, have provided a good fit between estimated and observed data, with R(2) values exceeding 0.95. Subsequently, its operation has been verified making use of the data for the year 2008. The correlation coefficients obtained between the data estimated by ANNs and those observed were in all cases superior to 0.85, confirming the capacity of ANNs as a model for predicting average and maximum daily flow 1 or 2 days in advance.
Armbruster, Benjamin
2011-01-01
We analyze random networks that change over time. First we analyze a dynamic Erdos-Renyi model, whose edges change over time. We describe its stationary distribution, its convergence thereto, and the SI contact process on the network, which has relevance for connectivity and the spread of infections. Second, we analyze the effect of node turnover, when nodes enter and leave the network, which has relevance for network models incorporating births, deaths, aging, and other demographic factors.
Brocade: Optimal flow placement in SDN networks
CERN. Geneva
2015-01-01
Today' network poses several challanges to network providers. These challanges fall in to a variety of areas ranging from determining efficient utilization of network bandwidth to finding out which user applications consume majority of network resources. Also, how to protect a given network from volumetric and botnet attacks. Optimal placement of flows deal with identifying network issues and addressing them in a real-time. The overall solution helps in building new services where a network is more secure and more efficient. Benefits derived as a result are increased network efficiency due to better capacity and resource planning, better security with real-time threat mitigation, and improved user experience as a result of increased service velocity.
Institute of Scientific and Technical Information of China (English)
Peter Mora; Yucang Wang; Fernando Alonso-Marroquin
2015-01-01
SUMMARY:Realizing the potential of geothermal energy as a cheap, green, sustainable resource to provide for the planet’s future energy demands that a key geophysical problem be solved first:how to develop and maintain a network of multiple fluid flow pathways for the time required to deplete the heat within a given region. We present the key components for micro-scale particle-based nu-merical modeling of hydraulic fracture, and fluid and heat flow in geothermal reservoirs. They are based on the latest developments of ESyS-Particle—the coupling of the lattice solid model (LSM) to simulate the nonlinear dynamics of complex solids with the lattice Boltzmann method (LBM) ap-plied to the nonlinear dynamics of coupled fluid and heat flow in the complex solid-fluid system. The coupled LSM/LBM can be used to simulate development of fracture systems in discontinuous media, elastic stress release, fluid injection and the consequent slip at joint surfaces, and hydraulic fractur-ing; heat exchange between hot rocks and water within flow pathways created through hydraulic fracturing;and fluid flow through complex, narrow, compact and gouge-or powder-filled fracture and joint systems. We demonstrate the coupled LSM/LBM to simulate the fundamental processes listed above, which are all components for the generation and sustainability of the hot-fractured rock geothermal energy fracture systems required to exploit this new green-energy resource.
Employment Growth through Labor Flow Networks
Guerrero, Omar A.; Axtell, Robert L.
2013-01-01
It is conventional in labor economics to treat all workers who are seeking new jobs as belonging to a labor pool, and all firms that have job vacancies as an employer pool, and then match workers to jobs. Here we develop a new approach to study labor and firm dynamics. By combining the emerging science of networks with newly available employment micro-data, comprehensive at the level of whole countries, we are able to broadly characterize the process through which workers move between firms. Specifically, for each firm in an economy as a node in a graph, we draw edges between firms if a worker has migrated between them, possibly with a spell of unemployment in between. An economy's overall graph of firm-worker interactions is an object we call the labor flow network (LFN). This is the first study that characterizes a LFN for an entire economy. We explore the properties of this network, including its topology, its community structure, and its relationship to economic variables. It is shown that LFNs can be useful in identifying firms with high growth potential. We relate LFNs to other notions of high performance firms. Specifically, it is shown that fewer than 10% of firms account for nearly 90% of all employment growth. We conclude with a model in which empirically-salient LFNs emerge from the interaction of heterogeneous adaptive agents in a decentralized labor market. PMID:23658682
Employment growth through labor flow networks.
Directory of Open Access Journals (Sweden)
Omar A Guerrero
Full Text Available It is conventional in labor economics to treat all workers who are seeking new jobs as belonging to a labor pool, and all firms that have job vacancies as an employer pool, and then match workers to jobs. Here we develop a new approach to study labor and firm dynamics. By combining the emerging science of networks with newly available employment micro-data, comprehensive at the level of whole countries, we are able to broadly characterize the process through which workers move between firms. Specifically, for each firm in an economy as a node in a graph, we draw edges between firms if a worker has migrated between them, possibly with a spell of unemployment in between. An economy's overall graph of firm-worker interactions is an object we call the labor flow network (LFN. This is the first study that characterizes a LFN for an entire economy. We explore the properties of this network, including its topology, its community structure, and its relationship to economic variables. It is shown that LFNs can be useful in identifying firms with high growth potential. We relate LFNs to other notions of high performance firms. Specifically, it is shown that fewer than 10% of firms account for nearly 90% of all employment growth. We conclude with a model in which empirically-salient LFNs emerge from the interaction of heterogeneous adaptive agents in a decentralized labor market.
Institute of Scientific and Technical Information of China (English)
GIOVANGIGLI; Vincent
2012-01-01
We present multicomponent flow models derived from the kinetic theory of gases and investigate the symmetric hyperbolic-parabolic structure of the resulting system of partial differential equations.We address the Cauchy problem for smooth solutions as well as the existence of deflagration waves,also termed anchored waves.We further discuss related models which have a similar hyperbolic-parabolic structure,notably the SaintVenant system with a temperature equation as well as the equations governing chemical equilibrium flows.We next investigate multicomponent ionized and magnetized flow models with anisotropic transport fluxes which have a different mathematical structure.We finally discuss numerical algorithms specifically devoted to complex chemistry flows,in particular the evaluation of multicomponent transport properties,as well as the impact of multicomponent transport.
Coupled One and Two Dimensional Model for River Network Flow and Sediment Transport%一二维耦合河网水沙模型研究
Institute of Scientific and Technical Information of China (English)
吕文丽; 张旭
2011-01-01
Based on previous research, a new one and two-dimensional coupled model of river water and sediment was proposed.With reference to the three-level solution for one-dimensional river network water mode, the two-dimensional river section will be generalized to river section within the river network.One and two dimensional coupled river network sediment model will be established with the balance of flow amount and sediment transport.The model sets up the chasing relationship between variables of water level and sediment content at the end and first section to further establish matrix equations of the whole one and two-dimensional river network node water level and sediment content.Though the verification and calculation for generalized river network from Datong to Zhenjiang in the lower reaches of the Yangtze River, it is found that the model is of great practical value.%借鉴河网水流的三级解法,将二维河段概化为河网内部河段,通过河网节点流量和输沙量的平衡,建立一二维耦合河网水沙模型.模型采用全隐式方法建立二维河段以首末断面的水位和含沙量为中间变量的矩阵追赶关系,进而建立整个一二维河网的节点水位及含沙量的矩阵方程组.对方程组的求解,可实现一二维水沙模型的耦合求解.通过对长江下游大通至镇江概化河网的验证计算,表明模型具有很好的实用价值.
Software defined networking with OpenFlow
Azodolmolky, Siamak
2013-01-01
A step-by-step, example-based guide which will help you gain hands-on experience with the platforms and debugging tools on OpenFlow.If you are a network engineer, architect, junior researcher or an application developer, this book is ideal for you. You will need to have some level of network experience, knowledge of broad networking concepts, and some familiarity with day- to- day operation of computer networks. Ideally, you should also be familiar with programing scripting/languages (especially Python and Java), and system virtualization.
El Kadiri, R.; Sultan, M.; Elbayoumi, T.; Sefry, S.
2013-12-01
Efforts to map the distribution of debris flows, to assess the factors controlling their development, and to identify the areas prone to their development are often hampered by the absence or paucity of appropriate monitoring systems and historical databases and the inaccessibility of these areas in many parts of the world. We developed methodologies that heavily rely on readily available observations extracted from remote sensing datasets and successfully applied these techniques over the the Jazan province, in the Red Sea hills of Saudi Arabia. We first identified debris flows (10,334 locations) from high spatial resolution satellite datasets (e.g., GeoEye, Orbview), and verified a subset of these occurrences in the field. We then constructed a GIS to host the identified debris flow locations together with co-registered relevant data (e.g., lithology, elevation) and derived products (e.g., slope, normalized difference vegetation index, etc). Spatial analysis of the data sets in the GIS sets indicated various degrees of correspondence between the distribution of debris flows and various variables (e.g., stream power index, topographic position index, normalized difference vegetation index, distance to stream, flow accumulation, slope and soil weathering index, aspect, elevation) suggesting a causal effect. For example, debris flows were found in areas of high slope, low distance to low stream orders and low vegetation index. To evaluate the extent to which these factors control landslide distribution, we constructed and applied: (1) a stepwise input selection by testing all input combinations to make the final model more compact and effective, (2) a statistic-based binary logistic regression (BLR) model, and (3) a mathematical-based artificial neural network (ANN) model. Only 80% (8267 locations) of the data was used for the construction of each of the models and the remaining samples (2067 locations) were used for the accuracy assessment purposes. Results
Modeling worldwide highway networks
Villas Boas, Paulino R.; Rodrigues, Francisco A.; da F. Costa, Luciano
2009-12-01
This Letter addresses the problem of modeling the highway systems of different countries by using complex networks formalism. More specifically, we compare two traditional geographical models with a modified geometrical network model where paths, rather than edges, are incorporated at each step between the origin and the destination vertices. Optimal configurations of parameters are obtained for each model and used for the comparison. The highway networks of Australia, Brazil, India, and Romania are considered and shown to be properly modeled by the modified geographical model.
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...
Bridging Minds: A Mixed Methodology to Assess Networked Flow.
Galimberti, Carlo; Chirico, Alice; Brivio, Eleonora; Mazzoni, Elvis; Riva, Giuseppe; Milani, Luca; Gaggioli, Andrea
2015-01-01
The main goal of this contribution is to present a methodological framework to study Networked Flow, a bio-psycho-social theory of collective creativity applying it on creative processes occurring via a computer network. First, we draw on the definition of Networked Flow to identify the key methodological requirements of this model. Next, we present the rationale of a mixed methodology, which aims at combining qualitative, quantitative and structural analysis of group dynamics to obtain a rich longitudinal dataset. We argue that this integrated strategy holds potential for describing the complex dynamics of creative collaboration, by linking the experiential features of collaborative experience (flow, social presence), with the structural features of collaboration dynamics (network indexes) and the collaboration outcome (the creative product). Finally, we report on our experience with using this methodology in blended collaboration settings (including both face-to-face and virtual meetings), to identify open issues and provide future research directions.
Characteristic particle methods for traffic flow simulations on highway networks
Farjoun, Yossi
2012-01-01
A characteristic particle method for the simulation of first order macroscopic traffic models on road networks is presented. The approach is based on the method "particleclaw", which solves scalar one dimensional hyperbolic conservations laws exactly, except for a small error right around shocks. The method is generalized to nonlinear network flows, where particle approximations on the edges are suitably coupled together at the network nodes. It is demonstrated in numerical examples that the resulting particle method can approximate traffic jams accurately, while only devoting a few degrees of freedom to each edge of the network.
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....
Packet flow analysis in IP networks via abstract interpretation
Komondoor, Raghavan; Seetharam, Deva P; Balodia, Sudha
2011-01-01
Static analysis (aka offline analysis) of a model of an IP network is useful for understanding, debugging, and verifying packet flow properties of the network. There have been static analysis approaches proposed in the literature for networks based on model checking as well as graph reachability. Abstract interpretation is a method that has typically been applied to static analysis of programs. We propose a new, abstract-interpretation based approach for analysis of networks. We formalize our approach, mention its correctness guarantee, and demonstrate its flexibility in addressing multiple network-analysis problems that have been previously solved via tailor-made approaches. Finally, we investigate an application of our analysis to a novel problem -- inferring a high-level policy for the network -- which has been addressed in the past only in the restricted single-router setting.
Artificial neural network modelling
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. .
Evolution of karst conduit networks in transition from pressurised flow to free surface flow
Perne, M.; Covington, M. D.; Gabrovšek, F.
2014-06-01
We present a novel modelling approach to study the evolution of conduit networks in soluble rocks. Unlike the models presented so far, the model allows a transition from pressurised (pipe) flow to a free surface (open channel) flow in evolving discrete conduit networks. It calculates flow, solute transport and dissolutional enlargement within each time step and steps through time until a stable flow pattern establishes. The flow in each time step is calculated by calling the EPA Storm Water Management Model (EPA SWMM), which efficiently solves the 1-D Saint Venant equations in a network of conduits. We present several cases with low dip and sub-vertical networks to demonstrate mechanisms of flow pathway selection. In low dip models the inputs were randomly distributed to several junctions. The evolution of pathways progresses upstream: initially pathways linking outlets to the closest inputs evolve fastest because the gradient along these pathways is largest. When a pathway efficiently drains the available recharge, the head drop along the pathway attracts flow from the neighbouring upstream junctions and new connecting pathways evolve. The mechanism progresses from the output boundary inwards until all inputs are connected to the stable flow system. In the pressurised phase, each junction is drained by at least one conduit, but only one conduit remains active in the vadose phase. The selection depends on the initial geometry of a junction, initial distribution of diameters, the evolution in a pressurised regime, and on the dip of the conduits, which plays an important role in vadose entrenchment. In high dip networks, the vadose zone propagates downwards and inwards from the rim of the massif. When a network with randomly distributed initial diameters is supplied with concentrated recharge from the adjacent area, the sink point regresses up upstream along junctions connected to the prominent pathways. Large conductive structures provide deep penetration of high
Evolution of karst conduit networks in transition from pressurised flow to free surface flow
Directory of Open Access Journals (Sweden)
M. Perne
2014-06-01
Full Text Available We present a novel modelling approach to study the evolution of conduit networks in soluble rocks. Unlike the models presented so far, the model allows a transition from pressurised (pipe flow to a free surface (open channel flow in evolving discrete conduit networks. It calculates flow, solute transport and dissolutional enlargement within each time step and steps through time until a stable flow pattern establishes. The flow in each time step is calculated by calling the EPA Storm Water Management Model (EPA SWMM, which efficiently solves the 1-D Saint Venant equations in a network of conduits. We present several cases with low dip and sub-vertical networks to demonstrate mechanisms of flow pathway selection. In low dip models the inputs were randomly distributed to several junctions. The evolution of pathways progresses upstream: initially pathways linking outlets to the closest inputs evolve fastest because the gradient along these pathways is largest. When a pathway efficiently drains the available recharge, the head drop along the pathway attracts flow from the neighbouring upstream junctions and new connecting pathways evolve. The mechanism progresses from the output boundary inwards until all inputs are connected to the stable flow system. In the pressurised phase, each junction is drained by at least one conduit, but only one conduit remains active in the vadose phase. The selection depends on the initial geometry of a junction, initial distribution of diameters, the evolution in a pressurised regime, and on the dip of the conduits, which plays an important role in vadose entrenchment. In high dip networks, the vadose zone propagates downwards and inwards from the rim of the massif. When a network with randomly distributed initial diameters is supplied with concentrated recharge from the adjacent area, the sink point regresses up upstream along junctions connected to the prominent pathways. Large conductive structures provide deep
Costa, Anna; Molnar, Peter
2017-04-01
Sediment transport rates along rivers and the grain size distribution (GSD) of coarse channel bed sediment are the result of the long term balance between transport capacity and sediment supply. Transport capacity, mainly a function of channel geometry and flow competence, can be altered by changes in climatic forcing as well as by human activities. In Alpine rivers it is hydropower production systems that are the main causes of modification to the transport capacity of water courses through flow regulation, leading over longer time scales to the adjustment of river bed GSDs. We developed a river network bedload transport model to evaluate the impacts of hydropower on the transfer of sediments and the GSDs of the Upper Rhône basin, a 5,200 km2 catchment located in the Swiss Alps. Many large reservoirs for hydropower production have been built along the main tributaries of the Rhône River since the 1960s, resulting in a complex system of intakes, tunnels, and pumping stations. Sediment storage behind dams and intakes, is accompanied by altered discharge due to hydropower operations, mainly higher flow in winter and lower in summer. It is expected that this change in flow regime may have resulted in different bedload transport. However, due the non-linear, threshold-based nature of the relation between discharge and sediment mobilization, the effects of changed hydraulic conditions are not easily deducible, and because observations of bedload in pre- and post-dam conditions are usually not available, a modelling approach is often necessary. In our modelling approach, the river network is conceptualized as a series of connected links (river reaches). Average geometric characteristics of each link (width, length, and slope of cross section) are extracted from digital elevation data, while surface roughness coefficients are assigned based on the GSD. Under the assumptions of rectangular prismatic cross sections and normal flow conditions, bed shear stress is estimated
Network structure of inter-industry flows
McNerney, James; Silverberg, Gerald
2012-01-01
We study the structure of inter-industry relationships using networks of money flows between industries in 20 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The community structure is hierarchical, with the top level of the hierarchy comprising five industry communities: food industries, chemical industries, manufacturing industries, service industries, and extraction industries.
Optimal Control of Transient Flow in Natural Gas Networks
Zlotnik, Anatoly; Backhaus, Scott
2015-01-01
We outline a new control system model for the distributed dynamics of compressible gas flow through large-scale pipeline networks with time-varying injections, withdrawals, and control actions of compressors and regulators. The gas dynamics PDE equations over the pipelines, together with boundary conditions at junctions, are reduced using lumped elements to a sparse nonlinear ODE system expressed in vector-matrix form using graph theoretic notation. This system, which we call the reduced network flow (RNF) model, is a consistent discretization of the PDE equations for gas flow. The RNF forms the dynamic constraints for optimal control problems for pipeline systems with known time-varying withdrawals and injections and gas pressure limits throughout the network. The objectives include economic transient compression (ETC) and minimum load shedding (MLS), which involve minimizing compression costs or, if that is infeasible, minimizing the unfulfilled deliveries, respectively. These continuous functional optimiza...
Huff, Emily Silver; Leahy, Jessica E; Hiebeler, David; Weiskittel, Aaron R; Noblet, Caroline L
2015-01-01
Privately owned woodlands are an important source of timber and ecosystem services in North America and worldwide. Impacts of management on these ecosystems and timber supply from these woodlands are difficult to estimate because complex behavioral theory informs the owner's management decisions. The decision-making environment consists of exogenous market factors, internal cognitive processes, and social interactions with fellow landowners, foresters, and other rural community members. This study seeks to understand how social interactions, information flow, and peer-to-peer networks influence timber harvesting behavior using an agent-based model. This theoretical model includes forested polygons in various states of 'harvest readiness' and three types of agents: forest landowners, foresters, and peer leaders (individuals trained in conservation who use peer-to-peer networking). Agent rules, interactions, and characteristics were parameterized with values from existing literature and an empirical survey of forest landowner attitudes, intentions, and demographics. The model demonstrates that as trust in foresters and peer leaders increases, the percentage of the forest that is harvested sustainably increases. Furthermore, peer leaders can serve to increase landowner trust in foresters. Model output and equations will inform forest policy and extension/outreach efforts. The model also serves as an important testing ground for new theories of landowner decision making and behavior.
Directory of Open Access Journals (Sweden)
Emily Silver Huff
Full Text Available Privately owned woodlands are an important source of timber and ecosystem services in North America and worldwide. Impacts of management on these ecosystems and timber supply from these woodlands are difficult to estimate because complex behavioral theory informs the owner's management decisions. The decision-making environment consists of exogenous market factors, internal cognitive processes, and social interactions with fellow landowners, foresters, and other rural community members. This study seeks to understand how social interactions, information flow, and peer-to-peer networks influence timber harvesting behavior using an agent-based model. This theoretical model includes forested polygons in various states of 'harvest readiness' and three types of agents: forest landowners, foresters, and peer leaders (individuals trained in conservation who use peer-to-peer networking. Agent rules, interactions, and characteristics were parameterized with values from existing literature and an empirical survey of forest landowner attitudes, intentions, and demographics. The model demonstrates that as trust in foresters and peer leaders increases, the percentage of the forest that is harvested sustainably increases. Furthermore, peer leaders can serve to increase landowner trust in foresters. Model output and equations will inform forest policy and extension/outreach efforts. The model also serves as an important testing ground for new theories of landowner decision making and behavior.
Mohanty, S.; Jha, Madan K.; Kumar, Ashwani; Panda, D. K.
2013-07-01
In view of worldwide concern for the sustainability of groundwater resources, basin-wide modeling of groundwater flow is essential for the efficient planning and management of groundwater resources in a groundwater basin. The objective of the present study is to evaluate the performance of finite difference-based numerical model MODFLOW and the artificial neural network (ANN) model developed in this study in simulating groundwater levels in an alluvial aquifer system. Calibration of the MODFLOW was done by using weekly groundwater level data of 2 years and 4 months (February 2004 to May 2006) and validation of the model was done using 1 year of groundwater level data (June 2006 to May 2007). Calibration of the model was performed by a combination of trial-and-error method and automated calibration code PEST with a mean RMSE (root mean squared error) value of 0.62 m and a mean NSE (Nash-Sutcliffe efficiency) value of 0.915. Groundwater levels at 18 observation wells were simulated for the validation period. Moreover, artificial neural network models were developed to predict groundwater levels in 18 observation wells in the basin one time step (i.e., week) ahead. The inputs to the ANN model consisted of weekly rainfall, evaporation, river stage, water level in the drain, pumping rate of the tubewells and groundwater levels in these wells at the previous time step. The time periods used in the MODFLOW were also considered for the training and testing of the developed ANN models. Out of the 174 data sets, 122 data sets were used for training and 52 data sets were used for testing. The simulated groundwater levels by MODFLOW and ANN model were compared with the observed groundwater levels. It was found that the ANN model provided better prediction of groundwater levels in the study area than the numerical model for short time-horizon predictions.
Correction model for flow calculation of plain river network%平原区河网水流计算校正模型
Institute of Scientific and Technical Information of China (English)
吴晓玲; 向小华; 李菲菲; 王船海
2012-01-01
Complex water movement and fewer measuring points are the unfavorable factors in improving the accuracy of flow calculation of river networks. For this reason, a correction model for a plain river network was developed based on the three-step method in dealing with the water levels at key nodes of a looping river network, with the coefficients in the equation considered to be the media carrying the correction information. The correction information of the model errors at observation stations was transmitted to the other cross sections of the river network to correct the forecasted values of water levels and discharges at the cross sections of neighboring rivers. A case study was conducted in the river network of the Chengtong section of the Yangtze River. Hydrological data in September 2004 were selected for model calculation. The results show that the proposed model can effectively transmit the correction information of model errors in a certain spatial range and has practical significance in improving the accuracy of forecasts for river networks.%为提高平原河网水流计算的精度,针对其中交错相连的复杂河网水流运动以及测点又相对较少的情况,提出以环状河网节点水位三级解法为基础、河段方程系数为载体的平原河网校正模型.将观测位置上的模型系统误差校正信息由测点向河网其他断面传播,修正相邻河道断面的水位、流量预报值.选择长江澄通段河网进行实例分析,选用2004年9月的水文资料进行演算.结果表明,该方法能在一定空间范围内有效地传播误差修正量,对提高河网水流预报精度具有实用意义.
Spike Code Flow in Cultured Neuronal Networks.
Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime; Kamimura, Takuya; Yagi, Yasushi; Mizuno-Matsumoto, Yuko; Chen, Yen-Wei
2016-01-01
We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of "1101" and "1011," which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the "maximum cross-correlations" among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network.
Modeling Network Interdiction Tasks
2015-09-17
minimize the operating costs for manufacturing 50 the item. This simple example illustrates the hierarchical structure that can be modeled using...fixed. The resulting model is linearized and the product of the dual variable and the (1−γij) term replaced with βij. This allows certain...the standard network interdiction model based on its tight linear programming relaxation. 2.3.3 Network Disruption. In practice, whenever an object is
Energy Technology Data Exchange (ETDEWEB)
McKoy, M.L., Sams, W.N.
1997-10-01
The US Department of Energy, Federal Energy Technology Center, has sponsored a project to simulate the behavior of tight, fractured, strata-bound gas reservoirs that arise from irregular discontinuous, or clustered networks of fractures. New FORTRAN codes have been developed to generate fracture networks, or simulate reservoir drainage/recharge, and to plot the fracture networks and reservoirs pressures. Ancillary codes assist with raw data analysis.
Information and material flows in complex networks
Helbing, Dirk; Armbruster, Dieter; Mikhailov, Alexander S.; Lefeber, Erjen
2006-04-01
In this special issue, an overview of the Thematic Institute (TI) on Information and Material Flows in Complex Systems is given. The TI was carried out within EXYSTENCE, the first EU Network of Excellence in the area of complex systems. Its motivation, research approach and subjects are presented here. Among the various methods used are many-particle and statistical physics, nonlinear dynamics, as well as complex systems, network and control theory. The contributions are relevant for complex systems as diverse as vehicle and data traffic in networks, logistics, production, and material flows in biological systems. The key disciplines involved are socio-, econo-, traffic- and bio-physics, and a new research area that could be called “biologistics”.
Quan, Guo-zheng; Yu, Chun-tang; Liu, Ying-ying; Xia, Yu-feng
2014-01-01
The stress-strain data of 20MnNiMo alloy were collected from a series of hot compressions on Gleeble-1500 thermal-mechanical simulator in the temperature range of 1173 ∼ 1473 K and strain rate range of 0.01 ∼ 10 s(-1). Based on the experimental data, the improved Arrhenius-type constitutive model and the artificial neural network (ANN) model were established to predict the high temperature flow stress of as-cast 20MnNiMo alloy. The accuracy and reliability of the improved Arrhenius-type model and the trained ANN model were further evaluated in terms of the correlation coefficient (R), the average absolute relative error (AARE), and the relative error (η). For the former, R and AARE were found to be 0.9954 and 5.26%, respectively, while, for the latter, 0.9997 and 1.02%, respectively. The relative errors (η) of the improved Arrhenius-type model and the ANN model were, respectively, in the range of -39.99% ∼ 35.05% and -3.77% ∼ 16.74%. As for the former, only 16.3% of the test data set possesses η-values within ± 1%, while, as for the latter, more than 79% possesses. The results indicate that the ANN model presents a higher predictable ability than the improved Arrhenius-type constitutive model.
Sample EP Flow Analysis of Severely Damaged Networks
Energy Technology Data Exchange (ETDEWEB)
Werley, Kenneth Alan [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); McCown, Andrew William [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-10-12
These are slides for a presentation at the working group meeting of the WESC SREMP Software Product Integration Team on sample EP flow analysis of severely damaged networks. The following topics are covered: ERCOT EP Transmission Model; Zoomed in to Houston and Overlaying StreetAtlas; EMPACT Solve/Dispatch/Shedding Options; QACS BaseCase Power Flow Solution; 3 Substation Contingency; Gen. & Load/100 Optimal Dispatch; Dispatch Results; Shed Load for Low V; Network Damage Summary; Estimated Service Areas (Potential); Estimated Outage Areas (potential).
Network as a computer: ranking paths to find flows
Pavlovic, Dusko
2008-01-01
We explore a simple mathematical model of network computation, based on Markov chains. Similar models apply to a broad range of computational phenomena, arising in networks of computers, as well as in genetic, and neural nets, in social networks, and so on. The main problem of interaction with such spontaneously evolving computational systems is that the data are not uniformly structured. An interesting approach is to try to extract the semantical content of the data from their distribution among the nodes. A concept is then identified by finding the community of nodes that share it. The task of data structuring is thus reduced to the task of finding the network communities, as groups of nodes that together perform some non-local data processing. Towards this goal, we extend the ranking methods from nodes to paths. This allows us to extract some information about the likely flow biases from the available static information about the network.
Exploring the potential of blood flow network data
Poelma, C.
2015-01-01
To gain a better understanding of the role of haemodynamic forces during the development of the cardiovascular system, a series of studies have been reported recently that describe flow fields in the vasculature of model systems. Such data sets, in particular those reporting networks at multiple sta
Modeling Epidemic Network Failures
DEFF Research Database (Denmark)
Ruepp, Sarah Renée; Fagertun, Anna Manolova
2013-01-01
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......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...
Modeling Evolving Innovation Networks
Koenig, Michael D.; Battiston, Stefano; Schweitzer, Frank
2007-01-01
We develop a new framework for modeling innovation networks which evolve over time. The nodes in the network represent firms, whereas the directed links represent unilateral interactions between the firms. Both nodes and links evolve according to their own dynamics and on different time scales. The model assumes that firms produce knowledge based on the knowledge exchange with other firms, which involves both costs and benefits for the participating firms. In order to increase their knowledge...
Advances in dynamic network modeling in complex transportation systems
Ukkusuri, Satish V
2013-01-01
This book focuses on the latest in dynamic network modeling, including route guidance and traffic control in transportation systems and other complex infrastructure networks. Covers dynamic traffic assignment, flow modeling, mobile sensor deployment and more.
On information flow in relay networks
El Gamal, A.
Preliminary investigations conducted by El Gamal and Cover (1980) have shown that a max-flow min-cut interpretation for the capacity expressions of the classes of degraded and semideterministic relay channels can be found. In this paper it is shown that such an interpretation can also be found for fairly general classes of discrete memoryless relay networks. Cover and El Gamal (1979) have obtained general lower and upper bounds to capacity. However, the capacity of the general relay channel is not known. Past results are here extended to establish the capacity of deterministic relay networks with no interference and degraded relay networks. A general upper bound is given to the capacity of any relay network with this upper bound being a natural generalization of Theorem 4 in the study conducted by Cover and El Gamal (1979).
Assessment of debris flow hazards using a Bayesian Network
Liang, Wan-jie; Zhuang, Da-fang; Jiang, Dong; Pan, Jian-jun; Ren, Hong-yan
2012-10-01
Comprehensive assessment of debris flow hazard risk is challenging due to the complexity and uncertainties of various related factors. A reasonable and reliable assessment should be based on sufficient data and realistic approaches. This study presents a novel approach for assessing debris flow hazard risk using BN (Bayesian Network) and domain knowledge. Based on the records of debris flow hazards and geomorphological/environmental data for the Chinese mainland, approaches based on BN, SVM (Support Vector Machine) and ANN (Artificial Neural Network) were compared. BN provided the highest values of hazard detection probability, precision, and AUC (area under the receiver operating characteristic curve). The BN model is useful for mapping and assessing debris flow hazard risk on a national scale.
Design of Optical Wireless Networks with Fair Traffic Flows
Directory of Open Access Journals (Sweden)
Artur Tomaszewski
2014-01-01
Full Text Available The paper presents a method for optimising the wireless optical network that carries elastic packet traffic. The particular focus is on modelling the effect of elastic traffic flows slowing down in response to the decrease of the optical transmission systems’ capacity at bad weather conditions. A mathematical programming model of the network design problem is presented that assumes that the packet rates of elastic traffic flows decrease fairly. While practically any subset of network links can be simultaneously affected by unfavourable transmission conditions, a particular challenge of solving the problem results from a huge number of network states considered in the model. Therefore, how the problem can be solved by generating the most unfavourable network states is presented. Moreover, it is proved that it is entirely sufficient to consider only the states that correspond to the decrease of capacity on a single link. Finally, as the general problem is nonlinear, it is shown that the problem can be transformed to a linear MIP problem and solved effectively when single-path routing of traffic flows is assumed.
Piri, Mohammad; Blunt, Martin J
2005-02-01
We use the model described in Piri and Blunt [Phys. Rev. E 71, 026301 (2005)] to predict two- and three-phase relative permeabilities of Berea sandstone using a random network to represent the pore space. We predict measured relative permeabilities for two-phase flow in a water-wet system. We then successfully predict the steady-state oil, water, and gas three-phase relative permeabilities measured by Oak (Proceedings of the SPE/DOE Seventh Symposium on Enhanced Oil Recovery, Tulsa, OK, 1990). We also study secondary and tertiary gas injection into media of different wettability and initial oil saturation and interpret the results in terms of pore-scale displacement processes.
Jia, Pin; Cheng, Linsong; Huang, Shijun; Xu, Zhongyi; Xue, Yongchao; Cao, Renyi; Ding, Guanyang
2017-08-01
This paper provides a comprehensive model for the flow behavior of a two-zone system with discrete fracture network. The discrete fracture network within the inner zone is represented explicitly by fracture segments. The Laplace-transform finite-difference method is used to numerically model discrete fracture network flow, with sufficient flexibility to consider arbitrary fracture geometries and conductivity distributions. Boundary-element method and line-source functions in the Laplace domain are employed to derive a semi-analytical flow solution for the two-zone system. By imposing the continuity of flux and pressure on discrete fracture surfaces, the semi-analytical two-zone system flow model and the numerical fracture flow model are coupled dynamically. The main advantage of the approach occurring in the Laplace domain is that simulation can be done with nodes only for discrete fractures and elements for boundaries and at predetermined, discrete times. Thus, stability and convergence problems caused by time discretization are avoided and the burden of gridding and computation is decreased without loss of important fracture characteristics. The model is validated by comparison with the results from an analytical solution and a fully numerical solution. Flow regime analysis shows that a two-zone system with discrete fracture network may develop six flow regimes: fracture linear flow, bilinear flow, inner zone linear flow, inner zone pseudosteady-state flow, outer zone pseudoradial flow and outer zone boundary-dominated flow. Especially, local solutions for the inner-zone linear flow have the same form with that of a finite conductivity planar fracture and can be correlated with the total length of discrete fractures and an intercept term. In the inner zone pseudosteady-state flow period, the discrete fractures, along with the boundary of the inner zone, will act as virtual closed boundaries, due to the pressure interference caused by fracture network and the
Clustering coefficient and periodic orbits in flow networks
Rodriguez-Mendez, Victor; Hernandez-Garcia, Emilio
2016-01-01
We show that the clustering coefficient, a standard measure in network theory, when applied to flow networks, i.e. graph representations of fluid flows in which links between nodes represent fluid transport between spatial regions, identifies the location of periodic trajectories in the flow system. This is true for steady flows and for periodic ones in which the time interval $\\tau$ used to construct the network is the period of the flow or a multiple of it. In other situations the clustering coefficient still identifies cyclic motion between regions of the fluid. Besides the fluid context, these ideas apply equally well to general dynamical systems. By varying the value of $\\tau$ used to construct the network, a kind of spectroscopy can be performed so that the observation of high values of mean clustering at a value of $\\tau$ reveals the presence of periodic orbits of period $3\\tau$. These results are illustrated with examples of increasing complexity, namely a steady and a periodically perturbed model two...
On network flow management in virtualized environments
Rodríguez Haro, Fernando; Freitag, Fèlix; Navarro Moldes, Leandro; Farías Mendoza, Nicandro; Guerrero Ibáñez, Juan Antonio
2009-01-01
Virtualization has enabled the use of virtual machine (VM) based resource providers to offer computing power as a service. However, one important feature is to have certainty about the planed or expected performance of the applications that run inside the VMs. Therefore, consolidating the work load of web-based applications, with time-varying resource requirements, should be done in an automated way. We present and evaluate a network flow management component for the admission control of HTTP...
Sensitivity analysis of permeability parameters for flows on Barcelona networks
Rarità, Luigi; D'Apice, Ciro; Piccoli, Benedetto; Helbing, Dirk
We consider the problem of optimizing vehicular traffic flows on an urban network of Barcelona type, i.e. square network with streets of not equal length. In particular, we describe the effects of variation of permeability parameters, that indicate the amount of flow allowed to enter a junction from incoming roads. On each road, a model suggested by Helbing et al. (2007) [11] is considered: free and congested regimes are distinguished, characterized by an arrival flow and a departure flow, the latter depending on a permeability parameter. Moreover we provide a rigorous derivation of the model from fluid dynamic ones, using recent results of Bretti et al. (2006) [3]. For solving the dynamics at nodes of the network, a Riemann solver maximizing the through flux is used, see Coclite et al. (2005) [4] and Helbing et al. (2007) [11]. The network dynamics gives rise to complicate equations, where the evolution of fluxes at a single node may involve time-delayed terms from all other nodes. Thus we propose an alternative hybrid approach, introducing additional logic variables. Finally we compute the effects of variations on permeability parameters over the hybrid dynamics and test the obtained results via simulations.
A speed-flow relationship model of highway traffic flow
Institute of Scientific and Technical Information of China (English)
WANG Wei; LI Wei; REN Gang
2005-01-01
In the view that the generally used speed-flow relationship model is insufficient in the traffic analysis under over-saturated conditions, this paper first establishes the theoretical models of speed flow relationship for each highway class based upon a large number of traffic data collected from the field. Then by analyzing the traffic flow dissipation mechanism under peak hour over-saturated traffic conditions, the speed flow relationship model structures for each highway class are reviewed under different traffic load conditions. Through curve-fitting of large numbers of observed data, functional equations of general speed-flow relationship models for each highway class under any traffic load conditions are established. The practical model parameters for each highway class under different design speeds are also put forward. This model is successful in solving the speed-forecasting problem of the traffic flow under peak hour over-saturated conditions. This provides the theoretical bases for the development of projects related to highway network planning, economic analysis, etc.
SIMULATION AND PREDICTION OF DEBRIS FLOW USING ARTIFICIAL NEURAL NETWORK
Institute of Scientific and Technical Information of China (English)
WANG Xie-kang; HUANG Er; CUI Peng
2003-01-01
Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-ful in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.
A Social Network Security Ontology Model Incorporating Information Flow%一种基于信息流的社交网络安全本体模型
Institute of Scientific and Technical Information of China (English)
刘一阳; 张保稳
2012-01-01
Social network, for its rapid development these years, becomes a new trend for the Internet. At this moment hundreds of millions of users use social networking websites represented by Facebook and Renren. However, rapid growth of SNS also brings such new information security problems as SNS spam, privacy violation and identity theft. Here the information flow in SNS is modeled by using ontology, and an automatic solution for recognizing SNS spam thus provided.%社交网站作为互联网的新兴应用，近年来发展迅速。以Facebook和人人网为代表的社交网站现在已经拥有数以百万计的活动用户，创造了一种新的生活方式。但是社交网络快速发展的同时也带来了新的安全问题，如垃圾信息、隐私泄露、身份盗用等。文中尝试用本体来描述社交网络中的信息流动，并提供一个能够识别垃圾信息发送者的自动化解决方案。
Flow enforcement algorithms for ATM networks
DEFF Research Database (Denmark)
Dittmann, Lars; Jacobsen, Søren B.; Moth, Klaus
1991-01-01
theory and partly on signal processing theory, is carried out. It is seen that the time constant involved increases with the increasing burstiness of the connection. It is suggested that the RMS measurement bandwidth be used to dimension linear algorithms for equal flow enforcement characteristics....... Implementations are proposed on the block diagram level, and dimensioning examples are carried out when flow enforcing a renewal-type connection using the four algorithms. The corresponding hardware demands are estimated aid compared......Four measurement algorithms for flow enforcement in asynchronous transfer mode (ATM) networks are presented. The algorithms are the leaky bucket, the rectangular sliding window, the triangular sliding window, and the exponentially weighted moving average. A comparison, based partly on teletraffic...
Wiratsudakul, Anuwat; Paul, Mathilde Cécile; Bicout, Dominique Joseph; Tiensin, Thanawat; Triampo, Wannapong; Chalvet-Monfray, Karine
2014-06-01
In Southeast Asia, traditional poultry marketing chains have been threatened by epidemics caused by the highly pathogenic avian influenza H5N1 (HPAI H5N1) virus. In Thailand, the trade of live backyard chickens is based on the activities of traders buying chickens from villages and supplying urban markets with chicken meat. This study aims to quantify the flows of chickens traded during a 1-year period in a province of Thailand. A compartmental stochastic dynamic model was constructed to illustrate trade flows of live chickens from villages to slaughterhouses. Live poultry movements present important temporal variations with increased activities during the 15 days preceding the Chinese New Year and, to a lesser extent, other festivals (Qingming Festival, Thai New Year, Hungry Ghost Festival, and International New Year). The average distance of poultry movements ranges from 4 to 25 km, defining a spatial scale for the risk of avian influenza that spread through traditional poultry marketing chains. Some characteristics of traditional poultry networks in Thailand, such as overlapping chicken supply zones, may facilitate disease diffusion over longer distances through combined expansion and relocation processes. This information may be of use in tailoring avian influenza and other emerging infectious poultry disease surveillance and control programs provided that the cost-effectiveness of such scenarios is also evaluated in further studies.
Models of educational institutions' networking
Shilova Olga Nikolaevna
2015-01-01
The importance of educational institutions' networking in modern sociocultural conditions and a definition of networking in education are presented in the article. The results of research levels, methods and models of educational institutions' networking are presented and substantially disclosed.
Models of educational institutions' networking
Shilova Olga Nikolaevna
2015-01-01
The importance of educational institutions' networking in modern sociocultural conditions and a definition of networking in education are presented in the article. The results of research levels, methods and models of educational institutions' networking are presented and substantially disclosed.
Traffic flow modeling: a Genealogy
Van Wageningen-Kessels, F.L.M.; Hoogendoorn, S.P.; Vuik, C.; Van Lint, J.W.C.
2014-01-01
80 years ago, Bruce Greenshields presented the first traffic flow model at the Annual Meeting of the Highway Research Board. Since then, many models and simulation tools have been developed. We show a model tree with four families of traffic flow models, all descending from Greenshields' model. The
Hannula, Manne; Alarousu, Erkki; Prykäri, Tuukka; Myllylä, Risto
2007-03-01
Low Coherence Doppler Flowmetry (LCDF) measurement produces a signal, which frequency domain characteristics are in connection to the speed of the flow. In this study performances of Artificial Neural Network (ANN) and Multilinear Regression (MLR) methods in prediction of pulp flow speed from the LCDF measurement data were compared. In the study the pulp flow speed was estimated distinctly from consecutive frequency bands of the LCDF data with both methods. The smallest estimation error in flow speed with the ANN method was 20% and with the MLR method 30%, depending on the selected frequency band. The results indicate the relationship between characteristics of the LCDF measurement and pulp flow speed includes remarkable number of nonlinear components. The result is in line with theoretical calculations about the Doppler shifts occurrence in the LCDF data.
Dynamics of blood flow in a microfluidic ladder network
Maddala, Jeevan; Zilberman-Rudenko, Jevgenia; McCarty, Owen
The dynamics of a complex mixture of cells and proteins, such as blood, in perturbed shear flow remains ill-defined. Microfluidics is a promising technology for improving the understanding of blood flow under complex conditions of shear; as found in stent implants and in tortuous blood vessels. We model the fluid dynamics of blood flow in a microfluidic ladder network with dimensions mimicking venules. Interaction of blood cells was modeled using multiagent framework, where cells of different diameters were treated as spheres. This model served as the basis for predicting transition regions, collision pathways, re-circulation zones and residence times of cells dependent on their diameters and device architecture. Based on these insights from the model, we were able to predict the clot formation configurations at various locations in the device. These predictions were supported by the experiments using whole blood. To facilitate platelet aggregation, the devices were coated with fibrillar collagen and tissue factor. Blood was perfused through the microfluidic device for 9 min at a physiologically relevant venous shear rate of 600 s-1. Using fluorescent microscopy, we observed flow transitions near the channel intersections and at the areas of blood flow obstruction, which promoted larger thrombus formation. This study of integrating model predictions with experimental design, aids in defining the dynamics of blood flow in microvasculature and in development of novel biomedical devices.
Multi-Flow Attacks Against Network Flow Watermarks: Analysis and Countermeasures
Kiyavash, Negar; Borisov, Nikita
2012-01-01
In this paper, we analyze several recent schemes for watermarking network flows that are based on splitting the flow into timing intervals. We show that this approach creates time-dependent correlations that enable an attack that combines multiple watermarked flows. Such an attack can easily be mounted in nearly all applications of network flow watermarking, both in anonymous communication and stepping stone detection. The attack can be used to detect the presence of a watermark, recover the secret parameters, and remove the watermark from a flow. The attack can be effective even if different flows are marked with different values of a watermark. We analyze the efficacy of our attack using a probabilistic model and a Markov-Modulated Poisson Process (MMPP) model of interactive traffic. We also implement our attack and test it using both synthetic and real-world traces, showing that our attack is effective with as few as 10 watermarked flows. Finally, we propose possible countermeasures to defeat the multi-flo...
DEFF Research Database (Denmark)
Heiselberg, Per; Nielsen, Peter V.
Air distribution in ventilated rooms is a flow process that can be divided into different elements such as supply air jets, exhaust flows, thermal plumes, boundary layer flows, infiltration and gravity currents. These flow elements are isolated volumes where the air movement is controlled...... by a restricted number of parameters, and the air movement is fairly independent of the general flow in the enclosure. In many practical situations, the most convenient· method is to design the air distribution system using flow element theory....
Institute of Scientific and Technical Information of China (English)
Lihui CEN; Yugeng XI
2008-01-01
By considering the flow control of urban sewer networks to minimize the electricity consumption of pumping stations.a decomposition-coordination strategy for energy savings based on network community division is developed in this paper. A mathematical model characterizing the smady-state flow of urball sewer networks is first constructed,consisting of a set of algebraic equations with the structure transportation capacities captured as constraints.Since the sewer networks have no apparent natural hierarchical structure in general.it is very difficult to identify the clustered groups.A fast network division approach through calculating the betweenness of each edge is successfully applied to identify the groups and a sewer network with arbitrary configuration could be then decomposed into subnetworks.By integrating the coupling constraints of the subnetworks.the original problem is separated into N optimization subproblems in accordance with the network decomposition.Each subproblem is solved locally and the solutions to the subproblems are coordinated to form an appropriate global solution.Finally,an application to a specified large-scale sewer network is also investigated to demonstrate the validity of the proposed algorithm.
River Network Modeling Beyond Discharge at Gauges
David, C. H.; Famiglietti, J. S.; Salas, F. R.; Whiteaker, T. L.; Maidment, D. R.; Tolle, K.
2014-12-01
Over the past two decades, the estimation of water flow in river networks within hydro-meteorological models has mostly focused on simulations of natural processes and on their verification at available river gauges. Despite valuable existing skills in hydrologic modeling the accounting for anthropogenic actions in current models remains limited. The emerging availability of datasets containing measured dam outflows and reported irrigation withdrawals motivates their inclusion into simulations of flow in river networks. However, the development of advanced river network models accounting for such datasets of anthropogenic influences requires a detailed data model and a thorough handling of the various data types, sources and time scales. This contribution details the development of a consistent data model suitable for accounting some observations of anthropogenic modifications of the surface water cycle and presents the impact of such inclusion on simulations using the Routing Application for Parallel computatIon of Discharge (RAPID).
Graphical Models for Optimal Power Flow
Dvijotham, Krishnamurthy; Chertkov, Michael; Misra, Sidhant; Vuffray, Marc
2016-01-01
Optimal power flow (OPF) is the central optimization problem in electric power grids. Although solved routinely in the course of power grid operations, it is known to be strongly NP-hard in general, and weakly NP-hard over tree networks. In this paper, we formulate the optimal power flow problem over tree networks as an inference problem over a tree-structured graphical model where the nodal variables are low-dimensional vectors. We adapt the standard dynamic programming algorithm for inference over a tree-structured graphical model to the OPF problem. Combining this with an interval discretization of the nodal variables, we develop an approximation algorithm for the OPF problem. Further, we use techniques from constraint programming (CP) to perform interval computations and adaptive bound propagation to obtain practically efficient algorithms. Compared to previous algorithms that solve OPF with optimality guarantees using convex relaxations, our approach is able to work for arbitrary distribution networks an...
Routing strategies in traffic network and phase transition in network traffic flow
Indian Academy of Sciences (India)
Bing-Hong Wang; Wen-Xu Wang
2008-08-01
The dynamics of information traffic over scale-free networks has been investigated systematically. A series of routing strategies of data packets have been proposed, including the local routing strategy, the next-nearest-neighbour routing strategy, and the mixed routing strategy based on local static and dynamic information. The capacity of the network can be quantified by the phase transition from free flow state to congestion state. The optimal parameter values of each model leading to the highest efficiency of scale-free networked traffic systems have been found. Moreover, we have found hysteretic loop in networked traffic systems with finite packets delivering ability. Such hysteretic loop indicates the existence of the bi-stable state in the traffic dynamics over scale-free networks.
Institute of Scientific and Technical Information of China (English)
纪丽君; 林柏梁; 乔国会; 王金宝
2011-01-01
针对既有点-弧模型和弧-路模型的局限性做2点改进.第1点是定义1个0-1决策变最,使优化结果体现车流的径路;第2点是区分大股车流和小股车流,使优化结果符合实际车流组织的特点.基于多商品流模型,结合铁路运输组织的车流不拆散原则,对大股车流、线路能力和车站能力进行约束,构建铁路网车流分配和径路优化改进模制,采用Lingo 8.0软件求解.以简化的东北地区局部路网为例,采用模拟OD车流,按照本文构建的模型进行铁路网车流分配及径路优化.结果表明:采用该模型能够得出较理想的车流分配和径路方案,验证了模型的合理性与有效性.%By analyzing the drawbacks of node-arc and arc-route car flow assignment model, improvements were made on the following two aspects. One was to define a 0-1 decision variable which could make the optimization results reflect car flow routing. The other was to differentiate the car flows according to their volume to make the optimization results accord with the characteristics of the actual car flow organization.Based on multi-commodity flow model, the car flow assignment and routing optimization model of railway network was built, which subjected to each car flow moving on one route only, car flow with big volume,the capacity of lines and stations. The model could be solved by software Lingo 8. 0. Taking the simplified local railway network in the Northeast China for example, the car flow was assigned and the routing was optimized for railway network according to the proposed model by adopting the simulated OD car flow.Results show that more ideal car flow assignment and routing plan can be obtained by the proposed model,which has verified the rationality and the validity of the proposed model.
Directed network discovery with dynamic network modelling.
Anzellotti, Stefano; Kliemann, Dorit; Jacoby, Nir; Saxe, Rebecca
2017-05-01
Cognitive tasks recruit multiple brain regions. Understanding how these regions influence each other (the network structure) is an important step to characterize the neural basis of cognitive processes. Often, limited evidence is available to restrict the range of hypotheses a priori, and techniques that sift efficiently through a large number of possible network structures are needed (network discovery). This article introduces a novel modelling technique for network discovery (Dynamic Network Modelling or DNM) that builds on ideas from Granger Causality and Dynamic Causal Modelling introducing three key changes: (1) efficient network discovery is implemented with statistical tests on the consistency of model parameters across participants, (2) the tests take into account the magnitude and sign of each influence, and (3) variance explained in independent data is used as an absolute (rather than relative) measure of the quality of the network model. In this article, we outline the functioning of DNM, we validate DNM in simulated data for which the ground truth is known, and we report an example of its application to the investigation of influences between regions during emotion recognition, revealing top-down influences from brain regions encoding abstract representations of emotions (medial prefrontal cortex and superior temporal sulcus) onto regions engaged in the perceptual analysis of facial expressions (occipital face area and fusiform face area) when participants are asked to switch between reporting the emotional valence and the age of a face. Copyright © 2017 Elsevier Ltd. All rights reserved.
Adamczyk, John J.
1997-01-01
Last year, researchers at the NASA Lewis Research Center used the average passage code APNASA to complete the largest three-dimensional simulation of a multistage axial flow compressor to date. Consisting of 29 blade rows, the configuration is typical of those found in aeroengines today. The simulation, which was executed on the High Performance Computing and Communications (HPCC) Program IBM SP2 parallel computer located at the NASA Ames Research Center, took nearly 90 hr to complete. Since the completion of this activity, a fine-grain, parallel version of APNASA has been written by a team of researchers from General Electric, NASA Lewis, and NYMA. Timing studies performed on the SP2 have shown that, with eight processors assigned to each blade row, the simulation time is reduced by a factor of six. For this configuration, the simulation time would be 15 hr. The reduction in computing time indicates that an overnight turnaround of a multistage configuration simulation is feasible. In addition, average passage forms of two-equation turbulence models were formulated. These models are currently being incorporated into APNASA.
2012-01-01
Flow-injection mass spectrometry (FI/MS) represents a powerful analytical tool for the quality assessment of herbal formula in dietary supplements. In this study, we described a scaffold (proof-of-concept) adapted from spectroscopy to quantify Cordyceps sinensis and Ganoderma lucidum in a popular Cordyceps sinensis /Ganoderma lucidum -enriched health beverage by utilizing flow-injection/mass spectrometry/artificial neural network (FI/MS/ANN) model fingerprinting method with feature selection capability. Equal proportion of 0.1% formic acid and methanol (v/v) were used to convert extracts of Cordyceps sinensis and Ganoderma lucidum into their respective ions under positive MS polarity condition. No chromatographic separation was performed. The principal m/z values of Cordyceps sinensis and Ganoderma lucidum were identified as: 104.2, 116.2, 120.2, 175.2, 236.3, 248.3, 266.3, 366.6 and 498.6; 439.7, 469.7, 511.7, 551.6, 623.6, 637.7 and 653.6, respectively. ANN models representing Cordyceps sinensis and Ganoderma lucidum were individually trained and validated using three independent sets of matrix-free and matrix-matched calibration curves at concentration levels of 2, 20, 50, 100, 200 and 400 μg mL-1. Five repeat analyses provided a total of 180 spectra for herbal extracts of Cordyceps sinensis and Ganoderma lucidum. Root-mean-square-deviation (RMSE) were highly satisfactory at <4% for both training and validation models. Correlation coefficient (r2) values of between 0.9994 and 0.9997 were reported. Matrix blanks comprised of complex mixture of Lingzhi fermentation solution and collagen. Recovery assessment was performed over two days using six sets of matrix blank (n = 6) spiked at three concentration levels of approximately 83, 166 and 333 mg kg-1. Extraction using acetonitrile provided good overall recovery range of 92-118%. A quantitation limit of 0.2 mg L-1 was reported for both Cordyceps sinensis and Ganoderma lucidum. Intra-day and inter-day RMSE
Directory of Open Access Journals (Sweden)
S. K. Lahiri
2009-05-01
Full Text Available This paper describes a robust hybrid artificial neural network (ANN methodology which can offer a superior performance for the important process engineering problems. The method incorporates a hybrid artificial neural network and differential evolution technique (ANN-DE for the efficient tuning of ANN meta parameters. The algorithm has been applied for the prediction of the hold up of the solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved the prediction of hold up over a wide range of operating conditions, physical properties, and pipe diameters.
Flow interaction based propagation model and bursty influence behavior analysis of Internet flows
Wu, Xiao-Yu; Gu, Ren-Tao; Ji, Yue-Feng
2016-11-01
QoS (quality of service) fluctuations caused by Internet bursty flows influence the user experience in the Internet, such as the increment of packet loss and transmission time. In this paper, we establish a mathematical model to study the influence propagation behavior of the bursty flow, which is helpful for developing a deep understanding of the network dynamics in the Internet complex system. To intuitively reflect the propagation process, a data flow interaction network with a hierarchical structure is constructed, where the neighbor order is proposed to indicate the neighborhood relationship between the bursty flow and other flows. The influence spreads from the bursty flow to each order of neighbors through flow interactions. As the influence spreads, the bursty flow has negative effects on the odd order neighbors and positive effects on the even order neighbors. The influence intensity of bursty flow decreases sharply between two adjacent orders and the decreasing degree can reach up to dozens of times in the experimental simulation. Moreover, the influence intensity increases significantly when network congestion situation becomes serious, especially for the 1st order neighbors. Network structural factors are considered to make a further study. Simulation results show that the physical network scale expansion can reduce the influence intensity of bursty flow by decreasing the flow distribution density. Furthermore, with the same network scale, the influence intensity in WS small-world networks is 38.18% and 18.40% lower than that in ER random networks and BA scale-free networks, respectively, due to a lower interaction probability between flows. These results indicate that the macro-structural changes such as network scales and styles will affect the inner propagation behaviors of the bursty flow.
Bibhab Kumar Lodh; Ajoy K Das
2015-01-01
This paper will present the large eddy simulation of turbulence modeling for wind flow over a wall mounted 3D cubical model. The LES Smagorinsky scheme is employed for the numerical simulation. The domain for this study is of the size of 60 cm x 30 cm x 30 cm. The 3D cube model is taken of the size of 6 cm x 6 cm x 4 cm. The Reynolds number for the flow in respect of the height of the cube i.e, 4 cm is 5.3x104 . The hexahedral grids are used for the meshing of the flow domain. ...
Mapping information flow in sensorimotor networks.
Directory of Open Access Journals (Sweden)
Max Lungarella
2006-10-01
Full Text Available Biological organisms continuously select and sample information used by their neural structures for perception and action, and for creating coherent cognitive states guiding their autonomous behavior. Information processing, however, is not solely an internal function of the nervous system. Here we show, instead, how sensorimotor interaction and body morphology can induce statistical regularities and information structure in sensory inputs and within the neural control architecture, and how the flow of information between sensors, neural units, and effectors is actively shaped by the interaction with the environment. We analyze sensory and motor data collected from real and simulated robots and reveal the presence of information structure and directed information flow induced by dynamically coupled sensorimotor activity, including effects of motor outputs on sensory inputs. We find that information structure and information flow in sensorimotor networks (a is spatially and temporally specific; (b can be affected by learning, and (c can be affected by changes in body morphology. Our results suggest a fundamental link between physical embeddedness and information, highlighting the effects of embodied interactions on internal (neural information processing, and illuminating the role of various system components on the generation of behavior.
Implementing network constraints in the EMPS model
Energy Technology Data Exchange (ETDEWEB)
Helseth, Arild; Warland, Geir; Mo, Birger; Fosso, Olav B.
2010-02-15
This report concerns the coupling of detailed market and network models for long-term hydro-thermal scheduling. Currently, the EPF model (Samlast) is the only tool available for this task for actors in the Nordic market. A new prototype for solving the coupled market and network problem has been developed. The prototype is based on the EMPS model (Samkjoeringsmodellen). Results from the market model are distributed to a detailed network model, where a DC load flow detects if there are overloads on monitored lines or intersections. In case of overloads, network constraints are generated and added to the market problem. Theoretical and implementation details for the new prototype are elaborated in this report. The performance of the prototype is tested against the EPF model on a 20-area Nordic dataset. (Author)
LSSVM Network Flow Prediction Based on the Self-adaptive Genetic Algorithm Optimization
Directory of Open Access Journals (Sweden)
Liao Wenjing
2013-02-01
Full Text Available In order to change the insufficiency of traditional network flow prediction and improve its accuracy, the paper proposed a kind of network flow prediction method based on the self-adaptive genetic least square support vector machine optimization. Through analyzing the individual parameter of the LS-SVM principle and self-adaptive remains algorithm, the network flow prediction model structure of GA-LSSVM, and the genetic model global operation parameters, this paper would conduct a performance test to the network flow simulation experiment. The simulation result showed that: compared with the traditional forecasting methods, the accuracy of its network flow prediction was higher than the traditional forecasting methods by using the least square support vector machine genetic optimization.
Energy Technology Data Exchange (ETDEWEB)
P. Dixon
2004-02-11
The purpose of this Model Report is to document the unsaturated zone (UZ) fluid flow and tracer transport models and submodels as well as the flow fields generated utilizing the UZ Flow and Transport Model of Yucca Mountain (UZ Model), Nevada. This work was planned in ''Technical Work Plan (TWP) for: Performance Assessment Unsaturated Zone'' (BSC 2002 [160819], Section 1.10, Work Package AUZM06). The UZ Model has revised, updated, and enhanced the previous UZ Flow Model REV 00 ICN 01 (BSC 2001 [158726]) by incorporation of the conceptual repository design with new grids, recalibration of property sets, and more comprehensive validation effort. The flow fields describe fracture-fracture, matrix-matrix, and fracture-matrix liquid flow rates and their spatial distributions as well as moisture conditions in the UZ system. These 3-D UZ flow fields are used directly by Performance Assessment (PA). The model and submodels evaluate important hydrogeologic processes in the UZ as well as geochemistry and geothermal conditions. These provide the necessary framework to test conceptual hypotheses of flow and transport at different scales and predict flow and transport behavior under a variety of climatic conditions. In addition, this Model Report supports several PA activities, including abstractions, particle-tracking transport simulations, and the UZ Radionuclide Transport Model.
Application of digital elevation model in delineating drainage networks
Institute of Scientific and Technical Information of China (English)
SUN Yan-ling; XIE De-ti; LIU Hong-bin; WEI Chao-fu
2005-01-01
A practical method to extract drainage network from DEM (digital elevation model) is introduced. DEM pretreatment includes depression and flat areas treatment. The flow direction of each grid cell in DEM is calculated according to the 8-direction pour point model, and then the flow accumulation grid from the flow direction grid. With the flow accumulation grid, streams are defined according to the given threshold value of flow accumulation. Taking Gufo River watershed as an example, the extraction of drainage network was done from DEM. The results are basically consistent with the digitized drainage network from the relief maps.
Laminar flow of two miscible fluids in a simple network
Karst, Casey M; Geddes, John B
2012-01-01
When a fluid comprised of multiple phases or constituents flows through a network, non-linear phenomena such as multiple stable equilibrium states and spontaneous oscillations can occur. Such behavior has been observed or predicted in a number of networks including the flow of blood through the microcirculation, the flow of picoliter droplets through microfluidic devices, the flow of magma through lava tubes, and two-phase flow in refrigeration systems. While the existence of non-linear phenomena in a network with many inter-connections containing fluids with complex rheology may seem unsurprising, this paper demonstrates that even simple networks containing Newtonian fluids in laminar flow can demonstrate multiple equilibria. The paper describes a theoretical and experimental investigation of the laminar flow of two miscible Newtonian fluids of different density and viscosity through a simple network. The fluids stratify due to gravity and remain as nearly distinct phases with some mixing occurring only by d...
Renormalization Group Flows of Hamiltonians Using Tensor Networks
Bal, M.; Mariën, M.; Haegeman, J.; Verstraete, F.
2017-06-01
A renormalization group flow of Hamiltonians for two-dimensional classical partition functions is constructed using tensor networks. Similar to tensor network renormalization [G. Evenbly and G. Vidal, Phys. Rev. Lett. 115, 180405 (2015), 10.1103/PhysRevLett.115.180405; S. Yang, Z.-C. Gu, and X.-G. Wen, Phys. Rev. Lett. 118, 110504 (2017), 10.1103/PhysRevLett.118.110504], we obtain approximate fixed point tensor networks at criticality. Our formalism, however, preserves positivity of the tensors at every step and hence yields an interpretation in terms of Hamiltonian flows. We emphasize that the key difference between tensor network approaches and Kadanoff's spin blocking method can be understood in terms of a change of the local basis at every decimation step, a property which is crucial to overcome the area law of mutual information. We derive algebraic relations for fixed point tensors, calculate critical exponents, and benchmark our method on the Ising model and the six-vertex model.
Renormalization Group Flows of Hamiltonians Using Tensor Networks.
Bal, M; Mariën, M; Haegeman, J; Verstraete, F
2017-06-23
A renormalization group flow of Hamiltonians for two-dimensional classical partition functions is constructed using tensor networks. Similar to tensor network renormalization [G. Evenbly and G. Vidal, Phys. Rev. Lett. 115, 180405 (2015)PRLTAO0031-900710.1103/PhysRevLett.115.180405; S. Yang, Z.-C. Gu, and X.-G. Wen, Phys. Rev. Lett. 118, 110504 (2017)PRLTAO0031-900710.1103/PhysRevLett.118.110504], we obtain approximate fixed point tensor networks at criticality. Our formalism, however, preserves positivity of the tensors at every step and hence yields an interpretation in terms of Hamiltonian flows. We emphasize that the key difference between tensor network approaches and Kadanoff's spin blocking method can be understood in terms of a change of the local basis at every decimation step, a property which is crucial to overcome the area law of mutual information. We derive algebraic relations for fixed point tensors, calculate critical exponents, and benchmark our method on the Ising model and the six-vertex model.
A perturbation-theoretic approach to Lagrangian flow networks
Fujiwara, Naoya; Kirchen, Kathrin; Donges, Jonathan F.; Donner, Reik V.
2017-03-01
Complex network approaches have been successfully applied for studying transport processes in complex systems ranging from road, railway, or airline infrastructures over industrial manufacturing to fluid dynamics. Here, we utilize a generic framework for describing the dynamics of geophysical flows such as ocean currents or atmospheric wind fields in terms of Lagrangian flow networks. In this approach, information on the passive advection of particles is transformed into a Markov chain based on transition probabilities of particles between the volume elements of a given partition of space for a fixed time step. We employ perturbation-theoretic methods to investigate the effects of modifications of transport processes in the underlying flow for three different problem classes: efficient absorption (corresponding to particle trapping or leaking), constant input of particles (with additional source terms modeling, e.g., localized contamination), and shifts of the steady state under probability mass conservation (as arising if the background flow is perturbed itself). Our results demonstrate that in all three cases, changes to the steady state solution can be analytically expressed in terms of the eigensystem of the unperturbed flow and the perturbation itself. These results are potentially relevant for developing more efficient strategies for coping with contaminations of fluid or gaseous media such as ocean and atmosphere by oil spills, radioactive substances, non-reactive chemicals, or volcanic aerosols.
Analysis of Cortical Flow Models In Vivo
Benink, Hélène A.; Mandato, Craig A.; Bement, William M.
2000-01-01
Cortical flow, the directed movement of cortical F-actin and cortical organelles, is a basic cellular motility process. Microtubules are thought to somehow direct cortical flow, but whether they do so by stimulating or inhibiting contraction of the cortical actin cytoskeleton is the subject of debate. Treatment of Xenopus oocytes with phorbol 12-myristate 13-acetate (PMA) triggers cortical flow toward the animal pole of the oocyte; this flow is suppressed by microtubules. To determine how this suppression occurs and whether it can control the direction of cortical flow, oocytes were subjected to localized manipulation of either the contractile stimulus (PMA) or microtubules. Localized PMA application resulted in redirection of cortical flow toward the site of application, as judged by movement of cortical pigment granules, cortical F-actin, and cortical myosin-2A. Such redirected flow was accelerated by microtubule depolymerization, showing that the suppression of cortical flow by microtubules is independent of the direction of flow. Direct observation of cortical F-actin by time-lapse confocal analysis in combination with photobleaching showed that cortical flow is driven by contraction of the cortical F-actin network and that microtubules suppress this contraction. The oocyte germinal vesicle serves as a microtubule organizing center in Xenopus oocytes; experimental displacement of the germinal vesicle toward the animal pole resulted in localized flow away from the animal pole. The results show that 1) cortical flow is directed toward areas of localized contraction of the cortical F-actin cytoskeleton; 2) microtubules suppress cortical flow by inhibiting contraction of the cortical F-actin cytoskeleton; and 3) localized, microtubule-dependent suppression of actomyosin-based contraction can control the direction of cortical flow. We discuss these findings in light of current models of cortical flow. PMID:10930453
An Improved Car-Following Model in Vehicle Networking Based on Network Control
Directory of Open Access Journals (Sweden)
D. Y. Kong
2014-01-01
Full Text Available Vehicle networking is a system to realize information interoperability between vehicles and people, vehicles and roads, vehicles and vehicles, and cars and transport facilities, through the network information exchange, in order to achieve the effective monitoring of the vehicle and traffic flow. Realizing information interoperability between vehicles and vehicles, which can affect the traffic flow, is an important application of network control system (NCS. In this paper, a car-following model using vehicle networking theory is established, based on network control principle. The car-following model, which is an improvement of the traditional traffic model, describes the traffic in vehicle networking condition. The impact that vehicle networking has on the traffic flow is quantitatively assessed in a particular scene of one-way, no lane changing highway. The examples show that the capacity of the road is effectively enhanced by using vehicle networking.
Spontaneous oscillations of capillary blood flow in artificial microvascular networks.
Forouzan, Omid; Yang, Xiaoxi; Sosa, Jose M; Burns, Jennie M; Shevkoplyas, Sergey S
2012-09-01
Previous computational studies have suggested that the capillary blood flow oscillations frequently observed in vivo can originate spontaneously from the non-linear rheological properties of blood, without any regulatory input. Testing this hypothesis definitively in experiments involving real microvasculature has been difficult because in vivo the blood flow in capillaries is always actively controlled by the host. The objective of this study was to test the hypothesis experimentally and to investigate the relative contribution of different blood cells to the capillary blood flow dynamics under static boundary conditions and in complete isolation from the active regulatory mechanisms mediated by the blood vessels in vivo. To accomplish this objective, we passed whole blood and re-constituted blood samples (purified red blood cells suspended in buffer or in autologous plasma) through an artificial microvascular network (AMVN) comprising completely inert, microfabricated vessels with the architecture inspired by the real microvasculature. We found that the flow of blood in capillaries of the AMVN indeed oscillates with characteristic frequencies in the range of 0-0.6 Hz, which is in a very good agreement with previous computational studies and in vivo observations. We also found that the traffic of leukocytes through the network (typically neglected in computational modeling) plays an important role in generating the oscillations. This study represents the key piece of experimental evidence in support of the hypothesis that spontaneous, self-sustained oscillations of capillary blood flow can be generated solely by the non-linear rheological properties of blood flowing through microvascular networks, and provides an insight into the mechanism of this fundamentally important microcirculatory phenomenon.
Flow networks: A characterization of geophysical fluid transport
Ser-Giacomi, Enrico; Lopez, Cristobal; Hernandez-Garcia, Emilio
2014-01-01
We represent transport between different regions of a fluid domain by flow networks, constructed from the discrete representation of the Perron-Frobenius or transfer operator associated to the fluid advection dynamics. The procedure is useful to analyze fluid dynamics in geophysical contexts, as illustrated by the construction of a flow network associated to the surface circulation in the Mediterranean sea. We use network-theory tools to analyze the flow network and gain insights into transport processes. In particular we quantitatively relate dispersion and mixing characteristics, classically quantified by Lyapunov exponents, to the degree of the network nodes. A family of network entropies is defined from the network adjacency matrix, and related to the statistics of stretching in the fluid, in particular to the Lyapunov exponent field. Finally we use a network community detection algorithm, Infomap, to partition the Mediterranean network into coherent regions, i.e. areas internally well mixed, but with lit...
Flux flow pinning and resistive behavior in superconducting networks
Energy Technology Data Exchange (ETDEWEB)
Teitel, S.
1990-10-01
We have studied the behavior of superconducting networks in terms of XY and Coulomb gas models. The dynamics of frustrated Josephson junction arrays has been simulated, with a view toward understanding the effects of vortex correlations on flux flow resistance. Randomness has been introduced, and its effects on the superconducting transition, and vortex mobility, have been studied. A three dimensional network has been simulated to study the effects of vortex line entanglement in high temperature superconductors. Preliminary calculations are in progress. The two dimensional classical Coulomb gas where charges map onto vortices in the superconducting network, has been simulated. The melting transitions of ordered charge (vortex) lattices have been studied, and we find clear evidence that these transitions do not have the critical behavior expected from standard symmetry analysis.
Towards effective flow simulations in realistic discrete fracture networks
Berrone, Stefano; Pieraccini, Sandra; Scialò, Stefano
2016-04-01
We focus on the simulation of underground flow in fractured media, modeled by means of Discrete Fracture Networks. Focusing on a new recent numerical approach proposed by the authors for tackling the problem avoiding mesh generation problems, we further improve the new family of methods making a step further towards effective simulations of large, multi-scale, heterogeneous networks. Namely, we tackle the imposition of Dirichlet boundary conditions in weak form, in such a way that geometrical complexity of the DFN is not an issue; we effectively solve DFN problems with fracture transmissivities spanning many orders of magnitude and approaching zero; furthermore, we address several numerical issues for improving the numerical solution also in quite challenging networks.
Analysis of HRCT-derived xylem network reveals reverse flow in some vessels
Flow in xylem vessels is modeled based on constructions of three dimensional xylem networks derived from High Resolution Computed Tomography (HRCT) images of grapevine (Vitis vinifera) stems. Flow in 6-14% of the vessels was found to be oriented in the opposite direction to the bulk flow under norma...
Directory of Open Access Journals (Sweden)
Zhang Weiyang
2016-03-01
Full Text Available Previous empirical research on urban networks has used data on infrastructure networks to guesstimate actual inter-city flows. However, with the exception of recent research on airline networks in the context of the world city literature, relatively limited attention has been paid to the degree to which the outline of these infrastructure networks reflects the actual flows they undergird. This study presents a method to improve our estimation of urban interaction in and through infrastructure networks by focusing on the example of passenger railways, which is arguably a key potential data source in research on urban networks in metropolitan regions. We first review common biases when using infrastructure networks to approximate actual inter-city flows, after which we present an alternative approach that draws on research on operational train scheduling. This research has shown that ‘dwell time’ at train stations reflects the length of the alighting and boarding process, and we use this insight to estimate actual interaction through the application of a bimodal network projection function. We apply our method to the high-speed railway (HSR network within the Yangtze River Delta (YRD region, discuss the difference between our modelled network and the original network, and evaluate its validity through a systemic comparison with a benchmark dataset of actual passenger flows.
Directory of Open Access Journals (Sweden)
P. Bala Anki Reddy
2016-09-01
Full Text Available In this paper, the prediction of the magnetohydrodynamic boundary layer slip flow over a permeable stretched cylinder with chemical reaction is investigated by using some mathematical techniques, namely Runge–Kutta fourth order method along with shooting technique and artificial neural network (ANN. A numerical method is implemented to approximate the flow of heat and mass transfer characteristics as a function of some input parameters, explicitly the curvature parameter, magnetic parameter, permeability parameter, velocity slip, Grashof number, solutal Grashof number, Prandtl number, temperature exponent, Schmidt number, concentration exponent and chemical reaction parameter. The non-linear partial differential equations of the governing flow are converted into a system of highly non-linear ordinary differential equations by using the suitable similarity transformations, which are then solved numerically by a Runge–Kutta fourth order along with shooting technique and then ANN is applied to them. The Back Propagation Neural Network is applied for forecasting the desired outputs. The reported numerical values and the ANN values are in good agreement than those published works on various special cases. According to the findings of this study, the ANN approach is reliable, effective and easily applicable for simulating heat and mass transfer flow over a stretched cylinder.
An improved BP artificial neural network algorithm for urban traffic flow intelligent prediction
Institute of Scientific and Technical Information of China (English)
XIONG Shi-yong; ZHANG Yi
2009-01-01
The traffic flow is interrelated to traffic congestion, the big traffic flow directly results in traffic congestion of some section. In this paper, on the basis of the research of overseas traffic accident, considering the characteristic of Chinese traffic, artificial neural network was used to predict traffic accident, and an improved BP artificial neural network model according with Chinese the situation of a country was proposed. The urban traffic flow prediction was simulated under the particular situation, the simulation result shows that the improved BP artificial neural network can fit the urban traffic flow prediction very well and have high performance.
Model Diagnostics for Bayesian Networks
Sinharay, Sandip
2006-01-01
Bayesian networks are frequently used in educational assessments primarily for learning about students' knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A…
Monitoring individual traffic flows within the ATLAS TDAQ network
Energy Technology Data Exchange (ETDEWEB)
Sjoen, R; Batraneanu, S M; Leahu, L; Martin, B; Al-Shabibi, A [CERN, 1211 Geneva 23 (Switzerland); Stancu, S; Ciobotaru, M, E-mail: rune.velle.sjoen@cern.c [' Politehnica' University of Bucharest (Romania)
2010-04-01
The ATLAS data acquisition system consists of four different networks interconnecting up to 2000 processors using up to 200 edge switches and five multi-blade chassis devices. The architecture of the system has been described in [1] and its operational model in [2]. Classical, SNMP-based, network monitoring provides statistics on aggregate traffic, but for performance monitoring and troubleshooting purposes there was an imperative need to identify and quantify single traffic flows. sFlow [3] is an industry standard based on statistical sampling which attempts to provide a solution to this. Due to the size of the ATLAS network, the collection and analysis of the sFlow data from all devices generates a data handling problem of its own. This paper describes how this problem is addressed by making it possible to collect and store data either centrally or distributed according to need. The methods used to present the results in a relevant fashion for system analysts are discussed and we explore the possibilities and limitations of this diagnostic tool, giving an example of its use in solving system problems that arise during the ATLAS data taking.
Monitoring individual traffic flows within the ATLAS TDAQ network
Sjoen, R; Ciobotaru, M; Batraneanu, S M; Leahu, L; Martin, B; Al-Shabibi, A
2010-01-01
The ATLAS data acquisition system consists of four different networks interconnecting up to 2000 processors using up to 200 edge switches and five multi-blade chassis devices. The architecture of the system has been described in [1] and its operational model in [2]. Classical, SNMP-based, network monitoring provides statistics on aggregate traffic, but for performance monitoring and troubleshooting purposes there was an imperative need to identify and quantify single traffic flows. sFlow [3] is an industry standard based on statistical sampling which attempts to provide a solution to this. Due to the size of the ATLAS network, the collection and analysis of the sFlow data from all devices generates a data handling problem of its own. This paper describes how this problem is addressed by making it possible to collect and store data either centrally or distributed according to need. The methods used to present the results in a relevant fashion for system analysts are discussed and we explore the possibilities a...
Three-phase flow simulations in discrete fracture networks
Geiger, S.; Niessner, J.; Matthai, S. K.; Helmig, R.
2006-12-01
Fractures are often the key conduits for fluid flow in otherwise low permeability rocks. Their presence in hydrocarbon reservoirs leads to complex production histories, unpredictable coupling of wells, rapidly changing flow rates, possibly early water breakthrough, and low final recovery. Recently, it has been demonstrated that a combination of finite volume and finite element discretization is well suited to model incompressible, immiscible two-phase flow in 3D discrete fracture networks (DFN) representing complexly fractured rocks. Such an approach has been commercialized in Golder Associates' FracMan Reservoir Edition software. For realistic reservoir simulations, however, it would be desirable if a third compressible gas phase can be included which is often present at reservoir conditions. Here we present the extension of an existing node-centred finite volume - finite element (FEFV) discretization for the efficient and accurate simulations of three-component - three-phase flow in geologically realistic representations of fractured porous media. Two possible types of fracture networks can be used: In 2D, they are detailed geometrical representations of fractured rock masses mapped in field studies. In 3D, they are geologically constrained, stochastically generated discrete fracture networks. Flow and transport can be simulated for fractures only or for fractures and matrix combined. The governing equations are solved decoupled using an implicit-pressure, explicit-saturation (IMPES) approach. Flux and concentration terms can be treated with higher-order accuracy in the finite volume scheme to preserve shock fronts. The method is locally mass conservative and works on unstructured, spatially refined grids. Flash calculations are carried out by a new description of the Black-Oil model. Capillary and gravity effects are included in this formulation. The robustness and accuracy of this formulation is shown in several applications. First, grid convergence is
Energy Technology Data Exchange (ETDEWEB)
Yeh, G.; Cheng, H.; Cheng, J.; Lin, H.C.; Martin, W.D.
1998-07-01
This report presents the development of a numerical model simulating water flow and contaminant and sediment transport in watershed systems of one-dimensional river/stream network, two-dimensional overland regime, and three-dimensional subsurface media. The model is composed of two modules: flow and transport. Three options are provided in modeling the flow module in river/ stream network and overland regime: the kinematic wave approach, diffusion wave approach, and dynamic wave approach. The kinematic and diffusion wave approaches are known to be numerically robust in terms of numerical convergency and stability; i.e., they can generate convergent and stable simulations over a wide range of ground surface slopes in the entire watershed. The question is the accuracy of these simulations. The kinematic wave approach usually produces accurate solutions only over the region of steep slopes. The diffusion wave approach normally gives accurate solutions over the region of mild to steep slopes. However, neither approach has the ability to yield accurate solutions over the region of small slopes, in which the inertial forces are no longer negligible compared to the gravitational forces. The kinematic wave approach cannot address the problems of backwater effects. On the other hand, a dynamic wave approach, having included all forces, can theoretically have the potential to generate accurate simulations over all ranges of slopes in a watershed. The subsurface flow is described by Richard`s equation where water flow through saturated-unsaturated porous media is accounted for.
Directed and Almost-Directed Flow Loops in Real Networks
Directory of Open Access Journals (Sweden)
M. Todinov
2013-09-01
Full Text Available Directed flow loops are highly undesirable because they are associated with wastage of energy for maintaining them and entail big losses to the world economy. It is shown that directed flow loops may appear in networks even if the dispatched commodity does not physically travel along a closed contour. Consequently, a theorem giving the necessary and sufficient condition of a directed flow loop on randomly oriented straight-line flow paths has been formulated and a close-form expression has been derived for the probability of a directed flow loop. The results show that even for a relatively small number of intersecting flow paths, the probability of a directed flow loop is very large, which means that the existence of directed flow loops in real networks is practically inevitable. Consequently, a theorem and an efficient algorithm have been proposed related to discovering and removing directed flow loops in a network with feasible flows. The new concept ‘almost-directed flow loop’ has also been introduced for the first time. It is shown that the removal of an almost-directed flow loop also results in a significant decrease of the losses. It is also shown that if no directed flow loops exist in the network, the removal of an almost-directed flow loop cannot create a directed flow loop.
Energy Technology Data Exchange (ETDEWEB)
Y. Wu
2004-11-01
The purpose of this report is to document the unsaturated zone (UZ) flow models and submodels, as well as the flow fields that have been generated using the UZ flow model(s) of Yucca Mountain, Nevada. In this report, the term ''UZ model'' refers to the UZ flow model and the several submodels, which include tracer transport, temperature or ambient geothermal, pneumatic or gas flow, and geochemistry (chloride, calcite, and strontium) submodels. The term UZ flow model refers to the three-dimensional models used for calibration and simulation of UZ flow fields. This work was planned in the ''Technical Work Plan (TWP) for: Unsaturated Zone Flow Analysis and Model Report Integration'' (BSC 2004 [DIRS 169654], Section 1.2.7). The table of included Features, Events, and Processes (FEPs), Table 6.2-11, is different from the list of included FEPs assigned to this report in the ''Technical Work Plan for: Unsaturated Zone Flow Analysis and Model Report Integration'' (BSC 2004 [DIRS 169654], Table 2.1.5-1), as discussed in Section 6.2.6. The UZ model has revised, updated, and enhanced the previous UZ model (BSC 2001 [DIRS 158726]) by incorporating the repository design with new grids, recalibration of property sets, and more comprehensive validation effort. The flow fields describe fracture-fracture, matrix-matrix, and fracture-matrix liquid flow rates, and their spatial distributions as well as moisture conditions in the UZ system. These three-dimensional UZ flow fields are used directly by Total System Performance Assessment (TSPA). The model and submodels evaluate important hydrogeologic processes in the UZ as well as geochemistry and geothermal conditions. These provide the necessary framework to test hypotheses of flow and transport at different scales, and predict flow and transport behavior under a variety of climatic conditions. In addition, the limitations of the UZ model are discussed in Section 8.11.
Maps of sparse Markov chains efficiently reveal community structure in network flows with memory
Persson, Christian; Edler, Daniel; Rosvall, Martin
2016-01-01
To better understand the flows of ideas or information through social and biological systems, researchers develop maps that reveal important patterns in network flows. In practice, network flow models have implied memoryless first-order Markov chains, but recently researchers have introduced higher-order Markov chain models with memory to capture patterns in multi-step pathways. Higher-order models are particularly important for effectively revealing actual, overlapping community structure, but higher-order Markov chain models suffer from the curse of dimensionality: their vast parameter spaces require exponentially increasing data to avoid overfitting and therefore make mapping inefficient already for moderate-sized systems. To overcome this problem, we introduce an efficient cross-validated mapping approach based on network flows modeled by sparse Markov chains. To illustrate our approach, we present a map of citation flows in science with research fields that overlap in multidisciplinary journals. Compared...
Network models of dissolution of porous media
Budek, Agnieszka
2012-01-01
We investigate the chemical dissolution of porous media using a network model in which the system is represented as a series of interconnected pipes with the diameter of each segment increasing in proportion to the local reactant consumption. Moreover, the topology of the network is allowed to change dynamically during the simulation: as the diameters of the eroding pores become comparable with the interpore distances, the pores are joined together thus changing the interconnections within the network. With this model, we investigate different growth regimes in an evolving porous medium, identifying the mechanisms responsible for the emergence of specific patterns. We consider both the random and regular network and study the effect of the network geometry on the patterns. Finally, we consider practically important problem of finding an optimum flow rate that gives a maximum increase in permeability for a given amount of reactant.
Directory of Open Access Journals (Sweden)
Tsz Leung Yip
2013-03-01
Full Text Available A model is developed for studying marine traffic flow through classical traffic flow theories, which can provide us with a better understanding of the phenomenon of traffic flow of ships. On one hand, marine traffic has its special features and is fundamentally different from highway, air and pedestrian traffic. The existing traffic models cannot be simply extended to marine traffic without addressing marine traffic features. On the other hand, existing literature on marine traffic focuses on one ship or two ships but does not address the issues in marine traffic flow.
Jiménez, Juan; Smits, Alexander
2003-11-01
Experimental investigation over a DARPA SUBOFF submarine model (SUBOFF Model) was performed using flow visualization and Digital Particle Image Velocimetry (DPIV). The model has an axisymmetric body with sail and fins, and it was supported by a streamlined strut that was formed by the extension of the sail appendage. The range of flow conditions studied correspond to a Reynolds numbers based on model length, Re_L, of about 10^5. Velocity vector fields, turbulence intensities, vorticity fields, and flow visualization in the vicinity of the junction flows are presented. In the vicinity of the control surface and sail hull junctions, the presence of streamwise vortices in the form of horseshoe or necklace vortices locally dominates the flow. The effects of unsteady motions about an axis passing through the sail are also investigated to understand the evolution of the unsteady wake.
Numerical Simulation of Unsteady Blood Flow through Capillary Networks.
Davis, J M; Pozrikidis, C
2011-08-01
A numerical method is implemented for computing unsteady blood flow through a branching capillary network. The evolution of the discharge hematocrit along each capillary segment is computed by integrating in time a one-dimensional convection equation using a finite-difference method. The convection velocity is determined by the local and instantaneous effective capillary blood viscosity, while the tube to discharge hematocrit ratio is deduced from available correlations. Boundary conditions for the discharge hematocrit at divergent bifurcations arise from the partitioning law proposed by Klitzman and Johnson involving a dimensionless exponent, q≥1. When q=1, the cells are partitioned in proportion to the flow rate; as q tends to infinity, the cells are channeled into the branch with the highest flow rate. Simulations are performed for a tree-like, perfectly symmetric or randomly perturbed capillary network with m generations. When the tree involves more than a few generations, a supercritical Hopf bifurcation occurs at a critical value of q, yielding spontaneous self-sustained oscillations in the absence of external forcing. A phase diagram in the m-q plane is presented to establish conditions for unsteady flow, and the effect of various geometrical and physical parameters is examined. For a given network tree order, m, oscillations can be induced for a sufficiently high value of q by increasing the apparent intrinsic viscosity, decreasing the ratio of the vessel diameter from one generation to the next, or by decreasing the diameter of the terminal vessels. With other parameters fixed, oscillations are inhibited by increasing m. The results of the continuum model are in excellent agreement with the predictions of a discrete model where the motion of individual cells is followed from inlet to outlet.
Mining and modeling character networks
Bonato, Anthony; Elenberg, Ethan R; Gleich, David F; Hou, Yangyang
2016-01-01
We investigate social networks of characters found in cultural works such as novels and films. These character networks exhibit many of the properties of complex networks such as skewed degree distribution and community structure, but may be of relatively small order with a high multiplicity of edges. Building on recent work of beveridge, we consider graph extraction, visualization, and network statistics for three novels: Twilight by Stephanie Meyer, Steven King's The Stand, and J.K. Rowling's Harry Potter and the Goblet of Fire. Coupling with 800 character networks from films found in the http://moviegalaxies.com/ database, we compare the data sets to simulations from various stochastic complex networks models including random graphs with given expected degrees (also known as the Chung-Lu model), the configuration model, and the preferential attachment model. Using machine learning techniques based on motif (or small subgraph) counts, we determine that the Chung-Lu model best fits character networks and we ...
Hierarchicality of trade flow networks reveals complexity of products.
Shi, Peiteng; Zhang, Jiang; Yang, Bo; Luo, Jingfei
2014-01-01
With globalization, countries are more connected than before by trading flows, which amounts to at least 36 trillion dollars today. Interestingly, around 30-60 percents of exports consist of intermediate products in global. Therefore, the trade flow network of particular product with high added values can be regarded as value chains. The problem is weather we can discriminate between these products from their unique flow network structure? This paper applies the flow analysis method developed in ecology to 638 trading flow networks of different products. We claim that the allometric scaling exponent η can be used to characterize the degree of hierarchicality of a flow network, i.e., whether the trading products flow on long hierarchical chains. Then, it is pointed out that the flow networks of products with higher added values and complexity like machinary, transport equipment etc. have larger exponents, meaning that their trade flow networks are more hierarchical. As a result, without the extra data like global input-output table, we can identify the product categories with higher complexity, and the relative importance of a country in the global value chain by the trading network solely.
Hierarchicality of trade flow networks reveals complexity of products.
Directory of Open Access Journals (Sweden)
Peiteng Shi
Full Text Available With globalization, countries are more connected than before by trading flows, which amounts to at least 36 trillion dollars today. Interestingly, around 30-60 percents of exports consist of intermediate products in global. Therefore, the trade flow network of particular product with high added values can be regarded as value chains. The problem is weather we can discriminate between these products from their unique flow network structure? This paper applies the flow analysis method developed in ecology to 638 trading flow networks of different products. We claim that the allometric scaling exponent η can be used to characterize the degree of hierarchicality of a flow network, i.e., whether the trading products flow on long hierarchical chains. Then, it is pointed out that the flow networks of products with higher added values and complexity like machinary, transport equipment etc. have larger exponents, meaning that their trade flow networks are more hierarchical. As a result, without the extra data like global input-output table, we can identify the product categories with higher complexity, and the relative importance of a country in the global value chain by the trading network solely.
Seamless Flow Migration on Smartphones without Network Support
Rahmati, Ahmad; Tossell, Chad; Nicoara, Angela; Zhong, Lin; Kortum, Phil; Singh, Jatinder
2010-01-01
This paper addresses the following question: Is it possible to migrate TCP/IP flows between different networks on modern mobile devices, without infrastructure support or protocol changes? To answer this question, we make three research contributions. (i) We report a comprehensive characterization of IP traffic on smartphones using traces collected from 27 iPhone 3GS users for three months. (ii) Driven by the findings from the characterization, we devise two novel system mechanisms for mobile devices to sup-port seamless flow migration without network support, and extensively evaluate their effectiveness using our field collected traces of real-life usage. Wait-n-Migrate leverages the fact that most flows are short lived. It establishes new flows on newly available networks but allows pre-existing flows on the old network to terminate naturally, effectively decreasing, or even eliminating, connectivity gaps during network switches. Resumption Agent takes advantage of the functionality integrated into many mod...
Information flow among neural networks with Bayesian estimation
Institute of Scientific and Technical Information of China (English)
LI Yan; LI XiaoLi; OUYANG GaoXiang; GUAN XinPing
2007-01-01
Estimating the interaction among neural networks is an interesting issue in neuroscience. Some methods have been proposed to estimate the coupling strength among neural networks; however, few estimations of the coupling direction (information flow) among neural networks have been attempted. It is known that Bayesian estimator is based on a priori knowledge and a probability of event occurrence. In this paper, a new method is proposed to estimate coupling directions among neural networks with conditional mutual information that is estimated by Bayesian estimation. First, this method is applied to analyze the simulated EEG series generated by a nonlinear lumped-parameter model. In comparison with the conditional mutual information with Shannon entropy, it is found that this method is more successful in estimating the coupling direction, and is insensitive to the length of EEG series. Therefore, this method is suitable to analyze a short time series in practice. Second, we demonstrate how this method can be applied to the analysis of human intracranial epileptic electroencephalogram (EEG) recordings, and to indicate the coupling directions among neural networks. Therefore, this method helps to elucidate the epileptic focus localization.
Modeling Size Polydisperse Granular Flows
Lueptow, Richard M.; Schlick, Conor P.; Isner, Austin B.; Umbanhowar, Paul B.; Ottino, Julio M.
2014-11-01
Modeling size segregation of granular materials has important applications in many industrial processes and geophysical phenomena. We have developed a continuum model for granular multi- and polydisperse size segregation based on flow kinematics, which we obtain from discrete element method (DEM) simulations. The segregation depends on dimensionless control parameters that are functions of flow rate, particle sizes, collisional diffusion coefficient, shear rate, and flowing layer depth. To test the theoretical approach, we model segregation in tri-disperse quasi-2D heap flow and log-normally distributed polydisperse quasi-2D chute flow. In both cases, the segregated particle size distributions match results from full-scale DEM simulations and experiments. While the theory was applied to size segregation in steady quasi-2D flows here, the approach can be readily generalized to include additional drivers of segregation such as density and shape as well as other geometries where the flow field can be characterized including rotating tumbler flow and three-dimensional bounded heap flow. Funded by The Dow Chemical Company and NSF Grant CMMI-1000469.
Follin, S.; Stigsson, M.; Levén, J.
2006-12-01
Difference flow logging is a relatively new hydraulic test method. It offers a superior geometrical resolution compared to the classic double-packer injection test method. Other significant features of the difference flow logging method are the long duration of the test period and the line source flow regime. These three features are vital for the characterization and the modeling of the conductive fracture frequency in crystalline rocks. Further, combining difference flow logging with core mapping and in situ borehole wall image logging (BIPS) allows for an enhanced geological cross correlation and structural interpretation. The data and analyses presented here come from the ongoing site investigations for a high-level nuclear waste repository in Forsmark managed by the Swedish Nuclear Fuel and Waste Management Co. First, we demonstrate the statistical properties of the fracture transmissivities acquired by difference flow logging for a number of one-kilometer-long cored boreholes. Secondly, we make a hydraulic comparison between these data and the transmissivities acquired by double-packer injection tests. Thirdly, we present a method for investigating the geometrical connectivity of open fractures in fracture network simulations and how this connectivity can be cross correlated to the fracture transmissivity distribution acquired by difference flow logging. Finally, we discuss the geometrical properties of flowing fractures as acquired by BIPS data and the correlation to the current stress situation in Forsmark. The geometrical anisotropy observed in the transmissivity data suggests that the current stress situation is very important for the flow field in Forsmark. This puts constraints on the collection and use of geological/structural data for hydrogeological discrete fracture network modeling.
Detecting Communities by Revised Max-flow Method in Networks
Institute of Scientific and Technical Information of China (English)
LIU Chuan-Jian; ZHU Zhi-Qiang; WU Jian-Liang
2013-01-01
A ubiquitous phenomenon in networks is the presence of communities within which the network connections are dense and between which they are sparser.This paper proposes a max-flow algorithm in bipartite networks to detect communities in general networks.Firstly,we construct a bipartite network in accordance with a general network and derive a revised max-flow problem in order to uncover the community structure.Then we present a local heuristic algorithm to find the optimal solution of the revised max-flow problem.This method is applied to a variety of real-world and artificial complex networks,and the partition results confirm its effectiveness and accuracy.
Modeling of curvilinear suspension flows
Morris, Jeffrey F.; Boulay, Fabienne
1996-11-01
The curvilinear parallel-plate and cone-and-plate rheometric flows of monodisperse noncolloidal suspensions have been modeled. Although nonuniform in shear rate, dotγ, the parallel-plate flow has been shown experimentally(A. W. Chow, S. W. Sinton, J. H. Iwayima & T. S. Stephens 1994 Phys. Fluids) 6, 2561. not to exhibit particle migration, contrary to predictions of prior suspension-flow modeling. Predictions of nonuniform particle volume fraction, φ, by the suspension-balance model(P. R. Nott & J. F. Brady 1994 J. Fluid Mech.) 275, 157. for parallel-plate and cone-and-plate flow without normal stress differences are presented. The ``nonmigration'' in parallel-plate flow may be attributed to bulk suspension normal stress differences: assuming the bulk stress has the form Σ ~ η dotγ Q(φ) with η the fluid viscosity, nonmigration is predicted for parallel-plate flow provided that Q_33 = (1/2) Q_11 at the bulk φ of interest, with 1 the flow direction and 3 the vorticity direction. Extending the model to include normal stress differences satisfying this requirement, a range of migration behavior is predicted for the cone-and-plate flow depending upon the ratio Q_11/Q_22.
Energy Technology Data Exchange (ETDEWEB)
Hoch, A
1998-05-01
Groundwater flow in many low permeability rocks is believed to occur within discrete fractures. Radionuclides in solution can diffuse away from the fractures into immobile water in the rock matrix. This process (rock-matrix diffusion) is important in determining the performance of a repository situated in fractured rock because it retards the transport of radionuclides that might otherwise return more rapidly towards the biosphere. NAPSAC is a computer program used to model groundwater flow and radionuclide transport in fractured rock. This report describes the implementation of a rock-matrix diffusion model in NAPSAC. (author)
Flow Routing for Delineating Supraglacial Meltwater Channel Networks
Directory of Open Access Journals (Sweden)
Leonora King
2016-12-01
Full Text Available Growing interest in supraglacial channels, coupled with the increasing availability of high-resolution remotely sensed imagery of glacier surfaces, motivates the development and testing of new approaches to delineating surface meltwater channels. We utilized a high-resolution (2 m digital elevation model of parts of the western margin of the Greenland Ice Sheet (GrIS and retention of visually identified sinks (i.e., moulins to investigate the ability of a standard D8 flow routing algorithm to delineate supraglacial channels. We compared these delineated channels to manually digitized channels and to channels extracted from multispectral imagery. We delineated GrIS supraglacial channel networks in six high-elevation (above 1000 m and one low-elevation (below 1000 m catchments during and shortly after peak melt (July and August 2012, and investigated the effect of contributing area threshold on flow routing performance. We found that, although flow routing is sensitive to data quality and moulin identification, it can identify 75% to 99% of channels observed with multispectral analysis, as well as low-order, high-density channels (up to 15.7 km/km2 with a 0.01 km2 contributing area threshold in greater detail than multispectral methods. Additionally, we found that flow routing can delineate supraglacial channel networks on rough ice surfaces with widespread crevassing. Our results suggest that supraglacial channel density is sufficiently high during peak melt that low contributing area thresholds can be employed with little risk of overestimating the channel network extent.
Base Flow Model Validation Project
National Aeronautics and Space Administration — The program focuses on turbulence modeling enhancements for predicting high-speed rocket base flows. A key component of the effort is the collection of high-fidelity...
Institute of Scientific and Technical Information of China (English)
朱磊; 陈玖泓; 刘德东
2016-01-01
Preferential flow refers to such a flow transport pattern that water and solute flow round soil matrix, which accelerates soil water movement. Soil cracks are a part of reasons that lead to preferential flow. This study aimed to analyze soil preferential flow pattern in order to establish a model of preferential model based on soil fracture network. A field color tracer experiment was carried out in Ningxia University from July to September in 2015 under precipitation intensity of 20 and 50 mm/h, and rainfall duration of 20-60 min. The results showed that when the rainfall intensity was 20 mm/h, the water infiltration depth increased with the increase of rainfall duration and the preferential flow began to develop, but it was still overall-matrix flow and the preferential flow was only locally developed. When precipitation intensity was 50mm/h and the duration of rainfall was 20min, water flows bypassed the soil matrix and preferential flow phenomena was observed obviously. The infiltration depth exceeded the fracture depth after duration of rainfall increased to 50min, soil fracture had not play a major role in the soil water movement, and the flow patterns were overall-matrix flow. The results showed that the flow patterns were mainly matrix flow under lower rainfall or irrigation intensity, and preferential flow under higher rainfall or irrigation intensity. A coupling model of soil matrix and fracture network for simulating preferential flow was established (CP model). In this model, the soil medium was divided into matrix and fracture networks formed by flow channels, each having its own saturated hydraulic conductivity and water movement equation. These equations were solved by using the control volume finite element method, and the nonlinear equations were linearized by Newton-Raphson methods to improve the convergence, and shorten the operation time. Equation of water movement in matrix and fracture network flow equation were coupled using global approach
Numerical experiments modelling turbulent flows
Directory of Open Access Journals (Sweden)
Trefilík Jiří
2014-03-01
Full Text Available The work aims at investigation of the possibilities of modelling transonic flows mainly in external aerodynamics. New results are presented and compared with reference data and previously achieved results. For the turbulent flow simulations two modifications of the basic k – ω model are employed: SST and TNT. The numerical solution was achieved by using the MacCormack scheme on structured non-ortogonal grids. Artificial dissipation was added to improve the numerical stability.
Computational modeling of concrete flow
DEFF Research Database (Denmark)
Roussel, Nicolas; Geiker, Mette Rica; Dufour, Frederic
2007-01-01
This paper provides a general overview of the present status regarding computational modeling of the flow of fresh concrete. The computational modeling techniques that can be found in the literature may be divided into three main families: single fluid simulations, numerical modeling of discrete...
The Network Picture of Labor Flow
López, Eduardo; Axtell, Robert L
2015-01-01
We construct a data-driven model of flows in graphs that captures the essential elements of the movement of workers between jobs in the companies (firms) of entire economic systems such as countries. The model is based on the observation that certain job transitions between firms are often repeated over time, showing persistent behavior, and suggesting the construction of static graphs to act as the scaffolding for job mobility. Individuals in the job market (the workforce) are modelled by a discrete-time random walk on graphs, where each individual at a node can possess two states: employed or unemployed, and the rates of becoming unemployed and of finding a new job are node dependent parameters. We calculate the steady state solution of the model and compare it to extensive micro-datasets for Mexico and Finland, comprised of hundreds of thousands of firms and individuals. We find that our model possesses the correct behavior for the numbers of employed and unemployed individuals in these countries down to t...
Hadgu, Teklu; Karra, Satish; Kalinina, Elena; Makedonska, Nataliia; Hyman, Jeffrey D.; Klise, Katherine; Viswanathan, Hari S.; Wang, Yifeng
2017-10-01
One of the major challenges of simulating flow and transport in the far field of a geologic repository in crystalline host rock is related to reproducing the properties of the fracture network over the large volume of rock with sparse fracture characterization data. Various approaches have been developed to simulate flow and transport through the fractured rock. The approaches can be broadly divided into Discrete Fracture Network (DFN) and Equivalent Continuum Model (ECM). The DFN explicitly represents individual fractures, while the ECM uses fracture properties to determine equivalent continuum parameters. We compare DFN and ECM in terms of upscaled observed transport properties through generic fracture networks. The major effort was directed on making the DFN and ECM approaches similar in their conceptual representations. This allows for separating differences related to the interpretation of the test conditions and parameters from the differences between the DFN and ECM approaches. The two models are compared using a benchmark test problem that is constructed to represent the far field (1 × 1 × 1 km3) of a hypothetical repository in fractured crystalline rock. The test problem setting uses generic fracture properties that can be expected in crystalline rocks. The models are compared in terms of the: 1) effective permeability of the domain, and 2) nonreactive solute breakthrough curves through the domain. The principal differences between the models are mesh size, network connectivity, matrix diffusion and anisotropy. We demonstrate how these differences affect the flow and transport. We identify the factors that should be taken in consideration when selecting an approach most suitable for the site-specific conditions.
Runoff Modelling in Urban Storm Drainage by Neural Networks
DEFF Research Database (Denmark)
Rasmussen, Michael R.; Brorsen, Michael; Schaarup-Jensen, Kjeld
1995-01-01
network is used to compute flow or water level at selected points in the sewer system, and to forecast the flow from a small residential area. The main advantages of the neural network are the build-in self calibration procedure and high speed performance, but the neural network cannot be used to extract......A neural network is used to simulate folw and water levels in a sewer system. The calibration of th neural network is based on a few measured events and the network is validated against measureed events as well as flow simulated with the MOUSE model (Lindberg and Joergensen, 1986). The neural...... knowledge of the runoff process. The neural network was found to simulate 150 times faster than e.g. the MOUSE model....
Compression of flow can reveal overlapping modular organization in networks
Esquivel, Alcides Viamontes
2011-01-01
To better understand the overlapping modular organization of large networks with respect to flow, here we introduce the map equation for overlapping modules. In this information-theoretic framework, we use the correspondence between compression and regularity detection. The generalized map equation measures how well we can compress a description of flow in the network when we partition it into modules with possible overlaps. When we minimize the generalized map equation over overlapping network partitions, we detect modules that capture flow and determine which nodes at the boundaries between modules should be classified in multiple modules and to what degree. With a novel greedy search algorithm, we find that some networks, for example, the neural network of C. Elegans, are best described by modules dominated by hard boundaries, but that others, for example, the sparse road network of California, have a highly overlapping modular organization. To compare our approach with other clustering algorithms, we sugg...
Directory of Open Access Journals (Sweden)
Adesina, L.M
2015-07-01
Full Text Available The solution to power flow is one of the most important problems in electrical power systems. Traditional methods have been previously used for power flow analysis, but with prevalent drawbacks such as abnormal operating solutions and divergences in heavy loads. This paper presents power flow analysis in a power system, by modelling a typical 33kV Distribution Network, and simulating using the NEPLAN software for power flow studies. Island Business Unit’s (IBU 33kV network of Eko Electricity Distribution Plc (EKEDP for a scenario day is taken as case study in the analysis. The most important parameters of power flow analysis is utilized to find the magnitude and phase angles of the voltages at each Busbar, as well as the real and reactive power flowing through each distribution line within the network under consideration.
Application of Chaos Theory in the Prediction of Motorised Traffic Flows on Urban Networks
Directory of Open Access Journals (Sweden)
Aderemi Adewumi
2016-01-01
Full Text Available In recent times, urban road networks are faced with severe congestion problems as a result of the accelerating demand for mobility. One of the ways to mitigate the congestion problems on urban traffic road network is by predicting the traffic flow pattern. Accurate prediction of the dynamics of a highly complex system such as traffic flow requires a robust methodology. An approach for predicting Motorised Traffic Flow on Urban Road Networks based on Chaos Theory is presented in this paper. Nonlinear time series modeling techniques were used for the analysis of the traffic flow prediction with emphasis on the technique of computation of the Largest Lyapunov Exponent to aid in the prediction of traffic flow. The study concludes that algorithms based on the computation of the Lyapunov time seem promising as regards facilitating the control of congestion because of the technique’s effectiveness in predicting the dynamics of complex systems especially traffic flow.
VEPCO network model reconciliation of LANL and MZA model data
Energy Technology Data Exchange (ETDEWEB)
NONE
1992-12-15
The LANL DC load flow model of the VEPCO transmission network shows 210 more substations than the AC load flow model produced by MZA utility Consultants. MZA was requested to determine the source of the difference. The AC load flow model used for this study utilizes 2 standard network algorithms (Decoupled or Newton). The solution time of each is affected by the number of substations. The more substations included, the longer the model will take to solve. In addition, the ability of the algorithms to converge to a solution is affected by line loadings and characteristics. Convergence is inhibited by numerous lightly loaded and electrically short lines. The MZA model reduces the total substations to 343 by creating equivalent loads and generation. Most of the omitted substations are lightly loaded and rated at 115 kV. The MZA model includes 16 substations not included in the LANL model. These represent new generation including Non-Utility Generator (NUG) sites, additional substations and an intertie (Wake, to CP and L). This report also contains data from the Italian State AC power flow model and the Duke Power Company AC flow model.
Internet Network Resource Information Model
Institute of Scientific and Technical Information of China (English)
陈传峰; 李增智; 唐亚哲; 刘康平
2002-01-01
The foundation of any network management systens is a database that con-tains information about the network resources relevant to the management tasks. A networkinformation model is an abstraction of network resources, including both managed resources andmanaging resources. In the SNMP-based management framework, management information isdefined almost exclusively from a "device" viewpoint, namely, managing a network is equiva-lent to managing a collection of individual nodes. Aiming at making use of recent advances indistributed computing and in object-oriented analysis and design, the Internet management ar-chitecture can also be based on the Open Distributed Processing Reference Model (RM-ODP).The purpose of this article is to provide an Internet Network Resource Information Model.First, a layered management information architecture will be discussed. Then the Internetnetwork resource information model is presented. The information model is specified usingObject-Z.
Complex Networks in Psychological Models
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.
Mutiscale Modeling of Segregation in Granular Flows
Energy Technology Data Exchange (ETDEWEB)
Sun, Jin [Iowa State Univ., Ames, IA (United States)
2007-01-01
Modeling and simulation of segregation phenomena in granular flows are investigated. Computational models at different scales ranging from particle level (microscale) to continuum level (macroscale) are employed in order to determine the important microscale physics relevant to macroscale modeling. The capability of a multi-fluid model to capture segregation caused by density difference is demonstrated by simulating grain-chaff biomass flows in a laboratory-scale air column and in a combine harvester. The multi-fluid model treats gas and solid phases as interpenetrating continua in an Eulerian frame. This model is further improved by incorporating particle rotation using kinetic theory for rapid granular flow of slightly frictional spheres. A simplified model is implemented without changing the current kinetic theory framework by introducing an effective coefficient of restitution to account for additional energy dissipation due to frictional collisions. The accuracy of predicting segregation rate in a gas-fluidized bed is improved by the implementation. This result indicates that particle rotation is important microscopic physics to be incorporated into the hydrodynamic model. Segregation of a large particle in a dense granular bed of small particles under vertical. vibration is studied using molecular dynamics simulations. Wall friction is identified as a necessary condition for the segregation. Large-scale force networks bearing larger-than-average forces are found with the presence of wall friction. The role of force networks in assisting rising of the large particle is analyzed. Single-point force distribution and two-point spatial force correlation are computed. The results show the heterogeneity of forces and a short-range correlation. The short correlation length implies that even dense granular flows may admit local constitutive relations. A modified minimum spanning tree (MST) algorithm is developed to asymptotically recover the force statistics in the
Directory of Open Access Journals (Sweden)
Bocewicz Grzegorz
2017-06-01
Full Text Available The problems of designing supply networks and traffic flow routing and scheduling are the subject of intensive research. The problems encompass the management of the supply of a variety of goods using multi-modal transportation. This research also takes into account the various constraints related to route topology, the parameters of the available fleet of vehicles, order values, delivery due dates, etc. Assuming that the structure of a supply network, constrained by a transport network topology that determines its behavior, we develop a declarative model which would enable the analysis of the relationships between the structure of a supply network and its potential behavior resulting in a set of desired delivery-flows. The problem in question can be reduced to determining sufficient conditions that ensure smooth flow in a transport network with a fractal structure. The proposed approach, which assumes a recursive, fractal network structure, enables the assessment of alternative delivery routes and associated schedules in polynomial time. An illustrative example showing the quantitative and qualitative relationships between the morphological characteristics of the investigated supply networks and the functional parameters of the assumed delivery-flows is provided.
2008-12-01
well, then a Euclidean distance would be appropriate. The quadratic assignment procedure ( QAP ) (Krackhardt, 1987) could be used to compare the...Networks. Journal of Applied Psychology, 71(1): 50-55. Krackhardt, D. (1987). QAP Partialling as a Test of Spuriousness. Social Networks, 9, 171-186
Assortative model for social networks
Catanzaro, Michele; Caldarelli, Guido; Pietronero, Luciano
2004-09-01
In this Brief Report we present a version of a network growth model, generalized in order to describe the behavior of social networks. The case of study considered is the preprint archive at cul.arxiv.org. Each node corresponds to a scientist, and a link is present whenever two authors wrote a paper together. This graph is a nice example of degree-assortative network, that is, to say a network where sites with similar degree are connected to each other. The model presented is one of the few able to reproduce such behavior, giving some insight on the microscopic dynamics at the basis of the graph structure.
Developing Personal Network Business Models
DEFF Research Database (Denmark)
Saugstrup, Dan; Henten, Anders
2006-01-01
on the 'state of the art' in the field of business modeling. Furthermore, the paper suggests three generic business models for PNs: a service oriented model, a self-organized model, and a combination model. Finally, examples of relevant services and applications in relation to three different cases......The aim of the paper is to examine the issue of business modeling in relation to personal networks, PNs. The paper builds on research performed on business models in the EU 1ST MAGNET1 project (My personal Adaptive Global NET). The paper presents the Personal Network concept and briefly reports...... are presented and analyzed in light of business modeling of PN....
2011-07-31
18]) General Charles Campbell noted that , although…. “the Army has a system for organizing, staffing, equipping, training, deploying, sustaining...Harrell, Charles , Ghosh, Biman K., & Bowden Jr.,Royce O. 2004. Simulation Using ProModel. Second edition. McGraw Hill, New York. [22] Klimas, J...RUNS: A Senior Leader Reference Handbook. U.S. Army War College, Carlisle, PA. [24] McNeill , Dan K. 2005 (August). Army Force Generation
Introduction to stochastic models of transportation flows. Part I
Malyshev, V A
2011-01-01
We consider here probabilistic models of transportation flows. The main goal of this introduction is rather not to present various techniques for problem solving but to present some intuition to invent adequate and natural models having visual simplicity and simple (but rigorous) formulation, the main objects being cars not abstract flows. The papers consists of three parts. First part considers mainly linear flows on short time scale - dynamics of the flow due to changing driver behavior. Second part studies linear flow on longer time scales - individual car trajectory from entry to exit from the road. Part three considers collective car movement in complex transport networks.
Cilia driven flow networks in the brain
Wang, Yong; Faubel, Regina; Westendorf, Chrsitian; Eichele, Gregor; Bodenschatz, Eberhard
Neurons exchange soluble substances via the cerebrospinal fluid (CSF) that fills the ventricular system. The walls of the ventricular cavities are covered with motile cilia that constantly beat and thereby induce a directional flow. We recently discovered that cilia in the third ventricle generate a complex flow pattern leading to partitioning of the ventricular volume and site-directed transport paths along the walls. Transient and daily recurrent alterations in the cilia beating direction lead to changes in the flow pattern. This has consequences for delivery of CSF components along the near wall flow. The contribution of this cilia-induced flow to overall CSF flow remains to be investigated. The state-of-art lattice Boltzmann method is adapted for studying the CFS flow. The 3D geometry of the third ventricle at high resolution was reconstructed. Simulation of CSF flow without cilia in this geometry confirmed that the previous idea about unidirectional flow does not explain how different components of CSF can be delivered to their various target sites. We study the contribution of the cilia-induced flow pattern to overall CSF flow and identify target areas for site-specific delivery of CSF-constituents with respect to the temporal changes.
Network Traffic Anomalies Detection and Identification with Flow Monitoring
Nguyen, Huy; Kim, Dong Il; Choi, Deokjai
2010-01-01
Network management and security is currently one of the most vibrant research areas, among which, research on detecting and identifying anomalies has attracted a lot of interest. Researchers are still struggling to find an effective and lightweight method for anomaly detection purpose. In this paper, we propose a simple, robust method that detects network anomalous traffic data based on flow monitoring. Our method works based on monitoring the four predefined metrics that capture the flow statistics of the network. In order to prove the power of the new method, we did build an application that detects network anomalies using our method. And the result of the experiments proves that by using the four simple metrics from the flow data, we do not only effectively detect but can also identify the network traffic anomalies.
Network models in anatomical systems.
Esteve-Altava, Borja; Marugán-Lobón, Jesús; Botella, Héctor; Rasskin-Gutman, Diego
2011-01-01
Network theory has been extensively used to model the underlying structure of biological processes. From genetics to ecology, network thinking is changing our understanding of complex systems, specifically how their internal structure determines their overall behavior. Concepts such as hubs, scale-free or small-world networks, common in the complexity literature, are now used more and more in sociology, neurosciences, as well as other anthropological fields. Even though the use of network models is nowadays so widely applied, few attempts have been carried out to enrich our understanding in the classical morphological sciences such as in comparative anatomy or physical anthropology. The purpose of this article is to introduce the usage of network tools in morphology; specifically by building anatomical networks, dealing with the most common analyses and problems, and interpreting their outcome.
Minimum cost network flows: Problems, algorithms, and software
Directory of Open Access Journals (Sweden)
Sifaleras Angelo
2013-01-01
Full Text Available We present a wide range of problems concerning minimum cost network flows, and give an overview of the classic linear single-commodity Minimum Cost Network Flow Problem (MCNFP and some other closely related problems, either tractable or intractable. We also discuss state-of-the-art algorithmic approaches and recent advances in the solution methods for the MCNFP. Finally, optimization software packages for the MCNFP are presented.
Telecommunications network modelling, planning and design
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.
Eigenanalysis of a neural network for optic flow processing
Weber, F.; Eichner, H.; Cuntz, H.; Borst, A.
2008-01-01
Flies gain information about self-motion during free flight by processing images of the environment moving across their retina. The visual course control center in the brain of the blowfly contains, among others, a population of ten neurons, the so-called vertical system (VS) cells that are mainly sensitive to downward motion. VS cells are assumed to encode information about rotational optic flow induced by self-motion (Krapp and Hengstenberg 1996 Nature 384 463-6). Recent evidence supports a connectivity scheme between the VS cells where neurons with neighboring receptive fields are connected to each other by electrical synapses at the axonal terminals, whereas the boundary neurons in the network are reciprocally coupled via inhibitory synapses (Haag and Borst 2004 Nat. Neurosci. 7 628-34 Farrow et al 2005 J. Neurosci. 25 3985-93 Cuntz et al 2007 Proc. Natl Acad. Sci. USA). Here, we investigate the functional properties of the VS network and its connectivity scheme by reducing a biophysically realistic network to a simplified model, where each cell is represented by a dendritic and axonal compartment only. Eigenanalysis of this model reveals that the whole population of VS cells projects the synaptic input provided from local motion detectors on to its behaviorally relevant components. The two major eigenvectors consist of a horizontal and a slanted line representing the distribution of vertical motion components across the fly's azimuth. They are, thus, ideally suited for reliably encoding translational and rotational whole-field optic flow induced by respective flight maneuvers. The dimensionality reduction compensates for the contrast and texture dependence of the local motion detectors of the correlation-type, which becomes particularly pronounced when confronted with natural images and their highly inhomogeneous contrast distribution.
Operator Splitting Method for Simulation of Dynamic Flows in Natural Gas Pipeline Networks
Dyachenko, Sergey A; Chertkov, Michael
2016-01-01
We develop an operator splitting method to simulate flows of isothermal compressible natural gas over transmission pipelines. The method solves a system of nonlinear hyperbolic partial differential equations (PDEs) of hydrodynamic type for mass flow and pressure on a metric graph, where turbulent losses of momentum are modeled by phenomenological Darcy-Weisbach friction. Mass flow balance is maintained through the boundary conditions at the network nodes, where natural gas is injected or withdrawn from the system. Gas flow through the network is controlled by compressors boosting pressure at the inlet of the adjoint pipe. Our operator splitting numerical scheme is unconditionally stable and it is second order accurate in space and time. The scheme is explicit, and it is formulated to work with general networks with loops. We test the scheme over range of regimes and network configurations, also comparing its performance with performance of two other state of the art implicit schemes.
Dynamic economic dispatch combining network flow and interior point method
Institute of Scientific and Technical Information of China (English)
韩学山; 赵建国; 柳焯
2003-01-01
Under the environment of electric power market, economic dispatch (ED) problem should consider network constraints, unit ramp rates, besides the basic constraints. For this problem, it is important to establish the effective model and algorithm. This paper examines the decoupled conditions that affect the solution optimality to this problem. It proposes an effective model and solution method. Based on the look-ahead technique, it finds the number of time intervals to guarantee the solution optimality. Next, an efficient technique for finding the optimal solution via the interior point methods is described. Test cases, which include dispatching six units over 5 time intervals on the IEEE 30 test system with line flows and ramp constraints are presented. Results indicate that the computational effort as measured by iteration counts or execution time varies only modestly with the problem size.
Confidence intervals in Flow Forecasting by using artificial neural networks
Panagoulia, Dionysia; Tsekouras, George
2014-05-01
One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input
Radial basis function neural network for power system load-flow
Energy Technology Data Exchange (ETDEWEB)
Karami, A.; Mohammadi, M.S. [Faculty of Engineering, The University of Guilan, P.O. Box 41635-3756, Rasht (Iran)
2008-01-15
This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)
Analysis and control of flows in pressurized hydraulic networks
Gupta, R.K.
2006-01-01
Analysis, design and flow control problems in pressurized hydraulic networks such as water transmission and distribution systems consisting of pipes and other appurtenant components such as reservoirs, pumps, valves and surge devices are dealt with from the prospective of network synthesis aiming at
Analysis and control of flows in pressurized hydraulic networks
Gupta, R.K.
2006-01-01
Analysis, design and flow control problems in pressurized hydraulic networks such as water transmission and distribution systems consisting of pipes and other appurtenant components such as reservoirs, pumps, valves and surge devices are dealt with from the prospective of network synthesis aiming at
Spray flow-network flow transition of binary Lennard-Jones particle system
Inaoka, Hajime
2010-07-01
We simulate gas-liquid flows caused by rapid depressurization using a molecular dynamics model. The model consists of two types of Lennard-Jones particles, which we call liquid particles and gas particles. These two types of particles are distinguished by their mass and strength of interaction: a liquid particle has heavier mass and stronger interaction than a gas particle. By simulations with various initial number densities of these particles, we found that there is a transition from a spray flow to a network flow with an increase of the number density of the liquid particles. At the transition point, the size of the liquid droplets follows a power-law distribution, while it follows an exponential distribution when the number density of the liquid particles is lower than the critical value. The comparison between the transition of the model and that of models of percolation is discussed. The change of the average droplet size with the initial number density of the gas particles is also presented. © 2010 Elsevier B.V. All rights reserved.
Campus network security model study
Zhang, Yong-ku; Song, Li-ren
2011-12-01
Campus network security is growing importance, Design a very effective defense hacker attacks, viruses, data theft, and internal defense system, is the focus of the study in this paper. This paper compared the firewall; IDS based on the integrated, then design of a campus network security model, and detail the specific implementation principle.
Flow-Based Network Analysis of the Caenorhabditis elegans Connectome
Bacik, Karol A.; Schaub, Michael T.; Billeh, Yazan N.; Barahona, Mauricio
2016-01-01
We exploit flow propagation on the directed neuronal network of the nematode C. elegans to reveal dynamically relevant features of its connectome. We find flow-based groupings of neurons at different levels of granularity, which we relate to functional and anatomical constituents of its nervous system. A systematic in silico evaluation of the full set of single and double neuron ablations is used to identify deletions that induce the most severe disruptions of the multi-resolution flow structure. Such ablations are linked to functionally relevant neurons, and suggest potential candidates for further in vivo investigation. In addition, we use the directional patterns of incoming and outgoing network flows at all scales to identify flow profiles for the neurons in the connectome, without pre-imposing a priori categories. The four flow roles identified are linked to signal propagation motivated by biological input-response scenarios. PMID:27494178
Maximum entropy analysis of flow and reaction networks
Niven, Robert K.; Abel, Markus; Schlegel, Michael; Waldrip, Steven H.
2015-01-01
We present a generalised MaxEnt method to infer the stationary state of a flow network, subject to "observable" constraints on expectations of various parameters, as well as "physical" constraints arising from frictional properties (resistance functions) and conservation laws (Kirchhoff laws). The method invokes an entropy defined over all uncertainties in the system, in this case the internal and external flow rates and potential differences. The proposed MaxEnt framework is readily extendable to the analysis of networks with uncertainty in the network structure itself.
Neural network modeling of emotion
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.
Behavioral analysis of network flow traffic
Heller, Mark D.
2010-01-01
Approved for public release, distribution unlimited Network Behavior Analysis (NBA) is a technique to enhance network security by passively monitoring aggregate traffic patterns and noting unusual action or departures from normal operations. The analysis is typically performed offline, due to the huge volume of input data, in contrast to conventional intrusion prevention solutions based on deep packet inspection, signature detection, and real-time blocking. After establishing a benchmar...
Nonequilibrium model on Apollonian networks.
Lima, F W S; Moreira, André A; Araújo, Ascânio D
2012-11-01
We investigate the majority-vote model with two states (-1,+1) and a noise parameter q on Apollonian networks. The main result found here is the presence of the phase transition as a function of the noise parameter q. Previous results on the Ising model in Apollonian networks have reported no presence of a phase transition. We also studied the effect of redirecting a fraction p of the links of the network. By means of Monte Carlo simulations, we obtained the exponent ratio γ/ν, β/ν, and 1/ν for several values of rewiring probability p. The critical noise q{c} and U were also calculated. Therefore, the results presented here demonstrate that the majority-vote model belongs to a different universality class than equilibrium Ising model on Apollonian network.
Edge anisotropy and the geometric perspective on flow networks
Molkenthin, Nora; Tupikina, Liubov; Marwan, Norbert; Donges, Jonathan F; Feudel, Ulrike; Kurths, Jürgen; Donner, Reik V
2016-01-01
Spatial networks have recently attracted great interest in various fields of research. While the traditional network-theoretic viewpoint is commonly restricted to their topological characteristics (often disregarding existing spatial constraints), this work takes a geometric perspective, which considers vertices and edges as objects in a metric space and quantifies the corresponding spatial distribution and alignment. For this purpose, we introduce the concept of edge anisotropy and define a class of measures characterizing the spatial directedness of connections. Specifically, we demonstrate that the local anisotropy of edges incident to a given vertex provides useful information about the local geometry of geophysical flows based on networks constructed from spatio-temporal data, which is complementary to topological characteristics of the same flow networks. Taken both structural and geometric viewpoints together can thus assist the identification of underlying flow structures from observations of scalar v...
Edge anisotropy and the geometric perspective on flow networks
Molkenthin, Nora; Kutza, Hannes; Tupikina, Liubov; Marwan, Norbert; Donges, Jonathan F.; Feudel, Ulrike; Kurths, Jürgen; Donner, Reik V.
2017-03-01
Spatial networks have recently attracted great interest in various fields of research. While the traditional network-theoretic viewpoint is commonly restricted to their topological characteristics (often disregarding the existing spatial constraints), this work takes a geometric perspective, which considers vertices and edges as objects in a metric space and quantifies the corresponding spatial distribution and alignment. For this purpose, we introduce the concept of edge anisotropy and define a class of measures characterizing the spatial directedness of connections. Specifically, we demonstrate that the local anisotropy of edges incident to a given vertex provides useful information about the local geometry of geophysical flows based on networks constructed from spatio-temporal data, which is complementary to topological characteristics of the same flow networks. Taken both structural and geometric viewpoints together can thus assist the identification of underlying flow structures from observations of scalar variables.
Algebraic Statistics for Network Models
2014-02-19
AFRL-OSR-VA-TR-2014-0070 (DARPA) Algebraic Statistics for Network Models SONJA PETROVIC PENNSYLVANIA STATE UNIVERSITY 02/19/2014 Final Report...DARPA GRAPHS Phase I Algebraic Statistics for Network Models FA9550-12-1-0392 Sonja Petrović petrovic@psu.edu1 Department of Statistics Pennsylvania...Department of Statistics, Heinz College , Machine Learning Department, Cylab Carnegie Mellon University 1. Abstract This project focused on the family of
Institute of Scientific and Technical Information of China (English)
梁红杰
2015-01-01
当大型混合网络受到异常攻击或大规模登录时，流量会发生短时间的巨幅变化，使得单个节点面临瘫痪的风险，导致传统基于单个节点的大型混合网络突变流量控制模型，由于不能适应流量大幅度变化，无法有效实现突变流量控制。提出一种基于自适应PD流量控制算法的大型混合网络突变流量控制模型，将大型混合网络流量统计时间划分成几个周期，获取周期个数的估计值，求出大型混合网络操作行为数据序列中的各统计周期中数据的离均差平方、与组间离均差平方和以及各周期中的方差比值，对流量成分模型进行塑造，通过简单PD控制算法对突变流量进行初步控制，引入延时环节，获取各闭环极点的位置，求出PD控制器参数求出交换节点瓶颈链接个数的估测值，从而实现突变流量控制。仿真实验结果表明，所提方法在控制突变流量方面具有很高的优越性及高效性。%When a large hybrid network anomaly attacks or massive logs in, flow can produce the huge change in a short pe⁃riod of time, make the paralyzed risk faced by a single node.Lead to traditional based on big mutation hybrid network flow control model of a single node, due to the large variation can not adapt to flow, flow control, cannot effectively mutations presents a flow based on adaptive PD control algorithm of large mutation hybrid network flow control model, mix a large net⁃work traffic statistics of time into several cycles, obtaining estimates of the number of cycles, and the large hybrid network operating behavior of each cycle of data in a sequence of data from the divided difference square, alienation and group were sum of squared residuals and variance ratio of each cycle, to shape the traffic composition model, through a simple PD con⁃trol algorithm to control the initial mutation flow, introducing delay link, obtain the closed-loop pole
Dynamical evolution processes of traffic flow and travel cost in urban transportation networks
Institute of Scientific and Technical Information of China (English)
Guo Ren-Yong; Huang Hai-Jun
2008-01-01
Considering such a fact that travellers dynamically adjust their routes and the resultant link traffic flows in a network evolve over time,this paper proposes a dynamical evolutionary model of the traffic assignment problem with endogenous origin-destination (OD) demands.The model's stability is analysed and the resultant user equilibrium (UE) state is shown to be stable under certain conditions.Numerical results in a grid network indicate that the model can generate convergent flow patterns and finally terminates at the UE state.Impacts by the parameters associated with OD demand function and link cost function are also investigated.
Exact Convex Relaxation of Optimal Power Flow in Radial Networks
Energy Technology Data Exchange (ETDEWEB)
Gan, LW; Li, N; Topcu, U; Low, SH
2015-01-01
The optimal power flow (OPF) problem determines a network operating point that minimizes a certain objective such as generation cost or power loss. It is nonconvex. We prove that a global optimum of OPF can be obtained by solving a second-order cone program, under a mild condition after shrinking the OPF feasible set slightly, for radial power networks. The condition can be checked a priori, and holds for the IEEE 13, 34, 37, 123-bus networks and two real-world networks.
Speed limit and ramp meter control for traffic flow networks
Goatin, Paola; Göttlich, Simone; Kolb, Oliver
2016-07-01
The control of traffic flow can be related to different applications. In this work, a method to manage variable speed limits combined with coordinated ramp metering within the framework of the Lighthill-Whitham-Richards (LWR) network model is introduced. Following a 'first-discretize-then-optimize' approach, the first order optimality system is derived and the switch of speeds at certain fixed points in time is explained, together with the boundary control for the ramp metering. Sequential quadratic programming methods are used to solve the control problem numerically. For application purposes, experimental setups are presented wherein variable speed limits are used as a traffic guidance system to avoid traffic jams on highway interchanges and on-ramps.
2016-11-09
standpoint remains more of an art than a science. Even when well executed, the ongoing evolution of the network may violate initial, security-critical design... Internet and compromises the LAN (this step compromises only the LAN). We describe this as a functional information flow between the Internet and the...vulnerabilities will stem from the delivery of email, access to the Internet , or access to an internal document or data repository. If any of these are
Network-based representation of energy transfer in unsteady separated flow
Nair, Aditya; Taira, Kunihiko
2015-11-01
We construct a network-based representation of energy pathways in unsteady separated flows using a POD-Galerkin projection model. In this formulation, we regard the POD modes as the network nodes and the energy transfer between the modes as the network edges. Based on the energy transfer analysis performed by Noack et al. (2008), edge weights are characterized on the interaction graph. As an example, we examine the energy transfer within the two-dimensional incompressible flow over a circular cylinder. In particular, we analyze the energy pathways involved in flow transition from the unstable symmetric steady state to periodic shedding cycle. The growth of perturbation energy over the network is examined to highlight key features of flow physics and to determine how the energy transfer can be influenced. Furthermore, we implement closed-loop flow control on the POD-Galerkin model to alter the energy interaction path and modify the global behavior of the wake dynamics. The insights gained will be used to perform further network analysis on fluid flows with added complexity. Work supported by US Army Research Office (W911NF-14-1-0386) and US Air Force Office of Scientific Research (YIP: FA9550-13-1-0183).
Advances in theoretical models of network science
Institute of Scientific and Technical Information of China (English)
FANG Jin-qing; BI Qiao; LI Yong
2007-01-01
In this review article, we will summarize the main advances in network science investigated by the CIAE Group of Complex Network in this field. Several theoretical models of network science were proposed and their topological and dynamical properties are reviewed and compared with the other models. Our models mainly include a harmonious unifying hybrid preferential model, a large unifying hybrid network model, a quantum interference network, a hexagonal nanowire network, and a small-world network with the same degree. The models above reveal some new phenomena and findings, which are useful for deeply understanding and investigating complex networks and their applications.
Natural gas distribution network modelling and leak minimization
Westering, W.H.P. van; Hellendoorn, H.; Brasjen, B.J.; Linden, R.J.P. van der
2014-01-01
A gas network model has been constructed based on the steady-state Weymouth equation. A fast and robust solution algorithm is proposed and subsequently used to calculate all flows and pressures in a gas network with over 40,000 pipes. The obtained result is mathematically accurate within 0.1% and ha
Zhou, Bao-Rong; Liu, Si-Liang; Zhang, Yong-Jun; Yi, Ying-Qi; Lin, Xiao-Ming
2017-05-01
To mitigate the impact on the distribution networks caused by the stochastic characteristic and high penetration of photovoltaic, a multi-objective optimal power flow model is proposed in this paper. The regulation capability of capacitor, inverter of photovoltaic and energy storage system embedded in active distribution network are considered to minimize the expected value of active power the T loss and probability of voltage violation in this model. Firstly, a probabilistic power flow based on cumulant method is introduced to calculate the value of the objectives. Secondly, NSGA-II algorithm is adopted for optimization to obtain the Pareto optimal solutions. Finally, the best compromise solution can be achieved through fuzzy membership degree method. By the multi-objective optimization calculation of IEEE34-node distribution network, the results show that the model can effectively improve the voltage security and economy of the distribution network on different levels of photovoltaic penetration.
Online traffic flow model applying dynamic flow-density relation
Kim, Y
2002-01-01
This dissertation describes a new approach of the online traffic flow modelling based on the hydrodynamic traffic flow model and an online process to adapt the flow-density relation dynamically. The new modelling approach was tested based on the real traffic situations in various homogeneous motorway sections and a motorway section with ramps and gave encouraging simulation results. This work is composed of two parts: first the analysis of traffic flow characteristics and second the development of a new online traffic flow model applying these characteristics. For homogeneous motorway sections traffic flow is classified into six different traffic states with different characteristics. Delimitation criteria were developed to separate these states. The hysteresis phenomena were analysed during the transitions between these traffic states. The traffic states and the transitions are represented on a states diagram with the flow axis and the density axis. For motorway sections with ramps the complicated traffic fl...
Structure learning for Bayesian networks as models of biological networks.
Larjo, Antti; Shmulevich, Ilya; Lähdesmäki, Harri
2013-01-01
Bayesian networks are probabilistic graphical models suitable for modeling several kinds of biological systems. In many cases, the structure of a Bayesian network represents causal molecular mechanisms or statistical associations of the underlying system. Bayesian networks have been applied, for example, for inferring the structure of many biological networks from experimental data. We present some recent progress in learning the structure of static and dynamic Bayesian networks from data.
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.
A Topological Phase Transition in Models of River Networks
Oppenheim, Jacob; Magnasco, Marcelo
2012-02-01
The classical Scheidegger model of river network formation and evolution is investigated on non-Euclidean geometries, which model the effects of regions of convergent and divergent flows - as seen around lakes and drainage off mountains, respectively. These new models may be differentiated by the number of basins formed. Using the divergence as an order parameter, we see a phase transition in the number of distinct basins at the point of a flat landscape. This is a surprising property of the statistics of river networks and suggests significantly different properties for riverine networks in uneven topography and vascular networks of arteries versus those of veins among others.
Loan and nonloan flows in the Australian interbank network
Sokolov, Andrey; Webster, Rachel; Melatos, Andrew; Kieu, Tien
2012-05-01
High-value transactions between banks in Australia are settled in the Reserve Bank Information and Transfer System (RITS) administered by the Reserve Bank of Australia. RITS operates on a real-time gross settlement (RTGS) basis and settles payments and transfers sourced from the SWIFT payment delivery system, the Austraclear securities settlement system, and the interbank transactions entered directly into RITS. In this paper, we analyse a dataset received from the Reserve Bank of Australia that includes all interbank transactions settled in RITS on an RTGS basis during five consecutive weekdays from 19 February 2007 inclusive, a week of relatively quiescent market conditions. The source, destination, and value of each transaction are known, which allows us to separate overnight loans from other transactions (nonloans) and reconstruct monetary flows between banks for every day in our sample. We conduct a novel analysis of the flow stability and examine the connection between loan and nonloan flows. Our aim is to understand the underlying causal mechanism connecting loan and nonloan flows. We find that the imbalances in the banks' exchange settlement funds resulting from the daily flows of nonloan transactions are almost exactly counterbalanced by the flows of overnight loans. The correlation coefficient between loan and nonloan imbalances is about -0.9 on most days. Some flows that persist over two consecutive days can be highly variable, but overall the flows are moderately stable in value. The nonloan network is characterised by a large fraction of persistent flows, whereas only half of the flows persist over any two consecutive days in the loan network. Moreover, we observe an unusual degree of coherence between persistent loan flow values on Tuesday and Wednesday. We probe static topological properties of the Australian interbank network and find them consistent with those observed in other countries.
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.
Forecasting Beijing Transportation Hub Areas’s Pedestrian Flow Using Modular Neural Network
Directory of Open Access Journals (Sweden)
Shuwei Wang
2015-01-01
Full Text Available Along with the increasing proportion of urban public transportation trip, pedestrian flow in transportation hub areas increased. For effectively improving the emergency handling ability of related management apartments and preventing the incident of pedestrian congestion, this paper studied the method of pedestrian flow forecast in Beijing transportation hub areas. Firstly, 34 typical sidewalks in Beijing transportation hub areas were surveyed to obtain 2200 valid data. Secondly, correlation analysis was used to analyze the relationship between pedestrian flow and its influential factors. 11 significant influential factors were extracted. Thirdly, forecasting model was established with modular neural network. The surveyed pedestrian flow sample was fuzzy clustered according to the regional land use where the transportation hub existed. Then, membership function based on the distance measure was constructed. Through fuzzy discrimination, online selection for the subnetwork of the information can be achieved. Consequently, the self-adaptation of the neural network on information processing was improved. Finally, this paper tested the pedestrian flow sample of a transportation hub in Beijing. It was concluded that the accuracy of pedestrian flow forecasting model using modular neural network was higher than other neural network models. There was also improvement in the adaptability to environment.
Study of Real—Time Simulation for Two—Phase Flow Network
Institute of Scientific and Technical Information of China (English)
XieMaoqing; ZhuWen; 等
1995-01-01
To meet the need of real-time simulation for two-phase flow network in power plants,this paper presents a mathematical model based on basic principles and a numerical solution method.This model can be used to describe nonhomogeneous two-phase flow networks.The algorithm presented in this paper makes use of the sparsity,the symmetyr and the diagonal dominancy of the coefficient matrix to save storage space,reduce computational time and meet the requirements of real-time simulations.
Optimal power flow for distribution networks with distributed generation
Directory of Open Access Journals (Sweden)
Radosavljević Jordan
2015-01-01
Full Text Available This paper presents a genetic algorithm (GA based approach for the solution of the optimal power flow (OPF in distribution networks with distributed generation (DG units, including fuel cells, micro turbines, diesel generators, photovoltaic systems and wind turbines. The OPF is formulated as a nonlinear multi-objective optimization problem with equality and inequality constraints. Due to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties, a probabilisticalgorithm is introduced in the OPF analysis. The Weibull and normal distributions are employed to model the input random variables, namely the wind speed, solar irradiance and load power. The 2m+1 point estimate method and the Gram Charlier expansion theory are used to obtain the statistical moments and the probability density functions (PDFs of the OPF results. The proposed approach is examined and tested on a modified IEEE 34 node test feeder with integrated five different DG units. The obtained results prove the efficiency of the proposed approach to solve both deterministic and probabilistic OPF problems for different forms of the multi-objective function. As such, it can serve as a useful decision-making supporting tool for distribution network operators. [Projekat Ministarstva nauke Republike Srbije, br. TR33046
Scaling of Peak Flows with Constant Flow Velocity in Random Self-Similar Networks
Mantilla, R.; Gupta, V. K.; Troutman, B. M.
2010-12-01
We present a methodology to understand the role of the statistical self-similar topology of real river networks on flow hydrographs for rainfall-runoff events. Monte Carlo generated ensembles of 1000 Random Self-similar Networks (RSNs) with geometrically distributed interior and exterior generators are created. We show how these networks emulate the statistical self-similarity present in real networks by presenting results for 30 river networks in the continental USA. Hydrographs for every link in each of these networks are obtained by numerically solving the link-based mass and momentum conservation equation under the assumption of constant flow velocity. From these simulated hydrographs for an ensemble of RSNs, the scaling parameters for the peak of the width function (β) and the hydrograph peak flow (φ) are estimated. It was found that φ > β, which supports a similar finding first reported for the Walnut Gulch basin, Arizona, and that is qualitatively different from previous results on idealized river networks (e.g. Peano Network, Mandelbrot- Viscek Network). Scaling of peak flows for individual rainfall runoff events is a new area of research that offers a path to physically understand regional scaling of flood quantiles. It addresses an important open problem in river network hydrology through studying the statistics of ensembles of multiple events in RSNs. In addition, our methodology provides a reference framework to study scaling exponents and intercepts under more complex scenarios of flow dynamics and runoff generation processes using ensembles of RSNs. Preliminary examples of such scenarios will also be given.
Stochastic models for turbulent reacting flows
Energy Technology Data Exchange (ETDEWEB)
Kerstein, A. [Sandia National Laboratories, Livermore, CA (United States)
1993-12-01
The goal of this program is to develop and apply stochastic models of various processes occurring within turbulent reacting flows in order to identify the fundamental mechanisms governing these flows, to support experimental studies of these flows, and to further the development of comprehensive turbulent reacting flow models.
World Migration Degree Global migration flows in directed networks
2015-01-01
In this article we analyze the global flow of migrants from 206 source countries to 145 destination countries (2006-2010) and focus on the differences in the migration network pattern between destination and source counters as represented by its degree and weight distribution. Degree represents the connectivity of a country to the global migration network, and plays an important role in defining migration processes and characteristics. Global analysis of migration degree distribution offers a...
Optimal Power Flow Solution Using Ant Manners for Electrical Network
Directory of Open Access Journals (Sweden)
ALLAOUA, B.
2009-02-01
Full Text Available This paper presents ant manners and the collective intelligence for electrical network. Solutions for Optimal Power Flow (OPF problem of a power system deliberate via an ant colony optimization metaheuristic method. The objective is to minimize the total fuel cost of thermal generating units and also conserve an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors, transformers tap-setting and power flow of transmission lines. Simulation results on the IEEE 30-bus electrical network show that the ant colony optimization method converges quickly to the global optimum.
On Event Detection and Localization in Acyclic Flow Networks
Suresh, Mahima Agumbe
2013-05-01
Acyclic flow networks, present in many infrastructures of national importance (e.g., oil and gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures have been proven costly and imprecise, particularly when dealing with large-scale distribution systems. In this article, to the best of our knowledge, for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. We propose the idea of using sensors that move along the edges of the network and detect events (i.e., attacks). To localize the events, sensors detect proximity to beacons, which are devices with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensors and beacons deployed) in a predetermined zone of interest, while ensuring a degree of coverage by sensors and a required accuracy in locating events using beacons. We propose algorithms for solving the aforementioned problem and demonstrate their effectiveness with results obtained from a realistic flow network simulator.
An improved model for reduced-order physiological fluid flows
San, Omer; 10.1142/S0219519411004666
2012-01-01
An improved one-dimensional mathematical model based on Pulsed Flow Equations (PFE) is derived by integrating the axial component of the momentum equation over the transient Womersley velocity profile, providing a dynamic momentum equation whose coefficients are smoothly varying functions of the spatial variable. The resulting momentum equation along with the continuity equation and pressure-area relation form our reduced-order model for physiological fluid flows in one dimension, and are aimed at providing accurate and fast-to-compute global models for physiological systems represented as networks of quasi one-dimensional fluid flows. The consequent nonlinear coupled system of equations is solved by the Lax-Wendroff scheme and is then applied to an open model arterial network of the human vascular system containing the largest fifty-five arteries. The proposed model with functional coefficients is compared with current classical one-dimensional theories which assume steady state Hagen-Poiseuille velocity pro...
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.
Lagrangian Flow networks: a new way to characterize transport and connectivity in geophysical flows
Ser-Giacomi, Enrico; Hernandez-Garcia, Emilio; Lopez, Cristobal; Rossi, Vincent; Vasile, Ruggero
2015-04-01
Water and air transport are among the basic processes shaping the climate of our planet. Heat and salinity fluxes change sea water density, and thus drive the global thermohaline circulation. Atmospheric winds force the ocean motion, and also transport moisture, heat or chemicals, impacting the regional climate. We describe transport among different regions of the ocean or the atmosphere by flow networks, giving a discrete and robust representation of the fluid advection dynamics. We use network-theory tools to gain insights into transport problem. Local and global features of the networks are extracted from many numerical experiments to give a time averaged description of the system. Classical concepts like dispersion, mixing and connectivity are finally related to a set of network-like objects contributing to build a "dictionary" between network measures and physical quantities in geophysical flows.
Reentrant Information Flow in Electrophysiological Rat Default Mode Network
Jing, Wei; Guo, Daqing; Zhang, Yunxiang; Guo, Fengru; Valdés-Sosa, Pedro A.; Xia, Yang; Yao, Dezhong
2017-01-01
Functional MRI (fMRI) studies have demonstrated that the rodent brain shows a default mode network (DMN) activity similar to that in humans, offering a potential preclinical model both for physiological and pathophysiological studies. However, the neuronal mechanism underlying rodent DMN remains poorly understood. Here, we used electrophysiological data to analyze the power spectrum and estimate the directed phase transfer entropy (dPTE) within rat DMN across three vigilance states: wakeful rest (WR), slow-wave sleep (SWS), and rapid-eye-movement sleep (REMS). We observed decreased gamma powers during SWS compared with WR in most of the DMN regions. Increased gamma powers were found in prelimbic cortex, cingulate cortex, and hippocampus during REMS compared with WR, whereas retrosplenial cortex showed a reverse trend. These changed gamma powers are in line with the local metabolic variation of homologous brain regions in humans. In the analysis of directional interactions, we observed well-organized anterior-to-posterior patterns of information flow in the delta band, while opposite patterns of posterior-to-anterior flow were found in the theta band. These frequency-specific opposite patterns were only observed in WR and REMS. Additionally, most of the information senders in the delta band were also the receivers in the theta band, and vice versa. Our results provide electrophysiological evidence that rat DMN is similar to its human counterpart, and there is a frequency-dependent reentry loop of anterior-posterior information flow within rat DMN, which may offer a mechanism for functional integration, supporting conscious awareness. PMID:28289373
Kerner, Boris S
2016-01-01
We show that the minimization of travel times in a network as generally accepted in classical traffic and transportation theories deteriorates the traffic system through a considerable increase in the probability of traffic breakdown in the network. We introduce a network characteristic {\\it minimum network capacity} that shows that rather than the minimization of travel times in the network, the minimization of the probability of traffic breakdown in the network maximizes the network throughput at which free flow persists in the whole network.
Towards Effective Intra-flow Network Coding in Software Defined Wireless Mesh Networks
Directory of Open Access Journals (Sweden)
Donghai Zhu
2016-01-01
Full Text Available Wireless Mesh Networks (WMNs have potential to provide convenient broadband wireless Internet access to mobile users.With the support of Software-Defined Networking (SDN paradigm that separates control plane and data plane, WMNs can be easily deployed and managed. In addition, by exploiting the broadcast nature of the wireless medium and the spatial diversity of multi-hop wireless networks, intra-flow network coding has shown a greater benefit in comparison with traditional routing paradigms in data transmission for WMNs. In this paper, we develop a novel OpenCoding protocol, which combines the SDN technique with intra-flow network coding for WMNs. Our developed protocol can simplify the deployment and management of the network and improve network performance. In OpenCoding, a controller that works on the control plane makes routing decisions for mesh routers and the hop-by-hop forwarding function is replaced by network coding functions in data plane. We analyze the overhead of OpenCoding. Through a simulation study, we show the effectiveness of the OpenCoding protocol in comparison with existing schemes. Our data shows that OpenCoding outperforms both traditional routing and intra-flow network coding schemes.
A Generalized Minimum Cost Flow Model for Multiple Emergency Flow Routing
Directory of Open Access Journals (Sweden)
Jianxun Cui
2014-01-01
Full Text Available During real-life disasters, that is, earthquakes, floods, terrorist attacks, and other unexpected events, emergency evacuation and rescue are two primary operations that can save the lives and property of the affected population. It is unavoidable that evacuation flow and rescue flow will conflict with each other on the same spatial road network and within the same time window. Therefore, we propose a novel generalized minimum cost flow model to optimize the distribution pattern of these two types of flow on the same network by introducing the conflict cost. The travel time on each link is assumed to be subject to a bureau of public road (BPR function rather than a fixed cost. Additionally, we integrate contraflow operations into this model to redesign the network shared by those two types of flow. A nonconvex mixed-integer nonlinear programming model with bilinear, fractional, and power components is constructed, and GAMS/BARON is used to solve this programming model. A case study is conducted in the downtown area of Harbin city in China to verify the efficiency of proposed model, and several helpful findings and managerial insights are also presented.
A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks.
Directory of Open Access Journals (Sweden)
Huan Chen
Full Text Available This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN. Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.
Samnett: the EMPS model with power flow constraints: implementation details
Energy Technology Data Exchange (ETDEWEB)
Helseth, Arild; Warland, Geir; Mo, Birger; Fosso, Olav B.
2011-12-15
This report describes the development and implementation of Samnett. Samnett is a new prototype for solving the coupled market and transmission network problem. The prototype is based on the EMPS model (Samkjoeringsmodellen). Results from the market model are distributed to a detailed transmission network model, where a DC power flow detects if there are overloads on monitored lines or interconnections. In case of overloads, power flow constraints are generated and added to the market problem. This report is an updated version of TR A6891 {sup I}mplementing Network Constraints in the EMPS model{sup .} It further elaborates on theoretical and implementation details in Samnett, but does not contain the case studies and file descriptions presented in TR A6891.(auth)
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 repor....... Working through these cases, students will learn to manage and evaluate realistic intelligence accounts.......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......, 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...
DEFF Research Database (Denmark)
Ding, Tao; Li, Cheng; Yang, Yongheng
2017-01-01
The detailed topology of renewable resource bases may have the impact on the optimal power flow of the VSC-HVDC transmission network. To address this issue, this paper develops an optimal power flow with the hybrid VSC-HVDC transmission and active distribution networks to optimally schedule...... the generation output and voltage regulation of both networks, which leads to a non-convex programming model. Furthermore, the non-convex power flow equations are based on the Second Order Cone Programming (SOCP) relaxation approach. Thus, the proposed model can be relaxed to a SOCP that can be tractably solved...
Truthful Unsplittable Flow for Large Capacity Networks
Azar, Yossi; Gutner, Shai
2008-01-01
In this paper, we focus our attention on the large capacities unsplittable flow problem in a game theoretic setting. In this setting, there are selfish agents, which control some of the requests characteristics, and may be dishonest about them. It is worth noting that in game theoretic settings many standard techniques, such as randomized rounding, violate certain monotonicity properties, which are imperative for truthfulness, and therefore cannot be employed. In light of this state of affairs, we design a monotone deterministic algorithm, which is based on a primal-dual machinery, which attains an approximation ratio of $\\frac{e}{e-1}$, up to a disparity of $\\epsilon$ away. This implies an improvement on the current best truthful mechanism, as well as an improvement on the current best combinatorial algorithm for the problem under consideration. Surprisingly, we demonstrate that any algorithm in the family of reasonable iterative path minimizing algorithms, cannot yield a better approximation ratio. Conseque...
Governing cruise tourism at Bonaire: a networks and flows approach
Bets, van L.K.J.; Lamers, M.A.J.; Tatenhove, van J.P.M.
2016-01-01
Conceptual approaches to thoroughly study governance of cruise tourism are lacking in the literature. Relying on Castells’ network society, we analyze how two interconnected flows of cruise ships and passengers are governed by a marine community of users and policy makers. Bonaire is used as a case
Contraction driven flow in the extended vein networks of Physarum polycephalum
Alim, Karen; Amselem, Gabriel; Peaudecerf, Francois; Pringle, Anne; Brenner, Michael P.
2011-11-01
The true slime mold Physarum polycephalum is a basal organism that forms an extended network of veins to forage for food. P. polycephalum is renown for its adaptive changes of vein structure and morphology in response to food sources. These rearrangements presumably occur to establish an efficient transport and mixing of resources throughout the networks thus presenting a prototype to design transport networks under the constraints of laminar flow. The physical flows of cytoplasmic fluid enclosed by the veins exhibit an oscillatory flow termed ``shuttle streaming.'' The flow exceed by far the volume required for growth at the margins suggesting that the additional energy cost for generating the flow is spent for efficient and/or targeted redistribution of resources. We show that the viscous shuttle flow is driven by the radial contractions of the veins that accompany the streaming. We present a model for the fluid flow and resource dispersion arising due to radial contractions. The transport and mixing properties of the flow are discussed.
Computation of gradually varied flow in compound open channel networks
Indian Academy of Sciences (India)
H Prashanth Reddy; M Hanif Chaudhry; Jasim Imran
2014-12-01
Although, natural channels are rarely rectangular or trapezoidal in cross section, these cross sections are assumed for the computation of steady, gradually varied flow in open channel networks. The accuracy of the computed results, therefore, becomes questionable due to differences in the hydraulic and geometric characteristics of the main channel and floodplains. To overcome these limitations, an algorithm is presented in this paper to compute steady, gradually varied flow in an open-channel network with compound cross sections. As compared to the presently available methods, the methodology is more general and suitable for application to compound and trapezoidal channel cross sections in series channels, tree-type or looped networks. In this method, the energy and continuity equations are solved for steady, gradually varied flow by the Newton–Raphson method and the proposed methodology is applied to tree-type and looped-channel networks. An algorithm is presented to determine multiple critical depths in a compound channel. Modifications in channel geometry are presented to avoid the occurrence of multiple critical depths. The occurrence of only one critical depth in a compound cross section with modified geometry is demonstrated for a tree-type channel network.
A network flow algorithm to position tiles for LAMOST
Institute of Scientific and Technical Information of China (English)
Guang-Wei Li; Gang Zhao
2009-01-01
We introduce the network flow algorithm used by the Sloan Digital Sky Survey (SDSS) into the sky survey of the Large sky Area Multi-Object fiber Spectroscopic Telescope (LAMOST) to position tiles.Because fibers in LAMOST's focal plane are distributed uniformly,we cannot use SDSS' method directly.To solve this problem,firstly we divide the sky into many small blocks,and we also assume that all the targets that are in the same block have the same position,which is the center of the block.Secondly,we give a value to limit the number of the targets that the LAMOST focal plane can collect in one square degree so that it cannot collect too many targets in one small block.Thirdly,because the network flow algorithm used in this paper is a bipartite network,we do not use the general solution algorithm that was used by SDSS.Instead,we give our new faster solution method for this special network.Compared with the Convergent Mean Shift Algorithm,the network flow algorithm can decrease observation times with improved mean imaging quality.This algorithm also has a very fast running speed.It can distribute millions of targets in a few minutes using a common personal computer.
Frequency tuning allows flow direction control in microfluidic networks with passive features.
Jain, Rahil; Lutz, Barry
2017-05-02
Frequency tuning has emerged as an attractive alternative to conventional pumping techniques in microfluidics. Oscillating (AC) flow driven through a passive valve can be rectified to create steady (DC) flow, and tuning the excitation frequency to the characteristic (resonance) frequency of the underlying microfluidic network allows control of flow magnitude using simple hardware, such as an on-chip piezo buzzer. In this paper, we report that frequency tuning can also be used to control the direction (forward or backward) of the rectified DC flow in a single device. Initially, we observed that certain devices provided DC flow in the "forward" direction expected from previous work with a similar valve geometry, and the maximum DC flow occurred at the same frequency as a prominent peak in the AC flow magnitude, as expected. However, devices of a slightly different geometry provided the DC flow in the opposite direction and at a frequency well below the peak AC flow. Using an equivalent electrical circuit model, we found that the "forward" DC flow occurred at the series resonance frequency (with large AC flow peak), while the "backward" DC flow occurred at a less obvious parallel resonance (a valley in AC flow magnitude). We also observed that the DC flow occurred only when there was a measurable differential in the AC flow magnitude across the valve, and the DC flow direction was from the channel with large AC flow magnitude to that with small AC flow magnitude. Using these observations and the AC flow predictions from the equivalent circuit model, we designed a device with an AC flowrate frequency profile that was expected to allow the DC flow in opposite directions at two distinct frequencies. The fabricated device showed the expected flow reversal at the expected frequencies. This approach expands the flow control toolkit to include both magnitude and direction control in frequency-tuned microfluidic pumps. The work also raises interesting questions about the
Applications of flow-networks to opinion-dynamics
Tupikina, Liubov; Kurths, Jürgen
2015-04-01
Networks were successfully applied to describe complex systems, such as brain, climate, processes in society. Recently a socio-physical problem of opinion-dynamics was studied using network techniques. We present the toy-model of opinion-formation based on the physical model of advection-diffusion. We consider spreading of the opinion on the fixed subject, assuming that opinion on society is binary: if person has opinion then the state of the node in the society-network equals 1, if the person doesn't have opinion state of the node equals 0. Opinion can be spread from one person to another if they know each other, or in the network-terminology, if the nodes are connected. We include into the system governed by advection-diffusion equation the external field to model such effects as for instance influence from media. The assumptions for our model can be formulated as the following: 1.the node-states are influenced by the network structure in such a way, that opinion can be spread only between adjacent nodes (the advective term of the opinion-dynamics), 2.the network evolution can have two scenarios: -network topology is not changing with time; -additional links can appear or disappear each time-step with fixed probability which requires adaptive networks properties. Considering these assumptions for our system we obtain the system of equations describing our model-dynamics which corresponds well to other socio-physics models, for instance, the model of the social cohesion and the famous voter-model. We investigate the behavior of the suggested model studying "waiting time" of the system, time to get to the stable state, stability of the model regimes for different values of model parameters and network topology.
A Universal Model of Commuting Networks
Lenormand, Maxime; Gargiulo, Floriana; Deffuant, Guillaume
2012-01-01
We test a recently proposed model of commuting networks on 80 case studies from different regions of the world (Europe and United-States) and with geographic units of different sizes (municipality, county, region). The model takes as input the number of commuters coming in and out of each geographic unit and generates the matrix of commuting flows betwen the geographic units. We show that the single parameter of the model, which rules the compromise between the influence of the distance and job opportunities, follows a universal law that depends only on the average surface of the geographic units. We verified that the law derived from a part of the case studies yields accurate results on other case studies. We also show that our model significantly outperforms the two other approaches proposing a universal commuting model (Balcan et al. (2009); Simini et al. (2012)), particularly when the geographic units are small (e.g. municipalities).
Yan, Liangming; An, Di; Shi, Ge; Bian, Mingzhe; Khanra, Asit Kumar; Wang, Huiting
2017-05-01
Isothermal and constant strain rate compression tests of as-homogenized Mg-5.9Zn-1.6Zr-1.6Nd-0.9Y alloy were performed by using Gleeble-3800 thermo-mechanical simulator at temperature between 523 and 723 K and strain rate range from 0.001 to 1 s-1, respectively. The deformation behavior of the alloy at elevated temperature was investigated. A strain-dependent constitutive model based on the Arrhenius model and back-propagation neural network (BPNN) model of the flow stress was established. The predictability of the two models was evaluated by average absolute relative error (AARE) and correlation coefficient ( R c), respectively. The results show that the BPNN model has more accurate predictability than strain-dependent constitutive model. Processing maps are obtained according to the principles of the dynamic materials modeling and microstructures of the compressed samples. The microstructure of the samples was observed by optical microscope and electron backscattered diffraction. Processing maps show that the instability domain is at a strain rate of range of 0.015-1 s-1 and a temperature between 523 and 623 K. The alloy has good workability at 630-700 K and 0.008-0.1 s-1, wherein dynamic recrystallization occurs.
Directory of Open Access Journals (Sweden)
Shan Yang
2016-01-01
Full Text Available Power flow calculation and short circuit calculation are the basis of theoretical research for distribution network with inverter based distributed generation. The similarity of equivalent model for inverter based distributed generation during normal and fault conditions of distribution network and the differences between power flow and short circuit calculation are analyzed in this paper. Then an integrated power flow and short circuit calculation method for distribution network with inverter based distributed generation is proposed. The proposed method let the inverter based distributed generation be equivalent to Iθ bus, which makes it suitable to calculate the power flow of distribution network with a current limited inverter based distributed generation. And the low voltage ride through capability of inverter based distributed generation can be considered as well in this paper. Finally, some tests of power flow and short circuit current calculation are performed on a 33-bus distribution network. The calculated results from the proposed method in this paper are contrasted with those by the traditional method and the simulation method, whose results have verified the effectiveness of the integrated method suggested in this paper.
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.
Three-Phase Unbalanced Load Flow Tool for Distribution Networks
DEFF Research Database (Denmark)
Demirok, Erhan; Kjær, Søren Bækhøj; Sera, Dezso;
2012-01-01
This work develops a three-phase unbalanced load flow tool tailored for radial distribution networks based on Matlab®. The tool can be used to assess steady-state voltage variations, thermal limits of grid components and power losses in radial MV-LV networks with photovoltaic (PV) generators where...... most of the systems are single phase. New ancillary service such as static reactive power support by PV inverters can be also merged together with the load flow solution tool and thus, the impact of the various reactive power control strategies on the steady-state grid operation can be simply...... investigated. Performance of the load flow solution tool in the sense of resulting bus voltage magnitudes is compared and validated with IEEE 13-bus test feeder....
Quantification of blood flow and topology in developing vascular networks.
Directory of Open Access Journals (Sweden)
Astrid Kloosterman
Full Text Available Since fluid dynamics plays a critical role in vascular remodeling, quantification of the hemodynamics is crucial to gain more insight into this complex process. Better understanding of vascular development can improve prediction of the process, and may eventually even be used to influence the vascular structure. In this study, a methodology to quantify hemodynamics and network structure of developing vascular networks is described. The hemodynamic parameters and topology are derived from detailed local blood flow velocities, obtained by in vivo micro-PIV measurements. The use of such detailed flow measurements is shown to be essential, as blood vessels with a similar diameter can have a large variation in flow rate. Measurements are performed in the yolk sacs of seven chicken embryos at two developmental stages between HH 13+ and 17+. A large range of flow velocities (1 µm/s to 1 mm/s is measured in blood vessels with diameters in the range of 25-500 µm. The quality of the data sets is investigated by verifying the flow balances in the branching points. This shows that the quality of the data sets of the seven embryos is comparable for all stages observed, and the data is suitable for further analysis with known accuracy. When comparing two subsequently characterized networks of the same embryo, vascular remodeling is observed in all seven networks. However, the character of remodeling in the seven embryos differs and can be non-intuitive, which confirms the necessity of quantification. To illustrate the potential of the data, we present a preliminary quantitative study of key network topology parameters and we compare these with theoretical design rules.
Computational modelling of SCC flow
DEFF Research Database (Denmark)
Geiker, Mette Rica; Thrane, Lars Nyholm; Szabo, Peter
2005-01-01
To benefit from the full potential of self-compacting concrete (SCC) prediction tools are needed for the form filling of SCC. Such tools should take into account the properties of the concrete, the shape and size of the structural element, the position of rebars, and the casting technique. Exampl...... of computational models for the time dependent flow behavior are given, and advantages and disadvantages of discrete particle and single fluid models are briefly described.......To benefit from the full potential of self-compacting concrete (SCC) prediction tools are needed for the form filling of SCC. Such tools should take into account the properties of the concrete, the shape and size of the structural element, the position of rebars, and the casting technique. Examples...
CNEM: Cluster Based Network Evolution Model
Directory of Open Access Journals (Sweden)
Sarwat Nizamani
2015-01-01
Full Text Available This paper presents a network evolution model, which is based on the clustering approach. The proposed approach depicts the network evolution, which demonstrates the network formation from individual nodes to fully evolved network. An agglomerative hierarchical clustering method is applied for the evolution of network. In the paper, we present three case studies which show the evolution of the networks from the scratch. These case studies include: terrorist network of 9/11 incidents, terrorist network of WMD (Weapons Mass Destruction plot against France and a network of tweets discussing a topic. The network of 9/11 is also used for evaluation, using other social network analysis methods which show that the clusters created using the proposed model of network evolution are of good quality, thus the proposed method can be used by law enforcement agencies in order to further investigate the criminal networks
Prediction of flow stress of Ti-15-3 alloy with artificial neural network
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Hot compression experiments were conducted on Ti-15-3 alloy specimens using Gleeble-1500 Thermal Simulator．These tests were focused to obtain the flow stress data under various conditions of strain，strain rate and temperature. On the basis of these data， the predicting model for the nonlinear relation between flow stress and deformation strain，strain rate and temperature for Ti-15-3 alloy was developed with a back-propagation artificial neural network method. Results show that the neural network can reproduce the flow stress in the sampled data and predict the nonsampled data well. Thus the neural network method has been verified to be used to tackle hot deformation problems of Ti-15-3 alloy.
A unified pore-network algorithm for dynamic two-phase flow
Sheng, Qiang; Thompson, Karsten
2016-09-01
This paper describes recent work on image-based network modeling of multiphase flow. The algorithm expands the range of flow scenarios and boundary conditions that can be implemented using dynamic network modeling, the most significant advance being the ability to model simultaneous injection of immiscible fluids under either transient or steady-state conditions using non-periodic domains. Pore-scale saturation distributions are solved rigorously from two-phase mass conservation equations simultaneously within each pore. Results show that simulations using a periodic network fail to track saturation history because periodic domains limit how the bulk saturation can evolve over time. In contrast, simulations using a non-periodic network with fractional flow as the boundary condition can account for behavior associated with both hysteresis and saturation history, and can capture phenomena such as the long pressure and saturation tails that are observed during dynamic drainage processes. Results include a sensitivity analysis of relative permeability to different model variables, which may provide insight into mechanisms for a variety of transient, viscous dominated flow processes.
Impact of trucking network flow on preferred biorefinery locations in the southern United States
Timothy M. Young; Lee D. Han; James H. Perdue; Stephanie R. Hargrove; Frank M. Guess; Xia Huang; Chung-Hao Chen
2017-01-01
The impact of the trucking transportation network flow was modeled for the southern United States. The study addresses a gap in existing research by applying a Bayesian logistic regression and Geographic Information System (GIS) geospatial analysis to predict biorefinery site locations. A one-way trucking cost assuming a 128.8 km (80-mile) haul distance was estimated...
Selection or Network Effects? Migration Flows into 27 OECD Countries, 1990-2000
DEFF Research Database (Denmark)
Pedersen, P.J.; Pytlikova, Mariola; Smith, Nina
of 11 years, 1990- 2000. Using a panel data model, we analyze the determinants of the migration flows. Our results indicate that traditional factors as cultural and linguistic distance are important. Network effects are also strong, but vary between source and destination countries. We do not find clear...
Unified pipe network method for simulation of water flow in fractured porous rock
Ren, Feng; Ma, Guowei; Wang, Yang; Li, Tuo; Zhu, Hehua
2017-04-01
Rock masses are often conceptualized as dual-permeability media containing fractures or fracture networks with high permeability and porous matrix that is less permeable. In order to overcome the difficulties in simulating fluid flow in a highly discontinuous dual-permeability medium, an effective unified pipe network method is developed, which discretizes the dual-permeability rock mass into a virtual pipe network system. It includes fracture pipe networks and matrix pipe networks. They are constructed separately based on equivalent flow models in a representative area or volume by taking the advantage of the orthogonality of the mesh partition. Numerical examples of fluid flow in 2-D and 3-D domain including porous media and fractured porous media are presented to demonstrate the accuracy, robustness, and effectiveness of the proposed unified pipe network method. Results show that the developed method has good performance even with highly distorted mesh. Water recharge into the fractured rock mass with complex fracture network is studied. It has been found in this case that the effect of aperture change on the water recharge rate is more significant in the early stage compared to the fracture density change.
The prescribed output pattern regulates the modular structure of flow networks
Emanuel Beber, Moritz; Armbruster, Dieter; Hütt, Marc-Thorsten
2013-11-01
Modules are common functional and structural properties of many social, technical and biological networks. Especially for biological systems it is important to understand how modularity is related to function and how modularity evolves. It is known that time-varying or spatially organized goals can lead to modularity in a simulated evolution of signaling networks. Here, we study a minimal model of material flow in networks. We discuss the relation between the shared use of nodes, i.e., the cooperativity of modules, and the orthogonality of a prescribed output pattern. We study the persistence of cooperativity through an evolution of robustness against local damages. We expect the results to be valid for a large class of flow-based biological and technical networks. Supplementary material in the form of one pdf file available from the Journal web page at http://dx.doi.org/10.1140/epjb/e2013-40672-3
Haahti, Kersti; Koivusalo, Harri; Younis, Bassam; Stenberg, Leena
2014-05-01
One third of the land area in Finland is covered by peatlands and today 4.5 million ha of peatlands are drained for forestry purposes. In order to sustain forest productivity, ditch networks are maintained annually on an area of approximately 60 000 ha. The suspended solid (SS) load from drained peatland forest sites after ditch network maintenance causes one of the largest strains on the water system by forestry in Finland. Understanding the hydraulic processes in newly maintained ditch networks is necessary for quantifying the SS load generation and transport in the source areas. In this study we developed a hydraulic unsteady-flow model to predict the behavior of flow in a drainage network in a boreal forested peatland site. The input to this model was in the form of a discharge hydrograph that was produced by a hydrological model (FEMMA). The simulations were performed using the algorithm of Zhu et al. (2011) for unsteady flows in a network of channels. In this iterative procedure, the Saint-Venant equations that govern the flow in each of the network channels were solved separately, and the flow depths at the junction-points were corrected using the method of characteristics. The algorithm was programmed using the numeric computing environment MATLAB by MathWorks. Based on the hydraulic conditions produced by the model, erosion risk within the network was evaluated. The model was applied to a peatland catchment drained for forestry in Koivupuro in Eastern Finland (63°53' N, 28°40' E). In August 2011, most of the Koivupuro catchment ditch network was maintained creating simultaneously a smaller nested catchment (area 5.2 ha) which was the focus of this study. The ditch network consisted of 15 branches, altogether 1.6 km in length, and 8 junctions. The model performance was evaluated against flow depth measurements at 5 locations in the network. The simulations in the small ditches of Koivupuro with mostly very low flow rate (resistance depended highly on the
Ising model for distribution networks
Hooyberghs, H; Giuraniuc, C; Van Schaeybroeck, B; Indekeu, J O
2012-01-01
An elementary Ising spin model is proposed for demonstrating cascading failures (break-downs, blackouts, collapses, avalanches, ...) that can occur in realistic networks for distribution and delivery by suppliers to consumers. A ferromagnetic Hamiltonian with quenched random fields results from policies that maximize the gap between demand and delivery. Such policies can arise in a competitive market where firms artificially create new demand, or in a solidary environment where too high a demand cannot reasonably be met. Network failure in the context of a policy of solidarity is possible when an initially active state becomes metastable and decays to a stable inactive state. We explore the characteristics of the demand and delivery, as well as the topological properties, which make the distribution network susceptible of failure. An effective temperature is defined, which governs the strength of the activity fluctuations which can induce a collapse. Numerical results, obtained by Monte Carlo simulations of t...
A Mathematical Model to Improve the Performance of Logistics Network
Directory of Open Access Journals (Sweden)
Muhammad Izman Herdiansyah
2012-01-01
Full Text Available The role of logistics nowadays is expanding from just providing transportation and warehousing to offering total integrated logistics. To remain competitive in the global market environment, business enterprises need to improve their logistics operations performance. The improvement will be achieved when we can provide a comprehensive analysis and optimize its network performances. In this paper, a mixed integer linier model for optimizing logistics network performance is developed. It provides a single-product multi-period multi-facilities model, as well as the multi-product concept. The problem is modeled in form of a network flow problem with the main objective to minimize total logistics cost. The problem can be solved using commercial linear programming package like CPLEX or LINDO. Even in small case, the solver in Excel may also be used to solve such model.Keywords: logistics network, integrated model, mathematical programming, network optimization
A Mathematical Model to Improve the Performance of Logistics Network
Directory of Open Access Journals (Sweden)
Muhammad Izman Herdiansyah
2012-01-01
Full Text Available The role of logistics nowadays is expanding from just providing transportation and warehousing to offering total integrated logistics. To remain competitive in the global market environment, business enterprises need to improve their logistics operations performance. The improvement will be achieved when we can provide a comprehensive analysis and optimize its network performances. In this paper, a mixed integer linier model for optimizing logistics network performance is developed. It provides a single-product multi-period multi-facilities model, as well as the multi-product concept. The problem is modeled in form of a network flow problem with the main objective to minimize total logistics cost. The problem can be solved using commercial linear programming package like CPLEX or LINDO. Even in small case, the solver in Excel may also be used to solve such model.Keywords: logistics network, integrated model, mathematical programming, network optimization
Neural Network Predictive Control for Vanadium Redox Flow Battery
Directory of Open Access Journals (Sweden)
Hai-Feng Shen
2013-01-01
Full Text Available The vanadium redox flow battery (VRB is a nonlinear system with unknown dynamics and disturbances. The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system. Too low or too high flowrate is unfavorable for the safety and performance of VRB. This paper presents a neural network predictive control scheme to enhance the overall performance of the battery. A radial basis function (RBF network is employed to approximate the dynamics of the VRB system. The genetic algorithm (GA is used to obtain the optimum initial values of the RBF network parameters. The gradient descent algorithm is used to optimize the objective function of the predictive controller. Compared with the constant flowrate, the simulation results show that the flowrate optimized by neural network predictive controller can increase the power delivered by the battery during the discharge and decrease the power consumed during the charge.
Modelling Traffic in IMS Network Nodes
Directory of Open Access Journals (Sweden)
BA Alassane
2013-07-01
Full Text Available IMS is well integrated with existing voice and data networks, while adopting many of their keycharacteristics.The Call Session Control Functions (CSCFs servers are the key part of the IMS structure. They are themain components responsible for processing and routing signalling messages.When CSCFs servers (P-CSCF, I-CSCF, S-CSCF are running on the same host, the SIP message can beinternally passed between SIP servers using a single operating system mechanism like a queue. It increasesthe reliability of the network [5], [6]. We have proposed in a last work for each type of service (between ICSCFand S-CSCF (call, data, multimedia.[23], to use less than two servers well dimensioned andrunning on the same operating system.Instead dimensioning servers, in order to increase performance, we try to model traffic on IMS nodes,particularly on entries nodes; it will provide results on separation of incoming flows, and then offer moresatisfactory service.
Institute of Scientific and Technical Information of China (English)
温旭红; 林柏梁; 王龙; 陈雷; 纪丽君
2013-01-01
We proposed an optimization model for railway car flow assignment and routing plan based on Multi-commodity network flow theory.The outcomes of the optimization model could reflect composition of the railway car flow for arcs and directly show the routings of every demand by setting 0-1 type decision variables,which represented whether the car flow passed through an arc or not.The proposed model was subjected to each railway car flow moving on one route only and the capacity limit of lines.Moreover,it could keep the reasonable detour for all the car flows,which reflected requirements for transportation service quality.We verified the optimization model by invoking CPLEX optimizer in MATLAB software and used simulation OD data in a certain local railway network of our country.The results show that we can get an ideal railway car flow assignment plan and the routing plan,which indicates the rationality and the validity of the proposed model.%根据多商品网络流理论构建铁路车流分配及径路优化模型,模型中设置0-1型决策变量表示该股车流是否通过路网中的弧段,使优化结果既能体现各个弧段的车流构成情况,又能反映每股车流的走行径路.模型的约束除了考虑传统模型中的弧段通过能力限制和车流不可拆散的原则外,将路径的合理绕行纳入约束体系,使结果更加符合铁路运输实际.最后,在MATLAB软件中调用CPLEX优化器,采用模拟车流OD数据在我国某地区局部路网中对模型进行验证.结果表明该模型能得出比较理想的车流分配的优化方案,验证了模型的合理有效性.
Scaling of peak flows with constant flow velocity in random self-similar networks
Mantilla, R.; Gupta, V. K.; Troutman, B. M.
2011-07-01
A methodology is presented to understand the role of the statistical self-similar topology of real river networks on scaling, or power law, in peak flows for rainfall-runoff events. We created Monte Carlo generated sets of ensembles of 1000 random self-similar networks (RSNs) with geometrically distributed interior and exterior generators having parameters pi and pe, respectively. The parameter values were chosen to replicate the observed topology of real river networks. We calculated flow hydrographs in each of these networks by numerically solving the link-based mass and momentum conservation equation under the assumption of constant flow velocity. From these simulated RSNs and hydrographs, the scaling exponents β and φ characterizing power laws with respect to drainage area, and corresponding to the width functions and flow hydrographs respectively, were estimated. We found that, in general, φ > β, which supports a similar finding first reported for simulations in the river network of the Walnut Gulch basin, Arizona. Theoretical estimation of β and φ in RSNs is a complex open problem. Therefore, using results for a simpler problem associated with the expected width function and expected hydrograph for an ensemble of RSNs, we give heuristic arguments for theoretical derivations of the scaling exponents β(E) and φ(E) that depend on the Horton ratios for stream lengths and areas. These ratios in turn have a known dependence on the parameters of the geometric distributions of RSN generators. Good agreement was found between the analytically conjectured values of β(E) and φ(E) and the values estimated by the simulated ensembles of RSNs and hydrographs. The independence of the scaling exponents φ(E) and φ with respect to the value of flow velocity and runoff intensity implies an interesting connection between unit hydrograph theory and flow dynamics. Our results provide a reference framework to study scaling exponents under more complex scenarios of flow
Nonparametric Bayesian Modeling of Complex Networks
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Mørup, Morten
2013-01-01
Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...... for complex networks can be derived and point out relevant literature....
Garbin, Silvia; Alessi Celegon, Elisa; Fanton, Pietro; Botter, Gianluca
2017-04-01
The temporal variability of river flow regime is a key feature structuring and controlling fluvial ecological communities and ecosystem processes. In particular, streamflow variability induced by climate/landscape heterogeneities or other anthropogenic factors significantly affects the connectivity between streams with notable implication for river fragmentation. Hydrologic connectivity is a fundamental property that guarantees species persistence and ecosystem integrity in riverine systems. In riverine landscapes, most ecological transitions are flow-dependent and the structure of flow regimes may affect ecological functions of endemic biota (i.e., fish spawning or grazing of invertebrate species). Therefore, minimum flow thresholds must be guaranteed to support specific ecosystem services, like fish migration, aquatic biodiversity and habitat suitability. In this contribution, we present a probabilistic approach aiming at a spatially-explicit, quantitative assessment of hydrologic connectivity at the network-scale as derived from river flow variability. Dynamics of daily streamflows are estimated based on catchment-scale climatic and morphological features, integrating a stochastic, physically based approach that accounts for the stochasticity of rainfall with a water balance model and a geomorphic recession flow model. The non-exceedance probability of ecologically meaningful flow thresholds is used to evaluate the fragmentation of individual stream reaches, and the ensuing network-scale connectivity metrics. A multi-dimensional Poisson Process for the stochastic generation of rainfall is used to evaluate the impact of climate signature on reach-scale and catchment-scale connectivity. The analysis shows that streamflow patterns and network-scale connectivity are influenced by the topology of the river network and the spatial variability of climatic properties (rainfall, evapotranspiration). The framework offers a robust basis for the prediction of the impact of
IDENTIFICATION OF GAS-LIQUID FLOW REGIMES IN A HORIZONTAL FLOW USING NEURAL NETWORK
Institute of Scientific and Technical Information of China (English)
JIA Zhi-hai; NIU Gang; WANG Jing
2005-01-01
The knowledge of flow regimes is very important in the study of a two-phase flow system. A new flow regime identification method based on a Probability Density Function (PDF) and a neural network is proposed in this paper. The instantaneous differential pressure signals of a horizontal flow were acquired with a differential pressure sensor. The characters of differential pressure signals for different flow regimes are analyzed with the PDF. Then, four characteristic parameters of the PDF curves are defined, the peak number (K1), the maximum peak value (K2), the peak position (K3) and the PDF variance (K4). The characteristic vectors which consist of the four characteristic parameters as the input vectors train the neural network to classify the flow regimes. Experimental results show that this novel method for identifying air-water two-phase flow regimes has the advantages with a high accuracy and a fast response. The results clearly demonstrate that this new method could provide an accurate identification of flow regimes.
Numerical Simulation of Flow and Suspended Sediment Transport in the Distributary Channel Networks
Directory of Open Access Journals (Sweden)
Wei Zhang
2014-01-01
Full Text Available Flow and suspended sediment transport in distributary channel networks play an important role in the evolution of deltas and estuaries, as well as the coastal environment. In this study, a 1D flow and suspended sediment transport model is presented to simulate the hydrodynamics and suspended sediment transport in the distributary channel networks. The governing equations for river flow are the Saint-Venant equations and for suspended sediment transport are the nonequilibrium transport equations. The procedure of solving the governing equations is firstly to get the matrix form of the water level and suspended sediment concentration at all connected junctions by utilizing the transformation of the governing equations of the single channel. Secondly, the water level and suspended sediment concentration at all junctions can be obtained by solving these irregular spare matrix equations. Finally, the water level, discharge, and suspended sediment concentration at each river section can be calculated. The presented 1D flow and suspended sediment transport model has been applied to the Pearl River networks and can reproduce water levels, discharges, and suspended sediment concentration with good accuracy, indicating this that model can be used to simulate the hydrodynamics and suspended sediment concentration in the distributary channel networks.
Ultrasensitive quantification of cerebral capillary flow networks and dynamics
You, Jiang; Park, Ki; Du, Congwu; Pan, Yingtian
2015-03-01
Ultra-high resolution optical Doppler coherence tomography (μODT) is a promising tool for brain functional imaging. However, its sensitivity for detecting slow flows in capillary beds may limit its utility in visualizing and quantifying subtle changes in brain microcirculation. To address this limitation, we developed a novel method called contrast-enhanced μODT (c-μODT) in which intralipid is injected into mouse tail vein to enhance μODT detection sensitivity. We demonstrate that after intralipid injection, the flow detection sensitivity of μODT is dramatically enhanced by 230% as quantified by the fill factor (FF) of microvasculature. More importantly, we show that c-μODT preserves the quantitative properties for flow imaging, i.e., showing a comparable change ratio of hypercapnia-induced flow increase in the capillary network before and after injecting intralipid.
Wienhöfer, J.; Zehe, E.
2012-04-01
Rapid lateral flow processes via preferential flow paths are widely accepted to play a key role for rainfall-runoff response in temperate humid headwater catchments. A quantitative description of these processes, however, is still a major challenge in hydrological research, not least because detailed information about the architecture of subsurface flow paths are often impossible to obtain at a natural site without disturbing the system. Our study combines physically based modelling and field observations with the objective to better understand how flow network configurations influence the hydrological response of hillslopes. The system under investigation is a forested hillslope with a small perennial spring at the study area Heumöser, a headwater catchment of the Dornbirnerach in Vorarlberg, Austria. In-situ points measurements of field-saturated hydraulic conductivity and dye staining experiments at the plot scale revealed that shrinkage cracks and biogenic macropores function as preferential flow paths in the fine-textured soils of the study area, and these preferential flow structures were active in fast subsurface transport of artificial tracers at the hillslope scale. For modelling of water and solute transport, we followed the approach of implementing preferential flow paths as spatially explicit structures of high hydraulic conductivity and low retention within the 2D process-based model CATFLOW. Many potential configurations of the flow path network were generated as realisations of a stochastic process informed by macropore characteristics derived from the plot scale observations. Together with different realisations of soil hydraulic parameters, this approach results in a Monte Carlo study. The model setups were used for short-term simulation of a sprinkling and tracer experiment, and the results were evaluated against measured discharges and tracer breakthrough curves. Although both criteria were taken for model evaluation, still several model setups
Towards Optimal Event Detection and Localization in Acyclic Flow Networks
Agumbe Suresh, Mahima
2012-01-03
Acyclic flow networks, present in many infrastructures of national importance (e.g., oil & gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures, have been proven costly and imprecise, especially when dealing with large scale distribution systems. In this paper, to the best of our knowledge for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. Sensor nodes move along the edges of the network and detect events (i.e., attacks) and proximity to beacon nodes with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensor and beacon nodes deployed), while ensuring a degree of sensing coverage in a zone of interest and a required accuracy in locating events. We propose algorithms for solving these problems and demonstrate their effectiveness with results obtained from a high fidelity simulator.
Mathematical Modelling Plant Signalling Networks
Muraro, D.
2013-01-01
During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more comprehensive modelling studies of hormonal transport and signalling in a multi-scale setting. © EDP Sciences, 2013.
Random Boolean network models and the yeast transcriptional network
Kauffman, Stuart; Peterson, Carsten; Samuelsson, Björn; Troein, Carl
2003-12-01
The recently measured yeast transcriptional network is analyzed in terms of simplified Boolean network models, with the aim of determining feasible rule structures, given the requirement of stable solutions of the generated Boolean networks. We find that for ensembles of generated models, those with canalyzing Boolean rules are remarkably stable, whereas those with random Boolean rules are only marginally stable. Furthermore, substantial parts of the generated networks are frozen, in the sense that they reach the same state regardless of initial state. Thus, our ensemble approach suggests that the yeast network shows highly ordered dynamics.
Cellular automata models for synchronized traffic flow
Jiang Rui
2003-01-01
This paper presents a new cellular automata model for describing synchronized traffic flow. The fundamental diagrams, the spacetime plots and the 1 min average data have been analysed in detail. It is shown that the model can describe the outflow from the jams, the light synchronized flow as well as heavy synchronized flow with average speed greater than approximately 24 km h sup - sup 1. As for the synchronized flow with speed lower than 24 km h sup - sup 1 , it is unstable and will evolve into the coexistence of jams, free flow and light synchronized flow. This is consistent with the empirical findings (Kerner B S 1998 Phys. Rev. Lett. 81 3797).
Plant Growth Models Using Artificial Neural Networks
Bubenheim, David
1997-01-01
In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.
Generalization performance of regularized neural network models
DEFF Research Database (Denmark)
Larsen, Jan; Hansen, Lars Kai
1994-01-01
Architecture optimization is a fundamental problem of neural network modeling. The optimal architecture is defined as the one which minimizes the generalization error. This paper addresses estimation of the generalization performance of regularized, complete neural network models. Regularization...
Random field Ising model and community structure in complex networks
Son, S.-W.; Jeong, H.; Noh, J. D.
2006-04-01
We propose a method to determine the community structure of a complex network. In this method the ground state problem of a ferromagnetic random field Ising model is considered on the network with the magnetic field Bs = +∞, Bt = -∞, and Bi≠s,t=0 for a node pair s and t. The ground state problem is equivalent to the so-called maximum flow problem, which can be solved exactly numerically with the help of a combinatorial optimization algorithm. The community structure is then identified from the ground state Ising spin domains for all pairs of s and t. Our method provides a criterion for the existence of the community structure, and is applicable equally well to unweighted and weighted networks. We demonstrate the performance of the method by applying it to the Barabási-Albert network, Zachary karate club network, the scientific collaboration network, and the stock price correlation network. (Ising, Potts, etc.)
Technology and knowledge flow the power of networks
Trentin, Guglielmo
2011-01-01
This book outlines how network technology can support, foster and enhance the Knowledge Management, Sharing and Development (KMSD) processes in professional environments through the activation of both formal and informal knowledge flows. Understanding how ICT can be made available to such flows in the knowledge society is a factor that cannot be disregarded and is confirmed by the increasing interest of companies in new forms of software-mediated social interaction. The latter factor is in relation both to the possibility of accelerating internal communication and problem solving processes, an
Scalable Capacity Bounding Models for Wireless Networks
Du, Jinfeng; Medard, Muriel; Xiao, Ming; Skoglund, Mikael
2014-01-01
The framework of network equivalence theory developed by Koetter et al. introduces a notion of channel emulation to construct noiseless networks as upper (resp. lower) bounding models, which can be used to calculate the outer (resp. inner) bounds for the capacity region of the original noisy network. Based on the network equivalence framework, this paper presents scalable upper and lower bounding models for wireless networks with potentially many nodes. A channel decoupling method is proposed...
Çebi, A.; Akdoğan, E.; Celen, A.; Dalkilic, A. S.
2017-02-01
An artificial neural network (ANN) model of friction factor in smooth and microfin tubes under heating, cooling and isothermal conditions was developed in this study. Data used in ANN was taken from a vertically positioned heat exchanger experimental setup. Multi-layered feed-forward neural network with backpropagation algorithm, radial basis function networks and hybrid PSO-neural network algorithm were applied to the database. Inputs were the ratio of cross sectional flow area to hydraulic diameter, experimental condition number depending on isothermal, heating, or cooling conditions and mass flow rate while the friction factor was the output of the constructed system. It was observed that such neural network based system could effectively predict the friction factor values of the flows regardless of their tube types. A dependency analysis to determine the strongest parameter that affected the network and database was also performed and tube geometry was found to be the strongest parameter of all as a result of analysis.
Çebi, A.; Akdoğan, E.; Celen, A.; Dalkilic, A. S.
2016-06-01
An artificial neural network (ANN) model of friction factor in smooth and microfin tubes under heating, cooling and isothermal conditions was developed in this study. Data used in ANN was taken from a vertically positioned heat exchanger experimental setup. Multi-layered feed-forward neural network with backpropagation algorithm, radial basis function networks and hybrid PSO-neural network algorithm were applied to the database. Inputs were the ratio of cross sectional flow area to hydraulic diameter, experimental condition number depending on isothermal, heating, or cooling conditions and mass flow rate while the friction factor was the output of the constructed system. It was observed that such neural network based system could effectively predict the friction factor values of the flows regardless of their tube types. A dependency analysis to determine the strongest parameter that affected the network and database was also performed and tube geometry was found to be the strongest parameter of all as a result of analysis.
A toy terrestrial carbon flow model
Parton, William J.; Running, Steven W.; Walker, Brian
1992-01-01
A generalized carbon flow model for the major terrestrial ecosystems of the world is reported. The model is a simplification of the Century model and the Forest-Biogeochemical model. Topics covered include plant production, decomposition and nutrient cycling, biomes, the utility of the carbon flow model for predicting carbon dynamics under global change, and possible applications to state-and-transition models and environmentally driven global vegetation models.
A network-oriented business modeling environment
Bisconti, Cristian; Storelli, Davide; Totaro, Salvatore; Arigliano, Francesco; Savarino, Vincenzo; Vicari, Claudia
The development of formal models related to the organizational aspects of an enterprise is fundamental when these aspects must be re-engineered and digitalized, especially when the enterprise is involved in the dynamics and value flows of a business network. Business modeling provides an opportunity to synthesize and make business processes, business rules and the structural aspects of an organization explicit, allowing business managers to control their complexity and guide an enterprise through effective decisional and strategic activities. This chapter discusses the main results of the TEKNE project in terms of software components that enable enterprises to configure, store, search and share models of any aspects of their business while leveraging standard and business-oriented technologies and languages to bridge the gap between the world of business people and IT experts and to foster effective business-to-business collaborations.
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.
Liu, Zugang
Network systems, including transportation and logistic systems, electric power generation and distribution networks as well as financial networks, provide the critical infrastructure for the functioning of our societies and economies. The understanding of the dynamic behavior of such systems is also crucial to national security and prosperity. The identification of new connections between distinct network systems is the inspiration for the research in this dissertation. In particular, I answer two questions raised by Beckmann, McGuire, and Winsten (1956) and Copeland (1952) over half a century ago, which are, respectively, how are electric power flows related to transportation flows and does money flow like water or electricity? In addition, in this dissertation, I achieve the following: (1) I establish the relationships between transportation networks and three other classes of complex network systems: supply chain networks, electric power generation and transmission networks, and financial networks with intermediation. The establishment of such connections provides novel theoretical insights as well as new pricing mechanisms, and efficient computational methods. (2) I develop new modeling frameworks based on evolutionary variational inequality theory that capture the dynamics of such network systems in terms of the time-varying flows and incurred costs, prices, and, where applicable, profits. This dissertation studies the dynamics of such network systems by addressing both internal competition and/or cooperation, and external changes, such as varying costs and demands. (3) I focus, in depth, on electric power supply chains. By exploiting the relationships between transportation networks and electric power supply chains, I develop a large-scale network model that integrates electric power supply chains and fuel supply markets. The model captures both the economic transactions as well as the physical transmission constraints. The model is then applied to the New
Outlier Detection Method Use for the Network Flow Anomaly Detection
Directory of Open Access Journals (Sweden)
Rimas Ciplinskas
2016-06-01
Full Text Available New and existing methods of cyber-attack detection are constantly being developed and improved because there is a great number of attacks and the demand to protect from them. In prac-tice, current methods of attack detection operates like antivirus programs, i. e. known attacks signatures are created and attacks are detected by using them. These methods have a drawback – they cannot detect new attacks. As a solution, anomaly detection methods are used. They allow to detect deviations from normal network behaviour that may show a new type of attack. This article introduces a new method that allows to detect network flow anomalies by using local outlier factor algorithm. Accom-plished research allowed to identify groups of features which showed the best results of anomaly flow detection according the highest values of precision, recall and F-measure.
Flow distribution analysis on the cooling tube network of ITER thermal shield
Nam, Kwanwoo; Chung, Wooho; Noh, Chang Hyun; Kang, Dong Kwon; Kang, Kyoung-O.; Ahn, Hee Jae; Lee, Hyeon Gon
2014-01-01
Thermal shield (TS) is to be installed between the vacuum vessel or the cryostat and the magnets in ITER tokamak to reduce the thermal radiation load to the magnets operating at 4.2K. The TS is cooled by pressurized helium gas at the inlet temperature of 80K. The cooling tube is welded on the TS panel surface and the composed flow network of the TS cooling tubes is complex. The flow rate in each panel should be matched to the thermal design value for effective radiation shielding. This paper presents one dimensional analysis on the flow distribution of cooling tube network for the ITER TS. The hydraulic cooling tube network is modeled by an electrical analogy. Only the cooling tube on the TS surface and its connecting pipe from the manifold are considered in the analysis model. Considering the frictional factor and the local loss in the cooling tube, the hydraulic resistance is expressed as a linear function with respect to mass flow rate. Sub-circuits in the TS are analyzed separately because each circuit is controlled by its own control valve independently. It is found that flow rates in some panels are insufficient compared with the design values. In order to improve the flow distribution, two kinds of design modifications are proposed. The first one is to connect the tubes of the adjacent panels. This will increase the resistance of the tube on the panel where the flow rate is excessive. The other design suggestion is that an orifice is installed at the exit of tube routing where the flow rate is to be reduced. The analysis for the design suggestions shows that the flow mal-distribution is improved significantly.
Computer-Aided Analysis of Flow in Water Pipe Networks after a Seismic Event
Directory of Open Access Journals (Sweden)
Won-Hee Kang
2017-01-01
Full Text Available This paper proposes a framework for a reliability-based flow analysis for a water pipe network after an earthquake. For the first part of the framework, we propose to use a modeling procedure for multiple leaks and breaks in the water pipe segments of a network that has been damaged by an earthquake. For the second part, we propose an efficient system-level probabilistic flow analysis process that integrates the matrix-based system reliability (MSR formulation and the branch-and-bound method. This process probabilistically predicts flow quantities by considering system-level damage scenarios consisting of combinations of leaks and breaks in network pipes and significantly reduces the computational cost by sequentially prioritizing the system states according to their likelihoods and by using the branch-and-bound method to select their partial sets. The proposed framework is illustrated and demonstrated by examining two example water pipe networks that have been subjected to a seismic event. These two examples consist of 11 and 20 pipe segments, respectively, and are computationally modeled considering their available topological, material, and mechanical properties. Considering different earthquake scenarios and the resulting multiple leaks and breaks in the water pipe segments, the water flows in the segments are estimated in a computationally efficient manner.
The flow of power law fluids in elastic networks and porous media
Sochi, Taha
2015-01-01
The flow of power law fluids, which include shear thinning and shear thickening as well as Newtonian as a special case, in networks of interconnected elastic tubes is investigated using a residual based pore scale network modeling method with the employment of newly derived formulae. Two relations describing the mechanical interaction between the local pressure and local cross sectional area in distensible tubes of elastic nature are considered in the derivation of these formulae. The model can be used to describe shear dependent flows of mainly viscous nature. The behavior of the proposed model is vindicated by several tests in a number of special and limiting cases where the results can be verified quantitatively or qualitatively. The model, which is the first of its kind, incorporates more than one major non-linearity corresponding to the fluid rheology and conduit mechanical properties, that is non-Newtonian effects and tube distensibility. The formulation, implementation and performance indicate that the...
Suspended sediment dynamics in a tidal channel network under peak river flow
Achete, Fernanda Minikowski; van der Wegen, Mick; Roelvink, Dano; Jaffe, Bruce
2016-05-01
Peak river flows transport fine sediment, nutrients, and contaminants that may deposit in the estuary. This study explores the importance of peak river flows on sediment dynamics with special emphasis on channel network configurations. The Sacramento-San Joaquin Delta, which is connected to San Francisco Bay (California, USA), motivates this study and is used as a validation case. Besides data analysis of observations, we applied a calibrated process-based model (D-Flow FM) to explore and analyze high-resolution (˜100 m, ˜1 h) dynamics. Peak river flows supply the vast majority of sediment into the system. Data analysis of six peak flows (between 2012 and 2014) shows that on average, 40 % of the input sediment in the system is trapped and that trapping efficiency depends on timing and magnitude of river flows. The model has 90 % accuracy reproducing these trapping efficiencies. Modeled deposition patterns develop as the result of peak river flows after which, during low river flow conditions, tidal currents are not able to significantly redistribute deposited sediment. Deposition is quite local and mainly takes place at a deep junction. Tidal movement is important for sediment resuspension, but river induced, tide residual currents are responsible for redistributing the sediment towards the river banks and to the bay. We applied the same forcing for four different channel configurations ranging from a full delta network to a schematization of the main river. A higher degree of network schematization leads to higher peak-sediment export downstream to the bay. However, the area of sedimentation is similar for all the configurations because it is mostly driven by geometry and bathymetry.
Emerging communities in networks - a flow of ties
Gawronski, Przemyslaw; Kulakowski, Krzysztof
2015-01-01
Algorithms for search of communities in networks usually consist discrete variations of links. Here we discuss a flow method, driven by a set of differential equations. Two examples are demonstrated in detail. First is a partition of a signed graph into two parts, where the proposed equations are interpreted in terms of removal of a cognitive dissonance by agents placed in the network nodes. There, the signs and values of links refer to positive or negative interpersonal relationships of different strength. Second is an application of a method akin to the previous one, dedicated to communities identification, to the Sierpinski triangle of finite size. During the time evolution, the related graphs are weighted; yet at the end the discrete character of links is restored. In the case of the Sierpinski triangle, the method is supplemented by adding a small noise to the initial connectivity matrix. By breaking the symmetry of the network, this allows to a successful handling of overlapping nodes.
Site-Scale Saturated Zone Flow Model
Energy Technology Data Exchange (ETDEWEB)
G. Zyvoloski
2003-12-17
The purpose of this model report is to document the components of the site-scale saturated-zone flow model at Yucca Mountain, Nevada, in accordance with administrative procedure (AP)-SIII.lOQ, ''Models''. This report provides validation and confidence in the flow model that was developed for site recommendation (SR) and will be used to provide flow fields in support of the Total Systems Performance Assessment (TSPA) for the License Application. The output from this report provides the flow model used in the ''Site-Scale Saturated Zone Transport'', MDL-NBS-HS-000010 Rev 01 (BSC 2003 [162419]). The Site-Scale Saturated Zone Transport model then provides output to the SZ Transport Abstraction Model (BSC 2003 [164870]). In particular, the output from the SZ site-scale flow model is used to simulate the groundwater flow pathways and radionuclide transport to the accessible environment for use in the TSPA calculations. Since the development and calibration of the saturated-zone flow model, more data have been gathered for use in model validation and confidence building, including new water-level data from Nye County wells, single- and multiple-well hydraulic testing data, and new hydrochemistry data. In addition, a new hydrogeologic framework model (HFM), which incorporates Nye County wells lithology, also provides geologic data for corroboration and confidence in the flow model. The intended use of this work is to provide a flow model that generates flow fields to simulate radionuclide transport in saturated porous rock and alluvium under natural or forced gradient flow conditions. The flow model simulations are completed using the three-dimensional (3-D), finite-element, flow, heat, and transport computer code, FEHM Version (V) 2.20 (software tracking number (STN): 10086-2.20-00; LANL 2003 [161725]). Concurrently, process-level transport model and methodology for calculating radionuclide transport in the saturated zone at Yucca
Generalized multidirectional fuzzy map model of the logistics system networks
Ji, Chun-Rong; Liu, Ming-Yuan; Li, Yan; He, Yue M.
1997-07-01
By conducting [0, 1] treatment to time consuming of logistics system network key links, and regarding the time consumed by manufacture, inspection, storage, assembling, packing and market as a kind of existent extent of the joint and the time consumed by materials handling, transportation and logistics information as the connection strength between joints in a generalized multi-directional fuzzy map, a generalized multi-directional fuzzy map model of logistics system networks is built. The mutual flow among network joints and the special form of generalized fuzzy matrix is analyzed. Finally, an example of model building is given.
Comparison of Conventional and ANN Models for River Flow Forecasting
Jain, A.; Ganti, R.
2011-12-01
Hydrological models are useful in many water resources applications such as flood control, irrigation and drainage, hydro power generation, water supply, erosion and sediment control, etc. Estimates of runoff are needed in many water resources planning, design development, operation and maintenance activities. River flow is generally estimated using time series or rainfall-runoff models. Recently, soft artificial intelligence tools such as Artificial Neural Networks (ANNs) have become popular for research purposes but have not been extensively adopted in operational hydrological forecasts. There is a strong need to develop ANN models based on real catchment data and compare them with the conventional models. In this paper, a comparative study has been carried out for river flow forecasting using the conventional and ANN models. Among the conventional models, multiple linear, and non linear regression, and time series models of auto regressive (AR) type have been developed. Feed forward neural network model structure trained using the back propagation algorithm, a gradient search method, was adopted. The daily river flow data derived from Godavari Basin @ Polavaram, Andhra Pradesh, India have been employed to develop all the models included here. Two inputs, flows at two past time steps, (Q(t-1) and Q(t-2)) were selected using partial auto correlation analysis for forecasting flow at time t, Q(t). A wide range of error statistics have been used to evaluate the performance of all the models developed in this study. It has been found that the regression and AR models performed comparably, and the ANN model performed the best amongst all the models investigated in this study. It is concluded that ANN model should be adopted in real catchments for hydrological modeling and forecasting.
Modeling interregional freight flow by distribution systems
Davydenko, I.; Tavasszy, L.A.; Blois, C.J. de
2013-01-01
Distribution Centers with a warehousing function have an important influence on the flow of goods from production to consumption, generating substantial goods flow and vehicle movements. This paper extends the classical 4-step freight modeling framework with a logistics chain model, explicitly model
Directory of Open Access Journals (Sweden)
Ming-Chorng Hwang
2015-01-01
Full Text Available A theoretic formulation on how traffic time information distributed by ITS operations influences the trajectory of network flows is presented in this paper. The interactions between users and ITS operator are decomposed into three parts: (i travel time induced path flow dynamics (PFDTT; (ii demand induced path flow dynamics (PFDD; and (iii predicted travel time dynamics for an origin-destination (OD pair (PTTDOD. PFDTT describes the collective results of user’s daily route selection by pairwise comparison of path travel time provided by ITS services. The other two components, PTTDOD and PFDD, are concentrated on the evolutions of system variables which are predicted and observed, respectively, by ITS operators to act as a benchmark in guiding the target system towards an expected status faster. In addition to the delivered modelings, the stability theorem of the equilibrium solution in the sense of Lyapunov stability is also provided. A Lyapunov function is developed and employed to the proof of stability theorem to show the asymptotic behavior of the aimed system. The information of network flow dynamics plays a key role in traffic control policy-making. The evaluation of ITS-based strategies will not be reasonable without a well-established modeling of network flow evolutions.
Modelling of the Czochralski flow
Jan Franc
1998-01-01
The Czochralski method of the industrial production of a silicon single crystal consists of pulling up the single crystal from the silicon melt. The flow of the melt during the production is called the Czochralski flow. The mathematical description of the flow consists of a coupled system of six P.D.E. in cylindrical coordinates containing Navier-Stokes equations (with the stream function), heat convection-conduction equations, convection-diffusion equation for oxygen impurity and an equation...
Overland flow : interfacing models with measurements
Loon, van E.E.
2002-01-01
Index words: overland flow, catchment scale, system identification, ensemble simulations.This study presents new techniques to identify scale-dependent overland flow models and use these for ensemble-based predictions. The techniques are developed on the basis of overland flow, rain, discharge, soil
Flow version of statistical neurodynamics for oscillator neural networks
Uchiyama, Satoki
2012-04-01
We consider a neural network of Stuart-Landau oscillators as an associative memory. This oscillator network with N elements is a system of an N-dimensional differential equation, works as an attractor neural network, and is expected to have no Lyapunov functions. Therefore, the technique of equilibrium statistical physics is not applicable to the study of this system in the thermodynamic limit. However, the simplicity of this system allows us to extend statistical neurodynamics [S. Amari, K. Maginu, Neural Netw. 1 (1988) 63-73], which was originally developed to analyse the discrete time evolution of the Hopfield model, into the version for continuous time evolution. We have developed and attempted to apply this method in the analysis of the phase transition of our model network.
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.
Dynamic ANN Modeling for Flood Forecasting in a River Network
Roy, Parthajit; Choudhury, P. S.; Saharia, Manabendra
2010-10-01
An experiment on predicting flood flows at each of the upstream and a down stream section of a river network is presented using focused Time Lagged Recurrent Neural Network with three different memories like TDNN memory, Gamma memory and Laguarre memory. This paper focuses on application of memory to the input layer of a TLRN in developing flood forecasting models for multiple sections in a river system. The study shows the Gamma memory has better applicability followed by TDNN and Laguarre memory.
Design of robust flow processing networks with time-programmed responses
Kaluza, P.; Mikhailov, A. S.
2012-04-01
Can artificially designed networks reach the levels of robustness against local damage which are comparable with those of the biochemical networks of a living cell? We consider a simple model where the flow applied to an input node propagates through the network and arrives at different times to the output nodes, thus generating a pattern of coordinated responses. By using evolutionary optimization algorithms, functional networks - with required time-programmed responses - were constructed. Then, continuing the evolution, such networks were additionally optimized for robustness against deletion of individual nodes or links. In this manner, large ensembles of functional networks with different kinds of robustness were obtained, making statistical investigations and comparison of their structural properties possible. We have found that, generally, different architectures are needed for various kinds of robustness. The differences are statistically revealed, for example, in the Laplacian spectra of the respective graphs. On the other hand, motif distributions of robust networks do not differ from those of the merely functional networks; they are found to belong to the first Alon superfamily, the same as that of the gene transcription networks of single-cell organisms.
Simulation of Code Spectrum and Code Flow of Cultured Neuronal Networks.
Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime
2016-01-01
It has been shown that, in cultured neuronal networks on a multielectrode, pseudorandom-like sequences (codes) are detected, and they flow with some spatial decay constant. Each cultured neuronal network is characterized by a specific spectrum curve. That is, we may consider the spectrum curve as a "signature" of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution. In the present study, we used an integrate-and-fire model of neurons with intrinsic and instantaneous fluctuations of characteristics for performing a simulation of a code spectrum from multielectrodes on a 2D mesh neural network. We showed that it is possible to estimate the characteristics of neurons such as the distribution of number of neurons around each electrode and their refractory periods. Although this process is a reverse problem and theoretically the solutions are not sufficiently guaranteed, the parameters seem to be consistent with those of neurons. That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network. Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence.
Network Dependency in Migration Flows – A Space-time Analysis for Germany since Re-unification
DEFF Research Database (Denmark)
Mitze, Timo
The contribution of this paper is to analyse the role of network interdependencies in a dynamic panel data model for German internal migration fl ows since re-unification. So far, a capacious account of spatial patterns in German migration data is still missing in the empirical literature....... In the context of this paper, network dependencies are associated with correlations of migration flows strictly attributable to proximate flows in geographic space. Using the neoclassical migration model, we start from its aspatial specification and show by means of residual testing that network dependency eff...
Modeling Fluid Flow in Faulted Basins
Directory of Open Access Journals (Sweden)
Faille I.
2014-07-01
Full Text Available This paper presents a basin simulator designed to better take faults into account, either as conduits or as barriers to fluid flow. It computes hydrocarbon generation, fluid flow and heat transfer on the 4D (space and time geometry obtained by 3D volume restoration. Contrary to classical basin simulators, this calculator does not require a structured mesh based on vertical pillars nor a multi-block structure associated to the fault network. The mesh follows the sediments during the evolution of the basin. It deforms continuously with respect to time to account for sedimentation, erosion, compaction and kinematic displacements. The simulation domain is structured in layers, in order to handle properly the corresponding heterogeneities and to follow the sedimentation processes (thickening of the layers. In each layer, the mesh is unstructured: it may include several types of cells such as tetrahedra, hexahedra, pyramid, prism, etc. However, a mesh composed mainly of hexahedra is preferred as they are well suited to the layered structure of the basin. Faults are handled as internal boundaries across which the mesh is non-matching. Different models are proposed for fault behavior such as impervious fault, flow across fault or conductive fault. The calculator is based on a cell centered Finite Volume discretisation, which ensures conservation of physical quantities (mass of fluid, heat at a discrete level and which accounts properly for heterogeneities. The numerical scheme handles the non matching meshes and guaranties appropriate connection of cells across faults. Results on a synthetic basin demonstrate the capabilities of this new simulator.
RECOVERY ACT - Robust Optimization for Connectivity and Flows in Dynamic Complex Networks
Energy Technology Data Exchange (ETDEWEB)
Balasundaram, Balabhaskar [Oklahoma State Univ., Stillwater, OK (United States); Butenko, Sergiy [Texas A & M Univ., College Station, TX (United States); Boginski, Vladimir [Univ. of Florida, Gainesville, FL (United States); Uryasev, Stan [Univ. of Florida, Gainesville, FL (United States)
2013-12-25
The goal of this project was to study robust connectivity and flow patterns of complex multi-scale systems modeled as networks. Networks provide effective ways to study global, system level properties, as well as local, multi-scale interactions at a component level. Numerous applications from power systems, telecommunication, transportation, biology, social science, and other areas have benefited from novel network-based models and their analysis. Modeling and optimization techniques that employ appropriate measures of risk for identifying robust clusters and resilient network designs in networks subject to uncertain failures were investigated in this collaborative multi-university project. In many practical situations one has to deal with uncertainties associated with possible failures of network components, thereby affecting the overall efficiency and performance of the system (e.g., every node/connection has a probability of partial or complete failure). Some extreme examples include power grid component failures, airline hub failures due to weather, or freeway closures due to emergencies. These are also situations in which people, materials, or other resources need to be managed efficiently. Important practical examples include rerouting flow through power grids, adjusting flight plans, and identifying routes for emergency services and supplies, in the event network elements fail unexpectedly. Solutions that are robust under uncertainty, in addition to being economically efficient, are needed. This project has led to the development of novel models and methodologies that can tackle the optimization problems arising in such situations. A number of new concepts, which have not been previously applied in this setting, were investigated in the framework of the project. The results can potentially help decision-makers to better control and identify robust or risk-averse decisions in such situations. Formulations and optimal solutions of the considered problems need
Robust optimal control of material flows in demand-driven supply networks
Laumanns, Marco; Lefeber, Erjen
2006-04-01
We develop a model based on stochastic discrete-time controlled dynamical systems in order to derive optimal policies for controlling the material flow in supply networks. Each node in the network is described as a transducer such that the dynamics of the material and information flows within the entire network can be expressed by a system of first-order difference equations, where some inputs to the system act as external disturbances. We apply methods from constrained robust optimal control to compute the explicit control law as a function of the current state. For the numerical examples considered, these control laws correspond to certain classes of optimal ordering policies from inventory management while avoiding, however, any a priori assumptions about the general form of the policy.
Numerical modeling of fluidic flow meters
Choudhury, D.; Patel, B. R.
1992-05-01
The transient fluid flow in fluidic flow meters has been modeled using Creare.x's flow modeling computer program FLUENT/BFC that solves the Navier-Stokes equations in general curvilinear coordinates. The numerical predictions of fluid flow in a fluidic flow meter have been compared with the available experimental results for a particular design, termed the PC-4 design. Overall flow structures such as main jet bending, and primary and secondary vortices predicted by FLUENT/BFC are in excellent agreement with flow visualization results. The oscillation frequencies of the PC-4 design have been predicted for a range of flow rates encompassing laminar and turbulent flow and the results are in good agreement with experiments. The details of the flow field predictions reveal that an important factor that determines the onset of oscillations in the fluidic flow meter is the feedback jet momentum relative to the main jet momentum. The insights provided by the analysis of the PC-4 fluidic flow meter design have led to an improved design. The improved design has sustained oscillations at lower flow rates compared with the PC-4 design and has a larger rangeability.
A time delay artificial neural network approach for flow routing in a river system
Directory of Open Access Journals (Sweden)
M. J. Diamantopoulou
2006-09-01
Full Text Available River flow routing provides basic information on a wide range of problems related to the design and operation of river systems. In this paper, three layer cascade correlation Time Delay Artificial Neural Network (TDANN models have been developed to forecast the one day ahead daily flow at Ilarionas station on the Aliakmon river, in Northern Greece. The networks are time lagged feed-formatted with delayed memory processing elements at the input layer. The network topology is using multiple inputs, which include the time lagged daily flow values further up at Siatista station on the Aliakmon river and at Grevena station on the Venetikos river, which is a tributary to the Aliakmon river and a single output, which are the daily flow values at Ilarionas station. The choice of the input variables introduced to the input layer was based on the cross-correlation. The use of cross-correlation between the ith input series and the output provides a short cut to the problem of the delayed memory determination. Kalman's learning rule was used to modify the artificial neural network weights. The networks are designed by putting weights between neurons, by using the hyperbolic-tangent function for training. The number of nodes in the hidden layer was determined based on the maximum value of the correlation coefficient. The results show a good performance of the TDANN approach for forecasting the daily flow values, at Ilarionas station and demonstrate its adequacy and potential for river flow routing. The TDANN approach introduced in this study is sufficiently general and has great potential to be applicable to many hydrological and environmental applications.
MODELS FOR NETWORK DYNAMICS - A MARKOVIAN FRAMEWORK
LEENDERS, RTAJ
1995-01-01
A question not very often addressed in social network analysis relates to network dynamics and focuses on how networks arise and change. It alludes to the idea that ties do not arise or vanish randomly, but (partly) as a consequence of human behavior and preferences. Statistical models for modeling
Modelling delay propagation within an airport network
Pyrgiotis, N.; Malone, K.M.; Odoni, A.
2013-01-01
We describe an analytical queuing and network decomposition model developed to study the complex phenomenon of the propagation of delays within a large network of major airports. The Approximate Network Delays (AND) model computes the delays due to local congestion at individual airports and capture
Modelling delay propagation within an airport network
Pyrgiotis, N.; Malone, K.M.; Odoni, A.
2013-01-01
We describe an analytical queuing and network decomposition model developed to study the complex phenomenon of the propagation of delays within a large network of major airports. The Approximate Network Delays (AND) model computes the delays due to local congestion at individual airports and
A system dynamics model for communications networks
Awcock, A. J.; King, T. E. G.
1985-09-01
An abstract model of a communications network in system dynamics terminology is developed as implementation of this model by a FORTRAN program package developed at RSRE is discussed. The result of this work is a high-level simulation package in which the performance of adaptive routing algorithms and other network controls may be assessed for a network of arbitrary topology.
Power flow tracing in a simplified highly renewable European electricity network
Tranberg, Bo; Rodriguez, Rolando A; Andresen, Gorm B; Schäfer, Mirko; Greiner, Martin
2015-01-01
The increasing transmission capacity needs in a future energy system raise the question how associated costs should be allocated to the users of a strengthened power grid. In contrast to straightforward oversimplified methods, a flow tracing based approach provides a fair and consistent nodal usage and thus cost assignment of transmission investments. This technique follows the power flow through the network and assigns the link capacity usage to the respective sources or sinks using a diffusion-like process, thus taking into account the underlying network structure and injection pattern. As a showcase, we apply power flow tracing to a simplified model of the European electricity grid with a high share of renewable wind and solar power generation, based on long-term weather and load data with an hourly temporal resolution.
Decompositions of injection patterns for nodal flow allocation in renewable electricity networks
Schäfer, Mirko; Tranberg, Bo; Hempel, Sabrina; Schramm, Stefan; Greiner, Martin
2017-08-01
The large-scale integration of fluctuating renewable power generation represents a challenge to the technical and economical design of a sustainable future electricity system. In this context, the increasing significance of long-range power transmission calls for innovative methods to understand the emerging complex flow patterns and to integrate price signals about the respective infrastructure needs into the energy market design. We introduce a decomposition method of injection patterns. Contrary to standard flow tracing approaches, it provides nodal allocations of link flows and costs in electricity networks by decomposing the network injection pattern into market-inspired elementary import/export building blocks. We apply the new approach to a simplified data-driven model of a European electricity grid with a high share of renewable wind and solar power generation.
Assessment of distributed arterial network models.
Segers, P; Stergiopulos, N; Verdonck, P; Verhoeven, R
1997-11-01
The aim of this study is to evaluate the relative importance of elastic non-linearities, viscoelasticity and resistance vessel modelling on arterial pressure and flow wave contours computed with distributed arterial network models. The computational results of a non-linear (time-domain) and a linear (frequency-domain) mode were compared using the same geometrical configuration and identical upstream and downstream boundary conditions and mechanical properties. pressures were computed at the ascending aorta, brachial and femoral artery. In spite of the identical problem definition, computational differences were found in input impedance modulus (max. 15-20%), systolic pressure (max. 5%) and pulse pressure (max. 10%). For the brachial artery, the ratio of pulse pressure to aortic pulse pressure was practically identical for both models (3%), whereas for the femoral artery higher values are found for the linear model (+10%). The aortic/brachial pressure transfer function indicates that pressure harmonic amplification is somewhat higher in the linear model for frequencies lower than 6 Hz while the opposite is true for higher frequencies. These computational disparities were attributed to conceptual model differences, such as the treatment of geometric tapering, rather than to elastic or convective non-linearities. Compared to the effect of viscoelasticity, the discrepancy between the linear and non-linear model is of the same importance. At peripheral locations, the correct representation of terminal impedance outweight the computational differences between the linear and non-linear models.
Network models in epidemiology: an overview
Lloyd, Alun L.; Valeika, Steve
In this chapter we shall discuss the development and use of network models in epidemiology. While network models have long been discussed in the theoretical epidemiology literature, they have recently received a large amount of attention amongst the statistical physics community. This has been fueled by the desire to better understand the structure of social and large-scale technological networks, and the increases in computational power that have made the simulation of reasonably-sized network models a feasible proposition. A main aim of this review is to bridge the epidemiologic and statistical physics approaches to network models for infectious diseases, highlighting the important contributions made by both research communities.
Modeling gene regulatory networks: A network simplification algorithm
Ferreira, Luiz Henrique O.; de Castro, Maria Clicia S.; da Silva, Fabricio A. B.
2016-12-01
Boolean networks have been used for some time to model Gene Regulatory Networks (GRNs), which describe cell functions. Those models can help biologists to make predictions, prognosis and even specialized treatment when some disturb on the GRN lead to a sick condition. However, the amount of information related to a GRN can be huge, making the task of inferring its boolean network representation quite a challenge. The method shown here takes into account information about the interactome to build a network, where each node represents a protein, and uses the entropy of each node as a key to reduce the size of the network, allowing the further inferring process to focus only on the main protein hubs, the ones with most potential to interfere in overall network behavior.
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.
Modelling of the Czochralski flow
Directory of Open Access Journals (Sweden)
Jan Franc
1998-01-01
Full Text Available The Czochralski method of the industrial production of a silicon single crystal consists of pulling up the single crystal from the silicon melt. The flow of the melt during the production is called the Czochralski flow. The mathematical description of the flow consists of a coupled system of six P.D.E. in cylindrical coordinates containing Navier-Stokes equations (with the stream function, heat convection-conduction equations, convection-diffusion equation for oxygen impurity and an equation describing magnetic field effect.
Blood flow in the cerebral venous system: modeling and simulation.
Miraucourt, Olivia; Salmon, Stéphanie; Szopos, Marcela; Thiriet, Marc
2017-04-01
The development of a software platform incorporating all aspects, from medical imaging data, through three-dimensional reconstruction and suitable meshing, up to simulation of blood flow in patient-specific geometries, is a crucial challenge in biomedical engineering. In the present study, a fully three-dimensional blood flow simulation is carried out through a complete rigid macrovascular circuit, namely the intracranial venous network, instead of a reduced order simulation and partial vascular network. The biomechanical modeling step is carefully analyzed and leads to the description of the flow governed by the dimensionless Navier-Stokes equations for an incompressible viscous fluid. The equations are then numerically solved with a free finite element software using five meshes of a realistic geometry obtained from medical images to prove the feasibility of the pipeline. Some features of the intracranial venous circuit in the supine position such as asymmetric behavior in merging regions are discussed.
An evolutionary model of social networks
Ludwig, M.; Abell, P.
2007-07-01
Social networks in communities, markets, and societies self-organise through the interactions of many individuals. In this paper we use a well-known mechanism of social interactions — the balance of sentiment in triadic relations — to describe the development of social networks. Our model contrasts with many existing network models, in that people not only establish but also break up relations whilst the network evolves. The procedure generates several interesting network features such as a variety of degree distributions and degree correlations. The resulting network converges under certain conditions to a steady critical state where temporal disruptions in triangles follow a power-law distribution.
Modeling of D-STATCOM in distribution systems load flow
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
This paper presents modeling of Distribution STATCOM (D-STATCOM) in load flow calculations for the steadystate voltage compensation. An accurate model for D-STATCOM is derived to use in load flow calculations. The rating of this device as well as the direction of required reactive power injection for voltage compensation in the desired value (1 p.u.) is derived and discussed analytically and mathematically by the phasor diagram method. Furthermore, an efficient method for node and line identification used in load flow calculations is presented. The validity of the proposed model is examined by using two standard distribution systems consisting of 33 and 69 nodes, respectively. The best location of D-STATCOM for under voltage problem mitigation approach in the distribution networks is determined. The results validate the proposed model for DSTATCOM in large distribution systems.
Scaling of peak flows with constant flow velocity in random self-similar networks
Directory of Open Access Journals (Sweden)
R. Mantilla
2011-07-01
Full Text Available A methodology is presented to understand the role of the statistical self-similar topology of real river networks on scaling, or power law, in peak flows for rainfall-runoff events. We created Monte Carlo generated sets of ensembles of 1000 random self-similar networks (RSNs with geometrically distributed interior and exterior generators having parameters p_{i} and p_{e}, respectively. The parameter values were chosen to replicate the observed topology of real river networks. We calculated flow hydrographs in each of these networks by numerically solving the link-based mass and momentum conservation equation under the assumption of constant flow velocity. From these simulated RSNs and hydrographs, the scaling exponents β and φ characterizing power laws with respect to drainage area, and corresponding to the width functions and flow hydrographs respectively, were estimated. We found that, in general, φ > β, which supports a similar finding first reported for simulations in the river network of the Walnut Gulch basin, Arizona. Theoretical estimation of β and φ in RSNs is a complex open problem. Therefore, using results for a simpler problem associated with the expected width function and expected hydrograph for an ensemble of RSNs, we give heuristic arguments for theoretical derivations of the scaling exponents β^{(E} and φ^{(E} that depend on the Horton ratios for stream lengths and areas. These ratios in turn have a known dependence on the parameters of the geometric distributions of RSN generators. Good agreement was found between the analytically conjectured values of β^{(E} and φ^{(E} and the values estimated by the simulated ensembles of RSNs and hydrographs. The independence of the scaling exponents φ^{(E} and φ with respect to the value of flow velocity and runoff intensity implies an interesting connection between unit
Analyzing the international exergy flow network of ferrous metal ores.
Qi, Hai; An, Haizhong; Hao, Xiaoqing; Zhong, Weiqiong; Zhang, Yanbing
2014-01-01
This paper employs an un-weighted and weighted exergy network to study the properties of ferrous metal ores in countries worldwide and their evolution from 2002 to 2012. We find that there are few countries controlling most of the ferrous metal ore exports in terms of exergy and that the entire exergy flow network is becoming more heterogeneous though the addition of new nodes. The increasing of the average clustering coefficient indicates that the formation of an international exergy flow system and regional integration is improving. When we contrast the average out strength of exergy and the average out strength of currency, we find both similarities and differences. Prices are affected largely by human factors; thus, the growth rate of the average out strength of currency has fluctuated acutely in the eleven years from 2002 to 2012. Exergy is defined as the maximum work that can be extracted from a system and can reflect the true cost in the world, and this parameter fluctuates much less. Performing an analysis based on the two aspects of exergy and currency, we find that the network is becoming uneven.
Analyzing the International Exergy Flow Network of Ferrous Metal Ores
Qi, Hai; An, Haizhong; Hao, Xiaoqing; Zhong, Weiqiong; Zhang, Yanbing
2014-01-01
This paper employs an un-weighted and weighted exergy network to study the properties of ferrous metal ores in countries worldwide and their evolution from 2002 to 2012. We find that there are few countries controlling most of the ferrous metal ore exports in terms of exergy and that the entire exergy flow network is becoming more heterogeneous though the addition of new nodes. The increasing of the average clustering coefficient indicates that the formation of an international exergy flow system and regional integration is improving. When we contrast the average out strength of exergy and the average out strength of currency, we find both similarities and differences. Prices are affected largely by human factors; thus, the growth rate of the average out strength of currency has fluctuated acutely in the eleven years from 2002 to 2012. Exergy is defined as the maximum work that can be extracted from a system and can reflect the true cost in the world, and this parameter fluctuates much less. Performing an analysis based on the two aspects of exergy and currency, we find that the network is becoming uneven. PMID:25188407
Microgravity two-phase flow regime modeling
Energy Technology Data Exchange (ETDEWEB)
Lee, D.; Best, F.R.; Faget, N.
1987-01-01
A flow pattern or flow regime is the characteristics spatial distribution of the phases of fluid in a duct. Since heat transfer and pressure drop are dependent on the characteristic distribution of the phases, it is necessary to describe flow patterns in an appropriate manner so that a hydrodynamic or heat transfer theory applicable to that pattern can be chosen. The objective of the present analysis is to create a flow regime map based on physical modeling of vapor/liquid interaction phenomena in a microgravity environment. In the present work, four basic flow patterns are defined: dispersed flow, stratified flow, slug flow, and annular flow. Fluid properties, liquid and vapor flow rates, and pipe size were chosen as the principal parameters. It is assumed that a transition from one flow pattern to another will occur when there is a change in the dominant force which controls that flow pattern. The forces considered in this modeling are surface tension force, both force, inertial force, friction, and turbulent fluctuations.
The model of social crypto-network
Directory of Open Access Journals (Sweden)
Марк Миколайович Орел
2015-06-01
Full Text Available The article presents the theoretical model of social network with the enhanced mechanism of privacy policy. It covers the problems arising in the process of implementing the mentioned type of network. There are presented the methods of solving problems arising in the process of building the social network with privacy policy. It was built a theoretical model of social networks with enhanced information protection methods based on information and communication blocks
Average-passage flow model development
Adamczyk, John J.; Celestina, Mark L.; Beach, Tim A.; Kirtley, Kevin; Barnett, Mark
1989-01-01
A 3-D model was developed for simulating multistage turbomachinery flows using supercomputers. This average passage flow model described the time averaged flow field within a typical passage of a bladed wheel within a multistage configuration. To date, a number of inviscid simulations were executed to assess the resolution capabilities of the model. Recently, the viscous terms associated with the average passage model were incorporated into the inviscid computer code along with an algebraic turbulence model. A simulation of a stage-and-one-half, low speed turbine was executed. The results of this simulation, including a comparison with experimental data, is discussed.
Modeling interregional freight flow by distribution systems
Davydenko, I.; Tavasszy, L.A.; Blois, C.J. de
2013-01-01
Distribution Centers with a warehousing function have an important influence on the flow of goods from production to consumption, generating substantial goods flow and vehicle movements. This paper extends the classical 4-step freight modeling framework with a logistics chain model, explicitly
Chen, Bor-Sen; Li, Cheng-Wei
2015-01-01
In general, it is very difficult to measure the information flow in a cellular network directly. In this study, based on an information flow model and microarray data, we measured the information flow in cellular networks indirectly by using a systems biology method. First, we used a recursive least square parameter estimation algorithm to identify the system parameters of coupling signal transduction pathways and the cellular gene regulatory network (GRN). Then, based on the identified parameters and systems theory, we estimated the signal transductivities of the coupling signal transduction pathways from the extracellular signals to each downstream protein and the information transductivities of the GRN between transcription factors in response to environmental events. According to the proposed method, the information flow, which is characterized by signal transductivity in coupling signaling pathways and information transductivity in the GRN, can be estimated by microarray temporal data or microarray sample data. It can also be estimated by other high-throughput data such as next-generation sequencing or proteomic data. Finally, the information flows of the signal transduction pathways and the GRN in leukemia cancer cells and non-leukemia normal cells were also measured to analyze the systematic dysfunction in this cancer from microarray sample data. The results show that the signal transductivities of signal transduction pathways change substantially from normal cells to leukemia cancer cells.
Mathematical modeling of local perfusion in large distensible microvascular networks
Causin, Paola; Malgaroli, Francesca
2017-08-01
Microvessels -blood vessels with diameter less than 200 microns- form large, intricate networks organized into arterioles, capillaries and venules. In these networks, the distribution of flow and pressure drop is a highly interlaced function of single vessel resistances and mutual vessel interactions. In this paper we propose a mathematical and computational model to study the behavior of microcirculatory networks subjected to different conditions. The network geometry is composed of a graph of connected straight cylinders, each one representing a vessel. The blood flow and pressure drop across the single vessel, further split into smaller elements, are related through a generalized Ohm's law featuring a conductivity parameter, function of the vessel cross section area and geometry, which undergo deformations under pressure loads. The membrane theory is used to describe the deformation of vessel lumina, tailored to the structure of thick-walled arterioles and thin-walled venules. In addition, since venules can possibly experience negative transmural pressures, a buckling model is also included to represent vessel collapse. The complete model including arterioles, capillaries and venules represents a nonlinear system of PDEs, which is approached numerically by finite element discretization and linearization techniques. We use the model to simulate flow in the microcirculation of the human eye retina, a terminal system with a single inlet and outlet. After a phase of validation against experimental measurements, we simulate the network response to different interstitial pressure values. Such a study is carried out both for global and localized variations of the interstitial pressure. In both cases, significant redistributions of the blood flow in the network arise, highlighting the importance of considering the single vessel behavior along with its position and connectivity in the network.
A source-controlled data center network model.
Yu, Yang; Liang, Mangui; Wang, Zhe
2017-01-01
The construction of data center network by applying SDN technology has become a hot research topic. The SDN architecture has innovatively separated the control plane from the data plane which makes the network more software-oriented and agile. Moreover, it provides virtual multi-tenancy, effective scheduling resources and centralized control strategies to meet the demand for cloud computing data center. However, the explosion of network information is facing severe challenges for SDN controller. The flow storage and lookup mechanisms based on TCAM device have led to the restriction of scalability, high cost and energy consumption. In view of this, a source-controlled data center network (SCDCN) model is proposed herein. The SCDCN model applies a new type of source routing address named the vector address (VA) as the packet-switching label. The VA completely defines the communication path and the data forwarding process can be finished solely relying on VA. There are four advantages in the SCDCN architecture. 1) The model adopts hierarchical multi-controllers and abstracts large-scale data center network into some small network domains that has solved the restriction for the processing ability of single controller and reduced the computational complexity. 2) Vector switches (VS) developed in the core network no longer apply TCAM for table storage and lookup that has significantly cut down the cost and complexity for switches. Meanwhile, the problem of scalability can be solved effectively. 3) The SCDCN model simplifies the establishment process for new flows and there is no need to download flow tables to VS. The amount of control signaling consumed when establishing new flows can be significantly decreased. 4) We design the VS on the NetFPGA platform. The statistical results show that the hardware resource consumption in a VS is about 27% of that in an OFS.
A source-controlled data center network model
Yu, Yang; Liang, Mangui; Wang, Zhe
2017-01-01
The construction of data center network by applying SDN technology has become a hot research topic. The SDN architecture has innovatively separated the control plane from the data plane which makes the network more software-oriented and agile. Moreover, it provides virtual multi-tenancy, effective scheduling resources and centralized control strategies to meet the demand for cloud computing data center. However, the explosion of network information is facing severe challenges for SDN controller. The flow storage and lookup mechanisms based on TCAM device have led to the restriction of scalability, high cost and energy consumption. In view of this, a source-controlled data center network (SCDCN) model is proposed herein. The SCDCN model applies a new type of source routing address named the vector address (VA) as the packet-switching label. The VA completely defines the communication path and the data forwarding process can be finished solely relying on VA. There are four advantages in the SCDCN architecture. 1) The model adopts hierarchical multi-controllers and abstracts large-scale data center network into some small network domains that has solved the restriction for the processing ability of single controller and reduced the computational complexity. 2) Vector switches (VS) developed in the core network no longer apply TCAM for table storage and lookup that has significantly cut down the cost and complexity for switches. Meanwhile, the problem of scalability can be solved effectively. 3) The SCDCN model simplifies the establishment process for new flows and there is no need to download flow tables to VS. The amount of control signaling consumed when establishing new flows can be significantly decreased. 4) We design the VS on the NetFPGA platform. The statistical results show that the hardware resource consumption in a VS is about 27% of that in an OFS. PMID:28328925
Gathering knowledge about disruptions in material flow in network supply chain
Directory of Open Access Journals (Sweden)
Marzena Kramarz
2015-03-01
Full Text Available Background: The article aims at presenting a study into disruptions in a network supply chain of metallurgic products. The research was carried out in the years 2011-2013. Network supply chains include key chain links for building the resistance. These chain links affect material flows of the whole supply chain through silencing disruptions. Those predisposed to form a strategy strengthening the resistance of the whole network supply chain are organizations fulfilling the assumptions of flagship enterprise. Material and methods: The research is carried out in two stages: the stage of identification of disruptions (risk factors and the stage of identification of strengthening disruptions zones. The authors carried out simulation experiments based on three models built in the management system dynamics technique (VensimDSS. The proposed methodology required constructing original tools for measuring disruptions and zones strengthening the disruptions in the form of cards for measuring disruptions. Results: The value added is grouping of disruptions in risk factors distinguished in terms of the frequency of the occurrence of disruptions and their results. The authors proposed and defined the notion of zones of strengthening disruptions. The zones are formed from sets of factors strengthening disruptions with similar influence on disruptions. Conclusions: The IT system composed of a module for identification of disruptions in material flows and a simulation model is a proposal dedicated to organizations controlling material flows in a network supply chain in the conditions of disruptions.
Numerical simulation of gas flow process in mining-induced crack network
Institute of Scientific and Technical Information of China (English)
Zhou; Hongwei; Liu; Jinfeng; Xue; Dongjie; Yi; Haiyang; Xue; Junhua
2012-01-01
The exploitation of coal bed methane or coal gas is one of the most effective solutions of the problem of coal gas hazard.A better understanding of gas flow in mining-induced cracks plays an important role in comprehensive development and utilization of coal gas as well as prevention of coal gas hazard.This paper presents a case study of gas flow in mining-induced crack network regarding the situation of low permeability of coal seam.A two-dimensional physical model is constructed on the basis of geological background of mining face No.1122(1) in coal seam No.11-2,Zhangji Coal Mine,Huainan Mining Group Corporation.The mining-induced stress and cracks in overburden rocks are obtained by simulating an extraction in physical model.An evolution of mining-induced cracks in the process of advancing of coal mining face is characterized and three typical crack networks are taken from digital photos by means of image analysis.Moreover,the numerical software named COMSOL Multiphysics is employed to simulate the process of gas flow in three representative crack networks.Isograms of gas pressure at various times in mining-induced crack networks are plotted,suggesting a shape and dimension of gas accumulation area.
De Domenico, Manlio; Arenas, Alex; Rosvall, Martin
2014-01-01
Unveiling the community structure of networks is a powerful methodology to comprehend interconnected systems across the social and natural sciences. To identify different types of functional modules in interaction data aggregated in a single network layer, researchers have developed many powerful methods. For example, flow-based methods have proven useful for identifying modular dynamics in weighted and directed networks that capture constraints on flow in the systems they represent. However, many networked systems consist of agents or components that exhibit multiple layers of interactions. Inevitably, representing this intricate network of networks as a single aggregated network leads to information loss and may obscure the actual organization. Here we propose a method based on compression of network flows that can identify modular flows in non-aggregated multilayer networks. Our numerical experiments on synthetic networks show that the method can accurately identify modules that cannot be identified in agg...
Volume fraction prediction in biphasic flow using nuclear technique and artificial neural network
Energy Technology Data Exchange (ETDEWEB)
Salgado, Cesar M.; Brandao, Luis E.B., E-mail: otero@ien.gov.br, E-mail: brandao@ien.gov.br [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)
2015-07-01
The volume fraction is one of the most important parameters used to characterize air-liquid two-phase flows. It is a physical value to determine other parameters, such as the phase's densities and to determine the flow rate of each phase. These parameters are important to predict the flow pattern and to determine a mathematical model for the system. To study, for example, heat transfer and pressure drop. This work presents a methodology for volume fractions prediction in water-gas stratified flow regime using the nuclear technique and artificial intelligence. The volume fractions calculate in biphasic flow systems is complex and the analysis by means of analytical equations becomes very difficult. The approach is based on gamma-ray pulse height distributions pattern recognition by means of the artificial neural network. The detection system uses appropriate broad beam geometry, comprised of a ({sup 137}Cs) energy gamma-ray source and a NaI(Tl) scintillation detector in order measure transmitted beam whose the counts rates are influenced by the phases composition. These distributions are directly used by the network without any parameterization of the measured signal. The ideal and static theoretical models for stratified regime have been developed using MCNP-X code, which was used to provide training, test and validation data for the network. The detector also was modeled with this code and the results were compared to experimental photopeak efficiency measurements of radiation sources. The proposed network could obtain with satisfactory prediction of the volume fraction in water-gas system, demonstrating to be a promising approach for this purpose. (author)
A dynamic network model for interbank market
Xu, Tao; He, Jianmin; Li, Shouwei
2016-12-01
In this paper, a dynamic network model based on agent behavior is introduced to explain the formation mechanism of interbank market network. We investigate the impact of credit lending preference on interbank market network topology, the evolution of interbank market network and stability of interbank market. Experimental results demonstrate that interbank market network is a small-world network and cumulative degree follows the power-law distribution. We find that the interbank network structure keeps dynamic stability in the network evolution process. With the increase of bank credit lending preference, network clustering coefficient increases and average shortest path length decreases monotonously, which improves the stability of the network structure. External shocks are main threats for the interbank market and the reduction of bank external investment yield rate and deposits fluctuations contribute to improve the resilience of the banking system.
Modeling Diagnostic Assessments with Bayesian Networks
Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego
2007-01-01
This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…
Modeling Diagnostic Assessments with Bayesian Networks
Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego
2007-01-01
This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…
Improved higher lead time river flow forecasts using sequential neural network with error updating
Directory of Open Access Journals (Sweden)
Prakash Om
2014-03-01
Full Text Available This paper presents a novel framework to use artificial neural network (ANN for accurate forecasting of river flows at higher lead times. The proposed model, termed as sequential ANN (SANN, is based on the heuristic that a mechanism that provides an accurate representation of physical condition of the basin at the time of forecast, in terms of input information to ANNs at higher lead time, helps improve the forecast accuracy. In SANN, a series of ANNs are connected sequentially to extend the lead time of forecast, each of them taking a forecast value from an immediate preceding network as input. The output of each network is modified by adding an expected value of error so that the residual variance of the forecast series is minimized. The applicability of SANN in hydrological forecasting is illustrated through three case examples: a hypothetical time series, daily river flow forecasting of Kentucky River, USA and hourly river flow forecasting of Kolar River, India. The results demonstrate that SANN is capable of providing accurate forecasts up to 8 steps ahead. A very close fit (>94% efficiency was obtained between computed and observed flows up to 1 hour in advance for all the cases, and the deterioration in fit was not significant as the forecast lead time increased (92% at 8 steps ahead. The results show that SANN performs much better than traditional ANN models in extending the forecast lead time, suggesting that it can be effectively employed in developing flood management measures.
Bayesian Network Webserver: a comprehensive tool for biological network modeling.
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.
Approximate Model for Turbulent Stagnation Point Flow.
Energy Technology Data Exchange (ETDEWEB)
Dechant, Lawrence [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2016-01-01
Here we derive an approximate turbulent self-similar model for a class of favorable pressure gradient wedge-like flows, focusing on the stagnation point limit. While the self-similar model provides a useful gross flow field estimate this approach must be combined with a near wall model is to determine skin friction and by Reynolds analogy the heat transfer coefficient. The combined approach is developed in detail for the stagnation point flow problem where turbulent skin friction and Nusselt number results are obtained. Comparison to the classical Van Driest (1958) result suggests overall reasonable agreement. Though the model is only valid near the stagnation region of cylinders and spheres it nonetheless provides a reasonable model for overall cylinder and sphere heat transfer. The enhancement effect of free stream turbulence upon the laminar flow is used to derive a similar expression which is valid for turbulent flow. Examination of free stream enhanced laminar flow suggests that the rather than enhancement of a laminar flow behavior free stream disturbance results in early transition to turbulent stagnation point behavior. Excellent agreement is shown between enhanced laminar flow and turbulent flow behavior for high levels, e.g. 5% of free stream turbulence. Finally the blunt body turbulent stagnation results are shown to provide realistic heat transfer results for turbulent jet impingement problems.
Backbone of complex networks of corporations: The flow of control
Glattfelder, J. B.; Battiston, S.
2009-09-01
We present a methodology to extract the backbone of complex networks based on the weight and direction of links, as well as on nontopological properties of nodes. We show how the methodology can be applied in general to networks in which mass or energy is flowing along the links. In particular, the procedure enables us to address important questions in economics, namely, how control and wealth are structured and concentrated across national markets. We report on the first cross-country investigation of ownership networks, focusing on the stock markets of 48 countries around the world. On the one hand, our analysis confirms results expected on the basis of the literature on corporate control, namely, that in Anglo-Saxon countries control tends to be dispersed among numerous shareholders. On the other hand, it also reveals that in the same countries, control is found to be highly concentrated at the global level, namely, lying in the hands of very few important shareholders. Interestingly, the exact opposite is observed for European countries. These results have previously not been reported as they are not observable without the kind of network analysis developed here.
Backbone of complex networks of corporations: the flow of control.
Glattfelder, J B; Battiston, S
2009-09-01
We present a methodology to extract the backbone of complex networks based on the weight and direction of links, as well as on nontopological properties of nodes. We show how the methodology can be applied in general to networks in which mass or energy is flowing along the links. In particular, the procedure enables us to address important questions in economics, namely, how control and wealth are structured and concentrated across national markets. We report on the first cross-country investigation of ownership networks, focusing on the stock markets of 48 countries around the world. On the one hand, our analysis confirms results expected on the basis of the literature on corporate control, namely, that in Anglo-Saxon countries control tends to be dispersed among numerous shareholders. On the other hand, it also reveals that in the same countries, control is found to be highly concentrated at the global level, namely, lying in the hands of very few important shareholders. Interestingly, the exact opposite is observed for European countries. These results have previously not been reported as they are not observable without the kind of network analysis developed here.
Modelling trafﬁc congestion using queuing networks
Indian Academy of Sciences (India)
Tushar Raheja
2010-08-01
Trafﬁc studies have been carried out predominantly using simulation models which are both time and capital intensive. In this paper, an analytical model of uninterrupted single-lane trafﬁc is proposed using queuing analysis. Well-known Trafﬁc Flow-Density diagrams are obtained using simple Jackson queuing network analysis. Such simple analytical models can be used to capture the effect of nonhomogenous trafﬁc.
World Migration Degree Global migration flows in directed networks
Porat, Idan
2015-01-01
In this article we analyze the global flow of migrants from 206 source countries to 145 destination countries (2006-2010) and focus on the differences in the migration network pattern between destination and source counters as represented by its degree and weight distribution. Degree represents the connectivity of a country to the global migration network, and plays an important role in defining migration processes and characteristics. Global analysis of migration degree distribution offers a strong potential contribution to understanding of migration as a global phenomenon. In regard to immigration, we found that it is possible to classify destination countries into three classes: global migration hubs with high connectivity and high migration rate; local migration hubs with low connectivity and high migration rate; and local migration hubs with opposite strategy of high connectivity and low migration rate. The different migration strategies of destination countries are emerging from similar and homogenies p...
Modeling of regional warehouse network generation
Directory of Open Access Journals (Sweden)
Popov Pavel Vladimirovich
2016-08-01
Full Text Available One of the factors that has a significant impact on the socio-economic development of the Russian Federation’s regions is the logistics infrastructure. It provides integrated transportation and distribution service of material flows. One of the main elements of logistics infrastructure is a storage infrastructure, which includes distribution center, distribution-and-sortout and sortout warehouses. It is the most expedient to place distribution center in the vicinity of the regional center. One of the tasks of the distribution network creation within the regions of the Russian Federation is to determine the location, capacity and number of stores. When determining regional network location of general purpose warehouses methodological approaches to solving the problems of location of production and non-production can be used which depend on various economic factors. The mathematical models for solving relevant problems are the deployment models. However, the existing models focus on the dimensionless power storage. The purpose of the given work is to develop a model to determine the optimal location of general-purpose warehouses on the Russian Federation area. At the first stage of the work, the authors assess the main economic indicators influencing the choice of the location of general purpose warehouses. An algorithm for solving the first stage, based on ABC, discriminant and cluster analysis were proposed by the authors in earlier papers. At the second stage the specific locations of general purpose warehouses and their power is chosen to provide the cost minimization for the construction and subsequent maintenance of warehouses and transportation heterogeneous products. In order to solve this problem the authors developed a mathematical model that takes into account the possibility of delivery in heterogeneous goods from suppliers and manufacturers in the distribution and storage sorting with specified set of capacities. The model allows
DEFF Research Database (Denmark)
Yuan, Hao; You, Zhenjiang; Shapiro, Alexander
2013-01-01
Colloidal-suspension flow in porous media is modelled simultaneously by the large scale population balance equations and by the microscale network model. The phenomenological parameter of the correlation length in the population balance model is determined from the network modelling. It is found ...... out that the correlation length in the population balance model depends on the particle size. This dependency calculated by two-dimensional network has the same tendency as that obtained from the laboratory tests in engineered porous media....
Directory of Open Access Journals (Sweden)
Juan R. Diaz
2014-01-01
Full Text Available Multimedia traffic can be forwarded through a wireless ad hoc network using the available resources of the nodes. Several models and protocols have been designed in order to organize and arrange the nodes to improve transmissions along the network. We use a cluster-based framework, called MWAHCA architecture, which optimizes multimedia transmissions over a wireless ad hoc network. It was proposed by us in a previous research work. This architecture is focused on decreasing quality of service (QoS parameters like latency, jitter, and packet loss, but other network features were not developed, like load balance or fault tolerance. In this paper, we propose a new fault tolerance mechanism, using as a base the MWAHCA architecture, in order to recover any multimedia flow crossing the wireless ad hoc network when there is a node failure. The algorithm can run independently for each multimedia flow. The main objective is to keep the QoS parameters as low as possible. To achieve this goal, the convergence time must be controlled and reduced. This paper provides the designed protocol, the analytical model of the algorithm, and a software application developed to test its performance in a real laboratory.
Analytical models for complex swirling flows
Borissov, A.; Hussain, V.
1996-11-01
We develops a new class of analytical solutions of the Navier-Stokes equations for swirling flows, and suggests ways to predict and control such flows occurring in various technological applications. We view momentum accumulation on the axis as a key feature of swirling flows and consider vortex-sink flows on curved axisymmetric surfaces with an axial flow. We show that these solutions model swirling flows in a cylindrical can, whirlpools, tornadoes, and cosmic swirling jets. The singularity of these solutions on the flow axis is removed by matching them with near-axis Schlichting and Long's swirling jets. The matched solutions model flows with very complex patterns, consisting of up to seven separation regions with recirculatory 'bubbles' and vortex rings. We apply the matched solutions for computing flows in the Ranque-Hilsch tube, in the meniscus of electrosprays, in vortex breakdown, and in an industrial vortex burner. The simple analytical solutions allow a clear understanding of how different control parameters affect the flow and guide selection of optimal parameter values for desired flow features. These solutions permit extension to other problems (such as heat transfer and chemical reaction) and have the potential of being significantly useful for further detailed investigation by direct or large-eddy numerical simulations as well as laboratory experimentation.
Modeling and simulation of reactive flows
Bortoli, De AL; Pereira, Felipe
2015-01-01
Modelling and Simulation of Reactive Flows presents information on modeling and how to numerically solve reactive flows. The book offers a distinctive approach that combines diffusion flames and geochemical flow problems, providing users with a comprehensive resource that bridges the gap for scientists, engineers, and the industry. Specifically, the book looks at the basic concepts related to reaction rates, chemical kinetics, and the development of reduced kinetic mechanisms. It considers the most common methods used in practical situations, along with equations for reactive flows, and va
Energy minimization for the flow in ducts and networks
Sochi, Taha
2014-01-01
The present paper is an attempt to demonstrate how the energy minimization principle may be considered as a governing rule for the physical equilibrium that determines the flow fields in tubes and networks. We previously investigated this issue using a numerical stochastic method, specifically simulated annealing, where we demonstrated the problem by some illuminating examples and concluded that energy minimization principle can be a valid hypothesis. The investigation in this paper is more general as it is based to a certain extent on an analytical approach.
Maximum Flow in Planar Networks with Exponentially Distributed Arc Capacities.
1984-12-01
avoid constructing the dual, are described in Itai and Shiloach P 97 91. In this paper, we consider the maximum flow problem in (st) planar networks...use arc e and lies completely below P. If no such path exists we say P(e) - *. An algorithm tc construct P(e) given P and e is described in Itai and...suggested in Ford and Fulkerson [1956], developed in Berge and Ghouila-Houri [1962] and its time complexity is reduced to 0( IVI log IVI ) by Itai and
Centrality Fingerprints for Power Grid Network Growth Models
Gurfinkel, Aleks Jacob; Rikvold, Per Arne
2015-01-01
In our previous work, we have shown that many of the properties of the Florida power grid are reproduced by deterministic network growth models based on the minimization of energy dissipation $E_\\mathrm{diss}$. As there is no $a~ priori$ best $E_\\mathrm{diss}$ minimizing growth model, we here present a tool, called the "centrality fingerprint," for probing the behavior of different growth models. The centrality fingerprints are comparisons of the current flow into/out of the network with the values of various centrality measures calculated at every step of the growth process. Finally, we discuss applications to the Maryland power grid.
Renormalization procedure for random tensor networks and the canonical tensor model
Sasakura, Naoki
2015-01-01
We discuss a renormalization procedure for random tensor networks, and show that the corresponding renormalization-group flow is given by the Hamiltonian vector flow of the canonical tensor model, which is a discretized model of quantum gravity. The result is the generalization of the previous one concerning the relation between the Ising model on random networks and the canonical tensor model with N=2. We also prove a general theorem which relates discontinuity of the renormalization-group flow and the phase transitions of random tensor networks.
Network Dependency in Migration Flows – A Space-time Analysis for Germany since Re-unification
DEFF Research Database (Denmark)
Mitze, Timo
. In the context of this paper, network dependencies are associated with correlations of migration flows strictly attributable to proximate flows in geographic space. Using the neoclassical migration model, we start from its aspatial specification and show by means of residual testing that network dependency eff...... equation and at the same time qualify the model to pass essential IV diagnostic tests. The basic message for future research is that space-time dynamics is highly relevant for modelling German internal migration flows.......The contribution of this paper is to analyse the role of network interdependencies in a dynamic panel data model for German internal migration fl ows since re-unification. So far, a capacious account of spatial patterns in German migration data is still missing in the empirical literature...
Intelligent Flow Control Technique of ABR Service in ATM Networks Based on Fuzzy Neural Networks
Institute of Scientific and Technical Information of China (English)
ZhangLiangjie; LiYanda; 等
1997-01-01
The ATM Forum voted to implement the rate-based flow control(RBFC)scheme to manage traffic in asynchronous transfer mode(ATM)networks.RBFC will be used specifically to manage available bit rate(ABR)service.Through the study of the transmission rate adjusting of the ABR traffic source,we propose and enhanced bit rate feedback(EBRF)scheme,which is the dynamic bit rate adjusting scheme based on fuzzy neural network(FNN).Simulation results show that it can enhance the switch buffer utilization on the premise of a full link utilization.
Coupling entropy of co-processing model on social networks
Zhang, Zhanli
2015-08-01
Coupling entropy of co-processing model on social networks is investigated in this paper. As one crucial factor to determine the processing ability of nodes, the information flow with potential time lag is modeled by co-processing diffusion which couples the continuous time processing and the discrete diffusing dynamics. Exact results on master equation and stationary state are achieved to disclose the formation. In order to understand the evolution of the co-processing and design the optimal routing strategy according to the maximal entropic diffusion on networks, we propose the coupling entropy comprehending the structural characteristics and information propagation on social network. Based on the analysis of the co-processing model, we analyze the coupling impact of the structural factor and information propagating factor on the coupling entropy, where the analytical results fit well with the numerical ones on scale-free social networks.
Flux Flow, Pinning, and Resistive Behavior in Superconducting Networks
Energy Technology Data Exchange (ETDEWEB)
Stephen Teitel
2005-05-03
Numerical simulators are used to study the behavior of interacting quantized vortices and vortex lines in superconducting networks, films, and three dimensional bulk samples. An emphasis is on the explanation of the phenomenological behavior of the ''high-Tc'' copper-oxide superconductors and related model systems.
Flow in a model turbine stator
Buggeln, R. C.; Shamroth, S. J.; Briley, W. R.
1985-10-01
In view of the complex nature of the flowfield in the hot section of gas turbine engines, the need to predict heat transfer and flow losses, the possible appearance of separation and strong secondary flows, etc., the present effort is focusing upon a Navier-Stokes approach to the three dimensional turbine stator problem. The advantages of a full Navier-Stokes approach are clear since when combined with a suitable turbulence model these equations represent the flow and heat transfer physics. In particular, the Navier-Stokes equations accurately represent possible separated regions and regions of significant secondary flow. In addition, the Navier-Stokes approach allows representation of the entire flow field by a single set of equations, thus avoiding problems associated with representing different regions of the flow by different equations and then matching flow regions.
NUMERICAL MODEL FOR FLOW MOTION WITH VEGETATION
Institute of Scientific and Technical Information of China (English)
ZHANG Jian-tao; SU Xiao-hui
2008-01-01
A set of governing equations for turbulent flows in vegetated area were derived with the assumption that vegetation is of straight and rigid cylinder. The effect of vegetation on flow motion was represented by additional inertial and drag forces. The new model was validated by available experimental data for open channel flows passing through vegetated areas with different vegetation size, density and distribution. Numerical results are in good agreement with the experimental data. Finally, the flow around a supposed isolated vegetated pile was simulated and the effects of vegetation density on the wake flow were discussed. It is found that the presence of vegetation, even at a very low density, has the pronounced influence on the dissipation of flow energy, both inside the vegetation domain and outside it in the wake flow region.
Regression modeling of ground-water flow
Cooley, R.L.; Naff, R.L.
1985-01-01
Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)
Dynamic modelling of packaging material flow systems.
Tsiliyannis, Christos A
2005-04-01
A dynamic model has been developed for reused and recycled packaging material flows. It allows a rigorous description of the flows and stocks during the transition to new targets imposed by legislation, product demand variations or even by variations in consumer discard behaviour. Given the annual reuse and recycle frequency and packaging lifetime, the model determines all packaging flows (e.g., consumption and reuse) and variables through which environmental policy is formulated, such as recycling, waste and reuse rates and it identifies the minimum number of variables to be surveyed for complete packaging flow monitoring. Simulation of the transition to the new flow conditions is given for flows of packaging materials in Greece, based on 1995--1998 field inventory and statistical data.
Physical Modeling of Scaled Water Distribution System Networks.
Energy Technology Data Exchange (ETDEWEB)
O' Hern, Timothy J.; Hammond, Glenn Edward; Orear, Leslie ,; van Bloemen Waanders, Bart G.; Paul Molina; Ross Johnson
2005-10-01
Threats to water distribution systems include release of contaminants and Denial of Service (DoS) attacks. A better understanding, and validated computational models, of the flow in water distribution systems would enable determination of sensor placement in real water distribution networks, allow source identification, and guide mitigation/minimization efforts. Validation data are needed to evaluate numerical models of network operations. Some data can be acquired in real-world tests, but these are limited by 1) unknown demand, 2) lack of repeatability, 3) too many sources of uncertainty (demand, friction factors, etc.), and 4) expense. In addition, real-world tests have limited numbers of network access points. A scale-model water distribution system was fabricated, and validation data were acquired over a range of flow (demand) conditions. Standard operating variables included system layout, demand at various nodes in the system, and pressure drop across various pipe sections. In addition, the location of contaminant (salt or dye) introduction was varied. Measurements of pressure, flowrate, and concentration at a large number of points, and overall visualization of dye transport through the flow network were completed. Scale-up issues that that were incorporated in the experiment design include Reynolds number, pressure drop across nodes, and pipe friction and roughness. The scale was chosen to be 20:1, so the 10 inch main was modeled with a 0.5 inch pipe in the physical model. Controlled validation tracer tests were run to provide validation to flow and transport models, especially of the degree of mixing at pipe junctions. Results of the pipe mixing experiments showed large deviations from predicted behavior and these have a large impact on standard network operations models.3
Modeling process flow using diagrams
Kemper, B.; de Mast, J.; Mandjes, M.
2010-01-01
In the practice of process improvement, tools such as the flowchart, the value-stream map (VSM), and a variety of ad hoc variants of such diagrams are commonly used. The purpose of this paper is to present a clear, precise, and consistent framework for the use of such flow diagrams in process
Modeling process flow using diagrams
Kemper, B.; de Mast, J.; Mandjes, M.
2010-01-01
In the practice of process improvement, tools such as the flowchart, the value-stream map (VSM), and a variety of ad hoc variants of such diagrams are commonly used. The purpose of this paper is to present a clear, precise, and consistent framework for the use of such flow diagrams in process improv
Bayesian estimation of the network autocorrelation model
Dittrich, D.; Leenders, R.T.A.J.; Mulder, J.
2017-01-01
The network autocorrelation model has been extensively used by researchers interested modeling social influence effects in social networks. The most common inferential method in the model is classical maximum likelihood estimation. This approach, however, has known problems such as negative bias of
Object Oriented Modeling Of Social Networks
Zeggelink, Evelien P.H.; Oosten, Reinier van; Stokman, Frans N.
1996-01-01
The aim of this paper is to explain principles of object oriented modeling in the scope of modeling dynamic social networks. As such, the approach of object oriented modeling is advocated within the field of organizational research that focuses on networks. We provide a brief introduction into the f
Agent-based modeling and network dynamics
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...
Directory of Open Access Journals (Sweden)
Afshin Mehrsai
2013-01-01
Full Text Available Alternative material flow strategies in logistics networks have crucial influences on the overall performance of the networks. Material flows can follow push, pull, or hybrid systems. To get the advantages of both push and pull flows in networks, the decoupling-point strategy is used as coordination mean. At this point, material pull has to get optimized concerning customer orders against pushed replenishment-rates. To compensate the ambiguity and uncertainty of both dynamic flows, fuzzy set theory can practically be applied. This paper has conceptual and mathematical parts to explain the performance of the push-pull flow strategy in a supply network and to give a novel solution for optimizing the pull side employing Conwip system. Alternative numbers of pallets and their lot-sizes circulating in the assembly system are getting optimized in accordance with a multi-objective problem; employing a hybrid approach out of meta-heuristics (genetic algorithm and simulated annealing and fuzzy system. Two main fuzzy sets as triangular and trapezoidal are applied in this technique for estimating ill-defined waiting times. The configured technique leads to smoother flows between push and pull sides in complex networks. A discrete-event simulation model is developed to analyze this thesis in an exemplary logistics network with dynamics.
Modelling Canopy Flows over Complex Terrain
Grant, Eleanor R.; Ross, Andrew N.; Gardiner, Barry A.
2016-06-01
Recent studies of flow over forested hills have been motivated by a number of important applications including understanding CO_2 and other gaseous fluxes over forests in complex terrain, predicting wind damage to trees, and modelling wind energy potential at forested sites. Current modelling studies have focussed almost exclusively on highly idealized, and usually fully forested, hills. Here, we present model results for a site on the Isle of Arran, Scotland with complex terrain and heterogeneous forest canopy. The model uses an explicit representation of the canopy and a 1.5-order turbulence closure for flow within and above the canopy. The validity of the closure scheme is assessed using turbulence data from a field experiment before comparing predictions of the full model with field observations. For near-neutral stability, the results compare well with the observations, showing that such a relatively simple canopy model can accurately reproduce the flow patterns observed over complex terrain and realistic, variable forest cover, while at the same time remaining computationally feasible for real case studies. The model allows closer examination of the flow separation observed over complex forested terrain. Comparisons with model simulations using a roughness length parametrization show significant differences, particularly with respect to flow separation, highlighting the need to explicitly model the forest canopy if detailed predictions of near-surface flow around forests are required.
Modelling Canopy Flows over Complex Terrain
Grant, Eleanor R.; Ross, Andrew N.; Gardiner, Barry A.
2016-12-01
Recent studies of flow over forested hills have been motivated by a number of important applications including understanding CO_2 and other gaseous fluxes over forests in complex terrain, predicting wind damage to trees, and modelling wind energy potential at forested sites. Current modelling studies have focussed almost exclusively on highly idealized, and usually fully forested, hills. Here, we present model results for a site on the Isle of Arran, Scotland with complex terrain and heterogeneous forest canopy. The model uses an explicit representation of the canopy and a 1.5-order turbulence closure for flow within and above the canopy. The validity of the closure scheme is assessed using turbulence data from a field experiment before comparing predictions of the full model with field observations. For near-neutral stability, the results compare well with the observations, showing that such a relatively simple canopy model can accurately reproduce the flow patterns observed over complex terrain and realistic, variable forest cover, while at the same time remaining computationally feasible for real case studies. The model allows closer examination of the flow separation observed over complex forested terrain. Comparisons with model simulations using a roughness length parametrization show significant differences, particularly with respect to flow separation, highlighting the need to explicitly model the forest canopy if detailed predictions of near-surface flow around forests are required.
Incorporating groundwater flow into the WEPP model
William Elliot; Erin Brooks; Tim Link; Sue Miller
2010-01-01
The water erosion prediction project (WEPP) model is a physically-based hydrology and erosion model. In recent years, the hydrology prediction within the model has been improved for forest watershed modeling by incorporating shallow lateral flow into watershed runoff prediction. This has greatly improved WEPP's hydrologic performance on small watersheds with...
Analysis and Visualization of Discrete Fracture Networks Using a Flow Topology Graph.
Aldrich, Garrett; Hyman, Jeffrey; Karra, Satish; Gable, Carl; Makedonska, Nataliia; Viswanathan, Hari; Woodring, Jonathan; Hamann, Bernd
2016-06-20
We present an analysis and visualization prototype using the concept of a flow topology graph (FTG) for characterization of flow in constrained networks, with a focus on discrete fracture networks (DFN), developed collaboratively by geoscientists and visualization scientists. Our method allows users to understand and evaluate flow and transport in DFN simulations by computing statistical distributions, segment paths of interest, and cluster particles based on their paths. The new approach enables domain scientists to evaluate the accuracy of the simulations, visualize features of interest, and compare multiple realizations over a specific domain of interest. Geoscientists can simulate complex transport phenomena modeling large sites for networks consisting of several thousand fractures without compromising the geometry of the network. However, few tools exist for performing higher-level analysis and visualization of simulated DFN data. The prototype system we present addresses this need. We demonstrate its effectiveness for increasingly complex examples of DFNs, covering two distinct use cases - hydrocarbon extraction from unconventional resources and transport of dissolved contaminant from a spent nuclear fuel repository.
Edge exchangeable models for network data
Crane, Harry
2016-01-01
Exchangeable models for vertex labeled graphs cannot replicate the large sample behaviors of sparsity and power law degree distributions observed in many network datasets. Out of this mathematical impossibility emerges the question of how network data can be modeled in a way that reflects known empirical behaviors and respects basic statistical principles. We address this question by observing that edges, not vertices, act as the statistical units in most network datasets, making a theory of edge labeled networks more natural for most applications. Within this context we introduce the new invariance principle of {\\em edge exchangeability}, which unlike its vertex exchangeable counterpart can produce networks with sparse and/or power law structure. We characterize the class of all edge exchangeable network models and identify a particular two parameter family of models with suitable theoretical properties for statistical inference. We discuss issues of estimation from edge exchangeable models and compare our a...
Simulation and modeling of turbulent flows
Gatski, Thomas B; Lumley, John L
1996-01-01
This book provides students and researchers in fluid engineering with an up-to-date overview of turbulent flow research in the areas of simulation and modeling. A key element of the book is the systematic, rational development of turbulence closure models and related aspects of modern turbulent flow theory and prediction. Starting with a review of the spectral dynamics of homogenous and inhomogeneous turbulent flows, succeeding chapters deal with numerical simulation techniques, renormalization group methods and turbulent closure modeling. Each chapter is authored by recognized leaders in their respective fields, and each provides a thorough and cohesive treatment of the subject.
Evolutionary Phylogenetic Networks: Models and Issues
Nakhleh, Luay
Phylogenetic networks are special graphs that generalize phylogenetic trees to allow for modeling of non-treelike evolutionary histories. The ability to sequence multiple genetic markers from a set of organisms and the conflicting evolutionary signals that these markers provide in many cases, have propelled research and interest in phylogenetic networks to the forefront in computational phylogenetics. Nonetheless, the term 'phylogenetic network' has been generically used to refer to a class of models whose core shared property is tree generalization. Several excellent surveys of the different flavors of phylogenetic networks and methods for their reconstruction have been written recently. However, unlike these surveys, this chapte focuses specifically on one type of phylogenetic networks, namely evolutionary phylogenetic networks, which explicitly model reticulate evolutionary events. Further, this chapter focuses less on surveying existing tools, and addresses in more detail issues that are central to the accurate reconstruction of phylogenetic networks.