International Journal of Modeling, Simulation, and Scientific Computing

A novel approach for partitioning and coordinating the collaborative design optimization of complex systems is described. A partitioning metric has been formulated to select the best partitioning solutions among the total possibilities of dividing the complex design optimization problem. Then, an agent-supported approach is used for the coordination of the collaborative design optimization. The approach has been applied to the case of a preliminary design of an electric vehicle, to demonstrate how various agents can effectively communicate with each other to provide support to the collaborative design optimization of complex systems.

Multi-agent system (MAS) models have been increasingly applied to the simulation of complex phenomena in different areas, providing successful and credible results. Citizens behavior related to a specific urban activity (i.e., recreation activities in a park, using bicycle for mobility purposes) can be modeled as an agent (actor) with several affinities and preferences which are dependent on aspects that affect the activity. A particular application of a MAS approach is in area of urban policy design, in which policies should be designed considering citizens needs, preferences and behavior. Once an open space in a city is available (i.e., an industry is moved to an industrial area), a land use policy should contribute to identify the new use for the urban space. There are different land use policies that can be applied depending on which services or facilities must be empowered in the city. It is important to identify the correct policy in order to satisfy present citizens needs but considering also the future needs in a social changing context. A socio-technological simulation model has been developed to allow citizens to get a better understanding of the urban problem, its dynamics and explore the sustainability of the different solutions., enhancing citizens to participate in the urban decisions through new technologies (i.e., e-participation). This paper illustrates an open space MAS simulation model for land use design policies in which citizens can check their opinion and get a better understanding of the different choices and its acceptability by the community considering not only present neighborhood profiles, but also future neighborhood configurations. It is the first step before the development of the final software including a user friendly interface to let citizens with different cultural profiles to perform simulations as an essential and neutral tool to reach consensus during the decision-making process in urban policy design.

The original restricted Boltzmann machines (RBMs) are extended by replacing the binary visible and hidden variables with clusters of binary units, and a new learning algorithm for training deep Boltzmann machine of this new variant is proposed. The sum of binary units of each cluster is approximated by a Gaussian distribution. Experiments demonstrate that the proposed Boltzmann machines can achieve good performance in the MNIST handwritten digital recognition task.

External counterpulsation therapy (ECP) is a non-invasive method to assist the circulatory system. The main principle of ECP is to initiate a diastolic pulse wave in the arterial system by squeezing the inner leg vessels. Superficial and low veins are compressed due to muscle contractions, which are triggered by functional electrical stimulation (FES). In this work, a new trigger method for the stimulation is proposed. Blood flow is determined by measuring the pulsatile change of conductivity in the observed segment by impedance plethysmography. The proposed “adaptive stimulation” allows the automatic adjustment of the start and duration of the stimulation to improve transport of venous blood. For this purpose, a mathematical circulation model was developed for optimization of the adaptive functional electrical stimulation. The model takes into account the effects of gravity, muscle pump with or without FES, and venous regurgitation. The model was shown to have a behavior similar to that of clinical measurements. The simulation showed a flow enhancement of up to 14% and, furthermore, that the start of the stimulation is less important than the duration of each stimulation phase. Therefore, an adaptation of the heart rate seems necessary, as the expulsion and refill times must remain relatively constant for an optimal pumping result. For a practical approach, it is reasonable to adapt the duration of stimulation to 56% of the cardiac cycle in order to reach a maximum flow.

Two-dimensional (2D) and axisymmetric minimum length nozzles (MLNs) with constant and variable specific heat were strictly designed using the method of characteristics (MOCs). MOC is a numerical technique which has great advantages in accuracy and efficiency for solving hyperbolic partial differential equations. According to previous MLN designs, violent vibrations of upper wall discrete points at the inlet were observed for 2D nozzles. Meanwhile, slight compressions could be observed in the flow field of axisymmetric nozzles designed by those methods. We proposed a novel technique in which the inlet grid is intensified to overcome the limitations mentioned above. Inviscid numerical simulations by CFD revealed that the proposed nozzle could meet the requirements for exit Mach number and flow field uniformity. Additionally, asymmetric MLNs could be used to hypersonic vehicles. The preliminary performances of 2D asymmetric nozzle with constant specific heat were investigated.

How to predict and change organizational performance has been a focus problem drawing economists and managers’ attention for a long time. The Big-five Factor theory is very popular among psychologists and ABMS (Agent-Based Modeling and Simulation) method is widely used in Systems Science. By integrating Big-five Factor and ABMS, we proposed a model to predict organizational performance. Fuzzy rules were built up to describe one-on-one cooperation in the five dimensions of personalities and an unsymmetrical network model was established to depict cooperation relationships between team members. What is more, a series of cases was studied and the result was proved to be rational.

Aiming at the human–computer interaction control (HCIC) requirements of multi operators in collaborative virtual maintenance (CVM), real-time motion capture and simulation drive of multi operators with optical human motion capture system (HMCS) is proposed. The detailed realization process of real-time motion capture and data drive for virtual operators in CVM environment is presented to actualize the natural and online interactive operations. In order to ensure the cooperative and orderly interactions of virtual operators with the input operations of actual operators, collaborative HCIC model is established according to specific planning, allocating and decision-making of different maintenance tasks as well as the human–computer interaction features and collaborative maintenance operation features among multi maintenance trainees in CVM process. Finally, results of the experimental implementation validate the effectiveness and practicability of proposed methods, models, strategies and mechanisms.

Environment model is an important component of an artificial society. To level up the resolution of residence-building environment models in artificial Beijing, we divide residential buildings in Beijing into two categories, which are noncommunity residential buildings and community residential buildings, and statistically analyze the numbers of floors and areas of the real residential buildings. In this paper, we have exhibited the distribution of numbers of floors in noncommunity residential buildings and community residential buildings. Furthermore, we analyze the distribution of areas of noncommunity residential buildings and residence-use proportions of community zones by Nonlinear Regression. Results indicate that areas of noncommunity residential buildings in Beijing obey the log-normal distribution and residence-use proportions of community zones obey normal distribution. According to the statistical models, the virtual residential buildings in artificial Beijing can be endowed with rational attribute values.

The basic configuration of step up converter usually used in photovoltaic solar systems to increase the DC voltage generated at their outputs suffers from some drawbacks just like high ripple in the output voltage, greater losses in the system and unstable dynamic behavior. To eliminate these drawbacks, this paper introduces a two-phase connection of step up converter with uncoupled smoothing reactors. Detailed analysis, simulation and control strategy have been proposed in this paper to investigate the advantages of using such connection with uncoupled reactors. This paper is intended to prove that two-phase connection with uncoupled reactors helps increasing the output power of the converter, minimizing its output ripple and making its control easier and more efficient. It also increases the converter chopping frequency and consequently decreases the size of smoothing reactors and filters used in the system. Concerning the design of such converters, it requires a long working period of time with a significant cost and specific technical tests at nominal operating points. Therefore, simulation can essentially decrease economic and development costs. Using modulation and simulation software techniques (Simplorer, Simulink, and Matlab) throughout this paper helped simulation of very fast the converter behavior and accurate determination of its dynamic characteristics. Moreover, the paper deals with modulation of voltage control technique using Matlab and Simplorer, thus regulating the converter output current and voltage. Simulation results show that this control technique provides robust output current and voltage of step up converters and is more feasible for their chopper up conversion technique.

Artificial neural networks (ANNs), a branch of artificial intelligence, has become a very interesting domain since the eighties when back-propagation (BP) learning algorithm for multilayer feed-forward architecture was introduced to solve nonlinear problems. It is used extensively to solve complex nonalgorithmic problems such as prediction, pattern recognition and clustering. However, in the context of a holistic study, there may be a need to integrate ANN with other models developed in various paradigms to solve a problem. In this paper, we suggest discrete event system specification (DEVS) be used as a model of computation (MoC) to make ANN models interoperable with other models (since all discrete event models can be expressed in DEVS, and continuous models can be approximated by DEVS). By combining ANN and DEVS, we can model the complex configuration of ANNs and express its internal workings. Therefore, we are extending the DEVS-based ANN proposed by Toma et al. [A new DEVS-based generic artficial neural network modeling approach, The 23rd European Modeling and Simulation Symp. (Simulation in Industry), Rome, Italy, 2011] for comparing multiple configuration parameters and learning algorithms and also to do prediction. The DEVS models are described using the high level language for system specification (HiLLS), [Maïga et al., A new approach to modeling dynamic structure systems, The 29th European Modeling and Simulation Symp. (Simulation in Industry), Leicester, United Kingdom, 2015] a graphical modeling language for clarity. The developed platform is a tool to transform ANN models into DEVS computational models, making them more reusable and more interoperable in the context of larger multi-perspective modeling and simulation (MAS).

We demonstrate a new nonuniform mesh finite difference method to obtain accurate solutions for the elliptic partial differential equations in two dimensions with nonlinear first-order partial derivative terms. The method will be based on a geometric grid network area and included among the most stable differencing scheme in which the nine-point spatial finite differences are implemented, thus arriving at a compact formulation. In general, a third order of accuracy has been achieved and a fourth-order truncation error in the solution values will follow as a particular case. The efficiency of using geometric mesh ratio parameter has been shown with the help of illustrations. The convergence of the scheme has been established using the matrix analysis, and irreducibility is proved with the help of strongly connected characteristics of the iteration matrix. The difference scheme has been applied to test convection diffusion equation, steady state Burger’s equation, ocean model and a semi-linear elliptic equation. The computational results confirm the theoretical order and accuracy of the method.

The flow of Ree–Eyring and Casson non-Newtonian fluids is investigated using a variational principle to optimize the total stress. The variationally obtained solutions are compared to the analytical solutions derived from the Weissenberg–Rabinowitsch–Mooney equation and the results are found to be identical within acceptable numerical errors and modeling approximations.

We apply a second-order symmetric Runge–Kutta method and a second-order symplectic Runge–Kutta method directly to the gyrocenter dynamics which can be expressed as a noncanonical Hamiltonian system. The numerical simulation results show the overwhelming superiorities of the two methods over a higher order nonsymmetric nonsymplectic Runge–Kutta method in long-term numerical accuracy and near energy conservation. Furthermore, they are much faster than the midpoint rule applied to the canonicalized system to reach given precision.

Direct Simulation Monte Carlo (DSMC) solves the Boltzmann equation with large Knudsen number. The Boltzmann equation generally consists of three terms: the force term, the diffusion term and the collision term. While the first two terms of the Boltzmann equation can be discretized by numerical methods such as the finite volume method, the third term can be approximated by DSMC, and DSMC simulates the physical behaviors of gas molecules. However, because of the low sampling efficiency of Monte Carlo Simulation in DSMC, this part usually occupies large portion of computational costs to solve the Boltzmann equation. In this paper, by Markov Chain Monte Carlo (MCMC) and multicore programming, we develop Direct Simulation Multi-Chain Markov Chain Monte Carlo (DSMC3): a fast solver to calculate the numerical solution for the Boltzmann equation. Computational results show that DSMC3 is significantly faster than the conventional method DSMC.

We propose a MATLAB implementation of the $P^1$ finite element method for the numerical solutions of the Poisson problem and the linear elasticity problem in two-dimensional (2D) and three-dimensional (3D). The code consists of vectorized (and short) assembling functions for the matrices (mass and stiffness) and the right-hand sides. Since for the $P^1$ finite element, the element mass matrix and right-hand side are simple, the implementation uses only the MATLAB function sparse on the elements volume. For the stiffness matrix, to obtain a MATLAB implementation close to the standard form, cell-arrays are used to store the gradients of the element basis functions. The assembling procedure can then use matrix/vector products on small size cell-arrays. Numerical experiments show that our implementation is fast, scalable with respect to time, and outperforms existing vectorized MATLAB FEM codes.