International Journal of Modeling, Simulation, and Scientific Computing

In Discrete Event System Specification (DEVS), the dynamics of a network is constituted only by the dynamics of its basic components. The state of each component is fully encapsulated. Control in the network is fully decentralized to each component. At dynamic structure level, DEVS should permit the same level of decentralization. However, it is hard to ensure structure consistency while letting all components achieve structure changes. Besides, this solution can be complex to implement. To avoid these difficulties, usual dynamic structure approaches ensure structure consistency allowing structure changes to be done only by the network having newly added dynamics change capabilities. This is a safe and simple way to achieve dynamic structure. However, it should be possible to simply allow components of a network to modify the structure of their network, other components and/or their own structure — without having to modify the usual definition a DEVS network. In this manuscript, it is shown that a simple fully decentralized approach is possible while ensuring full modularity and structure consistency.

The paper presents a study of model development for predicting the oxide thickness on metals under high temperature solid-particle erosion. The model is created based on the theory of solid-particle erosion that characterizes the erosion damage as deformation wear and cutting wear, incorporating the effect of the oxide scale on the eroded surface under high temperature erosion. Then the instantaneous oxide thickness is the result of the synergetic effect of erosion and oxidation. The developed model is applied on a Ni-based Al-containing (Ni–Al) alloy to investigate the oxide thickness variation with erosion duration of the alloy at high temperatures. The results show that the thickness of the oxide scale on the alloy surface increases with the exposure time and temperature when the surface is not attacked by particles. However, when particles impact on the alloy surface, the oxide thickness is reduced, although oxidation is continuing. This indicates that oxidation does not benefit the erosion resistance of this alloy at high temperatures due to the low growth rate of the oxide.

Depth map contains the space information of objects and is almost free from the influence of light, and it attracts many research interests in the field of machine vision used for human detection. Therefore, hunting a suitable image feature for human detection on depth map is rather attractive. In this paper, we evaluate the performance of the typical features on depth map. A depth map dataset containing various indoor scenes with human is constructed by using Microsoft's Kinect camera as a quantitative benchmark for the study of methods of human detection on depth map. The depth map is smoothed with pixel filtering and context filtering so as to reduce particulate noise. Then, the performance of five image features and a new feature is studied and compared for human detection on the dataset through theoretic analysis and simulation experiments. Results show that the new feature outperforms other descriptors.

Dynamic market optimization with respect to price, advertisement and investment is presented. The market model is nonlinear. Its main parameters are the elasticities with respect to price, advertisement and consumer income. Dynamic elements has been added to the static model based on the market elasticities. The parameters like seasonal index and consumer income are functions of time, and the whole market can grow due to the investment. The tools of the optimal control theory are applied to calculate optimal policy for product price, advertisement and investment, controlled simultaneously. The total revenue at the end of a fixed time interval is maximized.

In this paper, a fractional differential equation is introduced to describe the transverse vibrations of an axially moving viscoelastic string. An iterative algorithm is constructed to analyze the dynamical behavior. By conveying the memory effect of the fractional differential terms step by step, the computation cost can be greatly reduced. As a numerical example, the effects of the viscoelastic parameters on a moving string are investigated.

Being a new-generation C⁴ISR system simulation method, the construction approach of net-centric simulation (NCS) is developing toward net-centric from the traditional approach of platform-centric. NCS is mainly completed by the construction of the simulation task community (STC), the key to which being the dynamic integration of the various services spread in the network in order to form a new STC that meets the requirements of different users. In this study, a simulation task community service selection algorithm (STCSSA) is proposed. The main idea of this algorithm is to transform the construction of STC to the searching of optimal multi-objectives services with QoS global constraints. This paper first introduces the QoS model of STC and evaluates the service composition process, then presents the detailed operating process of STCSSA and design of the dynamic inertia weight strategy of the algorithm, and also proposes an optional variation method. Comparative tests were performed on STCSSA with other particle swarm optimization algorithms. It was validated from the perspective of performance that the proposed algorithm has advantages in improving the rate of convergence and avoiding local optimum, and from the perspective of practical application STCSSA also demonstrated feasibility in the construction of large-scale NCS task community.

A numerical method based on the explicit two-step method in time direction and the mixed finite element method in spatial direction is presented for the symmetric regularized long wave (SRLW) equation. The optimal a priori error estimates (O((Δt)² + h^m+1 + h^k+1)) for fully discrete explicit two-step mixed scheme are derived. Moreover, a numerical example is provided to confirm our theoretical results.

Nanofluid is a suspension of nanoparticles (solid particles with diameters below 100 nm) in a conventional base fluid with significantly improved heat transfer characteristics compared to the original fluid. The heat transfer coefficient is a quantitative characteristic of the convective heat transfer. The purpose of this paper is to study the effect of the nanoparticle size (diameter) on the heat transfer coefficient of forced convective heat transfer of nanofluid in the fully developed laminar region of a horizontal tube. Using thermal conductivity model which is a function of the nanoparticle size, flow of a nanofluid (water + Al₂O₃) in a circular tube submitted to a constant wall temperature is numerically investigated with two particle sizes of 11 nm and 47 nm. The calculated results show that the nanoparticle size does not significantly affect the heat transfer coefficient, however, the heat transfer coefficient decreases as the particle size increases.

Powder mixed electric discharge machining (PMEDM) is one of the modern developments in electric discharge machining (EDM) process. In the present work, finite element modeling has been carried out considering randomly oriented multiple sparks during PMEDM. Transient thermal analysis is done to obtain temperature distribution, volume removal, and proportion of volume removed by melting and evaporation at different current, pulse on time and fraction of heat that enters to work piece. Gradually growing spark behavior and Gaussian distribution of heat source is used to simulate multiple craters. Temperature distribution along radial direction shows peak temperature at center of spark and thereafter a gradual decrease with increase in radial distance. Along depth direction temperature sharply decreases that forms wider craters with shallow depth in PMEDM. Peak temperature and volume removal increases with current more rapidly. Volume removal by melting is much higher than evaporation at lower current settings and with higher current almost equal amount of material is removed by melting and evaporation thus reducing the re-solidification of melted material. Current plays a significant role behind the contribution of material removal by evaporation followed by fraction of heat. Increase in pulse on duration increases the total volume of material removal however does not significantly increase the proportion of volume removal by vaporization.

To reduce the running time of network simulation in heterogeneous computing environment, a network simulation task partition method, named LBPHCE, is put forward. In this method, the network simulation task is partitioned in comprehensive consideration of the load balance of both routing computing simulation and packet forwarding simulation. First, through benchmark experiments, the computation ability and routing simulation ability of each simulation machine are measured in the heterogeneous computing environment. Second, based on the computation ability of each simulation machine, the network simulation task is initially partitioned to meet the load balance of packet forwarding simulation in the heterogeneous computing environment, and then according to the routing computation ability, the scale of each partition is fine-tuned to satisfy the balance of the routing computing simulation, meanwhile the load balance of packet forwarding simulation is guaranteed. Experiments based on PDNS indicate that, compared to traditional uniform partition method, the LBPHCE method can reduce the total simulation running time by 26.3% in average, and compared to the liner partition method, it can reduce the running time by 18.3% in average.

Electric load movement forecast is increasingly importance for the industry. This study addresses the load movement forecast modeling based on complex matrix interpolation of the S-transform (ST). In complex matrix of time-frequency representation of the ST, each row follows conjugate symmetric property and each column appears a certain degree of similarity. Based on these characteristics, a complex matrix interpolation method for the time-frequency representation of the ST is proposed to interpolate each row of the complex matrix based on the conjugate symmetric property, and then to perform nearest-neighbor interpolation on each column. Then with periodic extension for daily and yearly electric load movement, a forecast model employing the complex matrix interpolation of the ST is introduced. The forecast approach is applied to predict daily load movement of the European Network on Intelligent Technologies (EUNITE) load dataset and annual electric load movement of State Gird Corporation of China and its branches in 2005 and 2006. Result analysis indicates workability and effectiveness of the proposed method.