Yonghua Zhou¹, Xun Yang¹, Chao Mi¹

CMES-Computer Modeling in Engineering & Sciences, Vol.92, No.5, pp. 477-491, 2013, DOI:10.3970/cmes.2013.092.477

Abstract Route selections for vehicles can be equivalent to determine the optimized operation processes for vehicles which intertwine with each other. This paper attempts to utilize the whole methodology of model predictive control to engender rational routes for vehicles, which involves three important parts, i.e. simulation prediction, rolling optimization and feedback adjustment. The route decisions are implemented over the rolling prediction horizon taking the real-time feedback information and the future intertwined operation processes into account. The driving behaviors and route selection speculations of drivers and even traffic propagation models are on-line identified and adapted for the simulation prediction in next prediction… More >

X.F. Guan¹, X. Liu², J.Z. Cui³

CMES-Computer Modeling in Engineering & Sciences, Vol.91, No.6, pp. 425-444, 2013, DOI:10.3970/cmes.2013.091.425

Abstract In this paper, a stochastic geometrical modeling method for reconstructing three dimensional multiscale pore structures of porous materials is presented. In this method, the pore structure in porous materials is represented by a random but spatially correlated pore-network, in which the results of the Mercury Intrusion Porosimetry (MIP) experiment are used as the basic input information. Beside that, based on the Monte Carlo techniques, an effective computer generation algorithm is developed, and the quantities to evaluate the properties of porous materials are defined and described. Furthermore, numerical implementations are conducted based on experimental data afterwards. This method can be used… More >

J.L. Morales¹, J.A. Moreno², F. Alhama³

CMES-Computer Modeling in Engineering & Sciences, Vol.76, No.1, pp. 1-18, 2011, DOI:10.3970/cmes.2011.076.001

Abstract Following the rules of the network simulation method, a general purpose network model is designed and numerically solved for linear elastostatic problems formulated by the Navier equations. Coupled and nonlinear terms of the PDE, as well as boundary conditions, are easily implemented in the model by means of general purpose electrical devices named controlled current (or voltage) sources. The complete model is run in the commercial software PSPICE and the numerical results are post-processed by MATLAB to facilitate graphical representation. To demonstrate the reliability and efficiency of the proposed method two applications are presented: a cantilever loaded at one end… More >

N. Mai-Duy^1,2, T. Tran-Cong², R.I. Tanner³

CMES-Computer Modeling in Engineering & Sciences, Vol.16, No.3, pp. 177-186, 2006, DOI:10.3970/cmes.2006.016.177

Abstract This paper presents a new high-order time-kernel boundary-integral-equation method (BIEM) for numerically solving transient problems governed by the Burgers equation. Instead of using high-order Lagrange polynomials such as quadratic and quartic interpolation functions, the proposed method employs integrated radial-basis-function networks (IRBFNs) to represent the unknown functions in boundary and volume integrals. Numerical implementations of ordinary and double integrals involving time in the presence of IRBFNs are discussed in detail. The proposed method is verified through the solution of diffusion and convection-diffusion problems. A comparison of the present results and those obtained by low-order BIEMs and other methods is also given. More >

S. Noroozi¹, P. Sewell¹, J. Vinney¹

CMES-Computer Modeling in Engineering & Sciences, Vol.14, No.3, pp. 171-180, 2006, DOI:10.3970/cmes.2006.014.171

Abstract A method that combines a modified back propagation Artificial Neural Network (ANN) and Boundary Element Analysis (BEA) was introduced and discussed in the author's previous papers. This paper discusses the development of an automated inverse boundary element problem engine. This inverse problem engine can be applied to both potential and elastostatic problems.
In this study, BEA solutions of a two-dimensional potential problem is utilised to test the system and to train a back propagation Artificial Neural Network (ANN). Once training is completed and the transfer function is created, the solution to any subsequent or new problems can be obtained… More >

Yonghua Zhou¹, Chao Mi¹

CMES-Computer Modeling in Engineering & Sciences, Vol.83, No.1, pp. 1-22, 2012, DOI:10.3970/cmes.2012.083.001

Abstract Cellular automata (CAs), model the dynamics of complex systems as the state update of cells restricted from their own neighbors. This paper regards the tempo-spatial constraints as dummy neighborhoods of cells for train movement, such as scheduled movement authority and speed restriction, equivalent to the maximum displacements during the future certain time steps and each time step, respectively. Under the framework of CA modeling, this paper attempts to propose an improved CA model for moving-block railway network which incorporates the tempo-spatial constraints to capture the restrictive, synergistic and autonomous dynamics. We divide the one-dimensional cell lattice into several segments, called… More >

C.-D. Tran¹, D.-A. An-Vo¹, N. Mai-Duy¹, T. Tran-Cong¹

CMES-Computer Modeling in Engineering & Sciences, Vol.82, No.2, pp. 137-162, 2011, DOI:10.32604/cmes.2011.082.137

Abstract This paper presents a numerical approach for macro-micro multi-scale modelling of visco-elastic fluid flows based on the Integrated Radial Basis Function Networks (IRBFNs) and the Stochastic Simulation Technique (SST). The extra stress is calculated using the Brownian configuration fields (BCFs) technique while the velocity field is locally approximated at a set of collocation points using 1D-IRBFNs. In this approach, the stress is decoupled from the velocity field and computed from the molecular configuration directly without the need for a closed form rheological constitutive equation. The equations governing the macro flow field are discretised using a meshless collocation method where the… More >

D.P. Boso¹, M. Lefik²

CMES-Computer Modeling in Engineering & Sciences, Vol.55, No.3, pp. 319-338, 2010, DOI:10.3970/cmes.2010.055.319

Abstract In this paper we study numerically the mechanical behaviour of wire ropes, particularly the influence of the geometrical configuration on the overall stiffness of the cables. Modelling the behaviour of a cable is a difficult problem, given the complexity of the geometrical layout, contact phenomena occurring among wires and possible yielding of the material. For this reason we pursue a "hierarchical beam approach", to substitute recursively, at each cabling stage, the bundle of wires with an equivalent single strand, having the characteristics computed from the previous level. We consider the first two levels of the bundle hierarchy and investigate the… More >

Itsuo Hanasaki, Hirofumi Shintaku, Satoshi Matsunami, Satoyuki Kawano¹

CMES-Computer Modeling in Engineering & Sciences, Vol.46, No.2, pp. 191-208, 2009, DOI:10.3970/cmes.2009.046.191

Abstract Self-assembly is one of the physical phenomena that are promising for the manufacturing process of the devices on which DNA molecules are mounted as the components. We have conducted a structural study of self-assembled poly(dA)\discretionary poly(dT) DNA networks on mica surface to discuss the design requirements. The results indicate that the network formation process consists of the adsorption and the subsequent coarsening. The final form of the component filaments are roughly straight. These characteristics imply the possible tensile loads during the network formation. Therefore, we have conducted molecular dynamics simulations of tensile tests of a short DNA fragment to elucidate… More >

D. S. Liu¹, C.Y. Tsai¹

CMES-Computer Modeling in Engineering & Sciences, Vol.39, No.1, pp. 29-48, 2009, DOI:10.3970/cmes.2009.039.029

Abstract Utilizing a thin copper substrate for illustration purposes, this study presents a novel numerical method for extracting the thermo-mechanical properties of a thin-film. In the proposed approach, molecular dynamics (MD) simulations are performed to establish the load-displacement response of a thin copper substrate nanoindented at temperatures ranging from 300~1400 K. The load data are then input to an artificial neural network (ANN), trained using a finite element model (FEM), in order to extract the material constants of the copper substrate. The material constants are then used to construct the corresponding stress-strain curve, from which the elastic modulus and the plastic… More >