Yaohui Li^1,2, Yizhong Wu^1,3, Zhengdong Huang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.97, No.4, pp. 323-357, 2014, DOI:10.3970/cmes.2014.097.323

Abstract Kriging model, which provides an exact interpolation and minimizes the error estimates, is a highly-precise global approximation model in contrast with other traditional response surfaces. Therefore, sequential exploratory experimental design (SEED) with Kriging model is crucial for globally approximating a complex black-box function. However, the more sampling points are, the longer time it would take to update the Kriging model during sequential exploratory design. This paper, therefore, proposes a new construction method called incremental Kriging method (IKM) to improve the constructing efficiency with just a little and controllable loss of accuracy for Kriging model. The… More >

W. Zhao^1,2, J.K. Liu³, X.Y. Li², Q.W. Yang⁴, Y.Y. Chen⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.93, No.6, pp. 469-488, 2013, DOI:10.3970/cmes.2013.093.469

Abstract In order to obtain reliable structural design, it is of extreme importance to evaluate the failure probability, safety levels of structure (reliability analysis) and the effect of a change in a variable parameter on structural safety (sensitivity analysis) when uncertainties are considered. With a computationally cheaper approximation of the limit state function, various response surface methods (RSMs) have emerged as a convenient tool to solve this especially for complex problems. However, the traditional RSMs may produce large errors in some conditions especially for those highly non-linear limit state functions. Instead of the traditional least squares… More >

Zichun Yang^1,2,3, Wencai Sun²

CMES-Computer Modeling in Engineering & Sciences, Vol.92, No.2, pp. 193-212, 2013, DOI:10.3970/cmes.2013.092.193

Abstract The set-based structural eigenvalue problem is defined, by expressing the uncertainties of the structural parameters in terms of various convex sets. A new method based on Kriging model and Particle Swarm Optimization (PSO) is proposed for solving this problem. The introduction of the Kriging model into this approach can effectively reduce the computational burden especially for largescale structures. The solutions of the non-linear and non-monotonic problems are more accurate than those obtained by other methods in the literature with the PSO algorithm. The experimental points for Kriging model are sampled according to Latin hypercube sampling More >

Leiting Dong^1,2, Salah H. Gamal³, Satya N. Atluri^2,4

CMC-Computers, Materials & Continua, Vol.37, No.1, pp. 1-21, 2013, DOI:10.3970/cmc.2013.037.001

Abstract In this paper, a simple and reliable procedure of stochastic computation is combined with the highly accurate and efficient Trefftz Computational Grains (TCG), for a direct numerical simulation (DNS) of heterogeneous materials with microscopic randomness. Material properties of each material phase, and geometrical properties such as particles sizes and distribution, are considered to be stochastic with either a uniform or normal probabilistic distributions. The objective here is to determine how this microscopic randomness propagates to the macroscopic scale, and affects the stochastic characteristics of macroscopic material properties. Four steps are included in this procedure: (1)… More >

Zhenhan Yao, Yintao Wei

The International Conference on Computational & Experimental Engineering and Sciences, Vol.16, No.2, pp. 51-52, 2011, DOI:10.3970/icces.2011.016.051

Abstract Shape optimization is very important in many engineering fields. As conventional engineering design, the shape optimization is generally based on the finite element analysis. Because many engineering strutures are related to different nonlinear problems in their working state, the analysis for each design sample is quite time consuming. For example for the shape optimization of automotive tires, it is related to the geometrical, material nonlinearity, and boundary nonlinearity caused by the contact problem. Therefore, the finite element analysis combined with sensitivity analysis to get more information for each design sample is a strategy usually adopted.… More >

S. Rajagopal¹, Ranjan Ganguli²

CMES-Computer Modeling in Engineering & Sciences, Vol.81, No.1, pp. 1-34, 2011, DOI:10.3970/cmes.2011.081.001

Abstract The preliminary wing design of a low speed, long endurance UAV is formulated as a two step optimization problem. The first step performs a single objective aerodynamic optimization and the second step involves a coupled dual objective aerodynamic and structural optimization. During the first step, airfoil geometry is optimized to get maximum endurance parameter at a 2D level with maximum thickness to chord ratio and maximum camber as design variables. Leading edge curvature, trailing edge radius, zero lift drag coefficient and zero lift moment coefficient are taken as constraints. Once the airfoil geometry is finalized,… More >

Hua Zou¹, Hua Li^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.67, No.1, pp. 55-78, 2010, DOI:10.3970/cmes.2010.067.055

Abstract A novel meshless technique termed the Random Integral Quadrature (RIQ) method is developed in this paper for solving the generalized integral equations. By the RIQ method, the governing equations in the integral form are discretized directly with the field nodes distributed randomly or uniformly, which is achieved by discretizing the integral governing equations with the generalized integral quadrature (GIQ) technique over a set of background virtual nodes, and then interpolating the function values at the virtual nodes over a set of field nodes with Local Kriging method, where the field nodes are distributed either randomly… More >

Vikas Namdeo^1,2, C S Manohar^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.32, No.3, pp. 123-160, 2008, DOI:10.3970/cmes.2008.032.123

Abstract The problem of identification of nonlinear system parameters from measured time histories of response under known excitations is considered. Solutions to this problem are obtained by using the force state mapping technique with two alternative functional representation schemes. These schemes are based on the application of reproducing kernel particle method (RKPM) and kriging techniques to fit the force state map. The RKPM has the capability to reproduce exactly polynomials of specified order at any point in a given domain. The kriging based methods represent the function under study as a random field and the parameters… More >