著者

Kenichi NANBU

出版者

The Japan Society of Mechanical Engineers

雑誌

Journal of Computational Science and Technology (ISSN:18816894)

巻号頁・発行日

vol.6, no.3, pp.147-156, 2012 (Released:2012-09-26)

参考文献数

15

被引用文献数

1 1

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New solution algorithms of finite-difference systems for 1D and 2D Poisson's equations were obtained by modifying Gaussian elimination. Introduction of a new naming of row and column for coefficient matrices of the systems made it possible to derive the elimination algorithms explicitly. Since the finite-difference systems for Poisson's equations are diagonally dominant, solutions with high accuracy can be obtained with no use of pivoting in the present algorithm. The computer execution time for the present algorithm would appear to be one order smaller than the time for SOR with Chebyshev acceleration.

著者

KANAZAKI Masahiro SETO Naoto

出版者

日本機械学会

雑誌

Journal of Computational Science and Technology (ISSN:18816894)

巻号頁・発行日

vol.6, no.1, pp.1-15, 2012

被引用文献数

3

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Efficient global optimization (EGO) was applied to the multi-objective design and knowledge discovery of a supersonic transport (SST) wing. The objective functions considered here are employed to maximize the lift–to-drag ratio at supersonic cruise, to minimize the sonic boom intensity and to minimize wing structural weight, simultaneously. The EGO process is based on Kriging surrogate models, which were constructed using several sample designs. Subsequently, the solution space could be explored through the maximization of expected improvement (EI) values that corresponded to the objective function of each Kriging model because the surrogate models provide an estimate of the uncertainty at the predicted point. Once a number of solutions have been obtained for the EI maximization problem by means of a multi-objective genetic algorithm (MOGA), the sample designs could be used to improve the models' accuracy and identify the optimum solutions at the same time. In this paper, 108 sample points are evaluated for the constructions of the Kriging models. In order to obtain further information about the design space, two knowledge discovery techniques are applied once the sampling process is completed. First, through functional analysis of variance (ANOVA), quantitative information is gathered and then, self-organizing maps (SOMs) are created to qualitatively evaluate the aircraft design. The proposed design process provides valuable information for the efficient design of an SST wing.