Non-parametric approach for uncertainty-based multidisciplinary design optimization considering discrete information
- Non-parametric approach for uncertainty-based multidisciplinary design optimization considering discrete information
- Issue Date
- 11th World Congress on Structural and Multidisciplinary Optimisation, page. 1-1
- Uncertainty-based multidisciplinary design optimization (UMDO) has been widely
acknowledged as an advanced methodology to address competing objectives and reliable
constraints of complex systems by coupling relationship of disciplines involved in the system.
One of the hot issues in the UMDO research is uncertainty propagation in the multi-disciplines
because it makes multidisciplinary analysis (MDA) difficult in a complex system. In an MDA
phase, the traditional methods regard uncertainty as a certain parametric distribution, for
instance, normal distribution. However, in a realistic experiment and/or environment,
uncertainties exist in discrete form because experiments or exploitations are limited by a cost
problem or an environmental problem When the well-known probability density functions
(PDF) cannot identify the phenomena or the distribution is misestimated in a uncertainty
modeling step, a serious error can be caused. Therefore, a novel UMDO method directly
adopting discrete information should be proposed.
In this paper, a non-parametric approach for UMDO is suggested to consider discrete
information of uncertainty. In a non-parametric approach, because discrete information of
uncertainty of variables is directly used, each discipline, experiment or simulation should
combine discrete information of uncertainty of each variable. Thus, as a data transferring step, a
sequence of autocorrelation is employed to make discrete samples uncorrelated before
analysing disciplines at each iteration. And then, Kolmogorov–Smirnov (KS) test, which
calculates maximum distance between empirical cumulative distribution functions of previous
and current iterations, is employed to measure the uncertainty propagation of coupled variables.
An MDA phase is terminated if the distance is within limits. At last, an uncertainty analysis
based on Akaike information criterion (AIC) is proposed. AIC method selects the best fitted
distribution from several candidate distributions. To verify the performance of the proposed
method, mathematical and engineering examples are illustrated.
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- COLLEGE OF ENGINEERING[S](공과대학) > DEPARTMENT OF AUTOMOTIVE ENGINEERING(미래자동차공학과) > Articles
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