Aircraft Design Optimization with Uncertainty Based on Fuzzy Clustering Analysis

Abstract

Uncertainty always exists in any design problems; conventional aircraft design with deterministic optimization may achieve underdesign or overdesign. Therefore, it is necessary to consider uncertainty analysis in aircraft concept design. Traditional uncertainty analyses need many sampling points to simulate the uncertain models. These methods include a large number of calculations to achieve the required accuracy. To increase the efficiency of uncertainty analysis and reduce the effect of error propagation on uncertainty models, a method with dynamic surrogate models based on fuzzy clustering analysis is proposed in this paper. Among the design spaces, the sampling points with little influence on response surface are abandoned by dynamic screening until the surrogate model reaches the expected level of accuracy. This method is applied to the optimization of a hypothetical aircraft concept design, which shows that the calculated amount of uncertainty analysis can be reduced effectively while the optimized performance can satisfy the reliability and robustness.

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