An adaptive Sequential Enhanced PCE approach and its application in aerodynamic uncertainty quantification

Uncertainty is common in the life cycle of an aircraft, and has great influence on flight quality. Among the approaches of uncertainty quantification, the polynomial chaos expansion (PCE) method has been more and more widely used. However, existing works indicate that the original PCE considering all the features is not so efficient, and has difficulties in its application in robust optimization considering uncertainty. Therefore, a novel PCE method, named as SEPCE, is proposed in this paper. A feature selecting module and a sequential sampling module is integrated in SEPCE. In this case, the SEPCE is capable of picking out the crucial features, and gradually appending new samples under requirements of model refinement. Three benchmark functions are utilized for testing the performance of SEPCE. After that, it is applied in the quantification of aerodynamic uncertainty of RAE2822 airfoil. The comparison between the SEPCE and the original PCE is also performed. Results show that the SEPCE model is more efficient and accurate. It is believed that this model will have better performance when applied to robust optimization.