Multi-fidelity and multi-objective optimization under uncertainty in aerospace design

点击翻译按钮获取中文摘要

In the past, design optimization was used to finding the best design at a single operating point which would usually have degraded performance at off-design conditions. Nowadays, the focus has shifted to guiding the optimization process towards finding designs such that they achieve as good performance as possible over a range of uncertain operating conditions. This project will focus on improving recent techniques in optimization under uncertainty which find stochastically non-dominated designs such as horsetail matching. Since CFD computations are expensive, uncertainty propagation is done by first building a surrogate model over the uncertainty space and then sampling it many times. This project aims at reducing the computational cost associated with building surrogate models, such as improving current sampling techniques and using multiple fidelity models. In addition, improving efficiency of multiple stage machine (i.e. jet engine) optimization under uncertain inputs and targets will be looked at. Suitable applications include turbomachinery and hypersonic vehicle designs.