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

在过去，设计优化是用来寻找最佳的设计在一个单一的操作点，通常会有降级的性能在非设计条件。如今，重点已经转移到引导优化过程，以找到设计，使它们在一系列不确定的操作条件下实现尽可能好的性能。这个项目将集中在改进最近的技术在优化下的不确定性，发现随机非支配设计，如马尾匹配。由于计算流体力学计算是昂贵的，不确定性传播是通过首先在不确定性空间上建立一个代理模型，然后多次采样来完成的。该项目旨在减少与构建代理模型相关的计算成本，例如改进当前的采样技术和使用多个保真度模型。此外，提高效率的多级机（即喷气发动机）优化不确定的输入和目标下将被视为。合适的应用包括超音速飞行器和高超音速飞行器设计。

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.