We propose a data-efficient workflow to optimize the efficiency of a radial turbine design under a strict budget of high-fidelity computational fluid dynamics simulations. Assuming anisotropic parameter impact, we use a maximum-projection initial experimental design to ensure space-filling and strong projection properties on low-dimensional subspaces. Bayesian optimization is performed using Gaussian process surrogates with an upper confidence bound acquisition function. In parallel, polynomial chaos expansions provide variance-based global sensitivity analysis metrics, which allow to identify a reduced subspace with the most influential parameters, wherein the optimization is continued. Turbine efficiency is increased from 85.77% initially to 91.77% at the end of the workflow, with a total budget of 330 simulations.

翻译：本文提出一种数据高效的工作流程，用于在高保真计算流体动力学模拟的严格预算下优化径向涡轮设计的效率。在假设参数影响各向异性的前提下，我们采用基于最大投影的初始实验设计，以确保设计在低维子空间上具有空间填充性和强投影特性。贝叶斯优化通过采用上置信界采集函数的高斯过程代理模型实现。同时，多项式混沌展开提供基于方差的全局敏感性分析指标，从而识别出包含最具影响力参数的降维子空间，并在该子空间内继续执行优化。经过总计330次模拟，涡轮效率从初始的85.77%提升至工作流程结束时的91.77%。