Data-driven aerodynamic shape design with distributionally robust optimization approaches

TL;DR

This paper introduces a data-driven approach for aerodynamic shape design using distributionally robust optimization, connecting it with the Taguchi method, and demonstrates promising results in transonic turbulent flow simulations.

Contribution

It establishes a novel link between distributionally robust optimization and the Taguchi method in aerodynamic design, with preliminary computational validation.

Findings

Promising design results in transonic turbulent flow

Connection between DRO and Taguchi method demonstrated

Initial computational experiments show potential

Abstract

We formulate and solve data-driven aerodynamic shape design problems with distributionally robust optimization (DRO) approaches. Building on the findings of the work \cite{gotoh2018robust}, we study the connections between a class of DRO and the Taguchi method in the context of robust design optimization. Our preliminary computational experiments on aerodynamic shape optimization in transonic turbulent flow show promising design results.