Uncertainty-Aware Data-Based Method for Fast and Reliable Shape Optimization

TL;DR

This paper introduces an uncertainty-aware data-based optimization framework that improves shape optimization for aerodynamics by reducing surrogate model errors and accelerating the process.

Contribution

It develops a probabilistic surrogate model with uncertainty quantification and integrates it into the optimization to enhance robustness and efficiency.

Findings

UA-DBO reduces prediction errors in optimized samples.

UA-DBO achieves better performance than original DBO.

UA-DBO accelerates optimization compared to full simulations.

Abstract

Data-based optimization (DBO) offers a promising approach for efficiently optimizing shape for better aerodynamic performance by leveraging a pretrained surrogate model for offline evaluations during iterations. However, DBO heavily relies on the quality of the training database. Samples outside the training distribution encountered during optimization can lead to significant prediction errors, potentially misleading the optimization process. Therefore, incorporating uncertainty quantification into optimization is critical for detecting outliers and enhancing robustness. This study proposes an uncertainty-aware data-based optimization (UA-DBO) framework to monitor and minimize surrogate model uncertainty during DBO. A probabilistic encoder-decoder surrogate model is developed to predict uncertainties associated with its outputs, and these uncertainties are integrated into a model-confidence-aware objective function to penalize samples with large prediction errors during data-based optimization process. The UA-DBO framework is evaluated on two multipoint optimization problems aimed at improving airfoil drag divergence and buffet performance. Results demonstrate that UA-DBO consistently reduces prediction errors in optimized samples and achieves superior performance gains compared to original DBO. Moreover, compared to multipoint optimization based on full computational simulations, UA-DBO offers comparable optimization effectiveness while significantly accelerating optimization speed.