Uncertainty-Aware Data-Based Method for Fast and Reliable Shape Optimization
This addresses the robustness issue in data-based shape optimization for aerodynamic design, offering a more reliable and faster method, though it is incremental as it builds on existing surrogate modeling approaches.
The study tackled the problem of unreliable shape optimization in aerodynamics due to surrogate model errors from out-of-distribution samples, proposing an uncertainty-aware framework that reduces prediction errors and achieves superior performance gains compared to original data-based optimization, with results showing comparable effectiveness to full simulations while significantly accelerating speed.
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.