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.

翻译：数据驱动优化（DBO）通过利用预训练的代理模型在迭代过程中进行离线评估，为高效优化形状以提升气动性能提供了一种前景广阔的方法。然而，DBO在很大程度上依赖于训练数据库的质量。优化过程中遇到的、超出训练分布的样本可能导致显著的预测误差，从而可能误导优化过程。因此，将不确定性量化纳入优化对于检测异常值并增强鲁棒性至关重要。本研究提出了一种不确定性感知的数据驱动优化（UA-DBO）框架，用于在DBO过程中监测并最小化代理模型的不确定性。我们开发了一种概率编码器-解码器代理模型，用于预测其输出相关的不确定性，并将这些不确定性整合到一个模型置信度感知的目标函数中，以便在数据驱动优化过程中对具有较大预测误差的样本进行惩罚。该UA-DBO框架在两个旨在改善翼型阻力发散和抖振性能的多点优化问题上进行了评估。结果表明，与原始DBO相比，UA-DBO持续降低了优化样本中的预测误差，并实现了更优的性能增益。此外，与基于全计算仿真的多点优化相比，UA-DBO在提供相当优化效果的同时，显著加快了优化速度。