空气动力学学报2025，Vol.43Issue(1)：12-21,10.DOI:10.7638/kqdlxxb-2024.0045
基于不确定性预测的气动力建模与主动采样
Aerodynamic modeling and active sampling based on uncertainty prediction
摘要
Abstract
Neural network methods,as an efficient and accurate modeling approach,have been widely used in various fields.However,the"black-box"feature of neural networks,combined with the engineering problem of few-shot phenomenon,leads to insufficient model reliability and high uncertainty in the prediction results,severely limiting the use of neural network models.In order to enhance the engineering applicability of neural network models,this study focuses on the unsteady aerodynamic characteristic and utilizes time convolutional networks(TCN)to model the temporal unsteady aerodynamic forces in large-amplitude oscillatory wind tunnel tests.The MC-Dropout technique is employed to evaluate the uncertainty of prediction results.Based on the uncertainty analysis results,active sampling of wind tunnel test samples is conducted.The results indicate that model uncertainty can be used as a prior evaluation of the prediction accuracy.There is a strong linear relationship between the model prediction error and the uncertainty.The active sampling strategy can reduce the required samples by up to 40%compared to the random sampling strategy.This validates the effectiveness of the present method in improving the trustworthiness of black-box models and reducing the number of modeling samples required.关键词
大迎角/风洞试验/非定常气动力/神经网络/不确定性/主动采样Key words
high angle of attack/wind tunnel test/unsteady aerodynamic/neural network/uncertainty/active sampling引用本文复制引用
张子军,李怀璐,赵彤,王旭,张伟伟..基于不确定性预测的气动力建模与主动采样[J].空气动力学学报,2025,43(1):12-21,10.基金项目
国家自然科学基金(U2441211) （U2441211）
