面向民机标模的宽域高雷诺数小样本气动建模方法研究

Research on few-shot aerodynamic modeling methods for civil aircraft model with wide-range high Reynolds number

目前飞行雷诺数下复杂工况地面试验需要的运行成本极高,高雷诺数流态下的试验数据少而稀,变雷诺数气动力精确模化存在严重的数据不均衡和小样本问题,因此,气动仿真精度亟待提升。为解决变雷诺数气动力获取成本与模型精度之间的矛盾,聚焦后续飞行器设计的降本增效,以CHN-T1运输机标模为研究对象,通过数据融合和信息迁移两大策略,降低了预测模型对高雷诺数样本的依赖,实现了宽域高雷诺数小样本气动特性的快速预测。本研究利用覆盖亚跨声速、百万至千万量级变雷诺数下的18条气动力曲线构建了宽域变雷诺数气动数据集,设计了涵盖不同速域、雷诺数和马赫数外插等多种复杂度的算例进行分析验证,将仅利用单一来源试验数据构建的模型精度作为基准,对比了不同方法的特点。结果表明,利用10条左右的高精度气动力曲线作为建模数据时,采用基于神经网络的数据融合方法得到的气动预测模型均方根误差可降低50%以上,信息迁移方法得到的气动力预测模均方根误差也可降低至少40%。

To enhance the independent development capability and efficiency of new strategic aircraft,it is crucial to accurately acquire aerodynamic data over a wide range of high Reynolds numbers.There is an urgent need to improve the accuracy of aerodynamic simulations for complex flow conditions at flight Reynolds numbers,and to address the high cost associated with ground testing in cryogenic wind tunnels.The experimental data at high Reynolds numbers are few and sparse,and there are serious data imbalance and few-shot problems in the accurate aerodynamic modeling under wide-range Reynolds numbers.To solve the contradiction between the cost and accuracy of the aerodynamic model under variable Reynolds number,and focus on the cost reduction and efficiency increase of subsequent aircraft design,this study takes the CHN-T1 transport aircraft as the research objective.Based on data fusion and information transfer techniques,we achieve the rapid prediction of variable Reynolds number aerodynamics through few-shot learning.This approach reduces the reliance on high Reynolds number samples in the modeling process.In the present study,a wide-ranging variable Reynolds number aerodynamic dataset is constructed using a combination of 18 aerodynamic curves.These curves encompass sub-transonic speeds and variable Reynolds numbers ranging from millions to tens of millions.Additionally,various complexity cases are designed for demonstration.As a verification,a benchmark is established using high-fidelity experimental data through a single source method.The characteristics of different methods are then compared.The results demonstrate that when approximately 10 high-fidelity aerodynamic curves are utilized as modeling data,the data fusion neural network reduces the root mean square error of aerodynamic modeling by over 50%.Additionally,the information transfer method reduces the error by at least 40%.