摘要
摩擦阻力的精准高效预示对飞行器设计至关重要.然而摩阻分布的计算不仅代价大,且对网格密度、湍流模式和数值算法的依赖性强,而试验测量更具挑战性.为此,提出了一种数据驱动的高泛化性摩阻分布机器学习建模方法.该方法在Euler方程数值解的基础上,结合RANS计算的少量摩阻分布数据样本,构建了表面无黏流动特征与摩阻分布的关联关系模型,从而实现摩阻的预测.由于该建模方法嵌入Euler方程这一物理模型,使得在很少的样本下就能保证模型的高泛化性和高精度;另一方面,相比于RANS数值计算,由于只用求解Euler方程,计算量降低约一个量级.研究通过典型翼型和机翼的测试算例来展示该方法对于气动设计中变外形气动力的预测效果.相比于端到端的分布力深度学习建模,该方法在减少5倍样本量的情况下仍能取得很高的建模精度(阻力误差约2%~3%),且对于工况与外形变化具有较强的外插预测能力,结果的分散度低.该研究为附着流机翼的摩阻分布预测和机翼优化设计提供了一种新的高效研究手段.
Accurate and efficient prediction of skin friction drag is crucial for aircraft design.However,the computation of the skin friction drag distribution is not only costly but also highly dependent on mesh density,turbulence patterns and numerical algorithms,while experimental measurements are more challenging.To this end,this paper proposes a datadriven machine-learning modeling method for skin friction drag distribution with high generalizability.Based on the numerical solution of Euler's equation,the method combines a small number of skin friction distribution samples computed by RANS to construct a model of the correlation relationship between the surface inviscid flow feature and the friction distribution,so as to realize the prediction of friction.Since the physical model of Euler's equation is embedded in this modeling method,the high generalizability and accuracy of the model can be ensured with few samples;on the other hand,compared with the numerical computation of RANS,the amount of computation is reduced by about one order of magnitude because only the Euler's equation is solved.The study demonstrates the effectiveness of the method for predicting variable geometric shapes skin friction in aerodynamic design by means of test cases for typical airfoils and wings.Compared to end-to-end distributed force deep learning modeling,the method achieves high modeling accuracy(drag error of about 2%~3%)despite a fivefold reduction in sample size,and has strong generalization ability for working conditions and shape changes with low dispersion of results.This study provides a new and efficient research tool for the prediction of the friction distribution of attached-flow airfoils and the optimal design of airfoils.
作者
赵书乐
张伟伟
Zhao Shule;Zhang Weiwei(School of Aeronautic,Northwestern Polytechnical University,Xi'an 710072,China;International Joint Institute of Artificial Intelligence on Fluid Mechanics,Northwestern Polytechnical University,Xi’an 710072,China)
出处
《力学学报》
EI
CAS
CSCD
北大核心
2024年第8期2243-2258,共16页
Chinese Journal of Theoretical and Applied Mechanics
基金
国家自然科学基金(92152301,12072282)
科技部重点研发项目(2022YFB4300200)资助。
关键词
摩擦阻力
边界层
机器学习
数据驱动
关联建模
skin friction
boundary layer
machine learning
data driven
correlation modelling
