稀疏发现湍流边界层壁面函数公式  

Sparse Formula Discovery in Wall Function of Turbulent Boundary Layer

作　　者：张晖[1] 叶涛 王新光[2] 万钊[2] ZHANG Hui;YE Tao;WANG Xin-guang;WAN Zhao(School of Computer Science and Technology,Southwest University of Science and Technology,Mianyang Sichuan 621000,China;China Aerodynamics Research and Development Center,Mianyang,Sichuan 621000,China)

机构地区：[1]西南科技大学计算机科学与技术学院,四川绵阳621000 [2]中国空气动力研究与发展中心计算空气动力研究所,四川绵阳621000

出　　处：《计算机仿真》2023年第7期387-392,共6页Computer Simulation

基　　金：国家数值风洞NNW支持项目;国家自然科学基金(11972362);四川省教育厅项目(06zd1610);四川省科技厅重点研发项目(2021YGF0031)。

摘　　要：壁面函数在处理边界层计算时发挥着重要作用,但目前学术界对统一壁面函数这一经验公式的正确性存在争议。提出了一种被称为顺序阈值弹性网络算法的稀疏机器学习方法,将上述算法同LASSO算法对壁面实验数据进行挖掘得到统一壁面函数。二者所得到的统一壁面函数与真实的实验值对比误差小,准确度高,能够很好地匹配实验数据,其中顺序阈值弹性网络算法与LASSO算法相比误差更小,准确度更高。将所得结果应用于工程中能够缩短数值模拟的时间,同时也能够为理论分析提供参考。The wall function plays an important role in the calculation of the boundary layer,however,there is currently controversy in the academic community regarcing the correctness of the empirical formula of the unified wall function.A sparse machine learning method called sequential threshold elastic network algorithm is proposed.The proposed algorithm and LASSO algorithm were used to mine the wall experimental data to obtain a unified wall function which is suitable for engineering applications.It is found that the two unified wall functions obtained by the two algorithms have small error and high accuracy compared with the real experimental data,which can match the experimental data well;Among them,the sequential threshold elastic network algorithm achieves smaller error and higher accuracy than the LASSO algorithm.Applying it to engineering will speed the numerical simulation process,and provide more accurate wall data for theoretical analysis.

关 键 词：壁面函数 稀疏回归 机器学习 数据挖掘 边界层 

分 类 号：TP3-05[自动化与计算机技术—计算机科学与技术]