摘要
针对叶型表面边界层转捩问题,基于机器学习XGBoost模型的层流/湍流界面识别方法,建立了一种不依赖于指定阈值的预测转捩位置的新方法。在本方法中,根据大涡模拟计算得到的可控扩散叶型绕流的高精度流场,考虑到流动的间歇性,利用机器学习方法统计出不同时刻下边界层中不同位置处层流状态的比例,并依据其在叶型弦长方向上的变化率,得出转捩区域位置。通过对不同影响参数的考察,检验该方法的有效性,从而验证了其具有较好的通用性。与传统判据相比,本方法预测转捩位置准确,且在结果研判上不依赖于主观判断。此外,利用当前方法,发现对于可控扩散叶型,其边界层转捩除了受湍动能影响较大之外,也取决于涡量的大小及其空间分布。
For identification of the transition position on the blade surface,a turbulence/non-turbulence interface identification method based on XGBoost model without specified thresholds was introduced.According to this method,the high-precision flow field around the controlled diffusion airfoil was solved by the large eddy simulation method.Considering the intermittent flow,the proportions of laminar flow state at different positions in the boundary layer at different times were calculated by the machine learning method,and the transition position was obtained according to the change rate in the chord length direction of the airfoil.The method was verified by investigating different influencing parameters.Compared with traditional criteria,this method could accurately predict transition positions without subjective judgment.In addition,using the present method,it was found that for a controlled diffusion airfoil,the boundary layer transition depended not only on the turbulent energy,but also on the size of vortices and the space distribution feature.
作者
李昌林
童歆
虞培祥
欧阳华
LI Changlin;TONG Xin;YU Peixiang;OUYANG Hua(School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;Engineering Research Center of Gas Turbine and Civil Aero Engine,Ministry of Education,Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《航空动力学报》
EI
CAS
CSCD
北大核心
2024年第11期184-193,共10页
Journal of Aerospace Power
基金
国家科技重大专项(2017-Ⅱ-0007-0021,J2019-Ⅱ-0005-0025)
中国联合重燃专项(19UGTC037)。
关键词
转捩位置预测
XGBoost模型
分离诱导转捩
可控扩散叶型
边界层
prediction of transition position
XGBoost model
separation-induced transition
controlled diffusion airfoil
boundary layer
作者简介
李昌林(1997-),男,硕士,主要从事压气机可控扩散叶型流场研究;通信作者:虞培祥(1986-),男,助理研究员,博士,主要从事计算流体力学及计算气动声学研究。E-mail:pxyu@sjtu.edu.cn。
