摘要
湍流相干结构演化规律的分析是探究沟槽减阻机理的一项重要研究手段。为探究Liutex参数与Omega准则在沟槽减阻机理研究中的适应性问题,采用大涡模拟方法,对设置有横向沟槽结构的槽道流域进行数值模拟,比较了Liutex参数及Omega准则和传统识别方法识别结果的异同。通过与传统涡识别方法综合对比后发现:Omega准则的阈值选取更加方便,且敏感度低,鲁棒性强,但对于壁面附近的小涡结构,相较于传统识别方法,并无突出优势;Liutex线相较于涡量线更加准确且规整,与涡结构的吻合性更佳;Omega准则与Liutex参数在横向沟槽结构流域的减阻机理分析中均具有很好的适应性,对于沟槽减阻机理的研究具有重要意义。
The analysis of the evolution of the turbulent coherent structure is an important tool in the investigation of the trench drag reduction mechanism.In order to make clear the adaptability of the Liutex parameter and the Omega criterion in the study of trench drag reduction mechanism,numerical simulations were carried out for a trench basin with a transverse trench structure using a large eddy simulation method,and the similarities and differences between the Liutex parameter and the Omega criterion and the traditional identification method were compared.A comprehensive comparison between this method and the traditional vortex identification methods reveals that:the Omega criterion is more convenient for threshold selection with low sensitivity and strong robustness,while for small vortex structures near the wall,it has no outstanding advantages compared with the traditional identification methods.The Liutex line is more accurate and regular compared with the vortex volume line,and it fits better with the vortex structure.The Omega criterion and the Liutex parameter are both well adapted in the analysis of the transverse trench.The Omega criterion and the Liutex parameters are both very suitable for the analysis of the drag reduction mechanism in the lateral groove structural basin,which is of great significance in the study of the drag reduction mechanism in the trench.
作者
吴正人
程相辉
石祎炜
吴浩
刘梅
WU Zhengren;CHENG Xianghui;SHI Yiwei;WU Hao;LIU Mei(School of Energy Power and Mechanical Engineering,North China Electric Power University,Baoding 071003,China;School of Economics and Management,North China Electric Power University,Baoding 071003,China)
出处
《华北电力大学学报(自然科学版)》
CAS
北大核心
2023年第6期118-126,共9页
Journal of North China Electric Power University:Natural Science Edition