Dynamic iterative approximate deconvolution model for large-eddy simulation of dense gas compressible turbulence

被引:8
作者
Zhang, Chao [1 ,2 ,3 ]
Yuan, Zelong [1 ,2 ,3 ]
Duan, Lishu [1 ,2 ,3 ]
Wang, Yunpeng [1 ,2 ,3 ]
Wang, Jianchun [1 ,2 ,3 ]
机构
[1] Southern Univ Sci & Technol, Natl Ctr Appl Math Shenzhen NCAMS, Shenzhen 518055, Peoples R China
[2] Southern Univ Sci & Technol, Dept Mech & Aerosp Engn, Shenzhen 518055, Peoples R China
[3] Southern Univ Sci & Technol, Guangdong Hong Kong Macao Joint Lab Data Driven Fl, Shenzhen 518055, Peoples R China
基金
中国国家自然科学基金;
关键词
SUBGRID-SCALE MODELS; CHANNEL FLOWS; EQUATION; SCHEMES;
D O I
10.1063/5.0128776
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
We study large-eddy simulation of compressible decaying isotropic turbulence of dense gas at initial turbulent Mach numbers of 0.4 and 0.8. The unclosed subgrid-scale (SGS) terms are approximated by the dynamic iterative approximate deconvolution (DIAD) model proposed by Yuan et al. [ "Dynamic iterative approximate deconvolution models for large-eddy simulation of turbulence, " Phys. Fluids 33, 085125 (2021)], and compared with the dynamic Smagorinsky (DSM) model. In an a priori test, the correlation coefficients of the DIAD model for most SGS terms are larger than 0.98, and the relative errors are smaller than 0.2, except for the SGS internal energy flux. In an a posteriori test, the DIAD model can well predict the probability density functions (PDFs) of SGS terms involving thermodynamic variables. Moreover, the DIAD model shows greater advantages than the DSM model in predicting various statistics and structures of compressible turbulence of dense gas, including spectra of velocity and thermodynamic variables, PDFs of SGS kinetic energy flux, deviatoric SGS stress and normalized strain-rate tensor, and the instantaneous spatial structures of vorticity. Published under an exclusive license by AIP Publishing.
引用
收藏
页数:24
相关论文
共 121 条
[1]   Implicit subgrid-scale modeling by adaptive deconvolution [J].
Adams, NA ;
Hickel, S ;
Franz, S .
JOURNAL OF COMPUTATIONAL PHYSICS, 2004, 200 (02) :412-431
[2]   NUMERICAL STUDY ON USING SULFUR-HEXAFLUORIDE AS A WIND-TUNNEL TEST GAS [J].
ANDERSON, WK .
AIAA JOURNAL, 1991, 29 (12) :2179-2180
[3]   Monotonicity preserving weighted essentially non-oscillatory schemes with increasingly high order of accuracy [J].
Balsara, DS ;
Shu, CW .
JOURNAL OF COMPUTATIONAL PHYSICS, 2000, 160 (02) :405-452
[4]  
Bardina J., 1980, 13 FLUID PLASM C, P1357
[5]   Deep neural networks for data-driven LES closure models [J].
Beck, Andrea ;
Flad, David ;
Munz, Claus-Dieter .
JOURNAL OF COMPUTATIONAL PHYSICS, 2019, 398
[6]  
Bethe H.A., 1998, CLASSIC PAPERS SHOCK, P421, DOI [DOI 10.1007/978-1-4612-2218-7_11, 10.1007/978-1-4612-2218-7_]
[7]   Variable density turbulence tunnel facility [J].
Bodenschatz, E. ;
Bewley, G. P. ;
Nobach, H. ;
Sinhuber, M. ;
Xu, H. .
REVIEW OF SCIENTIFIC INSTRUMENTS, 2014, 85 (09)
[8]  
BORIS JP, 1992, FLUID DYN RES, V10, P199, DOI 10.1016/0169-5983(92)90023-P
[9]   Application of Bethe-Zel'dovich-Thompson fluids in organic Rankine cycle engines [J].
Brown, BP ;
Argrow, BM .
JOURNAL OF PROPULSION AND POWER, 2000, 16 (06) :1118-1124
[10]   Machine Learning for Fluid Mechanics [J].
Brunton, Steven L. ;
Noack, Bernd R. ;
Koumoutsakos, Petros .
ANNUAL REVIEW OF FLUID MECHANICS, VOL 52, 2020, 52 :477-508