Investigations on high-fidelity low-dissipation scheme for unsteady turbulent separated flows

被引:2
|
作者
Chen, Shu-sheng [1 ]
Li, Zheng [2 ]
Yuan, Wu [3 ]
Yu, Hua-Feng [4 ]
Yan, Chao [5 ]
机构
[1] Northwestern Polytech Univ, Sch Aeronaut, Xian 710072, Peoples R China
[2] China Acad Launch Vehicle Technol, Beijing 100076, Peoples R China
[3] Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100190, Peoples R China
[4] China Elect Technol Grp Corp, Res Inst 52, Hangzhou 310000, Peoples R China
[5] Beihang Univ, Sch Aeronaut Sci & Engn, Beijing 100191, Peoples R China
关键词
Turbulent separated flow; Unsteady; Low-dissipation; Shock-capturing scheme; Low Mach number; RECONSTRUCTION METHOD; CIRCULAR-CYLINDER; EDDY SIMULATION; RIEMANN SOLVER; AUSM; BEHAVIOR; VERSION; SEQUEL;
D O I
10.1016/j.ast.2021.106785
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Unsteady turbulent separated flow widely exists in aerospace vehicles and has become a major research topic. For the precise turbulent flow prediction, efficient and accurate numerical methods are the premise and foundation. However, most shock-capturing schemes designed for compressible flows have some difficulties in low-speed flow regions, and reduce the reliability when simulating unsteady turbulent separated flow with rich low Mach number features. Thus, the paper proposes a high-fidelity low-dissipation scheme termed LD-Roe2 and investigates the performances of the novel approach in unsteady turbulent separated flow simulations using the Hybrid Reynolds Averaged Navier-Stokes Equations/Large Eddy Simulation Approach (Spalart-Allmaras Delayed Detached Eddy Simulation). Numerical results including flow over a circular cylinder at subcritical Reynolds number and flow over tandem cylinders, demonstrate that the novel low-dissipation scheme can better simulate the unsteady separated flow and depicts finer smaller-scale vortical structures compared with traditional numerical schemes. It is expected to provide a more accurate and applicable numerical solution for the refined turbulent prediction. (C) 2021 Elsevier Masson SAS. All rights reserved.
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页数:13
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