Near Space Technology

Uncertainty quantification of parameters in SST turbulence model for inlet simulation

  • Kailing ZHANG ,
  • Siyi LI ,
  • Yi DUAN ,
  • Chao YAN
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  • 1.Science and Technology on Space Physics Laboratory,China Academy of Launch Vehicle Technology,Beijing  100076,China
    2.School of Aeronautic Science and Engineering,Beijing University,Beijing  100083,China

Received date: 2023-08-10

  Revised date: 2023-08-14

  Accepted date: 2023-09-06

  Online published: 2023-09-21

Abstract

The inlet, as one of the key components of the air-breathing high Mach number vehicle, is significant to the performance of the whole propulsion system. In the numerically simulated inlet flow for engineering applications, RANS (Reynolds Averaged Navier-Stokes) still plays an irreplaceable role. However, the turbulence model frequently used in RANS would affect the reliability of the numerical results due to its parameter uncertainty. The purpose of this paper is to carry out quantitative analysis on parameter uncertainty in the SST (Shear Stress Transport) turbulence model, and evaluate the influence on the inlet flow. The hysteresis loop of the inlet start performance was firstly predicted by the SST turbulence model, the uncertainty of QoIs (Quantity of Interests) caused by the parameter uncertainty was then quantified by Non-Intrusive Polynomial Chaos (NIPC) method, and the key parameters were identified by the sensitivity analysis for both the start and unstart states of the inlet. The results show that the uncertainty of model parameters leads to a large uncertainty in the prediction results of the shock wave structure for the inlet start state and the separation flow for the inlet unstart state, further resulting in a 10% non-negligible uncertainty of the inlet performance parameters. According to the parameter sensitivity analysis, σω1 and a1 are the key model parameters contributing most to the QoIs uncertainty.

Cite this article

Kailing ZHANG , Siyi LI , Yi DUAN , Chao YAN . Uncertainty quantification of parameters in SST turbulence model for inlet simulation[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023 , 44(S2) : 729429 -729429 . DOI: 10.7527/S1000-6893.2023.29429

References

1 沈海军, 程凯, 杨莉. 近空间飞行器[M]. 北京: 航空工业出版社, 2012.
  SHEN H J, CHENG K, YANG L. Near space aerocraft[M]. Beijing: Aviation Industry Press, 2012 (in Chinese).
2 HEISER W, PRATT D, DALEY D, et al. Hypersonic airbreathing propulsion[M]. Reston: AIAA, 1994.
3 梁德旺, 袁化成, 张晓嘉. 影响高超声速进气道起动能力的因素分析[J]. 宇航学报200627(4): 714-719.
  LIANG D W, YUAN H C, ZHANG X J. Research on the effects of start ability of hypersonic inlet[J]. Journal of Astronautics200627(4): 714-719 (in Chinese).
4 王翼, 范晓樯, 何继宏, 等. 侧板构型对二维高超声速进气道启动性能的影响[J]. 航空学报201031(2): 217-222.
  WANG Y, FAN X Q, HE J H, et al. Effect of sidewall geometry on starting characteristics of two-dimensional hypersonic inlet[J]. Acta Aeronautica et Astronautica Sinica201031(2): 217-222 (in Chinese).
5 王卫星, 郭荣伟. 高超声速进气道自起动过程中流动非定常特性[J]. 航空学报201536(10): 3263-3274.
  WANG W X, GUO R W. Unsteady flow characteristics of hypersonic inlet during self-starting[J]. Acta Aeronautica et Astronautica Sinica201536(10): 3263-3274 (in Chinese).
6 LIU H K, YAN C, ZHAO Y T, et al. Active control method for restart performances of hypersonic inlets based on energy addition[J]. Aerospace Science and Technology201985: 481-494.
7 DURBIN P A. Some recent developments in turbulence closure modeling[J]. Annual Review of Fluid Mechanics201850: 77-103.
8 LAURENCE D. Large eddy simulation of industrial flows? Closure strategies for turbulent and transitional flows [M]. Cambridge: Cambridge University Press, 2000.
9 DURAISAMY K, SPALART P, Status RUMSEY C., emerging ideas and future directions of turbulence modeling research in aeronautics: NASA/TM-2017-219682[R]. Washington, D.C.: NASA, 2017.
10 BUSH R H, CHYCZEWSKI T S, DURAISAMY K, et al. Recommendations for future efforts in RANS modeling and simulation[C]∥ Proceedings of the AIAA Scitech 2019 Forum. Reston: AIAA, 2019.
11 SLOTNICK J, KHODADOUST A, ALONSO J, et al. CFD vision 2030 study: A path to revolutionary computational aerosciences: NASA/CR-2014-218178[R]. Washington, D.C.: NASA, 2014.
12 MENTER F R. Two-equation eddy-viscosity turbulence models for engineering applications[J]. AIAA Journal199432(8): 1598-1605.
13 MATYUSHENKO A A, GARBARUK A V. Adjustment of the k- ω SST turbulence model for prediction of airfoil characteristics near stall[J]. Journal of Physics: Conference Series2016769: 012082.
14 胡岳, 张涛. 分离涡流场数值仿真的参数影响研究[J]. 机械工程学报201652(12): 165-172.
  HU Y, ZHANG T. Research on the effects of numerical simulation parameters of separation vortex flow field[J]. Journal of Mechanical Engineering201652(12): 165-172 (in Chinese).
15 WIENER N. The homogeneous chaos[J]. American Journal of Mathematics193860(4): 897-936.
16 HOSDER S, WALTERS R, PEREZ R. A non-intrusive polynomial chaos method for uncertainty propagation in CFD simulations[C]∥ Proceedings of the 44th AIAA Aerospace Sciences Meeting and Exhibit. Reston: AIAA, 2006.
17 SCHAEFER J, HOSDER S, WEST T, et al. Uncertainty quantification of turbulence model closure coefficients for transonic wall-bounded flows[J]. AIAA Journal201755(1): 195-213.
18 ERB A, HOSDER S. Analysis and comparison of turbulence model coefficient uncertainty for canonical flow problems[J]. Computers & Fluids2021227: 105027.
19 ZHANG K L, ZHAO Y T, WANG Q, et al. Uncertainty analysis and calibration of SST turbulence model for free shear layer in cavity-ramp flow[J]. Acta Astronautica2022192: 168-181.
20 LI J P, ZENG F Z, JIANG Z H, et al. Investigations on turbulence model uncertainty for hypersonic shock-wave/boundary-layer interaction flows[J]. AIAA Journal202260(8): 4509-4522.
21 SCHOUTENS W. Stochastic processes and orthogonal polynomials[M]. New York: Springer, 2000.
22 ZHANG J C, FU S. An efficient approach for quantifying parameter uncertainty in the SST turbulence model[J]. Computers & Fluids2019181: 173-187.
23 MCKAY M D, BECKMAN R J, CONOVER W J. Comparison of three methods for selecting values of input variables in the analysis of output from a computer code[J]. Technometrics197921(2): 239-245.
24 ZHAO Y T, YAN C, WANG X Y, et al. Uncertainty and sensitivity analysis of SST turbulence model on hypersonic flow heat transfer[J]. International Journal of Heat and Mass Transfer2019136: 808-820.
25 SOBOL I. Sensitivity estimates for nonlinear mathematical models[J]. Mathematical Modeling and Computational Experiment19931(1): 112-118.
26 LI Z F, GAO W Z, JIANG H L, et al. Unsteady behaviors of a hypersonic inlet caused by throttling in shock tunnel[J]. AIAA Journal201351(10): 2485-2492.
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