进气道流动中SST湍流模型参数的不确定度量化
收稿日期: 2023-08-10
修回日期: 2023-08-14
录用日期: 2023-09-06
网络出版日期: 2023-09-21
Uncertainty quantification of parameters in SST turbulence model for inlet simulation
Received date: 2023-08-10
Revised date: 2023-08-14
Accepted date: 2023-09-06
Online published: 2023-09-21
进气道作为吸气式高马赫数飞行器的关键部件之一,对于整个推进系统的性能非常重要。在工程应用中对进气道流动进行数值模拟时,RANS(Reynolds Averaged Navier-Stokes)方法仍然起着不可替代的作用。然而,该方法中常用的湍流模型,由于其参数存在较大的不确定性,会影响数值结果的可信度。因此,对常用的SST(Shear Stress Transport)湍流模型参数开展不确定度量化分析,定量评估模型参数不确定性对进气道流动数值模拟的影响。首先,采用SST湍流模型预测进气道起动性能的迟滞环,再分别针对进气道的起动状态和不起动状态,通过非嵌入式混沌多项式(NIPC)方法量化由参数不确定性导致的所关注物理量的不确定度,并通过敏感性分析甄别出导致该不确定度的关键参数。研究结果表明:模型参数的不确定性会导致进气道起动状态下波系结构的预测以及不起动状态下分离区的预测产生较大的不确定性,并进一步导致进气道性能参数产生10%左右的不可忽略的不确定度。通过参数敏感性分析可知,在进气道起动状态,对所关注物理量不确定度贡献较大的关键模型参数为ω方程扩散项系数
张恺玲 , 李思怡 , 段毅 , 阎超 . 进气道流动中SST湍流模型参数的不确定度量化[J]. 航空学报, 2023 , 44(S2) : 729429 -729429 . DOI: 10.7527/S1000-6893.2023.29429
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.
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