In the present work, supersonic flows over an axisymmetric base and a 24-deg compression ramp are investigated using the generalized - (GEKO) model implemented in the commercial software, ANSYS FLUENT. GEKO is a two-equation model based on the - formulation, and some specified model coefficients can be tuned depending on the flow characteristics. Uncertainty quantification (UQ) analysis is incorporated to quantify the uncertainty of the model coefficients and to calibrate the coefficients. The Latin hypercube sampling (LHS) method is used for sampling independent input parameters as a uniform distribution. A metamodel is constructed based on general polynomial chaos expansion (gPCE) using ordinary least squares (OLS). The influential coefficient closure is obtained by using Sobol indices. The affine invariant ensemble algorithm (AIES) is selected to characterize the posterior distribution via Markov chain Monte Carlo sampling. Calibrated model coefficients are extracted from posterior distributions obtained through Bayesian inference, which is based on the point-collocation nonintrusive polynomial chaos (NIPC) method. Results obtained through calibrated model coefficients by Bayesian inference show superior prediction with available experimental measurements than those from original model ones.

中文翻译：

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可压缩流的 GEKO 模型系数的不确定性量化

另外，在本工作中，在轴对称的基超音速流动和24℃下压缩斜坡使用广义进行了研究-（GEKO）在商业软件，ANSYS FLUENT实施模型。GEKO 是一个基于-公式，并且可以根据流动特性调整一些指定的模型系数。结合不确定性量化 (UQ) 分析来量化模型系数的不确定性并校准系数。拉丁超立方采样 (LHS) 方法用于将独立输入参数作为均匀分布进行采样。使用普通最小二乘法 (OLS) 基于一般多项式混沌展开 (gPCE) 构建元模型。影响系数闭合是通过使用 Sobol 指数获得的。选择仿射不变集成算法 (AIES) 来通过马尔可夫链蒙特卡罗采样来表征后验分布。从通过贝叶斯推理获得的后验分布中提取校准模型系数，它基于点搭配非侵入多项式混沌（NIPC）方法。通过贝叶斯推理校准模型系数获得的结果表明，与原始模型的预测相比，可用实验测量具有更好的预测。