압축성 기저 유동에 대한 GEKO 모델 계수의 불확실성 정량화 연구

Abstract

The present work, supersonic flow over an axisymmetric base is simulated using Generalized k-ω (GEKO) model which is proposed by Menter. GEKO two-equation model based on the k-ω formulation provides free model coefficients which can be adjusted by user depending on the specified flow type. Uncertainty Quantification analysis (UQ) is adopted to quantify the uncertainty of the model coefficients and to calibrate the coefficients for the flow. Latin Hypercube Sampling (LHS) method is used for sampling input parameters which are independent as a uniform distribution. Surrogate model is constructed by using ordinary least-squares (OLS). In order to characterize the posterior via Markov Chain Monte Carlo sampling, Affine Invariant Ensemble Algorithm (AIES) is selected. Through Forward problem, the most effective coefficient among the coefficients of GEKO model is figured out (Sensitivity analysis). Calibrated model coefficients are obtained through Bayesian inference. The results obtained using the calibrated coefficients by UQ to the base flow shows better agreement with available experimental measurements than the results obtained using default model coefficients.