2篇
您的检索式：作者名="Fanzhi ZENG"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Bayesian parameter estimation of SST model for shock wave-boundary layer interaction flows with different strengths显示文摘The Shock Wave-Boundary Layer Interaction(SWBLI)flow generated by compression corner widely occurs in engineering.As one of the primary methods in engineering,the Reynolds Averaged Navier-Stokes(RANS)methods usually cannot correctly predict strong SWBLI flows.In addition to the defects of the eddy viscosity assumption,the uncertainty of the closure coeffi-cients in RANS models often significantly impacts the simulation results.This study performs para-metric sensitivity analysis and Bayesian calibration on the closure coefficients of the Menter k-x Shear-Stress Transport(SST)model based on the SWBLI with different strengths.Firstly,the para-metric sensitivity on prediction results is analyzed using the Sobol index.The results indicate that the Sobol indices of wall pressure and skin friction exhibited opposite fluctuation trends with the increase of SWBLI strength.Then,the Bayesian uncertainty quantification method is adopted to obtain the posterior probability distributions and Maximum A Posteriori(MAP)estimates of the closure coefficients and the posterior uncertainty of the Quantities of Interests(QoIs).The results indicate that the prediction ability for strong SWBLI of the SST model is significantly improved by using the MAP estimates,and the relative errors of QoIs are reduced dramatically. | Denggao TANG Jinping LI Fanzhi ZENG Yao LI Chao YAN | 2023 | Chinese Journal of Aeronautics2023，36，4： | 0 |
| 2 | A novel robust aerodynamic optimization technique coupled with adjoint solvers and polynomial chaos expansion显示文摘Uncertainty is common in the life cycle of an aircraft, and Robust Aerodynamic Optimization(RAO) that considers uncertainty is important in aircraft design. To avoid the curse of dimensionality in surrogate-based optimization, this study proposes an adjoint RAO technique called “R-Opt”. Polynomial Chaos Expansion(PCE) is coupled with the R-Opt technique to quantify uncertainty in the responses of the target(including its mean and standard deviation). Only one process of PCE model construction is required in each iteration, and the gradients of uncertainty can be inferred via chain rules. The proposed method is more efficient than prevalent methods,and avoids the problem of a disagreement over the best PCE basis from among a number of PCE models(especially in case of sparse PCE). It also supports the application of sparse PCE.Two benchmark tests and two airfoil cases were used to verify R-Opt, and the optimal solutions were deemed to be robust. It improved the mean aerodynamic performance and reduced the standard deviation of the target. | Wei ZHANG Qiang WANG Fanzhi ZENG Chao YAN | 2022 | Chinese Journal of Aeronautics2022，35，10： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员