Uncertainty Quantification for CFD Simulation of Stochastic Drag Flow Based on Non-Intrusive Polynomial Chaos Method

被引：0
|
作者：
Xia L. [1 ]
Zou Z. [1 ,2 ]
Yuan S. [1 ]
Zeng Z. [1 ]
机构：
[1] School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai
[2] State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai
来源：
Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2020年 / 54卷 / 06期
关键词：
Computational fluid dynamics (CFD); Latin hypercube sampling; Polynomial chaos method; Uncertainty quantification;
D O I：
10.16183/j.cnki.jsjtu.2019.062
中图分类号：
学科分类号：
摘要：
In this paper, verification & validation and uncertainty quantification in uncertainty analysis for computational fluid dynamics (CFD) simulation are compared. A state-of-the-art method for uncertainty quantification problems, i.e., the non-intrusive polynomial chaos (NIPC) method, is introduced and applied to quantifying the uncertainty of two-dimensional stochastic drag flow, together with the Monte-Carlo (MC) method. For the MC method, the random sampling (RS) method and the Latin hypercube sampling (LHS) method are adopted. The uncertainty of the stochastic drag flow induced by the inlet and outlet pressure boundaries is studied, with the boundaries treated as stochastic variables with uniform distribution. It is shown that there is no big difference between LHS and RS, and the NIPC method can simulate the uncertainty propagation better. © 2020, Shanghai Jiao Tong University Press. All right reserved.
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页码：584 / 591
页数：7
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