Non-Intrusive Polynomial Chaos Methods for Uncertainty Quantification in Fluid Dynamics

Abstract

This paper examines uncertainty quantification in computational fluid dynamics (CFD) with non-intrusive polynomial chaos (NIPC) methods which require no modification to the existing deterministic models. The NIPC methods have been increasingly used for uncertainty propagation in high-fidelity CFD simulations due to their non-intrusive nature and strong potential for addressing the computational efficiency and accuracy requirements associated with large-scale complex stochastic simulations. We give the theory and description of various NIPC methods used for non-deterministic CFD simulations. We also present several stochastic fluid dynamics examples to demonstrate the application and effectiveness of NIPC methods for uncertainty quantification in fluid dynamics. These examples include stochastic computational analysis of a laminar boundary layer flow over a flat plate, supersonic expansion wave problem, and inviscid transonic flow over a three-dimensional wing with rigid and aeroelastic assumptions.