CFD Uncertainty Quantification using PCE-HDMR – Exemplary Application to a Buoyancy-Driven Mixing Process

Abstract

For the investigation of uncertainties in high dimensional spaces of computationally expensive engineering applications, reliable Uncertainty Quantification (UQ) methods are needed. These methods should provide accurate and efficient High-Dimensional Model Representations (HDMR) of stochastic results using a reasonable number of calculations. Therefore, the PCE-HDMR approach is utilized to qualify appropriate UQ methods for large-scale computations in the field of Computational Fluid Dynamics (CFD). This technique is a combination of Cut-HDMR, a hierarchical decomposition modeling approach, with Polynomial Chaos Expansion (PCE). To demonstrate its effectiveness, the PCE-HDMR methodology in conjunction with complementary modeling techniques is applied for the UQ analysis of a buoyancy-driven mixing process between two miscible fluids within the Differentially Heated Cavity (DHC) of aspect ratio 4. The results include a thorough probabilistic representation of time-dependent response quantities that comprehensively describe the mixing process. The stochastic models are derived from Large Eddy Simulations (LES) using PCE HDMR and the Sparse Grid Method (SGM), which serves as a reference for the results from PCE-HDMR. The results show that PCE-HDMR provides accurate statistics of the modeled time-dependent stochastic processes and shows good agreement with the SGM reference. Thus, PCE-HDMR indicates great potential for UQ of technical-scale computations due to its efficiency and flexibility in the construction of stochastic models.