Bi-fidelity approximation for uncertainty quantification and sensitivity analysis of irradiated particle-laden turbulence

TL;DR

This paper introduces a bi-fidelity approximation method to efficiently quantify uncertainties in irradiated particle-laden turbulent flows, significantly reducing computational costs while maintaining accuracy in statistical estimates.

Contribution

The study develops a non-intrusive bi-fidelity approach leveraging low-rank structures to efficiently estimate uncertainty statistics in complex turbulent flow simulations.

Findings

Accurate uncertainty quantification achieved with fewer high-fidelity simulations.

Bi-fidelity method reduces computational cost significantly.

Effective for high-dimensional stochastic problems.

Abstract

Efficiently performing predictive studies of irradiated particle-laden turbulent flows has the potential of providing significant contributions towards better understanding and optimizing, for example, concentrated solar power systems. As there are many uncertainties inherent in such flows, uncertainty quantification is fundamental to improve the predictive capabilities of the numerical simulations. For large-scale, multi-physics problems exhibiting high-dimensional uncertainty, characterizing the stochastic solution presents a significant computational challenge as many methods require a large number of high-fidelity solves. This requirement results in the need for a possibly infeasible number of simulations when a typical converged high-fidelity simulation requires intensive computational resources. To reduce the cost of quantifying high-dimensional uncertainties, we investigate the application of a non-intrusive, bi-fidelity approximation to estimate statistics of quantities of interest associated with an irradiated particle-laden turbulent flow. This method relies on exploiting the low-rank structure of the solution to accelerate the stochastic sampling and approximation processes by means of cheaper-to-run, lower fidelity representations. The application of this bi-fidelity approximation results in accurate estimates of the QoI statistics while requiring a small number of high-fidelity model evaluations.