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Efficiently performing predictive studies of irradiated particle-ladenturbulent flows has the potential of providing significant contributionstowards better understanding and optimizing, for example, concentrated solarpower systems. As there are many uncertainties inherent in such flows,uncertainty quantification is fundamental to improve the predictivecapabilities of the numerical simulations. For large-scale, multi-physicsproblems exhibiting high-dimensional uncertainty, characterizing the stochasticsolution presents a significant computational challenge as many methods requirea large number of high-fidelity solves. This requirement results in the needfor a possibly infeasible number of simulations when a typical convergedhigh-fidelity simulation requires intensive computational resources. To reducethe cost of quantifying high-dimensional uncertainties, we investigate theapplication of a non-intrusive, bi-fidelity approximation to estimatestatistics of quantities of interest associated with an irradiatedparticle-laden turbulent flow. This method relies on exploiting the low-rankstructure of the solution to accelerate the stochastic sampling andapproximation processes by means of cheaper-to-run, lower fidelityrepresentations. The application of this bi-fidelity approximation results inaccurate estimates of the QoI statistics while requiring a small number ofhigh-fidelity model evaluations.

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