  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.
