Multi-fidelity uncertainty quantification strategies for large-scale multiphysics applications: PSAAP II particle-based solar energy receiver
The study of complex systems is often based on computationally intensive, high-fidelity, simulations. To build confidence or improve the prediction accuracy of such simulations, the impact of uncertainties in model inputs, or even the structure of the models, on the quantities of interest must be measured. This, however, requires a computational budget that is a possibly large multiple of the cost of a single simulation, and thus may become infeasible for expensive simulation models featuring a large number of uncertain inputs and highly non-linear behavior. Therefore, this work explores multi-fidelity strategies to accelerate the estimation of the effect of uncertainties. The main idea behind multi-fidelity methods is to utilize cheaper, lower-fidelity models – than the intended high-fidelity, expensive model of the problem – to generate a baseline solution that together with relatively small number of high-fidelity simulations can lead to accurate predictions. The methods are briefly presented, and their performance assessed on an irradiated particle-laden turbulent flow case related to Stanford’s PSAAP II particle-based solar energy receiver.