Uncertainty Quantification and Metamodeling of Multifidelity CFD Computation of a Heated Fuel Assembly

This study proposes to quantify the uncertainty in a CPU-time costlyComputational Fluid Dynamics (CFD) model used to evaluate local temperaturefield in the situation of blocked fuel assembly in a PWR transfertube. Several uncertain parameters are identified and a first uncertaintypropagation study is conducted on a low-fidelity (poorly refined) mesh forCPU cost issues. Then, using the concept of “support points”, an algorithmis employed to reduce the size of the initial design of experiments.A high-fidelity model (finer mesh, more CPU-time expensive) is then runon this small-size design of experiments. A metamodel was finally builton those high-fidelity results to propagate uncertainties and finely analyzethe results. The successful results that are obtained show that metamodelinghas the potential to overcome the issue of costly CPU-time CFDmodels in the near future. Despite good quantitative results, the mainpurpose of the present study remains the novel methodology that was setup for uncertainty propagation in CFD.