Development of a multi-fidelity CFD framework for efficient parametric studies in nuclear thermal hydraulics

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The direct simulation of turbulence is typically infeasible. A more pragmatic approach is to employ a mesh that is coarser than the smallest-scale turbulent structures. However, this introduces potential modelling and discretisation errors. We propose a combined data-science/engineering simulation approach, whereby Convolutional Neural Networks "correct" coarse-grid CFD (CG-CFD); providing the "best estimate" plus uncertainty. Unlike existing approaches, we target a feature vector set that will enable the prediction corrections for both modelling and discretisation errors. We propose to use CG-CFD as a "low-fidelity" component in a multi-fidelity framework for parametric sweeps. The framework will use Deep Gaussian Process Regression to bridge fidelities based on existing methods in the literature. The project has gained support of both Rolls Royce and IBM Research UK, who have indicated in-kind contribution via their direct involvement in the work. The resulting method will be demonstrated primarily on test cases relevant to Nuclear Thermal Hydraulics, but developments are expected to be relevant to other ongoing research areas such as fluid-structure interaction and multiphase flow.