Multi-Fidelity Machine Learning from Adaptive-and Multi-Grid RANS Simulations

A generalized multi-fidelity (MF) metamodel of CFD (computational fluid dynamics) computations is presented for design- and operational-space exploration, based on machine learning from an arbitrary number of fidelity levels. The method is based on stochastic radial basis functions (RBF) with least squares regression and in-the-loop optimization of RBF parameters to deal with noisy data. The method is intended to accurately predict ship performance while reducing the computational effort required by simulation-based optimization (SBDO) and/or uncertainty quantification problems. The present formulation here exploits the potential of simulation methods that naturally produce results spanning a range of fidelity levels through adaptive grid refinemen and/or multi-grid resolution (i.e. varying the grid resolution). The performance of the method is assessed for one analytical test and three SBDO problems based on CFD simulations, namely a NACA hydrofoil, the DTMB 5415 model, and a roll-on/roll-off passenger ferry in calm water. Under the assumption of a limited budget of function evaluations, the proposed MF method shows better performance in comparison with its single-fidelity counterpart. The method also shows very promising results in dealing with and learning from noisy CFD data.