Resistance and payload optimization of a sea vehicle by adaptive multi-fidelity metamodeling

An adaptive multi-fidelity metamodel is presented for efficient simulation-based design optimization processes. A multi-fidelity approximation is built by correcting a low-fidelity-trained metamodel with the metamodel of the difference (error) between high- and low-fidelity simulations. The metamodel prediction is based on the expected value of stochastic radial basis functions, which also provide the uncertainty associated to the prediction. New training points are placed where the prediction uncertainty is maximum. The prediction uncertainty of both the low-fidelity and the metamodel of the error is considered for an adaptive refinement of the low- and high-fidelity training sets, respectively. The method is demonstrated for the hull-form optimization of a SWATH (small water-plane area twin hull). The hydrodynamic performance is evaluated with RANSE (high-fidelity) and potential flow (low-fidelity) solvers and the optimization aims at the minimization of the hydrodynamic resistance and maximization of the payload.