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Autonomous surface vehicles (ASV) allow the investigation of coastal areas,ports and harbors as well as harsh and dangerous environments such as thearctic regions. Despite receiving increasing attention, the hydrodynamicanalysis of ASV performance subject to variable operational parameters islittle investigated. In this context, this paper presents a multi-fidelity (MF)hydrodynamic analysis of an ASV, namely the Shallow Water AutonomousMultipurpose Platform (SWAMP), at surveying speed in calm water and subject tovariable payload and location of the center of mass, accounting for the varietyof equipment that the vehicle can carry. The analysis is conducted in deepwater, which is the condition mostly encountered by the ASV during surveys ofcoastal and harbors areas. Quantities of interest are the resistance, thevehicle attitude, and the wave generated in the region between the catamaranhulls. These are assessed using a Reynolds Averaged Navier Stokes Equation(RANSE) code and a linear potential flow (PF) solver. The objective is toaccurately assess the quantities of interest, along with identifying thelimitation of PF analysis in the current context. Finally, a multi-fidelityGaussian Process (MF-GP) model is obtained combining RANSE and PF solutions.The latter also include variable grid refinement and coupling betweenhydrodynamic loads and rigid body equations of motion. The surrogate model isiteratively refined using an active learning approach. Numerical results showthat the MF-GP is effective in producing response surfaces of the SWAMPperformance with a limited computational cost. It is highlighted how the SWAMPperformance is significantly affected not only by the payload, but also by thelocation of the center of mass. The latter can be therefore properly calibratedto minimize the resistance and allow for longer-range operations.

Multi-fidelity hydrodynamic analysis of an autonomous surface vehicle at surveying speed in deep water subject to variable payload