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

TL;DR

This paper develops a multi-fidelity modeling approach combining RANSE and potential flow methods to analyze the hydrodynamic performance of an autonomous surface vehicle under variable payload conditions, optimizing design and operation.

Contribution

It introduces a multi-fidelity Gaussian Process model for efficient hydrodynamic analysis of ASV, accounting for payload and center of mass variations, with active learning refinement.

Findings

MF-GP effectively predicts response surfaces with limited computational cost.

Payload and center of mass significantly influence the vehicle's resistance and attitude.

Calibration of center of mass can reduce resistance and extend operational range.

Abstract

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