Accurate and Practical Thruster Modeling for Underwater Vehicles

TLDR

The thruster, as the lowest layer in an underwater vehicle’s control loop, is crucial, yet no accurate practical model exists, and conventional thrust maps are explained by a classification of ambient flow and propeller states. The paper proposes an accurate and practical thrust model for underwater vehicles that accounts for ambient flow velocity. The model represents the non‑measurable axial flow velocity using ambient flow and propeller shaft velocity, divides the thrust map into three states with a Critical Advance Ratio boundary, and is validated against experimental data with an error within ±2 N. The model is practical, using only measurable states, achieves an overall force error within ±2 N, and outperforms conventional models in matching performance.

Abstract

The thruster is the crucial factor of an underwater vehicle system, because it is the lowest layer in the control loop of the system. However, an accurate and practical thruster model has not been utilized yet. In this paper, we propose an accurate and practical thrust modeling for underwater vehicles which considers the effects of ambient flow velocity. In this model, the axial flow velocity of the thruster, which is non-measurable, is represented by ambient flow velocity and propeller shaft velocity. Hence, contrary to previous models, the proposed model is practical since it uses only measurable states. Next, the whole thrust map is divided into three states according to the state of ambient flow and propeller shaft velocity, and one of the borders of the states is defined as Critical Advance Ratio (CAR). This classification explains the physical phenomenon of conventional experimental thrust maps. The proposed model is evaluated by comparing experimental data with numerical model simulation data, and it accurately covers overall flow conditions within ± 2N force error. The comparison results show that the new model's matching performance is significantly better than conventional models'.