Drag coefficient estimation model to simulate dynamic control of autonomous underwater vehicle (AUV) motion

Abstract

A vehicle dynamics model is crucial for the design of control system for an autonomous underwater vehicle (AUV). However, it is not a simple task to determine the hydrodynamic forces especially the drag coefficient involved for any particular vehicle model. This paper describes a novel approach to approximate the drag coefficient of any given vehicle shapes and sizes using fourth order regression method. The vehicle is subjected to pre-conditioning phase, where it can be done with CFD modelling or subject to simple experimental test within an open environment. In the pre-conditioning phase, the vehicle is required to navigate freely around custom test environment to obtain the drag profile in real-time. With sufficient data, using the correlation 3D graph of drag coefficient and the change in angles, the drag profile of any given shape can be determined. The accuracy of the model is based on the frequency of trial runs, as well as the efficiency of the vehicle’s on-board inertial navigation sensors. In this paper, the proposed approach is being demonstrated using ANSYS-CFX and the results obtained provide close approximation to the real drag coefficient. Therefore, the proposed novel approach is promising and can be used to find the drag coefficients for any given underwater vehicle at any conditions.