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Type: Conference paper
Title: Drag coefficient estimation model to simulate dynamic control of autonomous underwater vehicle (AUV) motion
Author: Tan, K.
Lu, T.
Anvar, A.
Citation: MODSIM2013: 20th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2013 / J. Piantadosi, R. S. Anderssen and J. Boland (eds.): pp.963-969.
Publisher: The Modelling and Simulation Society of Aust & NZ
Publisher Place: Australia
Issue Date: 2013
ISBN: 9780987214331
Conference Name: International Congress on Modelling and Simulation (20th : 2013 : Adelaide, South Australia)
Statement of
Kuan M.Tan, Tien-Fu Lu, Amir Anvar
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.
Keywords: Autonomous Underwater Vehicle; Robotics; Hydrodynamics; CFD; System Identification; Modeling; Simulation; Control; Drag
Description: 22nd National Conference of the Australian Society for Operations Research — ASOR 2013 DSTO led Defence Operations Research Symposium — DORS 2013
Rights: Copyright status unknown
RMID: 0020133451
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Appears in Collections:Mechanical Engineering conference papers

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