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Computational Fluid Dynamics Prediction of the Dynamic Behavior of Autonomous Underwater Vehicles
- Source :
- IEEE Journal of Oceanic Engineering. :1-16
- Publication Year :
- 2019
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2019.
-
Abstract
- Prediction of the dynamic behavior of autonomous underwater vehicles (AUVs) is the basis for developing the control system and optimizing the hydrodynamic shape of AUVs. A fast and efficient prediction method of dynamic behavior of AUVs was developed with the secondary programming of a commercial computational fluid dynamics (CFD) code of FLUENT. In the proposed CFD method, mass properties of the AUV and thrust of the propeller were coupled to the 6-degree-of-freedom (DOF) dynamic equations of AUV in the form of user defined functions. A subregional unstructured mixed meshing strategy combining static meshes and dynamic meshes was proposed to improve the computational efficiency and to realize the 6-DOF movement of AUV. The proposed CFD prediction method can predict not only the dynamic behavior of the AUV, but also its surrounding flow field. Dynamic behavior of a seafloor mapping AUV in straight running and turn running were predicted using the developed CFD strategy. Comparison between the prediction results from the CFD method and those from the sea trials proved the proposed method to be accurate and effective. Some measures were put forward to improve motion performance of the vehicle according to the prediction results.
- Subjects :
- business.industry
Computer science
Mechanical Engineering
Sea trial
Propeller
Ocean Engineering
Thrust
Aerodynamics
Solid modeling
Computational fluid dynamics
Computer Science::Robotics
Vehicle dynamics
Static mesh
Electrical and Electronic Engineering
business
ComputingMethodologies_COMPUTERGRAPHICS
Marine engineering
Subjects
Details
- ISSN :
- 23737786 and 03649059
- Database :
- OpenAIRE
- Journal :
- IEEE Journal of Oceanic Engineering
- Accession number :
- edsair.doi...........799d1c1975d61f428fdaee82eda1d94d
- Full Text :
- https://doi.org/10.1109/joe.2019.2916119