Conference proceeding
Predicting Vehicle Motion in Shallow Water with Data-Driven Hydrodynamics Model
Volume 10: 19th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC)
ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Boston, Massachusetts, USA, Aug. 20 - 23, 2023
08/20/2023
DOI: 10.1115/DETC2023-115254
Abstract
Abstract
In this study, a numerical procedure for predicting vehicle mobility in shallow water is proposed with the data-driven hydrodynamic force and moment model. To this end, the high-fidelity coupled CFD-MBD model is developed to characterize the hydrodynamic loads exerted on the vehicle in shallow water and used to generate the training dataset for the proposed data-driven model. The neural networks are called from the MBD mobility solver every time step to determine the hydrodynamic loads, given the current vehicle motion states and water conditions, allowing for predicting the transient responses of the vehicle interacting with shallow water. It is demonstrated by several numerical examples that the complex vehicle-water interaction behavior was accurately predicted by the proposed data-driven hydrodynamics model while achieving a substantial computational speedup.
Details
- Title: Subtitle
- Predicting Vehicle Motion in Shallow Water with Data-Driven Hydrodynamics Model
- Creators
- Hiroki Yamashita - University of IowaJuan E. Martin - University of IowaHiroyuki Sugiyama - University of IowaNathan Tison - United States Department of the ArmyArkady GruninParamsothy Jayakumar - United States Department of the Army
- Resource Type
- Conference proceeding
- Publication Details
- Volume 10: 19th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC)
- Conference
- ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Boston, Massachusetts, USA, Aug. 20 - 23, 2023
- Publisher
- American Society of Mechanical Engineers
- DOI
- 10.1115/DETC2023-115254
- Language
- English
- Date published
- 08/20/2023
- Academic Unit
- Iowa Technology Institute; IIHR--Hydroscience and Engineering; Mechanical Engineering
- Record Identifier
- 9984521375402771
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