In this study, a data-driven hydrodynamics model is proposed to enable quick prediction of vehicle mobility in shallow water, considering the effect of tire-soil interaction. To this end, a high-fidelity coupled vehicle-water interaction model using computational fluid dynamics (CFD) and multibody dynamics (MBD) solvers is developed to characterize the hydrodynamic loads exerted on a vehicle operated in shallow water, and it is used to generate training data for the data-driven hydrodynamics model. To account for the history-dependent hydrodynamic behavior, a Long Short-Term Memory (LSTM) neural network is introduced to incorporate effects of the historical variation of vehicle motion states as the input to the data-driven model, and it is used to predict hydrodynamic loads online exerted on vehicle components in the MBD mobility simulation. The impacts of hydrodynamic loads on the vehicle mobility capability in shallow water are examined for different water depths and incoming flow speeds using the high-fidelity coupled CFD-MBD model. Furthermore, it is demonstrated that the vehicle-water interaction behavior in scenarios not considered in the training data can be predicted using the proposed LSTM data-driven hydrodynamics model. However, the use of non-LSTM layers, which do not account for the sequential variation of vehicle motion states as the input, leads to an inaccurate prediction. A substantial computational speedup is achieved with the proposed LSTM-MBD vehicle-water interaction model while ensuring accuracy, compared to the computationally expensive high-fidelity coupled CFD-MBD model.

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