Abstract

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.

References

1.
Mechergui
,
D.
, and
Jayakumar
,
P.
,
2020
, “
Efficient Generation of Accurate Mobility Maps Using Machine Learning Algorithms
,”
J. Terramech.
,
88
, pp.
53
63
.10.1016/j.jterra.2019.12.002
2.
Dallas
,
J.
,
Cole
,
M. P.
,
Jayakumar
,
P.
, and
Ersal
,
T.
,
2021
, “
Terrain Adaptive Trajectory Planning and Tracking on Deformable Terrains
,”
IEEE Trans. Veh. Technol.
,
70
(
11
), pp.
11255
11268
.10.1109/TVT.2021.3114088
3.
Pazouki
,
A.
,
Serban
,
R.
, and
Negrut
,
D.
,
2014
, “
A Lagrangian–Lagrangian Framework for the Simulation of Rigid and Deformable Bodies in Fluid
,”
Multibody Dynamics: Computational Methods and Applications
,
Springer International Publishing
,
Berlin, Germany
, pp.
33
52
.10.1007/978-3-319-07260-9_2
4.
Wasfy
,
T. M.
,
Wasfy
,
H. M.
, and
Peters
,
J. M.
,
2015
, “
Coupled Multibody Dynamics and Smoothed Particle Hydrodynamics for Modeling Vehicle Water Fording
,”
ASME
Paper No. DETC2015-47142. 10.1115/DETC2015-47142
5.
Canelas
,
R. B.
,
Brito
,
M.
,
Feal
,
O. G.
,
Domínguez
,
J. M.
, and
Crespo
,
A. J. C.
,
2018
, “
Extending DualSPHysics With a Differential Variational Inequality: Modeling Fluid-Mechanism Interaction
,”
Appl. Ocean Res.
,
76
, pp.
88
97
.10.1016/j.apor.2018.04.015
6.
Tison
,
N.
,
2019
, “
Wheeled Amphibious Vehicle Water Egress M&S Using CFD and Simplified Vehicle Modeling Methodologies
,”
Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium
, Novi, MI¸ Aug. 13–15, pp.
13
15
.https://events.esd.org/wp-content/uploads/2019/08/Amphibious-Vehicle-Water-Egress-Modeling-and-Simulation-Using-CFD-and-Wong%E2%80%99s-Methodology.pdf
7.
Yamashita
,
H.
,
Arnold
,
A.
,
Carrica
,
P. M.
,
Noack
,
R. W.
,
Martin
,
J. E.
,
Sugiyama
,
H.
, and
Harwood
,
C.
,
2022
, “
Coupled Multibody Dynamics and Computational Fluid Dynamics Approach for Amphibious Vehicles in the Surf Zone
,”
Ocean Eng.
,
257
, p.
111607
.10.1016/j.oceaneng.2022.111607
8.
Banazadeh
,
A.
,
Seif
,
M. S.
,
Khodaei
,
M. J.
, and
Rezaie
,
M.
,
2017
, “
Identification of the Equivalent Linear Dynamics and Controller Design for an Unmanned Underwater Vehicle
,”
Ocean Eng.
,
139
, pp.
152
168
.10.1016/j.oceaneng.2017.04.048
9.
Martin
,
J. E.
,
Hammond
,
M.
,
Rober
,
N.
,
Kim
,
Y.
,
Cichella
,
V.
, and
Carrica
,
P.
,
2022
, “
Reduced Order Model of a Generic Submarine for Maneuvering Near the Surface
,”
Symposium on Naval Hydrodynamics
,
Washington, DC
, June 26–July 1.10.48550/arXiv.2212.09821
10.
Brunton
,
S. L.
,
Noack
,
B. R.
, and
Koumoutsakos
,
P.
,
2020
, “
Machine Learning for Fluid Mechanics
,”
Annu. Rev. Fluid Mech.
,
52
(
1
), pp.
477
508
.10.1146/annurev-fluid-010719-060214
11.
Vinuesa
,
R.
, and
Brunton
,
S. L.
,
2022
, “
Enhancing Computational Fluid Dynamics With Machine Learning
,”
Nat. Comput. Sci.
,
2
(
6
), pp.
358
366
.10.1038/s43588-022-00264-7
12.
Kochkov
,
D.
,
Smith
,
J. A.
,
Alieva
,
A.
,
Wang
,
Q.
,
Brenner
,
M. P.
, and
Hoyer
,
S.
,
2021
, “
Machine Learning–Accelerated Computational Fluid Dynamics
,”
Proc. Natl. Acad. Sci.
,
118
(
21
), p.
e2101784118
.10.1073/pnas.2101784118
13.
Hesthaven
,
J. S.
, and
Ubbiali
,
S.
,
2018
, “
Non-Intrusive Reduced Order Modeling of Nonlinear Problems Using Neural Networks
,”
J. Comput. Phys.
,
363
, pp.
55
78
.10.1016/j.jcp.2018.02.037
14.
Wang
,
Z.
,
Xiao
,
D.
,
Fang
,
F.
,
Govindan
,
R.
,
Pain
,
C. C.
, and
Guo
,
Y.
,
2018
, “
Model Identification of Reduced Order Fluid Dynamics Systems Using Deep Learning
,”
Int. J. Numer. Methods Fluids
,
86
(
4
), pp.
255
268
.10.1002/fld.4416
15.
Hong
,
S. H.
,
House
,
A.
,
Kaminsky
,
A. L.
,
Tison
,
N.
,
Ruan
,
Y.
,
Korivi
,
V.
,
Wang
,
Y.
, and
Pant
,
K.
,
2021
, “
Machine Learning-Based Thermal and Flow Simulation on Heterogeneous Platform for Signature Prediction
,”
Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium
, Novi, MI, Aug. 10–12.http://gvsets.ndia-mich.org/documents/MS2/2021/MS2%20140PM%20Machine%20Learning%20Based%20Thermal%20and%20Flow%20Simulation%20on%20Heterogeneous%20Platform%20for%20Signature%20Prediction.pdf
16.
Abadía-Heredia
,
R.
,
López-Martín
,
M.
,
Carro
,
B.
,
Arribas
,
J. I.
,
Pérez
,
J. M.
, and
Le Clainche
,
S.
,
2022
, “
A Predictive Hybrid Reduced Order Model Based on Proper Orthogonal Decomposition Combined With Deep Learning Architectures
,”
Expert Syst. Appl.
,
187
, p.
115910
.10.1016/j.eswa.2021.115910
17.
del Águila Ferrandis
,
J.
,
Triantafyllou
,
M. S.
,
Chryssostomidis
,
C.
, and
Karniadakis
,
G. E.
,
2021
, “
Learning Functionals Via LSTM Neural Networks for Predicting Vessel Dynamics in Extreme Sea States
,”
Proc. R. Soc. A
,
477
(
2245
), p.
20190897
.10.1098/rspa.2019.0897
18.
Xu
,
W.
,
Maki
,
K. J.
, and
Silva
,
K. M.
,
2021
, “
A Data-Driven Model for Nonlinear Marine Dynamics
,”
Ocean Eng.
,
236
, p.
109469
.10.1016/j.oceaneng.2021.109469
19.
Carrica
,
P. M.
,
Wilson
,
R. V.
, and
Stern
,
F.
,
2007
, “
An Unsteady Single‐Phase Level Set Method for Viscous Free Surface Flows
,”
Int. J. Numer. Methods Fluids
,
53
(
2
), pp.
229
256
.10.1002/fld.1279
20.
Huang
,
J.
,
Carrica
,
P. M.
, and
Stern
,
F.
,
2008
, “
Semi‐Coupled Air/Water Immersed Boundary Approach for Curvilinear Dynamic Overset Grids With Application to Ship Hydrodynamics
,”
Int. J. Numer. Methods Fluids
,
58
(
6
), pp.
591
624
.10.1002/fld.1758
21.
Li
,
J.
,
Yuan
,
B.
, and
Carrica
,
P. M.
,
2020
, “
Modeling Bubble Entrainment and Transport for Ship Wakes: Progress Using Hybrid RANS/LES Methods
,”
J. Ship Res.
,
64
(
04
), pp.
328
345
.10.5957/JOSR.09180071
22.
Carrica
,
P. M.
,
Kerkvliet
,
M.
,
Quadvlieg
,
F.
, and
Martin
,
J. E.
,
2021
, “
CFD Simulations and Experiments of a Submarine in Turn, Zigzag, and Surfacing Maneuvers
,”
J. Ship Res.
,
65
(
04
), pp.
293
308
.10.5957/JOSR.07200045
23.
Noack
,
R.
,
Boger
,
D.
,
Kunz
,
R. E.
, and
Carrica
,
P.
,
2009
, “
Suggar++: An Improved General Overset Grid Assembly Capability
,”
AIAA
Paper No. 2009-3992. 10.2514/6.2009-3992
24.
Yamashita
,
H.
,
Jayakumar
,
P.
, and
Sugiyama
,
H.
,
2016
, “
Physics-Based Flexible Tire Model Integrated With LuGre Tire Friction for Transient Braking and Cornering Analysis
,”
ASME J. Comput. Nonlinear Dyn.
,
11
(
3
), p.
031017
.10.1115/1.4032855
25.
Yamashita
,
H.
,
Jayakumar
,
P.
,
Alsaleh
,
M.
, and
Sugiyama
,
H.
,
2018
, “
Physics-Based Deformable Tire–Soil Interaction Model for Off-Road Mobility Simulation and Experimental Validation
,”
ASME J. Comput. Nonlinear Dyn.
,
13
(
2
), p.
021002
.10.1115/1.4037994
26.
Yamashita
,
H.
,
Chen
,
G.
,
Ruan
,
Y.
,
Jayakumar
,
P.
, and
Sugiyama
,
H.
,
2019
, “
Hierarchical Multiscale Modeling of Tire–Soil Interaction for Off-Road Mobility Simulation
,”
ASME J. Comput. Nonlinear Dyn.
,
14
(
6
), p.
061007
.10.1115/1.4042510
27.
Yamashita
,
H.
,
Chen
,
G.
,
Ruan
,
Y.
,
Jayakumar
,
P.
, and
Sugiyama
,
H.
,
2020
, “
Parallelized Multiscale Off-Road Vehicle Mobility Simulation Algorithm and Full-Scale Vehicle Validation
,”
ASME J. Comput. Nonlinear Dyn.
,
15
(
9
), p.
091007
.10.1115/1.4046666
28.
Chen
,
G.
,
Yamashita
,
H.
,
Ruan
,
Y.
,
Jayakumar
,
P.
,
Knap
,
J.
,
Leiter
,
K. W.
,
Yang
,
X.
, and
Sugiyama
,
H.
,
2021
, “
Enhancing Hierarchical Multiscale Off-Road Mobility Model by Neural Network Surrogate Model
,”
ASME J. Comput. Nonlinear Dyn.
,
16
(
8
), p.
081005
.10.1115/1.4051271
29.
Chen
,
G.
,
Yamashita
,
H.
,
Ruan
,
Y.
,
Jayakumar
,
P.
,
Gorsich
,
D.
,
Knap
,
J.
,
Leiter
,
K. W.
,
Yang
,
X.
, and
Sugiyama
,
H.
,
2023
, “
Hierarchical MPM-ANN Multiscale Terrain Model for High-Fidelity Off-Road Mobility Simulations: A Coupled MBD-FE-MPM-ANN Approach
,”
ASME J. Comput. Nonlinear Dyn.
,
18
(
7
), p.
071001
.10.1115/1.4062204
30.
Wong
,
J. Y.
,
2022
,
Theory of Ground Vehicles
,
Wiley
,
Hoboken, NJ
.
31.
Ishigami
,
G.
,
Miwa
,
A.
,
Nagatani
,
K.
, and
Yoshida
,
K.
,
2007
, “
Terramechanics‐Based Model for Steering Maneuver of Planetary Exploration Rovers on Loose Soil
,”
J. Field Rob.
,
24
(
3
), pp.
233
250
.10.1002/rob.20187
32.
Serban
,
R.
,
Taves
,
J.
, and
Zhou
,
Z.
,
2023
, “
Real-Time Simulation of Ground Vehicles on Deformable Terrain
,”
ASME J. Comput. Nonlinear Dyn.
,
18
(
8
), p.
081007
.10.1115/1.4056851
33.
Hochreiter
,
S.
, and
Schmidhuber
,
J.
,
1997
, “
Long Short-Term Memory
,”
Neural Comput.
,
9
(
8
), pp.
1735
1780
.10.1162/neco.1997.9.8.1735
34.
Chollet
,
F.
,
2021
,
Deep Learning With Python
,
Simon and Schuster
,
New York
.
35.
Behara
,
S.
,
Arnold
,
A.
,
Martin
,
J. E.
,
Harwood
,
C. M.
, and
Carrica
,
P. M.
,
2020
, “
Experimental and Computational Study of Operation of an Amphibious Craft in Calm Water
,”
Ocean Eng.
,
209
, p.
107460
.10.1016/j.oceaneng.2020.107460
36.
Letherwood
,
M.
,
Jayakumar
,
P.
,
Gerth
,
R.
, and
Dasch
,
J.
,
2020
, “Cooperative Demonstration of Technology (CDT) for Next-Generation NATO Reference Mobility Model (NG-NRMM),”
North Atlantic Treaty Organization, Science and Technology Organization
, Report No.
STO-TM-AVT-308
.https://apps.dtic.mil/sti/trecms/pdf/AD1183601.pdf
You do not currently have access to this content.