Abstract

Data-driven modeling is considered as a prospective approach for many conventional physical problems including ocean applications. Among various machine learning techniques, support vector machine stands out as one of the most widely used algorithms to establish models connecting pertinent features to physical quantities of interest. This paper takes the experimental data for a fixed cylinder in shallow water as the baseline data set and explores the modeling of nonlinear wave loads by the support vector machine (SVM) regression method. Different feature and target selections are studied in this paper to establish the nonlinear mapping relations from ambient wave elevations and kinematics to nonlinear wave loads. The performance of the SVM regression model is discussed and compared with nonlinear potential flow theory focusing on the overall statistics (standard deviation and kurtosis), which is critical for fatigue and extreme statistics analysis.

References

1.
Wang
,
J.
,
Wu
,
J.
, and
Xiao
,
H.
,
2017
, “
Physics-Informed Machine Learning Approach for Reconstructing Reynolds Stress Modeling Discrepancies Based on DNS Data
,”
Phys. Rev. Fluids
,
2
(
3
), p.
034603
.
2.
Duriez
,
T.
,
Brunton
,
S. L.
, and
Noack
,
B. R.
,
2017
,
Machine Learning Control-Taming Nonlinear Dynamics and Turbulence
,
Fluid Mechanics and Its Applications, Springer
, pp.
11
48
.
3.
Raissi
,
M.
, and
Karniadakis
,
G. E.
,
2017
, “
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
,”
J. Comput. Phys.
, pp.
125
141
.
4.
Luo
,
W.
,
Soares
,
C. G.
, and
Zou
,
Z.
,
2016
, “
Parameter Identification of Ship Maneuvering Model Based on Support Vector Machines and Particle Swarm Optimization
,”
ASME J. Offshore Mech. Arctic Eng.
,
138
(
3
), p.
031101
.10.1115/1.4032892
5.
Ahn
,
Y.
,
Kim
,
Y.
, and
Kim
,
S.-Y.
,
2019
, “
Database of Model-Scale Sloshing Experiment for LNG Tank and Application of Artificial Neural Network for Sloshing Load Prediction
,”
Marine Struct.
,
66
, pp.
66
82
. 10.1016/j.marstruc.2019.03.005
6.
Bukka
,
S. R.
,
Magee
,
A. R.
, and
Jaiman
,
R. K.
,
2020
, “
Deep Convolutional Recurrent Autoencoders
for
Flow Field Prediction
,”
Proceedings of the ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering
,
Fort Lauderdale, FL
,
June 28–July 3, 2020
.
7.
Schløer
,
S.
,
2013
, “
Fatigue and Extreme Wave Loads on Bottom Fixed Offshore Wind Turbines. Effects From Fully Nonlinear Wave Forcing on the Structural Dynamics
,”
Ph.D. thesis
,
Technical University of Denmark
.
8.
Naess
,
A.
, and
Moan
,
T.
,
2012
,
Stochastic Dynamics of Marine Structures
,
Cambridge University Press
.
9.
Moarefzadeh
,
M. R.
, and
Melchers
,
R. E.
,
2006
, “
Nonlinear Wave Theory in Reliability Analysis of Offshore Structures
,”
Probabilistic Eng. Mech.
10.
Zhang
,
Y.
,
2019
, “
Offshore Wind Turbine Nonlinear Wave Loads and Their Statistics
,”
Ph.D. thesis
,
Massachusetts Institute of Technology
.
11.
Gu
,
X.
, and
Moan
,
T.
,
2002
, “
Long-term Fatigue Damage of Ship Structures Under Nonlinear Wave Loads
,”
Marine Technol.
, SNAME N,
39
(
2
), pp.
95
104
.10.5957/mt1.2002.39.2.95
12.
Marino
,
E.
,
Borri
,
C.
, and
Peil
,
U.
,
2011
, “
A Fully Nonlinear Wave Model to Account for Breaking Wave Impact Loads on Offshore Wind Turbines
,”
J. Wind Eng. Ind. Aerodyn.
,
99
(
4
), pp.
483
490
.10.1016/j.jweia.2010.12.015
13.
Newman
,
J. N.
,
2018
,
Marine Hydrodynamics
,
MIT Press
.
14.
Sclavounos
,
P. D.
,
2012
, “
Nonlinear Impulse of Ocean Waves on Floating Bodies
,”
J. Fluid Mech.
,
697
, pp.
316
335
.10.1017/jfm.2012.68
15.
Cristianini
,
N.
, and
Swane-Taylor
,
J.
,
2000
,
Support Vector Machines
,
Cambridge University Press
.
16.
Sclavounos
,
P. D.
, and
Ma
,
Y.
,
2018
, “
Artificial Intelligence Machine Learning
,”
ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering
,
Madrid, Spain
.
17.
Suykens
,
J. A.
, and
Vandewalle
,
J.
,
1999
,
Least Squares Support Vector Machine Classifiers
,
Neural Process. Lett.
,
9
, pp.
293
300
.10.1023/A:1018628609742
18.
Fasshauer
,
G. E.
, and
McCourt
,
M. J.
,
2012
, “
Stable Evaluation of Gaussian RBF Interpolants
,”
SIAM J. Sci. Comput.
,
34
, pp.
737
762
.
19.
Bachynski
,
E. E.
,
Kristiansen
,
T.
, and
Thys
,
M.
,
2017
, “
Experimental and Numerical Investigations of Monopile Ringing in Irregular Finite-Depth Water Waves
,”
Appl. Ocean Res.
,
68
, pp.
154
170
.10.1016/j.apor.2017.08.011
20.
Sclavounos
,
P. D.
,
Zhang
,
Y.
,
Ma
,
Y.
, and
Larson
,
D. F.
,
2019
, “
Offshore Wind Turbine Nonlinear Wave Loads and Their Statistics
,”
ASME J. Offshore Mech. Arctic Eng.
,
141
(
3
), p.
031904
.10.1016/j.marstruc.2019.03.005
21.
Ma
,
Y.
,
Larson
,
D. F.
, and
Sclavounos
,
P. D.
,
2020
, “
Support Vector Machine Learning Model of Nonlinear Viscous Ship Roll Hydrodynamics
,”
33rd Symposium on Naval Hydrodynamics
,
Osaka, Japan
,
Oct. 18–23
.
You do not currently have access to this content.