Support Vector Machines Model of the Nonlinear Hydrodynamics of Fixed Cylinders
Name
omae_143_5_051701.pdf
Description
Published version
Size
1016.38 KB
Format
Adobe PDF
Checksum (MD5)
13120f981e8b4a8cb37d190503f9ef5c
Author(s)
Ma, Yu
Sclavounos, Paul D
Date Issued
2021
Journal
Journal of Offshore Mechanics and Arctic Engineering
Publisher
ASME International
Citation
Ma, Yu and Sclavounos, Paul D. 2021. "Support Vector Machines Model of the Nonlinear Hydrodynamics of Fixed Cylinders." Journal of Offshore Mechanics and Arctic Engineering, 143 (5).
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1115/1.4049731
