A novel reduced-order model (ROM) based on higher order dynamic mode decomposition (HODMD) is proposed for the time series prediction of ship course-keeping motion in waves. The proposed ROM is validated by using the data of course-keeping tests of an ONR tumblehome ship model. First, modes are decomposed from the model test data by standard DMD and HODMD, and the dominant modes are selected according to the energy index. Then, the decomposed dominant modes are used to reconstruct and predict the dynamics of ship motion. The dynamic characteristics in the dynamical systems are revealed according to the energy index, growth rates, and frequencies of the decomposed modes. In addition, the effects of the tunable parameter in HODMD on prediction accuracy and computational times are analyzed by a parametric study. The prediction results by HODMD show better agreement with the model test data than those by standard DMD.
Skip Nav Destination
Article navigation
September 2023
Research Article|
September 22 2023
Time series prediction of ship course keeping in waves using higher order dynamic mode decomposition
Special Collection:
Recent Advances in Marine Hydrodynamics
Chang-Zhe Chen (陈昌哲)
;
Chang-Zhe Chen (陈昌哲)
(Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft)
1
School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University
, Shanghai 200240, China
Search for other works by this author on:
Zao-Jian Zou (邹早建)
;
Zao-Jian Zou (邹早建)
a)
(Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – review & editing)
1
School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University
, Shanghai 200240, China
2
State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University
, Shanghai 200240, China
a)Author to whom correspondence should be addressed: zjzou@sjtu.edu.cn
Search for other works by this author on:
Lu Zou (邹璐)
;
Lu Zou (邹璐)
(Conceptualization, Funding acquisition, Project administration, Supervision)
1
School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University
, Shanghai 200240, China
2
State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University
, Shanghai 200240, China
Search for other works by this author on:
Ming Zou (邹明)
;
Ming Zou (邹明)
(Formal analysis, Methodology, Software)
1
School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University
, Shanghai 200240, China
Search for other works by this author on:
Jia-Qing Kou (寇家庆)
Jia-Qing Kou (寇家庆)
(Formal analysis, Methodology, Software, Writing – review & editing)
3
Institute of Aerodynamics, RWTH Aachen University
, Aachen 52062, Germany
Search for other works by this author on:
a)Author to whom correspondence should be addressed: zjzou@sjtu.edu.cn
Physics of Fluids 35, 097139 (2023)
Article history
Received:
June 29 2023
Accepted:
September 03 2023
Citation
Chang-Zhe Chen, Zao-Jian Zou, Lu Zou, Ming Zou, Jia-Qing Kou; Time series prediction of ship course keeping in waves using higher order dynamic mode decomposition. Physics of Fluids 1 September 2023; 35 (9): 097139. https://doi.org/10.1063/5.0165665
Download citation file:
Sign in
Don't already have an account? Register
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Pay-Per-View Access
$40.00
Citing articles via
Hidden turbulence in van Gogh's The Starry Night
Yinxiang Ma (马寅翔), 马寅翔, et al.
On Oreology, the fracture and flow of “milk's favorite cookie®”
Crystal E. Owens, Max R. Fan (范瑞), et al.
Fluid–structure interaction on vibrating square prisms considering interference effects
Zengshun Chen (陈增顺), 陈增顺, et al.
Related Content
A reduced-order model for compressible flows with buffeting condition using higher order dynamic mode decomposition with a mode selection criterion
Physics of Fluids (January 2018)
Spatiotemporal Koopman decomposition of second mode instability from a hypersonic schlieren video
Physics of Fluids (September 2024)
Extraction and analysis of flow features in planar synthetic jets using different machine learning techniques
Physics of Fluids (September 2023)
Higher order dynamic mode decomposition to identify and extrapolate flow patterns
Physics of Fluids (August 2017)
An alternative method to study cross-flow instabilities based on high order dynamic mode decomposition
Physics of Fluids (September 2019)