Research on Replacing Numerical Simulation of Mooring System with Machine Learning Methods
Source abstract
Abstract
Time-domain numerical simulation is generally considered an accurate method to predict the mooring system performance, but it is also time and resource-consuming. This paper attempts to completely replace the time-domain numerical simulation with machine learning approaches, using a catenary anchor leg mooring (CALM) system design as an example. An adaptive sampling method is proposed to determine the dataset of various parameters in the CALM mooring system in order to train and validate the generated machine learning models. Reasonable prediction accuracy is achieved by the five assessed machine learning algorithms, namely random forest, extremely randomized trees, K-nearest neighbor, decision tree, and gradient boosting decision tree, among which random forest is found to perform the best if the sampling density is high enough.
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Research context
OpenAlex topics: Ocean Waves and Remote Sensing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, Dalian University of Technology, Dalian 116024, China
Dalian Shipbuilding Industry Co., Ltd., Dalian 116005, China
Ningbo Institute of Dalian University of Technology, Ningbo 315016, China
Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
Funding
National Natural Science Foundation of China · Fundamental Research Funds for the Central Universities
References (32)
Show source-provided references
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