A PRELIMINARY STUDY OF LEARNING A WAVE ENERGY CONVERTER SYSTEM USING PHYSICS-INFORMED NEURAL NETWORK METHOD

Bo Chen Chen, Yi HSiang Yu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Physics-informed neural network (PINN) is a new type of neural network method that can be used to solve the physical problem by providing a given data set in the machine learning process with embedded physics information directly described by differential equations. A PINN model was applied in this study to solve the governing equation of motion for the analysis of a floating sphere wave energy converter (WEC), and a time-series segmentation approach was implemented to effectively handle the time-dependent problem. A series of PINN simulations are presented in this paper, including a decay test and a set of wave conditions, where the PINN solutions are verified against those obtained from other time-domain numerical models. This work aims to investigate the feasibility of using PINN for WEC applications with a focus on numerical benchmarking, and the studies of collection point resolution, the overall model accuracy, and the corresponding computational efficiency are also investigated.

Original languageEnglish
Title of host publicationOcean Renewable Energy
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791886908
DOIs
StatePublished - 2023
EventASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2023 - Melbourne, Australia
Duration: 11 Jun 202316 Jun 2023

Publication series

NameProceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE
Volume8

Conference

ConferenceASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2023
Country/TerritoryAustralia
CityMelbourne
Period11/06/2316/06/23

Keywords

  • Physics-informed neural networks
  • floating sphere
  • mass-spring-damper system
  • time-domain simulation
  • wave energy converter

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