System Identification of OSWEC Response Using Physics-Informed Neural Network

Mahmoud Ayyad, Alaa Ahmed, Lisheng Yang, Muhammad R. Hajj, Raju Datla, Lei Zuo

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

Abstract

Optimizing the geometry and increasing the efficiency through PTO control of oscillating surge wave energy converters require the development of effective reduced-order models that can predict their hydrodynamic response. We implement a multi-step approach to identify the coefficients of the equation governing this response. Data from quasi-static, free decay and torque-forced experiments are used to respectively identify and represent the stiffness, the radiation damping, and the added mass and nonlinear damping terms. Particularly, we implement a data-driven system discovery, referred to as Physics-Informed Neural Network, to identify the added mass and nonlinear damping coefficients in the governing equations. Validation is performed via comparing time series predicted by the reduced order model to the measured time series.

Original languageEnglish
Title of host publicationOCEANS 2023 - Limerick, OCEANS Limerick 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350332261
DOIs
StatePublished - 2023
Event2023 OCEANS Limerick, OCEANS Limerick 2023 - Limerick, Ireland
Duration: Jun 5 2023Jun 8 2023

Publication series

NameOCEANS 2023 - Limerick, OCEANS Limerick 2023

Conference

Conference2023 OCEANS Limerick, OCEANS Limerick 2023
Country/TerritoryIreland
CityLimerick
Period6/5/236/8/23

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Oceanography
  • Automotive Engineering
  • Management, Monitoring, Policy and Law
  • Acoustics and Ultrasonics

Keywords

  • Oscillating Surge Wave Energy Converter (OSWEC)
  • Physics-Informed Neural Network (PINN)
  • Reduced-Order Model
  • System Identification

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