SPH hydrodynamic modeling and data-driven power performance optimization of a swing-wing wave energy converter

Figshare 2026 Dataset Open access

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Authors: Kai Liu
DOI: 10.6084/m9.figshare.31486774
Cited by: 0

Abstract

In this work, we developed a numerical and optimization framework for enhancing the power performance of SW-WEC by integrating smoothed particle hydrodynamics (SPH), radial basis function neural network (RBFNN), and genetic algorithm (GA).

Fields of Study & Subjects

Wave and Wind Energy Systems Fluid Dynamics Simulations and Interactions Ship Hydrodynamics and Maneuverability

Publication details

Journal / source
Figshare
Publication date
4 March 2026
Language
EN
Document type
Dataset