Multi-scale computational fluid dynamics and machine learning integration for hydrodynamic optimization of floating photovoltaic systems

Multi-scale computational fluid dynamics and machine learning integration for hydrodynamic optimization of floating photovoltaic systems

Abstract This paper presents a new and multidisciplinary systematic analysis of floating photovoltaic (FPV) systems that integrates recent advances in computational modelling and intelligent optimization to address persistent issues with performance, hydrodynamics, and adaptability. The review is or...

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Main Authors: Fadhil Khadoum Alhousni, Samuel Chukwujindu Nwokolo, Edson L. Meyer, Theyab R. Alsenani, Humaid Abdullah Alhinai, Chinedu Christian Ahia, Paul C. Okonkwo, Yaareb Elias Ahmed
Format: Article
Language:English
Published: SpringerOpen 2025-08-01
Series:Energy Informatics
Subjects:
Floating photovoltaic (FPV)
Self-Adaptive AI & CFD integration
Digital Twin-Based climate resilience
Real-Time mooring & VIV mitigation
Machine Learning-Driven FPV optimization
Hydrodynamic stability & energy sustainability
Online Access:https://doi.org/10.1186/s42162-025-00567-9
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https://doi.org/10.1186/s42162-025-00567-9

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