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<p>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 organized according to five main goals: (i) to publish experimental and empirical results in FPV literature; (ii) to develop a unified computational approach that combines CFD and ML; (iii) to assess system improvements through multi-scale hydrodynamic modelling and AI-driven adjustments; (iv) to introduce the Bidirectional Conceptual Feedback Loop (BCFL) as a dynamic optimization model; and (v) to develop a scalable, climate-resilient FPV model for the global energy transition. Scopus, Web of Science, Google Scholar, ScienceDirect, SpringerLink, and Taylor &amp; Francis were the sources of 404 research publications in all. 189 high-impact publications were found through a careful curation of online databases, with a focus on computational innovations, machine learning (ML)-based optimization, and hydrodynamic analysis. Following a strict inclusion and exclusion process and using Mendeley reference management software to remove duplicate records during the screening stage, authors evaluated a collection of high-impact literature, technology developments, and verified empirical data related to mooring systems, wave-wind interactions, structural stability, predictive analytics, and digital twin environments. According to the synthesis, real-time adaptation, predictive defect detection, and optimized energy yield are made possible by the clever fusion of CFD and ML, especially in dynamic aquatic environments. In order to meet the demands of both climate resilience and the scaling of renewable energy, FPV platforms must become cyber-physical, self-optimizing systems. This paper introduces a paradigm shift by using a methodical and theoretical approach to review and incorporate empirical research, advanced simulation, and AI-driven system intelligence. Future FPV development can be revolutionized by the proposed BCFL paradigm, which makes it easier to move from isolated innovation to integrative, flexible, and globally replicable FPV system design.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月4日

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  • Fadhil Khadoum Alhousni,
  • Samuel Chukwujindu Nwokolo,
  • Edson L. Meyer,
  • Theyab R. Alsenani,
  • Humaid Abdullah Alhinai,
  • Chinedu Christian Ahia,
  • Paul C. Okonkwo,
  • Yaareb Elias Ahmed

摘要

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 organized according to five main goals: (i) to publish experimental and empirical results in FPV literature; (ii) to develop a unified computational approach that combines CFD and ML; (iii) to assess system improvements through multi-scale hydrodynamic modelling and AI-driven adjustments; (iv) to introduce the Bidirectional Conceptual Feedback Loop (BCFL) as a dynamic optimization model; and (v) to develop a scalable, climate-resilient FPV model for the global energy transition. Scopus, Web of Science, Google Scholar, ScienceDirect, SpringerLink, and Taylor & Francis were the sources of 404 research publications in all. 189 high-impact publications were found through a careful curation of online databases, with a focus on computational innovations, machine learning (ML)-based optimization, and hydrodynamic analysis. Following a strict inclusion and exclusion process and using Mendeley reference management software to remove duplicate records during the screening stage, authors evaluated a collection of high-impact literature, technology developments, and verified empirical data related to mooring systems, wave-wind interactions, structural stability, predictive analytics, and digital twin environments. According to the synthesis, real-time adaptation, predictive defect detection, and optimized energy yield are made possible by the clever fusion of CFD and ML, especially in dynamic aquatic environments. In order to meet the demands of both climate resilience and the scaling of renewable energy, FPV platforms must become cyber-physical, self-optimizing systems. This paper introduces a paradigm shift by using a methodical and theoretical approach to review and incorporate empirical research, advanced simulation, and AI-driven system intelligence. Future FPV development can be revolutionized by the proposed BCFL paradigm, which makes it easier to move from isolated innovation to integrative, flexible, and globally replicable FPV system design.