Solomon Evro, Jacquelyn Veith, Akinmoladun Akinwale, Olusegun S. Tomomewo
{"title":"Enhancing floating offshore wind turbine systems through multi-scale coupled modeling","authors":"Solomon Evro, Jacquelyn Veith, Akinmoladun Akinwale, Olusegun S. Tomomewo","doi":"10.1016/j.seta.2025.104299","DOIUrl":null,"url":null,"abstract":"<div><div>Floating offshore wind turbines (FOWTs) harness consistent deep-sea wind resources to provide reliable renewable energy. This study examines Multi-Scale Coupled Modeling (MSCM) as a framework for improving FOWT design, efficiency, and reliability. MSCM integrates aerodynamic, hydrodynamic, and structural interactions across multiple scales, enhancing predictive accuracy while optimizing turbine stability and energy capture. Advanced computational techniques, including machine learning (ML) and reduced-order models (ROMs), enable real-time adaptability and efficient large-scale simulations. The study highlights key advancements in MSCM, such as nonlinear hydrodynamic modeling, integrated control strategies, and mooring system optimization. Findings indicate that incorporating high-fidelity computational fluid dynamics (CFD), finite element modeling (FEM), and probabilistic modeling enhances the robustness of FOWT simulations under extreme marine conditions. Furthermore, the integration of ML-based adaptive control improves turbine response to environmental variability, reducing fatigue loads and operational uncertainties. Experimental validation remains critical for refining MSCM frameworks, requiring collaboration between academia, industry, and research institutions to ensure real-world applicability. Additionally, the development of hybrid AI-physics models and digital twin frameworks presents new opportunities for predictive maintenance and real-time performance optimization. By advancing MSCM techniques, this study contributes to the scalability and economic viability of FOWTs, supporting the transition to sustainable offshore wind energy. The findings underscore the necessity of interdisciplinary collaboration and high-performance computing (HPC) solutions to address computational challenges while ensuring the long-term feasibility of floating wind technology. These insights provide a pathway for enhancing FOWT deployment and optimizing renewable energy generation in offshore environments.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"77 ","pages":"Article 104299"},"PeriodicalIF":7.1000,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Energy Technologies and Assessments","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213138825001304","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
Floating offshore wind turbines (FOWTs) harness consistent deep-sea wind resources to provide reliable renewable energy. This study examines Multi-Scale Coupled Modeling (MSCM) as a framework for improving FOWT design, efficiency, and reliability. MSCM integrates aerodynamic, hydrodynamic, and structural interactions across multiple scales, enhancing predictive accuracy while optimizing turbine stability and energy capture. Advanced computational techniques, including machine learning (ML) and reduced-order models (ROMs), enable real-time adaptability and efficient large-scale simulations. The study highlights key advancements in MSCM, such as nonlinear hydrodynamic modeling, integrated control strategies, and mooring system optimization. Findings indicate that incorporating high-fidelity computational fluid dynamics (CFD), finite element modeling (FEM), and probabilistic modeling enhances the robustness of FOWT simulations under extreme marine conditions. Furthermore, the integration of ML-based adaptive control improves turbine response to environmental variability, reducing fatigue loads and operational uncertainties. Experimental validation remains critical for refining MSCM frameworks, requiring collaboration between academia, industry, and research institutions to ensure real-world applicability. Additionally, the development of hybrid AI-physics models and digital twin frameworks presents new opportunities for predictive maintenance and real-time performance optimization. By advancing MSCM techniques, this study contributes to the scalability and economic viability of FOWTs, supporting the transition to sustainable offshore wind energy. The findings underscore the necessity of interdisciplinary collaboration and high-performance computing (HPC) solutions to address computational challenges while ensuring the long-term feasibility of floating wind technology. These insights provide a pathway for enhancing FOWT deployment and optimizing renewable energy generation in offshore environments.
期刊介绍:
Encouraging a transition to a sustainable energy future is imperative for our world. Technologies that enable this shift in various sectors like transportation, heating, and power systems are of utmost importance. Sustainable Energy Technologies and Assessments welcomes papers focusing on a range of aspects and levels of technological advancements in energy generation and utilization. The aim is to reduce the negative environmental impact associated with energy production and consumption, spanning from laboratory experiments to real-world applications in the commercial sector.