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Computation Modelling for Offshore Wind Turbines and Wind Farms

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A3: Wind, Wave and Tidal Energy".

Deadline for manuscript submissions: 5 January 2026 | Viewed by 775

Special Issue Editor


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Guest Editor
School of Ocean Engineering and Technology, Sun Yat-Sen University, B434, Haiqin Building, Tangjiawan Town, Xiangzhou District, Zhuhai City 519082, China
Interests: floating wind turbine; offshore wind turbine; hydrodynamics; aerodynamics; coupling dynamics; experiment

Special Issue Information

Dear Colleagues,

At present, offshore wind energy plays a crucial role in the advancement of renewable energy technologies, owing to its abundant resources, and in complex marine environments, offshore wind turbines are subjected to intricate loads from wind, waves, and currents, exhibiting dynamic behaviors characterized by multi-physical field coupling. Therefore, accurate computation modeling for offshore wind turbines and wind farms is of paramount importance for the design of offshore wind systems. Additionally, the efficiency of computational models is another significant factor that needs to be prioritized.

Computation modeling for offshore wind turbines can generally be categorized into frequency-domain and time-domain modeling approaches. Frequency-domain models often yield efficient computational models, making them suitable for the early stages of design. In contrast, time-domain models can account for the nonlinear dynamic behavior of offshore wind turbines, making them more appropriate for the later stages of design. Furthermore, time-domain modeling methods can be further divided into reduced nonlinear coupled methods, aero-servo-hydroelastic coupled methods, full CFD-FEM coupled methods, and others. Both frequency-domain or time-domain models are essential methodologies in the design of offshore wind turbines and wind farms, and there remains a significant demand for innovations in theory, numerical tools, and applications. Recently, with the rise of artificial intelligence (AI) technologies, the integration of computational models for offshore wind energy with AI presents a promising avenue for enhancing both the accuracy and efficiency of these models.

This Special Issue aims to present solutions to the challenges mentioned, including the development, validation, and application of computation modeling for offshore wind turbines and wind farms (both bottom-fixed offshore wind turbines and floating offshore wind turbines). Topics of interest include, but are not limited to:

  • Numerical modeling methods;
  • Aeroelastic and hydroelastic modeling;
  • Computational fluid dynamics (CFD); 
  • Model validation and prototype testing;
  • Wake modeling and analysis;
  • Wind farm optimization and forecasting;
  • Novel turbine design and simulation studies;
  • Installation and transportation simulation studies;
  • Fault scenario simulation studies;
  • New control strategies;
  • Applications of artificial intelligence (AI);
  • Offshore multi-energy integration equipment simulation studies.

Dr. Jiahao Chen
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • floating wind turbine
  • offshore wind turbine
  • hydrodynamics
  • aerodynamics
  • coupling dynamics
  • experiment

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Published Papers (1 paper)

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Review

26 pages, 2003 KB  
Review
Artificial Intelligence in Floating Offshore Wind Turbines: A Critical Review of Applications in Design, Monitoring, Control, and Digital Twins
by Ewelina Kostecka, Tymoteusz Miller, Irmina Durlik and Arkadiusz Nerć
Energies 2025, 18(22), 5937; https://doi.org/10.3390/en18225937 - 11 Nov 2025
Viewed by 355
Abstract
Floating offshore wind turbines (FOWTs) face complex aero-hydro-servo-elastic interactions that challenge conventional modeling, monitoring, and control. This review critically examines how artificial intelligence (AI) is being applied across four domains—design and surrogate modeling, structural health monitoring, control and operations, and digital twins—with explicit [...] Read more.
Floating offshore wind turbines (FOWTs) face complex aero-hydro-servo-elastic interactions that challenge conventional modeling, monitoring, and control. This review critically examines how artificial intelligence (AI) is being applied across four domains—design and surrogate modeling, structural health monitoring, control and operations, and digital twins—with explicit attention to uncertainty and reliability. Using PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a Scopus search identified 412 records; after filtering for articles, conference papers, and open access, 115 studies were analyzed. We organize the literature into a taxonomy covering classical supervised learning, deep neural surrogates, physics-informed and hybrid models, reinforcement learning, digital twins with online learning, and uncertainty-aware approaches. Neural surrogates accelerate coupled simulations; probabilistic encoders improve structural health monitoring; model predictive control and trust-region reinforcement learning enhance adaptive control; and digital twins integrate reduced-order physics with data-driven calibration for lifecycle management. The corpus reveals progress but also recurring limitations: simulation-heavy validation, inconsistent metrics, and insufficient field-scale evidence. We conclude with a bias-aware synthesis and propose priorities for future work, including shared benchmarks, safe RL with stability guarantees, twin-in-the-loop testing, and uncertainty-to-decision standards that connect model outputs to certification and operational risk. Full article
(This article belongs to the Special Issue Computation Modelling for Offshore Wind Turbines and Wind Farms)
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