Advances in Solid and Fluid Mechanics for Offshore Wind Turbines

A special issue of Wind (ISSN 2674-032X).

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 15080

Special Issue Editors


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Guest Editor
Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G1 1XQ, UK
Interests: fatigue design; corrosion–fatigue interactions; fractures; life prediction; structural integrity
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Guest Editor
Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
Interests: fluid mechanics; turbulence; wind energy; tidal energy

Special Issue Information

Dear Colleagues,

Offshore wind turbines (OWTs) operate in the hostile marine environment where their structures are subjected to variable-amplitude cyclic loads from the wind, waves and currents. The majority of OWTs also operate in large clusters where the performance of each turbine is affected by the presence of neighboring turbines, namely, wake and blockage effects. These facts highlight the importance of solid and fluid mechanics in the design, operation, damage analysis and life prediction of OWTs. In this Special Issue, we seek to publish a wide set of articles which address the advances in solid and fluid mechanics for OWTs. It is hoped that this open-access Special Issue will provide a platform for knowledge transfer between industrial and academic experts with the current state of the art for the design and life assessment of OWTs. Original research articles and reviews are welcome. Research areas may include, but are not limited to, the following themes:

  • Fluid–structure interaction;
  • Structural integrity;
  • Enhanced design and life assessment;
  • Damage analysis;
  • Fracture mechanics;
  • Monitoring and inspection;
  • Corrosion protection;
  • Atmospheric boundary layer;
  • Flow control;
  • Rotor aerodynamics;
  • Turbine wake interaction;
  • Wind farm blockage;
  • Wind farm optimization.

Experimental, numerical, and analytical studies with a sufficient level of contribution to knowledge are equally encouraged for publication in this Special Issue.

We look forward to receiving your contributions.

Prof. Dr. Ali Mehmanparast
Dr. Takafumi Nishino
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Wind is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 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

  • fluid–structure interaction
  • solid mechanics
  • fluid mechanics
  • offshore wind turbines
  • marine structures

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Published Papers (3 papers)

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Research

19 pages, 5018 KiB  
Article
Filling Missing and Extending Significant Wave Height Measurements Using Neural Networks and an Integrated Surface Database
by Damjan Bujak, Tonko Bogovac, Dalibor Carević and Hanna Miličević
Wind 2023, 3(2), 151-169; https://doi.org/10.3390/wind3020010 - 28 Mar 2023
Viewed by 1722
Abstract
Wave data play a critical role in offshore structure design and coastal vulnerability studies. For various reasons, such as equipment malfunctions, wave data are often incomplete. Despite the interest in completing the data, few studies have considered constructing a machine learning model with [...] Read more.
Wave data play a critical role in offshore structure design and coastal vulnerability studies. For various reasons, such as equipment malfunctions, wave data are often incomplete. Despite the interest in completing the data, few studies have considered constructing a machine learning model with publicly available wind measurements as input, while wind data from reanalysis models are commonly used. In this work, ANNs are constructed and tested to fill in missing wave data and extend the original wave measurements in a basin with limited fetch where wind waves dominate. Input features for the ANN are obtained from the publicly available Integrated Surface Database (ISD) maintained by NOAA. The accuracy of the ANNs is also compared to a state-of-the-art reanalysis wave model, MEDSEA, maintained at Copernicus Marine Service. The results of this study show that ANNs can accurately fill in missing wave data and also extend beyond the measurement period, using the wind velocity magnitude and wind direction from nearby weather stations. The MEDSEA reanalysis data showed greater scatter compared to the reconstructed significant wave heights from ANN. Specifically, MEDSEA showed a 22% higher HH index for expanding wave data and a 33% higher HH index for filling in missing wave data points. Full article
(This article belongs to the Special Issue Advances in Solid and Fluid Mechanics for Offshore Wind Turbines)
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20 pages, 13335 KiB  
Article
Tip Speed Ratio Optimization: More Energy Production with Reduced Rotor Speed
by Amir Hosseini, Daniel Trevor Cannon and Ahmad Vasel-Be-Hagh
Wind 2022, 2(4), 691-710; https://doi.org/10.3390/wind2040036 - 31 Oct 2022
Cited by 4 | Viewed by 10118
Abstract
A wind turbine’s tip speed ratio (TSR) is the linear speed of the blade’s tip, normalized by the incoming wind speed. For a given blade profile, there is a TSR that maximizes the turbine’s efficiency. The industry’s current practice is to impose the [...] Read more.
A wind turbine’s tip speed ratio (TSR) is the linear speed of the blade’s tip, normalized by the incoming wind speed. For a given blade profile, there is a TSR that maximizes the turbine’s efficiency. The industry’s current practice is to impose the same TSR that maximizes the efficiency of a single, isolated wind turbine on every turbine of a wind farm. This article proves that this strategy is wrong. The article demonstrates that in every wind direction, there is always a subset of turbines that needs to operate at non-efficient conditions to provide more energy to some of their downstream counterparts to boost the farm’s overall production. The aerodynamic interactions between the turbines cause this. The authors employed the well-known Jensen wake model in concert with Particle Swarm Optimization to demonstrate the effectiveness of this strategy at Lillgrund, a wind farm in Sweden. The model’s formulation and implementation were validated using large-eddy simulation results. The AEP of Lillgrund increased by approximately 4% by optimizing and actively controlling the TSR. This strategy also decreased the farm’s overall TSR, defined as the average TSR of the turbines, by 8%, leading to several structural and environmental benefits. Note that both these values are farm-dependent and change from one farm to another; hence, this research serves as a proof of concept. Full article
(This article belongs to the Special Issue Advances in Solid and Fluid Mechanics for Offshore Wind Turbines)
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14 pages, 2595 KiB  
Article
Parameterising the Impact of Roughness Evolution on Wind Turbine Performance
by Jack Kelly, Richard Willden and Christopher Vogel
Wind 2022, 2(2), 415-428; https://doi.org/10.3390/wind2020022 - 20 Jun 2022
Cited by 1 | Viewed by 2032
Abstract
This paper presents a study investigating the effects of surface roughness on airfoil performance and its consequences for wind turbine energy yield. This study examined 51 sets of experimental data across 16 airfoils to identify trends in roughened airfoil performance. The trends are [...] Read more.
This paper presents a study investigating the effects of surface roughness on airfoil performance and its consequences for wind turbine energy yield. This study examined 51 sets of experimental data across 16 airfoils to identify trends in roughened airfoil performance. The trends are used to formulate a novel ‘roughness evolution parameter’ that can be applied to airfoils with no roughened data available to predict the impact of roughness on performance. Blade element momentum theory is used to model the performance of the DTU 10 MW reference wind turbine, with uniformly roughened blades emulated using the roughness evolution parameter. An annual energy production loss between 0.6–9.6% is found for the DTU 10 MW turbine when considering a plausible range of values for the roughness evolution parameter derived from the experimental data. A framework has been developed to evaluate how the roughness evolution parameter changes over time, informed by observed changes in wind farm performance from previous studies. Full article
(This article belongs to the Special Issue Advances in Solid and Fluid Mechanics for Offshore Wind Turbines)
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