Articles | Volume 9, issue 3
https://doi.org/10.5194/wes-9-623-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-9-623-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Mar 2024
Research article |
| 14 Mar 2024
| 14 Mar 2024
Quantifying the impact of modeling fidelity on different substructure concepts for floating offshore wind turbines – Part 1: Validation of the hydrodynamic module QBlade-Ocean
Robert Behrens de Luna
CORRESPONDING AUTHOR
Chair of Fluid Dynamics, Hermann Föttinger Institute, Technische Universität Berlin, Müller-Breslau-Straße 8, 10623 Berlin, Germany
Sebastian Perez-Becker
Chair of Fluid Dynamics, Hermann Föttinger Institute, Technische Universität Berlin, Müller-Breslau-Straße 8, 10623 Berlin, Germany
Joseph Saverin
Chair of Fluid Dynamics, Hermann Föttinger Institute, Technische Universität Berlin, Müller-Breslau-Straße 8, 10623 Berlin, Germany
David Marten
Chair of Fluid Dynamics, Hermann Föttinger Institute, Technische Universität Berlin, Müller-Breslau-Straße 8, 10623 Berlin, Germany
Francesco Papi
Department of Industrial Engineering, University of Florence, via di Santa Marta 3, 50139 Firenze, Italy
Marie-Laure Ducasse
Saipem, 7 Av. de San Fernando, 78180 Montigny-le-Bretonneux, France
Félicien Bonnefoy
CNRS – École Centrale Nantes, 1 rue de la Noë, 44321 Nantes CEDEX 3, France
Alessandro Bianchini
Department of Industrial Engineering, University of Florence, via di Santa Marta 3, 50139 Firenze, Italy
Christian-Oliver Paschereit
Chair of Fluid Dynamics, Hermann Föttinger Institute, Technische Universität Berlin, Müller-Breslau-Straße 8, 10623 Berlin, Germany
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Wind Energ. Sci., 9, 981–1004, https://doi.org/10.5194/wes-9-981-2024, https://doi.org/10.5194/wes-9-981-2024, 2024
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Wind turbines need to be simulated for thousands of hours to estimate design loads. Mid-fidelity numerical models are typically used for this task to strike a balance between computational cost and accuracy. The considerable displacements of floating wind turbines may be a challenge for some of these models. This paper enhances comprehension of how modeling theories affect floating wind turbine loads by comparing three codes across three turbines, simulated in a real environment.
Leonardo Pagamonci, Francesco Papi, Gabriel Cojocaru, Marco Belloli, and Alessandro Bianchini
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-169, https://doi.org/10.5194/wes-2024-169, 2025
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Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-140, https://doi.org/10.5194/wes-2024-140, 2024
Revised manuscript under review for WES
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The article investigates the impact of large movements allowed by floating wind turbine foundations on their aerodynamics and wake behavior. Wind tunnel tests with a model turbine reveal that platform motions affect wake patterns and turbulence levels. Insights from these experiments are crucial for optimizing large-scale floating wind farms. The dataset obtained from the experiment is published and can aid in developing simulation tools for floating wind turbines.
Joseph Robert Saverin
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-73, https://doi.org/10.5194/wes-2024-73, 2024
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Wind Energ. Sci., 9, 1669–1688, https://doi.org/10.5194/wes-9-1669-2024, https://doi.org/10.5194/wes-9-1669-2024, 2024
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Successful wave tank experiments were conducted to evaluate the feedforward (FF) control strategy benefits in terms of structural loads and power quality of floating wind turbine components. The wave FF control strategy is effective when it comes to alleviating the effects of the wave forces on the floating offshore wind turbines, whereas wave FF control requires a significant amount of actuation to minimize the platform pitch motion, which makes such technology unfavorable for that objective.
Francesco Papi, Jason Jonkman, Amy Robertson, and Alessandro Bianchini
Wind Energ. Sci., 9, 1069–1088, https://doi.org/10.5194/wes-9-1069-2024, https://doi.org/10.5194/wes-9-1069-2024, 2024
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Wind Energ. Sci., 9, 601–622, https://doi.org/10.5194/wes-9-601-2024, https://doi.org/10.5194/wes-9-601-2024, 2024
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Stefano Cioni, Francesco Papi, Leonardo Pagamonci, Alessandro Bianchini, Néstor Ramos-García, Georg Pirrung, Rémi Corniglion, Anaïs Lovera, Josean Galván, Ronan Boisard, Alessandro Fontanella, Paolo Schito, Alberto Zasso, Marco Belloli, Andrea Sanvito, Giacomo Persico, Lijun Zhang, Ye Li, Yarong Zhou, Simone Mancini, Koen Boorsma, Ricardo Amaral, Axelle Viré, Christian W. Schulz, Stefan Netzband, Rodrigo Soto-Valle, David Marten, Raquel Martín-San-Román, Pau Trubat, Climent Molins, Roger Bergua, Emmanuel Branlard, Jason Jonkman, and Amy Robertson
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Jörg Alber, Marinos Manolesos, Guido Weinzierl-Dlugosch, Johannes Fischer, Alexander Schönmeier, Christian Navid Nayeri, Christian Oliver Paschereit, Joachim Twele, Jens Fortmann, Pier Francesco Melani, and Alessandro Bianchini
Wind Energ. Sci., 7, 943–965, https://doi.org/10.5194/wes-7-943-2022, https://doi.org/10.5194/wes-7-943-2022, 2022
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Rodrigo Soto-Valle, Stefano Cioni, Sirko Bartholomay, Marinos Manolesos, Christian Navid Nayeri, Alessandro Bianchini, and Christian Oliver Paschereit
Wind Energ. Sci., 7, 585–602, https://doi.org/10.5194/wes-7-585-2022, https://doi.org/10.5194/wes-7-585-2022, 2022
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Sebastian Perez-Becker, David Marten, and Christian Oliver Paschereit
Wind Energ. Sci., 6, 791–814, https://doi.org/10.5194/wes-6-791-2021, https://doi.org/10.5194/wes-6-791-2021, 2021
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Sirko Bartholomay, Tom T. B. Wester, Sebastian Perez-Becker, Simon Konze, Christian Menzel, Michael Hölling, Axel Spickenheuer, Joachim Peinke, Christian N. Nayeri, Christian Oliver Paschereit, and Kilian Oberleithner
Wind Energ. Sci., 6, 221–245, https://doi.org/10.5194/wes-6-221-2021, https://doi.org/10.5194/wes-6-221-2021, 2021
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Rodrigo Soto-Valle, Sirko Bartholomay, Jörg Alber, Marinos Manolesos, Christian Navid Nayeri, and Christian Oliver Paschereit
Wind Energ. Sci., 5, 1771–1792, https://doi.org/10.5194/wes-5-1771-2020, https://doi.org/10.5194/wes-5-1771-2020, 2020
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In this paper, a method to determine the angle of attack on a wind turbine rotor blade using a chordwise pressure distribution measurement was applied. The approach used a reduced number of pressure tap data located close to the blade leading edge. The results were compared with the measurements from three external probes mounted on the blade at different radial positions and with analytical calculations.
Jörg Alber, Rodrigo Soto-Valle, Marinos Manolesos, Sirko Bartholomay, Christian Navid Nayeri, Marvin Schönlau, Christian Menzel, Christian Oliver Paschereit, Joachim Twele, and Jens Fortmann
Wind Energ. Sci., 5, 1645–1662, https://doi.org/10.5194/wes-5-1645-2020, https://doi.org/10.5194/wes-5-1645-2020, 2020
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The aerodynamic impact of Gurney flaps is investigated on the rotor blades of the Berlin Research Turbine. The findings of this research project contribute to performance improvements of different-size rotor blades. Gurney flaps are considered a worthwhile passive flow-control device in order to alleviate the adverse effects of both early separation in the inner blade region and leading-edge erosion throughout large parts of the blade span.
Matthew Lennie, Johannes Steenbuck, Bernd R. Noack, and Christian Oliver Paschereit
Wind Energ. Sci., 5, 819–838, https://doi.org/10.5194/wes-5-819-2020, https://doi.org/10.5194/wes-5-819-2020, 2020
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This study presents a marriage of unsteady aerodynamics and machine learning. When airfoils are subjected to high inflow angles, the flow no longer follows the surface and the flow is said to be separated. In this flow regime, the forces experienced by the airfoil are highly unsteady. This study uses a range of machine learning techniques to extract infomation from test data to help us understand the flow regime and makes recomendations on how to model it.
Sebastian Perez-Becker, Francesco Papi, Joseph Saverin, David Marten, Alessandro Bianchini, and Christian Oliver Paschereit
Wind Energ. Sci., 5, 721–743, https://doi.org/10.5194/wes-5-721-2020, https://doi.org/10.5194/wes-5-721-2020, 2020
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Aeroelastic design load calculations play a key role in determining the design loads of the different wind turbine components. This study compares load estimations from calculations using a Blade Element Momentum aerodynamic model with estimations from calculations using a higher-order Lifting-Line Free Vortex Wake aerodynamic model. The paper finds and explains the differences in fatigue and extreme turbine loads for power production simulations that cover a wide range of turbulent wind speeds.
Annette Claudia Klein, Sirko Bartholomay, David Marten, Thorsten Lutz, George Pechlivanoglou, Christian Navid Nayeri, Christian Oliver Paschereit, and Ewald Krämer
Wind Energ. Sci., 3, 439–460, https://doi.org/10.5194/wes-3-439-2018, https://doi.org/10.5194/wes-3-439-2018, 2018
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The paper describes the experimental and numerical investigation of a model wind turbine with a diameter of 3.0 m in a narrow wind tunnel. The objectives of the study are the provision of validation data, the comparison and evaluation of methods of different fidelity, and the assessment of the influence of wind tunnel walls. It turned out that the accordance between the experimental and numerical results is good, but the wind tunnel walls have to be taken into account for the present setup.
Matthew Lennie, David Marten, George Pechlivanoglou, Christian Navid Nayeri, and Christian Oliver Paschereit
Wind Energ. Sci., 2, 671–683, https://doi.org/10.5194/wes-2-671-2017, https://doi.org/10.5194/wes-2-671-2017, 2017
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Floating platform wind turbines present a challenge for engineers to simulate. This paper explores some better methods for simulating the aerodynamics of wind turbines as they move about on a floating platform. We also derived a new way of investigating whether the aerodynamics of the wind turbine rotor help it stay stable.
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Thematic area: Wind technologies | Topic: Offshore technology
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Marco Caboni, Anna Elisa Schwarz, Henk Slot, and Harald van der Mijle Meijer
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Ana Fernandez-Navamuel, Nicolas Gorostidi, David Pardo, Vincenzo Nava, and Eleni Chatzi
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Revised manuscript accepted for WES
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This work employs Deep Neural Networks to identify damage in the mooring system of a Floating Offshore Wind Turbine (FOWT) using measurements from the platform response. We account for the effect of uncertainty in the assessment estimates caused by the existence of multiple solutions (different damage scenarios can cause the observed data). We describe the damage condition features using a distributional model based on a Gaussian Mixture, which captures the uncertainty in the predictions.
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Will Wiley, Jason Jonkman, and Amy Robertson
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Numerical models, used to assess loads on floating offshore wind turbines, require many input parameters to describe air and water conditions, system properties, and load calculations. All parameters have some possible range, due to uncertainty and/or variations with time. The selected values can have important effects on the uncertainty in the resulting loads. This work identifies the input parameters that have the most impact on ultimate and fatigue loads for extreme storm load cases.
Simon Daenens, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen
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Revised manuscript accepted for WES
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This study presents a novel model for predicting wind turbine power output at high temporal resolution in wind farms using a hybrid Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) architecture. By modeling the wind farm as a graph, the model captures both spatial and temporal dynamics, outperforming traditional power curve methods. Integrated within a Normal Behavior Model (NBM) framework, the model effectively identifies and analyzes power loss events.
Mohammad Youssef Mahfouz, Ericka Lozon, Matthew Hall, and Po Wen Cheng
Wind Energ. Sci., 9, 1595–1615, https://doi.org/10.5194/wes-9-1595-2024, https://doi.org/10.5194/wes-9-1595-2024, 2024
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As climate change increasingly impacts our daily lives, a transition towards cleaner energy is needed. With all the growth in floating offshore wind and the planned floating wind farms (FWFs) in the next few years, we urgently need new techniques and methodologies to accommodate the differences between the fixed bottom and FWFs. This paper presents a novel methodology to decrease aerodynamic losses inside an FWF by passively relocating the downwind floating wind turbines out of the wakes.
James Frech, Korak Saha, Paige D. Lavin, Huai-Min Zhang, James Reagan, and Brandon Fung
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-77, https://doi.org/10.5194/wes-2024-77, 2024
Revised manuscript accepted for WES
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A machine learning model is developed using lidar stations around the US coasts to extrapolate wind speed profiles up to the hub heights of wind turbines from surface wind speeds. Independent validation shows that our model vastly outperforms traditional methods for vertical wind extrapolation. We produce a new long-term gridded dataset of wind speed profiles from 20 to 200 m at 0.25°, 6-hourly resolution from 1987 to 2022 by applying this model to the NOAA/NCEI Blended Seawinds product.
Roger Bergua, Will Wiley, Amy Robertson, Jason Jonkman, Cédric Brun, Jean-Philippe Pineau, Quan Qian, Wen Maoshi, Alec Beardsell, Joshua Cutler, Fabio Pierella, Christian Anker Hansen, Wei Shi, Jie Fu, Lehan Hu, Prokopios Vlachogiannis, Christophe Peyrard, Christopher Simon Wright, Dallán Friel, Øyvind Waage Hanssen-Bauer, Carlos Renan dos Santos, Eelco Frickel, Hafizul Islam, Arjen Koop, Zhiqiang Hu, Jihuai Yang, Tristan Quideau, Violette Harnois, Kelsey Shaler, Stefan Netzband, Daniel Alarcón, Pau Trubat, Aengus Connolly, Seán B. Leen, and Oisín Conway
Wind Energ. Sci., 9, 1025–1051, https://doi.org/10.5194/wes-9-1025-2024, https://doi.org/10.5194/wes-9-1025-2024, 2024
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This paper provides a comparison for a floating offshore wind turbine between the motion and loading estimated by numerical models and measurements. The floating support structure is a novel design that includes a counterweight to provide floating stability to the system. The comparison between numerical models and the measurements includes system motion, tower loads, mooring line loads, and loading within the floating support structure.
Kaylie L. Roach, Matthew A. Lackner, and James F. Manwell
Wind Energ. Sci., 8, 1873–1891, https://doi.org/10.5194/wes-8-1873-2023, https://doi.org/10.5194/wes-8-1873-2023, 2023
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This paper presents an upscaling methodology for floating offshore wind turbine platforms using two case studies. The offshore wind turbine industry is trending towards fewer, larger offshore wind turbines within a farm, which is motivated by the per unit cost of a wind farm (including installation, interconnection, and maintenance costs). The results show the platform steel mass to be favorable with upscaling.
Will Wiley, Jason Jonkman, Amy Robertson, and Kelsey Shaler
Wind Energ. Sci., 8, 1575–1595, https://doi.org/10.5194/wes-8-1575-2023, https://doi.org/10.5194/wes-8-1575-2023, 2023
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A sensitivity analysis determined the modeling parameters for an operating floating offshore wind turbine with the biggest impact on the ultimate and fatigue loads. The loads were the most sensitive to the standard deviation of the wind speed. Ultimate and fatigue mooring loads were highly sensitive to the current speed; only the fatigue mooring loads were sensitive to wave parameters. The largest platform rotation was the most sensitive to the platform horizontal center of gravity.
Maciej M. Mroczek, Sanjay Raja Arwade, and Matthew A. Lackner
Wind Energ. Sci., 8, 807–817, https://doi.org/10.5194/wes-8-807-2023, https://doi.org/10.5194/wes-8-807-2023, 2023
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Benefits of orientating a three-legged offshore wind jacket relative to the metocean conditions for pile design are assessed considering the International Energy Agency 15 MW reference turbine and a reference site off the coast of Massachusetts. Results, based on the considered conditions, show that the pile design can be optimized by orientating the jacket relative to the dominant wave direction. This design optimization can be used on offshore wind projects to provide cost and risk reductions.
Patrick Connolly and Curran Crawford
Wind Energ. Sci., 8, 725–746, https://doi.org/10.5194/wes-8-725-2023, https://doi.org/10.5194/wes-8-725-2023, 2023
Short summary
Short summary
Mobile offshore wind energy systems are a potential way of producing green fuels from the untapped wind resource that lies far offshore. Herein, computational models of two such systems were developed and verified. The models are able to predict the power output of each system based on wind condition inputs. Results show that both systems have merits and that, contrary to existing results, unmoored floating wind turbines may produce as much power as fixed ones, given the right conditions.
Cited articles
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Short summary
A novel hydrodynamic module of QBlade is validated on three floating offshore wind turbine concepts with experiments and two widely used simulation tools. Further, a recently proposed method to enhance the prediction of slowly varying drift forces is adopted and tested in varying met-ocean conditions. The hydrodynamic capability of QBlade matches the current state of the art and demonstrates significant improvement regarding the prediction of slowly varying drift forces with the enhanced model.
A novel hydrodynamic module of QBlade is validated on three floating offshore wind turbine...
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