Articles | Volume 7, issue 2
https://doi.org/10.5194/wes-7-759-2022
© Author(s) 2022. 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-7-759-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling
Benjamin Sanderse
Scientific Computing group, Centrum Wiskunde & Informatica, Amsterdam, the Netherlands
Scientific Computing group, Centrum Wiskunde & Informatica, Amsterdam, the Netherlands
Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
Koen Boorsma
Energy Transition, TNO, Petten, the Netherlands
Gerard Schepers
Energy Transition, TNO, Petten, the Netherlands
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Maarten J. van den Broek, Marcus Becker, Benjamin Sanderse, and Jan-Willem van Wingerden
Wind Energ. Sci., 9, 721–740, https://doi.org/10.5194/wes-9-721-2024, https://doi.org/10.5194/wes-9-721-2024, 2024
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Wind turbine wakes negatively affect wind farm performance as they impinge on downstream rotors. Wake steering reduces these losses by redirecting wakes using yaw misalignment of the upstream rotor. We develop a novel control strategy based on model predictions to implement wake steering under time-varying conditions. The controller is tested in a high-fidelity simulation environment and improves wind farm power output compared to a state-of-the-art reference controller.
Maarten J. van den Broek, Delphine De Tavernier, Paul Hulsman, Daan van der Hoek, Benjamin Sanderse, and Jan-Willem van Wingerden
Wind Energ. Sci., 8, 1909–1925, https://doi.org/10.5194/wes-8-1909-2023, https://doi.org/10.5194/wes-8-1909-2023, 2023
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As wind turbines produce power, they leave behind wakes of slow-moving air. We analyse three different models to predict the effects of these wakes on downstream wind turbines. The models are validated with experimental data from wind tunnel studies for steady and time-varying conditions. We demonstrate that the models are suitable for optimally controlling wind turbines to improve power production in large wind farms.
Erik Fritz, Koen Boorsma, and Carlos Ferreira
Wind Energ. Sci., 9, 1617–1629, https://doi.org/10.5194/wes-9-1617-2024, https://doi.org/10.5194/wes-9-1617-2024, 2024
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This study presents results from a wind tunnel experiment on a model wind turbine with swept blades, thus blades curved in the rotor plane. Using a non-intrusive measurement technique, the flow around the turbine blades was measured from which blade-level aerodynamics are derived in post-processing. The detailed experimental database gives insight into swept-blade aerodynamics and has great value in validating numerical tools, which aim at simulating swept wind turbine blades.
Erik Fritz, André Ribeiro, Koen Boorsma, and Carlos Ferreira
Wind Energ. Sci., 9, 1173–1187, https://doi.org/10.5194/wes-9-1173-2024, https://doi.org/10.5194/wes-9-1173-2024, 2024
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This study presents results from a wind tunnel experiment on a model wind turbine. Using a non-intrusive measurement technique, the flow around the turbine blades was measured. In post-processing, the blade-level aerodynamics are derived from the measured flow fields. The detailed experimental database has great value in validating numerical tools of varying complexity, which aim at simulating wind turbine aerodynamics as accurately as possible.
Maarten J. van den Broek, Marcus Becker, Benjamin Sanderse, and Jan-Willem van Wingerden
Wind Energ. Sci., 9, 721–740, https://doi.org/10.5194/wes-9-721-2024, https://doi.org/10.5194/wes-9-721-2024, 2024
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Wind turbine wakes negatively affect wind farm performance as they impinge on downstream rotors. Wake steering reduces these losses by redirecting wakes using yaw misalignment of the upstream rotor. We develop a novel control strategy based on model predictions to implement wake steering under time-varying conditions. The controller is tested in a high-fidelity simulation environment and improves wind farm power output compared to a state-of-the-art reference controller.
Maarten J. van den Broek, Delphine De Tavernier, Paul Hulsman, Daan van der Hoek, Benjamin Sanderse, and Jan-Willem van Wingerden
Wind Energ. Sci., 8, 1909–1925, https://doi.org/10.5194/wes-8-1909-2023, https://doi.org/10.5194/wes-8-1909-2023, 2023
Short summary
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As wind turbines produce power, they leave behind wakes of slow-moving air. We analyse three different models to predict the effects of these wakes on downstream wind turbines. The models are validated with experimental data from wind tunnel studies for steady and time-varying conditions. We demonstrate that the models are suitable for optimally controlling wind turbines to improve power production in large wind farms.
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
Wind Energ. Sci., 8, 1659–1691, https://doi.org/10.5194/wes-8-1659-2023, https://doi.org/10.5194/wes-8-1659-2023, 2023
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Simulations of different fidelities made by the participants of the OC6 project Phase III are compared to wind tunnel wake measurements on a floating wind turbine. Results in the near wake confirm that simulations and experiments tend to diverge from the expected linearized quasi-steady behavior when the reduced frequency exceeds 0.5. In the far wake, the impact of platform motion is overestimated by simulations and even seems to be oriented to the generation of a wake less prone to dissipation.
Nirav Dangi, Koen Boorsma, Edwin Bot, Wim Bierbooms, and Wei Yu
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-90, https://doi.org/10.5194/wes-2023-90, 2023
Preprint withdrawn
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The wind turbine wake is a downstream region of velocity deficit, resulting in a power loss for downstream wind turbines. A turbulator is proposed to minimize this velocity deficit. In this work, a very successful field test campaign was executed which demonstrated the use of segmented Gurney Flaps as a promising add-on to promote enhanced wind turbine wake recovery for improved overall wind farm farm performance.
Roger Bergua, Amy Robertson, Jason Jonkman, Emmanuel Branlard, Alessandro Fontanella, Marco Belloli, Paolo Schito, Alberto Zasso, Giacomo Persico, Andrea Sanvito, Ervin Amet, Cédric Brun, Guillén Campaña-Alonso, Raquel Martín-San-Román, Ruolin Cai, Jifeng Cai, Quan Qian, Wen Maoshi, Alec Beardsell, Georg Pirrung, Néstor Ramos-García, Wei Shi, Jie Fu, Rémi Corniglion, Anaïs Lovera, Josean Galván, Tor Anders Nygaard, Carlos Renan dos Santos, Philippe Gilbert, Pierre-Antoine Joulin, Frédéric Blondel, Eelco Frickel, Peng Chen, Zhiqiang Hu, Ronan Boisard, Kutay Yilmazlar, Alessandro Croce, Violette Harnois, Lijun Zhang, Ye Li, Ander Aristondo, Iñigo Mendikoa Alonso, Simone Mancini, Koen Boorsma, Feike Savenije, David Marten, Rodrigo Soto-Valle, Christian W. Schulz, Stefan Netzband, Alessandro Bianchini, Francesco Papi, Stefano Cioni, Pau Trubat, Daniel Alarcon, Climent Molins, Marion Cormier, Konstantin Brüker, Thorsten Lutz, Qing Xiao, Zhongsheng Deng, Florence Haudin, and Akhilesh Goveas
Wind Energ. Sci., 8, 465–485, https://doi.org/10.5194/wes-8-465-2023, https://doi.org/10.5194/wes-8-465-2023, 2023
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This work examines if the motion experienced by an offshore floating wind turbine can significantly affect the rotor performance. It was observed that the system motion results in variations in the load, but these variations are not critical, and the current simulation tools capture the physics properly. Interestingly, variations in the rotor speed or the blade pitch angle can have a larger impact than the system motion itself.
Koen Boorsma, Gerard Schepers, Helge Aagard Madsen, Georg Pirrung, Niels Sørensen, Galih Bangga, Manfred Imiela, Christian Grinderslev, Alexander Meyer Forsting, Wen Zhong Shen, Alessandro Croce, Stefano Cacciola, Alois Peter Schaffarczyk, Brandon Lobo, Frederic Blondel, Philippe Gilbert, Ronan Boisard, Leo Höning, Luca Greco, Claudio Testa, Emmanuel Branlard, Jason Jonkman, and Ganesh Vijayakumar
Wind Energ. Sci., 8, 211–230, https://doi.org/10.5194/wes-8-211-2023, https://doi.org/10.5194/wes-8-211-2023, 2023
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Within the framework of the fourth phase of the International Energy Agency's (IEA) Wind Task 29, a large comparison exercise between measurements and aeroelastic simulations has been carried out. Results were obtained from more than 19 simulation tools of various fidelity, originating from 12 institutes and compared to state-of-the-art field measurements. The result is a unique insight into the current status and accuracy of rotor aerodynamic modeling.
Simone Mancini, Koen Boorsma, Gerard Schepers, and Feike Savenije
Wind Energ. Sci., 8, 193–210, https://doi.org/10.5194/wes-8-193-2023, https://doi.org/10.5194/wes-8-193-2023, 2023
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Modern wind turbines are subject to complex wind conditions that are far from the hypothesis of steady uniform inflow at the core of blade element momentum methods (the current industry standard for wind turbine design). Various corrections have been proposed to model this complexity. The present work focuses on modelling the unsteady evolution of wind turbine wakes (dynamic inflow), comparing the different corrections available and highlighting their effects on design load predictions.
Kisorthman Vimalakanthan, Harald van der Mijle Meijer, Iana Bakhmet, and Gerard Schepers
Wind Energ. Sci., 8, 41–69, https://doi.org/10.5194/wes-8-41-2023, https://doi.org/10.5194/wes-8-41-2023, 2023
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Leading edge erosion (LEE) is one of the most critical degradation mechanisms that occur with wind turbine blades. A detailed understanding of the LEE process and the impact on aerodynamic performance due to the damaged leading edge is required to optimize blade maintenance. Providing accurate modeling tools is therefore essential. This novel study assesses CFD approaches for modeling high-resolution scanned LE surfaces from an actual blade with LEE damages.
Alessandro Bianchini, Galih Bangga, Ian Baring-Gould, Alessandro Croce, José Ignacio Cruz, Rick Damiani, Gareth Erfort, Carlos Simao Ferreira, David Infield, Christian Navid Nayeri, George Pechlivanoglou, Mark Runacres, Gerard Schepers, Brent Summerville, David Wood, and Alice Orrell
Wind Energ. Sci., 7, 2003–2037, https://doi.org/10.5194/wes-7-2003-2022, https://doi.org/10.5194/wes-7-2003-2022, 2022
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The paper is part of the Grand Challenges Papers for Wind Energy. It provides a status of small wind turbine technology in terms of technical maturity, diffusion, and cost. Then, five grand challenges that are thought to be key to fostering the development of the technology are proposed. To tackle these challenges, a series of unknowns and gaps are first identified and discussed. Improvement areas are highlighted, within which 10 key enabling actions are finally proposed to the wind community.
Frederik Berger, Lars Neuhaus, David Onnen, Michael Hölling, Gerard Schepers, and Martin Kühn
Wind Energ. Sci., 7, 1827–1846, https://doi.org/10.5194/wes-7-1827-2022, https://doi.org/10.5194/wes-7-1827-2022, 2022
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We proof the dynamic inflow effect due to gusts in wind tunnel experiments with MoWiTO 1.8 in the large wind tunnel of ForWind – University of Oldenburg, where we created coherent gusts with an active grid. The effect is isolated in loads and rotor flow by comparison of a quasi-steady and a dynamic case. The observed effect is not caught by common dynamic inflow engineering models. An improvement to the Øye dynamic inflow model is proposed, matching experiment and corresponding FVWM simulations.
Tuhfe Göçmen, Filippo Campagnolo, Thomas Duc, Irene Eguinoa, Søren Juhl Andersen, Vlaho Petrović, Lejla Imširović, Robert Braunbehrens, Jaime Liew, Mads Baungaard, Maarten Paul van der Laan, Guowei Qian, Maria Aparicio-Sanchez, Rubén González-Lope, Vinit V. Dighe, Marcus Becker, Maarten J. van den Broek, Jan-Willem van Wingerden, Adam Stock, Matthew Cole, Renzo Ruisi, Ervin Bossanyi, Niklas Requate, Simon Strnad, Jonas Schmidt, Lukas Vollmer, Ishaan Sood, and Johan Meyers
Wind Energ. Sci., 7, 1791–1825, https://doi.org/10.5194/wes-7-1791-2022, https://doi.org/10.5194/wes-7-1791-2022, 2022
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The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings, and model complexities for the (initial) assessment of wind farm flow control benefits. Here we present the first part of the benchmark results for three blind tests with large-scale rotors and 11 participating models in total, via direct power comparisons at the turbines as well as the observed or estimated power gain at the wind farm level under wake steering control strategy.
Frederik Berger, David Onnen, Gerard Schepers, and Martin Kühn
Wind Energ. Sci., 6, 1341–1361, https://doi.org/10.5194/wes-6-1341-2021, https://doi.org/10.5194/wes-6-1341-2021, 2021
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Dynamic inflow denotes the unsteady aerodynamic response to fast changes in rotor loading and leads to load overshoots. We performed a pitch step experiment with MoWiTO 1.8 in the large wind tunnel of ForWind – University of Oldenburg. We measured axial and tangential inductions with a recent method with a 2D-LDA system and performed load and wake measurements. These radius-resolved measurements allow for new insights into the dynamic inflow phenomenon.
Vinit Dighe, Dhruv Suri, Francesco Avallone, and Gerard van Bussel
Wind Energ. Sci., 6, 1263–1275, https://doi.org/10.5194/wes-6-1263-2021, https://doi.org/10.5194/wes-6-1263-2021, 2021
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Ducted wind turbines (DWTs) can be used for energy harvesting in urban areas where non-uniform flows are caused by the presence of buildings or other surface discontinuities. For this reason, the aerodynamic performance of DWTs in yawed-flow conditions must be characterized. It is found that the duct cross-section camber offers not only insensitivity to yaw but also a gain in performance up to a specific yaw angle; thereafter any further increase in yaw results in a performance drop.
Gerard Schepers, Pim van Dorp, Remco Verzijlbergh, Peter Baas, and Harmen Jonker
Wind Energ. Sci., 6, 983–996, https://doi.org/10.5194/wes-6-983-2021, https://doi.org/10.5194/wes-6-983-2021, 2021
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In this article the aeroelastic loads on a 10 MW turbine in response to unconventional wind conditions selected from a year-long large-eddy simulation on a site at the North Sea are evaluated. Thereto an assessment is made of the practical importance of these wind conditions within an aeroelastic context based on high-fidelity wind modelling. Moreover the accuracy of BEM-based methods for modelling such wind conditions is assessed.
Simone Mancini, Koen Boorsma, Marco Caboni, Marion Cormier, Thorsten Lutz, Paolo Schito, and Alberto Zasso
Wind Energ. Sci., 5, 1713–1730, https://doi.org/10.5194/wes-5-1713-2020, https://doi.org/10.5194/wes-5-1713-2020, 2020
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This work characterizes the unsteady aerodynamic response of a scaled version of a 10 MW floating wind turbine subjected to an imposed platform motion. The focus has been put on the simple yet significant motion along the wind's direction (surge). For this purpose, different state-of-the-art aerodynamic codes have been used, validating the outcomes with detailed wind tunnel experiments. This paper sheds light on floating-turbine unsteady aerodynamics for a more conscious controller design.
Cited articles
Andrieu, C., De Freitas, N., Doucet, A., and Jordan, M. I.: An introduction to
MCMC for machine learning, Mach. Learn., 50, 5–43, 2003. a
Bak, C., Madsen, H. A., Gaunaa, M., Paulsen, U. S., Fuglsang, P., Romblad, J.,
Olesen, N. A., Enevoldsen, P., Laursen, J., and Jensen, L.: DAN-AERO MW:
Comparisons of airfoil characteristics for two airfoils tested in three
different wind tunnels, Torque 2010: The science of making torque from wind,
EWEA, 2010, 59–70, 2010. a
Bayes, T.: LII. An essay towards solving a problem in the doctrine of chances.
By the late Rev. Mr. Bayes, FRS communicated by Mr. Price, in a letter to
John Canton, AMFR S, Philos. T. R. Soc. Lond., 53, 370–418, 1763. a
Blatman, G.: Adaptive sparse polynomial chaos expansions foruncertainty
propagation and sensitivity analysis, PhD thesis, Universite Blaise Pascal,
Clermont-Ferrand, France, https://sudret.ibk.ethz.ch/research/publications/doctoralTheses/g--blatman.html, 2009. a
Boorsma, K. and Grasso, F.: ECN Aero-Module: User's Manual, Tech. rep., Energy
research Centre of the Netherlands, 2015. a
Boorsma, K. and Schepers, J.: Rotor experiments in controlled conditions
continued: New Mexico, J. Phys. Conf. Ser., 753, 022004,
https://doi.org/10.1088/1742-6596/753/2/022004, 2016. a, b, c
Boorsma, K., Grasso, F., and Holierhoek, J.: Enhanced approach for simulation
of rotor aerodynamic loads, Tech. Rep. ECN-M–12-003, Energy research Centre
of the Netherlands, https://repository.tno.nl//islandora/object/uuid:7a818ba7-6193-4f16-949a-caeb1827eb5a, 2012. a
Buhl, M. and Manjock, A.: A Comparison of Wind Turbine Aeroelastic Codes Used
for Certification, in: 44th AIAA Aerospace Sciences Meeting and Exhibit,
American Institute of Aeronautics and Astronautics, National Renewable Energy Laboratory (U.S.), Golden, Colorado, https://doi.org/10.2514/6.2006-786,
2006. a
Dutta, S., Ghosh, S., and Inamdar, M. M.: Optimisation of tensile membrane
structures under uncertain wind loads using PCE and kriging based metamodels,
Struct. Multidiscip. O., 57, 1149–1161, 2018. a
Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R.: Least angle
regression, Ann. Stat., 32, 407–499, 2004. a
Goodman, J. and Weare, J.: Ensemble samplers with affine invariance,
Comm. App. Math. Com. Sc., 5, 65–80,
2010. a
Guo, X., Dias, D., Carvajal, C., Peyras, L., and Breul, P.: Reliability
analysis of embankment dam sliding stability using the sparse polynomial
chaos expansion, Eng. Struct., 174, 295–307, 2018. a
Kumar, P., Sanderse, B., Boorsma, K., and Caboni, M.: Global sensitivity
analysis of model uncertainty in aeroelastic wind turbine models, J. Phys. Conf. Ser., 1618, 042034, https://doi.org/10.1088/1742-6596/1618/4/042034, 2020. a, b, c, d
Laloy, E., Rogiers, B., Vrugt, J. A., Mallants, D., and Jacques, D.: Efficient
posterior exploration of a high-dimensional groundwater model from two-stage
Markov chain Monte Carlo simulation and polynomial chaos expansion, Water
Resour. Res., 49, 2664–2682, 2013. a
Leishman, J. G.: Challenges in modelling the unsteady aerodynamics of wind
turbines, Wind Energy: An International Journal for Progress and Applications
in Wind Power Conversion Technology, 5, 85–132, 2002. a
Madsen, H., Bak, C., Schmidt Paulsen, U., Gaunaa, M., Fuglsang, P., Romblad,
J., Olesen, N., Enevoldsen, P., Laursen, J., and Jensen, L.: The DanAero MW
experiments: final report, Tech. Rep. Risø-R-1726(EN), Danmarks Tekniske
Universitet & Risø National laboratory, https://www.osti.gov/etdeweb/biblio/990865 (last access: 1 June 2021), 2010. a, b, c
Madsen, H. A., Sørensen, N. N., Bak, C., Troldborg, N., and Pirrung, G.:
Measured aerodynamic forces on a full scale 2MW turbine in comparison with
EllipSys3D and HAWC2 simulations, J. Phys. Conf. Ser.,
1037, 022011, https://doi.org/10.1088/1742-6596/1037/2/022011, 2018. a
Marelli, S. and Sudret, B.: UQLab: A framework for uncertainty quantification
in Matlab, in: Vulnerability, uncertainty, and risk: quantification,
mitigation, and management, 2554–2563, American Society of Civil
Engineers, https://doi.org/10.1061/9780784413609.257, 2014. a, b
Marelli, S. and Sudret, B.: UQLab user manual – Polynomial chaos expansions,
Tech. rep., Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich,
Switzerland, report # UQLab-V1.3-104, https://doi.org/10.13140/RG.2.1.3778.7366, 2019. a, b, c
Matthäus, D., Bortolotti, P., Loganathan, J., and Bottasso, C. L.:
Propagation of Uncertainties Through Wind Turbine Models for Robust Design
Optimization, in: 35th Wind Energy Symposium, AIAA SciTech Forum, 9–13
January 2017, Grapevine, Texas, American Institute of Aeronautics and
Astronautics, https://doi.org/10.2514/6.2017-1849, 2017. a
Murcia, J. P.: Uncertainty Quantification in Wind Farm Flow Models, PhD
thesis, Technical Unversity of Denmark, https://orbit.dtu.dk/en/publications/uncertainty-quantification-in-wind-farm-flow-models (last access: 1 June 2021), 2016. a
Murcia, J. P., Réthoré, P.-E., Dimitrov, N., Natarajan, A.,
Sørensen, J. D., Graf, P., and Kim, T.: Uncertainty propagation through an
aeroelastic wind turbine model using polynomial surrogates, Renew. Energ.,
119, 910–922, 2018. a
Oakley, J. E. and O'Hagan, A.: Probabilistic sensitivity analysis of complex
models: a Bayesian approach, J. R. Stat. Soc. B, 66, 751–769, 2004. a
Özçakmak, Ö. S., Madsen, H. A., Sørensen, N. N., Sørensen,
J. N., Fischer, A., and Bak, C.: Inflow Turbulence and Leading Edge Roughness
Effects on Laminar-Turbulent Transition on NACA 63-418 Airfoil, J. Phys. Conf. Ser., 1037, 022005,
https://doi.org/10.1088/1742-6596/1037/2/022005, 2018. a
Papageorgiou, A. and Traub, J.: Beating Monte Carlo, Risk, 9, 63–65, 1996. a
Sanderse, B., Dighe, V., and Kumar, P.: UQ4Wind, https://github.com/bsanderse/uq4wind/, last access: 1 February 2022. a
Schepers, J., Lutz, T., Boorsma, K., Gomez-Iradi, S., Herraez, I., Oggiano, L.,
Rahimi, H., Schaffarczyk, P., Pirrung, G., Madsen, H., Shen, W., and Weihing,
P.: Final report of IEA Wind Task 29 Mexnext (Phase 3), Tech. rep., Energy
research Centre of the Netherlands, eCN-E-18-003, https://repository.tno.nl/islandora/object/uuid:251f749d-41dc-4091-a6fa-08704eae2bab (last access: 1 June 2021), 2018. a, b, c
Schepers, J. G., Boorsma, K., Madsen, H. A, Pirrung, G. R., Bangga, G., Guma, G., Lutz, T., Potentier, T., Braud, C., Guilmineau, E., Croce, A., Cacciola, S., Schaffarczyk, A. P., Lobo, B. A., Ivanell, S., Asmuth, H., Bertagnolio, F., Sørensen, N. N., Shen, W. Z., Grinderslev, C., Forsting, A. M., Blondel, F., Bozonnet, P., Boisard, R., Yassin, K., Hoening, L., Stoevesandt, B., Imiela, M., Greco, L., Testa, C., Magionesi, F., Vijayakumar, G., Ananthan, S., Sprague, M. A., Branlard, E., Jonkman, J., Carrion, M., Parkinson, S., and Cicirello, E.: IEA
Wind TCP Task 29, Phase IV: Detailed Aerodynamics of Wind Turbines, Zenodo, https://doi.org/10.5281/zenodo.4813068, 2021. a, b, c
Schöbi, R.: Surrogate models for uncertainty quantification in the context
of imprecise probability modelling, IBK Bericht, 505, https://doi.org/10.3929/ethz-a-010870825, 2019. a
Severini, T. A.: Likelihood methods in statistics, Oxford University Press, ISBN-10 0198506503, 2000. a
Simms, D., Schreck, S., Hand, M., and Fingersh, L. J.: NREL unsteady
aerodynamics experiment in the NASA-Ames wind tunnel: a comparison of
predictions to measurements, Tech. rep., National Renewable Energy Lab.,
Golden, CO (US), https://doi.org/10.2172/783409, 2001. a
Sobol', I. M.: Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates, Math. Comput. Simulat., 55, 271–280, https://doi.org/10.1016/S0378-4754(00)00270-6, 2001. a
Soize, C. and Ghanem, R.: Physical systems with random uncertainties: chaos
representations with arbitrary probability measure, SIAM. J. Sci. Comput., 26, 395–410, 2004. a
Sørensen, J. D. and Toft, H. S.: Probabilistic design of wind turbines,
Energies, 3, 241–257, 2010. a
Sudret, B.: Global sensitivity analysis using polynomial chaos expansions,
Reliab. Eng. Syst. Safe, 93, 964–979, 2008. a
Sudret, B. and Marelli, S.: UQLab: A framework for uncertainty quantification
in Matlab, https://www.uqlab.com/, last access: 1 February 2022. a
van Beek, M. T., Viré, A., and Andersen, S. J.: Sensitivity and Uncertainty
of the FLORIS Model Applied on the Lillgrund Wind Farm, Energies, 14, 1293, https://doi.org/10.3390/en14051293, 2021. a
van Kuik, G. A. M., Peinke, J., Nijssen, R., Lekou, D., Mann, J., Sørensen, J. N., Ferreira, C., van Wingerden, J. W., Schlipf, D., Gebraad, P., Polinder, H., Abrahamsen, A., van Bussel, G. J. W., Sørensen, J. D., Tavner, P., Bottasso, C. L., Muskulus, M., Matha, D., Lindeboom, H. J., Degraer, S., Kramer, O., Lehnhoff, S., Sonnenschein, M., Sørensen, P. E., Künneke, R. W., Morthorst, P. E., and Skytte, K.: Long-term research challenges in wind energy – a research agenda by the European Academy of Wind Energy, Wind Energ. Sci., 1, 1–39, https://doi.org/10.5194/wes-1-1-2016, 2016. a
Wagner, P.-R., Fahrni, R., Klippel, M., Frangi, A., and Sudret, B.: Bayesian
calibration and sensitivity analysis of heat transfer models for fire
insulation panels, Eng. Struct., 205, 110063, https://doi.org/10.1016/j.engstruct.2019.110063, 2020. a
Short summary
An accurate prediction of loads and power of an offshore wind turbine is needed for an optimal design. However, such predictions are typically performed with engineering models that contain many inaccuracies and uncertainties. In this paper we have proposed a systematic approach to quantify and calibrate these uncertainties based on two experimental datasets. The calibrated models are much closer to the experimental data and are equipped with an estimate of the uncertainty in the predictions.
An accurate prediction of loads and power of an offshore wind turbine is needed for an optimal...
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