Articles | Volume 10, issue 6
https://doi.org/10.5194/wes-10-1123-2025
© Author(s) 2025. 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-10-1123-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimating microplastic emissions from offshore wind turbine blades in the Dutch North Sea
TNO, Wind Energy Technology, Westerduinweg 3, 1755 LE Petten, the Netherlands
Anna Elisa Schwarz
TNO, Climate, Air & Sustainability, Princetonlaan 6, 3584 CB Utrecht, the Netherlands
Henk Slot
TNO, Reliable Structures, Molengraaffsingel 8, 2629 JD Delft, the Netherlands
Harald van der Mijle Meijer
TNO, Wind Energy Technology, Westerduinweg 3, 1755 LE Petten, the Netherlands
Related authors
Marco Caboni and Gerwin van Dalum
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-174, https://doi.org/10.5194/wes-2024-174, 2024
Revised manuscript under review for WES
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Weather simulations carried out over a decade showed that the average erosivity of rainfall on wind turbine blades increases from the southwestern part of the Dutch North Sea to the northeastern region. These results suggest that future wind farms developed in the northeast are likely to encounter higher erosion rates compared to those currently operating in the southwest. This requires special attention when developing mitigation strategies.
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.
Marco Caboni and Gerwin van Dalum
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-174, https://doi.org/10.5194/wes-2024-174, 2024
Revised manuscript under review for WES
Short summary
Short summary
Weather simulations carried out over a decade showed that the average erosivity of rainfall on wind turbine blades increases from the southwestern part of the Dutch North Sea to the northeastern region. These results suggest that future wind farms developed in the northeast are likely to encounter higher erosion rates compared to those currently operating in the southwest. This requires special attention when developing mitigation strategies.
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.
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
Short summary
Short summary
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.
Related subject area
Thematic area: Wind technologies | Topic: Offshore technology
A new gridded offshore wind profile product for US coasts using machine learning and satellite observations
Sensitivity analysis of numerical modeling input parameters on floating offshore wind turbine loads in extreme idling conditions
Gaussian mixture autoencoder for uncertainty-aware damage identification in a floating offshore wind turbine
Effect of Rotor Design on Energy Performance and Cost of Stationary Unmoored Floating Offshore Wind Turbines
Experimental Validation of Parked Loads for a Floating Vertical Axis Wind Turbine: Wind-Wave Basin Tests
Spatio-Temporal Graph Neural Networks for Power Prediction in Offshore Wind Farms Using SCADA Data
Dynamic performance of a passively self-adjusting floating wind farm layout to increase the annual energy production
OC6 project Phase IV: validation of numerical models for novel floating offshore wind support structures
Quantifying the impact of modeling fidelity on different substructure concepts for floating offshore wind turbines – Part 1: Validation of the hydrodynamic module QBlade-Ocean
A new methodology for upscaling semi-submersible platforms for floating offshore wind turbines
Sensitivity analysis of numerical modeling input parameters on floating offshore wind turbine loads
Design optimization of offshore wind jacket piles by assessing support structure orientation relative to metocean conditions
Comparison of optimal power production and operation of unmoored floating offshore wind turbines and energy ships
James Frech, Korak Saha, Paige D. Lavin, Huai-Min Zhang, James Reagan, and Brandon Fung
Wind Energ. Sci., 10, 1077–1099, https://doi.org/10.5194/wes-10-1077-2025, https://doi.org/10.5194/wes-10-1077-2025, 2025
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A machine learning model is developed using lidar stations around 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° and 6-hourly resolution from 1987 to the present by applying this model to the National Oceanic and Atmospheric Administration (NOAA)/National Centers for Environmental Information (NCEI) Blended Seawinds product.
Will Wiley, Jason Jonkman, and Amy Robertson
Wind Energ. Sci., 10, 941–970, https://doi.org/10.5194/wes-10-941-2025, https://doi.org/10.5194/wes-10-941-2025, 2025
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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.
Ana Fernandez-Navamuel, Nicolas Gorostidi, David Pardo, Vincenzo Nava, and Eleni Chatzi
Wind Energ. Sci., 10, 857–885, https://doi.org/10.5194/wes-10-857-2025, https://doi.org/10.5194/wes-10-857-2025, 2025
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This work employs deep neural networks to identify damage in the mooring system of a floating offshore wind turbine using measurements from the platform response. We account for the effect of uncertainty caused by the existence of multiple solutions using a Gaussian mixture model to describe the damage condition estimates. The results reveal the capability of the methodology to discover the uncertainty in the assessment, which increases as the instrumentation system becomes more limited.
Aurélien Babarit, Maximilien André, and Vincent Leroy
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-15, https://doi.org/10.5194/wes-2025-15, 2025
Revised manuscript accepted for WES
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This study deals with energy performance optimization of Unmoored Floating Offshore Wind turbines (UFOWTs). UFOWTs use thrusters in lieu of mooring systems for position control. Previous studies have shown that net positive power generation can be achieved depending on design. In this study, we investigate the effect of rotor design. Results show that the optimal rated induction factor is smaller than the usual value of 1/3 both from the perspective of energy performance and cost of energy.
Md Sanower Hossain and D. Todd Griffith
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-156, https://doi.org/10.5194/wes-2024-156, 2024
Revised manuscript accepted for WES
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The document presents an experimental study on the parked loads of floating vertical axis wind turbines (VAWTs) in a wind and waves basin, focusing on the effects of wind speed, solidity, and floating platform dynamics. Findings show that higher wind speed, and higher solidity generally increase the parked loads, while a floating platform introduces additional effects due to tilting. A semi-numerical model was also presented to predict the parked loads, which helps enhance VAWT design.
Simon Daenens, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-113, https://doi.org/10.5194/wes-2024-113, 2024
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.
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.
Robert Behrens de Luna, Sebastian Perez-Becker, Joseph Saverin, David Marten, Francesco Papi, Marie-Laure Ducasse, Félicien Bonnefoy, Alessandro Bianchini, and Christian-Oliver Paschereit
Wind Energ. Sci., 9, 623–649, https://doi.org/10.5194/wes-9-623-2024, https://doi.org/10.5194/wes-9-623-2024, 2024
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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.
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
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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
4C-Offshore: Global Offshore Wind Farms Database, https://www.4coffshore.com/windfarms/, last access: 28 November 2024. a
Bak, C., Forsting, A. M., and Sorensen, N. N.: The influence of leading edge roughness, rotor control and wind climate on the loss in energy production, J. Phys. Conf. Ser., 1618, 052050, https://doi.org/10.1088/1742-6596/1618/5/052050, 2020. a
Barfknecht, N. and von Terzi, D.: Aerodynamic interaction of rain and wind turbine blades: the significance of droplet slowdown and deformation for leading-edge erosion, Wind Energ. Sci., 9, 2333–2357, https://doi.org/10.5194/wes-9-2333-2024, 2024. a
Bech, J. I., Hasager, C. B., and Bak, C.: Extending the life of wind turbine blade leading edges by reducing the tip speed during extreme precipitation events, Wind Energ. Sci., 3, 729–748, https://doi.org/10.5194/wes-3-729-2018, 2018. a, b
Bech, J. I., Johansen, N. F.-J., Madsen, M. B., Hannesdóttir, Á., and Hasager, C. B.: Experimental study on the effect of drop size in rain erosion test and on lifetime prediction of wind turbine blades, Renew. Energ., 197, 776–789, https://doi.org/10.1016/j.renene.2022.06.127, 2022. a, b
Caboni, M., Slot, H. M., Bergman, G., Wouters, D. A. J., and van der Mijle Meijer, H.: Evaluation of wind turbine blades' rain-induced leading edge erosion using rainfall measurements at offshore, coastal and onshore locations in the Netherlands, J. Phys. Conf. Ser., 2767, 062003, https://doi.org/10.1088/1742-6596/2767/6/062003, 2024. a, b, c
Copernicus Climate Change Service, C. D. S.: Land cover classification gridded maps from 1992 to present derived from satellite observation, https://doi.org/10.24381/cds.006f2c9a, 2019. a
Domenech, L., García-Peñas, V., Šakalytė, A., Puthukara Francis, D., Skoglund, E., and Sánchez, F.: Top Coating Anti-Erosion Performance Analysis in Wind Turbine Blades Depending on Relative Acoustic Impedance. Part 2: Material Characterization and Rain Erosion Testing Evaluation, Coatings, 10, 709, https://doi.org/10.3390/coatings10080709, 2020. a
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter, G., Abbas, N., Meng, F., Bortolotti, P., Skrzypinski, W., Scott, G., Feil, R., Bredmose, H., Dykes, K., Shields, M., Allen, C., and Viselli, A.: Definition of the IEA 15-Megawatt Offshore Reference Wind Turbine, Tech. Rep. NREL/TP-5000-75698, National Renewable Energy Laboratory (NREL), Golden, CO, USA, https://doi.org/10.2172/1603478, 2020. a, b
Gall, S. and Thompson, R.: The impact of debris on marine life, Mar. Pollut. Bull., 92, 170–179, https://doi.org/10.1016/j.marpolbul.2014.12.041, 2015. a
Gires, A., Tchiguirinskaia, I., and Schertzer, D.: 3D trajectories and velocities of rainfall drops in a multifractal turbulent wind field, Atmos. Meas. Tech., 15, 5861–5875, https://doi.org/10.5194/amt-15-5861-2022, 2022. a
Haščič, I. and Mackie, A.: Land Cover Change and Conversions: Methodology and Results for OECD and G20 Countries, OECD Green Growth Papers, No. 2018/04, OECD Publishing, Paris, https://doi.org/10.1787/72a9e331-en, 2018. a
Hawkins, S. and Nyboe, A.: The TEKNOBLADE REPAIR 9000 – a practical approach to Leading Edge Protection for wind turbine blades, Tech. rep., TEKNOS, https://www.worldclassmaintenance.com/wp-content/uploads/2019/12/2022.05.20-Teknos-A-practical-approach-to-Leading-Edge-Protection-for-wind-turbine-blades.pdf (last access: 12 June 2025), 2019. a, b
Herring, R., Dyer, K., MacLeod, A., and Ward, C.: Computational fluid dynamics methodology for characterisation of leading edge erosion in whirling arm test rigs, J. Phys. Conf. Ser., 1222, 012011, https://doi.org/10.1088/1742-6596/1222/1/012011, 2019. a
Herring, R., Domenech, L., Renau, J., Šakalytė, A., Ward, C., Dyer, K., and Sánchez, F.: Assessment of a Wind Turbine Blade Erosion Lifetime Prediction Model with Industrial Protection Materials and Testing Methods, Coatings, 11, 767, https://doi.org/10.3390/coatings11070767, 2021. a
Heymann, F.: Toward Quantitative Prediction of Liquid Impact Erosion, in: Characterization and Determination of Erosion Resistance, ASTM International, https://doi.org/10.1520/STP26871S, 1970. a
Heymann, F. J.: Conclusions from the ASTM interlaboratory test program with liquid impact erosion facilities, in: International Conference on Erosion by Liquid and Solid Impact, 5th edn., 3–6 September 1979, Cambridge, England, Proceedings (A80-25030 09-23), Cambridge, Cambridge University, 20-1–20-10, 1979. a, b, c, d
Huerta Lwanga, E., Gertsen, H., Gooren, H., Peters, P., Salanki, T., van der Ploeg, M., Besseling, E., Koelmans, A., and Geissen, V.: Microplastics in the terrestrial ecosystem: Implications for Lumbricus terrestris (Oligochaeta, Lumbricidae), Environ. Sci. Technol., 50, 2685–2691, https://doi.org/10.1021/acs.est.5b05478, 2016. a
KNMI: Jaaroverzicht Weer 2022, https://cdn.knmi.nl/knmi/map/page/klimatologie/gegevens/mow/jow_2022.pdf (last access: 12 June 2025), 2022. a
Krzyzanowski, J. and Szprengiel, Z.: The Influence of Droplet Size on the Turbine Blading Erosion Hazard, J. Eng. P., 100, 561–565, https://doi.org/10.1115/1.3446394, 1978. a
Kusumgar, N. G.: Kusumgar, Nerlfi & Growney publishes fourth study on the global paint & coatings industry, Focus on Powder Coatings, 2020, 7, https://doi.org/10.1016/j.fopow.2020.04.038, 2020. a
Lebreton, L. C., Slat, B., Ferrari, F., Sainte-Rose, B., Aitken, J., Marthouse, B., Hajbane, S., Cunsolo, S., Schwarz, A., Levivier, A., Noble, K., Debeljak, P., Maral, H., Schoeneich-Argent, R., Brambini, R., and Reisser, J.: Evidence that the Great Pacific Garbage Patch is rapidly accumulating plastic, Sci. Rep.-UK, 8, 1–10, https://doi.org/10.1038/s41598-018-22939-w, 2018. a
Leslie, H. A., van Velzen, M. J., Brandsma, S. H., Vethaak, A. D., Garcia-Vallejo, J. J., and Lamoree, M. H.: Discovery and quantification of plastic particle pollution in human blood, Environ. Int., 163, 107199, https://doi.org/10.1016/j.envint.2022.107199, 2022. a
Maniaci, D. C., Westergaard, C., Hsieh, A., and Paquette, J. A.: Uncertainty Quantification of Leading Edge Erosion Impacts on Wind Turbine Performance, J. Phys. Conf. Ser., 1618, 052082, https://doi.org/10.1088/1742-6596/1618/5/052082, 2020. a
Mishnaevsky, L., Hasager, C. B., Bak, C., Tilg, A.-M., Bech, J. I., Doagou Rad, S., and Fæster, S.: Leading edge erosion of wind turbine blades: Understanding, prevention and protection, Renew. Energ., 169, 953–969, https://doi.org/10.1016/j.renene.2021.01.044, 2021. a
Mishnaevsky, L., Tempelis, A., Kuthe, N., and Mahajan, P.: Recent developments in the protection of wind turbine blades against leading edge erosion: Materials solutions and predictive modelling, Renew. Energ., 215, 118966, https://doi.org/10.1016/j.renene.2023.118966, 2023. a
Pugh, K. and Stack, M. M.: Rain Erosion Maps for Wind Turbines Based on Geographical Locations: A Case Study in Ireland and Britain, Journal of Bio- and Tribo-Corrosion, 7, 34, https://doi.org/10.1007/s40735-021-00472-0, 2021. a
Sánchez, F., Sakalyte, A., Ansari, M. Q., Wu, C.-Y., Teuven, J., Young, T. M., Olivares, A., and Domenech, L.: Erosion Damage Progression Analysis for Wind Turbine Blade Material Coatings based on Comparison of Accelerated Rain Erosion Testing Methods and Polymer Properties, SSRN, https://doi.org/10.2139/ssrn.5172335, 2025. a, b, c
Schmitt, G. F.: Research for Improved Subsonic and Supersonic Rain Erosion Resistant Material, Tech. Rep. AFML-TR-67-211, Air Force Materials Laboratory, https://apps.dtic.mil/sti/html/tr/AD0828188/index.html (last access: 12 June 2025), 1968. a
Schwarz, A., Lensen, S., Langeveld, E., Parker, L., and Urbanus, J.: Plastics in the global environment assessed through material flow analysis, degradation and environmental transportation, Sci. Total Environ., 875, 162644, https://doi.org/10.1016/j.scitotenv.2023.162644, 2023. a, b, c, d
Seleznev, L. I., Ryzhenkov, V. A., and Mednikov, A. F.: Phenomenology of erosion wear of constructional steels and alloys by liquid particles, Therm. Eng.+, 57, 741–745, https://doi.org/10.1134/S004060151009003X, 2010. a
Slot, H.: A fatigue-based model for the droplet impingement erosion incubation period, PhD thesis, University of Twente, Enschede, the Netherlands, https://ris.utwente.nl/ws/portalfiles/portal/268387756/PhD_thesis_H_Slot.pdf (last access: 12 June 2025), 2021. a
Slot, H., Gelinck, E., Rentrop, C., and van der Heide, E.: Leading edge erosion of coated wind turbine blades: Review of coating life models, Renew. Energ., 80, 837–848, https://doi.org/10.1016/j.renene.2015.02.036, 2015. a, b, c
Solberg, A., Rimereit, B.-E., and Weinbach, J. E.: Leading Edge erosion and pollution from wind turbine blades, https://docs.wind-watch.org/Leading-Edge-erosion-and-
pollution-from-wind-turbine-blades_5_july_English.pdf (last access: 17 October 2024), 2021. a, b, c, d, e
Springer, G. S.: Erosion by liquid impact, John Wiley & Sons, https://doi.org/10.1017/S0001924000034552, 1976. a, b
Springer, G. S., Yang, C.-I., and Larsen, P. S.: Analysis of Rain Erosion of Coated Materials, J. Compos. Mater., 8, 229–252, https://doi.org/10.1177/002199837400800302, 1974. a
Tempelis, A. and Mishnaevsky, L.: Surface roughness evolution of wind turbine blade subject to rain erosion, Mater. Design, 231, 112011, https://doi.org/10.1016/j.matdes.2023.112011, 2023. a
Verma, A. S., Jiang, Z., Caboni, M., Verhoef, H., van der Mijle Meijer, H., Castro, S. G., and Teuwen, J. J.: A probabilistic rainfall model to estimate the leading-edge lifetime of wind turbine blade coating system, Renew. Energ., 178, 1435–1455, https://doi.org/10.1016/j.renene.2021.06.122, 2021. a
Verma, A. S., Yan, J., Hu, W., Jiang, Z., Shi, W., and Teuwen, J. J.: A review of impact loads on composite wind turbine blades: Impact threats and classification, Renew. Sust. Energ. Rev., 178, 113261, https://doi.org/10.1016/j.rser.2023.113261, 2023. a
Verschoor, A.: Emission of microplastics and potential mitigation measures Abrasive cleaning agents, paints and tyre wear, Tech. Rep. 2016-0026, RIVM, https://www.rivm.nl/bibliotheek/rapporten/2016-0026.pdf (last access: 12 June 2025), 2016. a
Vimalakanthan, K., van der Mijle Meijer, H., Bakhmet, I., and Schepers, G.: Computational fluid dynamics (CFD) modeling of actual eroded wind turbine blades, Wind Energ. Sci., 8, 41–69, https://doi.org/10.5194/wes-8-41-2023, 2023. a
Vitulli, J., Eeckels, C., Bot, E., Verhoef, J., Bergman, G., and der Werff, P. V.: Offshore wind energy deployment in the North Sea by 2030: long-term measurement campaign. LEG, 2014-2022, Tech. Rep. R10579, TNO, https://publications.tno.nl/publication/34640886/7ycJdI/TNO-2023-R10579.pdf (last access: 12 June 2025), 2023. a
Weigle, B. and Szprengiel, Z.: An attempt to assess the erosion damage due to the impact of a polyfractional rain of droplets, Transactions Institute of Fluid-Flow Machinery, 88, 45–70, 1985. a
Short summary
In this study, we assessed the total quantity of microplastics emitted by wind turbines currently operating in the Dutch North Sea. The estimates of microplastics currently emitted from offshore wind turbines in the Netherlands account for a very small portion of the total microplastics released offshore in the Netherlands, specifically less than 1 ‰.
In this study, we assessed the total quantity of microplastics emitted by wind turbines...
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