https://wes.copernicus.org/articles/ Combined list of the recent articles of the journal Wind Energy Science and the recent discussion forum Wind Energy Science Discussions en https://doi.org/10.5194/wes-11-2895-2026 Wind-tunnel analysis of wake-steering control strategies on a multi-column model wind farm Derek Micheletto, Jens Henrik Mikael Fransson, and Antonio Segalini Wind Energ. Sci., 11, 2895–2913, https://doi.org/10.5194/wes-11-2895-2026, 2026 We conducted wind tunnel experiments on nine wind turbine models and measured their power while intentionally yawing them away from the wind direction. By testing a broad range of yaw-angle combinations, we found that this method can increase the total power output by up to 5.3%. We also observed how different columns of turbines respond uniquely to these changes and how wind speed affects the overall improvement. Our findings could be useful in developing more accurate wind farm control models. Wed, 12 Aug 2026 21:58:12 +0200 https://doi.org/10.5194/wes-11-2939-2026 A multi-parametric composite approach for the optimization of wind turbine blades using Double-Double laminates Edgar Werthen, Gustavo Nunes Ribeiro, Sascha Dähne, Lennart Tönjes, David Zerbst, and Christian Hühne Wind Energ. Sci., 11, 2939–2960, https://doi.org/10.5194/wes-11-2939-2026, 2026 A multi-parametric panel approach, integrated into a wind turbine blade optimization workflow, is provided. The formulation enables sandwich panels to be represented as modular assemblies with independently parameterized materials. This capability enabled the examination of Double-Double laminates as viable replacements for conventional triaxial skins in a large-scale optimization, like demonstrated for a large blade. The results show a mass reduction without changing the production process. Wed, 12 Aug 2026 21:58:12 +0200 https://doi.org/10.5194/wes-11-2915-2026 Power output of turbines mounted on tension-leg platforms subjected to fully developed ocean gravity waves Juan M. Restrepo, Matthew Norman, Stuart Slattery, Lawrence Cheung, and Yihan Liu Wind Energ. Sci., 11, 2915–2938, https://doi.org/10.5194/wes-11-2915-2026, 2026 A comparison between the power generated by a 5 MW turbine mounted on a tension-leg platform and subjected to fully developed ocean wave movements, and the same platform/turbine not subjected to ocean motions shows that these wave motions have little effect on time-average power output over a large wind speed range. Wed, 12 Aug 2026 21:58:12 +0200 https://doi.org/10.5194/wes-11-2869-2026 Convolutional versus graph-based surrogate models for inter-farm wake prediction using multi-fidelity transfer learning Jens Peter Schøler, Frederik Peder Weilmann Rasmussen, M. Paul van der Laan, Alfredo Peña, and Pierre-Elouan Réthoré Wind Energ. Sci., 11, 2869–2893, https://doi.org/10.5194/wes-11-2869-2026, 2026 As offshore wind farms are built closer together, predicting how they affect each other becomes critical. We compared two AI approaches for this task, training both on cheap approximate data before refining them with expensive high-accuracy simulations. One predicts wake boundaries better, while the other estimates wind speeds more accurately, offering complementary tools for future wind farm design. Tue, 11 Aug 2026 21:58:12 +0200 https://doi.org/10.5194/wes-11-2845-2026 Validation of RANS-calibrated engineering models and ANN-based surrogate for wind farm flow simulation and layout optimization Jens Peter Schøler, Ernestas Simutis, M. Paul van der Laan, Julian Quick, and Pierre-Elouan Réthoré Wind Energ. Sci., 11, 2845–2868, https://doi.org/10.5194/wes-11-2845-2026, 2026 Wind turbines create wakes, which reduce downstream power. Optimizing turbine placement requires accounting for these reductions. We compared a neural network trained on numerical simulations against engineering wake models across various farm sizes. The neural network predicted flow most accurately but was slower. Surprisingly, a simple TurbOPark model produced layouts with higher validated energy output, suggesting that accuracy is not the only important metric for such models. Fri, 07 Aug 2026 21:58:12 +0200 https://doi.org/10.5194/wes-2026-129 Assessing Future Wind Speed Variations: Methodology for Climate Model Integration in Wind Resource Assessment Johanna Borowski, Kerstin Avila, and Martin Dörenkämper Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-129,2026 Preprint under review for WES (discussion: open, 0 comments) This study introduces a methodology that integrates climate model ensembles into the measure-correlate-predict framework for site-specific wind resource assessments. Using ERA5 reanalysis data and tall tower measurements from European sites, the approach bias-corrects climate model data to local conditions and reproduces historical wind climates. Results indicate decreases in summer wind speeds (typically 0.1–0.4 m s−¹) and slight winter increases but with greater ensemble spread in this season. Fri, 07 Aug 2026 21:58:12 +0200 https://doi.org/10.5194/wes-2026-136 Leading-edge erosion risk: Assessment of uncertainties Charlotte Bay Hasager, Abhiram Vinod, Laurids Dencker Di Stefano Toft, and Krystallia Dimitriadou Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-136,2026 Preprint under review for WES (discussion: open, 1 comment) Long-term rain gauge and satellite-based observations are suitable for assessing end of incubation of leading-edge erosion. Ten or more years and use of an advanced droplet slowdown model is recommended. Erosion safe operation efficiency in regions with short-lived precipitation events are recommended to be assessed from high frequency precipitation data. Fri, 07 Aug 2026 21:58:12 +0200 https://doi.org/10.5194/wes-11-2817-2026 Multi-strategy wind farm control: alternating wake steering and helix wake mixing on a large-scale wind farm Matteo Baricchio, Daan van der Hoek, Tim Dammann, Pieter M. O. Gebraad, Jenna Iori, and Jan-Willem van Wingerden Wind Energ. Sci., 11, 2817–2843, https://doi.org/10.5194/wes-11-2817-2026, 2026 Wind farm flow control mitigates wake effects within a wind farm by adjusting the turbine settings to improve the overall farm performance. This study quantifies the value of a combined strategy in which each turbine can apply wake steering or the helix wake mixing method. The proposed method is simulated for a large-scale wind farm, for which such a combined strategy provides higher gains in annual energy production compared to the individual techniques. Wed, 05 Aug 2026 21:58:12 +0200 https://doi.org/10.5194/wes-11-2801-2026 Fostering open science through a digital open innovation platform – structural health monitoring case study Sarah Barber, Shun Wang, Francesc Pozo, Yolanda Vidal, Marcela Rodrigues Machado, Amanda Aryda Silva Rodrigues de Sousa, Jefferson da Silva Coelho, Xukai Zhang, Yao-Teng Hu, Arash Noshadravan, Theodoros Varouxis, Mahmoud Abdelhak, Ramin Ghiasi, and Abdollah Malekjafarian Wind Energ. Sci., 11, 2801–2816, https://doi.org/10.5194/wes-11-2801-2026, 2026 WeDoWind is an open innovation ecosystem for enabling data sharing and alignment in the wind energy sector. In this work, a feasibility study of WeDoWind is presented, considering different technical, economic, legal, operational and scheduling aspects. The results showed strong governance, clear regulation, and promising scalability potential, but further progress is required to make it financially sustainable and to ensure adoption of the results in the sector. Tue, 04 Aug 2026 21:58:12 +0200 https://doi.org/10.5194/wes-2026-130 Integrated Wind Farm Layout Optimization Accounting for Wake-Induced Blade Fatigue and Wake-Steering Effects on LCOE Patrick Erik Marcel De Smet, Georg Jacobs, Dustin Frings, Leon Schenke, Stefan Witter, Thorsten Reichartz, and Martin Knops Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-130,2026 Preprint under review for WES (discussion: open, 0 comments) To accurately assess and optimize a wind farm's cost to produce energy over its entire lifetime in the planning phase, we integrate the effects from blade fatigue using a new design approach that considers both energy production and turbine lifetime. The method identifies wind farm layouts that reduce hidden costs caused by uneven loading on blades. Results show that lifetime-aware optimization can lower long-term costs without reducing energy output. Tue, 04 Aug 2026 21:58:12 +0200 https://doi.org/10.5194/wes-11-2783-2026 Impact of atmospheric stability and turbulence on wind turbine wake characteristics: a nacelle lidar study Julia Menken and Norman Wildmann Wind Energ. Sci., 11, 2783–2800, https://doi.org/10.5194/wes-11-2783-2026, 2026 Wind turbines interact with the atmosphere and affect the performance of nearby turbines. Using a large dataset from a wind farm in northern Germany, we studied how inflow conditions shape the flow behind a turbine, called a wake. Higher turbulence reduces the wake's length and impact, while stable conditions make it stronger. Wind direction changes with height can shift and skew the wake’s position. These findings help improve turbine wake models by considering varying atmospheric conditions. Mon, 03 Aug 2026 21:58:12 +0200 https://doi.org/10.5194/wes-11-2749-2026 Grand challenges in designing resilient wind energy systems in areas prone to tropical cyclones Georgios Deskos, Jiali Wang, Sanjay Arwade, Murray Fisher, Brian Hirth, Xiaoli Guo Larsén, Julie K. Lundquist, Andrew Myers, Weichiang Pang, William J. Pringle, Robert Rogers, Miguel Sanchez-Gomez, Chao Sun, Atsushi Yamaguchi, and Paul Veers Wind Energ. Sci., 11, 2749–2782, https://doi.org/10.5194/wes-11-2749-2026, 2026 Wind energy is expanding into regions exposed to tropical cyclones, where extreme winds and waves can damage turbines and disrupt energy production. We reviewed observations, simulations, engineering standards, and risk quantification frameworks to identify the most important gaps. Today's design rules, originally built for milder storms, do not capture cyclone hazards. Closing these gaps requires better offshore measurements, improved models, and risk-informed design standards. Mon, 03 Aug 2026 21:58:12 +0200 https://doi.org/10.5194/wes-11-2723-2026 Slow wake recovery and low turbulence behind wind farms parameterized in mesoscale simulations William C. Radünz, Jens H. Kasper, Richard J. A. M. Stevens, and Julie K. Lundquist Wind Energ. Sci., 11, 2723–2747, https://doi.org/10.5194/wes-11-2723-2026, 2026 Wind farms extract energy from the wind, creating slower, more turbulent flows that can affect other farms downstream. Using high-fidelity simulations for comparison, we find that mesoscale simulations that parameterize wind farms at coarser resolution may underestimate how quickly the wind recovers downstream. This slow recovery results from missing sharp wind gradients and low turbulence. Improving these aspects can help better predict wind energy production over long distances. Thu, 30 Jul 2026 21:58:12 +0200 https://doi.org/10.5194/wes-2026-133 Low-Frequency Fatigue monitoring using nacelle-mounted accelerometers Mustapha Chaar, Wout Weijtjens, and Christof Devriendt Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-133,2026 Preprint under review for WES (discussion: open, 0 comments) With advances in sensor technology, wind farm operators are installing low-cost sensors across the fleet to monitor structural integrity. However, these sensors cannot directly measure fatigue or remaining lifetime. This study shows that they can provide this information by learning from their peers with more instrumentation. The results demonstrate that slow-changing loads linked to high fatigue can be estimated accurately which are key to extending turbine lifetime. Thu, 30 Jul 2026 21:58:12 +0200 https://doi.org/10.5194/wes-11-2695-2026 A numerical study of the influence of terrain on wakes, blockage, wind farm efficiency, and turbine efficiency James Bleeg and Christiane Montavon Wind Energ. Sci., 11, 2695–2721, https://doi.org/10.5194/wes-11-2695-2026, 2026 Numerical simulations of 35 different combinations of terrain, wind farm layout, and atmospheric conditions indicate that terrain (i.e., ground elevation variation) can significantly influence wind farm flows and in turn energy extraction efficiency. An analysis of the simulation results identifies the main drivers behind these terrain effects. These influences should be accounted for when estimating the energy yield of a planned wind farm – at least for wind farms similar to those in this study. Tue, 28 Jul 2026 21:58:12 +0200 https://doi.org/10.5194/wes-2026-114 Dual-Doppler Radar Characterization during the Krummendeich Field Experiment: A Campaign Overview and Performance Assessment Arianna Marie Jordan, Lin-Ya Hung, Gerrit Wolken-Möhlmann, and Julia Gottschall Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-114,2026 Preprint under review for WES (discussion: open, 0 comments) Wind energy needs accurate airflow tools around turbines. We tested dual-Doppler radar at a research wind farm in Germany alongside lidar (a point-based wind sensor). The method matched lidar well at most heights and mapped wind shadows behind turbines, including wake meandering. Data quality depended on rainfall, with rainy periods giving the best coverage and fog the worst. This information is useful for implementation of dual-Doppler radar as a wind measurement tool for the industry. Tue, 28 Jul 2026 21:58:12 +0200 https://doi.org/10.5194/wes-2026-124 OptiWindNet RouteSets: a solver-diverse benchmark dataset for the offshore wind-farm cable routing problem Mauricio Souza de Alencar, Tuhfe Göçmen, and Nicolaos A. Cutululis Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-124,2026 Preprint under review for WES (discussion: open, 0 comments) This study created a large public collection of optimized cable layouts for real, built offshore wind farms, planned projects, and computer-generated sites. By comparing several design choices and solving approaches across thousands of cases, it shows how architectural constraints affect cable use and problem difficulty. The results reveal when branching can reduce cable needs and provide a reliable basis for improving future wind farm design, testing new methods, and training data-driven tools. Tue, 28 Jul 2026 21:58:12 +0200 https://doi.org/10.5194/wes-11-2669-2026 Large-eddy simulation of airborne wind energy systems flying in turbulent wind using model predictive control Jean-Baptiste Crismer, Thomas Haas, Matthieu Duponcheel, and Grégoire Winckelmans Wind Energ. Sci., 11, 2669–2694, https://doi.org/10.5194/wes-11-2669-2026, 2026 Wind energy is key to the energy transition. Airborne wind energy (AWE) is a technology based on kites. It has many advantages. However, their operation in gusts or in farm configurations remains unexplored. This work proposes a tool for studying AWE systems in such conditions. It is used to investigate a two-kite array. It is found that the second kite can avoid the wake of the first kite and stay unperturbed, while in other situations it produces 6 % less energy. Mon, 27 Jul 2026 21:58:12 +0200 https://doi.org/10.5194/wes-11-2621-2026 Wake-resolving acoustic tomography: advances through numerical covariance methods Nicholas Hamilton and Shreyas Bidadi Wind Energ. Sci., 11, 2621–2645, https://doi.org/10.5194/wes-11-2621-2026, 2026 This study explores improvements to an atmospheric measurement method called acoustic tomography, which uses sound travel times to estimate wind and temperature. We compare several ways of estimating how air conditions vary and show that models based on realistic wind turbine simulations yield more accurate results than traditional simplified methods. These findings support better observations of complex air flows around wind turbines, helping advance renewable energy research. Fri, 24 Jul 2026 21:58:12 +0200 https://doi.org/10.5194/wes-11-2647-2026 A consistent computational fluid dynamics surrogate model for wind turbine interaction including atmospheric stability Maarten Paul van der Laan, Alexander Meyer Forsting, and Pierre-Elouan Réthoré Wind Energ. Sci., 11, 2647–2668, https://doi.org/10.5194/wes-11-2647-2026, 2026 Wind turbine interaction can lead to energy losses. This article introduces a fast open-source wind turbine interaction model that can be used to design energy-efficient wind farms, including effects of atmospheric turbulence and temperature. The model can inherit the accuracy of a higher-fidelity model while being about 5 orders of magnitude faster. However, the model is an order of magnitude slower than analytic wind turbine interaction models, and more research is needed to reduce it. Fri, 24 Jul 2026 21:58:12 +0200