Articles | Volume 8, issue 3
https://doi.org/10.5194/wes-8-363-2023
© Author(s) 2023. 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-8-363-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Mar 2023
Research article |
| 22 Mar 2023
| 22 Mar 2023
Actuator line model using simplified force calculation methods
Gonzalo Pablo Navarro Diaz
CORRESPONDING AUTHOR
Wind Energy Section, Uppsala University, Campus Gotland, Visby, Sweden
Alejandro Daniel Otero
Facultad de Ingeniería, Universidad de Buenos Aires, Buenos Aires, Argentina
Computational Simulation Center (CSC-CONICET), Buenos Aires, Argentina
Henrik Asmuth
Wind Energy Section, Uppsala University, Campus Gotland, Visby, Sweden
Jens Nørkær Sørensen
Department of Wind and Energy Systems, Technical University of Denmark, Lyngby, Denmark
Stefan Ivanell
Wind Energy Section, Uppsala University, Campus Gotland, Visby, Sweden
Related authors
No articles found.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Ville Vakkari, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 9, 821–840, https://doi.org/10.5194/wes-9-821-2024, https://doi.org/10.5194/wes-9-821-2024, 2024
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Knowing the wind speed across the rotor of a wind turbine is key in making good predictions of the power production. However, models struggle to capture both the speed and the shape of the wind profile. Using machine learning methods based on the model data, we show that the predictions can be improved drastically. The work focuses on three coastal sites, spread over the Northern Hemisphere (the Baltic Sea, the North Sea, and the US Atlantic coast) with similar results for all sites.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 8, 1651–1658, https://doi.org/10.5194/wes-8-1651-2023, https://doi.org/10.5194/wes-8-1651-2023, 2023
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Low-level jets (LLJs) are special types of non-ideal wind profiles affecting both wind energy production and loads on a wind turbine. However, among LLJ researchers, there is no consensus regarding which definition to use to identify these profiles. In this work, we compare two different ways of identifying the LLJ – the falloff definition and the shear definition – and argue why the shear definition is better suited to wind energy applications.
Christoffer Hallgren, Heiner Körnich, Stefan Ivanell, Ville Vakkari, and Erik Sahlée
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-129, https://doi.org/10.5194/wes-2023-129, 2023
Preprint under review for WES
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Sometimes, the wind changes direction between the bottom and top part of a wind turbine. This affects both the power production and the loads on the turbine. In this study, a climatology of pronounced changes in wind direction across the rotor is created, focusing on Scandinavia. The weather conditions responsible for these changes in wind direction are investigated and the climatology is compared to measurements from two coastal sites, indicating an underestimation by the climatology.
Jens N. Sørensen
Wind Energ. Sci., 8, 1017–1027, https://doi.org/10.5194/wes-8-1017-2023, https://doi.org/10.5194/wes-8-1017-2023, 2023
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The paper presents a simple analytical model that, with surprisingly good accuracy, represents the loading for virtually any horizontal axis wind turbine, independent of size and operating regime. The aim of the model is to have a simple tool that may represent the loading of any wind turbine without having access to the details regarding the specific geometry and airfoil data, information that is normally kept confidential by the manufacturer of the turbine.
Mohammad Mehdi Mohammadi, Hugo Olivares-Espinosa, Gonzalo Pablo Navarro Diaz, and Stefan Ivanell
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-16, https://doi.org/10.5194/wes-2023-16, 2023
Revised manuscript accepted for WES
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This paper has put forward a set of recommendations regarding the actuator sector model implementation details to improve the capability of the model to reproduce similar results compared to those obtained by an actuator line model which is one of the most common ways used for numerical simulations of wind farms while providing significant computational savings. This includes among others the velocity sampling method and a correction of the sampled velocities to calculate the blade forces.
Christoffer Hallgren, Johan Arnqvist, Erik Nilsson, Stefan Ivanell, Metodija Shapkalijevski, August Thomasson, Heidi Pettersson, and Erik Sahlée
Wind Energ. Sci., 7, 1183–1207, https://doi.org/10.5194/wes-7-1183-2022, https://doi.org/10.5194/wes-7-1183-2022, 2022
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Non-idealized wind profiles with negative shear in part of the profile (e.g., low-level jets) frequently occur in coastal environments and are important to take into consideration for offshore wind power. Using observations from a coastal site in the Baltic Sea, we analyze in which meteorological and sea state conditions these profiles occur and study how they alter the turbulence structure of the boundary layer compared to idealized profiles.
Christoffer Hallgren, Stefan Ivanell, Heiner Körnich, Ville Vakkari, and Erik Sahlée
Wind Energ. Sci., 6, 1205–1226, https://doi.org/10.5194/wes-6-1205-2021, https://doi.org/10.5194/wes-6-1205-2021, 2021
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As wind power becomes more popular, there is a growing demand for accurate power production forecasts. In this paper we investigated different methods to improve wind power forecasts for an offshore location in the Baltic Sea, using both simple and more advanced techniques. The performance of the methods is evaluated for different weather conditions. Smoothing the forecast was found to be the best method in general, but we recommend selecting which method to use based on the forecasted weather.
Søren Juhl Andersen, Simon-Philippe Breton, Björn Witha, Stefan Ivanell, and Jens Nørkær Sørensen
Wind Energ. Sci., 5, 1689–1703, https://doi.org/10.5194/wes-5-1689-2020, https://doi.org/10.5194/wes-5-1689-2020, 2020
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The complexity of wind farm operation increases as the wind farms get larger and larger. Therefore, researchers from three universities have simulated numerous different large wind farms as part of an international benchmark. The study shows how simple engineering models can capture the general trends, but high-fidelity simulations are required in order to quantify the variability and uncertainty associated with power production of the wind farms and hence the potential profitability and risks.
Özge Sinem Özçakmak, Helge Aagaard Madsen, Niels Nørmark Sørensen, and Jens Nørkær Sørensen
Wind Energ. Sci., 5, 1487–1505, https://doi.org/10.5194/wes-5-1487-2020, https://doi.org/10.5194/wes-5-1487-2020, 2020
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Accurate prediction of the laminar-turbulent transition process is critical for design and prediction tools to be used in the industrial design process, particularly for the high Reynolds numbers experienced by modern wind turbines. Laminar-turbulent transition behavior of a wind turbine blade section is investigated in this study by means of field experiments and 3-D computational fluid dynamics (CFD) rotor simulations.
Henrik Asmuth, Hugo Olivares-Espinosa, and Stefan Ivanell
Wind Energ. Sci., 5, 623–645, https://doi.org/10.5194/wes-5-623-2020, https://doi.org/10.5194/wes-5-623-2020, 2020
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The presented work investigates the potential of the lattice Boltzmann method (LBM) for numerical simulations of wind turbine wakes. The LBM is a rather novel, alternative approach for computational fluid dynamics (CFD) that allows for significantly faster simulations. The study shows that the method provides similar results when compared to classical CFD approaches while only requiring a fraction of the computational demand.
Stefan Ivanell, Johan Arnqvist, Matias Avila, Dalibor Cavar, Roberto Aurelio Chavez-Arroyo, Hugo Olivares-Espinosa, Carlos Peralta, Jamal Adib, and Björn Witha
Wind Energ. Sci., 3, 929–946, https://doi.org/10.5194/wes-3-929-2018, https://doi.org/10.5194/wes-3-929-2018, 2018
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This article describes a study in which modellers were challenged to compute the wind at a forested site with moderately complex topography. The target was to match the measured wind profile at one exact location for three directions. The input to the models consisted of detailed information on forest densities and ground height. Overall, the article gives an overview of how well different types of models are able to capture the flow physics at a moderately complex forested site.
Jens N. Sørensen and Gunner C. Larsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2018-53, https://doi.org/10.5194/wes-2018-53, 2018
Preprint withdrawn
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The work assesses the potential of a massive exploitation of offshore wind power in the North Sea by combining a meteorological model with a cost model including a bathymetric analysis of the water depth of the North Sea. As an overall finding, it is shown that the electrical power demand of Europe can be fulfilled by exploiting an area corresponding to about 1/3 of the North Sea with 100.000 wind turbines of generator size 13 MW on water depths up to 45 m to a cost price of about 7.5 €cents/kWh.
Nikolaos Simisiroglou, Heracles Polatidis, and Stefan Ivanell
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2018-8, https://doi.org/10.5194/wes-2018-8, 2018
Preprint withdrawn
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The aim of the present study is to perform a comparative analysis of two actuator disc methods (ACD) and two analytical wake models for wind farm power production assessment. To do so wind turbine power production data from the Lillgrund offshore wind farm in Sweden is used. The measured power production for individual wind turbines is compared with results from simulations, done in the WindSim software.
Nikolaos Simisiroglou, Simon-Philippe Breton, and Stefan Ivanell
Wind Energ. Sci., 2, 587–601, https://doi.org/10.5194/wes-2-587-2017, https://doi.org/10.5194/wes-2-587-2017, 2017
G. A. M. van Kuik, J. Peinke, R. Nijssen, D. Lekou, J. Mann, J. N. Sørensen, C. Ferreira, J. W. van Wingerden, D. Schlipf, P. Gebraad, H. Polinder, A. Abrahamsen, G. J. W. van Bussel, J. D. Sørensen, P. Tavner, C. L. Bottasso, M. Muskulus, D. Matha, H. J. Lindeboom, S. Degraer, O. Kramer, S. Lehnhoff, M. Sonnenschein, P. E. Sørensen, R. W. Künneke, P. E. Morthorst, and K. Skytte
Wind Energ. Sci., 1, 1–39, https://doi.org/10.5194/wes-1-1-2016, https://doi.org/10.5194/wes-1-1-2016, 2016
Related subject area
Thematic area: Fluid mechanics | Topic: Wakes and wind farm aerodynamics
The near-wake development of a wind turbine operating in stalled conditions – Part 1: Assessment of numerical models
Data-driven optimisation of wind farm layout and wake steering with large-eddy simulations
Floating wind turbine motion signature in the far-wake spectral content – a wind tunnel experiment
Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 1: Large-eddy-simulation study
Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 2: Analytical modelling
Free-vortex models for wind turbine wakes under yaw misalignment – a validation study on far-wake effects
A method to correct for the effect of blockage and wakes on power performance measurements
Improvements to the Dynamic Wake Meandering Model by incorporating the turbulent Schmidt number
Wind Farm Structural Response and Wake Dynamics for an Evolving Stable Boundary Layer: Computational and Experimental Comparisons
Wind tunnel investigations of an individual pitch control strategy for wind farm power optimization
Vortex model of the aerodynamic wake of airborne wind energy systems
A new RANS-based wind farm parameterization and inflow model for wind farm cluster modeling
Investigating energy production and wake losses of multi-gigawatt offshore wind farms with atmospheric large-eddy simulation
The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data
An Actuator Sector Model for Wind Power Applications: A Parametric Study
Multi-point in situ measurements of turbulent flow in a wind turbine wake and inflow with a fleet of uncrewed aerial systems
Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model
Brief communication: A clarification of wake recovery mechanisms
Predictive and stochastic reduced-order modeling of wind turbine wake dynamics
Wind turbine wake simulation with explicit algebraic Reynolds stress modeling
Including realistic upper atmospheres in a wind-farm gravity-wave model
Pascal Weihing, Marion Cormier, Thorsten Lutz, and Ewald Krämer
Wind Energ. Sci., 9, 933–962, https://doi.org/10.5194/wes-9-933-2024, https://doi.org/10.5194/wes-9-933-2024, 2024
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This study evaluates different approaches to simulate the near-wake flow of a wind turbine. The test case is in off-design conditions of the wind turbine, where the flow is separated from the blades and therefore very difficult to predict. The evaluation of simulation techniques is key to understand their limitations and to deepen the understanding of the near-wake physics. This knowledge can help to derive new wind farm design methods for yield-optimized farm layouts.
Nikolaos Bempedelis, Filippo Gori, Andrew Wynn, Sylvain Laizet, and Luca Magri
Wind Energ. Sci., 9, 869–882, https://doi.org/10.5194/wes-9-869-2024, https://doi.org/10.5194/wes-9-869-2024, 2024
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This paper proposes a computational method to maximise the power production of wind farms through two strategies: layout optimisation and yaw angle optimisation. The proposed method relies on high-fidelity computational modelling of wind farm flows and is shown to be able to effectively maximise wind farm power production. Performance improvements relative to conventional optimisation strategies based on low-fidelity models can be attained, particularly in scenarios of increased flow complexity.
Benyamin Schliffke, Boris Conan, and Sandrine Aubrun
Wind Energ. Sci., 9, 519–532, https://doi.org/10.5194/wes-9-519-2024, https://doi.org/10.5194/wes-9-519-2024, 2024
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This paper studies the consequences of floater motions for the wake properties of a floating wind turbine. Since wake interactions are responsible for power production loss in wind farms, it is important that we know whether the tools that are used to predict this production loss need to be upgraded to take into account these aspects. Our wind tunnel study shows that the signature of harmonic floating motions can be observed in the far wake of a wind turbine, when motions have strong amplitudes.
Erwan Jézéquel, Frédéric Blondel, and Valéry Masson
Wind Energ. Sci., 9, 97–117, https://doi.org/10.5194/wes-9-97-2024, https://doi.org/10.5194/wes-9-97-2024, 2024
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Wind turbine wakes affect the production and lifecycle of downstream turbines. They can be predicted with the dynamic wake meandering (DWM) method. In this paper, the authors break down the velocity and turbulence in the wake of a wind turbine into several terms. They show that it is implicitly assumed in the DWM that some of these terms are neglected. With high-fidelity simulations, it is shown that this can lead to some errors, in particular for the maximum turbulence added by the wake.
Erwan Jézéquel, Frédéric Blondel, and Valéry Masson
Wind Energ. Sci., 9, 119–139, https://doi.org/10.5194/wes-9-119-2024, https://doi.org/10.5194/wes-9-119-2024, 2024
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Analytical models allow us to quickly compute the decreased power output and lifetime induced by wakes in a wind farm. This is achieved by evaluating the modified velocity and turbulence in the wake. In this work, we present a new model based on the velocity and turbulence breakdowns presented in Part 1. This new model is physically based, allows us to compute the whole turbulence profile (rather than the maximum value) and is built to take atmospheric stability into account.
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.
Alessandro Sebastiani, James Bleeg, and Alfredo Peña
Wind Energ. Sci., 8, 1795–1808, https://doi.org/10.5194/wes-8-1795-2023, https://doi.org/10.5194/wes-8-1795-2023, 2023
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The power curve of a wind turbine indicates the turbine power output in relation to the wind speed. Therefore, power curves are critically important to estimate the production of future wind farms as well as to assess whether operating wind farms are functioning correctly. Since power curves are often measured in wind farms, they might be affected by the interactions between the turbines. We show that these effects are not negligible and present a method to correct for them.
Peter Brugger, Corey Markfort, and Fernando Porté-Agel
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-150, https://doi.org/10.5194/wes-2023-150, 2023
Revised manuscript accepted for WES
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The dynamic wake meandering model (DWMM) assumes that wind turbine wakes are transported like a passive tracer by the large-scale turbulence of the atmospheric boundary layer. We show that both the streamwise transport and the lateral transport have differences to the passive tracer assumption. We then propose to include the turbulent Schmidt number into the DWMM to address the observed differences and show that it improves the quality of the model predictions.
Kelsey Shaler, Eliot Quon, Hristo Ivanov, and Jason Jonkman
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-138, https://doi.org/10.5194/wes-2023-138, 2023
Revised manuscript accepted for WES
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This paper presents a three-way verification and validation between an engineering-fidelity model, a high-fidelity model, and measured data for the wind farm structural response and wake dynamics during an evolving stable boundary layer of a small wind farm, generally with good agreement.
Franz Volker Mühle, Florian Marco Heckmeier, Filippo Campagnolo, and Christian Breitsamter
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-125, https://doi.org/10.5194/wes-2023-125, 2023
Revised manuscript accepted for WES
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Wind turbines influence each other and this wake effects limit the power production of downstream turbines. Controlling turbines collectively and not individually can limit such effects. We experimentally investigate a control strategy increasing mixing in the wake. We want to see the potential of this so called Helix control for power optimization and understand the flow physics. Our study shows that the control technique leads to clearly faster wake recovery and thus higher power production.
Filippo Trevisi, Carlo E. D. Riboldi, and Alessandro Croce
Wind Energ. Sci., 8, 999–1016, https://doi.org/10.5194/wes-8-999-2023, https://doi.org/10.5194/wes-8-999-2023, 2023
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Modeling the aerodynamic wake of airborne wind energy systems (AWESs) is crucial to properly estimating power production and to designing such systems. The velocities induced at the AWES from its own wake are studied with a model for the near wake and one for the far wake, using vortex methods. The model is validated with the lifting-line free-vortex wake method implemented in QBlade.
Maarten Paul van der Laan, Oscar García-Santiago, Mark Kelly, Alexander Meyer Forsting, Camille Dubreuil-Boisclair, Knut Sponheim Seim, Marc Imberger, Alfredo Peña, Niels Nørmark Sørensen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 8, 819–848, https://doi.org/10.5194/wes-8-819-2023, https://doi.org/10.5194/wes-8-819-2023, 2023
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Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses. In this work, an efficient numerical method is presented that can be used to estimate these energy losses. The novel method is verified with higher-fidelity numerical models and validated with measurements of an existing wind farm cluster.
Peter Baas, Remco Verzijlbergh, Pim van Dorp, and Harm Jonker
Wind Energ. Sci., 8, 787–805, https://doi.org/10.5194/wes-8-787-2023, https://doi.org/10.5194/wes-8-787-2023, 2023
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This work studies the energy production and wake losses of large offshore wind farms with a large-eddy simulation model. Therefore, 1 year of actual weather has been simulated for a suite of hypothetical 4 GW wind farm scenarios. The results suggest that production numbers increase significantly when the rated power of the individual turbines is larger while keeping the total installed capacity the same. Also, a clear impact of atmospheric stability on the energy production is found.
Robert Braunbehrens, Andreas Vad, and Carlo L. Bottasso
Wind Energ. Sci., 8, 691–723, https://doi.org/10.5194/wes-8-691-2023, https://doi.org/10.5194/wes-8-691-2023, 2023
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The paper presents a new method in which wind turbines in a wind farm act as local sensors, in this way detecting the flow that develops within the power plant. Through this technique, we are able to identify effects on the flow generated by the plant itself and by the orography of the terrain. The new method not only delivers a flow model of much improved quality but can also help in understanding phenomena that drive the farm performance.
Mohammad Mehdi Mohammadi, Hugo Olivares-Espinosa, Gonzalo Pablo Navarro Diaz, and Stefan Ivanell
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-16, https://doi.org/10.5194/wes-2023-16, 2023
Revised manuscript accepted for WES
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This paper has put forward a set of recommendations regarding the actuator sector model implementation details to improve the capability of the model to reproduce similar results compared to those obtained by an actuator line model which is one of the most common ways used for numerical simulations of wind farms while providing significant computational savings. This includes among others the velocity sampling method and a correction of the sampled velocities to calculate the blade forces.
Tamino Wetz and Norman Wildmann
Wind Energ. Sci., 8, 515–534, https://doi.org/10.5194/wes-8-515-2023, https://doi.org/10.5194/wes-8-515-2023, 2023
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In the present study, for the first time, the SWUF-3D fleet of multirotors is deployed for field measurements on an operating 2 MW wind turbine (WT) in complex terrain. The fleet of multirotors has the potential to fill the meteorological gap of observations in the near wake of WTs with high-temporal and high-spatial-resolution wind vector measurements plus temperature, humidity and pressure. The flow up- and downstream of the WT is measured simultaneously at multiple spatial positions.
Christopher J. Bay, Paul Fleming, Bart Doekemeijer, Jennifer King, Matt Churchfield, and Rafael Mudafort
Wind Energ. Sci., 8, 401–419, https://doi.org/10.5194/wes-8-401-2023, https://doi.org/10.5194/wes-8-401-2023, 2023
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This paper introduces the cumulative-curl wake model that allows for the fast and accurate prediction of wind farm energy production wake interactions. The cumulative-curl model expands several existing wake models to make the simulation of farms more accurate and is implemented in a computationally efficient manner such that it can be used for wind farm layout design and controller development. The model is validated against high-fidelity simulations and data from physical wind farms.
Maarten Paul van der Laan, Mads Baungaard, and Mark Kelly
Wind Energ. Sci., 8, 247–254, https://doi.org/10.5194/wes-8-247-2023, https://doi.org/10.5194/wes-8-247-2023, 2023
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Understanding wind turbine wake recovery is important to mitigate energy losses in wind farms. Wake recovery is often assumed or explained to be dependent on the first-order derivative of velocity. In this work we show that wind turbine wakes recover mainly due to the second-order derivative of the velocity, which transport momentum from the freestream towards the wake center. The wake recovery mechanisms and results of a high-fidelity numerical simulation are illustrated using a simple model.
Søren Juhl Andersen and Juan Pablo Murcia Leon
Wind Energ. Sci., 7, 2117–2133, https://doi.org/10.5194/wes-7-2117-2022, https://doi.org/10.5194/wes-7-2117-2022, 2022
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Simulating the turbulent flow inside large wind farms is inherently complex and computationally expensive. A new and fast model is developed based on data from high-fidelity simulations. The model captures the flow dynamics with correct statistics for a wide range of flow conditions. The model framework provides physical insights and presents a generalization of high-fidelity simulation results beyond the case-specific scenarios, which has significant potential for future turbulence modeling.
Mads Baungaard, Stefan Wallin, Maarten Paul van der Laan, and Mark Kelly
Wind Energ. Sci., 7, 1975–2002, https://doi.org/10.5194/wes-7-1975-2022, https://doi.org/10.5194/wes-7-1975-2022, 2022
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Wind turbine wakes in the neutral atmospheric surface layer are simulated with Reynolds-averaged Navier–Stokes (RANS) using an explicit algebraic Reynolds stress model. Contrary to standard two-equation turbulence models, it can predict turbulence anisotropy and complex physical phenomena like secondary motions. For the cases considered, it improves Reynolds stress, turbulence intensity, and velocity deficit predictions, although a more top-hat-shaped profile is observed for the latter.
Koen Devesse, Luca Lanzilao, Sebastiaan Jamaer, Nicole van Lipzig, and Johan Meyers
Wind Energ. Sci., 7, 1367–1382, https://doi.org/10.5194/wes-7-1367-2022, https://doi.org/10.5194/wes-7-1367-2022, 2022
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Recent research suggests that offshore wind farms might form such a large obstacle to the wind that it already decelerates before reaching the first turbines. Part of this phenomenon could be explained by gravity waves. Research on these gravity waves triggered by mountains and hills has found that variations in the atmospheric state with altitude can have a large effect on how they behave. This paper is the first to take the impact of those vertical variations into account for wind farms.
Cited articles
Arthur, R. S., Mirocha, J. D., Marjanovic, N., Hirth, B. D., Schroeder, J. L., Wharton, S., and Chow, F. K.: Multi-scale simulation of wind farm performance during a frontal passage, Atmosphere, 11, 245, https://doi.org/10.3390/atmos11030245, 2020. a
Bossuyt, J., Howland, M. F., Meneveau, C., and Meyers, J.: Wind tunnel study of the power output spectrum in a micro wind farm, J. Phys.: Conf. Ser., 753, 072002, https://doi.org/10.1088/1742-6596/753/7/072002, 2016.
a
Churchfield, M., Lee, S., Moriarty, P., Martinez, L., Leonardi, S.,
Vijayakumar, G., and Brasseur, J.: A large-eddy simulation of wind-plant
aerodynamics, in: 50th AIAA Aerospace Sciences Meeting including the New
Horizons Forum and Aerospace Exposition, 9–12 January 2012, Nashville, Tennessee, USA, p. 537, https://doi.org/10.2514/6.2012-537, 2012a. a
Churchfield, M. J., Lee, S., Michalakes, J., and Moriarty, P. J.: A numerical
study of the effects of atmospheric and wake turbulence on wind turbine
dynamics, J. Turbulence, 13, N14, https://doi.org/10.1080/14685248.2012.668191, 2012b. a, b, c
Englberger, A. and Dörnbrack, A.: Impact of the diurnal cycle of the
atmospheric boundary layer on wind-turbine wakes: a numerical modelling study, Bound.-Lay. Meteorol., 166, 423–448, 2018. a
Glauert, H.: Airplane propellers, in: Aerodynamic theory, Springer, 169–360, https://doi.org/10.1007/978-3-642-91487-4_3, 1935. a, b
Howland, M. F., Bossuyt, J., Martínez-Tossas, L. A., Meyers, J., and
Meneveau, C.: Wake structure in actuator disk models of wind turbines in yaw
under uniform inflow conditions, Journal of Renewable and Sustainable Energy,
8, 043301, https://doi.org/10.1063/1.4955091, 2016. a
Jha, P. K. and Schmitz, S.: Actuator curve embedding–an advanced actuator line model, J. Fluid Mech., 834, R2, https://doi.org/10.1017/jfm.2017.793, 2018. a, b
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5-MW
reference wind turbine for offshore system development, Technical Report No. NREL/TP-500-38060, National Renewable Energy Laboratory, Golden, CO, https://doi.org/10.2172/947422, 2009. a
Marjanovic, N., Mirocha, J. D., Kosović, B., Lundquist, J. K., and Chow,
F. K.: Implementation of a generalized actuator line model for wind turbine
parameterization in the Weather Research and Forecasting model, J. Renew. Sustain. Energ., 9, 063308, https://doi.org/10.1063/1.4989443, 2017. a
Martinez, L., Leonardi, S., Churchfield, M., and Moriarty, P.: A comparison of actuator disk and actuator line wind turbine models and best practices for
their use, in: 50th AIAA Aerospace Sciences Meeting including the New
Horizons Forum and Aerospace Exposition, 9–12 January 2012, Nashville, Tennessee, USA, p. 900, https://doi.org/10.2514/6.2012-900, 2012. a, b, c, d
Munters, W., Meneveau, C., and Meyers, J.: Shifted periodic boundary conditions for simulations of wall-bounded turbulent flows, Phys. Fluids, 28,
025112, https://doi.org/10.1063/1.4941912, 2016. a
Nathan, J., Masson, C., Dufresne, L., and Churchfield, M. J.: Analysis of the
sweeped actuator line method, in: E3S Web of Conferences, vol. 5, National
NREL – Renewable Energy Lab., Golden, CO, USA, https://doi.org/10.1051/e3sconf/20150501001, 2015. a
Nathan, J., Forsting, A. R. M., Troldborg, N., and Masson, C.: Comparison of
OpenFOAM and EllipSys3D actuator line methods with (NEW) MEXICO results, J. Phys.: Conf. Ser., 854, 012033, https://doi.org/10.1088/1742-6596/854/1/012033, 2017. a
Nathan, J., Masson, C., and Dufresne, L.: Near-wake analysis of actuator line method immersed in turbulent flow using large-eddy simulations, Wind Energ. Sci., 3, 905–917, https://doi.org/10.5194/wes-3-905-2018, 2018. a
Navarro Diaz, G. P., Saulo, A. C., and Otero, A. D.: Full wind rose wind farm
simulation including wake and terrain effects for energy yield assessment,
Energy, 237, 121642, https://doi.org/10.1016/j.energy.2021.121642, 2021. a
NREL: SOWFA, GitHub [code], https://github.com/NREL/SOWFA/, last access: 21 March 2023. a
Ponta, F. L., Otero, A. D., Lago, L. I., and Rajan, A.: Effects of rotor
deformation in wind-turbine performance: the dynamic rotor deformation blade
element momentum model (DRD–BEM), Renew. Energy, 92, 157–170, 2016. a
Porté-Agel, F., Bastankhah, M., and Shamsoddin, S.: Wind-turbine and
wind-farm flows: a review, Bound.-Lay. Meteorol., 174, 1–59, 2020. a
Sanderse, B., Pijl, S., and Koren, B.: Review of computational fluid dynamics
for wind turbine wake aerodynamics, Wind Energy, 14, 799–819, 2011. a
Sørensen, J. N. and Andersen, S. J.: Validation of analytical body force
model for actuator disc computations, J. Phys.: Conf. Ser., 1618, 052051, https://doi.org/10.1088/1742-6596/1618/5/052051, 2020. a, b
Sørensen, J. N. and Kock, C. W.: A model for unsteady rotor aerodynamics,
J. Wind Eng. Indust. Aerodynam., 58, 259–275, 1995. a
Sørensen, J. N. and Myken, A.: Unsteady actuator disc model for horizontal
axis wind turbines, J. Wind Eng. Indust. Aerodynam., 39, 139–149, 1992. a
Sørensen, J. N., Mikkelsen, R. F., Henningson, D. S., Ivanell, S., Sarmast, S., and Andersen, S. J.: Simulation of wind turbine wakes using the actuator line technique, Philos. T. Roy. Soc. A, 373, 20140071, https://doi.org/10.1098/rsta.2014.0071, 2015. a
Stevens, R. J., Martínez-Tossas, L. A., and Meneveau, C.: Comparison of
wind farm large eddy simulations using actuator disk and actuator line models
with wind tunnel experiments, Renew. Energy, 116, 470–478, 2018. a
Stieren, A., Gadde, S. N., and Stevens, R. J.: Modeling dynamic wind direction changes in large eddy simulations of wind farms, Renew. Energy, 170, 1342–1352, 2021. a
van der Laan, M. P., Andersen, S. J., Réthoré, P. E., Baungaard, M., Sørensen, J. N., and Troldborg, N.: Faster wind farm AEP calculations with CFD using a generalized wind turbine model, J. Phys.: Conf.
Ser., 2265, 022030, https://doi.org/10.1088/1742-6596/2265/2/022030, 2022. a
Wang, J., Wang, C., Castañeda, O., Campagnolo, F., and Bottasso, C.:
Large-eddy simulation of scaled floating wind turbines in a boundary layer
wind tunnel, J. Phys.: Conf. Ser., 1037, 072032, https://doi.org/10.1088/1742-6596/1037/7/072032, 2018. a
Yoshizawa, A.: Statistical theory for compressible turbulent shear flows, with the application to subgrid modeling, Phys. Fluids, 29, 2152–2164, 1986. a
Zhong, W., Wang, T. G., Zhu, W. J., and Shen, W. Z.: Evaluation of Tip Loss
Corrections to AD/NS Simulations of Wind Turbine Aerodynamic Performance,
Appl. Sci., 9, 4919, https://doi.org/10.1063/1.865552, 2019. a
Short summary
In this paper, the capacity to simulate transient wind turbine wake interaction problems using limited wind turbine data has been extended. The key novelty is the creation of two new variants of the actuator line technique in which the rotor blade forces are computed locally using generic load data. The analysis covers a partial wake interaction case between two wind turbines for a uniform laminar inflow and for a turbulent neutral atmospheric boundary layer inflow.
In this paper, the capacity to simulate transient wind turbine wake interaction problems using...
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