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Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm

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Cited by:

  1. Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(C).
  2. Yang, Kun & Deng, Xiaowei & Ti, Zilong & Yang, Shanghui & Huang, Senbin & Wang, Yuhang, 2023. "A data-driven layout optimization framework of large-scale wind farms based on machine learning," Renewable Energy, Elsevier, vol. 218(C).
  3. Mahdi Abkar & Jens Nørkær Sørensen & Fernando Porté-Agel, 2018. "An Analytical Model for the Effect of Vertical Wind Veer on Wind Turbine Wakes," Energies, MDPI, vol. 11(7), pages 1-10, July.
  4. Fan, Shuanglong & Liu, Zhenqing, 2023. "Proposal of fully-coupled actuated disk model for wind turbine operation modeling in turbulent flow field due to complex topography," Energy, Elsevier, vol. 284(C).
  5. Hegazy, Amr & Blondel, Frédéric & Cathelain, Marie & Aubrun, Sandrine, 2022. "LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models," Renewable Energy, Elsevier, vol. 181(C), pages 457-471.
  6. Deskos, Georgios & Laizet, Sylvain & Piggott, Matthew D., 2019. "Turbulence-resolving simulations of wind turbine wakes," Renewable Energy, Elsevier, vol. 134(C), pages 989-1002.
  7. Li, Qing'an & Murata, Junsuke & Endo, Masayuki & Maeda, Takao & Kamada, Yasunari, 2016. "Experimental and numerical investigation of the effect of turbulent inflow on a Horizontal Axis Wind Turbine (part II: Wake characteristics)," Energy, Elsevier, vol. 113(C), pages 1304-1315.
  8. Yang, Haoze & Ge, Mingwei & Abkar, Mahdi & Yang, Xiang I.A., 2022. "Large-eddy simulation study of wind turbine array above swell sea," Energy, Elsevier, vol. 256(C).
  9. Chen, Guang & Liang, Xi-Feng & Li, Xiao-Bai, 2022. "Modelling of wake dynamics and instabilities of a floating horizontal-axis wind turbine under surge motion," Energy, Elsevier, vol. 239(PB).
  10. Chanprasert, W. & Sharma, R.N. & Cater, J.E. & Norris, S.E., 2022. "Large Eddy Simulation of wind turbine fatigue loading and yaw dynamics induced by wake turbulence," Renewable Energy, Elsevier, vol. 190(C), pages 208-222.
  11. Tian, Linlin & Zhu, Weijun & Shen, Wenzhong & Song, Yilei & Zhao, Ning, 2017. "Prediction of multi-wake problems using an improved Jensen wake model," Renewable Energy, Elsevier, vol. 102(PB), pages 457-469.
  12. Zhang, Jincheng & Zhao, Xiaowei, 2020. "Quantification of parameter uncertainty in wind farm wake modeling," Energy, Elsevier, vol. 196(C).
  13. Barlas, Emre & Wu, Ka Ling & Zhu, Wei Jun & Porté-Agel, Fernando & Shen, Wen Zhong, 2018. "Variability of wind turbine noise over a diurnal cycle," Renewable Energy, Elsevier, vol. 126(C), pages 791-800.
  14. Dhiman, Harsh S. & Deb, Dipankar & Foley, Aoife M., 2020. "Bilateral Gaussian Wake Model Formulation for Wind Farms: A Forecasting based approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  15. Ti, Zilong & Deng, Xiao Wei & Zhang, Mingming, 2021. "Artificial Neural Networks based wake model for power prediction of wind farm," Renewable Energy, Elsevier, vol. 172(C), pages 618-631.
  16. Davide Astolfi & Fabrizio De Caro & Alfredo Vaccaro, 2023. "Characterizing the Wake Effects on Wind Power Generator Operation by Data-Driven Techniques," Energies, MDPI, vol. 16(15), pages 1-19, August.
  17. Pin Lyu & Wen-Li Chen & Hui Li & Lian Shen, 2019. "A Numerical Study on the Development of Self-Similarity in a Wind Turbine Wake Using an Improved Pseudo-Spectral Large-Eddy Simulation Solver," Energies, MDPI, vol. 12(4), pages 1-24, February.
  18. Chen, Long & Yao, Yu & Wang, Zhi-liang, 2020. "Development and validation of a prediction model for the multi-wake of tidal stream turbines," Renewable Energy, Elsevier, vol. 155(C), pages 800-809.
  19. Zhang, Ziyu & Huang, Peng, 2023. "Prediction of multiple-wake velocity and wind power using a cosine-shaped wake model," Renewable Energy, Elsevier, vol. 219(P1).
  20. Wu, Yu-Ting & Liao, Teh-Lu & Chen, Chang-Kuo & Lin, Chuan-Yao & Chen, Po-Wei, 2019. "Power output efficiency in large wind farms with different hub heights and configurations," Renewable Energy, Elsevier, vol. 132(C), pages 941-949.
  21. Amin Niayifar & Fernando Porté-Agel, 2016. "Analytical Modeling of Wind Farms: A New Approach for Power Prediction," Energies, MDPI, vol. 9(9), pages 1-13, September.
  22. Tristan Revaz & Fernando Porté-Agel, 2021. "Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models," Energies, MDPI, vol. 14(13), pages 1-22, June.
  23. Ye, Maokun & Chen, Hamn-Ching & Koop, Arjen, 2023. "High-fidelity CFD simulations for the wake characteristics of the NTNU BT1 wind turbine," Energy, Elsevier, vol. 265(C).
  24. Liu, Haixiao & Fu, Jianing & Liang, Zetao & Liang, Zhichang & Zhang, Yuming & Xiao, Zhong, 2022. "A simple method of fast evaluating full-field wake velocities for arbitrary wind turbine arrays on complex terrains," Renewable Energy, Elsevier, vol. 201(P1), pages 961-976.
  25. Pawar, Suraj & Sharma, Ashesh & Vijayakumar, Ganesh & Bay, Chrstopher J. & Yellapantula, Shashank & San, Omer, 2022. "Towards multi-fidelity deep learning of wind turbine wakes," Renewable Energy, Elsevier, vol. 200(C), pages 867-879.
  26. Kirchner-Bossi, Nicolas & Kathari, Gabriel & Porté-Agel, Fernando, 2024. "A hybrid physics-based and data-driven model for intra-day and day-ahead wind power forecasting considering a drastically expanded predictor search space," Applied Energy, Elsevier, vol. 367(C).
  27. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
  28. Jagdeep Singh & Jahrul M Alam, 2023. "Large-Eddy Simulation of Utility-Scale Wind Farm Sited over Complex Terrain," Energies, MDPI, vol. 16(16), pages 1-26, August.
  29. Wang, Qiang & Luo, Kun & Yuan, Renyu & Wang, Shuai & Fan, Jianren & Cen, Kefa, 2020. "A multiscale numerical framework coupled with control strategies for simulating a wind farm in complex terrain," Energy, Elsevier, vol. 203(C).
  30. Dhiman, Harsh S. & Deb, Dipankar & Foley, Aoife M., 2020. "Lidar assisted wake redirection in wind farms: A data driven approach," Renewable Energy, Elsevier, vol. 152(C), pages 484-493.
  31. Purohit, Shantanu & Ng, E.Y.K. & Syed Ahmed Kabir, Ijaz Fazil, 2022. "Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake," Renewable Energy, Elsevier, vol. 184(C), pages 405-420.
  32. Zilong, Ti & Xiao Wei, Deng, 2022. "Layout optimization of offshore wind farm considering spatially inhomogeneous wave loads," Applied Energy, Elsevier, vol. 306(PA).
  33. Dou, Bingzheng & Qu, Timing & Lei, Liping & Zeng, Pan, 2020. "Optimization of wind turbine yaw angles in a wind farm using a three-dimensional yawed wake model," Energy, Elsevier, vol. 209(C).
  34. Deepu Dilip & Fernando Porté-Agel, 2017. "Wind Turbine Wake Mitigation through Blade Pitch Offset," Energies, MDPI, vol. 10(6), pages 1-17, May.
  35. Huiwen Liu & Imran Hayat & Yaqing Jin & Leonardo P. Chamorro, 2018. "On the Evolution of the Integral Time Scale within Wind Farms," Energies, MDPI, vol. 11(1), pages 1-11, January.
  36. Shih-Chieh Liao & Shih-Chieh Chang & Tsung-Chi Cheng, 2021. "Managing the Volatility Risk of Renewable Energy: Index Insurance for Offshore Wind Farms in Taiwan," Sustainability, MDPI, vol. 13(16), pages 1-27, August.
  37. Jiufa Cao & Weijun Zhu & Xinbo Wu & Tongguang Wang & Haoran Xu, 2018. "An Aero-acoustic Noise Distribution Prediction Methodology for Offshore Wind Farms," Energies, MDPI, vol. 12(1), pages 1-16, December.
  38. Zhaobin Li & Xiaohao Liu & Xiaolei Yang, 2022. "Review of Turbine Parameterization Models for Large-Eddy Simulation of Wind Turbine Wakes," Energies, MDPI, vol. 15(18), pages 1-28, September.
  39. Zehtabiyan-Rezaie, Navid & Abkar, Mahdi, 2024. "An extended k−ɛ model for wake-flow simulation of wind farms," Renewable Energy, Elsevier, vol. 222(C).
  40. Guo-Wei Qian & Takeshi Ishihara, 2018. "A New Analytical Wake Model for Yawed Wind Turbines," Energies, MDPI, vol. 11(3), pages 1-24, March.
  41. Zhang, Jincheng & Zhao, Xiaowei, 2022. "Wind farm wake modeling based on deep convolutional conditional generative adversarial network," Energy, Elsevier, vol. 238(PB).
  42. Hanan M. Taleb & Bassam Abu Hijleh, 2021. "Optimizing the Power Generation of a Wind Farm in Low Wind Speed Regions," Sustainability, MDPI, vol. 13(9), pages 1-26, May.
  43. Mou Lin & Fernando Porté-Agel, 2023. "Power Production and Blade Fatigue of a Wind Turbine Array Subjected to Active Yaw Control," Energies, MDPI, vol. 16(6), pages 1-17, March.
  44. Cao, Jiu Fa & Zhu, Wei Jun & Shen, Wen Zhong & Sørensen, Jens Nørkær & Sun, Zhen Ye, 2020. "Optimizing wind energy conversion efficiency with respect to noise: A study on multi-criteria wind farm layout design," Renewable Energy, Elsevier, vol. 159(C), pages 468-485.
  45. Ka Ling Wu & Fernando Porté-Agel, 2017. "Flow Adjustment Inside and Around Large Finite-Size Wind Farms," Energies, MDPI, vol. 10(12), pages 1-23, December.
  46. Souaiby, Marwa & Porté-Agel, Fernando, 2024. "An improved analytical framework for flow prediction inside and downstream of wind farms," Renewable Energy, Elsevier, vol. 225(C).
  47. Li, Li & Wang, Bing & Ge, Mingwei & Huang, Zhi & Li, Xintao & Liu, Yongqian, 2023. "A novel superposition method for streamwise turbulence intensity of wind-turbine wakes," Energy, Elsevier, vol. 276(C).
  48. Qian, Guo-Wei & Ishihara, Takeshi, 2021. "Wind farm power maximization through wake steering with a new multiple wake model for prediction of turbulence intensity," Energy, Elsevier, vol. 220(C).
  49. Yang, Haoze & Ge, Mingwei & Gu, Bo & Du, Bowen & Liu, Yongqian, 2022. "The effect of swell on marine atmospheric boundary layer and the operation of an offshore wind turbine," Energy, Elsevier, vol. 244(PB).
  50. Yang, Shanghui & Deng, Xiaowei & Ti, Zilong & Yan, Bowen & Yang, Qingshan, 2022. "Cooperative yaw control of wind farm using a double-layer machine learning framework," Renewable Energy, Elsevier, vol. 193(C), pages 519-537.
  51. He, Ruiyang & Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2022. "Wind tunnel tests for wind turbines: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
  52. Dou, Bingzheng & Guala, Michele & Lei, Liping & Zeng, Pan, 2019. "Experimental investigation of the performance and wake effect of a small-scale wind turbine in a wind tunnel," Energy, Elsevier, vol. 166(C), pages 819-833.
  53. Kale, Baris & Buckingham, Sophia & van Beeck, Jeroen & Cuerva-Tejero, Alvaro, 2022. "Implementation of a generalized actuator disk model into WRF v4.3: A validation study for a real-scale wind turbine," Renewable Energy, Elsevier, vol. 197(C), pages 810-827.
  54. Sang Lee & Peter Vorobieff & Svetlana Poroseva, 2018. "Interaction of Wind Turbine Wakes under Various Atmospheric Conditions," Energies, MDPI, vol. 11(6), pages 1-15, June.
  55. Qian, Guo-Wei & Song, Yun-Peng & Ishihara, Takeshi, 2022. "A control-oriented large eddy simulation of wind turbine wake considering effects of Coriolis force and time-varying wind conditions," Energy, Elsevier, vol. 239(PA).
  56. Dongqin Zhang & Yang Liang & Chao Li & Yiqing Xiao & Gang Hu, 2022. "Applicability of Wake Models to Predictions of Turbine-Induced Velocity Deficit and Wind Farm Power Generation," Energies, MDPI, vol. 15(19), pages 1-26, October.
  57. Zhang, Jincheng & Zhao, Xiaowei, 2020. "A novel dynamic wind farm wake model based on deep learning," Applied Energy, Elsevier, vol. 277(C).
  58. Tian, Linlin & Song, Yilei & Wang, Zhenming & Zhao, Ning & Zhu, Chunling & Lu, Xiyun, 2024. "Predictive capability of an improved AD/RANS method for multiple wind turbines and wind farm wakes," Energy, Elsevier, vol. 297(C).
  59. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2024. "A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
  60. Wang, Longyan & Luo, Wei & Xu, Jian & Xie, Junhang & Luo, Zhaohui & Tan, Andy C.C., 2022. "Comparative study of decentralized instantaneous and wind-interval-based controls for in-line two scale wind turbines," Renewable Energy, Elsevier, vol. 189(C), pages 1218-1233.
  61. Zhang, Ziyu & Huang, Peng & Bitsuamlak, Girma & Cao, Shuyang, 2024. "Large-eddy simulation of upwind-hill effects on wind-turbine wakes and power performance," Energy, Elsevier, vol. 294(C).
  62. Dara Vahidi & Fernando Porté-Agel, 2022. "A New Streamwise Scaling for Wind Turbine Wake Modeling in the Atmospheric Boundary Layer," Energies, MDPI, vol. 15(24), pages 1-18, December.
  63. Sharma, V. & Cortina, G. & Margairaz, F. & Parlange, M.B. & Calaf, M., 2018. "Evolution of flow characteristics through finite-sized wind farms and influence of turbine arrangement," Renewable Energy, Elsevier, vol. 115(C), pages 1196-1208.
  64. Fredriksson, Sam T. & Broström, Göran & Bergqvist, Björn & Lennblad, Johan & Nilsson, Håkan, 2021. "Modelling Deep Green tidal power plant using large eddy simulations and the actuator line method," Renewable Energy, Elsevier, vol. 179(C), pages 1140-1155.
  65. Stevens, Richard J.A.M. & Martínez-Tossas, Luis A. & Meneveau, Charles, 2018. "Comparison of wind farm large eddy simulations using actuator disk and actuator line models with wind tunnel experiments," Renewable Energy, Elsevier, vol. 116(PA), pages 470-478.
  66. Eidi, Ali & Ghiassi, Reza & Yang, Xiang & Abkar, Mahdi, 2021. "Model-form uncertainty quantification in RANS simulations of wakes and power losses in wind farms," Renewable Energy, Elsevier, vol. 179(C), pages 2212-2223.
  67. Charlotte Bay Hasager & Nicolai Gayle Nygaard & Patrick J. H. Volker & Ioanna Karagali & Søren Juhl Andersen & Jake Badger, 2017. "Wind Farm Wake: The 2016 Horns Rev Photo Case," Energies, MDPI, vol. 10(3), pages 1-24, March.
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