IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i5p940-d212986.html
   My bibliography  Save this article

Development of a Computational System to Improve Wind Farm Layout, Part I: Model Validation and Near Wake Analysis

Author

Listed:
  • Rafael V. Rodrigues

    (Department of Mechanical and Materials Engineering, University of Denver, Denver, CO 80210, USA)

  • Corinne Lengsfeld

    (Department of Mechanical and Materials Engineering, University of Denver, Denver, CO 80210, USA)

Abstract

The first part of this work describes the validation of a wind turbine farm Computational Fluid Dynamics (CFD) simulation using literature velocity wake data from the MEXICO (Model Experiments in Controlled Conditions) experiment. The work is intended to establish a computational framework from which to investigate wind farm layout, seeking to validate the simulation and identify parameters influencing the wake. A CFD model was designed to mimic the MEXICO rotor experimental conditions and simulate new operating conditions with regards to tip speed ratio and pitch angle. The validation showed that the computational results qualitatively agree with the experimental data. Considering the designed tip speed ratio (TSR) of 6.6, the deficit of velocity in the wake remains at rate of approximately 15% of the free-stream velocity per rotor diameter regardless of the free-stream velocity applied. Moreover, analysis of a radial traverse right behind the rotor showed an increase of 20% in the velocity deficit as the TSR varied from TSR = 6 to TSR = 10, corresponding to an increase ratio of approximately 5% m·s −1 per dimensionless unit of TSR. We conclude that the near wake characteristics of a wind turbine are strongly influenced by the TSR and the pitch angle.

Suggested Citation

  • Rafael V. Rodrigues & Corinne Lengsfeld, 2019. "Development of a Computational System to Improve Wind Farm Layout, Part I: Model Validation and Near Wake Analysis," Energies, MDPI, vol. 12(5), pages 1-24, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:940-:d:212986
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/5/940/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/5/940/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shives, Michael & Crawford, Curran, 2016. "Adapted two-equation turbulence closures for actuator disk RANS simulations of wind & tidal turbine wakes," Renewable Energy, Elsevier, vol. 92(C), pages 273-292.
    2. Sarlak, H. & Meneveau, C. & Sørensen, J.N., 2015. "Role of subgrid-scale modeling in large eddy simulation of wind turbine wake interactions," Renewable Energy, Elsevier, vol. 77(C), pages 386-399.
    3. Zhong, Hongmin & Du, Pingan & Tang, Fangning & Wang, Li, 2015. "Lagrangian dynamic large-eddy simulation of wind turbine near wakes combined with an actuator line method," Applied Energy, Elsevier, vol. 144(C), pages 224-233.
    4. Hossain, M.Z. & Hirahara, H. & Nonomura, Y. & Kawahashi, M., 2007. "The wake structure in a 2D grid installation of the horizontal axis micro wind turbines," Renewable Energy, Elsevier, vol. 32(13), pages 2247-2267.
    5. Park, Jinkyoo & Law, Kincho H., 2015. "Layout optimization for maximizing wind farm power production using sequential convex programming," Applied Energy, Elsevier, vol. 151(C), pages 320-334.
    6. Serrano González, Javier & Burgos Payán, Manuel & Riquelme Santos, Jesús & González Rodríguez, Ángel Gaspar, 2015. "Maximizing the overall production of wind farms by setting the individual operating point of wind turbines," Renewable Energy, Elsevier, vol. 80(C), pages 219-229.
    7. Rafael V. Rodrigues & Corinne Lengsfeld, 2019. "Development of a Computational System to Improve Wind Farm Layout, Part II: Wind Turbine Wakes Interaction," Energies, MDPI, vol. 12(7), pages 1-27, April.
    8. Lee, Jaejoon & Son, Eunkuk & Hwang, Byungho & Lee, Soogab, 2013. "Blade pitch angle control for aerodynamic performance optimization of a wind farm," Renewable Energy, Elsevier, vol. 54(C), pages 124-130.
    9. Park, Jinkyoo & Law, Kincho H., 2016. "A data-driven, cooperative wind farm control to maximize the total power production," Applied Energy, Elsevier, vol. 165(C), pages 151-165.
    10. Sturge, D. & Sobotta, D. & Howell, R. & While, A. & Lou, J., 2015. "A hybrid actuator disc – Full rotor CFD methodology for modelling the effects of wind turbine wake interactions on performance," Renewable Energy, Elsevier, vol. 80(C), pages 525-537.
    11. Son, Eunkuk & Lee, Seungmin & Hwang, Byeongho & Lee, Soogab, 2014. "Characteristics of turbine spacing in a wind farm using an optimal design process," Renewable Energy, Elsevier, vol. 65(C), pages 245-249.
    12. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2012. "Unrestricted wind farm layout optimization (UWFLO): Investigating key factors influencing the maximum power generation," Renewable Energy, Elsevier, vol. 38(1), pages 16-30.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Takanori Uchida & Susumu Takakuwa, 2019. "A Large-Eddy Simulation-Based Assessment of the Risk of Wind Turbine Failures Due to Terrain-Induced Turbulence over a Wind Farm in Complex Terrain," Energies, MDPI, vol. 12(10), pages 1-19, May.
    2. Regodeseves, P. García & Morros, C. Santolaria, 2020. "Unsteady numerical investigation of the full geometry of a horizontal axis wind turbine: Flow through the rotor and wake," Energy, Elsevier, vol. 202(C).
    3. Rafael V. Rodrigues & Corinne Lengsfeld, 2019. "Development of a Computational System to Improve Wind Farm Layout, Part II: Wind Turbine Wakes Interaction," Energies, MDPI, vol. 12(7), pages 1-27, April.
    4. Shantanu Purohit & Ijaz Fazil Syed Ahmed Kabir & E. Y. K. Ng, 2021. "On the Accuracy of uRANS and LES-Based CFD Modeling Approaches for Rotor and Wake Aerodynamics of the (New) MEXICO Wind Turbine Rotor Phase-III," Energies, MDPI, vol. 14(16), pages 1-26, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gionfra, Nicolò & Sandou, Guillaume & Siguerdidjane, Houria & Faille, Damien & Loevenbruck, Philippe, 2019. "Wind farm distributed PSO-based control for constrained power generation maximization," Renewable Energy, Elsevier, vol. 133(C), pages 103-117.
    2. Lo Brutto, Ottavio A. & Guillou, Sylvain S. & Thiébot, Jérôme & Gualous, Hamid, 2017. "Assessing the effectiveness of a global optimum strategy within a tidal farm for power maximization," Applied Energy, Elsevier, vol. 204(C), pages 653-666.
    3. Lo Brutto, Ottavio A. & Thiébot, Jérôme & Guillou, Sylvain S. & Gualous, Hamid, 2016. "A semi-analytic method to optimize tidal farm layouts – Application to the Alderney Race (Raz Blanchard), France," Applied Energy, Elsevier, vol. 183(C), pages 1168-1180.
    4. Kuo, Jim Y.J. & Romero, David A. & Beck, J. Christopher & Amon, Cristina H., 2016. "Wind farm layout optimization on complex terrains – Integrating a CFD wake model with mixed-integer programming," Applied Energy, Elsevier, vol. 178(C), pages 404-414.
    5. Tanvir Ahmad & Abdul Basit & Muneeb Ahsan & Olivier Coupiac & Nicolas Girard & Behzad Kazemtabrizi & Peter C. Matthews, 2019. "Implementation and Analyses of Yaw Based Coordinated Control of Wind Farms," Energies, MDPI, vol. 12(7), pages 1-15, April.
    6. Cao, Yankai & Zavala, Victor M. & D’Amato, Fernando, 2018. "Using stochastic programming and statistical extrapolation to mitigate long-term extreme loads in wind turbines," Applied Energy, Elsevier, vol. 230(C), pages 1230-1241.
    7. Guirguis, David & Romero, David A. & Amon, Cristina H., 2017. "Gradient-based multidisciplinary design of wind farms with continuous-variable formulations," Applied Energy, Elsevier, vol. 197(C), pages 279-291.
    8. Li, Qing'an & Cai, Chang & Kamada, Yasunari & Maeda, Takao & Hiromori, Yuto & Zhou, Shuni & Xu, Jianzhong, 2021. "Prediction of power generation of two 30 kW Horizontal Axis Wind Turbines with Gaussian model," Energy, Elsevier, vol. 231(C).
    9. Serrano González, Javier & Burgos Payán, Manuel & Riquelme Santos, Jesús & González Rodríguez, Ángel Gaspar, 2015. "Maximizing the overall production of wind farms by setting the individual operating point of wind turbines," Renewable Energy, Elsevier, vol. 80(C), pages 219-229.
    10. Feng, Ju & Shen, Wen Zhong, 2017. "Design optimization of offshore wind farms with multiple types of wind turbines," Applied Energy, Elsevier, vol. 205(C), pages 1283-1297.
    11. Sun, Haiying & Yang, Hongxing & Gao, Xiaoxia, 2019. "Investigation into spacing restriction and layout optimization of wind farm with multiple types of wind turbines," Energy, Elsevier, vol. 168(C), pages 637-650.
    12. Guirguis, David & Romero, David A. & Amon, Cristina H., 2016. "Toward efficient optimization of wind farm layouts: Utilizing exact gradient information," Applied Energy, Elsevier, vol. 179(C), pages 110-123.
    13. Amin Allah, Veisi & Shafiei Mayam, Mohammad Hossein, 2017. "Large Eddy Simulation of flow around a single and two in-line horizontal-axis wind turbines," Energy, Elsevier, vol. 121(C), pages 533-544.
    14. 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.
    15. Tanvir Ahmad & Abdul Basit & Juveria Anwar & Olivier Coupiac & Behzad Kazemtabrizi & Peter C. Matthews, 2019. "Fast Processing Intelligent Wind Farm Controller for Production Maximisation," Energies, MDPI, vol. 12(3), pages 1-17, February.
    16. Brogna, Roberto & Feng, Ju & Sørensen, Jens Nørkær & Shen, Wen Zhong & Porté-Agel, Fernando, 2020. "A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain," Applied Energy, Elsevier, vol. 259(C).
    17. Veisi, Amin Allah & Shafiei Mayam, Mohammad Hossein, 2017. "Effects of blade rotation direction in the wake region of two in-line turbines using Large Eddy Simulation," Applied Energy, Elsevier, vol. 197(C), pages 375-392.
    18. Jim Kuo & Kevin Pan & Ni Li & He Shen, 2020. "Wind Farm Yaw Optimization via Random Search Algorithm," Energies, MDPI, vol. 13(4), pages 1-15, February.
    19. Wang, Yu & Wei, Shanbi & Yang, Wei & Chai, Yi, 2023. "Adaptive economic predictive control for offshore wind farm active yaw considering generation uncertainty," Applied Energy, Elsevier, vol. 351(C).
    20. Rafael V. Rodrigues & Corinne Lengsfeld, 2019. "Development of a Computational System to Improve Wind Farm Layout, Part II: Wind Turbine Wakes Interaction," Energies, MDPI, vol. 12(7), pages 1-27, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:940-:d:212986. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.