IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v70y2014icp129-141.html
   My bibliography  Save this article

Pseudo spectral analysis of the energy entrainment in a scaled down wind farm

Author

Listed:
  • Newman, A. Jensen
  • Drew, Donald A.
  • Castillo, Luciano

Abstract

Particle Image Velocimetry from the centerline of a 3 × 5 scaled down array of wind turbines in a wind tunnel was analyzed to gain further understanding of how turbulent transport brings mean kinetic energy (MKE) into the array from the neutrally stable Atmospheric Boundary Layer (ABL) above. Vertical fluxes of MKE due to the Reynolds stresses were computed i.e.−〈U〉〈u′v′〉 A modal expansion for 〈U〉〈u′v′〉 was constructed based on the Proper Orthogonal Decomposition (POD). By determining each mode's fractional contribution to the total entrainment it was shown that a small number of modes (the first 25) account for 75% of the entrainment. The remaining 25% is achieved asymptotically as the remainder of the modes are included in the representation. Based on this behavior the labels “idiosyncratic” and “asymptotic” were applied to the different mode types. A characteristic wavelength for each mode was defined as the length of a mode's longest positive contribution to the energy entrainment. By this definition it was shown that idiosyncratic and asymptotic modes are characterized by wavelengths greater than and less than D (rotor diameter) respectively so that large percentages of the energy brought into the wind farm are done at scales greater than D. Physical reasoning indicates the idiosyncratic modes are associated with larger scale coherent motions whereas the asymptotic modes are associated with small scale turbulent fluctuations. The analysis was repeated for PIV data without turbines. It was shown that the idiosyncratic modes represent the scales which are affected by the presence of the turbines. This further established that the idiosyncratic modes were connected with the larger scales of turbulent motion.

Suggested Citation

  • Newman, A. Jensen & Drew, Donald A. & Castillo, Luciano, 2014. "Pseudo spectral analysis of the energy entrainment in a scaled down wind farm," Renewable Energy, Elsevier, vol. 70(C), pages 129-141.
  • Handle: RePEc:eee:renene:v:70:y:2014:i:c:p:129-141
    DOI: 10.1016/j.renene.2014.02.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148114000822
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2014.02.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Jie & Chowdhury, Souma & Messac, Achille & Castillo, Luciano, 2012. "A Response Surface-Based Cost Model for Wind Farm Design," Energy Policy, Elsevier, vol. 42(C), pages 538-550.
    2. 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.
    3. Leonardo P. Chamorro & Fernando Porté-Agel, 2011. "Turbulent Flow Inside and Above a Wind Farm: A Wind-Tunnel Study," Energies, MDPI, vol. 4(11), pages 1-21, November.
    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. Dai, Xuan & Xu, Da & Zhang, Mengqi & Stevens, Richard J.A.M., 2022. "A three-dimensional dynamic mode decomposition analysis of wind farm flow aerodynamics," Renewable Energy, Elsevier, vol. 191(C), pages 608-624.

    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. Hayat, Imran & Chatterjee, Tanmoy & Liu, Huiwen & Peet, Yulia T. & Chamorro, Leonardo P., 2019. "Exploring wind farms with alternating two- and three-bladed wind turbines," Renewable Energy, Elsevier, vol. 138(C), pages 764-774.
    2. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2013. "Optimizing the arrangement and the selection of turbines for wind farms subject to varying wind conditions," Renewable Energy, Elsevier, vol. 52(C), pages 273-282.
    3. 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.
    4. Serrano González, Javier & Burgos Payán, Manuel & Santos, Jesús Manuel Riquelme & González-Longatt, Francisco, 2014. "A review and recent developments in the optimal wind-turbine micro-siting problem," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 133-144.
    5. Newman, A. Jensen & Cal, Raúl Bayoán & Castillo, Luciano, 2015. "Blade number effects in a scaled down wind farm," Renewable Energy, Elsevier, vol. 81(C), pages 472-481.
    6. Mattuella, J.M.L. & Loredo-Souza, A.M. & Oliveira, M.G.K. & Petry, A.P., 2016. "Wind tunnel experimental analysis of a complex terrain micrositing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 110-119.
    7. Kale, Baris & Buckingham, Sophia & van Beeck, Jeroen & Cuerva-Tejero, Alvaro, 2023. "Comparison of the wake characteristics and aerodynamic response of a wind turbine under varying atmospheric conditions using WRF-LES-GAD and WRF-LES-GAL wind turbine models," Renewable Energy, Elsevier, vol. 216(C).
    8. Yaqing Jin & Huiwen Liu & Rajan Aggarwal & Arvind Singh & Leonardo P. Chamorro, 2016. "Effects of Freestream Turbulence in a Model Wind Turbine Wake," Energies, MDPI, vol. 9(10), pages 1-12, October.
    9. Baheri, Ali & Ramaprabhu, Praveen & Vermillion, Christopher, 2018. "Iterative 3D layout optimization and parametric trade study for a reconfigurable ocean current turbine array using Bayesian Optimization," Renewable Energy, Elsevier, vol. 127(C), pages 1052-1063.
    10. Mittal, Prateek & Kulkarni, Kedar & Mitra, Kishalay, 2016. "A novel hybrid optimization methodology to optimize the total number and placement of wind turbines," Renewable Energy, Elsevier, vol. 86(C), pages 133-147.
    11. Kuo, Jim Y.J. & Romero, David A. & Amon, Cristina H., 2015. "A mechanistic semi-empirical wake interaction model for wind farm layout optimization," Energy, Elsevier, vol. 93(P2), pages 2157-2165.
    12. Razi, P. & Ramaprabhu, P. & Tarey, P. & Muglia, M. & Vermillion, C., 2022. "A low-order wake interaction modeling framework for the performance of ocean current turbines under turbulent conditions," Renewable Energy, Elsevier, vol. 200(C), pages 1602-1617.
    13. Lo Brutto, Ottavio A. & Nguyen, Van Thinh & Guillou, Sylvain S. & Thiébot, Jérôme & Gualous, Hamid, 2016. "Tidal farm analysis using an analytical model for the flow velocity prediction in the wake of a tidal turbine with small diameter to depth ratio," Renewable Energy, Elsevier, vol. 99(C), pages 347-359.
    14. Souma Chowdhury & Ali Mehmani & Jie Zhang & Achille Messac, 2016. "Market Suitability and Performance Tradeoffs Offered by Commercial Wind Turbines across Differing Wind Regimes," Energies, MDPI, vol. 9(5), pages 1-31, May.
    15. Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2020. "A review of full-scale wind-field measurements of the wind-turbine wake effect and a measurement of the wake-interaction effect," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    16. Muche, Thomas & Pohl, Ralf & Höge, Christin, 2016. "Economically optimal configuration of onshore horizontal axis wind turbines," Renewable Energy, Elsevier, vol. 90(C), pages 469-480.
    17. Sun, Haiying & Yang, Hongxing, 2020. "Numerical investigation of the average wind speed of a single wind turbine and development of a novel three-dimensional multiple wind turbine wake model," Renewable Energy, Elsevier, vol. 147(P1), pages 192-203.
    18. Yuanhang Qi & Peng Hou & Guisong Liu & Rongsen Jin & Zhile Yang & Guangya Yang & Zhaoyang Dong, 2021. "Cable Connection Optimization for Heterogeneous Offshore Wind Farms via a Voronoi Diagram Based Adaptive Particle Swarm Optimization with Local Search," Energies, MDPI, vol. 14(3), pages 1-21, January.
    19. 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.
    20. 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.

    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:eee:renene:v:70:y:2014:i:c:p:129-141. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.