10.1007/s40095-022-00531-3

Influence of nearby urban buildings on the wind field around a wind turbine: a case study in Dundalk Institute of Technology

Show Details XML
  • Yu-Chin Chien1,
  • Yan-Ting Lin2,
  • Huei Chu Weng*1,
  • Raymond Byrne3,
  • Pao-Hsiung Chiu4,
  1. Department of Mechanical Engineering, Chung Yuan Christian University, Taoyuan, 320314, TW
  2. Mechanical and System Engineering Program, Institute of Nuclear Energy Research, Atomic Energy Council, Taoyuan, 325207, TW
  3. Centre for Renewables and Energy, Dundalk Institute of Technology, Dundalk, Co. Louth, A91 K584, IE
  4. Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore, 138632, SG

Published in Issue 2022-09-22

How to Cite

Chien, Y.-C., Lin, Y.-T., Weng, H. C., Byrne, R., & Chiu, P.-H. (2022). Influence of nearby urban buildings on the wind field around a wind turbine: a case study in Dundalk Institute of Technology. International Journal of Energy and Environmental Engineering, 14(3 (September 2023). https://doi.org/10.1007/s40095-022-00531-3
Crossref
Scopus
Google Scholar
Europe PMC

Abstract

Abstract In this paper, an urban environmental wind field distribution around the wind turbine is numerically investigated by using ANSYS Fluent with the k – ω SST turbulence model. A computational domain designed with an octagonal prism shape is used to simulate wind from eight different directions. As a case study, the wind field and wind power output at the location of the 850 kW wind turbine in the Dundalk Institute of Technology campus are analyzed. The simulated inlet wind speeds adopted are 6 m/s, 12 m/s, 14 m/s, and 22 m/s. The simulation results show that, at both high and low wind speeds, specific wind directions would generate wakes behind tall narrow buildings and affect the wind speed at the location of the wind turbine. The local acceleration of the wind speed could only occur next to buildings. Moreover, in a low-speed southwesterly wind, the wind speed at the location of the wind turbine will be significantly reduced due to the presence of low-wide buildings. When the wind of 14 m/s comes from the south–east direction and the wind of 6 m/s comes from the north–east direction, the maximum percentage changes in wind speed and power output are − 2.41% and − 5.59%, respectively.

Keywords

  • Computational fluid dynamics,
  • Wind flow model,
  • Wind field simulation,
  • Urban environment,
  • Building aerodynamics

References

  1. Porte-Agel et al. (2020) Wind-turbine and wind-farm flows: a review (pp. 1-59) https://doi.org/10.1007/s10546-019-00473-0
  2. Olabi et al. (2021) Selection guidelines for wind energy technologies https://doi.org/10.3390/en14113244
  3. Chaudhuri et al. (2022) Energy conversion strategies for wind energy system: electrical, mechanical and material aspects https://doi.org/10.3390/ma15031232
  4. Díaz and Guedes Soares (2020) Review of the current status, technology and future trends of offshore wind farms https://doi.org/10.1016/j.oceaneng.2020.107381
  5. Farrugia and Sant (2013) Modelling wind speeds for cup anemometers mounted on opposite sides of a lattice tower: a case study (pp. 173-183) https://doi.org/10.1016/j.jweia.2012.11.006
  6. Baseer et al. (2016) Performance evaluation of cup-anemometers and wind speed characteristics analysis (pp. 733-744) https://doi.org/10.1016/j.renene.2015.08.062
  7. Hanslian and Hosek (2015) Combining the VAS 3D interpolation method and wind atlas methodology to produce a high-resolution wind resource map for the Czech Republic (pp. 291-299) https://doi.org/10.1016/j.renene.2014.12.013
  8. Ozelkan et al. (2016) Spatial estimation of wind speed: a new integrative model using inverse distance weighting and power law (pp. 733-747) https://doi.org/10.1080/17538947.2015.1127437
  9. Cheynet et al. (2017) Assessing the potential of a commercial pulsed lidar for wind characterisation at a bridge site (pp. 17-26) https://doi.org/10.1016/j.jweia.2016.12.002
  10. Rehman et al. (2018) Wind speed and power characteristics using LiDAR anemometer based measurements (pp. 46-62) https://doi.org/10.1016/j.seta.2018.03.009
  11. Dai et al. (2020) Multilevel validation of doppler wind lidar by the 325 m meteorological tower in the planetary boundary layer of Beijing https://doi.org/10.3390/atmos11101051
  12. Kogaki et al. (2020) Field measurements of wind characteristics using lidar on a wind farm with downwind turbines installed in a complex terrain region https://doi.org/10.3390/en13195135
  13. Lang and McKeogh (2011) LIDAR and SODAR measurements of wind speed and direction in upland terrain for wind energy purposes (pp. 1871-1901) https://doi.org/10.3390/rs3091871
  14. El Kasmi and Masson (2010) Turbulence modeling of atmospheric boundary layer flow over complex terrain: a comparison of models at wind tunnel and full scale (pp. 689-704) https://doi.org/10.1002/we.390
  15. Mattuella et al. (2016) Wind tunnel experimental analysis of a complex terrain micrositing (pp. 110-119) https://doi.org/10.1016/j.rser.2015.09.088
  16. Wang et al. (2022) Effect of topography truncation on experimental simulation of flow over complex terrain https://doi.org/10.3390/app12052477
  17. Dhunny et al. (2015) A high-resolution mapping of wind energy potentials for Mauritius using computational fluid dynamics (CFD) https://doi.org/10.12989/was.2015.20.4.565
  18. Yan and Li (2016) Coupled on-site measurement/CFD based approach for high-resolution wind resource assessment over complex terrains (pp. 351-366) https://doi.org/10.1016/j.enconman.2016.02.076
  19. Sessarego et al. (2018) CFD simulations of flows in a wind farm in complex terrain and comparisons to measurements https://doi.org/10.3390/app8050788
  20. Blocken et al. (2015) CFD simulation of wind flow over natural complex terrain: case study with validation by field measurements for Ria de Ferrol, Galicia Spain (pp. 43-57) https://doi.org/10.1016/j.jweia.2015.09.007
  21. Huang and Zhang (2019) Wind field simulation over complex terrain under different inflow wind directions (pp. 239-253) https://doi.org/10.12989/was.2019.28.4.239
  22. Tse et al. (2014) A comparative study of typhoon wind profiles derived from field measurements, meso-scale numerical simulations, and wind tunnel physical modeling (pp. 46-58) https://doi.org/10.1016/j.jweia.2014.05.001
  23. Niyomtham et al. (2022) Mesoscale/microscale and CFD modeling for wind resource assessment: application to the Andaman Coast of Southern Thailand https://doi.org/10.3390/en15093025
  24. Toparlar et al. (2017) A review on the CFD analysis of urban microclimate (pp. 1613-1640) https://doi.org/10.1016/j.rser.2017.05.248
  25. Ramponi et al. (2015) CFD simulation of outdoor ventilation of generic urban configurations with different urban densities and equal and unequal street widths (pp. 152-166) https://doi.org/10.1016/j.buildenv.2015.04.018
  26. Toja-Silva et al. (2018) A review of computational fluid dynamics (CFD) simulations of the wind flow around buildings for urban wind energy exploitation (pp. 66-87) https://doi.org/10.1016/j.jweia.2018.07.010
  27. Yang et al. (2016) Estimation of wind power generation in dense urban area (pp. 213-230) https://doi.org/10.1016/j.apenergy.2016.03.007
  28. Ku and Tsai (2020) Evaluating the influence of urban morphology on urban wind environment based on computational fluid dynamics simulation https://doi.org/10.3390/ijgi9060399
  29. Kalmikov, A., Dupont, G., Dykes, K., Chan, C.: Wind power resource assessment in complex urban environments: MIT campus case-study using CFD analysis. In: Proceedings of the AWEA 2010 WINDPOWER Conference, Dallas, TX, USA, 23–26 May 2010
  30. Jamdade and Jamdade (2015) Evaluation of wind energy potential for four sites in Ireland using the Weibull distribution model (pp. 48-53)
  31. Cooney et al. (2017) Performance characterisation of a commercial-scale wind turbine operating in an urban environment, using real data (pp. 44-54) https://doi.org/10.1016/j.esd.2016.11.001
  32. Byrne et al. (2018) Observed site obstacle impacts on the energy performance of a large scale urban wind turbine using an electrical energy rose (pp. 23-37) https://doi.org/10.1016/j.esd.2017.12.002
  33. Byrne et al. (2019) An assessment of the mesoscale to microscale influences on wind turbine energy performance at a peri-urban coastal location from the Irish wind atlas and onsite LiDAR measurements https://doi.org/10.1016/j.seta.2019.100537
  34. Vestas Wind Systems: V52-850 kW the turbine that goes anywhere.
  35. https://users.wpi.edu/~cfurlong/me3320/DProject/V52_850kW_US.pdf
  36. (2005)