DOI QR코드

DOI QR Code

A Study on Estimation of Inflow Wind Speeds in a CFD Model Domain for an Urban Area

도시 지역 대상의 CFD 모델 영역에서 유입류 풍속 추정에 관한 연구

  • Kang, Geon (Department of Environmental Atmospheric Sciences, Pukyong National University) ;
  • Kim, Jae-Jin (Department of Environmental Atmospheric Sciences, Pukyong National University)
  • 강건 (부경대학교 환경대기과학과) ;
  • 김재진 (부경대학교 환경대기과학과)
  • Received : 2016.11.14
  • Accepted : 2016.12.28
  • Published : 2017.03.31

Abstract

In this study, we analyzed the characteristics of flow around the Daeyeon automatic weather station (AWS 942) and established formulas estimating inflow wind speeds at a computational fluid dynamics (CFD) model domain for the area around Pukyong national university using a computational fluid dynamics (CFD) model. Simulated wind directions at the AWS 942 were quite similar to those of inflows, but, simulated wind speeds at the AWS 942 decreased compared to inflow wind speeds except for the northerly case. The decrease in simulated wind speed at the AWS 942 resulted from the buildings around the AWS 942. In most cases, the AWS 942 was included within the wake region behind the buildings. Wind speeds at the inflow boundaries of the CFD model domain were estimated by comparing simulated wind speeds at the AWS 942 and inflow boundaries and systematically increasing inflow wind speeds from $1m\;s^{-1}$ to $17m\;s^{-1}$ with an increment of $2m\;s^{-1}$ at the reference height for 16 inflow directions. For each inflow direction, calculated wind speeds at the AWS 942 were fitted as the third order functions of the inflow wind speed by using the Marquardt-Levenberg least square method. Estimated inflow wind speeds by the established formulas were compared to wind speeds observed at 12 coastal AWSs near the AWS 942. The results showed that the estimated wind speeds fell within the inter quartile range of wind speeds observed at 12 coastal AWSs during the nighttime and were in close proximity to the upper whiskers during the daytime (12~15 h).

Keywords

References

  1. Allwine, K. J., M. J. Leach, L. W. Stockham, J. S. Shinn, R. P. Hosker, J. F. Bowers, and J. C. Pace, 2004: Overview of Joint Urban 2003-An atmospheric dispersion study in Oklahoma City. Symp. Planning, Nowcasting, and Forecasting in the Urban Zone. AMS, Seattle WA, USA.
  2. Amorim, J. H., J. Valente, P. Cascao, V. Rodrigues, C. Pimentel, A. I. Miranda, and C. Borrego, 2013: Pedestrian exposure to air pollution in cities: Modeling the effect of roadside trees. Adv. Meteor., 2013, 7, doi:10.1155/2013/964904.
  3. Baik, J.-J., S.-B. Park, and J.-J. Kim, 2009: Urban flow and dispersion simulation using a CFD model coupled to a mesoscale model. J. Appl. Meteor. Climatol., 48, 1667-1681, doi:10.1175/2009JAMC2066.1.
  4. Balczo, M., C. Gromke, and B. Ruck, 2009: Numerical modeling of flow and pollutant dispersion in street canyons with tree planting. Meteor. Z., 18, 197-206, doi:10.1127/0941-2948/2009/0361.
  5. Blocken, B., 2015: Computational Fluid Dynamics for urban physics: Importance, scales, possibilities, limitations and ten tips and tricks towards accurate and reliable simulations. Build. Environ., 91, 219-245, doi:10.1016/j.buildenv.2015.02.015.
  6. Blocken, B., W. D. Janssen, and T. van Hooff, 2012: CFD simulation for pedestrian wind comfort and wind safety in urban areas: General decision framework and case study for the Eindhoven University campus. Environ. Model. Software, 30, 15-34, doi:10.1016/j.envsoft.2011.11.009.
  7. Castro, I. P., and D. D. Apsley, 1997: Flow and dispersionover topography: A comparison between numerical and laboratory data for two-dimensional flows. Atmos. Environ., 31, 839-850, doi:10.1016/S1352-2310(96)00248-8.
  8. Franke, J., A. Hellsten, H. Schlunzen, and B. Carissimo, 2007: Best Practice Guideline for the CFD Simulation of Flows in the Urban Environment, COST Action 732, Quality Assurance and Improvement of Microscale Meteorological Models, Hamburg, Germany.
  9. Gromke, C., R. Buccolieri, S. Di Sabatino, and B. Ruck, 2008: Dispersion study in a street canyon with tree planting by means of wind tunnel and numerical investigations-evaluation of CFD data with experimental data. Atmos. Environ., 42, 8640-8650, doi:10.1016/j.atmosenv.2008.08.019.
  10. Gromke, C., B. Blocken, W. Janssen, B. Merema, T. van Hooff, and H. Timmermans, 2015: CFD analysis of transpirational cooling by vegetation: Case study for specific meteorological conditions during a heat wave in Arnhem, Netherlands. Build. Environ., 83, 11-26, doi:10.1016/j.buildenv.2014.04.022.
  11. Gowardhan, A. A., E. R. Pardyjak, I. Senocak, and M. J. Brown, 2011: A CFD-based wind solver for an urban fast response transport and dispersion model. Environ. Fluid Mech., 11, 439-464, doi:10.1007/s10652-011-9211-6.
  12. Kang, G., and J.-J. Kim, 2015: Effects of trees on flow and scalar dispersion in an urban street Canyon. Atmosphere, 25, 685-692, doi:10.14191/Atmos.2015.25.4.685 (in Korean with English abstract).
  13. Kwa, S. M., and S. M. Salim, 2015: Numerical simulation of dispersion in an urban street canyon: Comparison between steady and fluctuating boundary conditions. Eng. Lett., 23, 55-64.
  14. Moonen, P., C. Gromke, and V. Dorer, 2013: Performance assessment of Large Eddy Simulation (LES) for modeling dispersion in an urban street canyon with tree planting. Atmos. Environ., 75, 66-76, doi:10.1016/j.atmosenv.2013.04.016.
  15. Patankar, S. V., 1980: Numerical Heat Transfer and Fluid Flow. McGraw-Hill, New York, 197 pp.
  16. Schatzmann, M., and B. Leitl, 2011: Issues with validation of urban flow and dispersion CFD models. J. Wind. Eng. Ind. Aerod., 99, 169-186, doi:10.1016/j.jweia.2011.01.005.
  17. Versteeg, H. K., and W. Malalasekera, 1995: An Introduction to Computational Fluid Dynamics: The Finite Volume Method. Longman, 257 pp.
  18. Yakhot, V., S. A. Orszag, S. Thangam, T. B. Gatski, and C. G. Speziale, 1992: Development of turbulence models for shear flow by a double expansion technique. Fluid Dyn., 4, 1510-1520, doi:10.1063/1.858424.
  19. Yang, H.-J., and J.-J. Kim, 2015: Assessment of observation environment for surface wind in urban areas using a CFD model. Atmosphere, 25, 449-459, doi:10.14191/Atmos.2015.25.3.449 (in Korean with English abstract).
  20. Wang, X., and K. F. McNamara, 2007: Effects of street orientation on dispersion at or near urban street intersections. J. Wind. Eng. Ind. Aerod., 95, 1526-1540, doi:10.1016/j.jweia.2007.02.021.
  21. World Meteorological Organization, 2010: Commission for Instruments and Methods of Observation. WMO-No. 1064, Geneva.

Cited by

  1. Analysis of Meteorological and Radiation Characteristics using WISE Observation Data vol.39, pp.1, 2018, https://doi.org/10.5467/JKESS.2018.39.1.89