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LES validation of urban flow, part I: flow statistics and frequency distributions

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Abstract

Essential prerequisites for a thorough model evaluation are the availability of problem-specific, quality-controlled reference data and the use of model-specific comparison methods. The work presented here is motivated by the striking lack of proportion between the increasing use of large-eddy simulation (LES) as a standard technique in micro-meteorology and wind engineering and the level of scrutiny that is commonly applied to assess the quality of results obtained. We propose and apply an in-depth, multi-level validation concept that is specifically targeted at the time-dependency of mechanically induced shear-layer turbulence. Near-surface isothermal turbulent flow in a densely built-up city serves as the test scenario for the approach. High-resolution LES data are evaluated based on a comprehensive database of boundary-layer wind-tunnel measurements. From an exploratory data analysis of mean flow and turbulence statistics, a high level of agreement between simulation and experiment is apparent. Inspecting frequency distributions of the underlying instantaneous data proves to be necessary for a more rigorous assessment of the overall prediction quality. From velocity histograms local accuracy limitations due to a comparatively coarse building representation as well as particular strengths of the model to capture complex urban flow features with sufficient accuracy are readily determined. However, the analysis shows that further crucial information about the physical validity of the LES needs to be obtained through the comparison of eddy statistics, which is focused on in part II. Compared with methods that rely on single figures of merit, the multi-level validation strategy presented here supports conclusions about the simulation quality and the model’s fitness for its intended range of application through a deeper understanding of the unsteady structure of the flow.

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References

  1. Guide for the verification and validation of computational fluid dynamics simulations (AIAA G-077-1998(2002)). American Institute of Aeronautics and Astronautics, Inc. doi:10.2514/4.472855.001

  2. Adrian RJ, Meneveau C, Moser RD, Riley J (2000) Final report on ‘turbulence measurements for LES’ workshop. Technical report. Department of Theoretical and Applied Mechanics, University of Illinois at Urbana-Champaign, Urbana (IL), USA

  3. Adrian RJ, Yao CS (1987) Power spectra of fluid velocities measured by laser Doppler velocimetry. Exp Fluids 5:17–28

    Google Scholar 

  4. ASME (2006) Guide for verification and validation in computational solid mechanics. ASME V&V 10-2006, The American Society of Mechanical Engineers, New York, NY, USA

  5. Book DL (2012) The conception, gestation, birth, and infancy of FCT. In: Kuzmin D, Löhner R, Turek S (eds) Flux-corrected transport: principles, algorithms, and applications, scientific computing, 2nd edn. Springer, Berlin, pp 1–21

    Chapter  Google Scholar 

  6. Boris J, Patnaik G, Obenschain K (2011) The how and why of nomografs for CT-Analyst. Report NRL/MR/6440-11-9326, Naval Research Laboratory, Washington, DC, USA

  7. Boris JP (1989) New directions in computational fluid dynamics. Annu Rev Fluid Mech 21:345–385

    Article  Google Scholar 

  8. Boris JP (1990) On large eddy simulation using subgrid turbulence models. In: Lumley JL (ed) Whither turbulence? Turbulence at the crossroads. Lecture Notes in Physics, vol 357. Springer, Berlin, pp 344–353

    Chapter  Google Scholar 

  9. Boris JP (2002) The threat of chemical and biological terrorism: preparing a response. Comput Sci Eng 4:22–32

    Article  Google Scholar 

  10. Boris JP (2005) Dust in the wind: challenges for urban aerodynamics. AIAA Paper 2005-5393

  11. Boris JP (2007) More for LES: a brief historical perspective of MILES. In: Grinstein FF, Margolin LG, Rider WJ (eds) Implicit large eddy simulation: computing turbulent fluid dynamics. Cambridge University Press, Cambridge, pp 9–38

    Chapter  Google Scholar 

  12. Boris JP, Book DL (1973) Flux-corrected transport I: SHASTA—a fluid transport algorithm that works. J Comput Phys 11:38–69

    Article  Google Scholar 

  13. Boris JP, Book DL (1976) Solution of the continuity equation by the method of flux-corrected transport. In: Alder B, Fernbach S, Rotenberg M, Killeen J (eds) Methods in computational physics, vol 16. Academic Press, Waltham, pp 85–129

    Google Scholar 

  14. Bradshaw P (1972) The understanding and prediction of turbulent flow. Aeronaut J 76:403–418

    Google Scholar 

  15. Britter R, Schatzmann M (eds) (2007a) Background and justification document to support the model evaluation guidance and protocol. COST Action 732. University of Hamburg, Germany

  16. Britter R, Schatzmann M (eds) (2007b) Model evaluation guidance and protocol document. COST Action 732. University of Hamburg, Germany

  17. Britter RE, Hanna SR (2003) Flow and dispersion in urban areas. Annu Rev Fluid Mech 35:469–496

    Article  Google Scholar 

  18. Chang JC, Hanna SR (2004) Air quality model performance evaluation. Meteorol Atmos Phys 87:167–196

    Article  Google Scholar 

  19. Cheng WC, Liu CH (2011) Large-eddy simulation of turbulent transports in urban street canyons in different thermal stabilities. J Wind Eng Ind Aerodyn 99:434–442

    Article  Google Scholar 

  20. Counihan J (1975) Adiabatic atmospheric boundary layers: a review and analysis of data from the period 1880–1972. Atmos Environ 9:871–905

    Article  Google Scholar 

  21. De Waele S, Broersen PMT (2000) Error measures for resampled irregular data. IEEE Trans Instrum Meas 49:216–222

    Article  Google Scholar 

  22. Edwards RV, Jensen AS (1983) Particle-sampling statistics in laser anemometers: sample-and-hold systems and saturable systems. J Fluid Mech 133:397–411

    Article  Google Scholar 

  23. ESDU (1985) Characteristics of atmospheric turbulence near the ground. Part II: single point data for strong winds (neutral atmosphere). ESDU 85020, Engineering Sciences Data Unit, London, UK

  24. Grimmond CSB, Oke TR (1999) Aerodynamic properties of urban areas derived from analysis of surface form. J Appl Meteorol 38:1262–1292

    Article  Google Scholar 

  25. Grinstein FF (2010) Verification and validation of CFD based turbulent flow experiments. In: Encyclopedia of aerospace engineering. Wiley, Hoboken, pp 515–523

  26. Hanna S, Chang J (2012) Acceptance criteria for urban dispersion model evaluation. Meteorol Atmos Phys 116(3):133–146

    Article  Google Scholar 

  27. Hanna SR, Brown MJ, Camelli FE, Chan ST, Coirier WJ, Kim S, Hansen OR, Huber AH, Reynolds RM (2006) Detailed simulations of atmospheric flow and dispersion in downtown Manhattan: an application of five computational fluid dynamics models. Bull Am Meteorol Soc 87(12):1713–1726

    Article  Google Scholar 

  28. Hanna SR, Hansen OR, Dharmavaram S (2004) FLACS CFD air quality model performance evaluation with Kit Fox, MUST, Prairie Grass, and EMU observations. Atmos Environ 38:4675–4687

    Article  Google Scholar 

  29. Hertwig D (2013) On aspects of large-eddy simulation validation for near-surface atmospheric flows. Ph.D. thesis, Universität Hamburg. http://ediss.sub.uni-hamburg.de/volltexte/2013/6289/pdf/Dissertation.pdf

  30. Hertwig D, Efthimiou GC, Bartzis JG, Leitl B (2012) CFD-RANS model validation of turbulent flow in a semi-idealized urban canopy. J Wind Eng Ind Aerodyn 111:61–72

    Article  Google Scholar 

  31. Hertwig D, Leitl B, Schatzmann M (2011) Organized turbulent structures—link between experimental data and LES. J Wind Eng Ind Aerodyn 99:296–307

    Article  Google Scholar 

  32. Huang H, Ooka R, Kato S (2005) Urban thermal environment measurements and numerical simulation for an actual complex urban area covering a large district heating and cooling system in summer. Atmos Environ 39(34):6362–6375

    Article  Google Scholar 

  33. Kaimal JC, Wyngaard JC, Izumi Y, Coté OR (1972) Spectral characteristics of surface-layer turbulence. Q J R Meteorol Soc 98:563–589

    Article  Google Scholar 

  34. Kanda M (2007) Progress in urban meteorology: a review. J Meteorol Soc Jpn Ser II(85B):363–383

    Article  Google Scholar 

  35. Kempf AM (2008) LES validation from experiments. Flow Turbul Combust 80:351–373

    Article  Google Scholar 

  36. Konow H (2015) Tall wind profiles in heterogeneous terrain. Ph.D. thesis, Universität Hamburg. http://ediss.sub.uni-hamburg.de/volltexte/2015/7202/pdf/Dissertation.pdf

  37. Leitl B (2000) Validation data for microscale dispersion modelling. EUROTRAC Newsl 22:28–32

    Google Scholar 

  38. Li XX, Britter RE, Koh TY, Norford LK, Liu CH, Entekhabi D, Leung DYC (2010) Large-eddy simulation of flow and pollutant transport in urban street canyons with ground heating. Bound Lay Meteorol 137:187–204

    Article  Google Scholar 

  39. Li XX, Britter RE, Norford LK, Koh TY, Entekhabi D (2012) Flow and pollutant transport in urban street canyons of different aspect ratios with ground heating: large-eddy simulation. Bound Lay Meteorol 142:289–304

    Article  Google Scholar 

  40. Li XX, Liu CH, Leung DYC, Lam KM (2006) Recent progress in CFD modelling of wind field and pollutant transport in street canyons. Atmos Environ 40:5640–5658

    Article  Google Scholar 

  41. Liu YS, Cui GX, Wang ZS, Zhang ZS (2011) Large eddy simulation of wind field and pollutant dispersion in downtown Macao. Atmos Environ 45:2849–2859

    Article  Google Scholar 

  42. Mochida A, Lun I (2008) Prediction of wind environment and thermal comfort at pedestrian level in urban area. J Wind Eng Ind Aerodyn 96:1498–1527

    Article  Google Scholar 

  43. Moonen P, Defraeye T, Dorer V, Blocken B, Carmeliet J (2012) Urban physics: effect of the micro-climate on comfort, health and energy demand. Front Archit Res 1:197–228

    Article  Google Scholar 

  44. Moonen P, Gromke C, Dorer V (2013) Performance assessment of large eddy simulation (LES) for modeling dispersion in an urban street canyon with tree planting. Atmos Environ 75:66–76

    Article  Google Scholar 

  45. Murakami S, Ooka R, Mochida A, Yoshida S, Kim S (1999) CFD analysis of wind climate from human scale to urban scale. J Wind Eng Ind Aerodyn 81:57–81

    Article  Google Scholar 

  46. Oberkampf WL, Barone MF (2006) Measures of agreement between computation and experiment: validation metrics. J Comput Phys 217:5–36

    Article  Google Scholar 

  47. Oberkampf WL, Trucano TG (2002) Verification and validation in computational fluid dynamics. Prog Aerosp Sci 38:209–272

    Article  Google Scholar 

  48. Oke TR (2007) Siting and exposure of meteorological instruments at urban sites. In: Borrego C, Norman AL (eds) Air pollution modeling and its application XVII, chap 66. Springer, Berlin, pp 615–631

  49. Park SB, Baik JJ, Raasch S, Letzel MO (2012) A large-eddy simulation study of thermal effects on turbulent flow and dispersion in and above a street canyon. J Appl Meteorol Climatol 51:829–841

    Article  Google Scholar 

  50. Patnaik G, Boris JP, Grinstein FF, Iselin JP, Hertwig D (2012) Large scale urban simulations with FCT. In: Kuzmin D, Löhner R, Turek S (eds) Flux-corrected transport: principles, algorithms, and applications, scientific computing, 2nd edn. Springer, Berlin, pp 91–117

    Chapter  Google Scholar 

  51. Patnaik G, Grinstein FF, Boris JP, Young TR, Parmhed O (2007) Large-scale urban simulations. In: Grinstein FF, Margolin LG, Rider WJ (eds) Implicit large eddy simulation: computing turbulent fluid dynamics. Cambridge University Press, Cambridge

    Google Scholar 

  52. Plate EJ (1999) Methods of investigating urban wind fields—physical models. Atmos Environ 33:3981–3989

    Article  Google Scholar 

  53. Ramond A, Millan P (2000) Measurements and treatment of LDA signals, comparison with hot-wire signals. Exp Fluids 28:58–63

    Article  Google Scholar 

  54. Salim SM, Buccolieri R, Chan A, Di Sabatino S (2011) Numerical simulation of atmospheric pollutant dispersion in an urban street canyon: comparison between RANS and LES. J Wind Eng Ind Aerodyn 99:103–113

    Article  Google Scholar 

  55. Schatzmann M, Leitl B (2010) Validation of urban flow and dispersion CFD models. In: Proceedings of the 5th international symposium on computational wind engineering. Chapel Hill, North Carolina

  56. Schatzmann M, Olesen H, Franke J (eds) (2010) COST 732 model evaluation case studies: approaches and results. COST Action 732. University of Hamburg, Germany. ISBN 3-00-018312-4

  57. Schlünzen KH (1997) On the validation of high-resolution atmospheric mesoscale models. J Wind Eng Ind Aerodyn 67(68):479–492

    Article  Google Scholar 

  58. Simiu E, Scanlan RH (1986) Wind effects on structures, 2nd edn. Wiley, Hoboken

    Google Scholar 

  59. Standen NM (1972) A spire array for generating thick turbulent shear layers for natural wind simulation in wind tunnels. Report LTR-LA-94, National Aeronautical Establishment, Canada

  60. Stathopoulos T (2006) Pedestrian level winds and outdoor human comfort. J Wind Eng Ind Aerodyn 94:769–780

    Article  Google Scholar 

  61. Tamura T (2008) Towards practical use of LES in wind engineering. J Wind Eng Ind Aerodyn 96:1451–1471

    Article  Google Scholar 

  62. Tominaga Y, Mochida A, Yoshie R, Kataoka H, Nozu T, Yoshikawa M, Shirasawa T (2008) AIJ guidelines for practical applications of CFD to pedestrian wind environment around buildings. J Wind Eng Ind Aerodyn 96:1749–1761

    Article  Google Scholar 

  63. Tominaga Y, Stathopoulos T (2011) CFD modeling of pollution dispersion in a street canyon: comparison between LES and RANS. J Wind Eng Ind Aerodyn 99:340–348

    Article  Google Scholar 

  64. Tominaga Y, Stathopoulos T (2012) CFD modeling of pollution dispersion in building array: evaluation of turbulent scalar flux modeling in RANS model using LES results. J Wind Eng Ind Aerodyn 104–106:484–491

    Article  Google Scholar 

  65. Tominaga Y, Stathopoulos T (2013) CFD simulation of near-field pollutant dispersion in the urban environment: a review of current modeling techniques. Atmos Environ 79:716–730

    Article  Google Scholar 

  66. VDI (2000) Environmental meteorology—physical modelling of flow and dispersion processes in the atmospheric boundary layer—application of wind tunnels. Guideline VDI-3783-12, Verein Deutscher Ingenieure (The Association of German Engineers), Beuth Verlag, Berlin

  67. VDI (2005) Environmental meteorology—prognostic microscale wind field models—evaluation for flow around buildings and obstacles. Guideline VDI-3783-9, Verein Deutscher Ingenieure (The Association of German Engineers), Beuth Verlag, Berlin

  68. Wettermast Hamburg, Universität Hamburg. http://www.wettermast-hamburg.zmaw.de

  69. Wilks DS (2005) Statistical methods in the atmospheric sciences, 2nd edn. Academic Press, Waltham

    Google Scholar 

  70. Winter AR, Graham LJW, Bremhorst K (1991) Effects of time scales on velocity bias in LDA measurements using sample and hold processing. Exp Fluids 11:147–152

    Google Scholar 

  71. Wyngaard JC, Peltier LJ (1996) Experimental micrometeorology in an era of turbulence simulation. Bound Lay Meteorol 78:71–86

    Article  Google Scholar 

  72. Xie ZT, Castro IP (2006) LES and RANS for turbulent flow over arrays of wall-mounted obstacles. Flow Turbul Combust 76:291–312

    Article  Google Scholar 

  73. Xie ZT, Castro IP (2009) Large-eddy simulation for flow and dispersion in urban streets. Atmos Environ 43:2174–2185

    Article  Google Scholar 

  74. Xie ZT, Hayden P, Wood C (2013) Large-eddy simulation of approaching-flow stratification on dispersion over arrays of buildings. Atmos Environ 71:64–74

    Article  Google Scholar 

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Acknowledgements

The numerical simulations with the LES code FAST3D-CT were carried out at the Laboratories for Computational Physics and Fluid Dynamics of the U.S. Naval Research Laboratory in Washington DC, USA. The authors wish to express their thanks to Jay Boris, Mi-Young Obenschain and other collaborators there. Further thanks is given to colleagues at the Environmental Wind Tunnel Laboratory at the University of Hamburg. Financial funding by the German Federal Office of Civil Protection and Disaster Assistance as well as by the Ministry of the Interior of the City of Hamburg within the “Hamburg Pilot Project” is gratefully acknowledged (BBK research contract no. BBK III.1-413-10-364). Parts of the wind-tunnel model construction were financially supported by the KlimaCampus at the University of Hamburg.

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  1. Denise Hertwig

    Present address: Department of Meteorology, University of Reading, P.O. Box 243, Reading, RG6 6BB, UK

Authors and Affiliations

  1. Meteorological Institute, University of Hamburg, Bundesstrasse 55, 20146, Hamburg, Germany

    Denise Hertwig & Bernd Leitl

  2. Laboratories for Computational Physics and Fluid Dynamics, U.S. Naval Research Laboratory, Washington, DC, USA

    Gopal Patnaik

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Hertwig, D., Patnaik, G. & Leitl, B. LES validation of urban flow, part I: flow statistics and frequency distributions. Environ Fluid Mech 17, 521–550 (2017). https://doi.org/10.1007/s10652-016-9507-7

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