Fire Technology

, Volume 54, Issue 5, pp 1443–1485 | Cite as

Wind and Fire Coupled Modelling—Part II: Good Practice Guidelines

  • Wojciech WęgrzyńskiEmail author
  • Tomasz Lipecki
  • Grzegorz Krajewski
Review Paper
Part of the following topical collections:
  1. Fire Science Reviews


The requirement to model wind is inherently connected with the modelling of many fire-related phenomena. With its defining influence on fire behaviour, spread and smoke transport, the solution of a problem with and without wind exposure will lead to substantially different results. As wind and fire are phenomena that often require different scales of analysis and approaches to modelling, their coupling is not a trivial task. This paper is the second part of a two-paper review of the coupling between fire safety engineering and computational wind engineering (CWE). Part I contained a review of historical interactions between these disciplines, sorted into six distinct areas: flames, indoor flows, natural ventilators, tunnels, wildfires and urban smoke dispersion. This part of the review contains practical information related to wind modelling in fire analysis, based on various available CWE best practice guidelines. As the authors conclude, the most relevant of these are guidelines related to urban physics and natural ventilation; however, many more are discussed and presented, together with the results of other essential wind engineering experiments and computations. Introduction of wind as a boundary condition is explained in details, both based on wind statistics, or meso/micro scale coupled modelling. The guidelines for wind/fire coupled analyses are subdivided into recommendations for: building the digital domain, spatial and temporal discretisation, the consequences of the choice of a turbulent flow model, and the procedure for optimising CFD analysis of both wind and fire phenomena.


Wind Fire Computational wind engineering Fire safety engineering Computational fluid dynamics 



Architectural Institute of Japan


Atmospheric boundary layer


Available safe evacuation time


Commonwealth Advisory Aeronautical Research Council (standardised test building)


Computational fluid dynamics


Courant–Friedrichs–Lewy (condition)


Silsoe cube building


Computational wind engineering


Detached eddy simulation


Direct numerical simulation


Differential stress model


Eddy viscosity model


Fire dynamics simulator


Fire safety engineering


Fluid–structure interaction


Large eddy simulation


Mesoscale meteorological model (also MMM)


Microscale meteorological model


National Institute of Standards and Technology (Gaithersburg, USA)


Natural smoke and heat exhaust ventilation


Reynold’s averaged Navier–Stokes (equations)


Required safe evacuation time


Reynold’s stress method


Scale adaptive simulation


Texas Tech Building


Unsteady RANS


Wildland–urban interface



  1. 1.
    Weinschenk CG, Overholt KJ, Madrzykowski D (2015) Simulation of an attic fire in a wood frame residential structure, Chicago, IL. Fire Technol 52:1629–1658. Google Scholar
  2. 2.
    Blocken B (2014) 50 years of computational wind engineering: past, present and future. J Wind Eng Ind Aerodyn 129:69–102. Google Scholar
  3. 3.
    Franke J, Hirsch C, Jensen AG, Krus HW, Schatzmann M, Westbury PS, Miles SD, Wisse JA, Wright NG (2004) Recommendations on the use of CFD in wind engineering. In: van Beeck JPAJ (ed) Proceedings of the international conference on urban wind engineering and building aerodynamics. COST action C14, impact of wind and storm on city life built environment, pp 1–11Google Scholar
  4. 4.
    Ramponi R, Blocken B (2012) CFD simulation of cross-ventilation for a generic isolated building: impact of computational parameters. Build Environ 53:34–48. Google Scholar
  5. 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. Google Scholar
  6. 6.
    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. Google Scholar
  7. 7.
    Franke J, Hellsten A, Schlünzen H, Carissimo B (2007) Best practice guideline for the CFD simulation of flows in the urban environment. COST Office BrusselsGoogle Scholar
  8. 8.
    Franke J, Hellsten A, Schlünzen KH, Carissimo B (2011) The COST 732 best practice guideline for CFD simulation of flows in the urban environment: a summary. Int J Environ Pollut 44:419. Google Scholar
  9. 9.
    Mochida A, Tominaga Y, Murakami S, Yoshie R, Ishihara T, Ooka R (2002) Comparison of various k–ε models and DSM applied to flow around a high-rise building—report on AIJ cooperative project for CFD prediction of wind environment. Wind Struct 5:227–244. Google Scholar
  10. 10.
    Tominaga Y, Mochida A, Shirasawa T, Yoshie R, Kataoka H, Harimoto K, Nozu T (2004) Cross comparisons of CFD results of wind environment at pedestrian level around a high-rise building and within a building complex. J Asian Archit Build Eng 70:63–70. Google Scholar
  11. 11.
    Yoshie R, Mochida A, Tominaga Y, Kataoka H, Harimoto K, Nozu T, Shirasawa T (2007) Cooperative project for CFD prediction of pedestrian wind environment in the Architectural Institute of Japan. J Wind Eng Ind Aerodyn 95:1551–1578. Google Scholar
  12. 12.
    Tamura T (2008) Towards practical use of LES in wind engineering. J Wind Eng Ind Aerodyn 96:1451–1471. Google Scholar
  13. 13.
    Blocken B, Janssen WD, van Hooff T (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 Softw 30:15–34. Google Scholar
  14. 14.
    Tominaga Y, Blocken B (2015) Wind tunnel experiments on cross-ventilation flow of a generic building with contaminant dispersion in unsheltered and sheltered conditions. Build Environ 92:452–461. Google Scholar
  15. 15.
    Tominaga Y, Blocken B (2016) Wind tunnel analysis of flow and dispersion in cross-ventilated isolated buildings: impact of opening positions. J Wind Eng Ind Aerodyn 155:74–88. Google Scholar
  16. 16.
    van Hooff T, Blocken B, Tominaga Y (2017) On the accuracy of CFD simulations of cross-ventilation flows for a generic isolated building: comparison of RANS, LES and experiments. Build Environ 114:148–165. Google Scholar
  17. 17.
    Uematsu Y, Watanabe K, Sasaki A, Motohiko Y, Hongo T (1999) Wind-induced dynamic response and resultant load estimation of a circular flat roof. J Wind Eng Ind Aerodyn 83:251–261. Google Scholar
  18. 18.
    Uematsu Y, Moteki T, Hongo T (2008) Model of wind pressure field on circular flat roofs and its application to load estimation. J Wind Eng Ind Aerodyn 96:1003–1014. Google Scholar
  19. 19.
    Richards PJ, Hoxey RP (2006) Flow reattachment on the roof of a 6 m cube. J Wind Eng Ind Aerodyn 94:77–99. Google Scholar
  20. 20.
    Richards PJ, Hoxey RP (2008) Wind loads on the roof of a 6 m cube. J Wind Eng Ind Aerodyn 96:984–993. Google Scholar
  21. 21.
    Gerhardt H, Kramer C (1992) Effects of building geometry on roof wind loading. J Wind Eng Ind Aerodyn 41–44:1765–1773Google Scholar
  22. 22.
    Lipecki T (2013) Pressure coefficient on flat roofs of rectangular buildings. In: 6th European and African conference on wind engineering. Robinson College, Cambridge, pp 1–8Google Scholar
  23. 23.
    Stathopoulos T, Marathe R, Wu H (1999) Mean wind pressures on flat roof corners affected by parapets: field and wind tunnel studies. Eng Struct 21:629–638. Google Scholar
  24. 24.
    Kareem A, Lu PC (1992) Pressure fluctuations on flat roofs with parapets. J Wind Eng Ind Aerodyn 41–44:1775–1786Google Scholar
  25. 25.
    Pindado S, Meseguer J (2003) Wind tunnel study on the influence of different parapets on the roof pressure distribution of low-rise buildings. J Wind Eng Ind Aerodyn 91:1133–1139. Google Scholar
  26. 26.
    Blessing C, Chowdhury AG, Lin J, Huang P (2009) Full-scale validation of vortex suppression techniques for mitigation of roof uplift. Eng Struct 31:2936–2946. Google Scholar
  27. 27.
    Mooneghi MA, Irwin P, Chowdhury AG (2014) Large-scale testing on wind uplift of roof pavers. J Wind Eng Ind Aerodyn 128:22–36. Google Scholar
  28. 28.
    Cao J, Tamura Y, Yoshida A (2012) Wind pressures on multi-level flat roofs of medium-rise buildings. J Wind Eng Ind Aerodyn 103:1–15. Google Scholar
  29. 29.
    Cao J, Tamura Y, Yoshida A (2013) Wind tunnel investigation of wind loads on rooftop model modules for green roofing systems. J Wind Eng Ind Aerodyn 118:20–34. Google Scholar
  30. 30.
    Pindado S, Meseguer J, Franchini S (2011) Influence of an upstream building on the wind-induced mean suction on the flat roof of a low-rise building. J Wind Eng Ind Aerodyn 99:889–893. Google Scholar
  31. 31.
    Wu F, Sarkar PP, Mehta KC, Zhao Z (2001) Influence of incident wind turbulence on pressure fluctuations near flat-roof corners. J Wind Eng Ind Aerodyn 89:403–420. Google Scholar
  32. 32.
    Tieleman HW, Reinhold TA, Hajj MR (2001) Detailed simulation of pressures in separated/reattached flows. J Wind Eng Ind Aerodyn 89:1657–1670Google Scholar
  33. 33.
    Tieleman HW, Ge Z, Hajj MR, Reinhold TA (2003) Pressures on a surface-mounted rectangular prism under varying incident turbulence. J Wind Eng Ind Aerodyn 91:1095–1115. Google Scholar
  34. 34.
    Kawai H (2002) Local peak pressure and conical vortex on building. J Wind Eng Ind Aerodyn 90:251–263. Google Scholar
  35. 35.
    Banks D, Meroney RN, Sarkar PP, Zhao Z, Wu F (2000) Flow visualization of conical vortices on flat roofs with simultaneous surface pressure measurement. J Wind Eng Ind Aerodyn 84:65–85. Google Scholar
  36. 36.
    Stathopoulos T, Zhou YS (1995) Numerical evaluation of wind pressures on flat roofs with the k–ε model. Build Environ 30:267–276. Google Scholar
  37. 37.
    Ono Y, Tamura T, Kataoka H (2008) LES analysis of unsteady characteristics of conical vortex on a flat roof. J Wind Eng Ind Aerodyn 96:2007–2018. Google Scholar
  38. 38.
    Richards PJ, Hoxey RP, Short LJ (2001) Wind pressures on a 6 m cube. J Wind Eng Ind Aerodyn 89:1553–1564. Google Scholar
  39. 39.
    Richards PJ, Hoxey RP (2002) Unsteady flow on the sides of a 6 m cube. J Wind Eng Ind Aerodyn 90:1855–1866. Google Scholar
  40. 40.
    Richards PJ, Hoxey RP, Connell BD, Lander DP (2007) Wind-tunnel modelling of the Silsoe Cube. J Wind Eng Ind Aerodyn 95:1384–1399. Google Scholar
  41. 41.
    Richards PJ, Hoxey RP (2012) Pressures on a cubic building-part 1: full-scale results. J Wind Eng Ind Aerodyn 102:72–86. Google Scholar
  42. 42.
    Richards PJ, Hoxey RP (2012) Pressures on a cubic building-part 2: quasi-steady and other processes. J Wind Eng Ind Aerodyn 102:87–96. Google Scholar
  43. 43.
    Easom G (2000) Improved turbulence models for computational wind engineering. Ph.D. Thesis, The University of NottinghamGoogle Scholar
  44. 44.
    Wright NGG, Easom GJJ (2003) Non-linear k–ε turbulence model results for flow over a building at full-scale. Appl Math Model 27:1013–1033. zbMATHGoogle Scholar
  45. 45.
    Richards P, Norris S (2015) LES modelling of unsteady flow around the Silsoe cube. J Wind Eng Ind Aerodyn 144:70–78. Google Scholar
  46. 46.
    King MF, Gough HL, Halios C, Barlow JF, Robertson A, Hoxey R, Noakes CJ (2017) Investigating the influence of neighbouring structures on natural ventilation potential of a full-scale cubical building using time-dependent CFD. J Wind Eng Ind Aerodyn 169:265–279. Google Scholar
  47. 47.
    King M-F, Khan A, Delbosc N, Gough HL, Halios C, Barlow JF, Noakes CJ (2017) Modelling urban airflow and natural ventilation using a GPU-based lattice-Boltzmann method. Build Environ 125:273–284. Google Scholar
  48. 48.
    Melbourne WH (1980) Comparison of measurements of the CAARC standard tall building model in simulated model wind flows. J Wind Eng Ind Aerodyn 6:78–88Google Scholar
  49. 49.
    Goliger AM, Milford RV (1988) Sensitivity of the CAARC standard building model to geometric scale and turbulence. J Wind Eng Ind Aerodyn 31:105–123Google Scholar
  50. 50.
    Tang UF, Kwok KCS (2004) Interference excitation mechanisms on a 3DOF aeroelastic CAARC building model. J Wind Eng Ind Aerodyn 92:1299–1314. Google Scholar
  51. 51.
    Huang S, Li QS, Xu S (2007) Numerical evaluation of wind effects on a tall steel building by CFD. J Constr Steel Res 63:612–627. Google Scholar
  52. 52.
    Huang MF, Lau IWH, Chan CM, Kwok KCS, Li G (2011) A hybrid RANS and kinematic simulation of wind load effects on full-scale tall buildings. J Wind Eng Ind Aerodyn 99:1126–1138. Google Scholar
  53. 53.
    Daniels SJ, Castro IP, Xie ZT (2013) Peak loading and surface pressure fluctuations of a tall model building. J Wind Eng Ind Aerodyn 120:19–28. Google Scholar
  54. 54.
    Elshaer A, Aboshosha H, Bitsuamlak G, El Damatty A, Dagnew A (2016) LES evaluation of wind-induced responses for an isolated and a surrounded tall building. Eng Struct 115:179–195. Google Scholar
  55. 55.
    Braun AL, Awruch AM (2009) Aerodynamic and aeroelastic analyses on the CAARC standard tall building model using numerical simulation. Comput Struct 87:564–581. Google Scholar
  56. 56.
    Levitan MC, Mehta KC (1992) Texas Tech field experiments for wind loads. Part I. Building and pressure measuring system. J Wind Eng Ind Aerodyn 43:1565–1576Google Scholar
  57. 57.
    Levitan MC, Mehta KC (1992) Texas tech field experiments for wind loads. Part II. Meteorological instrumentation and terrain parameters. J Wind Eng Ind Aerodyn 43:1577–1588Google Scholar
  58. 58.
    Cochran LS, Cermak JE (1992) Full- and model-scale cladding pressures on the Texas Tech University experimental building. J Wind Eng Ind Aerodyn 41–44:1589–1600Google Scholar
  59. 59.
    Okada H, Ha Y-C (1992) Comparison of wind tunnel and full-scale pressure measurement tests on the Texas Tech Building. J Wind Eng Ind Aerodyn 41–44:1601–1612Google Scholar
  60. 60.
    Cheung JCK, Holmes JD, Melbourne WH (1997) Pressures on a 110 scale model of the Texas Tech Building. J Wind Eng Ind Aerodyn 71:529–538Google Scholar
  61. 61.
    Tieleman HW, Surry D, Mehta KC (1996) Full/model-scale comparison of surface pressures on the Texas Tech experimental building. J Wind Eng Ind Aerodyn 61:1–23. Google Scholar
  62. 62.
    Lin JX, Surry D, Tieleman HW (1995) The distribution of pressure near roof corners of flat roof low buildings. J Wind Eng Ind Aerodyn 56:235–265. Google Scholar
  63. 63.
    Lin JX, Surry D (1998) The variation of peak loads with tributary area near corners on flat low building roofs. J Wind Eng Ind Aerodyn 77–78:185–196. Google Scholar
  64. 64.
    Endo M, Bienkiewicz B, Ham HJ (2006) Wind-tunnel investigation of point pressure on TTU test building. J Wind Eng Ind Aerodyn 94:553–578. Google Scholar
  65. 65.
    Selvam RP (1996) Computation of flow around Texas Tech building using k–epsilon and Kato–Launder k–epsilon turbulence model. Eng Struct 18:856–860Google Scholar
  66. 66.
    Selvam RP (1997) Computation of pressures on Texas Tech University building using large eddy simulation. J Wind Eng Ind Aerodyn 67–68:647–657. Google Scholar
  67. 67.
    Stathopoulos T (1997) Computational wind engineering: past achievements and future challenges. J Wind Eng Ind Aerodyn 67–68:509–532. Google Scholar
  68. 68.
    Senthooran S, Lee DD, Parameswaran S (2004) A computational model to calculate the flow-induced pressure fluctuations on buildings. J Wind Eng Ind Aerodyn 92:1131–1145. Google Scholar
  69. 69.
    Blocken B, Stathopoulos T, van Beeck JPAJ (2016) Pedestrian-level wind conditions around buildings: review of wind-tunnel and CFD techniques and their accuracy for wind comfort assessment. Build Environ 100:50–81. Google Scholar
  70. 70.
    Revuz J, Hargreaves DM, Owen JS (2012) On the domain size for the steady-state CFD modelling of a tall building. Wind Struct Int J 15:313–329. Google Scholar
  71. 71.
    Vanella M, Posa A, Balaras E (2014) Adaptive mesh refinement for immersed boundary methods. J Fluids Eng 136:40901. Google Scholar
  72. 72.
    Vanella M, McDermott R, Forney G (2015) A cut-cell immersed boundary technique for fire dynamics simulation. In: APS division of fluid dynamics meeting abstracts, p L7.003Google Scholar
  73. 73.
    McGrattan K, Hostikka S, McDermott R, Floyd J, Weinschenk C, Overholt K (2017) Fire Dynamics Simulator User’s Guide, 6th ednGoogle Scholar
  74. 74.
    McGrattan K, Hostikka S, McDermott R, Floyd J, Weinschenk C, Overholt K (2015) Fire Dynamics Simulator technical reference guide. Volume 2: verification, 6th edn. NIST Special Publication 1018Google Scholar
  75. 75.
    McGrattan K, Hostikka S, McDermott R, Floyd J, Weinschenk C, Overholt K (2015) Fire Dynamics Simulator technical reference guide. Volume 3: validation, 6th edn. NIST Special Publication 1018-3Google Scholar
  76. 76.
    Pope SB (2004) Ten questions concerning the large-eddy simulation of turbulent flows. New J Phys 6:35. Google Scholar
  77. 77.
    Roache PJ (1994) Perspective: a method for uniform reporting of grid refinement studies. J Fluids Eng 116:405. Google Scholar
  78. 78.
    Roache PJ (1997) Quantification of uncertainty in computational fluid dynamics. Annu Rev Fluid Mech 29:123–160. MathSciNetGoogle Scholar
  79. 79.
    van Hooff T, Blocken B (2010) Coupled urban wind flow and indoor natural ventilation modelling on a high-resolution grid: a case study for the Amsterdam ArenA stadium. Environ Model Softw 25:51–65. Google Scholar
  80. 80.
    McGrattan K, McDermott R, Floyd J, Hostikka S, Forney G, Baum H (2012) Computational fluid dynamics modelling of fire. Int J Comput Fluid Dyn 26:349–361. MathSciNetGoogle Scholar
  81. 81.
    Franke J (2007) Introduction to the prediction of wind loads on buildings by computational wind engineering (CWE). In: Stathopoulos T, Baniotopoulos CC (eds) Wind effects on buildings and design of wind-sensitive structures. Springer, Vienna, pp 67–103Google Scholar
  82. 82.
    Murakami S (1998) Overview of turbulence models applied in CWE-1997. J Wind Eng Ind Aerodyn 74–76:1–24. Google Scholar
  83. 83.
    Argyropoulos CD, Markatos NC (2015) Recent advances on the numerical modelling of turbulent flows. Appl Math Model 39:693–732. MathSciNetGoogle Scholar
  84. 84.
    Office of Nuclear Regulatory Research (2012) Computational fluid dynamics best practice guidelines for dry cask applications draft report for commentGoogle Scholar
  85. 85.
    Launder BE, Spalding JL (1972) Lectures in mathematical models of turbulence. Academic Press, New YorkzbMATHGoogle Scholar
  86. 86.
    Yakhot V, Orszag SAA, Thangam S, Gatski TBB, Speziale CGG (1992) Development of turbulence models for shear flows by a double expansion technique. Phys Fluids A Fluid Dyn 4:1510. MathSciNetzbMATHGoogle Scholar
  87. 87.
    Shih TH, Liou WW, Shabbir A, Yang Z, Zhu J (1995) A new k–e eddy viscosity model for high Reynolds number turbulent flows. Comput Fluids 24:227–238zbMATHGoogle Scholar
  88. 88.
    Wilcox DC (1988) Re-assessment of the scale-determining equation for advanced turbulence models. AIAA J 26:1299–1310MathSciNetzbMATHGoogle Scholar
  89. 89.
    Menter FR (1994) Two-equation eddy-viscosity turbulence models for engineering applications. AIAA J 32:1598–1605. Google Scholar
  90. 90.
    Smagorinsky J (1963) General circulation experiments with the primitive equations. I. The basic experiment. Mon Weather Rev 91:99–164Google Scholar
  91. 91.
    Germano M, Piomelli U, Moin P, Cabot WH (1991) A dynamic subgrid-scale eddy viscosity model. Phys Fluids A 3:1760–1765zbMATHGoogle Scholar
  92. 92.
    Sarwar M, Moinuddin K, Thorpe GR (2013) Large eddy simulation over backwards facing step using fire dynamics simulator (FDS). In: Fourteenth Asian congress of fluid dynamics, pp 469–474Google Scholar
  93. 93.
    A. Toms B (2015) Large-eddy simulation of flow over a backward facing step: assessment of inflow boundary conditions, eddy viscosity models, and wall functions. J Appl Mech Eng. Google Scholar
  94. 94.
    Tamura T, Nozawa K, Kondo K (2008) AIJ guide for numerical prediction of wind loads on buildings. J Wind Eng Ind Aerodyn 96:1974–1984. Google Scholar
  95. 95.
    Spalart PR (2009) Detached-eddy simulation. Annu Rev Fluid Mech 41:181–202. zbMATHGoogle Scholar
  96. 96.
    Blocken B, Stathopoulos T, Carmeliet J (2007) CFD simulation of the atmospheric boundary layer: wall function problems. Atmos Environ 41:238–252. Google Scholar
  97. 97.
    Wieringa J (1992) Updating the Davenport roughness classification. J Wind Eng Ind Aerodyn 41:357–368. Google Scholar
  98. 98.
    Richards PJ, Hoxey RP (1993) Appropriate boundary conditions for computational wind engineering models using the k–ε turbulence model. In: Computational wind engineering 1. Elsevier, Amsterdam, pp 145–153Google Scholar
  99. 99.
    Porté-Agel F, Wu Y-T, Lu H, Conzemius RJ (2011) Large-eddy simulation of atmospheric boundary layer flow through wind turbines and wind farms. J Wind Eng Ind Aerodyn 99:154–168. Google Scholar
  100. 100.
    Shur ML, Spalart PR, Strelets MK, Travin AK (2014) Synthetic turbulence generators for RANS-LES interfaces in zonal simulations of aerodynamic and aeroacoustic problems. Flow Turbul Combust 93:63–92. Google Scholar
  101. 101.
    Tabor GR, Baba-Ahmadi MH (2010) Inlet conditions for large eddy simulation: a review. Comput Fluids 39:553–567. MathSciNetzbMATHGoogle Scholar
  102. 102.
    Dyer AJ (1974) A review of flux-profile relationships. Bound Layer Meteorol 7:363–372. Google Scholar
  103. 103.
    Richards PJ, Norris SE (2011) Appropriate boundary conditions for computational wind engineering models revisited. J Wind Eng Ind Aerodyn 99:257–266. Google Scholar
  104. 104.
    Hargreaves DM, Wright NG (2007) On the use of the k–ε model in commercial CFD software to model the neutral atmospheric boundary layer. J Wind Eng Ind Aerodyn 95:355–369. Google Scholar
  105. 105.
    Yang Y, Gu M, Chen S, Jin X (2009) New inflow boundary conditions for modelling the neutral equilibrium atmospheric boundary layer in computational wind engineering. J Wind Eng Ind Aerodyn 97:88–95. Google Scholar
  106. 106.
    Parente A, Gorlé C, van Beeck J, Benocci C (2011) Improved k–ε model and wall function formulation for the RANS simulation of ABL flows. J Wind Eng Ind Aerodyn 99:267–278. Google Scholar
  107. 107.
    Richards PJ, Norris SE (2015) Appropriate boundary conditions for a pressure driven boundary layer. J Wind Eng Ind Aerodyn 142:43–52. Google Scholar
  108. 108.
    Deaves DM, Harris RI (1978) A mathematical model of the structure of strong winds. CIRIA, LondonGoogle Scholar
  109. 109.
    Bęc J, Lipecki T, Błazik-Borowa E (2011) Research on wind structure in the wind tunnel of wind engineering laboratory of Cracow University of Technology. J Phys Conf Ser 318:72003. Google Scholar
  110. 110.
    Tominaga Y, Mochida A, Murakami S, Sawaki S (2008) Comparison of various revised k–ε models and LES applied to flow around a high-rise building model with 1:1:2 shape placed within the surface boundary layer. J Wind Eng Ind Aerodyn 96:389–411. Google Scholar
  111. 111.
    CEN (2010) EN 1991-1-4:2005+A1: Eurocode 1: actions on structures—part 1–4: general actions–wind actionsGoogle Scholar
  112. 112.
    ASCE (2006) ASCE/SEI 7-05 minimum design loads for buildings and other structuresGoogle Scholar
  113. 113.
    AS-NZS (2011) AS-NZS 1170-2 structural design actions—part 2: wind actionsGoogle Scholar
  114. 114.
    AIJ (2004) AIJ-RBL-1996 recommendations for loads on buildingsGoogle Scholar
  115. 115.
    ISO (2009) ISO 4354:2009 wind actions on structuresGoogle Scholar
  116. 116.
    Dyrbye C, Hansen S. (1997) Wind loads on structures. Wiley, New YorkGoogle Scholar
  117. 117.
    Holmes JD (2004) Wind loading of structures. Taylor & Francis, LondonGoogle Scholar
  118. 118.
    Simiu E, Scanlan RH (1996) Wind effects on structures. Wiley, New YorkGoogle Scholar
  119. 119.
    Tamura Y, Kareem A (2014) Advanced structural wind engineering. Springer, BerlinGoogle Scholar
  120. 120.
    Hangan H, Refan M, Jubayer C, Romanic D, Parvu D, LoTufo J, Costache A (2017) Novel techniques in wind engineering. J Wind Eng Ind Aerodyn 171:12–33. Google Scholar
  121. 121.
    Schlünzen KH, Grawe D, Bohnenstengel SI, Schlüter I, Koppmann R (2011) Joint modelling of obstacle induced and mesoscale changes—current limits and challenges. J Wind Eng Ind Aerodyn 99:217–225. Google Scholar
  122. 122.
    Masson V (2006) Urban surface modeling and the meso-scale impact of cities. Theor Appl Climatol 84:35–45. Google Scholar
  123. 123.
    Garuma GF (2017) Review of urban surface parameterizations for numerical climate models. Urban Clim. Google Scholar
  124. 124.
    Ryu YH, Bou-Zeid E, Wang ZH, Smith JA (2016) Realistic representation of trees in an urban canopy model. Bound Layer Meteorol 159:193–220. Google Scholar
  125. 125.
    Krayenhoff ES, Christen A, Martilli A, Oke TR (2014) A multi-layer radiation model for urban neighbourhoods with trees. Bound Layer Meteorol 151:139–178. Google Scholar
  126. 126.
    Yamada T, Koike K (2011) Downscaling mesoscale meteorological models for computational wind engineering applications. J Wind Eng Ind Aerodyn 99:199–216. Google Scholar
  127. 127.
    Liu Y, Warner T, Liu Y, Vincent C, Wu W, Mahoney B, Swerdlin S, Parks K, Boehnert J (2011) Simultaneous nested modeling from the synoptic scale to the LES scale for wind energy applications. J Wind Eng Ind Aerodyn 99:308–319. Google Scholar
  128. 128.
    Mochida A, Iizuka S, Tominaga Y, Lun IYF (2011) Up-scaling CWE models to include mesoscale meteorological influences. J Wind Eng Ind Aerodyn 99:187–198. Google Scholar
  129. 129.
    Tominaga Y, Mochida A, Okaze T, Sato T, Nemoto M, Motoyoshi H, Nakai S, Tsutsumi T, Otsuki M, Uamatsu T, Yoshino H (2011) Development of a system for predicting snow distribution in built-up environments: combining a mesoscale meteorological model and a CFD model. J Wind Eng Ind Aerodyn 99:460–468. Google Scholar
  130. 130.
    Mughal MO, Lynch M, Yu F, Sutton J (2018) Forecasting and verification of winds in an East African complex terrain using coupled mesoscale- and micro-scale models. J Wind Eng Ind Aerodyn 176:13–20. Google Scholar
  131. 131.
    Baik J-J, Park S-B, Kim J-J (2009) Urban flow and dispersion simulation using a CFD model coupled to a mesoscale model. J Appl Meteorol Climatol 48:1667–1681. Google Scholar
  132. 132.
    Tewari M, Kusaka H, Chen F, Coirier WJ, Kim S, Wyszogrodzki AA, Warner TT (2010) Impact of coupling a microscale computational fluid dynamics model with a mesoscale model on urban scale contaminant transport and dispersion. Atmos Res 96:656–664. Google Scholar
  133. 133.
    Chahine A, Dupont E, Musson-Genon L, Legorgeu C, Carissimo B (2018) Long term modelling of the dynamical atmospheric flows over SIRTA site. J Wind Eng Ind Aerodyn 172:351–366. Google Scholar
  134. 134.
    Temel O, Bricteux L, van Beeck J (2018) Coupled WRF-OpenFOAM study of wind flow over complex terrain. J Wind Eng Ind Aerodyn 174:152–169. Google Scholar
  135. 135.
    Gopalan H, Gundling C, Brown K, Roget B, Sitaraman J, Mirocha JD, Miller WO (2014) A coupled mesoscale–microscale framework for wind resource estimation and farm aerodynamics. J Wind Eng Ind Aerodyn 132:13–26. Google Scholar
  136. 136.
    Kwak KH, Baik JJ, Ryu YH, Lee SH (2015) Urban air quality simulation in a high-rise building area using a CFD model coupled with mesoscale meteorological and chemistry-transport models. Atmos Environ 100:167–177. Google Scholar
  137. 137.
    Forthofer JM, Butler BW, Mchugh CW, Finney MA, Bradshaw LS, Stratton RD, Shannon KS, Wagenbrenner NS (2014) A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part II. An exploratory study of the effect of simulated winds on fire growth simulations. Int J Wildl Fire 23:982–994. Google Scholar
  138. 138.
    Forthofer JM, Butler BW, Wagenbrenner NS (2014) A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part I. Model formulation and comparison against measurements. Int J Wildland Fire 23:969–981. Google Scholar
  139. 139.
    Wagenbrenner NS, Forthofer JM, Lamb BK, Shannon KS, Butler BW (2016) Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja. Atmos Chem Phys 16:5229–5241. Google Scholar
  140. 140.
    Sanjuan G, Brun C, Margalef T, Cortés A (2016) Determining map partitioning to minimize wind field uncertainty in forest fire propagation prediction. J Comput Sci 14:28–37. Google Scholar
  141. 141.
    Sanjuan G, Margalef T, Cortés A (2016) Applying domain decomposition to wind field calculation. Parallel Comput 57:185–196. MathSciNetGoogle Scholar
  142. 142.
    Sanjuan G, Margalef T, Cortés A (2018) Wind field parallelization based on Schwarz alternating domain decomposition method. Future Gener Comput Syst 82:565–574. Google Scholar
  143. 143.
    Brun C, Margalef T, Cortés A (2013) Coupling diagnostic and prognostic models to a dynamic data driven forest fire spread prediction system. Procedia Comput Sci 18:1851–1860. Google Scholar
  144. 144.
    Węgrzyński W, Krajewski G, Sulik P (2016) Case study 2—production and storage building (Poland). In: 11th conference on performance-based codes and fire safety design methods. SFPE, WarszawaGoogle Scholar
  145. 145.
    CEN (2005) EN 1991-1-4:2005 Eurocode 1: actions on structures—part 1–4: general actions–wind actionsGoogle Scholar
  146. 146.
    Krajewski G, Węgrzyński W (2018) Use of computational fluid dynamics in optimization of natural smoke ventilation from a historical shopping mall—case study. AIP Conf Proc 1922:110009. Google Scholar
  147. 147.
    Morgan HP (1986) The horizontal flow of buoyant gases toward an opening. Fire Saf J 11:193–200. Google Scholar
  148. 148.
    Alpert RL (1975) Turbulent ceiling-jet induced by large-scale fires. Combust Sci Technol 11:197–213. Google Scholar
  149. 149.
    Karlsson B, Quintiere JG (2000) Enclosure fire dynamics. CRC Press, Boca RatonGoogle Scholar
  150. 150.
    Quintiere JG (2006) Fundamentals of fire phenomena. Wiley, New YorkGoogle Scholar
  151. 151.
    Babrauskas V (2003) Ignition handbook. Fire Science Publishers, IssaquahGoogle Scholar
  152. 152.
    Drysdale DD (2011) An introduction to fire dynamics, 3rd edn. Wiley, New YorkGoogle Scholar
  153. 153.
    Węgrzyński W, Sulik P (2016) The philosophy of fire safety engineering in the shaping of civil engineering development. Bull Polish Acad Sci Tech Sci 64:719–730. Google Scholar
  154. 154.
    McGrattan K, Miles S (2016) Modeling fires using computational fluid dynamics (CFD). In: SFPE handbook of fire protection engineering. Springer, New York, pp 1034–1065Google Scholar
  155. 155.
    Sztarbała G (2013) An estimation of conditions inside construction works during a fire with the use of computational fluid dynamics. Bull Polish Acad Sci Tech Sci 61:155–160. Google Scholar
  156. 156.
    Merci B, Beji T (2016) Fluid mechanics aspects of fire and smoke dynamics in enclosures. CRC Press, Boca RatonGoogle Scholar
  157. 157.
    Kuligowski ED (2016) Human behavior in fire. In: Hurley MJ, Gottuk DT, Hall JR Jr, Harada K, Kuligowski ED, Puchovsky M, Torero JL, Watts JM Jr, Wieczorek CJ (eds) SFPE handbook of fire protection engineering. Springer, New York, pp 2070–2114Google Scholar
  158. 158.
    Yamada T, Akizuki Y (2016) Visibility and human behavior in fire smoke. In: SFPE handbook of fire protection engineering. Springer, New York, pp 2181–2206Google Scholar
  159. 159.
    Purser DA, McAllister JL (2016) Assessment of hazards to occupants from smoke, toxic gases, and heat. In: SFPE handbook of fire protection engineering. Springer, New York, pp 2308–2428Google Scholar
  160. 160.
    Węgrzyński W, Vigne G (2017) Experimental and numerical evaluation of the influence of the soot yield on the visibility in smoke in CFD analysis. Fire Saf J 91:389–398. Google Scholar
  161. 161.
    Perez Segovia JF, Beji T, Merci B (2017) CFD simulations of pool fires in a confined and ventilated enclosure using the Peatross–Beyler correlation to calculate the mass loss rate. Fire Technol 53:1669–1703. Google Scholar
  162. 162.
    Bari S, Naser J (2005) Simulation of smoke from a burning vehicle and pollution levels caused by traffic jam in a road tunnel. Tunn Undergr Space Technol 20:281–290. Google Scholar
  163. 163.
    Yu LX, Beji T, Zadeh SE, Liu F, Merci B (2016) Simulations of smoke flow fields in a wind tunnel under the effect of an air curtain for smoke confinement. Fire Technol 52:1–20. Google Scholar
  164. 164.
    Król M, Król A (2017) Multi-criteria numerical analysis of factors influencing the efficiency of natural smoke venting of atria. J Wind Eng Ind Aerodyn 170:149–161. Google Scholar
  165. 165.
    Cooper LY (1983) A concept for estimating available safe egress time in fires. Fire Saf J 5:135–144. Google Scholar
  166. 166.
    Cooper LY (1982) A mathematical model for estimating available safe egress time in fires. Fire Mater 6:135–144. Google Scholar
  167. 167.
    Babrauskas V, Fleming JM, Don Russell B (2010) RSET/ASET, a flawed concept for fire safety assessment. Fire Mater 34:341–355. Google Scholar
  168. 168.
    Purser D (2003) ASET and RSET: addressing some issues in relation to occupant behaviour and tenability. Fire Saf Sci. Google Scholar
  169. 169.
    Kuligowski ED, Gwynne SM V, Hulse LM, Kinsey MJ (2016) Guidance for the model developer on representing human behavior in egress models. Fire Technol 52:775–800. Google Scholar
  170. 170.
    Węgrzyński W, Krajewski G (2017) Influence of wind on natural smoke and heat exhaust system performance in fire conditions. J Wind Eng Ind Aerodyn 164:44–53. Google Scholar
  171. 171.
    Węgrzyński W, Krajewski G, Kimbar G (2018) A concept of external aerodynamic elements in improving the performance of natural smoke ventilation in wind conditions. AIP Conf Proc 1922:110006. Google Scholar
  172. 172.
    Lovreglio R, Ronchi E, Maragkos G, Beji T, Merci B (2016) An integrated dynamic approach for the impact of a toxic gas dispersion hazard: coupling human behaviour and dispersion modelling. J Hazard Mater 318:758–771. Google Scholar
  173. 173.
    Mouilleau Y, Champassith A (2009) CFD simulations of atmospheric gas dispersion using the fire dynamics simulator (FDS). J Loss Prev Process Ind 22:316–323. Google Scholar
  174. 174.
    Tohidi A, Kaye NB (2017) Stochastic modeling of firebrand shower scenarios. Fire Saf J 91:91–102. Google Scholar
  175. 175.
    Tohidi A, Kaye NB (2017) Comprehensive wind tunnel experiments of lofting and downwind transport of non-combusting rod-like model firebrands during firebrand shower scenarios. Fire Saf J 90:95–111. Google Scholar
  176. 176.
    Rios O, Jahn W, Rein G (2014) Forecasting wind-driven wildfires using an inverse modelling approach. Nat Hazards Earth Syst Sci 14:1491–1503. Google Scholar
  177. 177.
    Węgrzyński W, Turkowski P (2015) Fire resistance of a roof tensile structure in parametric fire conditions calculated using CFD simulations and simplified calculation methods. In: SFPE Europe conference on fire safety engineeringGoogle Scholar
  178. 178.
    Węgrzyński W, Krajewski G (2015) ITB technical report 2752.3/15/Z00NPGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Fire Research DepartmentBuilding Research Institute (ITB)WarsawPoland
  2. 2.Faculty of Civil Engineering and ArchitectureLublin University of TechnologyLublinPoland

Personalised recommendations