Fire Technology

, Volume 54, Issue 1, pp 187–210 | Cite as

Estimating the Flashover Probability of Residential Fires Using Monte Carlo Simulations of the MQH Correlation

  • Morgan C. Bruns


An important indicator of fire hazard in residential fires is the occurrence of flashover in the room of fire origin. Since the variability of residential fire scenarios is large, many different cases must be considered to evaluate the hazard of a given flammable product. Efficiently predicting the occurrence of flashover of a naturally ventilated compartment is possible using the correlation of McCaffrey, Quintiere, and Harkleroad (MQH). The large variability in United States (U.S.) living room fire scenarios is characterized from available data and propagated through the MQH correlation using Monte Carlo (MC) simulation. For the parameters, for which no relevant data was found, uniform probability distributions were assumed. The scenarios sampled in the MC simulations generally fell within the range of scenarios for which the MQH correlation has been validated. Flashover probabilities were estimated for fires up to 5 MW in heat release rate (HRR) and up to 8 min in duration. It was found that fires with HRRs less than 400 kW have a flashover probability of less than 0.01% regardless of their duration. Typical furniture fires were used as example cases, and it was predicted that a three seat upholstered sofa with a peak HRR of 2.15 MW has a 90% chance of flashing over a randomly chosen U.S. living room. The results of a global sensitivity analysis indicates that the fire location parameter and the vent opening width are the most important parameters affecting the prediction of the occurrence of flashover in U.S. living rooms. The methodology presented is generalizable, and the results can be readily improved by the collection of more data and the use of higher fidelity fire models.


Hazard Probabilistic risk assessment Flashover Compartment fires Monte Carlo simulation Sensitivity analysis 


  1. 1.
    Ahrens M (2016) Home structure fires. Technical Report USS12G, National Fire Protection Association, Quincy, MassachusettsGoogle Scholar
  2. 2.
    Upadhyay RR, Ezekoye OA (2008) Treatment of design fire uncertainty using quadrature method of moments. Fire Saf J, 43:127–139CrossRefGoogle Scholar
  3. 3.
    Kong D, Johansson N, Lu S, Lo S (2012) A Monte Carlo analysis of the heat release rate uncertainty on available safe egress time. J Fire Prot Eng, 23:5–29CrossRefGoogle Scholar
  4. 4.
    Kong D, Lu S, Lo S (2011) Global sensitivity analysis of occupant egress safety model. Procedia Eng, 11:179–184Google Scholar
  5. 5.
    Kong D, Lu S, Xie Q (2011) Uncertainty and sensitivity analyses of heat fire detector model on monte carlo simulation. J Fire Sci, 29:317–337CrossRefGoogle Scholar
  6. 6.
    Bruns MC (2016) Inferring and propagating kinetic parameter uncertainty for condensed phase burning models. Fire Technol, 52(1):93–120CrossRefGoogle Scholar
  7. 7.
    Bruns MC (2016) Predicting the effects of barrier fabrics on residential upholstered furniture fire hazard. Technical Report NIST Technical Note 1920, National Institute of Standards and Technology, Gaithersburg, MarylandGoogle Scholar
  8. 8.
    McCaffrey BJ, Quintiere JG, Harkleroad MF (1981) Estimating room fire temperatures and the likelihood of flashoverusing fire test data correlations. Fire Technol, 17:98–119CrossRefGoogle Scholar
  9. 9.
    Baker G, Wade C, Spearpoint M, Fleischmann C (2013) Developing probabilisitic design fires for performance-based fire safety engineering. Procedia Eng, 62:639–647CrossRefGoogle Scholar
  10. 10.
    Peacock RD, McGrattan KB, Forney GP, Reneke PA (2016) CFAST—Consolidated model of fire growth and smoke transport (version 7) volume 1: technical reference guide. Technical Note 1889v1, National Institute of Standards and Technology, Gaithersburg, MarylandGoogle Scholar
  11. 11.
    McGrattan K, Hostikka S, McDermott R, Floyd J, Weinschenk C, Overholt K (2017) Fire dynamics simulator, user’s guide. Technical report, NISTGoogle Scholar
  12. 12.
    Sundström B (1995) Research Commission of the European Communities. Directorate-General for Science, Development, Commission of the European Communities, European Commission Measurement, and Testing Programme. CBUF: fire safety of upholstered furniture : the final report on the CBUF Research Programme. EUR (series). European Commission Measurements and TestingGoogle Scholar
  13. 13.
    International Organization for Standardization (ISO) (2017) ISO 13943, Fire safety—Vocabulary. International Organization for Standardization, Geneva, SwitzerlandGoogle Scholar
  14. 14.
    Hagglund B, Jansson R, Onnermark B (1974) Fire development in residential rooms after ignition from nuclear explosions. Technical report, Forsevarets ForskingsantaltGoogle Scholar
  15. 15.
    Thomas PH (1981) Testing products and materials for their contribution to flashover in rooms. Fire Mater, 5(3):103–111CrossRefGoogle Scholar
  16. 16.
    Walton WD, Thomas PH (2008) Estimating temperatures in compartment fires. In: Dinenno PJ, Drysdale D, Beyler CL, Walton WD (eds) SFPE handbook of fire protection engineering, 4th edn. National Fire Protection Association, QuincyGoogle Scholar
  17. 17.
    McGrattan K, Peacock R, Overholt K (2016) Validation of fire models applied to nuclear power plant safety. Fire Technol, 52:5–24CrossRefGoogle Scholar
  18. 18.
    Johansson N, Svensson S, van Hees P (2015) An evaluation of two methods to predict temperatures in multi-room compartment fires. Fire Saf J, 77:46–58CrossRefGoogle Scholar
  19. 19.
    Mowrer FW, Williamson RB (1987) Estimating room temperatures from fires along walls and in corners. Fire Technol, 23:133–145CrossRefGoogle Scholar
  20. 20.
    Sivia DS (2006) Data analysis, 2nd edn. Oxford University Press, OxfordMATHGoogle Scholar
  21. 21.
    International Code Council, Inc. (2015) International residential code. International Code Council, Inc., Country Club Hills, ILGoogle Scholar
  22. 22.
    Mehaffey JR, Cuerrier P, Carisse G (1994) A model for predicting heat transfer through gypsum-board/wood-studd walls exposed to fire. Fire Mater, 18:297–305CrossRefGoogle Scholar
  23. 23.
    Sultan MA (1996) A model for predicting heat transfer through noninsulated unloaded steel-stud gypsum board wall assemblies exposed to fire. Fire Technol, 32:239–259CrossRefGoogle Scholar
  24. 24.
    Wakili K Ghazi, Hugi E, Wullschleger L, Frank T (2007) Gypsum board in fire—modeling and experimental validation. J Fire Sci, 25:267–282CrossRefGoogle Scholar
  25. 25.
    Manzello SL, Park SH, Mizukami T, Bentz DP (2008) Measurement of thermal properties of gypsum board at elevated temperatures. In: Fifth international converence on structures in fire, Singapore, pp 656–665Google Scholar
  26. 26.
    Wakili K Ghazi, Hugi E (2009) Four types of gypsum plaster boards and their thermophysical properties under fire condition. J Fire Sci, 27:27–43CrossRefGoogle Scholar
  27. 27.
    U.S. Census Bureau (2013) Suitland, Maryland. American Housing Survey for the United States: 2013Google Scholar
  28. 28.
    Emrath P (2013) Spaces in new homes. Technical report, National Association of Home Builders (NAHB)Google Scholar
  29. 29.
    U.S. Energy Information Administration (2009) Washington, District of Columbia. Residential energy consumption surveyGoogle Scholar
  30. 30.
    Saltelli A, Annoni P (2010) How to avoid a perfunctory sensitivity analysis. Environ Model Softw, 25:1508–1517CrossRefGoogle Scholar
  31. 31.
    Sobol IM (2001) Global sensitivity indices for nonlinear mathematical models and their monte carlo estimates. Math Comput Simul, 55:271–280MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC (outside the USA) 2017

Authors and Affiliations

  1. 1.National Institute of Standards and TechnologyGaithersburgUSA

Personalised recommendations