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Fire Technology

, Volume 52, Issue 3, pp 847–864 | Cite as

A Mathematical Modeling of the Interaction Between Evacuees and Fire Through Radiation

  • Sungryong Bae
  • Hong Sun Ryou
Article

Abstract

We define radiation repulsive forces for representing the change of behavior pattern when the evacuees see the fire. Mathematical models of the radiation repulsive forces are applied on FDS + Evac which can simultaneously calculate the fire and evacuation simulation. Then, we analyze the characteristics of evacuation by comparing with the Helbing’s movement model and the obstruction model. As a result of analysis, when the anisotropy radiation repulsive force is applied, all evacuees change the walking direction for avoiding the fire. Moreover, after evacuees pass the fire, the evacuees moving in the central pathway are increased and the population density is decreased near the side wall. Therefore, the evacuation characteristics and the total evacuation time are the most reasonable when the anisotropy radiation repulsive force is applied. However, the additional studies for more reliable psychological factor of the evacuees are required for improving the reality of modified movement model.

Keywords

Evacuation Change of behavior pattern Psychological stress from fire Radiation repulsive force 

Notes

Acknowledgments

This research was supported by the Next Generation Fire Protection & Safety Core Technology Development Program funded by the National Emergency Management Agency (Grant Number: NEMA-NG-2014-53). In addition, we specially acknowledge to the developers of FDS + Evac who are providing source code by open access.

References

  1. 1.
    Custer RLP, Meacham B (1997) Introduction to performance based fire safety. Society of Fire Protection Engineers, BostonGoogle Scholar
  2. 2.
    Nelson H, Mowrer F (2002) Emergency movement. In: DiNenno P (ed) SFPE handbook of fire protection engineering, 3rd edn. NFPA, Quincy, pp 3/367–380Google Scholar
  3. 3.
    Klüpfel H, Meyer-König T, Wahle J, Schreckenberg M (2000) Microscopic simulation of evacuation processes on passenger ships. In: Bandini S, Worsch T (eds) Theory and practical issues on cellular automata. Springer, BerlinGoogle Scholar
  4. 4.
    Langston PA, Masling R, Asmar BN (2006) Crowd dynamics discrete element multi-circle model. Saf Sci 44:395–417CrossRefGoogle Scholar
  5. 5.
    Hamacher HW, Tjandra SA (2002) Mathematical modeling of evacuation problems: a state of the art. In: Sharma SD, Schrekenberg M (eds) Pedestrian and evacuation dynamics. Springer, HeidelbergGoogle Scholar
  6. 6.
    Chooramun N (2011) Implementing a hybrid spatial discretisation within an agent based evacuation model. University of Greenwich, LondonGoogle Scholar
  7. 7.
    Borrmann A, Kneidl A, Koster G, Ruzika S, Thiemann M (2012) Bidirectional coupling of macroscopic and microscopic pedestrian evacuation models. Saf Sci 50(8):1695–1703CrossRefGoogle Scholar
  8. 8.
    Ronchi E, Alvear D, Berloco N, Capote J, Colonna P, Cuesta A (2010) Human Behaviour in road tunnel fires: comparison between egress models (FDS + Evac, STEPS, Pathfinder). In: Proceedings of the 12th international Interflam 2010 conference, Nottingham, UK, pp 837–848Google Scholar
  9. 9.
    Tavares RM (2009) Evacuation processes versus evacuation models: Quo Vadimus? Fire Technol 45(4):419–430CrossRefGoogle Scholar
  10. 10.
    Frantzich H, Nilsson D, Eriksson O (2007) Evaluation and validation of evacuation programs. Report 3143, Department of Fire Safety Engineering and Systems Safety, Lund University, SwedenGoogle Scholar
  11. 11.
    Galea E (1998) A general approach to validating evacuation models with an application to EXODUS. J Fire Sci 16(6):414–436CrossRefGoogle Scholar
  12. 12.
    Gwynne S, Galea ER, Lawrence PJ, Owen M, Filippidis L (1999) A review of the methodologies used in the computer simulation of evacuation from the built environment. Build Environ 34:741–749CrossRefGoogle Scholar
  13. 13.
    Kuligowski ED, Peacock RD (2005) Review of building evacuation models, technical note 1471. National Institute of Standards and Technology, GaithersburgGoogle Scholar
  14. 14.
    Helbing D, Farkas I, Vicsek T (2000) Simulating dynamical features of escape panic. Nature 407(6803):487–490CrossRefGoogle Scholar
  15. 15.
    Jia H-F, Yang L-L, Tang M (2009) Pedestrian flow characteristics analysis and model parameter calibration in comprehensive transport terminal. J Transp Syst Eng Inf Technol 9(5):117–123Google Scholar
  16. 16.
    Jin T (1978) Visibility through fire smoke. J Fire Flammabil 9(April):135–155Google Scholar
  17. 17.
    Fridolf K, Andrée K, Nilsson D, Frantzich H (2014) The impact of smoke on walking speed. Fire Mater 38(7):744–759CrossRefGoogle Scholar
  18. 18.
    Purser DA (1995) Toxicity assessment of combustion products. In: Dinenno P (ed) SFPE handbook of fire protection engineering, 2nd edn. NFPA, Quincy, pp 2/85–146Google Scholar
  19. 19.
    Bae S, Ryou HS (2015) Development of a smoke effect model for representing the psychological pressure from the smoke. Saf Sci 77:57–65CrossRefGoogle Scholar
  20. 20.
    Korhonen T, Hostikka S (2009) Fire dynamic simulator: technical reference and user’s guide. VTT Technical Research Centre of Finland, Oct 2009Google Scholar
  21. 21.
    Jin T (2002) Visibility and human behavior in fire smoke. In: DiNenno P (ed) SFPE handbook of fire protection engineering, 3rd edn. NFPA, Quincy, pp 2/42–53Google Scholar
  22. 22.
    Quintiere JG (1998) Principles of fire behavior. Delmar Publishers, New YorkGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringChung-Ang UniversitySeoulKorea

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