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Methodology for DB construction of input parameters in FDS-based prediction models of smoke detector

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Abstract

The input parameters of the Heskestad and Cleary models—which are numerical models included in fire dynamics simulator (FDS)—are measured, and a sensitivity analysis is conducted on the effects of individual and common input parameters of the numerical models on the detection time. The input parameters are applied to the FDS, and the results predicted the activation time of the detector within +5 s. Compared to the individual input parameters, the obscuration per meter (OPM), which is a common input parameter, significantly affected the detection time. Finally, additional input parameters that correspond to combustion properties, such as the soot yield and mass specific extinction coefficient, are discovered to have a greater impact on the detection time than the input parameters in the detector’s numerical models. Considering various smoke detectors and combustibles, this study’s findings will contribute to the efficient use of resources to build a database of input parameters.

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References

  1. V. R. Beck, Performance-based fire engineering design and its application in Australia, Fire Safety Science, 5 (1997) 23–40.

    Article  Google Scholar 

  2. M. J. Hurley and E. R. Rosenbaum, Performance-based Fire Safety Design, 1st Ed., CRC Press, Taylor & Francis Group, Boca Raton, USA (2015) 1–21.

    Book  Google Scholar 

  3. G. V. Hadjisophocleous, N. Benichou and A. S. Tamin, Literature review of performance-based fire codes and design environment, Journal of Fire Protection Engineering, 9 (1), (1998), 12–40.

    Article  Google Scholar 

  4. D. A. Purser, ASET and RSET: addressing some issues in relation to occupant behavior and tenability, Fire Safety Science, 7 (2003) 91–102.

    Article  Google Scholar 

  5. G. Proulx, Movement of people: the evacuation timing, SFPE Handbook of Fire Protection Engineering, 3rd Ed., Maryland, USA, Section 3 (2002) 342–366.

    Google Scholar 

  6. W. Poh, Tenability criteria for design of smoke hazard management systems, Ecolibrium, 10 (7), (2011), 32–37.

    Google Scholar 

  7. K. T. Yang, Role of fire field models as a design tool for performance-based fire-code implementation, International Journal on Engineering Performance-Based Fire Codes, 1 (1), (1999), 11–17.

    Google Scholar 

  8. S. L. Poon, A dynamic approach to ASET/RSET assessment in performance based design, Procedia Engineering, 71 (2014) 173–181.

    Article  Google Scholar 

  9. Z. Yan, X. Han and M. Li, Accurate assessment of RSET for building fire based on engineering calculation and numerical simulation, MATEC Web of Conference, 04024, 61 (2016) 28–29.

    Google Scholar 

  10. Z.-X. Xing, X.-F. Zhao, H. Song and W.-L. Gao, Applied research of performance-based fire protection design in a large building, The 5th Conference on Performance-based Fire and Fire Protection Engineering, 11 (2011) 566–574.

    Google Scholar 

  11. V. Novozhilov, Computational fluid dynamics modeling of compartment fires, Progress in Energy and Combustion Science, 6 (2001) 611–666.

    Article  Google Scholar 

  12. S. Tavelli, R. Rota and M. Derudi, A critical comparison between CFD and zone models for the consequence analysis of fires in congested environments, Chemical Engineering transactions, 36 (2004) 247–252.

    Google Scholar 

  13. C. H. Hwang, A. Lock, M. Bundy, E. Johnsson and G. H. Ko, Studies on fire characteristics in over- and underventilated fullscale compartments, Journal of Fire Science, 28 (2010) 459–486.

    Article  Google Scholar 

  14. K. McGrattan, S. Hostikka, R. McDermott, J. Floyd, M. Vanella, C. Weinschenk and K. Overholt, Fire Dynamics Simulator User’s Guide, 6th Ed., NIST Special Publication 1019; National Institute of Standard and Technology, Gaithersburg, MD, USA (2017).

    Google Scholar 

  15. Office of Nuclear Regulatory Research, M. H. Salley, Electric Power Research Institute and R. Wachowiak, Nuclear Power Plant Fire Modeling Analysis Guidelines, NUREG-1934, EPRI 1023259, U.S. Nuclear Regulatory Commission (2012).

    Google Scholar 

  16. M. H. Salley, R. P. Kassawara, Electric Power Research Institute and Office of Nuclear Regulatory Research, Verification and Validation of Selected Fire Models for Nuclear Power Plant Applications: Fire Dynamics Simulator (FDS), NUREG-1824, U.S. Nuclear Regulatory Commission, 7 (2007).

  17. W. Jahn, G. Rein and J. L. Torero, The effect of model parameters on the simulation of fire dynamics, Fire Safety Science, 9 (2008) 1341–1352.

    Article  Google Scholar 

  18. A. Lock, M. Bundy, E. L. Johnsson, A. Hamins, G. H. Ko, C. H. Hwang, P. Fuss and R. Harris, Experimental Study of the Effect of Fuel Type, Fuel Distribution, and Vent Size on Full-Scale Underventilated Compartment Fires in an ISO 9705 Room, NIST Technical Note 1603, National Institute of Standard and Technology, Gaithersburg, MD, USA (2008).

    Book  Google Scholar 

  19. S. Y. Mun, C. H. Hwang and S. C. Kim, CO and soot yields of wood combustibles for a kitchen fire simulation, Fire Science and Engineering, 33 (1), (2019), 76–84

    Article  Google Scholar 

  20. V. Babrauskas and R. D. Peacock, Heat release rate: the single most important variable in fire hazard, Fire Safety Journal, 18 (3), (1992), 255–272.

    Article  Google Scholar 

  21. J. H. Cho, C. H. Hwang, J. S. Kim and S. K. Lee, Sensitivity analysis of FDS results for the input uncertainty of fire heat release rate, Journal of the Korean Society of Safety, 31 (1), (2016), 25–32.

    Article  Google Scholar 

  22. E. Brozovsky, V. Motevalli and R. P. Custer, A first approximation method for smoke detector placement based on design fire size, critical velocity, and detector aerosol entry lag time, Fire Technology, 31 (4), (1995), 336–354.

    Article  Google Scholar 

  23. V. T. D’souza, J. A. Sutula, S. M. Olenick, W. Zhang and R. J. Roby, Predicting smoke detector activation using the fire dynamics simulator, Fire Safety Science, 7 (2003) 187–195.

    Article  Google Scholar 

  24. H. Y. Jang and C. H. Hwang, Revision of the input parameters for the prediction models of smoke detectors based on the FDS, Fire Science and Engineering, 31 (2), (2017), 44–51.

    Article  Google Scholar 

  25. G. Heskestad, Generalized characteristics of smoke entry and response for products-of-combustion detectors, Proceedings, 7th International Conference on Problems of Automatic Fire Detection, Rheinish-Westfalischen Technischen Hochschule Aachen (1975) 267–310.

    Google Scholar 

  26. J. Björkman, M. A. Kokkalal and H. Ahola, Measurement of the characteristic lengths of smoke detectors, Fire Technology, 28 (1992) 99–106.

    Article  Google Scholar 

  27. T. Cleary, A. Chernovsky, W. Grosshandler and M. Anderson, Particulate entry lag in spot-type smoke detectors, Fire Safety Science-Proceedings of the 6th International Symposium, 6 (2000) 779–790.

    Article  Google Scholar 

  28. K. H. Kim and C. H. Hwang, Measurement of the device properties of a ionization smoke detector to improve predictive performance of the fire modeling, Korean Institute of Fire Science and Engineering, 27 (4), (2013), 27–34.

    Article  Google Scholar 

  29. J. H. Cho, S. Y. Mun, C. H. Hwang and D. G. Nam, Measurement of the device properties of photoelectric smoke detector for the fire modeling, Fire Science and Engineering, 28 (6), (2014), 62–68.

    Article  Google Scholar 

  30. F. W. Mowrer, Lag times associated with fire detection and suppression, Fire Technology, 26 (1990) 244–265.

    Article  Google Scholar 

  31. C. E. Marrion, Lag time modeling and effects of ceiling jet velocity on the placement of optical smoke detectors, Master’s Thesis, Worcester Polytechnic Institute, Center for Fire Safety Studies, Worcester (1989).

    Google Scholar 

  32. National Standards of the Republic of Korea, National Fire Safety Code: Fire Safety Standards for Automated Detection Facilities and Visual Alarm Devices, NFSC 203, National Fire Agency, Republic of Korea (2019) 56–62.

    Google Scholar 

  33. Y. Kametani, K. Fulkagata, R. Orlu and P. Scahlatter, Effect of uniform blowing/suction in a turbulent boundary layer at moderate reynolds number, International Journal of Heat and Fluid Flow, 55 (2015) 132–142.

    Article  Google Scholar 

  34. B. Munson, T. Okiishi, W. Huebsch and A. Rothmayer, Fundamentals of Fluid Mechanics, 7th Ed., John Wiley & Sons, Inc., Hoboken, USA (2013) 413–422.

    Google Scholar 

  35. G. W. Mulholland, E. L. Johnsson, M. G. Fernandez and D. A. Shear, Design and testing of new smoke concentration meter, Reprinted from the Fire and Materials, 24 (2000) 231–243.

    Article  Google Scholar 

  36. B. J. Kim, J. H. Cho, C. H. Hwang and S. H. Park, A study on the development of a low-cost device for measuring the optical smoke density, Journal of Korean Institute of Fire Science & Engineering, 29 (2015) 81–88.

    Article  Google Scholar 

  37. P. J. Dinenno, D. Drysdale, L. Craig, W. D. Waton, R. L. P. Custer, J. R. Hall and J. M. Watts, SFPE Handbook of Fire Protection Engineering, 3rd Ed., National Fire Protection Association Inc., Fosston, USA (2002) 560, 645.

    Google Scholar 

  38. K. McGrattan, S. Hostikka, R. McDermott, J. Floyd, M. Vanella, C. Weinschenk and K. Overholt, Fire Dynamics Simulator Technical Reference Guide, Vol. 1: Mathmetical Model, 6th Ed., NIST Special Publication 1018-1, National Institute of Standard and Technology, USA (2015).

    Google Scholar 

  39. National Standards of the Republic of Korea, Detailed Regulations for Model Approval & Inspection Technical Standards for Fire Detectors, Korea Fire Institute (KFI), National Fire Agency, Republic of Korea (2019) 23.

    Google Scholar 

  40. G. W. Mulholland and M. Y. Choi, Measurement of the mass specific extinction coefficient for acetylene and ethane smoke using the large agglomerate optics facility, Symposium (International) on Combustion, 27 (1), (1998), 1515–1522.

    Article  Google Scholar 

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Acknowledgments

This work was supported by the National Fire Agency of Korea through the field-based firefighting activity supporting technology development project (MPSS-Fire Safety-2015-66).

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Authors and Affiliations

  1. Department of Fire and Disaster Prevention, Daejeon University, 62 Daehak-ro, Dong-gu, Daejeon, 34520, Korea

    Hyo-Yeon Jang & Cheol-Hong Hwang

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  1. Hyo-Yeon Jang
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  2. Cheol-Hong Hwang
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Correspondence to Cheol-Hong Hwang.

Additional information

Recommended by Editor Yong Tae Kang

Hyo-Yeon Jang received a Master’s degree of Fire and Disaster Prevention from Daejeon University. She had worked as a researcher at Korea Fire Institute and currently works as a Post-Graduate Researcher at Daejeon University, South Korea. Her major research interests include validation of numerical models of smoke and heat detectors and construction of input parameter DB for reliable fire simulation.

Cheol-Hong Hwang received a Ph.D. degree of Mechanical Engineering from the Inha University, Korea in 2006. He currently works as an Associate Professor in the Department of Fire & Disaster Prevention at Daejeon University, South Korea. His major research interests include combustion and fire dynamics related to the understanding of fundamental phenomena and their practical applications.

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Jang, HY., Hwang, CH. Methodology for DB construction of input parameters in FDS-based prediction models of smoke detector. J Mech Sci Technol 34, 5327–5337 (2020). https://doi.org/10.1007/s12206-020-1133-0

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  • DOI: https://doi.org/10.1007/s12206-020-1133-0

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