Fire dynamics simulator (FDS) pyrolysis model analysis of heavy goods vehicle fires in road tunnels

Heavy goods vehicle (HGV) fires cause more serious fire safety problems than other vehicle fires in road tunnels due to the large fire size. There is a desire to use a cost-effective computational fluid dynamics (CFD) modelling method to study tunnel fires, such as fire dynamics simulator (FDS). The pyrolysis model in FDS can predict HRR based on fuel properties and environmental conditions. Therefore, the FDS pyrolysis model is adopted in this research to simulate a large-scale tunnel simulated HGV cargo experiment, which was carried out on behalf of the Land Transport Authority (LTA), Singapore. There are three major objectives in this research: to understand fuel properties for the application of the pyrolysis model; to understand influence of forced ventilation on the heat release rate (HRRL of tunnel fires; and to assess the predictive capability of the pyrolysis model in FDS to simulate tunnel fires. The ultimate objective is to assess the ability of the FDS pyrolysis model to predict the HRR in the small-scale and large-scale tunnel experiments. In the simulations, the decomposition reactions are described. The ventilation influences on burning efficiency are accounted for through heat of combustion. Unfortunately, FDS considerably under predicts the HRR and fire growth behaviour for both experiments. These results suggest that the FDS pyrolysis model is unable to predict fire burning behaviour for complex fuels with sufficient accuracy to be used in practical tunnel design. Overall, this research reveals an effective hand calculation method to derive kinetic properties; a manual optimisation process to determine thermal properties; a mathematical model to describe forced ventilation influence on fire size and to further estimate peak HRR for tunnel crib fires.

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Filing Info

  • Accession Number: 01660776
  • Record Type: Publication
  • Source Agency: ARRB
  • Files: ITRD, ATRI
  • Created Date: Feb 20 2018 11:06AM