An evaluation of two methods to predict temperatures in multi-room compartment fires

Johansson, Nils; Svensson, Stefan; Hees, Patrick Van

doi:10.1016/j.firesaf.2015.07.006

Cited by 14 publications

(2 citation statements)

References 21 publications

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“…Before starting the simulation, the first step was to calculate the temporary fire load [ 50 ] using Eq. (4) , taking into account the data in Table 1 :

where, p n temporary fire load (MJ/m 2 ) M i the mass of the n-th combustible material (kg) H i the calorific value of the n-th combustible material (MJ/kg) S the floor area of building (m 2 ) j the number of materials included in temporary fire load …”

Section: Calculationmentioning

confidence: 99%

Examination of effects of indoor fires on building structures and people

Kuti

Zólyomi²,

Gabriella

et al. 2023

Heliyon

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“…Before starting the simulation, the first step was to calculate the temporary fire load [ 50 ] using Eq. (4) , taking into account the data in Table 1 :

Section: Calculationmentioning

confidence: 99%

Examination of effects of indoor fires on building structures and people

Kuti

Zólyomi²,

Gabriella

et al. 2023

Heliyon

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“…1, and it is shown that the differences between modelers can be more than 50% when it comes to the calculation of temperature increase. This is much more than the model uncertainty that can be expected in various types of fire models, which can be as low as 5% to 20% [4,17] depending on model and scenario. The numbers should not be compared directly, however, they obviously imply that the uncertainty in the result is more associated with the designer than with the fire model used.…”

mentioning

confidence: 98%

Variation in Results Due to User Effects in a Simulation with FDS

Johansson

Ekholm

2017

Fire Technol

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Abstract. The results from a round-robin study in which practicing fire safety engineers simulated the same scenario are presented in this paper. The simulation task included the simulation of an 800 mm heptane pool in a three-room apartment. The participants, representing eight Swedish consultancy firms, simulated the well-specified scenario with FDS 5. The participants received information about the building, the fire mass loss rate and initial conditions. The task was performed a priori, meaning that the participants were not given any experimental or simulation results prior to performing the task. The study shows that there is a variation between the participants in how the input file was specified, the choice of input data and the types of devices used in FDS. The differences in how the fuel and the burner were described were relatively large, which resulted in large differences in mass loss rate and heat release rate. Furthermore, several of the participants made mistakes when the fire was prescribed and this resulted in a variation in the calculated parameters like the temperature increase, which was 300 K in the fire room and 50 K to 150 K in the adjacent rooms. However, the study shows that when the heat release rate and wall boundary conditions were well defined, good temperature predictions could be made.

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

Bruns

2017

Fire Technol

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An evaluation of two methods to predict temperatures in multi-room compartment fires

Cited by 14 publications

References 21 publications

Examination of effects of indoor fires on building structures and people

Examination of effects of indoor fires on building structures and people

Variation in Results Due to User Effects in a Simulation with FDS

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

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