Design Fire Characteristics for Probabilistic Assessments of Dwellings in England

Abstract

In England, there are no fixed requirements on the parameters adopted when considering residential design fires, and analyses undertaken are often deterministic with limited consideration given to probabilistic assessments and the sensitivity of parameters. The Home Office dwelling fires dataset has been analysed, considering the fire damage area and the time from ignition to fire and rescue service arrival. From this, lognormal distributions for the maximum heat release rate (HRR) and fire growth rate of residential fires have been approximated. The mean maximum HRR ranges from 900 kW to 1900 kW, with a standard deviation ranging from 2000 kW to 3700 kW, depending on property type and room of fire origin. The mean growth rate, assuming a t 2 relationship, ranges from 0.0022 kW/s 2 to 0.0034 kW/s 2 , with a standard deviation ranging from 0.0071 kW/s 2 to 0.0132 kW/s 2. When considering incidents which result in immediate fire and rescue service call out following ignition, the mean growth rate increases to a range of 0.0058 kW/s 2 to 0.0088 kW/s 2. As a result of the analyses, design fire distributions are provided which can be adopted for probabilistic assessments. For deterministic analyses, it is proposed that an approximate 95th percentile fire may be adopted, aligning with a medium growth rate of 0.0117 kW/s 2 and a maximum fuel-limited HRR in the region of 3800 kW to 4400 kW, depending on whether the dwelling is a house or an apartment. A 95th percentile design fire broadly aligns with values already specified in guidance, helping to substantiate the existing recommendations.

Household cooking oil use and its bearing on fire safety

“…The SFPE Handbook of Fire Protection Engineering 48 suggests that where a severe quantity of fuel is expected, an 80th to 95th percentile should be used. Previous work 49 on design fires for dwellings adopted a 95th percentile and so this is investigated herein. Data from the online survey gives the weighted average number of times when respondents shallow or deep fried (excluding using a separate deep fat frying appliance) as 9.4 days per month.…”

Fire safety design of open plan apartments in England

“…The HRR was estimated to be normally distributed, with a mean of 4.6 MW and a standard deviation of 0.07 MW. In their assessment of design fire characteristics for probabilistic assessments of dwellings, calculated from incident data for fires in England, Hopkin et al (2019b) determined an overall lognormal distribution for the maximum HRR of apartments, with a mean of 1 MW and a standard deviation of 2.3 MW.…”

“…This distribution is also represented in Figure 7, alongside the NF 19 data, where comparatively NF 19 appears to have underestimated the maximum HRR. (adapted from Fraser-Mitchell & Williams [2009b] and Hopkin et al [2019b]).…”

“…To accommodate the uncertainty associated with the potential fuel load in an apartment, a simplified alternative to the method adopted in NF 19 is to use distributions for fire growth rate and maximum HRR, and consider the interaction of the fire with the environment in terms of whether it becomes fuel-bed or ventilation controlled. Baker et al (2013), Holborn et al (2004), Nilsson et al (2014) Deguchi et al (2011 and Hopkin et al (2019b) have all provided examples of probabilistic distributions for design fire growth rates, mostly derived from the analysis of fire incident and fire investigation data. They each estimated that the growth rate could be represented by a lognormal distribution, with mean growth rates ranging from 0.006 kW/s 2 to 0.052 kW/s 2 depending on the study.…”

Uncertainty-based decision-making in fire safety: Analyzing the alternatives

“…The disadvantage for the use of such results is that their communication can mislead stakeholders in believing that the worst possible conditions are covered, which might not be the case depending on the engineers' trustworthiness on their assumptions such as the pessimistic parameters of P2. As additional data becomes available such as that presented by Hopkin et al [120], the possibilities for incorporating different conditions further enable the use of Pboxes.…”