A new correlation for gas temperature inside a burning enclosure

A new correlation for predicting enclosure gas temperature is presented in this paper based on the energy balance for adiabatic conditions, an estimate of the heat flux imposed on the enclosure boundary and the transient thermal response of the boundary. This correlation has been verified being able to predict enclosure gas temperature in both well- and under-ventilated fires in comparison with the existing experimental results. It is also compared with the well-known and widely used McCaffrey, Quintiere & Harkleroad (MQH) correlation.     

Comparison of FDS Prediction of Smoke Movement in a 10-Storey Building with Experimental Data

In this study, the Fire Dynamics Simulator (FDS), a computational fluid dynamics (CFD) model developed by National Institute of Standards and Technology (NIST) is used to simulate fire tests conducted at the National Research Council of Canada (CNRC). These tests were conducted in an experimental 10-storey tower to generate realistic smoke movement data. A full size FDS model of the tower was developed to predict smoke movement from fires that originate on the second floor. Three propane fire tests were modelled, and predictions of O₂, CO₂ concentrations and temperature on each floor are compared with the experimental data. This paper provides details of the tests, and the numerical modelling, and discusses the comparisons between the model results and the experiments. The 10-storey experimental tower was designed to simulate the centre core of high-rise buildings. It includes a compartment and corridor on each floor, a stair shaft, elevator shaft and service shafts. Three propane fire tests were conducted in 2006 and 2007 to study smoke movement through the stair shaft to the upper floors of the building. The fire was set in the compartment of the 2nd floor. Thermocouples and gas analyzers were placed on each floor to measure temperature and O₂, CO₂ and CO concentrations. Comparisons in the fire compartment and floor of fire show that the FDS model gives a good prediction of temperature and O₂ and CO₂ concentrations. In the stair shaft and upper floors there are some small differences which are due to the effect of heat transfer to the stairs that was not considered in the model. Overall the study demonstrates that FDS is capable of modelling fire development and smoke movement in a high rise building for well ventilated fires.     

The maximum temperature of buoyancy-driven smoke flow beneath the ceiling in tunnel fires

In order to detect a fire and provide adequate fire protection to a tunnel structure, the maximum gas temperature beneath the ceiling to which the structure is exposed needs to be estimated. Theoretical analysis of maximum gas temperature beneath a tunnel ceiling based on a plume theory is given. The heat release rate, longitudinal ventilation velocity and tunnel geometry are taken into account. Two series of model-scale experimental tests were also carried out. The results of both analysis and experiments show that the maximum excess gas temperature beneath the ceiling can be divided into two regions. When the dimensionless ventilation velocity is greater than 0.19, the maximum excess gas temperature beneath the tunnel ceiling increases linearly with the heat release rate and decreases linearly with the longitudinal ventilation velocity. When the dimensionless ventilation velocity is less than 0.19, the maximum excess gas temperature beneath the ceiling varies as the two-thirds power of the dimensionless heat release rate, independent of the longitudinal ventilation velocity. In both regions, the maximum excess gas temperature varies as the −5/3 power of the vertical distance between the fire source bottom and tunnel ceiling. The investigation presented here considers only the cases when the continuous flame region is lower than the ceiling height.     

Determination of the heat release rate inside operational road tunnels by comparison with CFD calculations

Usually, during a fire inside a tunnel, the average heat release rate (HRR) is estimated according to the type of vehicle. Frequently, the overall HRR is considered, however it is also necessary to know its time evolution to design real time systems, particularly ventilation, which respond to fire events or signals as fast as possible. Nowadays, there is not a well established and generally accepted procedure to know the power liberated at each instant of time inside an operational tunnel. That procedure could help in taking the correct actions to adapt the tunnel ventilation in order to diminish the effects of the fire and the smoke. This work shows a method to calculate the heat release rate using sensors that can be installed inside an operational road tunnel. Besides, the location of the fire could also be calculated accurately and quickly. To achieve the previous purposes, a stationary database that depends on HRR, its location, and the ventilation speed is calculated with CFD programs; the data are compared with temperatures measured by the sensors located inside the tunnel. The program used to generate the database is the simplified model UPMTUNNEL. The predictions of the model are compared with the results of calculations carried out using the general purpose code FLUENT, and with measurements done in a tunnel with a real fire, produced with a fuel tray.     

Statistical characterization of the time to reach peak heat release rate for nuclear power plant electrical enclosure fires

Since publication of NUREG/CR-6850 (EPRI 1011989), EPRI/NRC-RES Fire PRA Methodology for Nuclear Power Facilities in 2005, phenomenological modeling of fire growth to peak heat release rate (HRR) for electrical enclosure fires in nuclear power plant probabilistic risk assessment (PRA) has typically assumed an average 12-min rise time [1]. One previous analysis using the data from NUREG/CR-6850 from which this estimate derived indicated this could be represented by a gamma distribution with alpha (shape) and beta (scale) parameters of 8.66 and 1.31, respectively [2]. Completion of the test program by the US Nuclear Regulatory Commission (USNRC) for electrical enclosure heat release rates, documented in NUREG/CR-7197, Heat Release Rates of Electrical Enclosure Fires (HELEN-FIRE) in 2016, has provided substantially more data from which to characterize this growth time to peak HRR [3]. From these, the author develops probabilistic distributions that enhance the original NUREG/CR-6850 results for both qualified and unqualified cables.² The mean times to peak HRR are 13.3 and 10.1 min, respectively, with a mean of 12.4 min when all data are combined, confirming that the original NUREG/CR-6850 estimate of 12 min was quite reasonable.Via statistical-probabilistic analysis, the author shows that the time to peak HRR for qualified and unqualified cables can again be well represented by gamma distributions with alpha and beta parameters of 1.88 and 7.07, and 3.86 and 2.62, respectively. Working with the gamma distribution for All cables given the two cable types, the author performs simulations demonstrating that non-suppression probabilities, on average, are 30% and 10% higher than the use of a 12-min point estimate when the fire is assumed to be detected at its start and halfway between its start and the time it reaches its peak, respectively. This suggests that adopting a probabilistic approach enables more realistic modeling of this particular fire phenomenon (growth time).     

火灾试验用标准燃烧物的制备及燃烧特性

基于典型场所的火灾载荷密度及可燃物,制备了典型的塑料杯组合体和纸杯组合体标准燃烧物,开展两种标准燃烧物的燃烧特性试验研究.结果表明,两种典型的标准燃烧物的燃烧性能稳定,总热值、火灾增长速率数据偏差较小,实验的重现性良好;在一定条件下,塑料杯组合体标准燃烧物可近似代表火灾载荷约为157.8 MJ的近中速火,纸杯组合体标准燃烧物可近似代表火灾载荷约为51.1 MJ的慢速火.     

Measuring rate of heat release by oxygen consumption

This paper provides a comprehensive set of equations and guidelines to determine the rate of heat release in full-scale fire tests based on the O₂ consumption principle. The approach is different from other investigators as the enphasis is on full-scale fire test applications and the use of volumetric flow rates is avoided. Some general equations for flow rate (i.e., applicable irrespective of the configuration of the gas analysis system) are described first. In subsequent sections, distinctions are made between various gas analyzer combinations to derive the equations for rate of heat release. Procedures to calculate net rate of heat release from a specimen exposed to a gas burner or wood crib ignition source are also given. A summary at the end of the paper lists step by step procedures for all cases covered.     

Examining the feasibility of prediction models by monitoring data and management data for bioaerosols inside office buildings

Exposure to bioagents can cause several health problems, including acute allergies, infectious diseases, and myctoxicosis. Nevertheless, all conventional methods for measuring airborne bioaerosols have significant limitations such as high cost, prolonged measurement time, and discontinuous measurements.     

Developing a one-dimensional heat and mass transfer algorithm for describing the effect of green roofs on the built environment: Comparison with experimental results

This paper investigates the mathematical modelling of the effect of green roofs on mitigating raised urban temperatures. A dynamic, one-dimensional model is developed, describing heat and mass transfer in building materials, considered as capillary-porous bodies, the vegetated canopy, modelled as a combined plant–air canopy layer, the soil and the air. The model is validated with an experiment, conducted in the Welsh School of Architecture, in Cardiff, in summer 2004. The right choice of parameters that affect the accuracy of the model (such as the expression of the convective heat transfer coefficient and stomatal resistance) is discussed. Special attention is given to the comparison between the experimental results and the outputs of only heat transfer algorithms and heat and mass transfer expressions. Taking these comparisons into consideration, conclusions are drawn about developing an accurate algorithm describing the thermal effect of green roofs on the built microclimate.