Multi-scale analysis of the under-ventilated combustion regime for the case of a fire event in a confined and mechanically ventilated compartment

Prétrel, Hugues; Lafdal, Bouaza; Suard, Sylvain

doi:10.1016/j.firesaf.2020.103069

Cited by 13 publications

(8 citation statements)

References 18 publications

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“…On the other hand, the predictions at ∆m f = 0.2 kg were about 15% lower than the experiments for Test#7 and #9 in Table 4. The main reason would be expected to take place from the combustion efficiency in accordance with the opening area [20]. However, it is confirmed that the total of 27 experiments and the predicted values were in good agreement within ±5%.…”

Section: Analysis Of the Maximum Heat Release Rate Q Maxsupporting

confidence: 56%

“…In accordance with the previous research, it can be predicted that FIGRA would not be equal for the same material due to the thermal conditions. However, to the authors' knowledge, there have been no research to predict FIGRA since the values of Q max and t max cannot be closed with previous investigations [20][21][22][23][24]. Especially, if the value of FIGRA for a highly combustible material is evaluated too low at a specified test condition, it can cause a risk for the material to be used for building construction.…”

Section: Introductionmentioning

confidence: 97%

“…There are several studies that the fire-retardant performance test result is changed by the test conditions in the use of the same pipe insulation material. The temperature inside the compartment increases in proportion to the thermal ratio, as resulted in the study of H. Pretrel et al, analyzed by the correlation under the ventilated pool fire phenomenon by the thermal ratio of dimensionless variables including thermal conductivity coefficient, the thickness of the wall, and the opening area of the compartment [20].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis on the Fire Growth Rate Index Considering of Scale Factor, Volume Fraction, and Ignition Heat Source for Polyethylene Foam Pipe Insulation

Park

Lim²,

You

2020

Energies

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The fire growth rate index (FIGRA), which is the ratio of the maximum value of the heat release rate (Qmax) and the time (tmax) to reach the maximum heat release rate, is a general method to evaluate a material in the fire-retardant performance in fire technology. The object of this study aims to predict FIGRA of the polyethylene foam pipe insulation in accordance with the scale factor (Sf), the volume fraction of the pipe insulation (VF) and the ignition heat source (Qig). The compartments made of fireboard have been mock-up with 1/3, 1/4, and 1/5 reduced scales of the compartment as specified in ISO 20632. The heat release rate data of the pipe insulation with the variation of Sf, VF, and Qig are measured from 33 experiments to correlate with FIGRA. Based on a critical analysis of the heat transfer phenomenon from previous research literature, the predictions of Qmax and tmax are presented. It is noticeable that the fire-retardant grade of the polyethylene foam pipe insulation could have Grade B, C, and D in accordance with the test conditions within ±15% deviation of the predicted FIGRA. In case of establishing the database of various types of insulation, the prediction models could apply to evaluate the fire-retardant performance.

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Section: Analysis Of the Maximum Heat Release Rate Q Maxsupporting

confidence: 56%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis on the Fire Growth Rate Index Considering of Scale Factor, Volume Fraction, and Ignition Heat Source for Polyethylene Foam Pipe Insulation

Park

Lim²,

You

2020

Energies

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“…During a fire event, the airflow varies depending on the condition of the ventilation, which and affect the time series of the fire signatures measured by IoT sensors [22]. Temperature measurements can also be influenced by ventilation when internal and external temperatures differ significantly [33].…”

Section: A Modeling and Simulation Methodologymentioning

confidence: 99%

“…Fire signatures, such as temperature, smoke, and gas, are greatly affected by airflow, which depends on the structure and state of buildings [21]. In the event of a fire, the operation of a building's heating, ventilation, & air conditioning (HVAC) system or whether a door is open affects the inflow of oxygen and supports the combustion of materials [22]. In addition, the time series of fire signatures received by a sensor exhibit different trends due to the changes in airflow according to the structure or condition of the buildings.…”

Section: Trustworthy Building Fire Detection Frameworkmentioning

confidence: 99%

Trustworthy Building Fire Detection Framework With Simulation-Based Learning

Kim

Lee

et al. 2021

IEEE Access

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With the difficulty of collecting desirable training data due to the heterogeneities of IoT sensors in various buildings and the scarcity of fire events, it is time consuming and expensive to apply data-driven deep learning approaches to fire detection systems in specific building environments. Simulation-based learning has been actively researched to mitigate data scarcity problems by reproducing potential fire events. Since simulation-based learning mainly depends on synthetic training data, trained deep learning models may generate erroneous predictions in real-world scenarios that are unlike any of the training samples. In this paper, we propose a trustworthy building fire detection framework based on a multioutput encoder-decoder network, named MEDNet, which is designed for the practical usage of simulation-based learning in building fire detection. The fundamental steps of our approach are (1) modeling and simulating fire events to create realistic synthetic data that reflect data from actual buildings, (2) predicting a fire event and dissimilarities between real input data and synthetic training data based on the trained MEDNet model, and (3) operating a switching mechanism to use a knowledge-based method that does not depend on synthetic training data when dissimilarities exist. Finally, we perform simulation experiments based on a real building compartment where the proposed framework is compared with conventional time-series classification networks on various evaluation datasets. The proposed framework is trustworthy in practical usage because MEDNet with a switching mechanism achieves a 36.65% higher F1-score than conventional time-series classification networks and generates false-positive predictions lower than 0.02% even in unpredictable scenarios.INDEX TERMS Simulation-based learning, deep learning, modeling and simulation, encoder-decoder network, supervised learning-based rare event detection, building fire detection.

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Fire Safety Design of Confined Building Environment with Mechanical Ventilation

Chen

2024

Digital Innovations in Architecture, Engineering and Construction

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Multi-scale analysis of the under-ventilated combustion regime for the case of a fire event in a confined and mechanically ventilated compartment

Cited by 13 publications

References 18 publications

Analysis on the Fire Growth Rate Index Considering of Scale Factor, Volume Fraction, and Ignition Heat Source for Polyethylene Foam Pipe Insulation

Analysis on the Fire Growth Rate Index Considering of Scale Factor, Volume Fraction, and Ignition Heat Source for Polyethylene Foam Pipe Insulation

Trustworthy Building Fire Detection Framework With Simulation-Based Learning

Fire Safety Design of Confined Building Environment with Mechanical Ventilation

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