Pub Date : 2025-12-01Epub Date: 2025-10-08DOI: 10.1016/j.firesaf.2025.104549
Fernando Ebensperger, Raphael Ogabi, Albert Simeoni
Firebrands play a dominant role in wildland fire propagation, especially in spotting, where embers are carried away from the main fire to ignite new areas. Three mechanisms govern their dynamics: generation, transport, and landing. While transport and landing have received more attention in the literature, generation remains poorly understood due to challenges in real-time quantification and detection. This shortcoming undermines fire spread models that require accurate estimation of particle properties and numbers. To address this, a UNet-based convolutional neural network (CNN) image processing method was developed to detect and segment firebrands from images and video. The model demonstrated over 80 % accuracy with limited training data, suggesting potential for field use with low-cost, heterogeneous imaging systems. Experiments were conducted in large wind tunnel (1.5m 2.1m 6 m) at uniform wind speeds up to 6.2 m/s and test velocities between 0.5 to 1.5 m/s. Douglas Fir samples were burned, and mass loss was measured using balances. Fuel positioning affected firebrand production: free samples (1.5 kg) exhibited multiple intense burning stages due to deformation, whereas restrained samples (0.76 kg) burned more uniformly. The proposed CNN approach offers a promising tool for supporting firebrand detection.
{"title":"Methodology for detecting firebrand generation and the analysis of influential variables in quantification","authors":"Fernando Ebensperger, Raphael Ogabi, Albert Simeoni","doi":"10.1016/j.firesaf.2025.104549","DOIUrl":"10.1016/j.firesaf.2025.104549","url":null,"abstract":"<div><div>Firebrands play a dominant role in wildland fire propagation, especially in spotting, where embers are carried away from the main fire to ignite new areas. Three mechanisms govern their dynamics: generation, transport, and landing. While transport and landing have received more attention in the literature, generation remains poorly understood due to challenges in real-time quantification and detection. This shortcoming undermines fire spread models that require accurate estimation of particle properties and numbers. To address this, a UNet-based convolutional neural network (CNN) image processing method was developed to detect and segment firebrands from images and video. The model demonstrated over 80 % accuracy with limited training data, suggesting potential for field use with low-cost, heterogeneous imaging systems. Experiments were conducted in large wind tunnel (1.5m <span><math><mo>×</mo></math></span> 2.1m <span><math><mo>×</mo></math></span> 6 m) at uniform wind speeds up to 6.2 m/s and test velocities between 0.5 to 1.5 m/s. Douglas Fir samples were burned, and mass loss was measured using balances. Fuel positioning affected firebrand production: free samples (1.5 kg) exhibited multiple intense burning stages due to deformation, whereas restrained samples (0.76 kg) burned more uniformly. The proposed CNN approach offers a promising tool for supporting firebrand detection.</div></div>","PeriodicalId":50445,"journal":{"name":"Fire Safety Journal","volume":"158 ","pages":"Article 104549"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-09-06DOI: 10.1016/j.firesaf.2025.104523
Nigel B. Kaye , Khalid Moinuddin , Rahul Wadhwani
Spot-fire generation from embers blown ahead of a wildfire front is one of the leading causes of home destruction in wildland-urban interface (WUI) fires. It is, therefore, important to be able to model wind-driven ember flight accurately. This study presents the application of a stochastic debris flight model to this problem. The model embeds the uncertainty in flight conditions into the model by randomly perturbing the flight parameters (drag and lift forces) at each numerical integration time step. The stochastic flight model replicates the results of a series of ember flight tests run using the Victoria University ember dragon for both cubic and cylindrical model embers. Results show that the stochastic model produces very good predictions of the mean landing location of the embers tested. The model also provides reasonable estimates of the standard deviation and skewness of the landing location distribution in the direction of the initial launch for the cubic embers. The agreement with higher moment statistics is poorer for the cylindrical embers, though there is qualitative consistency between the experimental and model spatial distributions.
{"title":"Stochastic modeling of the flight of embers ejected from an ember dragon","authors":"Nigel B. Kaye , Khalid Moinuddin , Rahul Wadhwani","doi":"10.1016/j.firesaf.2025.104523","DOIUrl":"10.1016/j.firesaf.2025.104523","url":null,"abstract":"<div><div>Spot-fire generation from embers blown ahead of a wildfire front is one of the leading causes of home destruction in wildland-urban interface (WUI) fires. It is, therefore, important to be able to model wind-driven ember flight accurately. This study presents the application of a stochastic debris flight model to this problem. The model embeds the uncertainty in flight conditions into the model by randomly perturbing the flight parameters (drag and lift forces) at each numerical integration time step. The stochastic flight model replicates the results of a series of ember flight tests run using the Victoria University ember dragon for both cubic and cylindrical model embers. Results show that the stochastic model produces very good predictions of the mean landing location of the embers tested. The model also provides reasonable estimates of the standard deviation and skewness of the landing location distribution in the direction of the initial launch for the cubic embers. The agreement with higher moment statistics is poorer for the cylindrical embers, though there is qualitative consistency between the experimental and model spatial distributions.</div></div>","PeriodicalId":50445,"journal":{"name":"Fire Safety Journal","volume":"158 ","pages":"Article 104523"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-08-25DOI: 10.1016/j.firesaf.2025.104514
Rwayda Kh.S. Al-Hamd , Asad S. Albostami , Holly Warren
The bond between steel and concrete in reinforced concrete (RC) and fibre-reinforced concrete (FRC) structures is a multifaceted and intricate phenomenon. It refers to the adhesion and mechanical interlock between the steel reinforcement bars and the surrounding concrete matrix. The bond becomes more complex at elevated temperatures; however, having an accurate estimate is a crucial factor in design. Therefore, this paper employs advanced machine learning (ML) techniques to predict bond strength (Tb) at both ambient and elevated temperatures from a 394-point experimental database, which includes additional variables such as fibre content, geometric ratios, and thermal parameter conditions. Seven models were built and assessed, including Linear Regression (LR), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), k-nearest Neighbours (KNN), Decision Tree (DT), and Deep Learning (DLearning) Regressors. The GB, XGBoost, and DT models offered the best prediction results with R² above 0.95 for the testing datasets, lowest error metrics (mean absolute error (MAE) between 0.8 and 1.1 MPa), and highest reliability (a30%-index ≥ 90%), all outperforming those reported in earlier literature. According to SHapley Additive exPlanations (SHAP) analysis, the length-to-diameter ratio () and failure surface temperature () dominated as the predictors, followed by concrete compressive strength (), and cover-to-diameter ratio (), which is according to the existing mechanics of bond and thermal degradation. This study presents resolutions regarding the promise of data-driven models to accurately, reliably, and interpretably predict bond strength in post-fire conditions, which is of great merit in terms of resilient design practice. Future work may investigate hybrid ML–mechanistic frameworks and the integration of full-scale fire testing to further enhance engineering applicability.
{"title":"Data-driven and explainable AI models for evaluating bond strength in reinforced concrete at elevated temperatures","authors":"Rwayda Kh.S. Al-Hamd , Asad S. Albostami , Holly Warren","doi":"10.1016/j.firesaf.2025.104514","DOIUrl":"10.1016/j.firesaf.2025.104514","url":null,"abstract":"<div><div>The bond between steel and concrete in reinforced concrete (RC) and fibre-reinforced concrete (FRC) structures is a multifaceted and intricate phenomenon. It refers to the adhesion and mechanical interlock between the steel reinforcement bars and the surrounding concrete matrix. The bond becomes more complex at elevated temperatures; however, having an accurate estimate is a crucial factor in design. Therefore, this paper employs advanced machine learning (ML) techniques to predict bond strength (<em>T<sub>b</sub></em>) at both ambient and elevated temperatures from a 394-point experimental database, which includes additional variables such as fibre content, geometric ratios, and thermal parameter conditions. Seven models were built and assessed, including Linear Regression (LR), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), <em>k</em>-nearest Neighbours (KNN), Decision Tree (DT), and Deep Learning (DLearning) Regressors. The GB, XGBoost, and DT models offered the best prediction results with R² above 0.95 for the testing datasets, lowest error metrics (mean absolute error (MAE) between 0.8 and 1.1 MPa), and highest reliability (a30%-index ≥ 90%), all outperforming those reported in earlier literature. According to SHapley Additive exPlanations (SHAP) analysis, the length-to-diameter ratio (<span><math><mrow><mfrac><mi>l</mi><mi>d</mi></mfrac></mrow></math></span>) and failure surface temperature (<span><math><mrow><mi>T</mi></mrow></math></span>) dominated as the predictors, followed by concrete compressive strength (<span><math><mrow><msub><mi>f</mi><mi>c</mi></msub></mrow></math></span>), and cover-to-diameter ratio (<span><math><mrow><mfrac><mi>c</mi><mi>d</mi></mfrac></mrow></math></span>), which is according to the existing mechanics of bond and thermal degradation. This study presents resolutions regarding the promise of data-driven models to accurately, reliably, and interpretably predict bond strength in post-fire conditions, which is of great merit in terms of resilient design practice. Future work may investigate hybrid ML–mechanistic frameworks and the integration of full-scale fire testing to further enhance engineering applicability.</div></div>","PeriodicalId":50445,"journal":{"name":"Fire Safety Journal","volume":"158 ","pages":"Article 104514"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-09-12DOI: 10.1016/j.firesaf.2025.104539
Laura Schmidt, Rory M. Hadden
Smouldering combustion of timber presents a significant fire safety concern, particularly in scenarios where heat retention enables sustained char oxidation. This study isolates char oxidation from other smouldering processes to investigate its onset and sustained reaction under close-to-critical incident heat fluxes. Experiments using pre-pyrolysed char samples provided direct measurements of CO and CO2 generation, mass loss, and temperature evolution during char oxidation. The onset of char oxidation was characterised by a rapid increase in CO generation rate, occurring consistently at an external heat flux of 10 kW/m2. Among the methods tested, CO mass flow rates proved to be the most reliable indicator of char oxidation onset, offering greater precision than traditional mass loss measurements or temperature data in determining the char oxidation onset time. Once initiated, oxidation led to sustained heat release, with in-depth temperatures exceeding 400 °C, peak heat release rates of ∼29 kW/m2 and a mean effective heat of combustion of ∼30.3 kJ/g, close to the char's gross heat of combustion, at 10 kW/m2. These findings improve the understanding of char oxidation kinetics and support the development of predictive models for smouldering in engineered timber, informing fire hazard assessment and mitigation strategies.
{"title":"Conditions for onset and sustained char oxidation","authors":"Laura Schmidt, Rory M. Hadden","doi":"10.1016/j.firesaf.2025.104539","DOIUrl":"10.1016/j.firesaf.2025.104539","url":null,"abstract":"<div><div>Smouldering combustion of timber presents a significant fire safety concern, particularly in scenarios where heat retention enables sustained char oxidation. This study isolates char oxidation from other smouldering processes to investigate its onset and sustained reaction under close-to-critical incident heat fluxes. Experiments using pre-pyrolysed char samples provided direct measurements of CO and CO<sub>2</sub> generation, mass loss, and temperature evolution during char oxidation. The onset of char oxidation was characterised by a rapid increase in CO generation rate, occurring consistently at an external heat flux of 10 kW/m<sup>2</sup>. Among the methods tested, CO mass flow rates proved to be the most reliable indicator of char oxidation onset, offering greater precision than traditional mass loss measurements or temperature data in determining the char oxidation onset time. Once initiated, oxidation led to sustained heat release, with in-depth temperatures exceeding 400 °C, peak heat release rates of ∼29 kW/m<sup>2</sup> and a mean effective heat of combustion of ∼30.3 kJ/g, close to the char's gross heat of combustion, at 10 kW/m<sup>2</sup>. These findings improve the understanding of char oxidation kinetics and support the development of predictive models for smouldering in engineered timber, informing fire hazard assessment and mitigation strategies.</div></div>","PeriodicalId":50445,"journal":{"name":"Fire Safety Journal","volume":"158 ","pages":"Article 104539"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-09-19DOI: 10.1016/j.firesaf.2025.104540
F. Di Giorgio, C. Galizzi, M. Kühni
A characterization study was conducted on a novel experimental setup designed to investigate the spread of façade fires. This setup consists of a wall divided into an effusion zone, where methane injection simulates the pyrolysis process, and a large inert zone where the flame propagates. Various flow rates were applied to the effusion module and analyzed through direct visualizations, CH* and OH* chemiluminescence imaging, as well as temperature and heat flux. The results highlight and confirm the well-established influence of fuel injection rates on flame behavior and propagation. This standardized configuration serves as a benchmark for comparisons with more complex scenarios involving different arrangements of effusion and inert zones. Moreover, the data generated in this study provide a valuable basis for evaluating the reliability of fire engineering models and codes.
{"title":"Experimental study of fire propagation along a vertical wall in a lab scale setup","authors":"F. Di Giorgio, C. Galizzi, M. Kühni","doi":"10.1016/j.firesaf.2025.104540","DOIUrl":"10.1016/j.firesaf.2025.104540","url":null,"abstract":"<div><div>A characterization study was conducted on a novel experimental setup designed to investigate the spread of façade fires. This setup consists of a wall divided into an effusion zone, where methane injection simulates the pyrolysis process, and a large inert zone where the flame propagates. Various flow rates were applied to the effusion module and analyzed through direct visualizations, CH* and OH* chemiluminescence imaging, as well as temperature and heat flux. The results highlight and confirm the well-established influence of fuel injection rates on flame behavior and propagation. This standardized configuration serves as a benchmark for comparisons with more complex scenarios involving different arrangements of effusion and inert zones. Moreover, the data generated in this study provide a valuable basis for evaluating the reliability of fire engineering models and codes.</div></div>","PeriodicalId":50445,"journal":{"name":"Fire Safety Journal","volume":"158 ","pages":"Article 104540"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-08DOI: 10.1016/j.firesaf.2025.104552
Aobo Liu , Alexandros G. Venetsanos , Michael A. Delichatsios , Yiannis A. Levendis
Liquid nitrogen (LN2), a widely used cryogen, is proposed as an effective and environmentally benign fire suppression agent when tackling challenging fires, such as liquid hydrocarbon pool fires. Laboratory-scale experiments using liquid nitrogen were conducted to extinguish small alcohol pool fires (D = 20 cm). The axial temperature profile over the fuel surface and the mass history of the pool during the fire extinction were recorded and analyzed. The minimum quantity of LN2 required for fire extinction was experimentally determined. A physics-based numerical model was developed using the ADREA-HF CFD code to simulate interactions between the cryogen, the flame envelope and the alcohol pool. Axial flame temperatures, oxygen concentrations and liquid nitrogen mass fractions were predicted numerically before and after the application of the cryogen. Modelling predictions for the liquid jet touchdown time, the amount of liquid mass reaching the ground and the ensuing fire extinction timeframes were in line with experimental observations. The purpose of this model is to predict the minimum amount of this cryogen required for effective pool fire suppression. Such a tool may be used to optimize the application of LN2 for extinction of accidental fires of fuel spilled or spread on the ground.
{"title":"Experimental and numerical investigations on the use of liquid nitrogen streams to suppress alcohol pool fires","authors":"Aobo Liu , Alexandros G. Venetsanos , Michael A. Delichatsios , Yiannis A. Levendis","doi":"10.1016/j.firesaf.2025.104552","DOIUrl":"10.1016/j.firesaf.2025.104552","url":null,"abstract":"<div><div>Liquid nitrogen (LN<sub>2</sub>), a widely used cryogen, is proposed as an effective and environmentally benign fire suppression agent when tackling challenging fires, such as liquid hydrocarbon pool fires. Laboratory-scale experiments using liquid nitrogen were conducted to extinguish small alcohol pool fires (D = 20 cm). The axial temperature profile over the fuel surface and the mass history of the pool during the fire extinction were recorded and analyzed. The minimum quantity of LN<sub>2</sub> required for fire extinction was experimentally determined. A physics-based numerical model was developed using the ADREA-HF CFD code to simulate interactions between the cryogen, the flame envelope and the alcohol pool. Axial flame temperatures, oxygen concentrations and liquid nitrogen mass fractions were predicted numerically before and after the application of the cryogen. Modelling predictions for the liquid jet touchdown time, the amount of liquid mass reaching the ground and the ensuing fire extinction timeframes were in line with experimental observations. The purpose of this model is to predict the minimum amount of this cryogen required for effective pool fire suppression. Such a tool may be used to optimize the application of LN<sub>2</sub> for extinction of accidental fires of fuel spilled or spread on the ground.</div></div>","PeriodicalId":50445,"journal":{"name":"Fire Safety Journal","volume":"158 ","pages":"Article 104552"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-09-01DOI: 10.1016/j.firesaf.2025.104520
Hyewon Kim , Jun-ichi Yamaguchi , Hyun-woo Park , Yoshifumi Ohmiya
Currently, in two-zone models, smoke flow is calculated based on the assumption that the fire plume, which develops directly above the fire source, depends solely on the amount of entrained surrounding air. However, in large flat spaces, which have been growing larger in recent years, the horizontal travel distance of ceiling jet flow is long, and it is possible that the amount of smoke due to entrainment of air during the horizontal spread process is underestimated. In this study, we performed experiments that reproduced an unconfined ceiling without a vertical wall soffit and determined the amount of entrainment in the ceiling jet flow by analyzing the gas concentration of ceiling jet flow at various flow distances. Next, we formulated the ceiling jet flow rate by expressing this in terms of dimensionless flow rate and dimensionless flow distance. Furthermore, we derived a simple prediction equation for ceiling jet flow arrival time based on the model equation. Finally, we validated the proposed equation and range of applicability through comparison with the results of several experiments.
{"title":"Simple prediction equation for ceiling jet flow arrival time in space without vertical wall soffit","authors":"Hyewon Kim , Jun-ichi Yamaguchi , Hyun-woo Park , Yoshifumi Ohmiya","doi":"10.1016/j.firesaf.2025.104520","DOIUrl":"10.1016/j.firesaf.2025.104520","url":null,"abstract":"<div><div>Currently, in two-zone models, smoke flow is calculated based on the assumption that the fire plume, which develops directly above the fire source, depends solely on the amount of entrained surrounding air. However, in large flat spaces, which have been growing larger in recent years, the horizontal travel distance of ceiling jet flow is long, and it is possible that the amount of smoke due to entrainment of air during the horizontal spread process is underestimated. In this study, we performed experiments that reproduced an unconfined ceiling without a vertical wall soffit and determined the amount of entrainment in the ceiling jet flow by analyzing the gas concentration of ceiling jet flow at various flow distances. Next, we formulated the ceiling jet flow rate by expressing this in terms of dimensionless flow rate and dimensionless flow distance. Furthermore, we derived a simple prediction equation for ceiling jet flow arrival time based on the model equation. Finally, we validated the proposed equation and range of applicability through comparison with the results of several experiments.</div></div>","PeriodicalId":50445,"journal":{"name":"Fire Safety Journal","volume":"158 ","pages":"Article 104520"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-08DOI: 10.1016/j.firesaf.2025.104534
Rayna Vreeland , Kyle L. Fetter , Nicolas S.B. Jaeger , Yi Yan , Xiuqi Xi , James L. Urban , Daniel I. Pineda , R. Mitchell Spearrin
The production of incomplete combustion products from the burning of wood, medium density fiberboard (MDF), and nylon in an under-ventilated compartment fire was investigated using a reduced-scale compartment. Species measurements of carbon monoxide (CO) and carbon dioxide (CO2) were performed using Fourier Transform Infrared Spectroscopy (FTIR) and methane (CH4), hydrogen cyanide (HCN), benzene (C6H6), ethylene (C2H4) and acetylene (C2H2) were measured with Laser Absorption Spectroscopy (LAS) with three different interband cascade lasers. The fuels were burned in three different crib configurations; only wood, only MDF, and a mixture of wood and nylon, to examine the production of different toxicants. During the experiments, measurements were collected of CO, CO2, CH4, HCN, C2H2, and C6H6 species from the gas exiting the compartment, gas temperature from inside the compartment, and the flow into and out of the compartment. Consistent with under-ventilated combustion, the temperature inside the compartment typically exceeded 600 °C. CO was measured during all experiments and was two orders of magnitude less than the measured CO2 concentration. Significant amounts of unburned hydrocarbons were measured during all of the experiments, while HCN was only detected during the wood-nylon tests. Higher toxicant yields were measured for wood-nylon compared to pure wood and MDF.
{"title":"Toxicant production in under-ventilated compartment fires assessed by laser absorption spectroscopy","authors":"Rayna Vreeland , Kyle L. Fetter , Nicolas S.B. Jaeger , Yi Yan , Xiuqi Xi , James L. Urban , Daniel I. Pineda , R. Mitchell Spearrin","doi":"10.1016/j.firesaf.2025.104534","DOIUrl":"10.1016/j.firesaf.2025.104534","url":null,"abstract":"<div><div>The production of incomplete combustion products from the burning of wood, medium density fiberboard (MDF), and nylon in an under-ventilated compartment fire was investigated using a reduced-scale compartment. Species measurements of carbon monoxide (CO) and carbon dioxide (CO<sub>2</sub>) were performed using Fourier Transform Infrared Spectroscopy (FTIR) and methane (CH<sub>4</sub>), hydrogen cyanide (HCN), benzene (C<sub>6</sub>H<sub>6</sub>), ethylene (C<sub>2</sub>H<sub>4</sub>) and acetylene (C<sub>2</sub>H<sub>2</sub>) were measured with Laser Absorption Spectroscopy (LAS) with three different interband cascade lasers. The fuels were burned in three different crib configurations; only wood, only MDF, and a mixture of wood and nylon, to examine the production of different toxicants. During the experiments, measurements were collected of CO, CO<sub>2</sub>, CH<sub>4</sub>, HCN, C<sub>2</sub>H<sub>2</sub>, and C<sub>6</sub>H<sub>6</sub> species from the gas exiting the compartment, gas temperature from inside the compartment, and the flow into and out of the compartment. Consistent with under-ventilated combustion, the temperature inside the compartment typically exceeded 600 °C. CO was measured during all experiments and was two orders of magnitude less than the measured CO<sub>2</sub> concentration. Significant amounts of unburned hydrocarbons were measured during all of the experiments, while HCN was only detected during the wood-nylon tests. Higher toxicant yields were measured for wood-nylon compared to pure wood and MDF.</div></div>","PeriodicalId":50445,"journal":{"name":"Fire Safety Journal","volume":"158 ","pages":"Article 104534"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-13DOI: 10.1016/j.firesaf.2025.104556
Xiaoyi Lu , Ning Ren , Gang Xiong , Alex Krisman , Agarwal Gaurav , Hideki Yoshioka , Yuhei Nishio , Takafumi Noguchi , Yi Wang
This study models the JIS A 1310 fire test configuration of a cubic fire compartment with an attached non-combustible façade wall using the large-eddy simulation code FireFOAM. The focus is to understand heat and mass transfer processes in fully developed compartment fires, with a particular emphasis on radiative heat transfer to the façade. Engineering radiation modeling is typically based either on a prescribed radiant fraction or on a prescribed soot or smoke yield. Our numerical results, validated against experimental measurements of flame morphology, temperature distribution, and heat flux profiles, show that the commonly used radiant fraction model provides satisfactory predictions for fire window ejection but significantly underestimates radiative heat flux onto the façade from the ejected fire. By neglecting absorption and assuming that all emission is co-located with the flame sheet, the radiant fraction model fails to account for radiation emission from the non-flaming portion of the fire plume, which experimental results reveal to be hot and sooty and thus likely a significant source of radiation emission. Despite modeling challenges due to oversimplified soot chemistry processes as well as the need of calibrations, the soot yield-based model that captures radiation emission from both the flaming and non-flaming portions of the fire plumes, achieved improved agreement with the experimental heat flux data across a range of fire sizes.
本研究采用大涡模拟代码FireFOAM，模拟JIS A 1310中附不燃侧墙的立方体防火室的防火试验配置。重点是了解充分发展的隔间火灾的传热和传质过程，特别强调辐射传热到表面。工程辐射建模通常基于规定的辐射分数或规定的烟尘或烟雾产量。我们的数值结果，与火焰形态、温度分布和热流分布的实验测量相验证，表明常用的辐射分数模型对火窗喷射提供了令人满意的预测，但明显低估了从喷出的火到表面的辐射热通量。由于忽略吸收并假设所有发射都与火焰片位于同一位置，辐射分数模型无法考虑来自火焰羽流非燃烧部分的辐射发射，实验结果显示该部分是热的和煤烟的，因此可能是一个重要的辐射发射源。尽管由于过于简化的烟尘化学过程以及校准的需要，建模存在挑战，但基于烟尘产量的模型捕获了火焰羽流燃烧和非燃烧部分的辐射发射，在一系列火灾规模的实验热通量数据中取得了更好的一致性。
{"title":"A numerical study of transport phenomena and radiation transfer in compartment and façade fires","authors":"Xiaoyi Lu , Ning Ren , Gang Xiong , Alex Krisman , Agarwal Gaurav , Hideki Yoshioka , Yuhei Nishio , Takafumi Noguchi , Yi Wang","doi":"10.1016/j.firesaf.2025.104556","DOIUrl":"10.1016/j.firesaf.2025.104556","url":null,"abstract":"<div><div>This study models the JIS A 1310 fire test configuration of a cubic fire compartment with an attached non-combustible façade wall using the large-eddy simulation code FireFOAM. The focus is to understand heat and mass transfer processes in fully developed compartment fires, with a particular emphasis on radiative heat transfer to the façade. Engineering radiation modeling is typically based either on a prescribed radiant fraction or on a prescribed soot or smoke yield. Our numerical results, validated against experimental measurements of flame morphology, temperature distribution, and heat flux profiles, show that the commonly used radiant fraction model provides satisfactory predictions for fire window ejection but significantly underestimates radiative heat flux onto the façade from the ejected fire. By neglecting absorption and assuming that all emission is co-located with the flame sheet, the radiant fraction model fails to account for radiation emission from the non-flaming portion of the fire plume, which experimental results reveal to be hot and sooty and thus likely a significant source of radiation emission. Despite modeling challenges due to oversimplified soot chemistry processes as well as the need of calibrations, the soot yield-based model that captures radiation emission from both the flaming and non-flaming portions of the fire plumes, achieved improved agreement with the experimental heat flux data across a range of fire sizes.</div></div>","PeriodicalId":50445,"journal":{"name":"Fire Safety Journal","volume":"158 ","pages":"Article 104556"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-17DOI: 10.1016/j.firesaf.2025.104558
Richard M. Kesler , Nathaniel Sauer , Adam Barowy , Chandler Probert , Danielle L. Neumann , Alexander C. Mayer , Micah Niemeier-Walsh , Kenneth W. Fent , Heather M. Stapleton , Gavin P. Horn
The combustion of vehicles generates fire effluent that may contain compounds that pose unique health hazards. This study evaluated the composition of products of combustion that may present chronic health concerns from electric vehicles (EVs) and internal combustion engine vehicles (ICEVs). Six EVs and three ICEVs were ignited with a 30 kW burner and allowed to burn until all combustible components of the entire vehicle were consumed. Active and passive air samplers were deployed 3.0 m in front of and 4.5 m behind the vehicle’s bumpers, and in the smoke plume 8.7 m above the vehicle. Combustion gases were sampled for volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), heavy metals, acid gases, per- and polyfluoroalkyl substances (PFAS), and inhalable particulates. EVs and ICEVs burned for similar durations, though EVs took longer to initially catch fire with this particular ignition method. Acid gases, VOCs, PFAS, PAHs, and inhalable particulates in the smoke showed minor differences between the vehicle types, though fluoride particulate was notably greater in the EV fires. Greater amounts of heavy metals (specifically nickel, manganese, cobalt, and lithium) were detected in the EV fire effluent relative to the ICEVs. Both EV and ICEV fires produce combustion products that could present health hazards to responders or bystanders.
{"title":"Evaluation of combustion products in air from electric and internal combustion engine vehicles during full-scale fire experiments","authors":"Richard M. Kesler , Nathaniel Sauer , Adam Barowy , Chandler Probert , Danielle L. Neumann , Alexander C. Mayer , Micah Niemeier-Walsh , Kenneth W. Fent , Heather M. Stapleton , Gavin P. Horn","doi":"10.1016/j.firesaf.2025.104558","DOIUrl":"10.1016/j.firesaf.2025.104558","url":null,"abstract":"<div><div>The combustion of vehicles generates fire effluent that may contain compounds that pose unique health hazards. This study evaluated the composition of products of combustion that may present chronic health concerns from electric vehicles (EVs) and internal combustion engine vehicles (ICEVs). Six EVs and three ICEVs were ignited with a 30 kW burner and allowed to burn until all combustible components of the entire vehicle were consumed. Active and passive air samplers were deployed 3.0 m in front of and 4.5 m behind the vehicle’s bumpers, and in the smoke plume 8.7 m above the vehicle. Combustion gases were sampled for volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), heavy metals, acid gases, per- and polyfluoroalkyl substances (PFAS), and inhalable particulates. EVs and ICEVs burned for similar durations, though EVs took longer to initially catch fire with this particular ignition method. Acid gases, VOCs, PFAS, PAHs, and inhalable particulates in the smoke showed minor differences between the vehicle types, though fluoride particulate was notably greater in the EV fires. Greater amounts of heavy metals (specifically nickel, manganese, cobalt, and lithium) were detected in the EV fire effluent relative to the ICEVs. Both EV and ICEV fires produce combustion products that could present health hazards to responders or bystanders.</div></div>","PeriodicalId":50445,"journal":{"name":"Fire Safety Journal","volume":"158 ","pages":"Article 104558"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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