Fire

22 pages, 1705 KB

Open AccessEssay

Selection of Appropriate Clean Fire Suppression Agents in Aircraft Cargo Compartments by Ranking System

by Xiaotong Huang, Xuhong Jia, Tao Hu, Wanki Chow and Ziqi Zhao

Fire 2026, 9(6), 241; https://doi.org/10.3390/fire9060241 (registering DOI) - 5 Jun 2026

Appropriate Halon-free fire suppression agents for aircraft cargo compartments are important in civil aviation safety. There are many products available and selecting an appropriate agent for civil aircraft cargo compartments under specific requirements is important and will be discussed. An in-depth analysis of [...] Read more.

Appropriate Halon-free fire suppression agents for aircraft cargo compartments are important in civil aviation safety. There are many products available and selecting an appropriate agent for civil aircraft cargo compartments under specific requirements is important and will be discussed. An in-depth analysis of the technical characteristics and updated research achievements of clean fire suppression agents are reviewed first. Advantages, limitations, and application feasibility of clean fire suppression agents for cargo compartments are included with quantitative evaluation of their comprehensive attributes. A ranking system is proposed to select appropriate suppressants by considering their environmental impact, fire extinguishment efficiency, airworthiness, and safety. Two approaches were introduced with the first quantitatively evaluating all four performance areas of agents through weight allocations. The second approach excludes environmental performance as Halon is to be substituted. Only the remaining three areas are included and compared with Halon. The integrated application of a ranking system provides a reference example for selecting fire suppression agents for aircraft cargo compartments. Based on the above selected approaches, the optimal substitute suppression agents are Novec 1230/N₂, 2-BTP/CO₂, and Novec 1230/(CF₃CF₂)₃N for this example. Future development will also be guided by the dynamically updatable ranking system, which provides a pathway from technical screening to engineering design. Theoretical guidance for selecting suitable clean fire suppression agents can be achieved for aircraft cargo compartments. Full article

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17 pages, 6884 KB

Open AccessArticle

Characterization of Log-Normal Distribution of Vehicle Spacing in Highway Tunnels with Implications for Evacuation Safety

by Dingli Liu, Yantong Chen, Rongwei Bu, Jingya Wang, Junqi Liu, Lu Wu, Weijun Liu and Yao Huang

Fire 2026, 9(6), 240; https://doi.org/10.3390/fire9060240 (registering DOI) - 5 Jun 2026

Abstract

Timely and efficient evacuation is crucial for tunnel fire safety. Previous studies often assumed fixed vehicle spacing and vehicle type ratios, which did not reflect real traffic conditions. Based on high-resolution satellite imagery of congested scenarios nationwide, 1703 sets of static vehicle spacing [...] Read more.

Timely and efficient evacuation is crucial for tunnel fire safety. Previous studies often assumed fixed vehicle spacing and vehicle type ratios, which did not reflect real traffic conditions. Based on high-resolution satellite imagery of congested scenarios nationwide, 1703 sets of static vehicle spacing data were obtained. Descriptive statistical analysis and the K–S test were conducted. As a result, a log-normal distribution model of vehicle spacing was established, with an average spacing of 2.23 m, a logarithmic mean of 0.707, and a logarithmic standard deviation of 0.439. Analysis of 11,211 vehicle samples further revealed an uneven distribution of vehicle types across lanes. Based on the statistical data, a random vehicle arrangement algorithm was developed for highway tunnels, incorporating the log-normal characteristics of vehicle spacing. Using this algorithm, non-uniform vehicle layouts were generated for tunnel evacuation modeling. A total of 120 simulations across 12 different scenarios were then conducted to analyze the impact of log-normal vehicle spacing on evacuation time. Results showed that evacuation time was 2–5% longer under a log-normal distribution than under a uniform distribution. Therefore, future tunnel evacuation simulations should account for the log-normal distribution of vehicle spacing and the actual vehicle type ratio. Full article

(This article belongs to the Special Issue Advances in Decision Support for Urban Fire Risk Management and Policy Formulation)

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26 pages, 1211 KB

Open AccessArticle

Fire Performance Prediction of Naturally Ventilated Double-Skin Façades Using CFD and Machine Learning

by Mehmet Akif Yıldız and Merve Ertosun Yıldız

Fire 2026, 9(6), 239; https://doi.org/10.3390/fire9060239 - 4 Jun 2026

Abstract

Double-skin façade (DSF) systems are important for energy efficiency because they effectively utilize natural ventilation and daylight. However, the uninterrupted vertical gaps in these systems may pose safety risks in the event of a fire by causing the rapid spread of smoke and [...] Read more.

Double-skin façade (DSF) systems are important for energy efficiency because they effectively utilize natural ventilation and daylight. However, the uninterrupted vertical gaps in these systems may pose safety risks in the event of a fire by causing the rapid spread of smoke and hot gases. This study presents a hybrid approach that combines computational fluid dynamics (CFD)-based simulations and machine learning (ML) techniques to predict heat flow and fire-room control-volume heat release rate (FR-HRR). Within the scope of the study, 400 different scenarios were modeled with different combinations of basic natural ventilation design parameters consisting of gap width, gap height, window opening area, and air inlet and outlet area. The data obtained were evaluated with different ML models, including Fine Tree, Bagged Tree, Support Vector Machine, and Artificial Neural Network models; in particular, the Fine Tree model gave the most successful results with high accuracy rates (R² = 0.99 for FR-HRR; R² = 0.91 for heat flow). The analysis showed that DSF gap width provided a dominant model-based contribution within the investigated CFD-generated dataset. This approach provides a preliminary CFD-informed ML framework for the rapid comparative assessment of fire-related responses in open-boundary naturally ventilated DSF configurations during the early design stage. Full article

(This article belongs to the Special Issue Fire Safety in the Built Environment)

26 pages, 9359 KB

Open AccessArticle

Effects of Equivalence Ratio on Emission Characteristics of Ammonia–Hydrogen Dual-Fuel Engine

by Zhongcheng Wang, Jie Zhu, Xiaoyu Liu, Ron Wu, Jiale Du, Shenwei Weng and Jingjun Zhong

Fire 2026, 9(6), 238; https://doi.org/10.3390/fire9060238 - 4 Jun 2026

Abstract

Ammonia–hydrogen dual-fuel engines are regarded as a promising clean-energy power system, but nitrogen-based pollutants (NH₃, NO, N₂O, and NO₂) emitted during their operation restrict their popularization and application. To clarify the influence of equivalence ratio on the [...] Read more.

Ammonia–hydrogen dual-fuel engines are regarded as a promising clean-energy power system, but nitrogen-based pollutants (NH₃, NO, N₂O, and NO₂) emitted during their operation restrict their popularization and application. To clarify the influence of equivalence ratio on the emission characteristics of ammonia–hydrogen dual-fuel engines, the emission laws and formation mechanisms of nitrogen-based pollutants under different equivalence ratios were studied and the evolution of the in-cylinder cross-sectional mole fraction of NO, N₂O and NO₂ was analyzed. The results show that equivalence ratio has a significant regulatory effect on in-cylinder temperature, combustion efficiency and pollutant formation. Unburned NH₃ emission first decreases and then increases, reaching the minimum at an equivalence ratio of 1.05. NO emission peaks at 0.74 due to high temperature and combustion efficiency and is nearly zero at 1.48 under fuel-rich conditions. N₂O emission is highest at 0.5–0.62 and is inhibited at high equivalence ratios. NO₂ is mainly generated under low equivalence ratios and disappears when the equivalence ratio exceeds 0.74. This study reveals the influence mechanism of equivalence ratio on nitrogen-based pollutants of ammonia–hydrogen dual-fuel engines and provides a theoretical basis and experimental support for the optimization of engine emission-control strategies. Full article

(This article belongs to the Special Issue Advancements in Hydrogen Internal Combustion Engine Technology: Combustion, Performance, and Environmental Impact)

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17 pages, 26220 KB

Open AccessArticle

Oxidation Mechanisms of Low Molecular Valeric Acidic Compounds in Coal Spontaneous Combustion

by Shaobo Qu, Xiaobo Wang, Tianyi Yang, Wenhao Deng, Sichen Liu, Hanzhong Deng, Yafei Shan and Hongguang Ji

Fire 2026, 9(6), 237; https://doi.org/10.3390/fire9060237 - 3 Jun 2026

Abstract

Coal spontaneous combustion seriously threatens the safety of coal mine production, and studies on low molecular compounds in coal spontaneous combustion are limited. The chemical reaction process of low molecular compound valeric acid in coal spontaneous combustion was studied using B3LYP/6-311G quantum chemical [...] Read more.

Coal spontaneous combustion seriously threatens the safety of coal mine production, and studies on low molecular compounds in coal spontaneous combustion are limited. The chemical reaction process of low molecular compound valeric acid in coal spontaneous combustion was studied using B3LYP/6-311G quantum chemical density functional theory. The mechanism of valeric acid in coal spontaneous combustion was disclosed, which is the process of chemical bond formation and breakage. The findings implied that the active sites of valeric acid during combustion are C1, C5, C8, and C11 atoms. Twelve reaction channels have also been theoretically determined in the following order: Path6 > Path3 > Path8 > Path5 > Path4 > Path1 > Path11 > Path10 > Path9 > Path12 > Path7 > Path2. This is significant for developing low molecular spontaneous combustion inhibitors and preventing coal spontaneous combustion. Full article

(This article belongs to the Special Issue Fire Risk Management and Emergency Prevention)

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22 pages, 42421 KB

Open AccessArticle

Analysis of Syngas Inlet Position for Optimization of Flameless Combustion in a Biomass Pyrolyzer

by Andre Amba Matarru and Donghoon Shin

Fire 2026, 9(6), 236; https://doi.org/10.3390/fire9060236 - 2 Jun 2026

Abstract

A new biomass pyrolyzer, named Biochar Oven, has been developed using flameless combustion technology, which provides uniform high temperature in the pyrolysis reactor. A computational fluid dynamics (CFD) model of flameless combustion was developed to analyze how the fuel inlet depth controls the [...] Read more.

A new biomass pyrolyzer, named Biochar Oven, has been developed using flameless combustion technology, which provides uniform high temperature in the pyrolysis reactor. A computational fluid dynamics (CFD) model of flameless combustion was developed to analyze how the fuel inlet depth controls the reaction and heat transfer to a vertical biomass pyrolysis reactor. The combustor was modeled using the k–ε turbulence model, the discrete ordinates radiation model, and species transport with the reaction. The fuel nozzle relative depth ratios (RDR) of chamber height and equivalence ratios (ER) were varied to obtain optimal combustion and heat transfer performance. The internal recirculation ratio (Z) was calculated to evaluate the flameless combustion condition, with maximum values generally found at RDR 0.73 for each ER. Increasing depth strengthens the mixing zone closer to the reactor wall. With an ER of 0.9 and RDR of 0.73, the wall heat flux is up to 16.36 kW m⁻², the average wall reactor temperature is up to 900 °C, and the heat transfer efficiency is up to 59.79%. These flow patterns and chamber–reactor results indicate that deeper nozzle insertions (RDR 0.73) provide better overall performance by improving recirculation intensity, wall heat flux, and heat transfer efficiency with lower CO emissions. Full article

(This article belongs to the Special Issue Low Carbon Fuel Combustion and Pollutant Control)

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12 pages, 256 KB

Open AccessArticle

ActivAcción: Implementation Feasibility, Acceptability, and Safety of a Task-Specific Functional Activation Protocol for Forest Firefighters Occupationally Exposed to Wildfire—A Pilot Study in Chile

by Miguel Ángel Meneses Galaz, Lylian González Plate, Julio Ortega Calderón, José Luis Valín, Isabel Cuevas Quezada and Cristóbal Galleguillos Ketterer

Fire 2026, 9(6), 235; https://doi.org/10.3390/fire9060235 - 2 Jun 2026

Abstract

Forest firefighters face concurrent thermal stress, biomechanical loading, high metabolic demand, smoke, and time-constrained manual work, conditions that increase musculoskeletal disorder (MSD) risk. In Chile, occupational health regulation requires biomechanical risk management, but no task-specific activation protocol has been evaluated within wildfire suppression [...] Read more.

Forest firefighters face concurrent thermal stress, biomechanical loading, high metabolic demand, smoke, and time-constrained manual work, conditions that increase musculoskeletal disorder (MSD) risk. In Chile, occupational health regulation requires biomechanical risk management, but no task-specific activation protocol has been evaluated within wildfire suppression operations. This pilot study assessed the feasibility, acceptability, and safety of ActivAcción, a 5 min dynamic activation protocol integrated into Corporación Nacional Forestal (CONAF) wildfire brigade routines. A single-group mixed-methods feasibility study was conducted over 8 weeks at the Placilla CONAF base, Valparaíso Region, Chile. Ten firefighters and supervisors participated. The intervention included two educational sessions and an eight-exercise routine adapted from the WFX-FIT manual and delivered within existing safety briefings. Outcomes were adverse events, musculoskeletal complaints, perceived feasibility and acceptability, implementation fidelity, and qualitative themes. No adverse events or new musculoskeletal complaints were recorded. Feasibility and acceptability ratings were high among brigadistas (mean, 6.71/7.0, SD = 0.69) and maximal among supervisors (7.0/7.0). Protocol fidelity reached 75% (12/16 sessions). Qualitative findings identified task-specificity, ease of integration, and leadership endorsement as implementation facilitators. ActivAcción is feasible, acceptable, and safe under the studied low-exposure conditions; controlled high-season evaluation is required before inference on MSD prevention effectiveness. Full article

(This article belongs to the Special Issue Wildfire Exposure and Human Health: A Multidisciplinary Perspective)

18 pages, 26829 KB

Open AccessArticle

Scars on the Steppe: A Comparative Analysis of Soyuz First Stage and Abort Mode Impacts on Arid Ecosystems in Kazakhstan at 2024

by Ivan N. Semenkov, Andrey M. Karpachevskiy, Sergey A. Lednev and Tatyana V. Koroleva

Fire 2026, 9(6), 234; https://doi.org/10.3390/fire9060234 - 2 Jun 2026

Abstract

In 2024, eight Soyuz launch vehicles launched from the Baikonur Cosmodrome created 40 falling sites in Central and Northern Kazakhstan. During field work at all falling sites, the area of disturbances was assessed. The average area of zones containing large fragments was 63 [...] Read more.

In 2024, eight Soyuz launch vehicles launched from the Baikonur Cosmodrome created 40 falling sites in Central and Northern Kazakhstan. During field work at all falling sites, the area of disturbances was assessed. The average area of zones containing large fragments was 63 m², and the average area of scatter fields of small fragments was 11,605 m². The maximum area of jet fuel spills reached 125 m², and that of hydrogen peroxide spills reached 196 m². In 2024, due to falling debris, total mechanical disturbances covered 6912 m², and total pyrogenic impact effected 1,211,453 m². At the first stage falling sites during the plant growing season, the area affected by fires and H₂O₂ spills was significantly larger, while the area of very weak mechanical disturbance was smaller (p < 0.05). During the mud season, the area with very strong mechanical impact was greater compared to winter and summer (p < 0.002). Compared to the first stage falling sites, the Soyuz abort mode falling sites were characterized by a larger area of very strong mechanical impact (p < 0.02), evidenced by deep craters, and a smaller area covered by large fragments. The data obtained can be used when planning launch dates from the Baikonur Cosmodrome to minimize negative environmental impact on the fragile and slow-recovering arid ecosystems of Central and Northern Kazakhstan. Information on disturbance areas can be used to monitor the natural recovery of affected ecosystems and to calculate the environmental damage caused by rocket and space activities in 2024. Full article

(This article belongs to the Special Issue Fire and Explosion Prevention in Maritime and Aviation Transportation)

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17 pages, 5472 KB

Open AccessArticle

Intelligent Tunnel Fire Source Characteristic Inversion Technology Based on Large Language Model Multi-Agent Collaboration

by Ding Zeng, Ao Gao and Zhisheng Xu

Fire 2026, 9(6), 233; https://doi.org/10.3390/fire9060233 - 1 Jun 2026

Abstract

The integration of computational fluid dynamics (CFD) with deep learning in tunnel fire research is currently constrained by excessive reliance on manual operations and low overall efficiency. To address these limitations, this study presents a multi-agent collaborative framework driven by large language models, [...] Read more.

The integration of computational fluid dynamics (CFD) with deep learning in tunnel fire research is currently constrained by excessive reliance on manual operations and low overall efficiency. To address these limitations, this study presents a multi-agent collaborative framework driven by large language models, which enables full automation of the fire source characteristic inversion process. This framework reorganizes the conventional research pipeline into four dedicated, specialized agents: physical modeling, data governance, model training, and evaluation analysis. As a typical automated verification task, five deep learning models are systematically benchmarked under 45 experimental configurations to implement multi-task continuous regression inversion, which fully demonstrates the framework’s capability of automated, reproducible and large-scale comparative experiments. The experimental results demonstrated that the CNN-LSTM model outperforms other models in extracting spatiotemporal correlation features from temperature time-series data, enabling high-precision prediction of multiple fire parameters. With a 6 s observation window and 10 m sensor spacing, the average R² attains 0.942, an improvement of 2% over the baseline LSTM model, and the RMSE decreases by 28.8%. For sparse sensor deployment at 30 m spacing, the average R² remains at 0.917, confirming the effectiveness of integrating spatial feature extraction with temporal modeling. This study provides an efficient technical pathway for intelligent tunnel fire identification and advances the research paradigm by shifting the traditional manual optimization process to a multi-agent system-based optimization workflow. Full article

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28 pages, 3056 KB

Open AccessArticle

Development of a Mobile Application for Visualizing the Hazard Zone During a Fire at an Industrial Enterprise Based on Cellular Automata

by Fares Abu-Abed, Yuri Matveev, Ruslan Fedyakin, Olga Zhironkina and Sergey Zhironkin

Fire 2026, 9(6), 232; https://doi.org/10.3390/fire9060232 - 1 Jun 2026

Abstract

Accurate simulation modeling of the danger zone and real-time visualization of the toxic cloud spread during a fire and explosion at an industrial facility in a nearby urban area are in demand by rescue services conducting evacuation. Using a cellular automaton method allows [...] Read more.

Accurate simulation modeling of the danger zone and real-time visualization of the toxic cloud spread during a fire and explosion at an industrial facility in a nearby urban area are in demand by rescue services conducting evacuation. Using a cellular automaton method allows us to create an optimal predictive model of the danger zone spread, combine modeling accuracy with computational speed, and consider multiple input variables and the cascading nature of an accident during visualization. The objective of this study was to develop a mobile application for calculating the parameters of the danger zone during an accident at an industrial facility caused by a toxic cloud spreading into an urban area, based on the selection of a cellular automaton algorithm. The primary objective of the study was a highly detailed visualization of the danger zone with several predicted values of toxic substance concentrations in the air. The authors developed a cellular automaton-based model, which forms the basis of the mobile application. It takes into account several variables characterizing chemicals in the explosion and fire zone, climate factors, occupancy, building parameters, and the availability of respiratory protection. The FireSoft Mobile app was developed using the Visual Studio 2022 development environment, C# 10.0, and .NET MAUI, adapted for Android 8.0 and higher. The mobile app was tested to visualize a cloud of toxic pollutants forming a hazardous zone in an urban agglomeration for cases involving an ammonia tank explosion and a large fire involving a large amount of polyvinyl chloride. The results demonstrate the app’s feasibility and effectiveness in predicting, planning, and managing evacuation measures during accidents at an industrial facility. Full article

(This article belongs to the Special Issue Advances in Fire Safety Engineering and Management in Buildings and Industrial Facilities)

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19 pages, 2483 KB

Open AccessArticle

Fire–Climate–Vegetation Linkages in a Quaking Aspen Forest During the Late Holocene

by R. Justin DeRose and Jesse L. Morris

Fire 2026, 9(6), 231; https://doi.org/10.3390/fire9060231 - 1 Jun 2026

Abstract

Fire plays an important role in shaping forested ecosystems around the globe. Unlike many other fire-driven forest types, our understanding of pre-settlement fire behavior in quaking aspen (Populus tremuloides) systems is limited. To better understand the frequency and severity of fires [...] Read more.

Fire plays an important role in shaping forested ecosystems around the globe. Unlike many other fire-driven forest types, our understanding of pre-settlement fire behavior in quaking aspen (Populus tremuloides) systems is limited. To better understand the frequency and severity of fires in a putatively stable quaking aspen forest, a small, key watershed was selected for sediment coring to reconstruct fire history, vegetation change, and climatic variability. The study aim was to explore the fire–climate–vegetation linkages in an aspen-dominated catchment. For the past ~4000 years this basin has been dominated by quaking aspen but also subalpine fir (Abies lasiocarpa), and their relative composition has shifted inversely over this period. Large, stand-replacing fires occurred, on average, every ~178 years, with individual fire-free intervals ranging from 132 to 323 years. The occurrence of fire was not related to climatic conditions as characterized by either cool-season or warm-season moisture availability (drought proxies). Rather, fire occurrence was most strongly related to fuel accumulation associated with the predictable successional shift in species dominance from quaking aspen to subalpine fir. Unlike in climate-limited systems where managers have little control over fire occurrence due to climatic conditions (e.g., drought), fuel-limited systems are controlled from the bottom up, where the explicit reduction or redistribution of long-term fuel buildup is an effective approach to reducing the likelihood and/or effects of fire in the short-term. Full article

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28 pages, 13054 KB

Open AccessArticle

Study on Liquid Hydrogen Leakage Dispersion Behavior and Synergistic Mitigation by Barrier Walls and Air Curtains in a Hydrogen Production and Refueling Station

by Xingyu Liu, Bo Yuan, Shiyan Zeng, Linzhi Xu, Chunyan Song, Nianfeng Xu, Tianqi Yang, Yonghua Cai and Jinsheng Xiao

Fire 2026, 9(6), 230; https://doi.org/10.3390/fire9060230 - 1 Jun 2026

Abstract

Compared with gaseous hydrogen at ambient temperature, liquid hydrogen (LH₂) possesses a higher volumetric energy density and is therefore regarded as one of the most economically viable hydrogen storage and transportation options. However, the extremely large temperature difference between the storage [...] Read more.

Compared with gaseous hydrogen at ambient temperature, liquid hydrogen (LH₂) possesses a higher volumetric energy density and is therefore regarded as one of the most economically viable hydrogen storage and transportation options. However, the extremely large temperature difference between the storage temperature of LH₂ and the ambient environment may give rise to serious safety hazards once a leakage accident occurs. Focusing on an integrated hydrogen production and refueling station (IHPRS), this study investigates the suppression effect of a novel synergistic protection system—combining a barrier wall and an air curtain—on LH₂ leakage and dispersion. By comparing the dispersion distances of hydrogen clouds under different barrier wall–air curtain configurations, the optimal synergistic structure was identified as a barrier wall with a planar size of 36 m × 12 m and a height of 3 m, combined with an air curtain velocity of 40 m/s. The reliability of this structure is further evaluated under practical influencing factors: under varying natural wind conditions, the maximum downwind dispersion distance is reduced by up to 58.02%; at a flash evaporation mass fraction of 20%, horizontal dispersion is suppressed by 42.18% and 33.17% in the X- and Z-directions, respectively; and at a leakage mass flow rate of 5.15 kg/s, the X-direction dispersion distance is reduced by 33.88% with a 40.14% increase in cloud height. The results show that the proposed barrier wall–air curtain synergistic protection structure can effectively alter the dispersion path of the FHC (refers to the hydrogen cloud with a volume concentration within the flammable range between 4 and 75% vol) formed by LH₂ leakage, shorten the hazardous downwind distance, and enhance the vertical dispersion of the FHC. These findings provide theoretical support and safety guidance for the risk control of LH2 leakage accidents in IHPRS. Full article

(This article belongs to the Special Issue Hydrogen Leakage, Dispersion, Fires and Explosions: Advances in Hazard Mitigation and Risk Management)

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16 pages, 6205 KB

Open AccessArticle

Research on Characteristic Analysis of Typical Fire Accidents and Trend Prediction of Workplace Accidents

by Fangming Xue, Binbin Wu, Jiawei Ding, Chao Wang, Wei Ding and Fei Ren

Fire 2026, 9(6), 229; https://doi.org/10.3390/fire9060229 - 1 Jun 2026

Abstract

Workplace accidents pose a serious threat to people’s lives and property and hinder social and economic development. Among these accidents, fire accidents are typical due to their sudden occurrence and severe consequences. To better understand accident evolution laws and improve risk prevention, this [...] Read more.

Workplace accidents pose a serious threat to people’s lives and property and hinder social and economic development. Among these accidents, fire accidents are typical due to their sudden occurrence and severe consequences. To better understand accident evolution laws and improve risk prevention, this study analyzes the characteristics of typical national fire accidents based on 2015–2024 accident statistics. A linear-nonlinear combined Autoregressive Integrated Moving Average-Long Short-Term Memory (ARIMA-LSTM) model is established to predict trends of the number of national overall workplace accidents, deaths, injuries, and direct economic losses, and it is compared with Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Seasonal Autoregressive Integrated Moving Average-Long Short-Term Memory (SARIMA-LSTM) models. The results show that the ARIMA-LSTM model integrates the strengths of linear fitting and nonlinear learning, with stronger explanatory power and higher prediction accuracy, as reflected by lower Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) values. This study provides technical support for the precise prevention and control of fire accidents, trend prediction of work safety accidents, and helps to establish a scientific and forward-looking safety risk prevention and control system. Full article

(This article belongs to the Special Issue Relevance and Applicability of AI for Fire Engineering)

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23 pages, 860 KB

Open AccessArticle

How Information Source and User Attributes Affect Use of Fire Management Information

by George B. Frisvold, Ning Zhang, Charles Maxwell, Michael A. Crimmins and Daniel B. Ferguson

Fire 2026, 9(6), 228; https://doi.org/10.3390/fire9060228 - 29 May 2026

Abstract

This study examines how information source and fire manager attributes affect the use of 33 different information sources used for fire management. The approach is like that of recreation demand models that predict an individual’s travel to recreation sites based on individual and [...] Read more.

This study examines how information source and fire manager attributes affect the use of 33 different information sources used for fire management. The approach is like that of recreation demand models that predict an individual’s travel to recreation sites based on individual and site characteristics. Here, we predict “visits” to websites rather than campsites. The study develops and estimates a random utility model, using survey data from a representative sample of US Southwest fire managers. Results were consistent with predictions of economic value of information models. Significant predictors included the agency that a manager worked for, a manager‘s self-reported role within the agency, the interagency dispatch centers where they worked, the total number of fire management decisions, and the geographic and administrative scope of the dispatch center management area. Manager personal attributes (education, age, experience) only minutely improved model fit. Information use varied significantly by type of information source. The probability of use was greater for general weather or climate websites/portals than for specialized sources (such as drought, ENSO, or fire decision support tools (DSTs)). Fire management-specific sources (excluding fire DSTs) had a greater probability of use than general sources. Manager reliance on different sources of information shifted when moving from before to during the fire season. Future research could explore the internal dynamics of agencies and dispatch centers affecting information use, why fire managers do not use decision support systems more to support decisions, and the role of different types (and not just years) of experience. Full article

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21 pages, 3465 KB

Open AccessArticle

Analysis of Fire Incident Characteristics and Countermeasure Study in Municipal Districts: A Case Study of Xiaoshan District

by Huakai Sun, Ming Chen, Huiping Hu, Ke Wu, Sha Lu and Kai Zhu

Fire 2026, 9(6), 227; https://doi.org/10.3390/fire9060227 - 29 May 2026

Abstract

As key units in China’s new urbanization process, municipal districts exhibit distinct fire risk characteristics due to dense populations, concentrated infrastructure, and intensive socio-economic activities. Taking Xiaoshan District as an illustrative case of a highly urbanized and industrialized municipal district, this study analyzes [...] Read more.

As key units in China’s new urbanization process, municipal districts exhibit distinct fire risk characteristics due to dense populations, concentrated infrastructure, and intensive socio-economic activities. Taking Xiaoshan District as an illustrative case of a highly urbanized and industrialized municipal district, this study analyzes fire incidents from 2020 to 2023 from temporal, spatial, and causal perspectives. During the study period, 5011 fire incidents were recorded, resulting in 3 deaths, 2 injuries, and direct property loss of 73.41 million CNY. The results indicate that highly urbanized and industrialized districts such as Xiaoshan may simultaneously face frequent fire occurrence pressure, relatively low casualty levels, and strong sensitivity to large-loss incidents. Temporally, fire occurrence was strongly coupled with human activity patterns rather than being dominated solely by seasonal factors. The period from 4 p.m. to 8 p.m. accounted for 32.47% of daily fire incidents, whereas only 9.10% occurred between 2 a.m. and 6 a.m.; however, early morning fires were associated with more serious property loss. Spatially, resident population and industrial output value above designated size were identified as the primary socio-economic factors associated with the spatial differentiation of fire incidents and direct property loss at the town/subdistrict scale. In terms of causation, electrical issues were the leading cause of fire incidents, accounting for 31.95% of fires and 32.92% of direct property loss. In addition, direct property loss attributed to “other” causes was disproportionately high, highlighting the need to improve the professionalism, granularity, and consistency of fire cause investigation. These findings provide case-based empirical evidence for refined fire prevention, electrical fire control, early warning, and targeted fire safety management in highly urbanized and industrialized districts with similar development conditions. Full article

(This article belongs to the Special Issue Current Advances in the Assessment and Mitigation of Fire Risk in Buildings and Urban Areas: 2nd Edition)

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35 pages, 23622 KB

Open AccessArticle

Simulation and Experimental Study on Parameters of High-Frequency Acoustic Waves Affecting Kitchen Oil Fires

by Wenyue Ge and Zhaojun Tian

Fire 2026, 9(6), 226; https://doi.org/10.3390/fire9060226 - 28 May 2026

Abstract

This study systematically investigates the influence of sound waves on the flame morphology of oil pan fires in commercial kitchen fire scenarios through a combined approach of numerical simulation and experimental research. A two-dimensional numerical model was established using COMSOL Multiphysics to simulate [...] Read more.

This study systematically investigates the influence of sound waves on the flame morphology of oil pan fires in commercial kitchen fire scenarios through a combined approach of numerical simulation and experimental research. A two-dimensional numerical model was established using COMSOL Multiphysics to simulate the interaction mechanisms with flames under various sound source configurations, frequencies, and sound pressure levels. An experimental platform was then constructed to validate and refine the findings using flame morphology, center temperature, and combustion duration as metrics. Results confirm that sound waves effectively destabilize flames, with suppression effects exhibiting a nonlinear trend of initial enhancement followed by attenuation as frequency increases. At the optimal frequency, increasing sound pressure level significantly enhances suppression but exhibits saturation characteristics. Bilateral oblique sound sources simultaneously act on both sides of the flame root, synchronously thinning the boundary layer to create uniform suppression. This configuration also compensates for deflection effects at high frequencies in single-field scenarios, yielding higher efficiency. The determined optimal parameter combination is 1800 Hz and 50 dB, with bilateral oblique arrangement preferred. Full article

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15 pages, 4896 KB

Open AccessArticle

Numerical Investigation of the Effect of Fire-Resistant Overhanging Eaves on Vertical Fire Spread Along Building Facades

by Yan Jiao, Zicheng Nie, Chongwen Xiong, Hao Huang, Hong Zhu and Yufei Dai

Fire 2026, 9(6), 225; https://doi.org/10.3390/fire9060225 - 28 May 2026

Abstract

To investigate the influence of fire-resistant overhanging eave geometry (width and installation height) on vertical fire spread along building facades, a nine-story building model was developed using PyroSim (version 2023.2.0816), and simulations were conducted using Fire Dynamics Simulator. The results show that window-ejected [...] Read more.

To investigate the influence of fire-resistant overhanging eave geometry (width and installation height) on vertical fire spread along building facades, a nine-story building model was developed using PyroSim (version 2023.2.0816), and simulations were conducted using Fire Dynamics Simulator. The results show that window-ejected flames form a buoyant spill plume that adheres to the facade due to the Coandă effect and air entrainment, resulting in a rapid temperature rise above window openings. Increasing both eave width (W) and installation height exhibited measurable but limited effectiveness in suppressing vertical fire spread. Specifically, increasing eave width significantly enhances flame deflection and reduces facade-attached plume intensity, whereas increasing installation height primarily alters plume impingement location with comparatively weaker suppression effects. A critical transition is observed at an eave width of approximately 0.4 m, beyond which lateral spill flames are induced due to flow obstruction. The relationship between eave geometry and flame behavior is further interpreted through dimensionless analysis based on characteristic fire diameter (D*), revealing that effective suppression occurs when W/D* exceeds a threshold value. These findings provide simulation-based insights into facade fire protection design, although further investigation is required to incorporate wind effects, complex facade geometries, and combustible materials. Full article

(This article belongs to the Special Issue Experimental and Numerical Investigations into Fire Dynamics in Enclosed and Open Spaces)

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21 pages, 3049 KB

Open AccessArticle

Machine Learning Assessment of Fire Resistance in Seismically Designed Reinforced Concrete Structures

by Mohammadreza Amiraslankhan, Behrouz Behnam, Ehsan Nazerfard and Maedeh Haghbin Yousefi

Fire 2026, 9(6), 224; https://doi.org/10.3390/fire9060224 - 28 May 2026

Abstract

This research investigates the effect of seismic loading on FRRs of RC structures using different machine learning (ML) algorithms. First, 20 portal RC frames with varying span numbers and stories are designed for seismic loads. This is then expanded to over 1760 frames [...] Read more.

This research investigates the effect of seismic loading on FRRs of RC structures using different machine learning (ML) algorithms. First, 20 portal RC frames with varying span numbers and stories are designed for seismic loads. This is then expanded to over 1760 frames by including further specifications such as span length, soil type, and seismic levels. This dataset is derived using decision tree algorithms, ensuring a robust and comprehensive analysis of the relationship between seismic design considerations and FRRs. All the models are subjected to the ISO 834 fire curve. Employing different ML algorithms indicate that the Random Forest Regression provides an accuracy of 81.88%, outperforming alternative algorithms such as Gradient Boosting and Support Vector Regression. Overall, the results suggest that structural elements designed for higher seismic demands exhibit higher FRRs. Additionally, as the number of spans increases, the associated FRRs also increase. An equation is then proposed to correlate the required sprinklers and the FRRs of seismically designed structures, making it possible to adopt a cost-reduction strategy in establishing fire protection systems. The ML-based algorithms here present a functional approach that can assist engineers in reducing structural and fire protection design costs while meeting the fire safety needs. Full article

(This article belongs to the Special Issue Advances in Structural Fire Engineering)

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17 pages, 14756 KB

Open AccessArticle

FlameDet: A Computational Framework for Flame Detection via Physics-Inspired Heat Diffusion and Multi-Resolution Frequency Analysis

by Song Han, Fei Ren, Chenglin Liu, Zekang Zhang, Tianzhu Wang, Jiayin Liu and Honglei Che

Fire 2026, 9(6), 223; https://doi.org/10.3390/fire9060223 - 28 May 2026

Abstract

The development of robust and efficient computational methodologies is crucial for vision-based flame detection systems. While deep learning has shown promise, many existing models are direct applications of generic architectures, lacking principled methodologies to address the inherent physical characteristics of flame, such as [...] Read more.

The development of robust and efficient computational methodologies is crucial for vision-based flame detection systems. While deep learning has shown promise, many existing models are direct applications of generic architectures, lacking principled methodologies to address the inherent physical characteristics of flame, such as heat diffusion and multi-scale radiative patterns. To bridge this gap, this paper proposes FlameDet, a novel flame detection framework grounded in physics-inspired computing and multi-resolution analysis. Unlike conventional approaches, FlameDet formulates visual feature propagation through the lens of heat conduction physics. The core contribution is the Heat Diffusion Module, a computationally efficient backbone that explicitly models feature spread by solving a parameterized heat equation via discrete cosine transform. This physics-aligned design achieves a global receptive field with

O (N^{1.5})

complexity, processing high-resolution inputs 2× faster with 54% less memory than RT-DETR-ResNet50, while providing an interpretable computational process. Furthermore, a High–Low Frequency Analysis module is proposed, a multi-resolution computational strategy that decomposes features into low-frequency components for global context and high-frequency components for fine-grained details. To enhance contextual reasoning for small flames without computational penalty, a DSK_C3 module that employs dilated convolutions and structural re-parameterization is designed, expanding the receptive field and by 26.5%. Extensive experiments on FlameLife dataset demonstrate that FlameDet establishes a new state-of-the-art, improving the F1-score and AP50 by 3.5% and 4.0%, respectively, while maintaining superior efficiency. Full article

(This article belongs to the Special Issue Relevance and Applicability of AI for Fire Engineering)

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