Fire, Volume 6, Issue 10 (October 2023) – 41 articles

Pipeline transportation is widely regarded as the most cost-effective method for conveying substantial volumes of hydrogen across extensive distances. However, before hydrogen can be widely used, a new pipeline network must be built to reliably supply industrial users. An alternative way to rather expensive investments in new infrastructure could be to use the existing pipeline network to add pure hydrogen to natural gas and further transport the gas mixture in an industrially safe way. The new solution necessities will be examined for compression, transportation, and fire hazard accidents, which have not been scrutinized by other scholars. This study presents the results of a comprehensive analysis of the methane–hydrogen mixture compression process and a mathematical description of the main pipeline operation during gas mixture transportation, considering industrial fire safety issues. By examining a case study involving a main gas pipeline and its associated mathematical model for hydrogen transportation, it becomes feasible to assess the potential hazards associated with various leakage areas and the subsequent occurrence of fires. The findings of this investigation demonstrate that the spontaneous combustion of hydrogen due to leakage from a natural gas pipeline is directly influenced by the proportion of hydrogen present in the gas mixture. If the hydrogen percentage reaches a balanced ratio of 50–50%, it is plausible that the equipment at the compressor station could be subject to detrimental consequences, potentially leading to accidents and fires. Furthermore, the obtained results from modeling in ANSYS Fluent software propose two practical scenarios, which demonstrate that despite the limited research conducted on the safety aspects and the occurrence of fires during the operation of hydrogen gas pipelines, industrial and fire safety necessitate the inclusion of hydrogen transport infrastructure as a pivotal element within the broader framework of hydrogen infrastructure development. Full article

Like many places around the world, the wildland–urban interface areas surrounding urban regions are subject to variable levels of fire risk, threatening the natural habitats they contact. This risk has been assessed by various authors using many different methods and numerical models. Among these approaches, machine learning models have been successfully applied to determine the weights of criteria in risk assessment and risk prediction studies. In Istanbul, data have been collected for areas that are yet to be urbanized but are foreseen to be at risk using geographic information systems (GIS) and remote sensing technologies based on fires that occurred between 2000 and 2021. Here, the land use/land cover (LULC) characteristics of the region were examined, and machine learning techniques, including random forest (RF), extreme gradient boosting (XGB), and light gradient boosting (LGB) models, were applied to classify the factors that affect fires. The RF model yielded the best results, with an accuracy of 0.70, an F1 score of 0.71, and an area under the curve (AUC) value of 0.76. In the RF model, the grouping between factors that initiate fires and factors that influence the spread of fires was distinct, and this distinction was also somewhat observable in the other two models. Risk scores were generated through the multiplication of the variable importance values of the factors and their respective layer values, culminating in a risk map for the region. The distribution of risk is in alignment with the number of fires that have previously occurred, and the risk in wildland–urban interface areas was found to be significantly higher than the risk in wildland areas alone. Full article

SiO₂ aerogel is a super-insulating material that can be used for tunnel fireproofing to eliminate high-temperature spalling and extend the safe evacuation time of personnel. This study aimed to replace traditional aggregates with SiO₂ aerogel in mortar preparation and evaluate its mechanical properties, thermal conductivity, and durability (freeze–thaw, water, and moisture resistance). Furthermore, the high-temperature characteristics of SiO₂ aerogel and the damage evolution pattern of SiO₂ aerogel mortar were investigated with varying fire durations (0.5, 1, 1.5, 2, 2.5, and 3 h) and fire temperatures (1000, 1100, and 1200 °C) as environmental variables. The results revealed that the critical temperature and critical time of SiO₂ aerogel particles from amorphous to crystalline structures were about 1100 °C and 1.5 h, respectively. SiO₂ aerogel mortar exhibited a compressive strength of 3.5 MPa, a bond strength of 0.36 MPa, and a thermal conductivity of 0.165 W/m·K. The residual mass ratio and residual compressive strength of SiO₂ aerogel mortar were 81% and 1.8 MPa after 1100 °C for 2.5 h. The incorporation of SiO₂ aerogel significantly improved the fire resistance of the mortar. Therefore, SiO₂ aerogel mortar has the potential to be used as a fireproof coating and can be applied in tunnels to reduce high-temperature spalling and extend the safe evacuation time for personnel. Full article

Driving wind and slope of terrain can increase the rate of surface fire propagation. Previous physical modelling under higher driving wind (3–12.5 m/s) on slopes (Innocent et al., IJWF, 2023, 32(4), pp. 496–512 and 513–530) demonstrated that the averaged rate of fire spread (RoS) varied from that of empirical models. This study investigates the potential for better agreement at lower wind velocities (0.1 and 1 m/s), since empirical models are typically developed from experimental studies conducted under benign wind conditions. The same physical model WFDS is used. The results are analysed to understand the behaviour of various parameters (RoS, fire isochrone progression, fire intensity, flame dynamics, and heat fluxes) across different slopes. The RoS–slope angle relationship closely fits an exponential model, aligning with the findings from most experimental studies. The relative RoSs are aligned more closely with the Australian and Rothermel models’ slope corrections for 0.1 and 1 m/s, respectively. The relationship between flame length and fire intensity matches predictions from an empirical power–law correlation. Flame and plume dynamics reveal that the plume rises at a short distance from the ignition line and fire propagation is primarily buoyancy-driven. The Byram number analysis shows buoyancy-dominated fire propagation at these lower wind velocities. Convective heat fluxes are found to be more significant at greater upslopes. The study confirmed that “lighter & drier” fuel parameters accelerated the fire front movement, increasing the RoS by approximately 57–60% compared to the original parameters. Overall, this study underscores the nuanced interplay of wind speed, slope, and other factors in influencing grassfire behaviour, providing valuable insights for predictive modelling and firefighting strategies. Full article

Presently, as human activity and climate warming gradually increase, straw burning leads to more accidental burning in neighbouring wetlands, which threatens wetland carbon stores. Plants are important carbon fixers in wetlands, converting carbon dioxide to biomass through photosynthesis and releasing carbon into the soil as plants die off. Nitrogen and phosphorus limitation in wetlands is a key factor affecting plant growth, and different burning seasons have different effects on mitigating this limitation. To further elucidate the effects of nitrogen and phosphorus distribution on wetland inter-month nutrient dynamics after different burning seasons, we selected a Calamagrostis angustifolia wetland in the Sanjiang Plain that was burned in spring and autumn, respectively, and conducted a monthly survey from May to September. We found that the leaf nitrogen content in September at spring burning sites was 3.59 ± 2.69 g/kg, which was significantly lower than that in July, while the difference at the unburned sites was only 0.60 ± 3.72 g/kg, and after the autumn burning, soil nitrogen and phosphorus contents remained higher than at the unburned sites in August, being 0.55 ± 1.74 g/kg and 0.06 ± 0.12 g/kg, respectively. Our results indicate that spring burning immediately increased the nitrogen and phosphorus contents in soil and plants but that these effects only lasted for a short time, until June. In comparison, autumn burning had a long-term effect on soil nitrogen and phosphorus levels and significantly increased the aboveground biomass. Thus, we recommend that conducting autumn burning before the commencement of agricultural burning not only reduces combustible accumulation to prevent fires but also promotes nitrogen and phosphorus cycling in wetlands, and the increase in plant biomass after autumn burning also enhances the carbon fixation capacity of the wetland. Full article

PVC plastic products are common combustible substances seen in fires, but their thermal degradation behavior under different oxygen concentrations has not been adequately studied. The thermal degradation behavior of PVC materials in atmospheres with different oxygen concentrations was analyzed via thermogravimetric–Fourier transform infrared spectroscopy (TG-FTIR). The TG results show that the thermal degradation process of PVC under a non-oxygenated atmosphere occurred in two stages, and the activation energies of the two stages were 130–175 KJ mol⁻¹ and 230–320 KJ mol⁻¹, respectively; under the oxygenated atmosphere, the thermal degradation process occurred in three stages. The activation energies of the three stages were 130–175 KJ mol⁻¹, 145–510 KJ mol⁻¹ and 75–190 KJ mol⁻¹, respectively. And the reaction mechanism of the second stage of thermal degradation was changed from D-ZLT₃ to E_n by the higher oxygen concentration. Infrared spectroscopy (FTIR) was used to analyze the pyrolysis process of PVC in the non-oxygenated atmosphere, and the eight major components were as follows, in descending order according to amount released: C-H stretching > HCl > C-Cl stretching > H₂O > CO₂ > C-H bending > C-H aliphatic bending > CH₂. For the reaction of PVC at an oxygen concentration of 7%, the nine major components, in descending order according to amount released, were as follows: CO₂ > HCl > H₂O > CO > C-H stretching > C-Cl stretching > C-H aliphatic bending > C-H bending > CH₂. For PVC reactions at oxygen concentrations of 14% and 21%, the five major components, in descending order according to amount released, were CO₂ > HCl > CO > C-Cl stretching > H₂O. Full article

Urban areas adjacent to wildlands are very dangerous zones for residents and their properties during a wildfire event. We attempted to connect wildfire simulations with field inventories and surveys to create a framework that can be used to enhance the fire resistance of residential structures located in the wildland-urban interface (WUI). Legal restrictions and the lack of economic incentives for WUI residents greatly limit the potential to appropriately intervene to enhance their property’s fire resistance. By studying in situ the resilience of building materials and combining them with exposure metrics produced from wildfire simulations, we created an index that helps to assess fire risk at the property level. The proposed index can support property owners to optimally manage the vegetation near or inside their property. State agencies can use our proposed index to estimate with a consistent methodology which properties are more exposed and with higher risk from fire damage so that specific fuel and vegetation management practices on and around them can be suggested or enforced. Full article

Agricultural biomass burning plays a critical role in carbon emissions, with implications for climate change. This study aims to assess carbon (C) emissions and establish C, CO, CO₂ and CH₄ emission factors (EFs) by simultaneously testing the effects of climatic conditions and cropland category on gas emissions. In Burkina Faso, 96 experimental fires were conducted in accordance with farmers’ operations during the land-clearing season in two climatic zones (Sudanian and Sudano-Sahelian) and across two cropland categories (Cropland Remaining Cropland (CC) and Land Converted to Cropland (LC)). The carbon mass balance technique was applied to estimate emissions. Climate zone and cropland category significantly influenced carbon emissions and emission factors (p < 0.05). The Sudanian zone recorded the highest carbon emissions (0.24 ± 0.01 t C ha⁻¹). For cropland category, LC recorded the highest carbon emissions with an average value of 0.27 ± 0.01 t C ha⁻¹. CO₂ EFs ranged from 1661.44 ± 3.63 g kg⁻¹ in the Sudanian zone to 1716.51 ± 3.24 g kg⁻¹ in the Sudano-Sahelian zone. EFs showed a dependence on the cropland category, with the highest EFs in CC. Smart agricultural practices limiting cropland expansion and biomass burning need to be promoted. This study provides vital information useful for supporting decision making as part of Nationally Determined Contributions. Full article

High temperatures, toxic gases, and smoke resulting from indoor fires pose evident threats to the lives of both trapped individuals and firefighters. This study aims to predict indoor fire development effectively, facilitating rapid rescue decisions and minimizing casualties and property damage. A comprehensive database has been developed using Computational Fluid Dynamics (CFD) tools, primarily focused on basic fire scenarios. A total of 300 indoor fire scenarios have been simulated for different fire locations and severity levels. Using fire databases developed from simulation tools, artificial intelligence models have been developed to make spatial–temporal inferences on indoor temperature, CO concentration, and visibility. Detailed analysis has been conducted to optimize sensor system layouts while investigating the variations in prediction accuracy according to different prediction horizons. The research results show that, in combination with artificial intelligence models, the optimized sensor system can accurately predict temperature distribution, CO concentration, and visibility, achieving R² values of 91%, 72%, and 83%, respectively, while reducing initial hardware costs. The research results confirm the potential of artificial intelligence in predicting indoor fire scenarios and providing practical guidelines for smart firefighting. However, it is important to note that this study has certain limitations, including the scope of fire scenarios, data availability, and model generalization and interpretability. Full article

The new situations, problems, and challenges facing urban fire safety work are gradually increasing in China, so innovating urban fire safety governance modes is an urgent task. In the fire management practice of the Chinese government, the establishment of an urban fire safety co-management system is an important measure for aggregating fire safety management resources and improving the level of urban fire safety prevention, as well as control. In order to reveal and clarify the interacting relationships and influencing mechanisms among multiple subjects in an urban fire safety co-management system, we constructed an urban fire safety co-management game model comprising fire supervision departments, production management units, and the public based on evolutionary game theory. The stability of the urban fire safety co-management game system is explored from the perspective of game subjects. The influencing factors of strategy selection between game subjects in the game system were investigated using numerical simulation analysis. The research results show that elevating the informatization level of co-management, the risk perception level of the public, and the disclosure level of fire safety information are conducive to stimulating the public’s positivity to participate in co-management. Strengthening the accountability of the superior government is conducive to ensuring the supervision level of fire supervision departments. The above measures have positive value for optimizing China’s urban fire safety co-management systems, establishing urban fire safety management synergy, and ensuring the stability of social fire safety situations. Full article

Globally, most fire-related deaths and injuries occur in residential areas. The aim of this systematic review is to report on the economic costs of residential fires from a societal perspective. Five databases (MEDLINE, EMBASE, EconLit, CINAHL, and Scopus) and grey literature were searched to identify studies that report economic or societal costs of residential fires with data from 1978 to 2021. There were no restrictions on study design. A narrative synthesis was undertaken based on the societal and economic costs reported for each included study. Seven studies from the United States, Canada, Australia, and Kuwait reported costs of residential fires. The costs of injuries and deaths were between USD 12 million and USD 5 billion, and between USD 75 million and USD 26 billion, respectively. The costs of treatment ranged from USD 0.3 million to USD 551 million, lost productivity from USD 12 million to USD 4 billion, and property damage from USD 8 million to USD 10 billion. This systematic review provides the most comprehensive evidence to date on the economic costs of residential fires. This study would offer insights into the effects of residential fires on diverse economic agents and aid in community fire prevention messaging and incentives. Full article

FIRELAN was developed as a model expected to foster the resilience to fire and sustainability of a landscape that is based on a number of premises about fire behaviour. We critically review FIRELAN and find that flawed ecological concepts and terminology are used, and that six fallacies are pervasive throughout the paper, namely begging the question regarding the effectiveness of land cover changes; the appeal to nature on the preference of native species over non-native species; confirmation bias on the flammability of native vs. non-native species; the oversimplification of fire behaviour drivers; questionable causation regarding the effect of land cover on fire hazard; and non-sequitur in respect to the flammability–resilience relationship. We conclude that FIRELAN overall lacks supporting scientific evidence, both theoretical and empirical, and would be unable to deliver adequate wildfire mitigation. Recommendations are given to guide the landscape-level process of planning and implementing wildfire impacts mitigation. Full article

Climate and land-use changes have been contributing to the increase in the occurrence of extreme wildfires, shifting fire regimes and driving desertification, particularly in Mediterranean-climate regions. However, few studies have researched the influence of land use/cover on fire regimes and carbon storage at the broad national scale. To address this gap, we used spatially explicit data from annual burned areas in mainland Portugal to build a typology of fire regimes based on the accumulated burned area and its temporal concentration (Gini Index) between 1984 and 2019. This typology was then combined with carbon stock data and different landscapes to explore relationships between landscape types and two important ecosystem services: wildfire reduction and carbon stock. Multivariate analyses were performed on these data and the results revealed a strong relationship between landscapes dominated by maritime pine and eucalypt plantations and highly hazardous fire regimes, which in turn hold the highest carbon stocks. Shrubland and mixed landscapes were associated with low carbon stocks and less hazardous fire regimes. Specialized agricultural landscapes, as well as mixed native forests and mixed agroforestry landscapes, were the least associated with wildfires. In the case of agricultural landscapes, however, this good wildfire performance is achieved at the cost of the poorest carbon stock, whereas native forests and agroforestry landscapes strike the best trade-off between carbon stock and fire regime. Our findings support how nature-based solutions promoting wildfire mitigation and carbon stock ecosystem services may prevent and revert land degradation harming Mediterranean regions. Full article

Due to high stress, high ground temperature, high moisture, and other factors in deep mines, the risk of coal spontaneous combustion (CSC) is enhanced, seriously affecting the safety of coal mining. To achieve early prediction of spontaneous combustion in the No. 3 coal seam at the Juye coalfield in the deep mine, this paper employs a temperature-programmed device to analyze the changing pattern of single-index gases and composite gas indices with temperature derived from the gas produced during csc. It also optimizes the index gas of coal sample spontaneous combustion. Simultaneously, the characteristics of coal temperature and a four-level warning indicator system for CSC are determined based on the analysis of indicator gas growth rate method, carbon-to-oxygen ratio, and the characteristics of the indicator gas. The composite index gases of the No. 3 coal seam in Juye coalfield are selected in the initial oxidation stage (Rco), accelerated oxidation stage (R₁, G₁), intense oxidation stage (R₂, G₁, G₃), and oxidative decomposition stage (G₃). This leads to the construction of a six-level warning system consisting of initial warning value, blue, yellow, orange, red, and black levels. Meanwhile, warning thresholds are also established. Full article

Accurate fuel mapping is crucial for effectively determining wildfire risk and implementing management strategies. The primary challenge in fuel type mapping lies in the need to develop accurate and efficient methods for identifying and categorizing the various combustible materials present in an area, often on a large scale. In response to this need, this paper presents a comprehensive approach that combines remote sensing data and Convolutional Neural Network (CNN) to discriminate between fire behavior fuel models. In particular, a CNN-based classification approach that leverages Sentinel-2 imagery is exploited to accurately classify fuel types into seven preliminary main classes (broadleaf, conifers, shrubs, grass, bare soil, urban areas, and water bodies). To further refine the fuel mapping results, subclasses were generated from the seven principles by using biomass and bioclimatic maps. These additional maps provide complementary information about vegetation density and climatic conditions, respectively. By incorporating this information, we align our fuel type classification with the widely used Standard Scott and Burgan (2005) fuel classification system. The results are highly promising, showcasing excellent CNN training performance with all three metrics—accuracy, recall, and F1 score—achieving an impressive 0.99%. Notably, the network exhibits exceptional accuracy in a test case conducted in the southern region of Sardinia, successfully identifying Burnable classes in previously unseen pixels: broadleaf at 0.99%, conifer at 0.79%, shrub at 0.76%, and grass at 0.84%. The proposed approach presents a valuable tool for enhancing fire management, contributing to more effective wildfire prevention and mitigation efforts. Thus, this tool could be leveraged by fire management agencies, policymakers, and researchers to improve the determination of wildfire risk and management. Full article

Terrestrial LiDAR scans (TLS) offer a rich data source for high-fidelity vegetation characterization, addressing the limitations of traditional fuel sampling methods by capturing spatially explicit distributions that have a significant impact on fire behavior. However, large volumes of complex, high resolution data are difficult to use directly in wildland fire models. In this study, we introduce a novel method that employs a voxelization technique to convert high-resolution TLS data into fine-grained reference voxels, which are subsequently aggregated into lower-fidelity fuel cells for integration into physics-based fire models. This methodology aims to transform the complexity of TLS data into a format amenable for integration into wildland fire models, while retaining essential information about the spatial distribution of vegetation. We evaluate our approach by comparing a range of aggregate geometries in simulated burns to laboratory measurements. The results show insensitivity to fuel cell geometry at fine resolutions (2–8 cm), but we observe deviations in model behavior at the coarsest resolutions considered (16 cm). Our findings highlight the importance of capturing the fine scale spatial continuity present in heterogeneous tree canopies in order to accurately simulate fire behavior in coupled fire-atmosphere models. To the best of our knowledge, this is the first study to examine the use of TLS data to inform fuel inputs to a physics based model at a laboratory scale. Full article

This study investigates the behavior of air supply in tunnels with multiple vertical shafts during fire incidents, focusing on natural ventilation dynamics. Numerical simulation is utilized to analyze the effect of different variables on air supply within vertical shafts. The findings reveal that the position of the smoke front significantly influences the direction and flow rate of gases during fire development. The mass flow rate of air supply during the stable fire development stage is influenced by the geometric size and positioning of vertical shafts, with shafts closer to the fire source exhibiting higher air flow rates. To address this issue, this study introduces a predictive model for estimating air flow rates in vertical shafts. This model exhibits a high level of accuracy when compared to simulations, offering a reliable method for predicting air flow rates based on the geometric characteristics of vertical shafts. Overall, this research contributes to understanding the complexities of air supply in tunnels with multiple vertical shafts, aiding in the improvement of natural ventilation strategies during fire incidents. Full article

This work proposed a novel model to simultaneously reconstruct temperature distribution and soot volume fraction field for two-dimensional thin-slice flames using the knowledge of monochromatic radiation intensities at two wavelengths using a CCD camera. The deduction process and numerical analysis of the model were described. Effects of wavelength combinations and measurement errors on reconstruction accuracy were considered in detail. Numerical results have proven the model’s accuracy and showed that the temperature and soot volume fraction fields can be reconstructed well even with noisy input data from flame radiation. In addition, a series of experiments were conducted on a mesoscale combustor to obtain the real thin-slice flames for further experimental reconstruction via the model. The experimental results indicated that the proposed model can successfully reconstruct the flame temperature distribution and soot volume fraction field and the main features of thin-slice flames also can be reasonably reproduced. Full article

Hydrogen is gaining prominence as a sustainable energy source in the UK, aligning with the country’s commitment to advancing sustainable development across diverse sectors. However, a rigorous examination of the interplay between the hydrogen economy and the Sustainable Development Goals (SDGs) is imperative. This study addresses this imperative by comprehensively assessing the risks associated with hydrogen production, storage, transportation, and utilization. The overarching aim is to establish a robust framework that ensures the secure deployment and operation of hydrogen-based technologies within the UK’s sustainable development trajectory. Considering the unique characteristics of the UK’s energy landscape, infrastructure, and policy framework, this paper presents practical and viable recommendations to facilitate the safe and effective integration of hydrogen energy into the UK’s SDGs. To facilitate sophisticated decision making, it proposes using an advanced Decision-Making Trial and Evaluation Laboratory (DEMATEL) tool, incorporating regret theory and a 2-tuple spherical linguistic environment. This tool enables a nuanced decision-making process, yielding actionable insights. The analysis reveals that Incident Reporting and Learning, Robust Regulatory Framework, Safety Standards, and Codes are pivotal safety factors. At the same time, Clean Energy Access, Climate Action, and Industry, Innovation, and Infrastructure are identified as the most influential SDGs. This information provides valuable guidance for policymakers, industry stakeholders, and regulators. It empowers them to make well-informed strategic decisions and prioritize actions that bolster safety and sustainable development as the UK transitions towards a hydrogen-based energy system. Moreover, the findings underscore the varying degrees of prominence among different SDGs. Notably, SDG 13 (Climate Action) exhibits relatively lower overall distinction at 0.0066 and a Relation value of 0.0512, albeit with a substantial impact. In contrast, SDG 7 (Clean Energy Access) and SDG 9 (Industry, Innovation, and Infrastructure) demonstrate moderate prominence levels (0.0559 and 0.0498, respectively), each with its unique influence, emphasizing their critical roles in the UK’s pursuit of a sustainable hydrogen-based energy future. Full article

For the safe utilization and management of hydrogen energy within a fuel-cell room in a hydrogen-fueled house, an explosion test was conducted to evaluate the potential hazards associated with hydrogen accident scenarios. The overpressure and heat radiation were measured for an explosion accident at distances of 1, 2, 3, 5, and 10 m for hydrogen–air mixing ratios of 10%, 25%, 40%, and 60%. When the hydrogen–air mixture ratio was 40%, the greatest overpressure was 24.35 kPa at a distance of 1 m from the fuel-cell room. Additionally, the thermal radiation was more than 1.5 kW/m², which could cause burns at a distance of 5 m from the hydrogen fuel-cell room. Moreover, a thermal radiation in excess of 1.5 kW/m² was computed at a distance of 3 m from the hydrogen fuel-cell room when the hydrogen–air mixing ratio was 25% and 60%. Consequently, an explosion in the hydrogen fuel-cell room did not considerably affect fatality levels, but could affect the injury levels and temporary threshold shifts. Furthermore, the degree of physical damage did not reach major structural damage levels, causing only minor structural damage. Full article

Unlike traditional coal-powered energy generation, renewable energy sources do not generate carbon dioxide emissions. To enhance the efficiency of renewable energy systems, energy storage systems (ESSs) have been implemented. However, in South Korea, ESS fire incidents have emerged as a significant social problem. Consequently, a government-formed committee was established to investigate the cause of these fires through the analysis of the data collected from ESSs, stored in the battery management system (BMS) log data of the fire-resistant safe storage. In the first phase of the investigation, the committee was unable to identify the underlying characteristic of ESS fires. Nevertheless, in the second phase, the investigation committee could identify the key characteristics of ESS fires by analyzing the BMS log data. ESS fires were found to occur when the state of charge level was more than 95% and during the initiation of thermal runaway in specific cells. Despite these findings, the committee was unable to determine the root cause of ESS fires. Full article

In the history of human civilization, traditional villages and buildings have been significantly threatened by fire. As an essential part of Huizhou traditional architecture, fire seal walls play a crucial role in preserving Huizhou architecture by effectively blocking the spread of fire. However, with economic and social development, the Huizhou fire seal wall’s original fire prevention function has been unable to meet the needs of modern fire protection. This study aims to explore the fire performance of different types of Huizhou fire seal walls to provide a reference guide for future fire protection, optimization, and transformation of traditional buildings. In this paper, 3D models of traditional buildings with fire seal walls were built with FDS, and the performance of the different kinds of fire seal walls was simulated under the influence of wind speeds, building spacing, and the height of the vertical ridge of the fire seal wall. The results showed that, under the same conditions, a fire seal wall with a single eave is superior to fire seal walls with quintuple eaves in terms of performance, and fire seal walls with quintuple eaves are superior to fire seal walls with triple eaves in the middle and late stages of a fire. In addition, wind speeds, building spacing, and the height of the vertical ridge have different effects on the fire performance of seal walls. Lower wind speeds can reduce the fire performance of fire seal walls, and no wind and higher wind speeds have no significant effect on the fire performance of fire seal walls, while increasing the height of the vertical ridge and fire spacings appropriately can improve the fire performance of fire seal walls. This study provides a reference guide for future fire protection, optimization, and transformation of Huizhou fire seal walls, which can help improve the fire safety of traditional buildings. Full article

The fire effect and performance of bridge pylons under construction were investigated via an analysis conducted on two types of pylons with different wall thicknesses. Three fire scenarios, namely internal fire, external ring fire, and external side fire, were established for a 40 m high section of the bridge pylon under construction. The distribution of fire smoke and temperature was obtained using fire dynamics simulation software for different fire scenarios. In addition, a finite element simulation was performed using the thermal–mechanical coupling method to obtain the temperature, stress, and deformation of the columns. The simulation results demonstrate that the average temperature of the internal fire is higher. The chimney effect extends the height range of temperature influence. In the vertical direction, the temperature decrease curve for the internal fire follows a single negative exponential function, while the external fire adheres to a double negative exponential function. The thickness of the temperature influence in the bridge pylon is extended by heating to approximately 200 mm. The stress value considering the thermal expansion coefficient is nearly 27.5 times that without the expansion coefficient, while the deformation value increases by 1 to 8 times. In conclusion, the calculations of the coupled expansion coefficient are helpful in improving the fire safety of bridge pylons. Full article

Certain polymers, such as polyvinylidene fluoride (PVDF), polyimide (PI), and poly (phthalazinone ether sulfone ketone) (PPESK), are commonly used separator materials in batteries. However, during the thermal runaway (TR) processing of batteries, significant heat is released by the combustion of the polymer separator. Therefore, analysis of the fire behaviors of polymer separator materials will facilitate a more comprehensive quantitative evaluation of battery thermal risk. This paper investigated the combustion properties of three types of polymers, namely, PVDF, PI, and PPESK, as potential separator materials by cone calorimetry and thermogravimetry (TG). A series of characteristic parameters, including ignition time (TTI), heat release rate (HRR), smoke production rate (SPR), and total heat release (THR), were evaluated for three polymers and blends (PI/PVDF, PPESK/PVDF) under an external heat flux of 45 or 60 kW/m², respectively. The combustion characteristics and fire hazards of the three polymers and corresponding mixtures were analyzed through the comparative analysis of experimental data and phenomena. Under 60 kW/m², the HRR curves of all polymers presented two peaks, while PI/PVDF and PPESK/PVDF mixtures exhibited one obvious peak. Moreover, the peak HRR (pHRR) for the mixed polymers was higher, indicating a relatively higher fire risk. However, in the application scenario, the mixed state represents the main polymer form as the active separator materials in batteries. The results showed that the specific coupling behaviors were related primarily to the component type. This work will help evaluate the fire risk of polymeric separator materials based on the combustion characteristics to predict the safety of mixtures in batteries and develop new methods for fire suppression. Full article

In the Republic of Korea, a new rocket launchpad was constructed to launch the KSLV-II on an island, and all the launchpad facilities are located in basement. Because of the complex and diverse facilities, fire accidents have increased. Using the FDS (Fire Dynamics Simulator) to predict the damage from kerosene storage and drain tank pool fires is garnering more attention as a tool of choice. The FDS supports a sprinkler model, which is needed to analyze fire extinguishing by water sprinkling. To predict and estimate the resistance of the building and thermal damage, the main analysis factors for a kerosene tank pool fire accident are temperature and HRR (heat release rate per unit volume). In 3 ${\mathrm{m}}^3$ release cases, the maximum temperature decreased by 33% from 900 K to 600 K by sprinkled water, and the maximum HRR decreased by 70% from 20,000 kW/${\mathrm{m}}^3$ to 6000 kW/${\mathrm{m}}^3$. In 10 ${\mathrm{m}}^3$ release cases, the temperature and HRR decreased by 44%, from 800 K to 450 K and 68% from 25,000 kW/${\mathrm{m}}^3$ to 8000 kW/${\mathrm{m}}^3$, respectively. Full article

Fires are disastrous events with significant negative impacts on both people and the environment. Thus, timely and accurate fire detection and firefighting operations are crucial for social development and ecological protection. In order to segment the flame accurately, this paper proposes the GK-RGB unsupervised flame segmentation method. In this method, RGB segmentation is used as the central algorithm to extract flame features. Additionally, a Gaussian filtering method is applied to remove noise interference from the image. Moreover, K-means mean clustering is employed to address incomplete flame segmentation caused by flame colours falling outside the fixed threshold. The experimental results show that the proposed method achieves excellent results on four flame images with different backgrounds at different time periods: Accuracy: 97.71%, IOU: 81.34%, and F1-score: 89.61%. Compared with other methods, GK-RGB has higher segmentation accuracy and is more suitable for the detection of fire. Therefore, the method proposed in this paper helps the application of firefighting and provides a new reference value for the detection and identification of fires. Full article

To further improve the detection of smoke and small target smoke in complex backgrounds, a novel smoke detection model called RepVGG-YOLOv7 is proposed in this paper. Firstly, the ECA attention mechanism and SIoU loss function are applied to the YOLOv7 network. The network effectively extracts the feature information of small targets and targets in complex backgrounds. Also, it makes the convergence of the loss function more stable and improves the regression accuracy. Secondly, RepVGG is added to the YOLOv7 backbone network to enhance the ability of the model to extract features in the training phase, while achieving lossless compression of the model in the inference phase. Finally, an improved non-maximal suppression algorithm is used to improve the detection in the case of dense smoke. Numerical experiments show that the detection accuracy of the proposed algorithm can reach about 95.1%, which contributes to smoke detection in complex backgrounds and small target smoke. Full article

Fire is the origin of serious environmental and social impacts in Mediterranean-like landscapes, such as those in California, Australia, and southern Europe. Portugal is one of the southern European countries most affected by fire, which has increased in intensity and extent in the recent decades in response to variations in climate, but mostly due to changes in land systems (LSs), characterized by land use and land cover and also by factors such as management intensity, livestock composition, land ownership structure, and demography. Agricultural activities, which contributed to the management of fuel in the overall landscape, were allocated to the most productive areas, while the steepest areas were occupied by extensive areas of shrubland and monospecific forests, creating landscapes of high fire-proneness. These challenging circumstances call for landscape transformation actions focusing on reducing the burned area, but the spatial distribution of LS is highly conditioned by land morphology (LM), which limits the actions (e.g., farming operations) that can be taken. Considering the constraints posed by the LM, this study investigates whether there is a possibility of transforming the landscape by single modifying the LS from more to less fire prone. To better understand landscape–fire relationships, the individual and interactive effects of the LS and LM on burned areas were also analyzed. Even in the more fire-prone LM types, a 40% proportion of agricultural uses in the landscape results in an effective reduction in the burned area. Full article

The limit parameters of coal spontaneous combustion are important indicators for determining the risk of spontaneous combustion in coal seams. By analyzing the limit parameters of coal spontaneous combustion, the dangerous areas of coal spontaneous combustion can be determined, and corresponding measures can be taken to avoid the occurrence of fires. In order to accurately predict the limit parameters of coal spontaneous combustion, the prediction model of coal spontaneous combustion limit parameters based on GA-SVM was constructed by coupling genetic algorithm (GA) and support vector machine (SVM). Meanwhile, the GA and particle swarm optimization algorithm (PSO) were used to optimize the back propagation neural network (BPNN) to construct the GA-BPNN and PSO-BPNN prediction models, respectively. To predict the intensity of air leakage of the upper limit of coal spontaneous combustion in the goaf, the prediction results of the models were compared and analyzed using MAE, MAPE, RMSE, and R² as the prediction performance evaluation indexes. The results show that the MAE of the GA-SVM model, the PSO-BPNN model, and the GA-BPNN model are 0.0960, 0.1086, and 0.1309, respectively; the MAPE is 2.46%, 3.11%, and 3.69%, respectively; the RMSE is 0.1180, 0.1789, and 0.2212, respectively; and the R² is 0.9921, 0.9818, and 0.9722. The prediction results of the GA-SVM model are the most optimal in four evaluation indexes, followed by the PSO-BPNN and the GA-BPNN models. Applying each model to the prediction of minimum residual coal thickness in the goaf of a coal mine in Shanxi, the GA-SVM model has higher accuracy, which further verifies the universality and stability of the model and its suitability for the prediction of coal spontaneous combustion limit parameters. Full article

In the face of fire in buildings, people need to quickly plan their escape routes. Intelligent optimization algorithms can achieve this goal, including the sparrow search algorithm (SSA). Despite the powerful search ability of the SSA, there are still some areas that need improvements. Aiming at the problem that the sparrow search algorithm reduces population diversity and is easy to fall into local optimum when solving the optimal solution of the objective function, a hybrid improved sparrow search algorithm is proposed. First, logistic-tent mapping is used to initialize the population and enhance diversity in the population. Also, an adaptive period factor is introduced into the producer’s update position equation. Then, the Lévy flight is introduced to the position of the participant to improve the optimization ability of the algorithm. Finally, the adaptive disturbance strategy is adopted for excellent individuals to strengthen the ability of the algorithm to jump out of the local optimum in the later stage. In order to prove the improvement of the optimization ability of the improved algorithm, the improved sparrow algorithm is applied to five kinds of maps for evacuation path planning and compared with the simulation results of other intelligent algorithms. The ultimate simulation results show that the optimization algorithm proposed in this paper has better performance in path length, path smoothness, and algorithm convergence, showing better optimization performance. Full article