Search Results (413)

Fire spread between buildings is the primary cause of extensive fire damage in traditional village timber structure clusters. Accurately assessing fire spread risk is crucial for the preservation of these architectural ensembles. During the development and conservation of traditional villages, fire risk dynamics may shift due to fire-resistant retrofits or layout modifications, necessitating repeated risk reevaluations. To address challenges such as the computational intensity of fire spread simulations, high costs, and data acquisition difficulties, this study proposes a directed graph-based method for fire spread risk analysis and risk level classification in timber structure clusters, accounting for their unique fire propagation characteristics. First, localized fire spread paths and propagation times between nodes (buildings) are determined through fire spread simulations, constructing an adjacency matrix for the directed graph of the building cluster. Path search algorithms then identify the spread range and velocity under specific fire scenarios. Subsequently, a zoned risk assessment model for individual buildings is developed based on critical fire spread loss and velocity, integrating each building’s fire resistance and its probability of exposure to different risk zones to determine the overall cluster’s fire spread risk level. The method is validated using a case study of a typical village in Yunnan Province. Results demonstrate that the approach efficiently computes fire spread characteristics across different scenarios and quantitatively evaluates risk levels, enabling targeted fire safety interventions based on village-specific spread patterns. Case analysis reveals significant variations in fire spread behavior: Village 1, Village 2, and Village 3 exhibit fire resistance indices of 0.59, 0.757, and 0.493, corresponding to high, moderate, and high fire spread risk levels, respectively. Full article

Mathematical modeling and computer visualization of hazardous zones of toxic substance cloud spread that occur during different accidents at industrial enterprises located near residential areas are in high demand to support the operational planning of evacuation measures and accident response. The possible chain-like nature of fires and explosions of containers with toxic substances inside increases the importance of predicting changes in hazardous zone parameters in real time. The objective of this study is to develop algorithms for the development of a mathematical model of a hazardous zone during an explosion and fire at an enterprise. The subject of this study is a software tool created for the visualization of hazardous substance emission zones in real time, superimposed onto a development map to determine potential damage to human health and for the operational planning of evacuation measures. The proposed model takes into account variables such as the air temperature, wind speed and direction, the mass of the substance at each explosion and fire site, etc. C# and Visual Studio 2022 languages and an SQL database were used to create a software tool for visualizing the hazardous area. The testing of the calculation model and software used for the visualization of the hazardous zones of toxic substance cloud spread are presented on the basis of explosion cases involving a railway tank containing ammonia and the combustion of polyvinyl chloride at a chemical industry enterprise. The results confirmed the operability of the software and the prospects of its use in regard to the mitigation of the consequences of human-made accidents. Full article

As part of the work, polymer composites dedicated to rapid prototyping were developed, especially for 3D printing using the material extrusion technique. For this purpose, a polymer matrix was selected, which was an acrylonitrile-butadiene-styrene (ABS) terpolymer and a flame retardant, which was tetrakis (2,6-dimethylphenyl)-m-phenylenebisphosphate, commercially known as PX200. The effect of the presence and amount (5, 10, 15 and 20 wt.%) of the introduced additive on the rheological properties, structural properties, flammability (limiting oxygen index, LOI; UL94) and flame retardant properties (microcone calorimeter, MLC) of ABS-based composites was investigated. In addition, the mechanism of thermal degradation and flame resistance was investigated using thermogravimetric analysis, TGA and Fourier transform infrared spectroscopy, FT-IR of the residue after the MLC test. In the first part of the work, using the author’s technological line, filaments were obtained from unfilled ABS and its composites. Samples for testing were obtained by 3D printing in Fused Deposition Modeling (FDM) technology. In order to determine the quantitative and qualitative spread of fire and the effectiveness of the phosphorus flame retardant PX200 in the produced composites, the Maximum Average Rate of Heat Emission (MARHE); Fire Growth Rate Index (FIGRA); Fire Potential Index (FPI) and Flame Retardancy Index (FRI) were determined. Based on the obtained results, it was found that the aryl biphosphate used in this work exhibits activity in the gas phase, which was confirmed by quantitative assessment using data from a microcone calorimeter and non-residues after combustion and thermolysis at 700 °C. As a result, the flammability class did not change (HB40), and the LOI slightly increased to 20% for the composite with 20% flame retardant content. Moreover, this composite was characterized by the following flammability indices: pHRR = 482.9 kW/m² (−40.3%), MARHE = 234 kW/m² (−40.7%), FIGRA = 3.1 kW/m²·s (−56.3%), FPI = 0.061 m²·s/kW (+64.9%), FRI = 2.068 (+106.8%). Full article

Metropolitan city express line tunnels are fully enclosed and often span long distances between stations, allowing multiple trains within a single interval. Traditional segmented ventilation ensures only one train per section, but ultra-long tunnels with shaftless designs introduce new challenges under fire conditions. This study investigates smoke behavior in an ultra-long inter-district tunnel during multi-train blockage scenarios. A numerical model evaluates the effects of train spacing, fire source location, and receding spacing on smoke back-layering, temperature distribution, and flow velocity. Results indicate that when train spacing exceeds 200 m and longitudinal wind speed is above 1.2 m/s, the impact of train spacing on smoke back-layering becomes negligible. Larger train spacing increases back-layering under constant wind speed, while higher wind speeds reduce it. Fire source location and evacuation spacing affect the extent and pattern of smoke spread and high-temperature zones, especially under reverse ventilation conditions. These findings provide quantitative insights into fire-induced smoke dynamics in ultra-long tunnels, offering theoretical support for optimizing ventilation control and evacuation strategies in urban express systems. Full article

Accurate real-time tracking of forest fires using UAV platforms is crucial for timely early warning, reliable spread prediction, and effective autonomous suppression. Existing detection-based multi-object tracking methods face challenges in accurately associating targets and maintaining smooth tracking trajectories in complex forest environments. These difficulties stem from the highly nonlinear movement of flames relative to the observing UAV and the lack of robust fire-specific feature modeling. To address these challenges, we introduce AO-OCSORT, an association-optimized observation-centric tracking framework designed to enhance robustness in dynamic fire scenarios. AO-OCSORT builds on the YOLOX detector. To associate detection results across frames and form smooth trajectories, we propose a temporal–physical similarity metric that utilizes temporal information from the short-term motion of targets and incorporates physical flame characteristics derived from optical flow and contours. Subsequently, scene classification and low-score filtering are employed to develop a hierarchical association strategy, reducing the impact of false detections and interfering objects. Additionally, a virtual trajectory generation module is proposed, employing a kinematic model to maintain trajectory continuity during flame occlusion. Locally evaluated on the 1080P-resolution FireMOT UAV wildfire dataset, AO-OCSORT achieves a 5.4% improvement in MOTA over advanced baselines at 28.1 FPS, meeting real-time requirements. This improvement enhances the reliability of fire front localization, which is crucial for forest fire management. Furthermore, AO-OCSORT demonstrates strong generalization, achieving 41.4% MOTA on VisDrone, 80.9% on MOT17, and 92.2% MOTA on DanceTrack. Full article

Timely and accurate detection of forest fires is crucial for protecting forest ecosystems. However, traditional monitoring methods face significant challenges in effectively detecting forest fires, primarily due to the dynamic spread of flames and smoke, irregular morphologies, and the semi-transparent nature of smoke, which make it extremely difficult to extract key visual features. Additionally, deploying these detection systems to edge devices with limited computational resources remains challenging. To address these issues, this paper proposes a lightweight hybrid receptive field model (LHRF-YOLO), which leverages deep learning to overcome the shortcomings of traditional monitoring methods for fire detection on edge devices. Firstly, a hybrid receptive field extraction module is designed by integrating the 2D selective scan mechanism with a residual multi-branch structure. This significantly enhances the model’s contextual understanding of the entire image scene while maintaining low computational complexity. Second, a dynamic enhanced downsampling module is proposed, which employs feature reorganization and channel-wise dynamic weighting strategies to minimize the loss of critical details, such as fine smoke textures, while reducing image resolution. Furthermore, a scale weighted Fusion module is introduced to optimize multi-scale feature fusion through adaptive weight allocation, addressing the issues of information dilution and imbalance caused by traditional fusion methods. Finally, the Mish activation function replaces the SiLU activation function to improve the model’s ability to capture flame edges and faint smoke textures. Experimental results on the self-constructed Fire-SmokeDataset demonstrate that LHRF-YOLO achieves significant model compression while further improving accuracy compared to the baseline model YOLOv11. The parameter count is reduced to only 2.25M (a 12.8% reduction), computational complexity to 5.4 GFLOPs (a 14.3% decrease), and mAP50 is increased to 87.6%, surpassing the baseline model. Additionally, LHRF-YOLO exhibits leading generalization performance on the cross-scenario M4SFWD dataset. The proposed method balances performance and resource efficiency, providing a feasible solution for real-time and efficient fire detection on resource-constrained edge devices with significant research value. Full article

Assessments of subway train fires were conducted based on full-scale experiments and numerical simulations. The experimental platform and simulation model were established according to a real subway train in China. The results show that there was no obvious flame spread, and all the electrical circuitry maintained its integrity during a standard luggage fire. The maximum HRR (heat release rate) of the luggage fire obtained through the full-scale experiment was 155.5 kW, which was almost the same as the standard HRR curve provided in EN 45545-1. However, the fire only lasted approximately 180 s, which was much shorter than a standard fire (600 s). Through numerical simulations of an entire subway train, the side wall and roof ignited quickly, and the fire continually spread to the adjacent compartment under the extreme scenario with a gasoline pool fire and exposed winterproof material. The maximum HRRs of the luggage and gasoline pool fires were 179.7 and 17,800.0 kW, respectively. According to the experimental and simulation results, the Duggan method, which assumes that all combustibles inside a train compartment burn at the same time, was not appropriate for assessing the fires in the subway train, and a simple revised frame was proposed instead. Full article

Electrical cabinet fire scenarios constitute a significant risk within nuclear facilities, emphasizing the need to mitigate uncertainties in risk evaluations. Owing to the disparate nature of electrical cabinet parameters, only a few factors have been experimentally explored and statistically analyzed to assess their impact on peak HRR. In this study, we conducted both a cabinet parameter study and a combustible configuration study to systematically evaluate their influence on peak HRR and time-to-peak HRR. A series of 51 simulation matrices were created using statistical experiment design (SED) and ANOVA to quantify the influence of cabinet volume, combustible surface area, vent area, ignition characteristics, and burning behavior (e.g., HRRPUA and duration). A computational fluid dynamics (CFD) model, specifically a Fire Dynamics Simulator (FDS), was used to model the ignition source and flame spread inside of the electrical cabinet that influence peak HRR. The most impactful parameters influencing peak HRR and time-to-peak HRR were identified. The findings revealed that the configuration of combustibles and the placement of the ignition source play a pivotal role in determining the peak HRR. A partition screening analysis was conducted to identify the conditions under which the ventilation area becomes a more significant parameter. Additionally, a comparison between experimental results and numerical simulations demonstrated good agreement, further validating the predictive capability of the model. Full article

This study proposes a deflection-based criterion for the assessment of barrel support brackets to ensure the structural stability of large fire shutters installed in large-scale buildings such as logistics facilities. While the current extended application method in the BS EN 15269 standard allows for the evaluation of the structural adequacy of the barrel—primarily based on stress analysis—this research aims to establish a more reliable design guideline by additionally considering the deflection of barrel support brackets, which may become structurally vulnerable under high-temperature conditions. To achieve this, the bracket was modeled as a cantilever beam, and deflection equations were applied. The deflection and stress were analyzed for various rectangular hollow sections. Furthermore, the support capacities at ambient temperature and at 700 °C were compared, and regression analysis was conducted to assess the Accuracy and error rates associated with different deflection limits (L/180 to L/480). The results indicate that setting the deflection limit to L/180 yields the most favorable outcome in terms of structural safety and error minimization across most conditions. It is expected that the adoption of deflection criteria for barrel support brackets in the design of large fire shutters will contribute significantly to preventing the spread of fire and ensuring structural safety. Full article

This article discusses and compares approaches to the visualization of the danger zone formed as a result of spreading toxic substances during a fire at an industrial enterprise, to create predictive models and scenarios for evacuation and environmental protection measures. The purpose of this study is to analyze the features and conditions for the application of algorithms for predicting the spread of a danger zone, based on the Gauss equation and the probabilistic algorithm of a cellular automaton. The research is also aimed at the analysis of the consequences of a fire at an industrial enterprise, taking into account natural and climatic conditions, the development of the area, and the scale of the fire. The subject of this study is the development of software and algorithmic support for the visualization of the danger zone and analysis of the consequences of a fire, which can be confirmed by comparing a computational experiment and actual measurements of toxic substance concentrations. The main research methods include a Gaussian model and probabilistic, frontal, and empirical cellular automation. The results of the study represent the development of algorithms for a cellular automation model for the visual forecasting of a dangerous zone. They are characterized by taking into consideration the rules for filling the dispersion ellipse, as well as determining the effects of interaction with obstacles, which allows for a more accurate mathematical description of the spread of a cloud of toxic combustion products in densely built-up areas. Since the main problems of the cellular automation approach to modeling the dispersion of pollutants are the problems of speed and numerical diffusion, in this article the frontal cellular automation algorithm with a 16-point neighborhood pattern is used, which takes into account the features of the calculation scheme for finding the shortest path. Software and algorithmic support for an integrated system for the visualization and analysis of fire consequences at an industrial enterprise has been developed; the efficiency of the system has been confirmed by computational analysis and actual measurement. It has been shown that the future development of the visualization of dangerous zones during fires is associated with the integration of the Bayesian approach and stochastic forecasting algorithms based on Markov chains into the simulation model of a dangerous zone for the efficient assessment of uncertainties associated with complex atmospheric processes. Full article

Fire doors are installed between compartments to prevent the spread of fire. During a fire, the temperature difference between the exposed and unexposed surfaces induces bending deformation of the door, thereby reducing its fire resistance performance. Excessive deformation may further compromise the structural integrity of the door. This study presents a thermo-mechanical model that idealizes the bending behavior of double swing fire doors based on the deflection equation of a simply supported beam subjected to a thermal gradient between the tensile and compressive sides. A criterion of deformation, quantifying the relationship between the meeting stile gap and the resulting maximum deflection, is introduced and compared with the predicted values. The validity of the proposed model was confirmed through fire resistance tests conducted on both insulated and non-insulated fire door specimens, demonstrating strong agreement with experimental results. Furthermore, by comparing the predicted deformation with the deformation criterion, the impact of increasing gap sizes on the service life of fire doors on their fire resistance performance was evaluated. Based on this analysis, appropriate gap size limits for different door specifications are proposed to ensure reliable fire performance. Full article

Wildfires pose significant threats to ecosystems, human lives, and property worldwide. Understanding the behavior of fire spread on sloped terrain is essential for developing effective firefighting strategies and improving fire prediction models. Previous research has successfully demonstrated the accuracy of numerical tools in comparison to laboratory experiments. This study focuses on the influence of terrain slope and wind speed on wildland fire behavior using Computational Fluid Dynamics (CFD) simulations. In the first phase, the numerical model was validated for a 5 m high single Douglas Fir tree under various mesh sizes, yielding heat release and mass loss rates in close agreement with experimental data. The second phase extends the model to simulate a plantation of 66 Douglas Fir trees under varying slopes and wind conditions. The results indicate that a downward slope of 30° reduces the peak heat release rate, while an upward slope of 30° increases it, with wind speed amplifying these effects. Based on these data, a new reduced-order model is proposed to quantify the influence of slope angle on the heat release rate (HRR) in wildland fires. These findings are critical for enhancing predictive fire models and mitigating wildfire risks in complex terrains. Full article

Fire safety is one of the critical concerns for the design and construction of modular structures. The lack of understanding of cavity fire spread in modular construction could create variations in the fire performance of structural members. This study aimed to assess the impact of cavity fire spread in modular buildings initiated by a room fire using validated fire dynamics and structural numerical models. A comprehensive parametric study was conducted to identify critical thermal conditions affecting adjacent structural members under plausible cavity fire scenarios. The identified critical cavity fire thermal conditions were used to examine the structural performance of cold-formed steel intermediate column specimens while varying geometric configurations, material properties, and boundary conditions. The results highlighted two distinct phases of restrained thermal expansion and lateral deformations under material yielding and buckling, resulting in the loss of structural integrity. The restrained thermal expansion significantly increased axial/restraint forces, reaching up to 155% of the initial load. This behavior decreased axial load capacity by 2.4% to 35% of the ambient capacity. Further, the study identifies a requirement for designing the intermediate columns and the connected intermodular connections for increased design action equivalent to 56% of the service load. Full article

The Wan’an Bridge, the longest wooden lounge bridge in China with a history of more than 900 years, was devastated by a catastrophic fire in 2022. This tragic event underscores the susceptibility of historical wooden structures to fire damage. In this article, the bridge’s intricate structure and the development of the fire incident are introduced in detail. To gain a deeper insight into the patterns of fire propagation across the bridge and assess the reliability of fire simulations in predicting fire spread in historical wooden structures, we utilized the Fire Dynamics Simulator (FDS), with a sophisticated pyrolysis model and thermal response parameters specifically tailored to ancient fir wood. The modeling results reveal that the FDS simulation reflects the actual fire spread process well. Both the investigation and simulation findings indicate that once the flame reaches above the bridge deck, it enters a rapid three-dimensional propagation phase that is exceptionally challenging to control. Furthermore, the modeling results suggest that the application of intumescent fire-retardant coatings can significantly delay fire spread, reduce heat release rates, and suppress smoke production, thereby making them an effective fire prevention measure for historical wooden buildings. Full article

The rapid growth in natural gas consumption by gas-fired generators and the emergence of power-to-hydrogen (P2H) technology have increased the interdependency of natural gas and power systems, presenting new challenges to energy system operators due to the heterogeneous uncertainties associated with power loads, renewable energy sources (RESs), and gas loads. These uncertainties can easily spread from one infrastructure to another, increasing the risk of cascading outages. Given the erratic nature of RESs, P2H technology provides a valuable solution for large-scale energy storage systems, crucial for the transition to economic, clean, and secure energy systems. This paper proposes a new approach for the co-optimized operation of gas and electric power systems, aiming to reduce combined operating costs by 10–15% without jeopardizing gas and energy supplies to customers. A mixed integer non-linear programming (MINLP) model is developed for the optimal day-ahead operation of these integrated systems, with a case study involving the IEEE 24-bus power system and a 20-node natural gas system. Simulation results demonstrate the model’s effectiveness in minimizing total costs by up to 20% and significantly reducing renewable energy curtailment by over 50%. The proposed approach supports UN Sustainable Development Goals by ensuring sustainable energy (SDG 7), fostering innovation and resilient infrastructure (SDG 9), enhancing energy efficiency for resilient cities (SDG 11), promoting responsible consumption (SDG 12), contributing to climate action (SDG 13), and strengthening partnerships (SDG 17). It promotes clean energy, technological innovation, resilient infrastructure, efficient resource use, and climate action, supporting the transition to sustainable energy systems. Full article