Fire Location Research Articles

Numerical Simulation of the Effects of Compartment Height, Fire Source Location, and Vent Location on Compartment Fire Phenomena with a Ceiling Vent

In this study, the numerical simulations of the effects of compartment height, fire source location, and vent location on the compartment fire phenomena with a ceiling vent were performed. Under the same fire source and vent location conditions, the compartment height of 5.5 m showed a higher mass flow rate across the vent than that of 11 m. For the fire source at the center, the vent on the left showed a lower mass flow rate than that at the center for both compartment heights. For the fire source on the left, the vents on the right and left exhibited the lowest mass flow rates at the compartment heights of 5.5 m and 11 m, respectively. Moreover, for the fire source on the left, a macroscopic rotational flow in the clockwise direction was observed at the compartment height of 5.5 m, whereas at the compartment height of 11 m, macroscopic rotational flows in the clockwise and counterclockwise directions appeared at its lower and upper portions, respectively. It was confirmed that these macroscopic rotational flows were closely related to the change in the mass flow rate across the vent with the vent location. The compartment height of 11 m had a lower average temperature inside the compartment than that of 5.5 m, which was because of the increase in the surrounding wall area. In addition, under each compartment height condition, the changes in the average temperature inside the compartment with the fire source and vent locations were minor.

Geo-Fenced Aerial Fire Response System

Purpose: We aim to create a prototype of a fire extinguisher drone equipped with advanced geo-fencing technology explicitly designed for use in high-rise buildings. This drone will be capable of both ground movement and flight, allowing it to quickly navigate narrow spaces and difficult-to-reach areas. Methodology: The study involves an in-depth analysis of various drones, features, and geo-fencing to develop a prototype for precise and targeted fire suppression. The aim is to minimize the damage caused by fires and improve overall safety by enabling swift intervention, particularly in confined or elevated locations where traditional firefighting methods may face challenges. Findings: The prototype drone, equipped with geo-fencing capabilities, has demonstrated its ability to be efficiently deployed within specific, pre-defined areas, significantly improving its precision and effectiveness in targeted fire suppression. This feature ensures that the drone operates only within designated zones, reducing the risk of interference or wandering into unsafe areas. The drone is controlled remotely, allowing it to fly freely and access hard-to-reach locations quickly. A small surveillance camera mounted on the drone enhances its effectiveness by providing real-time visuals of the fire's location and intensity. This capability allows operators to assess the situation more accurately and deploy the necessary response measures, making the drone a powerful tool for modern firefighting operations. Unique Contribution to Theory, Practice and Policy: The findings from this prototype provide valuable insights that can shape the future of fire response mechanisms and drone designs. The drones can target fires while minimizing risks to firefighting personnel, and they can potentially revolutionize emergency response strategies. The ability to remotely control drones, equipped with geo-fencing and surveillance capabilities, reduces the need for human intervention in dangerous situations and improves the speed and accuracy of fire suppression efforts. Furthermore, this technology can be applied to detect and combat wildfires, helping to minimize their destructive impact by enabling quicker detection and targeted responses in remote or difficult-to-access areas. These advancements pave the way for safer and more efficient firefighting methods in the future.

Predicting smoke temperature distribution beneath ceiling for a large subway station fire

Fire accident seriously threatens the stable operation and personnel security in the subway station, and clarifying the smoke temperature distribution is conducive to arranging reasonably fire alarm. In this study, model experiments and numerical simulations are conducted to investigate the ceiling temperature profiles, mainly altering the heat release rate (HRR) and fire locations in a large subway station. The repeatability of the experimental data is confirmed, and the numerical model is validated by the measurement data. During a fire happening in a large-wide space, the smoke movement beneath the ceiling can be divided into radial and one-dimensional flow, whose prediction models are proposed under fire source located at the center. On this basis, it can be found that the radial temperature profile is independent of fire source location. While, the one-dimensional temperature distribution model is modified by introducing the effects of transvers and longitudinal fire locations respectively. This work could provide data support and theoretical reference for clarifying the smoke movement for large subway station fire.

The impact of fire source location on hot gas layer temperature in multi-compartment pre-flashover fires: Analysis and semi-empirical model development

A numerical study of a two-room structure (multi-compartment) subjected to fire in the inner room was carried out, employing the CFD (Computational Fluid Dynamics) software called FDS (Fire Dynamics Simulator), to (i) analyze the influence of the fire source position on the hot gas layer temperature, Tu, and its behavior in a multi-compartment fire, and (ii) develop a semi-empirical engineering calculation model to predict Tu in the adjacent room to a pre-flashover well-ventilated fire room, taking into account the fire source position. The results showed that when the fire origin occurs close to walls or elevated above the floor level, a reduced air entrainment is observed in the fire plume and flame, causing an increase in the Tu and a reduction of the smoke production in the fire room. This reduction in the smoke production led to a lower increase in the Tu of the adjacent room in relation to the case when the fire source was at the center of the fire room. It was also observed that the Tu in both rooms was inversely proportional to the ventilation factor (A.H0.5), with a significant increase observed as the ventilation factor decreases, depending on the fire source position. Furthermore, a semi-empirical engineering calculation model was developed to predict the Tu in the adjacent room, considering different fire source locations at ground level and elevated. The model was validated against experimental data from the literature, showing a good agreement (deviation of ≈20 %), thus demonstrating its applicability to other cases.

A Tunnel Fire Detection Method Based on an Improved Dempster-Shafer Evidence Theory

Tunnel fires are generally detected using various sensors, including measuring temperature, CO concentration, and smoke concentration. To address the ambiguity and inconsistency in multi-sensor data, this paper proposes a tunnel fire detection method based on an improved Dempster-Shafer (DS) evidence theory for multi-sensor data fusion. To solve the problem of evidence conflict in the DS theory, a two-level multi-sensor data fusion framework is adopted. The first level of fusion involves feature fusion of the same type of sensor data, removing ambiguous data to obtain characteristic data, and calculating the basic probability assignment (BPA) function through the feature interval. The second-level fusion derives basic probability numbers from the BPA, calculates the degree of evidence conflict, normalizes the BPA to obtain the relative conflict degree, and optimizes the BPA using the trust coefficient. The classical DS evidence theory is then used to integrate and obtain the probability of tunnel fire occurrence. Different heat release rates, tunnel wind speeds, and fire locations are set, forming six fire scenarios. Sensor monitoring data under each simulation condition are extracted and fused using the improved DS evidence theory. The results show that there is a 67.5%, 83.5%, 76.8%, 83%, 79.6%, and 84.1% probability of detecting fire when it occurs, respectively, and identifies fire occurrence in approximately 2.4 s, an improvement from 64.7% to 70% over traditional methods. This demonstrates the feasibility and superiority of the proposed method, highlighting its significant importance in ensuring personnel safety.

Dual-agent intelligent fire detection method for large commercial spaces based on numerical databases and artificial intelligence

Fires in commercial buildings can lead to significant casualties and property damage. Thus, rapidly and accurately identifying the source of a fire and its intensity is crucial for guiding rescue operations. This study aims to explore the use of distributed fiber optic temperature sensing systems, combined with deep learning algorithms, to predict key information about fires in commercial buildings, including the location of the fire source, the intensity of fire development, and the critical planar distribution of carbon monoxide. Based on the Fire Dynamic Simulation (FDS) data, a dual-agent model was developed, and with a sensor sampling interval of 3 s, 30 s of historical data achieved prediction accuracies of 94.23 % for fire intensity and 99.28 % for fire location. Upon reducing the intervals for fire location, the accuracy reached 93.56 %. Additionally, the Random Forest algorithm for mapping carbon monoxide distribution demonstrated an overall Mean Absolute Percentage Error (MAPE) of 5.87 %. Within the 0–150 ppm range, the MAPE was 2.46 %, far below the error levels of existing CO sensors. The database configurations and the sensors' spatiotemporal arrangement were also optimized to rapid and reliable fire prediction. This research demonstrates the feasibility of using artificial intelligence for critical fire scene information detection.

Air temperature characteristics of cable-girder pin joint node of suspension bridge under pool fire

Under the action of high temperature, the mechanical properties of steel structure will be reduced. Cable-girder pin joint nodes (referred to as the node) of suspension bridges are composed of steel structures. Before studying the fire resistance of structures, it is necessary to accurately predict the air temperature characteristics under fire. In this paper, the FDS calculation model of the node is established for the pool fire. The air temperature characteristics of the node are clarified by numerical simulation and theoretical analysis. The effects of main factors such as fire location, wind speed, leakage hole radius and space height are studied. The unfavorable fire scenario and air heating mathematical model are obtained. The main conclusions are as follows: (1) As the fire source distance increases, the peak value of air temperature at the node gradually decreases, while the heating time steadily increases and eventually reaches a stable state. (2) The air temperature at the node initially increases and then gradually decreases as the wind speed increases. (3) Increasing the leakage hole radius from 0.02 m to 0.06 m results in a decrease of approximately 60 % in the air heating time at the node. (4) Increasing the space height from 0.25 m to 1.7 m leads to a maximum decrease of 72 % in air temperature at the node. (6) In the unfavorable fire scenario, the peak air temperature at the node can reach approximately 1200 °C, and the heating time ranges from 67 s to 149 s (7) A mathematical model of the air temperature rise curve, considering the influence of space height, is developed. This model can serve as a theoretical foundation and reference for the study of fire resistance of the node exposed to pool fires.

Firefighting Robot Extinguishment Decision‐Making Based on Visual Guidance: A Novel Attention and Scale U‐Net Model and Genetic Algorithm

ABSTRACTAt present, rescue firefighting relies mainly on firefighting robots, and robots with perception and decision‐making functions are the key elements for achieving intelligent firefighting. However, traditional firefighting robots often lack the ability for autonomous perception and decision‐making when extinguishing multiple fire sources, leading to low rescue efficiency and increased risk for rescue personnel, especially when making firefighting decisions in extreme fire scenes, which poses a challenge. To effectively handle firefighting tasks and ensure operational efficiency, a robot firefighting decision‐making method based on drone visual guidance is proposed. First, we introduce a novel Attention and Scale U‐Net (ASUNet) model to accurately capture crucial target information, including fire location and size, in a fire scene. The ASUNet model adopts an effective multiscale fusion strategy and attention mechanism to enhance the model's performance. Subsequently, based on the results of the ASUNet model, through pixel segmentation clustering and a genetic optimization algorithm, we obtain the robot's firefighting decision results, thereby guiding the robot to carry out firefighting operations systematically. Finally, through numerical experiments, it is verified that the proposed ASUNet model is superior and effective, as the model can perceive important information in a fire scene and extract it well. The use of improved genetic optimization can further accelerate algorithm convergence. To our knowledge, this study is the first to use drone‐based monocular vision guidance for firefighting decision‐making, providing significant engineering value.

Prediction of flashover time in a compartment fire by CNN-LSTM based deep neural network considering wearable data collection equipment

Real-time prediction of flashover in a compartment fire is of great significance for rescue. This paper investigated a CNN-LSTM neural network to predict the flashover based on the temperature and radiative heat flux in 24 compartment fire cases simulated with FDS. Multiple conditions affecting compartment fires have been taken into account, like room size, room height, fire source location, opening size, number of vents, and fire load. Radiative heat flux meters and thermocouples were positioned at specific heights, to simulate sensors worn by firefighters entering a fire scene. The proposed model consisted of a convolutional layer and three LSTM layers. After training, the deep learning model effectively identified the flashover in compartment fires, with an average relative error of 4.1 % for temperature prediction and 11.2 % for radiative heat flux prediction. In addition, the model was validated on other simulated fire cases and full-scale experimental data, and the results showed good performance. In this paper, the sensitivity of the model to lead time was analyzed, and the application range of the model was determined. The model performed well within the lead time of 25s. In view of the strong performance of the proposed deep learning model in predicting flashover based on wearable sensors (temperature and radiative heat flux), it is believed that this model holds potential for application in a broader range of fire scenarios. This demonstrates the feasibility of using deep learning artificial intelligence models to predict compartment fire development, providing scientific guidance for smart firefighting technology.

Analisis Lokasi Pos Damkar Berdasarkan Peta Kerawanan Kebakaran Menggunakan SIG Di Jakarta Timur

This research aims to analyze the range of fire post locations on the fire vulnerability map in East Jakarta. The methodology of this research is quantitative research with data analysis techniques using GIS analysis, namely network analysis to create fire post affordability, and overlay analysis to create fire vulnerability maps. Based on result there are still areas that have not been reached, especially in Duren Sawit, Cakung, Makasar, Cipayung and Ciracas Districts. When viewed in terms of fire disaster vulnerability, East Jakarta City has a medium-high level of vulnerability, while areas with a low level of vulnerability are very few with an area of only 15 hectares. Areas with a medium level of vulnerability dominate with a total area of 12,362 hectares, while areas with a high level of vulnerability cover an area of 6,124 hectares. If an overlay analysis is carried out on the distance between fire posts and the fire vulnerability of residential areas in East Jakarta, it can be seen that there are still areas with a high-medium fire vulnerability class that are not yet reached by fire posts covering an area of 6,018.08 hectares. In fact, 46% of all areas with a high level of fire vulnerability have not been reached by fire stations. These areas are found in Duren Sawit, Ciracas and Cipayung sub-districts. It is recommended that fire posts be built in unreached areas to ensure disaster management is more efficient. This research aims to analyze the range of fire post locations on the fire vulnerability map in East Jakarta. The methodology of this research is quantitative research with data analysis techniques using GIS analysis, namely network analysis to create fire post affordability, and overlay analysis to create fire vulnerability maps. Based on result there are still areas that have not been reached, especially in Duren Sawit, Cakung, Makasar, Cipayung and Ciracas Districts. When viewed in terms of fire disaster vulnerability, East Jakarta City has a medium-high level of vulnerability, while areas with a low level of vulnerability are very few with an area of only 15 hectares. Areas with a medium level of vulnerability dominate with a total area of 12,362 hectares, while areas with a high level of vulnerability cover an area of 6,124 hectares. If an overlay analysis is carried out on the distance between fire posts and the fire vulnerability of residential areas in East Jakarta, it can be seen that there are still areas with a high-medium fire vulnerability class that are not yet reached by fire posts covering an area of 6,018.08 hectares. In fact, 46% of all areas with a high level of fire vulnerability have not been reached by fire stations. These areas are found in Duren Sawit, Ciracas and Cipayung sub-districts. It is recommended that fire posts be built in unreached areas to ensure disaster management is more efficient.

Egress Safety for STUDIO Residential Buildings

In recent years, the number of studio residential buildings has increased significantly in Korea, as well as in many other countries, due to changes in living patterns. In Korea especially, there have been many fire accidents in studio residential buildings, which have caused a huge number of casualties and property damages, because the buildings were not adequately equipped for firefighting. In this study, the egress safety of a typical studio residential building in Korea is analyzed. Fire simulations were performed with variables of the fire location and the capacity of the smoke exhaust system to estimate the available safe egress time (ASET); egress simulations were also performed with the variable of egress delay time, and the required safe egress time (RSET) was determined. Then, the egress safety was evaluated, and the criteria for egress safety evaluation were proposed based on the simulation results. A studio residential building with a floor plan different from the prototype was used to validate the proposed egress safety criteria. Finally, a simple evaluation model is presented to estimate the required safe egress time (RSET) without simulation and to examine the impact of bottlenecks.

Robot-assisted pedestrian evacuation in fire scenarios based on deep reinforcement learning

Indoor fires pose a significant challenge to the safe evacuation of pedestrians. In response to fire hazards, pedestrians instinctively seek alternative evacuation routes to avoid the hazard zone. However, the specific location and intensity of the fire hazard zone can influence pedestrians' decisions, leading to varying congestion levels in different areas. To address this challenge and enhance overall evacuation efficiency, this paper introduces an improved social force model to depict pedestrian movement in fire scenarios and proposes a methodology that leverages dynamic robot for pedestrian evacuation, employing Deep Reinforcement Learning (DRL) and Human-Robot Interaction (HRI). The results show that in the no-robot scenario, pedestrians will detour according to the varying locations of fire hazard zones and emergency levels, resulting in congestion at different positions. In the static robot scenario, robots placed in different locations exhibit varied effects on evacuation depending on the fire hazard zones' locations and intensities. In the DRL-control robot scenario, the robot controlled by DRL and HRL can always navigate to the appropriate position to promote evacuation, regardless of the fire's location and emergency levels or the robot's initial placement. Furthermore, our findings reveal that strategically positioned robots can enhance evacuation efficiency by alleviating crowding and increasing the distance between pedestrians and fire hazard zones in most cases, thereby improving pedestrian safety. This study offers practical guidance for managing pedestrian evacuation during fire incidents and establishes a theoretical foundation for refining evacuation strategies and safety measures at fire scenes.

Spatial variability in Arctic–boreal fire regimes influenced by environmental and human factors

Wildfire activity in Arctic and boreal regions is rapidly increasing, with severe consequences for climate and human health. Regional long-term variations in fire frequency and intensity characterize fire regimes. The spatial variability in Arctic–boreal fire regimes and their environmental and anthropogenic drivers, however, remain poorly understood. Here we present a fire tracking system to map the sub-daily evolution of all circumpolar Arctic–boreal fires between 2012 and 2023 using 375 m Visible Infrared Imaging Radiometer Suite active fire detections and the resulting dataset of the ignition time, location, size, duration, spread and intensity of individual fires. We use this dataset to classify the Arctic–boreal biomes into seven distinct ‘pyroregions’ with unique climatic and geographic environments. We find that these pyroregions exhibit varying responses to environmental drivers, with boreal North America, eastern Siberia and northern tundra regions showing the highest sensitivity to climate and lightning density. In addition, anthropogenic factors play an important role in influencing fire number and size, interacting with other factors. Understanding the spatial variability of fire regimes and its interconnected drivers in the Arctic–boreal domain is important for improving future predictions of fire activity and identifying areas at risk for extreme events.

Experimental study on transverse fire source and blockage locations influence on tunnel fire temperature distribution

Previous research has commonly assumed that tunnel fire blockage occurs at the central longitudinal axis of tunnels. However, fires may arise at different points within tunnels, each with various distances from the tunnel sidewall. The study aimed to investigate the distribution of ceiling temperature fields across various scenarios of blockage and transverse fire source locations by conducting a sequence of fire experiments within a reduced-scale 1:10 horseshoe-shaped tunnel model. The findings indicate that at sufficiently low ventilation velocities (v’≤0.19), the maximum ceiling temperature rise in the tunnel remains virtually unchanged regardless of the locations of blockage and transverse fire source. For higher ventilation velocities (v’>0.19), the maximum ceiling temperature rise diminishes with greater blockage-fire source distance. Additionally, the maximum ceiling temperature rise increases as the distance between the sidewall and the fire source diminishes. Furthermore, a new model has been formulated to forecast the dimensionless maximum ceiling temperature rise, incorporating both blockage and transverse fire locations.

Understanding fire combustion characteristics and available safe egress time in underground metro trains: A simulation approach

Due to insufficient oxygen supply in the tunnel, incomplete combustion of the underground metro train fire easily produces toxic gases and smoke dust, causing difficulty in breathing for passengers and rescue. The experimental study of the fire combustion characteristics of metro trains in the tunnel is of great significance for designing metro fire extinguishing and fire prevention systems and providing reliable guidance for emergency evacuation in fire scenarios. A train fire model is established to study the effects of various factors on fire combustion characteristics and calculate the available safe egress time (ASET). The smoke spread characteristics under different conditions such as fire source power, fire source location, carriage layout, and train location are simulated. The simulation results show that the fire source power, location, and seat arrangement have a significant impact on the smoke spread characteristics of carriage fires and ASET.

Particle swarm optimization inverse tracing method for mine tunnel fire based on quasi-steady-state heat transfer mechanism

Rapidly locating and assessing fires in coal mines is essential for effective response and minimizing damage. Traditional methods often struggle to swiftly and accurately pinpoint fire locations and statuses. Hence, this study introduces a particle swarm inverse tracing approach based on the quasi-steady-state heat transfer between high-temperature flue gases and mine tunnel walls. This method aims to remotely monitor smoke flow temperatures and gas concentrations to determine fire locations and combustion trends. Validation of the fire source tracing model was conducted through comprehensive tunnel fire experiments and numerical simulations. The particle swarm optimization inverse tracing method yielded a maximum 7.65 % error in fire location determination compared to actual measurements, while accurately matching the measured fire source heat release rate curve. These findings underscore the method’s high precision and reliability, particularly evident during the developmental and stable stages of fire burning.

A deep learning–based surrogate model for spatial-temporal temperature field prediction in subway tunnel fires via CFD simulation

The smoke temperature and toxic gas generated by subway tunnel fires threaten trapped personnel and hinder rescue work. In this study, with a view to ensuring that smoke movement is acquired quickly and contributing to the future development of subway tunnel fires to guide emergency rescue, a deep learning surrogate model based on a transposed convolution neural network (TCNN) was proposed to predict the spatial-temporal temperature field. A numerical database of subway tunnel fires was constructed based on the computational fluid dynamics (CFD) method. Considering different fire locations, heat release rates (HRRs), and ventilation velocities, 135 scenarios were conducted to collect fire data. An adaptive up-projection unit (AUPU) and axial attention fusion block were proposed to improve the TCNN. The results showed that, with the inputs of fire location, HRR, and ventilation velocity, the surrogate model can quickly identify given features and reproduce the spatial-temporal temperature field with 94 % accuracy within 0.034 s. The proposed deep learning surrogate model can aid emergency control and rescue efforts in the context of subway tunnel fires.

Time Makes Space: Emergence of Place Fields in Networks Encoding Temporally Continuous Sensory Experiences.

The vertebrate hippocampus is believed to use recurrent connectivity in area CA3 to support episodic memory recall from partial cues. This brain area also contains place cells, whose location-selective firing fields implement maps supporting spatial memory. Here we show that place cells emerge in networks trained to remember temporally continuous sensory episodes. We model CA3 as a recurrent autoencoder that recalls and reconstructs sensory experiences from noisy and partially occluded observations by agents traversing simulated arenas. The agents move in realistic trajectories modeled from rodents and environments are modeled as continuously varying, high-dimensional, sensory experience maps (spatially smoothed Gaussian random fields). Training our autoencoder to accurately pattern-complete and reconstruct sensory experiences with a constraint on total activity causes spatially localized firing fields, i.e., place cells, to emerge in the encoding layer. The emergent place fields reproduce key aspects of hippocampal phenomenology: a) remapping (maintenance of and reversion to distinct learned maps in different environments), implemented via repositioning of experience manifolds in the network's hidden layer, b) orthogonality of spatial representations in different arenas, c) robust place field emergence in differently shaped rooms, with single units showing multiple place fields in large or complex spaces, and d) slow representational drift of place fields. We argue that these results arise because continuous traversal of space makes sensory experience temporally continuous. We make testable predictions: a) rapidly changing sensory context will disrupt place fields, b) place fields will form even if recurrent connections are blocked, but reversion to previously learned representations upon remapping will be abolished, c) the dimension of temporally smooth experience sets the dimensionality of place fields, including during virtual navigation of abstract spaces.

Simulation of fire combustion process in valve hall of DC converter power station

To investigate the fire danger of the valve hall, a 3D numerical model of the DC converter substation's valve hall is created by using the fire dynamics simulator (FDS) program and the fire burning process of the valve hall is simulated. In particular, the procedure of simulating the fire burning in the valve hall under various fire source positions is accomplished by varying certain parameters. The smoke spreading process, ambient temperature field, and heat release rate curve under various fire source placements are compared based on the results of the simulation calculations. The findings demonstrate that under various fire source locations, the valve hall's combustion process varies as well. This study compares the time for smoke to fill the entire valve hall, the maximum temperature within the valve hall, and the maximum fire source heat release efficiency in different fire scenes. The results indicate that in fire scenes where the fire source is closer to the middle position, the time for smoke to spread throughout the valve hall is shorter, and the fire source heat release rate is higher. Conversely, in fire scenes where the fire source is closer to the edge, the maximum temperature within the valve hall is higher. The numerical simulation of the valve hall in this DC converter station assessed the hazards under different fire scenes, aiming to maximize the safety of the valve hall. It provides reliable guidance for maximizing the safety of the valve hall and facilitating firefighting and rescue efforts, thus protecting personal safety and minimizing property damage.

Thermal Characteristics of Multiple Blockages with Various Sizes in Longitudinal Ventilated Tunnel Fire

In longitudinal ventilation tunnel fires, the thermal characteristics become more intricate due to the presence of blockages. This phenomenon becomes more complex when multiple blockages occur, which results in a unique interaction between the fire and longitudinal ventilation through gaps between the blockages. Most of the previous studies have only considered single obstacles or have only performed qualitative analyses and have not obtained predictive models. To fill this research gap, we conducted numerical simulations using the Fire Dynamic Simulator (FDS) to study the effects of vehicular blockages in three lanes and two fire locations. Our study highlights the differences in the flame behavior, maximum temperature rise, and smoke back-layering length in the presence of multiple blockages and reveals that as the ventilation velocity increases, the flame bifurcation angle increases and the smoke back-layering length decreases. Additionally, when the fire is in the side lane, the flame tilts towards the sidewall, leading to higher maximum temperatures compared to those in the middle lane. Based on these findings, we have developed modified formulas that predict the maximum temperature rise, smoke back-layering length, and maximum temperature ratio at different fire locations and blockage rates, which are linearly related.