Fire Accidents Research Articles

High-temperature mechanical properties and failure modes of UHSS wire for cable-supported bridge

The frequency of fire accidents in cable-supported bridges is increasing, but the ultra-high strength steel (UHSS) wire for cable-supported bridges lacks a unified expression of the degradation law of its high-temperature mechanical properties. Therefore, for the emerging 1960 and 2060 MPa UHSS wires, a series of steady-state tests are carried out in this study. The stress-strain response, elastic modulus, yield strength, ultimate strength, ultimate strain, and failure strain of the UHSS wires in the temperature range of 20–900 °C are obtained, as well as the failure parameters such as elongation and shrinkage after rupture. Based on the test data, a calculation model suitable for the prediction of the degradation law of high-temperature mechanical indicators of 1670‐–2060 MPa UHSS wire is established. Experimental investigations show that the stress-strain development process of UHSS wire at elevated temperature can be described by a three-stage model. Due to differences in chemical composition and manufacturing process, there are significant differences in the degradation patterns of mechanical indicators of different steel materials at elevated temperatures. The failure mode of the steel wire is brittle failure when the temperature does not exceed 200 °C, but the failure mode changes from brittle failure to plastic failure when the temperature exceeds 200 °C. Compared with the existing experimental results, it is obvious that the high-temperature mechanical indicator degradation model of high-strength steel wires proposed in this study can be used as input data for computer models to evaluate the fire resistance of cable members of cable-supported bridges.

Lagrangian plume rise and dispersion modelling of the large-scale lithium-ion battery fire in Morris, USA, 2021

This article puts a perspective to the health risks of smoke from lithium-ion battery (LIB) fires by retrospect simulations of the large-scale event in a warehouse in Morris, IL, USA where about 60 metric tonnes of LIB set on fire on of June 29, 2021. Possible scenarios are sketched where ground concentration maps of PM2.5 reveal large areas of tens of square kilometres exceeding the action levels set up by the authorities for the event. Besides high concentration in the close vicinity, the plume touchdown is found to be located 5–10 km away from the source. In contrast, irritant gases including HF did not exceed the action levels. The reconstruction of the event was conducted using details from interviews with the owner and first-responders on site, which subsequently was used as input data to a Lagrangian atmospheric transport model. Plume rise due to buoyant flux in a Lagrangian dispersion model was accomplished by adding a vertical speed component derived from discretization of the well-known Briggs formulation. Emission factors were collected from the published scientific literature and implemented in combination with warehouse inventory and visual observations. The importance of this work becomes apparent in view of the rapid increase of LIB’s in, e.g., transportation and energy storage with accompanied fire accidents. These findings support precautions in infrastructure handling LIBs and particularly in large amounts.

Technological Aspects of Methane–Hydrogen Mixture Transportation through Operating Gas Pipelines Considering Industrial and Fire Safety

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.

A Real-Time Flame Detection Method Using Deformable Object Detection and Time Sequence Analysis.

Timely and accurate flame detection is a very important and practical technology for preventing the occurrence of fire accidents effectively. However, the current methods of flame detection are still faced with many challenges in video surveillance scenarios due to issues such as varying flame shapes, imbalanced samples, and interference from flame-like objects. In this work, a real-time flame detection method based on deformable object detection and time sequence analysis is proposed to address these issues. Firstly, based on the existing single-stage object detection network YOLOv5s, the network structure is improved by introducing deformable convolution to enhance the feature extraction ability for irregularly shaped flames. Secondly, the loss function is improved by using Focal Loss as the classification loss function to solve the problems of the imbalance of positive (flames) and negative (background) samples, as well as the imbalance of easy and hard samples, and by using EIOU Loss as the regression loss function to solve the problems of a slow convergence speed and inaccurate regression position in network training. Finally, a time sequence analysis strategy is adopted to comprehensively analyze the flame detection results of the current frame and historical frames in the surveillance video, alleviating false alarms caused by flame shape changes, flame occlusion, and flame-like interference. The experimental results indicate that the average precision (AP) and the F-Measure index of flame detection using the proposed method reach 93.0% and 89.6%, respectively, both of which are superior to the compared methods, and the detection speed is 24-26 FPS, meeting the real-time requirements of video flame detection.

Sistem Deteksi Kebakaran Menggunakan Esp32 Dan Arduino

,. Fire disasters are disasters that occur more often than other disasters such as floods, landslides, earthquakes or tsunamis. This disaster can happen at any time and no one knows for sure when this disaster will come. The impact of a fire disaster is not only material but can result in the loss of human life. Factors that cause fire disasters often occur due to human negligence and fires often occur in houses where the occupants are abandoned. The research carried out this time focuses on creating a fire detection system using ESP32 and Arduino. The system uses three sensors, namely a temperature sensor, a gas sensor and a fire sensor. Temperature sensors are useful for monitoring room temperature conditions, fire sensors are useful for detecting the presence of fire in a fire disaster and gas sensors are useful for detecting the presence of smoke that appears as a result of a fire disaster. This system uses Arduino Uno and ESP32 microcontrollers. The results of the fire detection system are expected to reduce the occurrence of fire accidents and also losses caused by fire accidents

Moisture content effects on self-heating in stored biomass: An experimental study

Biomass is essential for modern bioenergy applications, but long-term storage of large volumes of it can pose challenges to plant safety due to the intrinsic self-heating characteristics of biomass piles. In recent years, serious fire accidents have occurred because of self-heating. However, little research has been conducted on this concern. The effects of different initial moisture contents (range of 20–95 %) on the self-heating characteristics of rice and wheat straw, which are two different agricultural biomass residues, are investigated in this study. Biomass samples with different initial moisture contents are stored in a well-insulated container, and the temperature and oxygen levels within the stored biomass samples are monitored. Based on the tests, the heat production rate, oxygen consumption rate and microorganism growth rate are derived, and the impacts of the initial moisture contents on the self-heating characteristics of the stored biomass samples are analysed. The highest temperatures in the stored rice straw and wheat straw are attained under initial moisture contents of 50 % and 20 %, respectively, while the largest heat production rates for both straw types are attained under an initial moisture content of 95 %. An increase in the initial moisture content greatly enhances the overall biological reactivity and oxygen consumption rate. The heat production and oxygen consumption levels exhibit a clear positive correlation. Under identical storage conditions, wheat straw is more susceptible to self-heating than rice straw, as shown by its higher heat generation, faster oxygen consumption, and shorter temperature peaking time. This study contributes to a quantitative understanding of the underlying processes and provides valuable experimental data for model development to guide safe biomass storage systems.

Autoencoder-Enhanced Regularized Prototypical Network for New Energy Vehicle battery fault detection

As the ownership of new energy vehicles (NEVs) is experiencing a sustained growth, the safety of NEVs has become increasingly prominent, with power battery faults emerging as the primary cause of fire accidents in NEVs. Successful detection of incipient faults can not only improve the safety and reliability but also provide optimal maintenance instructions for NEV batteries. Since NEVs operate under normal conditions most of the time, the occurrence of system faults leading to faulty samples is extremely rare, thus exhibiting the characteristics of a long-tail distribution. To resolve this problem, this paper proposes an Autoencoder-Enhanced Regularized Prototypical Network (ARPN) to overcome the limitations of class imbalance and uneven sample distribution in the data. The model consists of three key components. First, an autoencoder with an encoder and decoder structure is introduced to map the data to a low-dimensional latent space and back, effectively extracting key structural information from the original data and make full use of the limited number of faulty training samples. Secondly, a multi-layer regularized feature embedding strategy is designed to reduce the complexity of the embedding space, which makes the prototype vector get easier to be separated and distinguished and plays an important role in the proposed ARPN. Finally, a prototype-based classification method is adopted to achieve accurate classification by calculating the cosine similarity between the representation of query set samples in the embedding space and the prototype vectors of each class. Experiments on several battery datasets, obtained from the operational data of NEVs, demonstrate the effectiveness of the proposed method.

Computer vision-based characterization of large-scale jet flames using a synthetic infrared image generation approach

Different kinds of fire accidents can occur during industrial activities that involve hazardous materials, such as jet fires, which are often involved in a process known as a domino effect that generates a sequence of other accidents of greater magnitude. Jet fires present specific features that can significantly increase the probability of this domino effect, so they become relevant from a risk analysis perspective, making their proper characterization a crucial task. Data acquisition of jet fires involves expensive experiments, especially when infrared imagery is necessary. Therefore, this paper proposes a method that uses Generative Adversarial Networks to produce plausible infrared images from visible ones, making experiments less expensive and allowing for other potential applications. As validation, the infrared images are used in a fire characterization approach that employs Deep Learning to segment radiation zones and extracts the jet fire’s geometrical information. A comparison is done between the measurements obtained from real and generated infrared images. The results suggest that, with the proposed approach, it is possible to realistically replicate the analysis obtained from experiments carried out using both visible and infrared cameras.

An FDS Simulation to Predict the Kerosene Pool Fire Results at Rocket Launchpad Basement Facilities in the Republic of Korea

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 m3 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/m3 to 6000 kW/m3. In 10 m3 release cases, the temperature and HRR decreased by 44%, from 800 K to 450 K and 68% from 25,000 kW/m3 to 8000 kW/m3, respectively.

Tempnet: A graph convolutional network for temperature field prediction of fire-damaged concrete

Determining the damage level of the fire-damaged concrete structure is critical for the structural assessment and repair of buildings after fire. Existing methods assess the damage levels of concrete by measuring the remaining mechanical performance in a traditional manner, where they either have limited accuracy or efficiency due to the need of heavy machines and experienced laborers. In contrast with these methods, we propose a deep learning based approach called Tempnet to promote the efficiency and effectiveness of damage level assessment for concrete after fire. Tempnet incorporates a graph convolutional layer and a conventional convolutional layer to encode the temperature interdependency between neighboring areas in the images of fire-affected concrete to capture the exposed temperature fields of the concrete. Three closely related application scenarios together with their corresponding datasets have been proposed to evaluate the performance of Tempnet. Extensive comparative experiments and ablation studies have validated the model design, the high efficiency, and the robust performance of Tempnet, with a performance metric F1 value higher than 0.97 in all applications. Case studies were conducted further to provide insightful illustration of Tempnet’s impressive performance. It is envisioned that the Tempnet can contribute to the efficient maintenance of concrete structures following fire accidents for construction applications.

Knowledge Graph Construction to Facilitate Indoor Fire Emergency Evacuation

Indoor fire is a sudden and frequent disaster that severely threatens the safety of indoor people worldwide. Indoor fire emergency evacuation is crucial to reducing losses involving various objects and complex relations. However, traditional studies only rely on numerical simulation, which cannot provide adequate support for decision-making in indoor fire scenarios. The knowledge graph is a knowledge base that can fully utilize massive heterogeneous data to form a sound knowledge system; however, it has not been effectively applied in the fire emergency domain. This study is a preliminary attempt to construct a knowledge graph for indoor fire emergency evacuation. We constructed the indoor fire domain ontology and proposed a four-tuple knowledge representation model. A knowledge graph was constructed with 1852 nodes and 2364 relations from 25 indoor fire events. The proposed method was tested for the case study of Henan Pingdingshan ‘5.25’ Fire Accident in China. Results show that the proposed knowledge representation model and the corresponding knowledge graph can represent complicated indoor fire events and support indoor fire emergency evacuation.

An experimental investigation into the fire behaviors and smoke characteristics of continuous spill fires in road tunnels

Continuous fuel spill fires are a common accident scenario which needs to be considered for the safe operation of tunnels, given the potential threat posed to trapped persons. In this paper, a series of continuous spill fire experiments on a concrete surface were studied using a 1/8 reduced-scale tunnel model. Gasoline was used as the discharge fuel and the discharge rate varied from 1.5 to 5.2 ml/s. The burning area, steady regression rate, smoke front arrival time, and ceiling temperature were analyzed. The results show that the burning area spreads slowly following an initial rapid spill stage, with the spread process following a pattern of exponential decay. The steady regression rate is found to be approximately 63% of that of pool fires for the same burning area. At the same heat release rate, the smoke front moves faster for spill fires than pool fires because of the larger burning area, and a correlation is built to predict the smoke front arrival time. Finally, a ceiling temperature rise prediction model is developed which considers the spread and burning processes of the fire. The results are of practical importance in the quantitative risk assessment of liquid fuel fire accidents in tunnels, and their implications for personnel evacuation.

The causes and responses to cargo hold fire accidents in RoRo ships using AcciMap

ABSTRACT The recent string of cargo hold fire incidents on RoRo ships represents one of the most challenging types of ship fire accidents. The densely stowed vehicle cargo loading structure and large volume of RoRo ship cargo holds are factors that complicate firefighting efforts. In addition, vehicle cargoes have a higher likelihood of self-ignition compared to other types of cargo and produce dense smoke when a fire occurs. Combustible materials inside vehicles can also contribute to the spread of fires after they start. Particularly, the recent increase in demand for electric vehicles calls for changes in how we respond to potential cargo hold fires on RoRo ships and heightens the risk of such incidents. In this study, we analyzed major domestic and international RoRo cargo hold fire incidents using AcciMap, a system analysis method. Through this analysis, we identified needs for improved management of vehicle cargo conditions, responses to fixed firefighting systems and system inspections, as well as improvements to fire detection systems. By using a systems analysis approach, we were able to identify areas for improvement needed by ships, ports, governments, shippers, and shipping companies.

Pyrolytic kinetics, reaction models and gas evolution of wood material with kerosene by TG-FTIR

The pyrolysis process of typical oils (kerosene, gasoline, etc) penetrating solid combustibles is one of the key behaviors of arsons and fire accidents caused by the leaking and spilling of these flammable oils on solid combustibles. Previous researches have mainly focused on the combustion characteristics of combustible solids infiltrated with flammable oils. However, there is a lack of comprehensive and systematic research on their pyrolysis characteristics, kinetics, and gaseous products. This study explored the pyrolytic behaviors, kinetics, reaction models and pyrolytic gases of pine wood with different contents (0, 5, 10, 20 and 30 wt%) of kerosene via thermogravimetry-Fourier transform infrared analysis (TG-FTIR). The results exhibit that the thermal decomposition processes of the pure pine wood and pine wood-kerosene mixture both show a three-stage process. The average activation energy of the mixture pyrolysis gradually decreases from 181.87 to 129.87 kJ/mol with the increase of kerosene content. The results further identify that the kinetic process of mixtures pyrolysis in the region 2 followed the three-dimensional diffusion model: g(α)= [1 − (1 − α)1/3]2. The principal finding of this study is that the obtained kinetic parameters of mixtures pyrolysis can precisely reproduce and predict conversion degree curves. Moreover, during the pyrolysis of the mixtures, the primary gaseous products identified include H2O, hydrocarbon, CO2, CO, carbonyl compounds, phenols, hydroxyl compounds. As the kerosene content increases, there is a significant rise in the production of hydrocarbons observed at the initial peak (around 420 K). Additionally, the predominant gaseous product observed at the peak temperature is carbonyl compounds. It is expected that this study can provide a practical guideline for the prediction of the fire size, the prevention and control of arson accidents and accident investigation.

냉동창고 건설공사 화재예방시스템 개선에 관한 연구

The domestic logistics environment must provide services in real time, regardless of time and place, due to increased delivery volume, increased logistics warehouse functions, increased delivery speed, increased low-temperature products, and the 4th Industrial Revolution. In order to survive in this logistics environment, logistics companies and others are pursuing profits on a large scale and short construction periods as business survival strategies, so the exposure of risks to logistics warehouse construction is very serious. Among them, more than 1,000 fire accidents with very serious disaster intensity have continued to occur over the past 10 years since 2010, and fire accidents have caused dozens of lives and property damage of tens of billions of won every year. The purpose of this study is to reveal the problems of the fire prevention system of the frozen warehouse construction work through case analysis and related laws and literature that occurred in the construction of the frozen warehouse among the logistics warehouses and to suggest ways to improve them.
 The research method was to investigate and analyze fire status, accident cases (centered on accidents at frozen warehouses in Icheon, Gyeonggi-do in 2008), theoretical consideration such as 4M techniques, and suggest ways to improve the fire prevention system.
 Fire prevention mechanisms of 4M refrigeration warehouse construction were investigated. Fire control by unqualified firemen, fire monitors, and lack of management by supervisors existed mechanically. The construction method of fire prevention was improved S.H.P(Safety Hold Point) operation, replacement of frozen warehouse insulation with semi-non-combustible urethane foam, introducing a mandatory fire prevention education system, strengthening emergency simulation training, and finally operating a one-out system for those who do not fulfill fire prevention guidelines.
 While conducting this study, it was an opportunity to examine the seriousness of fire accidents in frozen warehouse construction work, and I hope that the measures proposed through this study will help prevent fire in frozen warehouse construction work.

A Basic Study on the Diagnosis Method of Internal Short Circuit based on Feature Extraction and Separation Technique

Lithium-ion batteries are widely used in energy storage systems (ESS) and electric vehicles due to superior performance. However, most safety problems in the use process of the lithium-ion battery are attributed to internal short circuits, there are a lot of factors leading to the internal short circuit. Short circuit in the lithium-ion battery affects the electrochemical reaction and then generating a larger short circuit current. Besides, the thermal runaway is the result of a failure of the separator inside Li-ion batteries, small discharge pulses of a few mV were detected at the time of separator damages. Therefore, the fault diagnosis by internal short circuit of lithium batteries has attracted more attention to prevent the fire accidents. This paper aimed to suggest a diagnostic model for separator of LFP (Lithium iron phosphate) pouch cell as a basic research work using continuous pulse analysis method such as feature extraction and separation technique of pulse source for timely diagnose the short circuit fault. In other words, this paper provided a method that can identify the separator failure before thermal runaway in the aspect of a potential explosion and fire prevention. The internal short circuit signal for diagnosis of LFP pouch cell was investigated by forming artificial defects at the separator part compared with normal battery. To investigate the short circuit signal, artificial defect specimens are prepared as a pouch type, pulse signal was analyzed in terms of pulse waveshape (Skewness) and frequency (Fast Fourier Transform, FFT) compared with normal battery and artificial defect pouch cell. To calculate the continuous acquisition pulse signal, python code was developed. As a result, it is found that the pulse signal of artificial defect specimen shown high frequency and low skewness characteristics. Whereas, in the case of a normal battery, it showed characteristics of relatively low frequency and high or middle skewness. From our research work, the results revealed that the pulse shape and feature contain useful information which can help to understand degradation behavior of the LFP pouch cells. This result is expected to be applicable various batteries with presence a defect at the separator of battery

Fire Accident Risk Analysis of Lithium Battery Energy Storage Systems during Maritime Transportation

The lithium battery energy storage system (LBESS) has been rapidly developed and applied in engineering in recent years. Maritime transportation has the advantages of large volume, low cost, and less energy consumption, which is the main transportation mode for importing and exporting LBESS; nevertheless, a fire accident is the leading accident type in the transportation process of LBESS. This paper applied fault tree analysis and Bayesian network methods to evaluate the fire accident risk of LBESS in the process of maritime transportation. The Bayesian network was constructed via GeNIe 2.3 software, and the probability of LBESS fire accidents during maritime transportation was calculated based on the probability of basic events occurring. The results showed that an unsuitable firefighting system for putting out lithium battery fires, high humidity, and monitoring equipment without a real-time alarm function have the most significant influence on the occurrence of LBESS fire accidents during maritime transportation. The research work of this paper provides a theoretical basis for the risk assessment of LBESS during maritime transportation.

Effect of angular motion on accidental hydrogen fires in conceptional ship fuel preparation room with mechanical ventilation

This paper presents a numerical analysis of the effects of angular motion on fire caused by an accidental hydrogen leak in a mechanically ventilated conceptual ship fuel preparation room. Three rotational motions (roll, pitch, and yaw) were applied with periods and amplitudes ranging from 8 s to 2°–10°, respectively. A distinctive feature of this study is the model construction of a hydrogen ship fuel preparation room with a ventilation system, considering the impact of ship motion on the combustion process of a hydrogen fire. The results indicate that the maximum ventilation flow rate varies by 10% depending on the amplitude of ship motion. However, the effect of ship motion on the range of radiated heat produced by a hydrogen fire is small. Regardless of the ship motion conditions, the maximum radiated heat of hydrogen storage tanks and pipelines is 62.24 kW/m2 and 20,110 kW/m2, respectively, which could cause significant damage to major equipment due to the fire. Consequently, to minimize the damage caused by hydrogen leakage, it is necessary to install additional sensors near major equipment and adjust the number of ventilations to quickly discharge the equipment before a fire occurs.

Long-Life Quasi-Solid-State Anode-Free Batteries Enabled by Li Compensation Coupled Interface Engineering.

Initially, anode-free Li metal batteries present a promising power source that merges the high production feasibility of Li-ion batteries with the superb energy capabilities of Li-metal batteries. However, their application confronts formidable challenges of extremely short lifespan due to the inadequacy of zero-Li-excess cell configuration against irreversible Li loss. A Li compensation coupled interface engineering strategy is reported for realizing long-life quasi-solid-state anode-free batteries. The Li2 S is utilized as a sacrificial Li supplement to effectively counterbalance irreversible Li loss without damage to cell chemistry. Meanwhile, it demonstrates remarkable efficacy in establishing a robust yet slender inorganic-organic hybrid solid-state interphase for inhibiting cell degradation by dead and dendritic Li. This strategy enables quasi-solid-state anode-free batteries with a long lifespan of 500 cycles. The Ah-scale quasi-solid-state pouch cells, featuring a high-loading LiFePO4 cathode and lean gel polymer electrolyte, exhibit a high specific energy of 300Whkgcell -1 . This achievement translates into an improvement of 46% in gravimetric energy and 94% in volumetric energy compared to LiFePO4 ||graphite batteries while outperforming LiFePO4 ||Li-metal batteries by 22-47% in volumetric energy. Such quasi-solid-state anode-free cells also demonstrate good safety, showcasing remarkable resistance against nail penetration in ambient air without failure, smoke, or fire accidents.

Experimental study of high temperature on the shear properties of early-age concrete

Fire accidents happen randomly which may occur in the construction of concrete buildings. In this study, the effects of high temperature and curing ages on shear properties of early-age concrete were experimentally studied. The pore structure of early-age concrete before heating, after heating, and re-curing after heating were examined through nuclear magnetic resonance and scanning electron microscope tests. Experimental results reveal that high temperature treatment of early-age concrete multiplies the number of pores but has little influence on the change of pore size distribution, while the re-curing treatment reduces pore size but has little influence on the change of pore numbers. Under the same exposed temperature, the shear strength of concretes before heating, after heating, and re-curing after heating increases gradually with curing ages. For concretes with certain curing ages, the shear strength increases first and then decreases with the exposed high temperature. While the cohesion and internal friction angle show a negative relationship with elevated temperature. The research can provide base data for buildings encountering fire accidents.