Fire Spread Research Articles

Study on the Fire Characteristics of Dual Fire Sources and the Difference in Power Temperature of Different Fire Sources in Tunnel

To investigate the combustion characteristics of multiple fire sources in the tunnel caused by ‘jumping’ discontinuous fire spread, we utilized scaled model experiments, numerical simulation software, and theoretical research. Our study focused on analyzing the influence of different fire source powers on the temperature characteristics of double fire sources in the tunnel. We examined the temperature characteristics, critical wind speed, and change rule under various wind speeds, fire source spacing, and fire source powers. Additionally, we explored the temperature characteristics, critical wind speed, and change rule of different fire source powers under varying wind speed conditions. The mathematical model for roof temperature decay and the temperature decay coefficients of dual source fires were established through the analysis of scale-down model experiments and numerical simulations. In comparison to single-source fires, the temperature variations in the tunnel of dual source fires exhibit a more intricate pattern, with higher average temperature and temperature peak values. These values are influenced by factors such as fire source spacing and power. Numerical simulation software was utilized to investigate the impact of fire source spacing at 10 m, 15 m, and 20 m, as well as the effect of varying fire source power on the temperature distribution within a tunnel under consistent fire source position and growth coefficient. The study revealed that, with consistent double fire source position and ventilation conditions in the tunnel, the upstream fire source exhibited greater power than the downstream fire source, resulting in the lowest average and peak temperatures in the tunnel. This observation could potentially enhance escape and rescue operations within the tunnel. Similarly, the lowest average and peak temperatures in the tunnel were also identified, offering potential benefits for optimizing escape and rescue strategies in tunnel scenarios.

Effect of different boundary conditions on the combustion properties and fire characteristics of typical woods

To provide guidance on the bioenergy potential of wood and its fire spread numerical simulation, insights into the combustion properties and fire characteristics of one hardwood (sassafras) and two softwoods (pine and fir) were investigated. The effect of different boundary conditions are focused. The condensed phase decomposition in thermogravimetric analysis indicated that the main degradation processes for all wood combustion could be divided into two stages, and the corresponding demarcation point of conversion rate is 0.4 and 0.35 for hardwood and softwood, respectively. Three specific regions could further be established based on the activation energies variations in Stage Ⅰ. The governing reaction mechanism of sassafras wood is determined to be the diffusion model connect with order-based model, while the reaction mechanism of softwoods (pine and fir) can be considered as the reaction order (1st to three-halves) type followed by diffusion model. The gas phase flaming in FPA combustion experiments suggest that fir and pine wood have a faster charring rate and produce less CO and CO2 during pyrolysis and combustion. Sassafras requires more time to be ignited owing to its high density, which causes higher thermal inertia therefore decelerating the temperature increase on the sample surface. In addition, the dense structure of sassafras leads to an early appearance of the second HRR and MLR peaks. As hardwood has a higher density than softwood, the thermal insulation in hardwood delays char cracking on sassafras wood surface and enhances the duration between ignition and flameout. Such findings should provide guidance for wood combustion modeling and flame spread simulation in future works.

The Meteorology of the August 2023 Maui Wildfire

Abstract On 8 August 2023, a wind-driven wildfire pushed across the city of Lahaina, located in West Maui, Hawaii, resulting in at least 100 deaths and an estimated economic loss of 4–6 billion dollars. The Lahaina wildfire was associated with strong, dry downslope winds gusting to 31–41 m s−1 (60–80 kt; 1 kt ≈ 0.51 m s−1) that initiated the fire by damaging power infrastructure. The fire spread rapidly in invasive grasses growing in abandoned agricultural land upslope from Lahaina. This paper describes the synoptic and mesoscale meteorology associated with this event, as well as its predictability. Stronger-than-normal northeast trade winds, accompanied by a stable layer near the crest level of the West Maui Mountains, resulted in a high-amplitude mountain-wave response and a strong downslope windstorm. Mesoscale model predictions were highly accurate regarding the location, strength, and timing of the strong winds. Hurricane Dora, which passed approximately 1300 km to the south of Maui, does not appear to have had a significant impact on the occurrence and intensity of the winds associated with the wildfire event. The Maui wildfire was preceded by a wetter-than-normal winter and near-normal summer conditions. Significance Statement The 2023 Maui wildfire was one of the most damaging of the past century, with at least 100 fatalities. This paper describes the meteorological conditions associated with the event and demonstrates that excellent model forecasts made the threat foreseeable.

Assessing the Impact of Clearing and Grazing on Fuel Management in a Mediterranean Oak Forest through Unmanned Aerial Vehicle Multispectral Data

Climate change has intensified the need for robust fire prevention strategies. Sustainable forest fuel management is crucial in mitigating the occurrence and rapid spread of forest fires. This study assessed the impact of vegetation clearing and/or grazing over a three-year period in the herbaceous and shrub parts of a Mediterranean oak forest. Using high-resolution multispectral data from an unmanned aerial vehicle (UAV), four flight surveys were conducted from 2019 (pre- and post-clearing) to 2021. These data were used to evaluate different scenarios: combined vegetation clearing and grazing, the individual application of each method, and a control scenario that was neither cleared nor purposely grazed. The UAV data allowed for the detailed monitoring of vegetation dynamics, enabling the classification into arboreal, shrubs, herbaceous, and soil categories. Grazing pressure was estimated through GPS collars on the sheep flock. Additionally, a good correlation (r = 0.91) was observed between UAV-derived vegetation volume estimates and field measurements. These practices proved to be efficient in fuel management, with cleared and grazed areas showing a lower vegetation regrowth, followed by areas only subjected to vegetation clearing. On the other hand, areas not subjected to any of these treatments presented rapid vegetation growth.

Potential forest fire during the long dry season Province Riau & Jambi

Background: Air pollution is a very serious problem nowadays. Air pollution can be caused by the problem of haze from forest fires that occurred in 2015 & 2019 in Province Riau & Jambi. The worst conditions occurred in the period 1997-1998 and in 2006-2007. In 2015 & 2019 the haze conditions returned to the 1997 period. Province Riau & Jambi contributed to the largest hotspots in forest fires compared to North Sumatra or Kalimantan. The increase in the haze that has continued for the past three months is very worrying because the haze is getting worse and thicker, making the visibility of the affected areas even smaller. The purpose of this research to know the capabilities being run the Riau provincial government has been in control of who impressed off guard because repeated continuously, and whether factors constr ai nts and defienc e i nflu enci ng th e c apab i li ti es of th e Province Riau & Jambi government. Method: Descriptive research is research that aims to describe, describe and analyze existing events and aims to obtain information about the impact of the haze in Province Riau & Jambi with existing theories so that it can be used in mitigation in different places. FIndings: The urgency in this study to see why the Province Riau & Jambi government off guard so persistent, because the capabilities of the system and the process will involve considering the fire that occurred not once or twice only. Conclusion: The main causes of forest and peatland fires in Riau are human activities, such as the use of fire for land clearing and agricultural practices, combined with natural conditions like dry land and extreme hot weather. These fires significantly impact human health, social interactions, and the environment, necessitating critical awareness among farmers to act responsibly and monitor fire spread to prevent future losses. Novelty/Originality of this article: By analyzing the inhibiting factors and definitions that influence local government capacity, this study opens new insights into the complexity of environmental disaster management involving interactions between human activities, natural conditions, and institutional capacity.

Experimental and numerical study on data-driven prediction for wildfire spread incorporating adaptive observation error adjustment

In recent wildfire prediction research, data assimilation (DA) methods like Ensemble Kalman filtering have gained traction for integrating observation data to enhance prediction accuracy. Most previous studies trusted that the observation data were accurate, and set a small observation error, which causes unreliable predicted results for scenarios with large observation error. To tackle this, our study introduced a method that iteratively adjusted the potential range of observation errors by comparing observation and simulation data over time. We conducted a 30-m experiment and kilometer-scale numerical simulations. Unlike prior research, we adopted larger error ranges (the similarity index with true data ranges from 0.6 to 1) for both real and synthetic observation data. In the experiment, to increase the complexity of fire spread, a heterogeneous fuel arrangement was employed. Irregular flame fronts appeared due to incomplete combustion and were difficult to replicate in simulations. Better accuracy was achieved using real observation data to revise predictions. Furthermore, to improve the applicability of the algorithm, numerical simulations were designed to consider observation error changing over time or not. The Root Mean Square Errors for the fire front prediction using the proposed method remained lower than that of the traditional DA approach.

Reduced scale test bench for investigating the upward flame heat impact on external thermal insulation composite system facades

The project aimed to develop a reduced scale test protocol designed to be repeatable and capable of providing the upward fire evaluation for assessing fire risk on external thermal insulation composite system facade samples. The variation of heat flux is simulated by radiant panel. This experimental setup permits the recording of dynamic variables in flame propagation, such as ignition time, flame extinguishing time, and variations in both surface and back temperatures of the insulation panel. Notably, the measurement of back temperature provides a means to assess the thermal insulation efficiency of the external thermal insulation composite system facade. Furthermore, the fire spreading temperature is collected during the test could also be useful for larger-scale testing.

Predicting the Integrated Fire Resistance of Wildland–Urban Interface Plant Communities by Spatial Structure Analysis Learning for Shanghai, China

Abstract: Fire is a prevalent hazard that poses a significant risk to public safety and societal progress. The continuous expansion of densely populated urban areas, exacerbated by global warming and the increasing intensification of urban heat islands, has led to a notable increase in the frequency and severity of fires worldwide. Incorporating measures to withstand different types of calamities has always been a crucial aspect of urban infrastructure. Well-designed plant communities play a pivotal role as a component of green space systems in addressing climate-related challenges, effectively mitigating the occurrence and spread of fires. This study conducted field research on 21 sites in the green belt around Shanghai, China, quantifying tree morphological indexes and coordinate positions. The spatial structure attributes of different plant communities were analyzed by principal component analysis, CRITIC weighting approach, and stepwise regression analysis to build a comprehensive fire resistance prediction model. Through this research, the relationship between community spatial structures and fire resistance was explored. A systematic construction of a prediction model based on community spatial structures for fire resistance was undertaken, and the fire resistance performance could be quickly judged by easily measured tree morphological indexes, providing valuable insights for the dynamic prediction of fire resistance. According to the evaluation and ranking conducted by the prediction model, the Celtis sinensis, Sapindus saponaria, Osmanthus fragrans, Koelreuteria paniculata, and Distylium racemosum + Populus euramericana ‘I-214’ communities exhibited a high level of fire resistance. On the other hand, the Koelreuteria bipinnata + Ligustrum lucidum, Ginkgo biloba + Camphora officinarum + Ligustrum lucidum, and Ligustrum lucidum + Sapindus saponaria communities obtained lower scores and were positioned lower in the ranking. It is emphasized that the integration of monitoring and regulation is essential to ensure the ecological integrity and well-being of green areas in the Wildland–Urban Interface.

Применение методов пространственного анализа при оценке степени горимости лесов Хабаровского края

Forest fires are the dominant factor in the transformation of natural landscapes in the boreal zone. The area of the Khabarovsk Territory, 93.6 % of which belongs to the forest fund lands, currently has a forest cover of only 66.5 %, which is largely due to forest fires. This makes urgent the problem of reducing the forest fire frequency in the region, including in terms of assessing the actual fire frequency for predicting the risks of landscape fires. The article shows the possibilities of using spatial analysis methods of the Spatial Analyst module of the ArcGIS 10.8 software package to assess the degree of actual forest fire frequency based on calculating the number of fires per 1 million and 250 thousand ha and the area of fires per 1000 ha for a sliding window of a given size. Based on the data from the Remote Monitoring Information System of the Federal Forestry Agency for 2000–2020 and the data on the number of fires from the Fire Information for Resource Management System website for 2000–2021, an assessment of the actual fire frequency for the Khabarovsk Territory has been carried out. The analysis of the results reflects the high spatial and seasonal heterogeneity of the spread of landscape fires in the territory and their proximity to transport and residential structures. Areas with high fire frequency often do not coincide with the boundaries of forestry enterprises, and the maximum indicators are typical for non-forest areas that are not covered by forestry statistics. For different seasons of the year, along with quantitative differences, spatial shifts of firing points and areas with their high density are also observed. Most of the lands in the region (88.3 %) in terms of fire danger belong to the “low” and “below medium” fire frequency categories, which is primarily due to the low degree of development of these areas. The assessment of the degree of fire frequency in the territory on the basis of annual and seasonal maps of the density of firing points, calculated using the sliding window method, makes it possible to fairly objectively and in detail reflect the average long-term risk of fire occurence outside of reference to forestry boundaries. In combination with a map of the distribution of forests by natural fire hazard classes based on pyrological characteristics, the results of the reseаrch can become the basis for calculating the background fire risk, reflecting the risk of fire occurrence and development.

A Study on the Impact of Window Partition Walls on the Spread of Fire on Building Facades

This paper investigates the impact of window partition walls on the spread of fire on building facades under the impact of environmental wind through Fire Dynamics Simulator simulation experiments. A four-story building model was constructed using a Fire Dynamics Simulator incorporating six different wind speed conditions and six different partition wall widths. The fire-blocking performance of window partition walls of varying widths was systematically compared and analyzed, and the data indicated: (1) Under calm wind conditions, the installation of window partition walls is observed to facilitate the vertical spread of facade fires. Moreover, as the width of these partition walls increases, this facilitative effect becomes increasingly prominent; (2) Under wind speeds of 0 to 5 m/s, the temperature on the leeward side is lower when window partition walls are present than when they are absent. This indicates that window partition walls inhibit the horizontal spread of building facade fires, and wider window partition walls have better horizontal fire resistance performance.

Smoldering ignition and transition to flaming in wooden mulch beds exposed to firebrands under wind

Spotting ignition by firebrands is a significant fire spread pathway at the wildland-urban interface (WUI), where mulch products are commonly used as landscaping materials. Mulch is typically organic in nature, thus it may be easily ignited into a smoldering mode by firebrands and subsequently transition to flaming, leading to direct flame contact and radiant heat exposure to siding materials of adjacent structures. This work quantified the thresholds of smoldering ignition of four common types of commercially available mulch (black mulch (BM), forest floor (FF), redwood (RW), and fir bark (FB)) exposed to heating by smoldering firebrand piles, and their propensity for smoldering-to-flaming transition under external winds (up to 1.4 m/s). We found that there was a minimum mass of firebrand pile to achieve smoldering ignition of mulch (e.g., ∼0.1 g for FF). Beyond this minimum mass, the required wind speed to trigger smoldering ignition generally decreased as the mass of the firebrand pile increased, agreeing well with theoretical analysis. After smoldering ignition, smoldering-to-flaming transition could be observed when the wind speed exceeded a critical value (e.g., ∼1 m/s for FF), which was not affected by the initial spotting process. To achieve smoldering-to-flaming transition, the glowing mulch had to reach a critical temperature of around 850 °C. Mulch samples with larger particle sizes were more likely to smolder and transition to flaming, due to increased oxygen supply through larger inter-particle pores and channels and better firebrand accumulation due to a more crevice-like geometry on the fuel surface. This work advances the fundamental understanding of the ignition and burning behavior of landscaping mulches, and thus contributes to the prevention of extreme WUI fire events.

High-performance real-time fire detection and forecasting framework for industrial cables

In industrial scenarios, cable fires have always been the most common threat, and traditional fire detection systems often rely on a large number of sensors, and the detection range is very limited, and it is impossible to effectively predict the fire situation in time. In this paper, we propose a detection and prediction scheme for industrial cable fire, which breaks the limitations of the previous research on multi-sensor signal input, and highly couples the detection and prediction modules to realize fire prediction based on video image input only. In fire detection, we design an object detection model using HSV for flame feature enhancement based on YOLOv8, and in fire prediction aspect, we use iTransformer as a time series prediction model to mine the correlation between various parameters to predict the spread of fire. In the experiments, the average absolute percentage error of the flame detection model for the detection of flame height, width and longitudinal position was 3.49%–10.64 %, 2.45%–8.89 % and 1.61%–9.31 %, respectively, and the MAPE of the time series prediction model for the above three parameters was 11.18%–15.06 % and 4.35%–8.18 %, 3.37%–6.62 %.The results of the above experiments verify that the proposed model has the ability to quantitatively analyze the fire spread trend in the actual fire and help firefighters make decisions.

The distributed strategy for asynchronous observations in data-driven wildland fire spread prediction

Background Asynchronous observations refer to observations that are obtained at multiple moments. The observation moments of fire fronts may differ throughout an entire wildfire area. Asynchronous observations include historical data, which hinders the effectiveness of data assimilation due to the lack of timely updates on changing fire fronts. Aims This paper proposed a distributed strategy combined with the Ensemble Transform Kalman filter (ETKF-distributed) for asynchronous observations. It can assimilate fire fronts immediately at any location by using new matching schemes between prediction and observation. Methods The ETKF-distributed undergoes testing using a wildland fire generated based on real terrain, vegetation, and historical weather data from the local area. In addition, the ETKF and ETKF-centralised proposed in our previous work were employed as comparisons. Observing System Simulation Experiments were conducted to generate asynchronous observation fire fronts. Key results The benefit of immediate assimilation enables the new method to maintain high accuracy predictions. Conclusions The allocation of observation resources can be focused in regions with high rates of speed when employing ETKF-distributed. Implications The ETKF-distributed has high efficiency and adaptability, making it highly promising for implementation in wildfire prediction.

СРЕДСТВА ПОЛУЧЕНИЯ И ПЕРСПЕКТИВЫ ПРИМЕНЕНИЯ КОМПРЕССИОННОЙ ПЕНЫ В ПОЖАРОТУШЕНИИ

The subject of the article is modern fire extinguishing technologies based on compression foam. The purpose of the study is to study the means of obtaining and the prospects for using compression foam in fire fighting. Methods: analysis and synthesis, modeling. The results obtained indicate that compression foam has a number of advantages compared to water and conventional air-aspirated foam, so it can be used to extinguish fires in many industries and areas. A diagram of the production of compression foam is presented (the foam concentrate acts as a foaming agent) and a diagram of an autonomous installation for the production of compression foam. It is noted that one of the significant advantages of CAFS is the ability to create a finished product that matches a specific type of fuel or a specific situation. The characteristics of the SmartCAFS product have been defined. A typology of the use of compression foam depending on the type of incident is presented. The results of fire resistance tests of various foams are presented. It is concluded that fire suppression systems using compression foam have significant advantages, such as minimizing the spread of fire, the volume of water required to cover large areas, costs and placement, and the integration of CAFS with digital technologies.

Metaheuristic algorithms for calibration of two-dimensional wildfire spread prediction model

Wildfires are complex phenomena with harmful consequences, ranging from environmental and property destruction to loss of human lives. In this sense, predicting wildfire behaviour is essential to mitigate its impacts and consequences. The Rothermel model is the most used fire rate of spread prediction model. However, input parameter uncertainty is a significant source of prediction error. In this paper, we propose the calibration of the input parameters of the fire propagation model by metaheuristic algorithms under a two-stage framework. The fire spread model consists on the Rothermel model in a two-dimensional approach for surface fires. The proposed calibration is performed in two stages iteratively repeated over time: (i) the calibration of the fire spread model’s input parameters and (ii) the wildfire spread prediction using the calibrated input parameters. The calibration was performed by the genetic algorithm, differential evolution, and simulated annealing, which calibrates the surface-area-to-volume ratio, fuel bed depth, live fuel moisture and dead fuel moisture. The symmetric difference between the real and predicted fire map shapes was defined as the fitness function of all three metaheuristic algorithms. For validation, simulations were done on two prescribed fires. The results for the real and estimated fire behaviour were then compared and revealed that all the tested metaheuristic algorithms produce a better fit to the real fire’s perimeter when compared to the uncalibrated Rothermel model. From the results, differential evolution provided the majority of best results when compared to genetic algorithm and simulated annealing algorithms in each scenario.

The Canadian Fire Spread Dataset

Satellite data are effective for mapping wildfires, particularly in remote locations where monitoring is rare. Geolocated fire detections can be used for enhanced fire management and fire modelling through daily fire progression mapping. Here we present the Canadian Fire Spread Dataset (CFSDS), encompassing interpolated progressions for fires >1,000 ha in Canada from 2002–2021, representing the day-of-burning and 50 environmental covariates for every pixel. Day-of-burning was calculated by ordinary kriging of active fire detections from the Moderate Resolution Imaging Spectroradiometer and the Visible Infrared Imaging Radiometer Suite, enabling a substantial improvement in coverage and resolution over existing datasets. Day of burning at each pixel was used to identify environmental conditions of burning such as daily weather, derived weather metrics, topography, and forest fuels characteristics. This dataset can be used in a broad range of research and management applications, such as retrospective analysis of fire spread, as a benchmark dataset for validating statistical or machine-learning models, and for forecasting the effects of climate change on fire activity.

Research Trends in Wildland Fire Prediction Amidst Climate Change: A Comprehensive Bibliometric Analysis

Wildfire prediction plays a vital role in the management and conservation of forest ecosystems. By providing detailed risk assessments, it contributes to the reduction of fire frequency and severity, safeguards forest resources, supports ecological stability, and ensures human safety. This study systematically reviews wildfire prediction literature from 2003 to 2023, emphasizing research trends and collaborative trends. Our findings reveal a significant increase in research activity between 2019 and 2023, primarily driven by the United States Forest Service and the Chinese Academy of Sciences. The majority of this research was published in prominent journals such as the International Journal of Wildland Fire, Forest Ecology and Management, Remote Sensing, and Forests. These publications predominantly originate from Europe, the United States, and China. Since 2020, there has been substantial growth in the application of machine learning techniques in predicting forest fires, particularly in estimating fire occurrence probabilities, simulating fire spread, and projecting post-fire environmental impacts. Advanced algorithms, including deep learning and ensemble learning, have shown superior accuracy, suggesting promising directions for future research. Additionally, the integration of machine learning with cellular automata has markedly improved the simulation of fire behavior, enhancing both efficiency and precision. The profound impact of climate change on wildfire prediction also necessitates the inclusion of extensive climate data in predictive models. Beyond conventional studies focusing on fire behavior and occurrence probabilities, forecasting the environmental and ecological consequences of fires has become integral to forest fire management and vital for formulating more effective wildfire strategies. The study concludes that significant regional disparities in knowledge exist, underscoring the need for improved research capabilities in underrepresented areas. Moreover, there is an urgent requirement to enhance the application of artificial intelligence algorithms, such as machine learning, deep learning, and ensemble learning, and to intensify efforts in identifying and leveraging various wildfire drivers to refine prediction accuracy. The insights generated from this field will profoundly augment our understanding of wildfire prediction, assisting policymakers and practitioners in managing forest resources more sustainably and averting future wildfire calamities.

Reflections on Lithium-Ion Cells. Genesis of the Problem and Theoretical Introduction

Aim: This article presents an identification and analysis of the hazards associated with the operation of lithium-ion technology, the range of applications, an overview of hazardous incidents domestically and also internationally, the brief history of lithium-ion batteries, as well as recommendations on the use and storage of batteries published not only by domestic but also foreign fire and occupational safety organizations. Introduction: The increased mobility of people doing their work with computers has created the need to produce portable devices that require concentrating as much energy as possible in a limited enclosure volume. These devices accompany the user in everyday life, during travel and also in a variety of work. In order to meet these criteria, it was necessary to solve these problems, first of all, the problem of power supply, which would allow to use the electricity stored by them in a long-lasting but also very efficient way. However, modern solutions bring a number of new risks that are not obvious at first glance and require detailed analysis. Project and methods: Based on a critical analysis of the literature, the authors presented the advantages and disadvantages of cells used in smartphones, laptops, portable power tools used in households across the globe and even electric cars. The development of lithium-ion battery technology and the expansion of their range of applications makes it necessary to develop solutions to enhance safety during their use, together with procedures to prevent possible explosions and fires, allowing effective life and health protection of users, as well as safeguarding all property and the environment. Methodology: Opierając się na krytycznej analizie literatury, autorzy przedstawili zalety i wady ogniw wykorzystywanych w smartfonach, laptopach, przenośnych elektronarzędziach, a nawet samochodach elektrycznych. Rozwój technologii produkcji akumulatorów litowo-jonowych oraz rozszerzenie zakresu ich zastosowania sprawia, że konieczne jest opracowanie rozwiązań zwiększających bezpieczeństwo podczas ich użytkowania oraz procedur zapobiegania ewentualnym wybuchom i pożarom, pozwalających na skuteczną ochronę życia oraz zdrowia użytkowników, a także zabezpieczenie wszelkiego mienia i środowiska. Conclusions: In order to prevent fires spreading, it is necessary to understand the mechanisms that lead to ignition and possible explosions of cells and batteries. From the point of view of preventing such failures and fires, it is important to recognize the response of burning batteries to various types of fire extinguishing agents and preventive measures. Continued research in this area can contribute to the evolution of more sophisticated and advanced safety technologies and the development of regulatory standards that in the future will guarantee the effective control of lithium-ion battery fires directly linked to their storage, use at high quantities, and the correct storage of waste (in the form of damaged and depleted batteries). Keywords: lithium-ion batteries, fire hazard, environmental protection, environmental pollution, toxic products of combustion

Simplified identification of fire spread risk in building clusters based on digital image processing technology

ABSTRACT Due to factors such as smoke, the non fire identification rate and fire identification accuracy in building fire identification are low, resulting in poor identification results. Therefore, a simplified identification method for building fire spread risk based on digital image processing technology is proposed. A fire spread model is constructed by considering both thermal radiation and thermal plumes, and based on this model, changes in smoke, temperature, and other factors during the occurrence of a fire are obtained. Using K-means to improve the dark channel defogging algorithm, the image after defogging is obtained, making the colour of the image more prominent and the feature information more abundant. Using the area threshold method to further extract the flame target contour in the defogged image, and determine whether the suspected flame area is a flame. Finally, four aspects of colour, sharp corner characteristics, area growth characteristics, and automatic estimation of propagation direction were identified to obtain simplified identification results for fire propagation risk. The experimental results show that the average non fire recognition rate of the proposed method reaches 95.8%, with good fire recognition accuracy and recognition effect, which verifies its feasibility.

Applying and Evaluating the Modified Method of the Rothermel Model under No-Wind Conditions for Pinus koraiensis Plantations

Pinus koraiensis is one of the important tree species in Northeast China. Due to its high pine-needle-oil content and the density of human activities in its habitat, the forest-fire prevention situation is severe in the context of climate change. The rate of surface-fire spread is one of the key indicators for scientifically advancing early fire prevention and is crucial for guiding forest firefighting operations. In this study, we investigated how moisture content, load, and slope impact the surface-fire spread rate using indoor-simulated fire-spread experiments. Furthermore, we analyzed the limitations in the Rothermel model for predicting the surface-fire spread rate in P. koraiensis plantations and proposed modifications to the model by the modification method of priority to no-wind or slope conditions and slope conditions. Additionally, we evaluated the prediction accuracy of the original Rothermel model and two other modified models on the surface-fire spread rate. A high moisture content and low slope demonstrated an absolute inhibitory effect on the rate of surface-fire spread, whereas the promotional effect of a low moisture content and high slope was easily disturbed by the other factors. Under high-slope conditions, an overestimation situation was observed in the Rothermel model. Both of the modification methods involving priority to no-wind or slope conditions and slope conditions could improve this situation. Furthermore, the modification method demonstrated a better improvement effect on the prediction accuracy. Our findings provide valuable insights for refining the Rothermel model and offer guidance for improving the accuracy of predicting fire spread rates and behavior for Pinus koraiensis. This bears immense significance for advancing the understanding and calculation of the ROS of forest fires in the region.