Fire Model Research Articles

Modelling LiDAR-Based Vegetation Geometry for Computational Fluid Dynamics Heat Transfer Models

Vegetation characteristics significantly influence the impact of wildfires on individual building structures, and these effects can be systematically analyzed using heat transfer modelling software. Close-range light detection and ranging (LiDAR) data obtained from uncrewed aerial systems (UASs) capture detailed vegetation morphology; however, the integration of dense vegetation and merged canopies into three-dimensional (3D) models for fire modelling software poses significant challenges. This study proposes a method for integrating the UAS–LiDAR-derived geometric features of vegetation components—such as bark, wooden core, and foliage—into heat transfer models. The data were collected from the natural woodland surrounding an elevated building in Samford, Queensland, Australia. Aboveground biomass (AGB) was estimated for 21 trees utilizing three 3D tree reconstruction tools, with validation against biomass allometric equations (BAEs) derived from field measurements. The most accurate reconstruction tool produced a tree mesh utilized for modelling vegetation geometry. A proof of concept was established with Eucalyptus siderophloia, incorporating vegetation data into heat transfer models. This non-destructive framework leverages available technologies to create reliable 3D tree reconstructions of complex vegetation in wildland–urban interfaces (WUIs). It facilitates realistic wildfire risk assessments by providing accurate heat flux estimations, which are critical for evaluating building safety during fire events, while addressing the limitations associated with direct measurements.

City-scale fire risk modeling based on spatial regression methods

City-scale fire risk modeling based on spatial regression methods

Parametric model order reduction for a wildland fire model via the shifted POD-based deep learning method

Parametric model order reduction techniques often struggle to accurately represent transport-dominated phenomena due to a slowly decaying Kolmogorov n-width. To address this challenge, we propose a non-intrusive, data-driven methodology that combines the shifted proper orthogonal decomposition (POD) with deep learning. Specifically, the shifted POD technique is utilized to derive a high-fidelity, low-dimensional model of the flow, which is subsequently utilized as input to a deep learning framework to forecast the flow dynamics under various temporal and parameter conditions. The efficacy of the proposed approach is demonstrated through the analysis of one- and two-dimensional wildland fire models with varying reaction rates, and its error is compared with the error of other similar methods. The results indicate that the proposed approach yields reliable results within the percent range, while also enabling rapid prediction of system states within seconds.

CNNM-FDI: Novel Convolutional Neural Network Model for Fire Detection in Images

Fires are a leading cause of accidents globally, often resulting in significant destruction, which can be challenging to quantify. It can happen anywhere and under any circumstances, with repercussions that transcend geographical and situational boundaries, causing widespread devastation across the social and economic domains. In this study, we introduced a fire detection model based on a deep learning methodology. The VGG16, VGG19, Inception, and Xception models are widely recognized as standards for image categorization challenges. In our method, we introduced a deep CNN model for fire detection, which was evaluated using a custom dataset. Our model utilized a multipath architecture that incorporated convolutional layers with a combination of small and large filters. This design enables the model to learn local and global features effectively, thereby improving its ability to extract comprehensive features. Varying size filters identify intricate patterns, textures, and structures, leading to the generation of stronger and more expressive representations. We utilized image data gathered from satellite and CCTV surveillance systems deployed in various environments, including forests, agricultural land, indoor and outdoor settings, and chemical plants for fire prevention. The experimental results demonstrate that our proposed model outperforms state-of-the-art methods (VGG16, VGG19, Inception, and Xception) in terms of Accuracy, Precision, Recall, F-score, and ROC-AUC score.

An offline coupling of fire spread models to simulate the 2021 Marshall Fire

Background Existing fire spread models focus exclusively on wildland or urban fire simulation. Aims This study aims at an offline coupling of two fire spread models to enable a continuous simulation of a wildfire incident transitioning from wildland into wildland–urban interface (WUI) communities, evaluate the effects of wind input on simulation results and study the influence of building types on fire spread patterns. Methods The selected models are WRF-Fire, a wildland fire behaviour simulation platform, and SWUIFT, a model for fire spread inside the WUI. The 2021 Marshall Fire serves as the case study. A map of the fire’s timeline and location is generated using public information. Three simulation scenarios are analysed to study the effects of wind input resolution and building type on the predicted fire spread and damage. Key results The most accurate results are obtained using a high-resolution wind input and when incorporating different building types. Conclusions The offline coupling of models provides a reliable solution for fire spread simulation. Fire-resistant buildings likely helped limit community fire spread during the Marshall Fire. Implications The research is a first step toward developing simulation capabilities to predict the spread of wildfires within the wildland, WUI and urban environments.

Enhanced CH4 emissions from global wildfires likely due to undetected small fires

Monitoring methane (CH4) emissions from terrestrial ecosystems is essential for assessing the relative contributions of natural and anthropogenic factors leading to climate change and shaping global climate goals. Fires are a significant source of atmospheric CH4, with the increasing frequency of megafires amplifying their impact. Global fire emissions exhibit large spatiotemporal variations, making the magnitude and dynamics difficult to characterize accurately. In this study, we reconstruct global fire CH4 emissions by integrating satellite carbon monoxide (CO)-based atmospheric inversion with well-constrained fire CH4 to CO emission ratio maps. Here we show that global fire CH4 emissions averaged 24.0 (17.7–30.4) Tg yr−1 from 2003 to 2020, approximately 27% higher (equivalent to 5.1 Tg yr−1) than average estimates from four widely used fire emission models. This discrepancy likely stems from undetected small fires and underrepresented emission intensities in coarse-resolution data. Our study highlights the value of atmospheric inversion based on fire tracers like CO to track fire-carbon-climate feedback.

A large-scale FireLine global positioning method based on a single unmanned aerial vehicle

ABSTRACT Wildfires exert a destructive influence on forest ecosystems. Precise positioning of the fire margin (large-scale fireline) of extensive wildfires facilitates the allocation of firefighting resources. However, the direct positioning of the entire fire field by a single unmanned aerial vehicle (UAV) remains a formidable challenge due to the extensive horizontal expanse of large-scale firelines, and asynchronous monitoring of the entire fire field results in suboptimal accuracy as a consequence of the dynamic propagation of the fireline. To address these limitations, this paper presents a global positioning method for large-scale firelines employing a single UAV. By asynchronously locating part fireline through the UAV’s airborne fireline real-time positioning system during UAV flight, the acquired asynchronous firelines are synchronized to the same time utilizing a fire spread prediction model, ultimately achieving global positioning of the firelines. The efficacy of the proposed method was validated using FARSITE-simulated wildfire data and two field-prefabricated wildfire experiment datasets. The outcomes demonstrate that the proposed method outperforms direct asynchronous positioning of the entire fireline by a single UAV in terms of accuracy.

Performance of Orthotropic Steel Plate under Combined Fire Load and Axial Compression

The orthotropic steel plate systems have been used widely in decks of long span bridges because of their large capacity and economic advantages. Early experimental and analytical research on orthotropic steel plate systems has mainly focused on their behaviors during ambient conditions. This paper presents a detailed investigation on the axial capacity of the orthotropic steel plates under fire using a sequential thermal stress analysis framework. Temperature-dependent stress-strain relationship and thermal properties of steel have been taken into consideration in the finite element modeling. Different models and parameters such as fire model, material model, geometric imperfection, residual stress and rib wall thickness have been discussed, and their effects on the axial strength of the plate have been studied and compared. Simulation results indicate that fire has significant deteriorating effects on the plate’s axial capacity. Conventional simple fire models which assume uniform surface fire loads don’t represent the real fire scenarios, and tend to overestimate the axial capacity of the plate compared that during realistic fire scenarios. Initial imperfection and residual stress in the orthotropic steel plate has negligible effect on the axial capacity of the orthotropic steel plate system under fire conditions. Increasing the thickness of rib wall could improve the fire resistance of the plate.

Correlating Fire Incidents with Meteorological Variables in Dry Temperate Forest

Forest fires pose a significant ecological threat, particularly in the Diamer District, Gilgit-Baltistan, Pakistan, where climatic factors combined with human activities have resulted in severe fire incidents. The present study sought to investigate the correlation between the incidence of forest fires and critical meteorological elements, including temperature, humidity, precipitation, and wind speed, over a period of 25 years, from 1998 to 2023. We analyzed 169 recorded fire events, collectively burning approximately 109,400 hectares of forest land. Employing sophisticated machine learning algorithms, Random Forest (RF), and Gradient Boosting Machine (GBM) revealed that temperature and relative humidity during the critical fire season, which spans May through July, are key factors influencing fire activity. Conversely, wind speed was found to have a negligible impact. The RF model demonstrated superior predictive accuracy compared to the GBM model, achieving an RMSE of 5803.69 and accounting for 49.47% of the variance in the burned area. This study presents a novel methodology for predictive fire risk modeling under climate change scenarios in the region, offering significant insights into fire management strategies. Our results underscore the necessity for real-time early warning systems and adaptive management strategies to mitigate the frequency and intensity of escalating forest fires driven by climate change.

YOLOGX: an improved forest fire detection algorithm based on YOLOv8

To tackle issues, including environmental sensitivity, inadequate fire source recognition, and inefficient feature extraction in existing forest fire detection algorithms, we developed a high-precision algorithm, YOLOGX. YOLOGX integrates three pivotal technologies: First, the GD mechanism fuses and extracts features from multi-scale information, significantly enhancing the detection capability for fire targets of varying sizes. Second, the SE-ResNeXt module is integrated into the detection head, optimizing feature extraction capability, reducing the number of parameters, and improving detection accuracy and efficiency. Finally, the proposed Focal-SIoU loss function replaces the original loss function, effectively reducing directional errors by combining angle, distance, shape, and IoU losses, thus optimizing the model training process. YOLOGX was evaluated on the D-Fire dataset, achieving a mAP@0.5 of 80.92% and a detection speed of 115 FPS, surpassing most existing classical detection algorithms and specialized fire detection models. These enhancements establish YOLOGX as a robust and efficient solution for forest fire detection, providing significant improvements in accuracy and reliability.

Evaluating a simulation-based wildfire burn probability map for the conterminous US

Background Wildfire simulation models are used to derive maps of burn probability (BP) based on fuels, weather, topography and ignition locations, and BP maps are key components of wildfire risk assessments. Aims Few studies have compared BP maps with real-world fires to evaluate their suitability for near-future risk assessment. Here, we evaluated a BP map for the conterminous US based on the large fire simulation model FSim. Methods We compared BP with observed wildfires from 2016 to 2022 across 128 regions representing similar fire regimes (‘pyromes’). We evaluated the distribution of burned areas across BP values, and compared burned area distributions among fire size classes. Key results Across all pyromes, mean BP was moderately correlated with observed burned area. An average of 71% of burned area occurred in higher-BP classes, vs 79% expected. BP underpredicted burned area in the Mountain West, especially for extremely large fires. Conclusions The FSim BP map was useful for estimating subsequent wildfire hazard, but may have underestimated burned areas where input data did not reflect recent climate change, vegetation change or human ignition patterns. Implications Our evaluations indicate that caution is needed when relying on simulation-based BP maps to inform management decisions. Our results also highlight potential opportunities to improve model estimates.

Respiratory surveillance and inward rectifier potassium channel expression in lung tissue within an experimental epilepsy model.

Epilepsy is characterized by neuronal discharges that occur as a result of disruption of the excitatory and inhibitory balance of the brain due to functional and structural changes. It has been shown in the literature that this neurological disorder may be related to the expression of ion channels. Any defect in the function or expression mechanism of these channels can lead to various neuronal disorders such as epilepsy. Epileptic seizures occur as a result of the accumulation of biological disorders in the circulatory, respiratory and nervous systems. In this study, we aimed to examine the changes in the expression of inward-directing potassium channels (Kir 3.1 and 6.2) in lung tissue and respiratory functions, considering that it will contribute to the elucidation of the mechanisms of sudden deaths thought to be caused by cardiorespiratory complications in epilepsy. In the study, 48 adult male Wistar albino rats weighing 250-300g were used in the study. During the research process, respiratory function tests were performed on epileptic rats induced with pentylenetetrazol (PTZ) firing model, and then histopathological changes in lung and hippocampus tissues, and expression levels of the Kir (3.1 and 6.2) channels were evaluated by immunohistochemistry, qRT-PCR and Western blot analysis. Memantine and tertiapin-Q have been shown to protect epileptic groups from histopathological harm induced by PTZ application and also reduce HIF-1α, Kir 3.1 and Kir 6.2 expression. The findings imply that memantine and tertiapin-Q would be suitable options for treating epilepsy patients.

Short-term impacts of operational fuel treatments on modelled fire behaviour and effects in seasonally dry forests of British Columbia, Canada

Background In response to increasing risk of extreme wildfire across western North America, forest managers are proactively implementing fuel treatments. Aims We assessed the efficacy of alternative combinations of thinning, pruning and residue fuel management to mitigate potential fire behaviour and effects in seasonally dry forests of interior British Columbia, Canada. Methods Across five community forests, we measured stand attributes before and after fuel treatments in 2021 and 2022, then modelled fire behaviour and effects using the Fire and Fuels Extension to the Forest Vegetation Simulator. Key results For our study area, field measurements combined with fire behaviour modelling indicated: (1) low-intensity thinning from below reduced potential of passive crown fire, whereas high-intensity thinning reduced potential of passive and active crown fire; (2) pruning after thinning from below did not further reduce potential of passive crown fire; and (3) chipping or pile burning of residue fuel mitigated potential of passive crown fire, but fire effects associated with chipping remain a concern. Conclusions and implications There is limited prior research on the impacts of fuel treatments in western Canada. This research contributes to better understanding the potential impacts of fuel treatments in the fire-prone forests of interior British Columbia.

Research on the development and application of fire real-time risk model for nuclear power plants

Research on the development and application of fire real-time risk model for nuclear power plants

Fire Testing and Modeling of a Novel Hybrid Timber Floor System

Fire Testing and Modeling of a Novel Hybrid Timber Floor System

Experimental and numerical investigations of cavity flame spread in double skin façade

Experimental and numerical investigations of cavity flame spread in double skin façade

Early fire hazard risk management model in urban environments : Leveraging optimized deep learning techniques

The rapid growth of IoT technology has revolutionized smart city applications, improving societal functionalities. This integration offers real-time applications for predicting crime events, monitoring environmental conditions, and managing health. However, current IoT-based smart city implementations face challenges in technological capacity and skill readiness. To address this, they propose a framework combining RNN with ALO to predict fire hazard risks early. IoT sensor devices are deployed across smart cities to monitor environmental conditions like drought code, temperature, and humidity. Data is securely stored in Firebase for processing with MATLAB. The model’s effectiveness is validated against conventional methods, demonstrating superior accuracy and minimal errors. This framework addresses technological challenges and offers promising outcomes in predicting and mitigating fire hazards through advanced data analysis.

Evaluation of the Uncertainty of Geometry Shape on Fire Modeling Analysis Caused by Ignition Area Change of Combustibles

In this study, an impact assessment of the uncertainty caused by the geometric shape of fuel in fire modeling analysis was conducted, considering various factors. To evaluate the impact, the geometric shape of the fuel was divided into the area of the ignition source and the flame propagation area of the secondary combustible material. The impact on the target cable tray was assessed by selecting a virtual nuclear power plant compartment as the cause of the fire in the simulation. The results indicated that the smaller the area of the ignition source, the greater the damage to the target. In addition, the flame propagation method of the secondary soft materials showed different trends, even in the same ignition area. Therefore, this tendency should be considered in fire modeling analysis.

Effects of Terrain Geometry Processing Methods on Fire Spread in Wildland Fire Modeling

This study was conducted to understand the characteristics of wildfire spread according to the processing methods of terrain geometry in the computational domain based on topographic data. The terrain geometry of the computational domain was handled using body-fitted (BF) grids and the cut-cell method (CCM), and the spread of the wildfire was simulated using a level-set model incorporating the heat release rate. The fire spread predicted by the BF grid and CCM showed similar overall trends despite local differences. A simple model was proposed to estimate the perimeter of the fireline based on the calculated heat release rate with fire spread, which showed good agreement with the field results predicted by the level-set method. The cut-cell method can enhance the applicability of wildland fire-spread models due to its ease of handling complex terrain geometry and reasonable agreement with the BF grid.

Research on Fire Suppression Characteristics of Compressed Air Foams in Full-Scale 220 kV Converter Transformer

To study the fire behavior of UHVDC (ultra-high-voltage direct current) converter transformers and the effectiveness of CAFs (compressed air foams) in suppressing fires, a full-scale model of a 220 kV converter transformer fire was constructed. The model mainly considered the oil pool fires and oil spill fires that form after explosions, causing the casing to completely fall out. The hot oil fire tests were conducted on the physical converter transformer. The fire suppression characteristics of the CAF system for converter transformer fires were studied. The temperature and changes in various locations of the fire model were analyzed under different foam supply strengths. The fire in a converter transformer is characterized by intense heat, high temperatures, and strong radiation. The highest temperature can exceed 1000 °C in cases of complete combustion. The fire in the converter transformer involves a dynamic oil spill and a large pool of oil, making it challenging to extinguish. The fire extinguishing performance and cooling effect of CAFs are outstanding. The recommended foam supply strength for the actual project is more than 8 L/(min·m2).