Fire Model Research Articles

Maximum temperature of steel bridges due to multiple under-bridge fire sources

In this study, an analytical model was developed and predictive equations were proposed to estimate the maximum temperature of steel bridges in the event of a fire originating from complex facilities beneath the bridge. Based on statistical data, a representative shape of steel bridges in South Korea was selected. A representative Heat Release Rate (HRR) curve for the complex facility was selected, and a parametric study was conducted, including variables such as Heat Release Rate Per Unit Area (HRRPUA), fully developed time, vertical clearance, and the area of the complex facility. Through correlation analysis, key parameters influencing the maximum temperature in steel bridges were examined. Based on the results, both linear and non-linear temperature prediction equations were suggested. The non-linear equation demonstrated higher predictive performance than the linear equation. This study provides foundational data for enhancing the accuracy of fire analysis methods and temperature prediction models for steel bridges.

Multi-factor coupled forest fire model based on cellular automata

The risk of forest fires is substantial due to uneven precipitation distributions and abnormal climate change. This study employs cellular automata principles to analyze forest fire behavior, taking into account meteorological elements, combustible material types, and terrain slopes. The Wang Zhengfei model is utilized to compute fire spread speed, and a multifactor coupled forest fire model is developed. Comparisons with experimental data show a mean calculated fire spread speed of 0.69 m/min, which is consistent with the experimental results. Using the forest fire in Anning city, Yunnan Province, as a case study with a mean burned area of 2281 ha, the burned area, rate of change in burned area, and burning area demonstrated an increasing trend, with fluctuating states in the rate of change of the burning area. Employing the controlled variable method to examine forest fire spreading patterns under varying factors such as wind speed, vegetation type, and maximum slope reveals that under wind influence, the fire site adopts an elliptical shape with the downwind direction as the major axis. Quantitatively, when the wind speed increases from 2 m/s to 10 m/s, the burned area expands by a factor of 1.37. The ratio of the combustible material configuration coefficient to the burned area remains consistent across the different vegetation types, and the burned area increases by a factor of 1.92 when the maximum slope increases from 5° to 25°.

Coupled Fire–Atmosphere Simulations of the Split, Croatia, Wildfire

Abstract The Weather Research and Forecasting Spread FIRE (WRF-SFIRE) model was used to simulate the evolution of the fire perimeter for the Split wildfire in Croatia, evaluated in our previous detailed reconstruction of the event. A four-domain configuration was applied, with the innermost domain having a resolution of 500 m with a planetary boundary layer parameterization. The coupled fire grid was further downscaled to 33.3-m resolution, using the best available estimates of topography and fuel load data. WRF-SFIRE simulated slightly smaller fire areas in the four observed fire perimeter stages of the Split wildfire event, especially the northwestward spread in the last stage along the Adriatic coast. Additional experiments with multiple ignitions in the WRF-SFIRE model, representing the spotting mechanism, successfully simulated the northwestward spread in the event. The study also included sensitivity experiments that turned off the heat and moisture flux coupling between WRF and the fire grid. It was found that the simulated fire area is larger than the model run that included heat and moisture feedback from the fire grid to the atmosphere, which is contrary to some previous studies. The large wind speed and convergence along the fire line in our feedback-on simulation, which constrain the fire when there are feedback processes, were likely the cause of the smaller fire area. Last, areas of pine that burnt in the Split fire event favored crown fire spread, which is not represented in the current modeling framework of WRF-SFIRE.

Assessment of a PAH-based soot production model in laminar coflow methane diffusion flames doped by gasoline surrogate fuels

This article assesses the capability of the PAH-based soot model developed by the authors and validated in ethylene non-premixed flames to predict soot production in flames fueled with gasoline surrogates. The soot model was coupled to a flamelet model and the Rank-Correlated Full-Spectrum k model to simulate laminar coflow nitrogen-diluted methane/air diffusion flames doped with n-heptane/toluene and iso-octane/toluene mixtures. Consistent with our previous studies, the simulation was conducted using the Kaust Mechanism 1, pyrene as soot precursor, and the same set of model parameters. The model reproduced reasonably-well the peak soot volume fraction. However, the soot production onset was predicted much earlier than measurements owing to the early formation of pyrene induced by the presence of toluene. These discrepancies can be partially corrected by selecting a larger PAH than pyrene with a similar level of concentrations as soot precursor. For the present mechanism, anthanthrene was found to be the best candidate. Model results show that different mechanisms dominate the soot mass growth in ethylene and gasoline surrogate flames. While the HACA is more important in the former, PAH condensation largely prevails in the latter. This suggests that ethylene may be not the most relevant reference fuel for developing semi-empirical soot models for fires. Further investigations are required to confirm this conjecture.

Assuring fire safety in nuclear plants with international standards

This paper presents the evolution and benefits of international standards for fire safety engineering11Fire safety engineering and performance-based fire safety are both widely used, and both represent an engineering process to determine performance of a fire protection plan based on the results of fire calculations. developed at the International Organization of Standardization (ISO). The history of fire safety in nuclear power plants along with the need for fire safety regulation is discussed to lay the foundation for the standards development work. The evolution of fire safety regulation from prescriptive to performance-based is also presented as the background for the research.Extensive research of the issues that arise for performance-based fire safety regulation, specifically for computer models for fire safety calculations, which included international benchmark exercises formed the basis to identify the need for developing international standards for fire safety engineering. The research focused both on the verification and validation (V&V) of computer fire models and determining their predictive capability. The paper for the first time takes new scientific and engineering data from benchmark exercises to assess the suitability and applicability of computer fire models for application in nuclear power plants. The paper reports on the findings of the benchmark exercises and their use in formulating new requirements in ISO international standards on verification and validation of fire calculation methods and the fire safety engineering process through a consensus process of international experts.Discussion of the foundational research at the ISO fire safety engineering committee (ISO TC 92 SC 4) led to a consensus to revise two international standards. One specifying the principles and general requirements for fire safety engineering, and another on the verification and validation procedures for fire calculation methods. The development and content of these two international standards are discussed in this paper. An international technical report was subsequently developed at the ISO committee for conformity assessment (CASCO) to provide guidance for the certification of the fire safety engineering process, and specific parts of it, namely the V&V of fire calculation methods.It is concluded that the fire safety engineering international standards presented in this paper can support rigorous performance-based fire safety regulation for nuclear power plants. These ISO international standards will be particularly useful to nations that have not yet developed the regulatory infrastructure for nuclear power regulation. The international standards are also available to those that would like to transition to the use of international standards and an ISO standards-based certification scheme. Regulations based on these international standards will provide citizens with confidence in nuclear power as a viable option to address climate change.

Fire after earthquake assessment of 3D reinforced concrete structures

Despite that, an earthquake's occurrence can lead to dramatic effects with significant damage in urban areas, including human and material losses. Fire after an earthquake amplifies the overall impact and becomes a catastrophic event. Most constructions in Algeria are made of reinforced concrete, and current regulations overlook fires after earthquakes. Structural designs are inadequate to handle such events. This study aims to investigate the behaviour of 3D reinforced concrete frames, as part of a residential building, designed according to internal codes, CBA93 and RPA99 v2003. This 3D RC structure is exposed to several load levels of its vertical load bearing capacity. The structural system is assumed to undergo seismic scenarios characterised by various levels of story drift and damage level types. The structure is then exposed to the standard fire ISO834 model. Numerical investigations are carried out for thermo-mechanical analysis using the finite element software ANSYS, taking into account geometric and material non-linearities. Results highlight the significant impact of vertical loads, story drifts, fire scenarios, and structural damage on a building's response to fire following an earthquake. These factors collectively influence the collapse probabilities, underlining the importance of holistic risk assessment in structural design.

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.

Comparing gas composition from fast pyrolysis of live foliage measured in bench-scale and fire-scale experiments

Background Fire models have used pyrolysis data from oxidising and non-oxidising environments for flaming combustion. In wildland fires pyrolysis, flaming and smouldering combustion typically occur in an oxidising environment (the atmosphere). Aims Using compositional data analysis methods, determine if the composition of pyrolysis gases measured in non-oxidising and ambient (oxidising) atmospheric conditions were similar. Methods Permanent gases and tars were measured in a fuel-rich (non-oxidising) environment in a flat flame burner (FFB). Permanent and light hydrocarbon gases were measured for the same fuels heated by a fire flame in ambient atmospheric conditions (oxidising environment). Log-ratio balances of the measured gases common to both environments (CO, CO2, CH4, H2, C6H6O (phenol), and other gases) were examined by principal components analysis (PCA), canonical discriminant analysis (CDA) and permutational multivariate analysis of variance (PERMANOVA). Key results Mean composition changed between the non-oxidising and ambient atmosphere samples. PCA showed that flat flame burner (FFB) samples were tightly clustered and distinct from the ambient atmosphere samples. CDA found that the difference between environments was defined by the CO-CO2 log-ratio balance. PERMANOVA and pairwise comparisons found FFB samples differed from the ambient atmosphere samples which did not differ from each other. Conclusion Relative composition of these pyrolysis gases differed between the oxidising and non-oxidising environments. This comparison was one of the first comparisons made between bench-scale and field scale pyrolysis measurements using compositional data analysis. Implications These results indicate the need for more fundamental research on the early time-dependent pyrolysis of vegetation in the presence of oxygen.

Effects of Fuel Bed Slope and Wind Direction on Flame Spread Characteristics Over a Bed of Pinus Silvestris Needles

ABSTRACT Important factors influencing the flame characteristics over a bed of dry pine needles from Siberian Boreal forests (Pinus silvestris) in lab-scale is provided. Factors such as slope of the fuel bed, wind velocity, and direction of spread (concurrent flow or opposed flow), which play significant roles in determining the flame spread rate, have been investigated. Systematic lab-scale experiments have been carried out in a wind tunnel with varying air velocities and on an inclined fuel bed in still air. Numerical simulations have been carried out using a three-dimensional physics-based flame spread model available in Fire Dynamics Simulator, and the results are validated against a wide range of measured flame characteristics. The validated numerical model is then used to simulate concurrent flow and opposed flow flame spread over a sloping fuel bed in the presence of wind. Gas phase temperature on and above the bed surface and total and radiative heat flux measurements for concurrent flow and opposed flow flame spread under the influence of wind as well as for upslope flame spread in still air are presented. A stationary flame propagation mode is observed for a flat fuel surface. Predicted temperature, flow, and species concentration fields have been reported for selected cases to understand the flow and flame dynamics in gas phase and within the fuel beds. These data serve as a validation of the numerical model used for simulating the experimental cases. Further, it can be used to validate popular fire models such as Fire Foam and Fire-Star 3D.

Size scale effect on mass burning flux and flame behavior of solid fuels

The study investigates the horizontal fuel size effect on free-burning fires for PMMA plates and wood cribs. The fuel size effect on mass burning flux and flame behavior is mainly discussed and compared with typical pool fires. For the PMMA plate and liquid pool, when the fuel size is small (< 10 cm), either the 3D sidewall burning of PMMA or the container wall heated by flame can promote the burning flux at the horizontal projection area. As the fuel size increases, these side wall burning or heating effects decrease, causing the drop in burning flux with fuel scale for both the PMMA plate and liquid pool. For small-scale wood cribs, fire cannot self-sustain due to the large airflow cooling. With the increase in wood crib size, the burning rate first remains constant and then gradually increases, driven by the enhanced internal radiation. As the fuel size increases above 20–30 cm, the flame radiation dominates the burning flux for all fuel types. Fire dynamics simulator (FDS) was adopted to simulate the horizontal size effect by setting a varied fire source (horizontal projection) area. First, the flame geometry and heat release rate (HRR) of simulations were validated against experimental results. Subsequently, the validated fire model generates cases covering a broad range of fire scales. Finally, a new correlation of flame height with the fire heat release rate and fuel size is proposed, and its prediction capability is validated in the numerical fire modeling. This study quantifies the size effect on the burning rate for common solid fuels and provides valuable information for the numerical modeling of multi-scale fires.

Study on Thermal Radiation Characteristics and the Multi-Point Source Model of Hydrogen Jet Fire

Study on Thermal Radiation Characteristics and the Multi-Point Source Model of Hydrogen Jet Fire

Assessing Delayed Collapse Risks in Load-Bearing Reinforced Concrete Walls Exposed to Parametric Fires: A Numerical Investigation

This study delves into the thermo-mechanical analysis of structures exposed to fire, focusing on the evolution of gas temperatures, thermal distribution in structural members, and mechanical behavior during fire scenarios. Traditional prescriptive approaches assume a monotonically increasing temperature, while contemporary performance-based designs incorporate both heating and cooling phases for a more realistic fire resistance assessment. The critical aspect of this research is the modeling of the fire's cooling phase, which, though not commonly practiced in design offices, is essential for evaluating the risk of delayed structural collapse. Utilizing the finite element program SAFIR, numerical simulations were conducted on reinforced concrete walls to investigate their behavior during and post-cooling phase. The findings indicate potential failure not only during the cooling phase but also after the fire has subsided and temperatures have returned to ambient levels. This highlights delayed temperature increases in the core of the element and the consequent loss of concrete strength during cooling as key mechanisms of failure. A parametric study was undertaken, examining various fire scenarios, wall geometries, load levels, wall heights, adjacency, and boundary conditions. The results underscore that short-duration fires pose the most significant risk for delayed failure, particularly for simply supported walls. Additionally, the study scrutinizes the mechanical properties of materials across the heating and cooling phases. This investigation underscores the necessity of incorporating cooling phase analyses in fire resistance evaluations to mitigate the risk of delayed collapses. The insights gained aim to inform safer structural designs and enhance fire safety protocols, emphasizing the importance of realistic fire modeling in performance-based design methodologies.

The importance of geography in forecasting future fire patterns under climate change

An increasing amount of California's landscape has burned in wildfires in recent decades, in conjunction with increasing temperatures and vapor pressure deficit due to climate change. As the wildland-urban interface expands, more people are exposed to and harmed by these extensive wildfires, which are also eroding the resilience of terrestrial ecosystems. With future wildfire activity expected to increase, there is an urgent demand for solutions that sustain healthy ecosystems and wildfire-resilient human communities. Those who manage disaster response, landscapes, and biodiversity rely on mapped projections of how fire activity may respond to climate change and other human factors. California wildfire is complex, however, and climate-fire relationships vary across the state. Given known geographical variability in drivers of fire activity, we asked whether the geographical extent of fire models used to create these projections may alter the interpretation of predictions. We compared models of fire occurrence spanning the entire state of California to models developed for individual ecoregions and then projected end-of-century future fire patterns under climate change scenarios. We trained a Maximum Entropy model with fire records and hydroclimatological variables from recent decades (1981 to 2010) as well as topographic and human infrastructure predictors. Results showed substantial variation in predictors of fire probability and mapped future projections of fire depending upon geographical extents of model boundaries. Only the ecoregion models, accounting for the unique patterns of vegetation, climate, and human infrastructure, projected an increase in fire in most forested regions of the state, congruent with predictions from other studies.

Real‐time fire and smoke detection with transfer learning based on cloud‐edge collaborative architecture

AbstractRecent years have seen increased interest in object detection‐based applications for fire detection in digital images and videos from edge devices. The environment's complexity and variability often lead to interference from factors such as fire and smoke characteristics, background noise, and camera settings like angle, sharpness, and exposure, which hampers the effectiveness of fire detection applications. Limited picture data for fire and smoke scenes further challenges model accuracy and robustness, resulting in high false detection and leakage rates. To address the need for efficient detection and adaptability to various environments, this paper focuses on (1) proposing a cloud‐edge collaborative architecture for real‐time fire and smoke detection, incorporating an iterative transfer learning strategy based on user feedback to enhance adaptability; (2) improving the detection capabilities of the base model YOLOv8 by enhancing the data augmentation method and introducing the coordinate attention mechanism to improve global feature extraction. The improved algorithm shows a 2‐point accuracy increase. After three iterations of transfer learning in the production environment, accuracy improves from 93.3% to 96.4%, and mAP0.5:0.95 increases by nearly 5 points. This program effectively addresses false detection issues in fire and smoke detection systems, demonstrating practical applicability.

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.

FireYOLO-Lite: Lightweight Forest Fire Detection Network with Wide-Field Multi-Scale Attention Mechanism

A lightweight forest fire detection model based on YOLOv8 is proposed in this paper in response to the problems existing in traditional sensors for forest fire detection. The performance of traditional sensors is easily constrained by hardware computing power, and their adaptability in different environments needs improvement. To balance the accuracy and speed of fire detection, the GhostNetV2 lightweight network is adopted to replace the backbone network for feature extraction of YOLOv8. The Ghost module is utilized to replace traditional convolution operations, conducting feature extraction independently in different dimensional channels, significantly reducing the complexity of the model while maintaining excellent performance. Additionally, an improved CPDCA channel priority attention mechanism is proposed, which extracts spatial features through dilated convolution, thereby reducing computational overhead and enabling the model to focus more on fire targets, achieving more accurate detection. In response to the problem of small targets in fire detection, the Inner IoU loss function is introduced. By adjusting the size of the auxiliary bounding boxes, this function effectively enhances the convergence effect of small target detection, further reducing missed detections, and improving overall detection accuracy. Experimental results indicate that, compared with traditional methods, the algorithm proposed in this paper significantly improves the average precision and FPS of fire detection while maintaining a smaller model size. Through experimental analysis, compared with YOLOv3-tiny, the average precision increased by 5.9% and the frame rate reached 285.3 FPS when the model size was only 4.9 M; compared with Shufflenet, the average precision increased by 2.9%, and the inference speed tripled. Additionally, the algorithm effectively addresses false positives, such as cloud and reflective light, further enhancing the detection of small targets and reducing missed detections.

Probabilistic Path Planning for UAVs in Forest Fire Monitoring: Enhancing Patrol Efficiency through Risk Assessment

Forest fire is a significant global natural disaster, and unmanned aerial vehicles (UAVs) have gained attention in wildfire prevention for their efficient and flexible monitoring capabilities. Proper UAV patrol path planning can enhance fire-monitoring accuracy and response speed. This paper proposes a probabilistic path planning (PPP) module that plans UAV patrol paths by combining real-time fire occurrence probabilities at different points. Initially, a forest fire risk logistic regression model is established to compute the fire probabilities at different patrol points. Subsequently, a patrol point filter is applied to remove points with low fire probabilities. Finally, combining fire probabilities with distances between patrol points, a dynamic programming (DP) algorithm is employed to generate an optimal UAV patrol route. Compared with conventional approaches, the experimental results demonstrate that the PPP module effectively improves the timeliness of fire monitoring and containment, and the introduction of DP, considering that the fire probabilities and the patrol point filter both contribute positively to the experimental outcomes. Different combinations of patrol point coordinates and their fire probabilities are further studied to summarize the applicability of this method, contributing to UAV applications in forest fire monitoring and prevention.

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.