Incipient Fire Research Articles

Identification of volatiles during thermal degradation of epoxy resins for detection of incipient fire conditions in composites

Identification of volatiles during thermal degradation of epoxy resins for detection of incipient fire conditions in composites

Impact of Depopulation on Forest Fires in Spain: Primary School Distribution as a Potential Socioeconomic Indicator

Socioeconomic factors are increasingly considered in the study of forest fires. However, there is a gap in the literature on the possible relationship between basic services and infrastructures such as small rural schools and forest fires. Population decline in rural areas is leading to an increase in forest fire risk and social vulnerability to forest fires due to the abandonment of traditional agroforestry practices and the expansion of unmanaged forest canopy. In addition, rural schools are supposed to make rural municipalities livable and promote the people’s sense of community. In parallel, there is controversy over the closure of small local schools in sparsely populated rural areas worldwide. Our study identified that the forest area burned in the province of Avila (Central Spain), during the period 1996 to 2023, was higher in municipalities without rural primary schools. The presence of rural schools was as statistically significant as the influence of orographic variations of the territory, the number of incipient fires, and the reduction of population density during the same period. Our work contributes to highlighting the potential links between the decline of essential services in rural areas and the increase in forest fire risk, to urge policymakers to take a collaborative and holistic view.

Simulated fire video collection for advancing understanding of human behavior in building fires.

The goal of the present research was to develop a video collection of simulated fires to investigate how people perceive growing building fires. In fire safety science, a critical factor to occupant responses to building fires is the pre-movement period, determined by how long it takes an individual to initiate taking protective action with an incipient fire. Key to studying the psychological processes that contribute to the duration of the pre-movement period is presenting human subjects with building fires. One approach used in previous research is to present videos of building fires to individuals via scenarios. The numerical simulations used to model fire dynamics can be used to render videos for these scenarios. However, such simulations have predominantly been used in fire protection engineering to design buildings and are relatively inaccessible to social scientists. The present study documents a collection of videos, based on numerical simulations, which can be used by researchers to study human behavior in fire. These videos display developing fires in different types of rooms, growing at different rates, different smoke thickness, among other characteristics. As part of a validation study, participants were presented with subsets of the video clips and were asked to rate the perceived risk posed by the simulated fire. We observed that ratings varied by the intensity and growth rate of the fires, smoke opacity, type of room, and where the viewpoint was located from the fire. These effects aligned with those observed in previous fire science research, providing evidence that the videos could elicit perceived risk using fire simulations. The present research indicates that future studies can utilize the video library of fire simulations to study human perceptions of developing building fires as situational factors are systematically manipulated.

Experimental and numerical investigations on fire development in a timber-based compartment with identification of characteristic events

An experiment and numerical simulations were carried out to investigate the mechanism of the incipient and growth stages of a building fire. The building, constructed in the 1980s with wooden components, has one compartment and one opening. The average fire load density was 1495.3 MJ/m2, including the wooden columns, beams, furniture, etc. The evolution of indoor temperature, gas concentration, gas flow rate and burning behaviors were obtained and analyzed. To better understand the fire development process, we identified characteristic events, including pilot fire, incipient fire, pre-flashover fire spread, ceiling jet period, and flashover and spill plume. By defining dimensionless time (τ = ti/tflashover), it was found that the fire development process simulated by Fire Dynamic Simulator experienced the same characteristic events as the experiment. In this work, when flashover occurred, the neutral plane height decreased to about half the height of the opening, the velocity of hot smoke outflow was higher than 3 m/s, and the maximum thickness of the wood charred layer reached 1 cm. Two theoretical methods to estimate the heat release rate (HRR) in the fuel-controlled stage before flashover were proposed. The HRRs at the time of flashover with the two methods are at the same order of 5 MW, and the error between them was within 18.1%, which verifies the feasibility of the above methods to a certain extent. This study helps to learn the characteristics of early and growth stages of building fires with wooden components.

Classification in Early Fire Detection Using Multi-Sensor Nodes-A Transfer Learning Approach.

Effective early fire detection is crucial for preventing damage to people and buildings, especially in fire-prone historic structures. However, due to the infrequent occurrence of fire events throughout a building's lifespan, real-world data for training models are often sparse. In this study, we applied feature representation transfer and instance transfer in the context of early fire detection using multi-sensor nodes. The goal was to investigate whether training data from a small-scale setup (source domain) can be used to identify various incipient fire scenarios in their early stages within a full-scale test room (target domain). In a first step, we employed Linear Discriminant Analysis (LDA) to create a new feature space solely based on the source domain data and predicted four different fire types (smoldering wood, smoldering cotton, smoldering cable and candle fire) in the target domain with a classification rate up to 69% and a Cohen's Kappa of 0.58. Notably, lower classification performance was observed for sensor node positions close to the wall in the full-scale test room. In a second experiment, we applied the TrAdaBoost algorithm as a common instance transfer technique to adapt the model to the target domain, assuming that sparse information from the target domain is available. Boosting the data from 1% to 30% was utilized for individual sensor node positions in the target domain to adapt the model to the target domain. We found that additional boosting improved the classification performance (average classification rate of 73% and an average Cohen's Kappa of 0.63). However, it was noted that excessively boosting the data could lead to overfitting to a specific sensor node position in the target domain, resulting in a reduction in the overall classification performance.

Investigation on the burning behaviors of the combustible ceiling with the impingement of an incipient fire source

This paper presents experimental and theoretical research on ceiling jet, burn-through, and flame spread behaviors of the combustible ceiling with the impingement of an incipient fire source. Medium-density fiberboard was utilized as the combustible ceiling in experiments, and the ceiling height above the fire source was varied. The flame radial length (rf), ceiling surface temperature, flame shape and spread rate after burn-through, and radiative heat flux were measured. Results showed that rf beneath the combustible ceiling was significantly longer than that of the non-combustible ceiling. Theoretical analysis was conducted for rf based on energy distribution, and an expression of rf beneath combustible ceiling was proposed. The burn-through behavior of the combustible ceiling was explored by combining the ceiling temperature, small-sized experiment and Fourier law. The advancement of char front along the ceiling thickness was calculated, which was the key point during burn-through. Upper surface flame spread in a circular shape after burn-through, and an expression of spread rate was obtained. A three-dimensional radiation model was adopted to calculate the radiative heat flux received by the unburned ceiling surface during the dynamic flame spread process, the calculated results agreed well with the experimental data.

Distance-Based Analysis of Early Fire Indicators on a New Indoor Laboratory Dataset with Distributed Multi-Sensor Nodes

This work analyzes a new indoor laboratory dataset looking at early fire indicators in controlled and realistic experiments representing different incipient fire scenarios. The experiments were performed within the constraints of an indoor laboratory setting using multiple distributed sensor nodes in different room positions. Each sensor node collected data of particulate matter (PM), volatile organic compounds (VOCs), carbon monoxide (CO), carbon dioxide (CO2), hydrogen (H2), ultraviolet radiation (UV), air temperature, and humidity in terms of a multivariate time series. These data hold immense value for researchers within the machine learning and data science communities who are keen to explore innovative and advanced statistical and machine learning techniques. They serve as a valuable resource for the development of early fire detection systems. The analysis of the collected data was carried out depending on the Manhattan distance between the fire source and the sensor node. We found that especially larger particles (>0.5 μm) and VOCs show a significant dependency with respect to the intensity as a function of the Manhattan distance to the source. Moreover, we observed differences in the propagation behavior of VOCs, PM, and CO, which are particularly relevant in incipient fire scenarios due to the presence of strand propagation effects.

Urban Firefighting Drones: Precise Throwing from UAV

In recent years, the use of unmanned aerial vehicles has spread across different fields of the industry due to their ease of deployment and minimal operational risk. Firefighting is a dangerous task for the humans involved, in which the use of UAVs presents itself as a good first-action protocol for a rapid response to an incipient fire because of their safety and speed of action. Current research is mainly focused on wildland fires, but fires in urban environments are barely mentioned in the bibliography. To motivate the research on this topic, ICUAS’22 organized an international competition inspired by this mission, with the challenge of a UAV traversing an area populated by obstacles, finding a target, and precisely throwing a ball to it. For this competition, the Computer Vision and Aerial Robotics (CVAR-UPM) team developed a solution composed of multiple modules and structured by a mission planner. In this paper, we describe our approach and the developed architecture that led us to be awarded the first prize in the competition.

Ancient Conflagration: a Reconstruction of a Middle Formative Fire at the Chiripa Mound, Bolivia

In this study, we assess competing interpretations of a burnt ceremonial structure from the terminal Middle Formative period (ca. 300–100 BCE) by analyzing the stepped platform mound at Chiripa, Bolivia, through a systematic reconstruction of the fire that destroyed it. We developed a model of potential fire pathways, their social contexts, and material indicators. Our approach contrasts incipient fires from accident or arson to planned fires initiated for functional or social ends. We assessed these pathways for the Chiripa mound fire through experimental, geoarchaeological, faunal, and botanical data. Experiments were aimed at deducing the temperature, duration, and oxidation conditions of the fire. The fire temperature and duration were approximated from geoarchaeological analyses of construction materials in comparison with controls, and thermal alteration of faunal bone. Fuels were reconstructed through paleoethnobotanical analysis of charred remains from discrete areas within the burnt structure. We conclude that an intentional fire burned the structures on the Chiripa mound to temperatures of 700 °C or higher under oxidizing conditions for several hours. The pattern of heat-altered materials recovered would have required a substantial supplemental fuel load. At the 3840 masl elevation of Chiripa, the effective control of a high temperature oxidizing fire demonstrates technical expertise in fire management. Our findings indicate the fire appears intentional, likely a ritual event. We believe the structures were burned to facilitate a socio-political change during a period of social transition at the end of the Middle Formative period in Bolivia.

Fast detection of compartment fires under different heating conditions of materials

The paper presents experimental findings for the characteristics of equipment activation for the detection of pyrolysis and flame combustion under different heating conditions of materials. Class A model fires in the experiments involved a group of typical indoor combustible materials (wood, linoleum, cardboard, paper). Three most common causes of fires were reproduced: careless handling of fire (open flame), unsafe operation of heating equipment and electrical short circuits. To identify the characteristics of pyrolysis and ignition (i.e., stages preceding a fire), an automated system featuring fire (heat, smoke, flame) detectors, contact and non-contact temperature measurement instruments, a gas analysis system (CO, CO2, O2) and video recording equipment was used. Following the experiments, the scope of technical equipment (smoke, heat and flame detectors, gas analytical sensors, video recording systems) for the detection of pyrolysis of combustible materials and flame combustion start was determined. The most efficient combinations of equipment, necessary and sufficient for the identification of pyrolysis of combustible materials, were established for different fire causes. The values of the efficiency coefficients of fire detectors are calculated. It is shown that their values for different types of detectors can vary in a wide range (0.1–0.9). Recommendations were made on upgrading the existing fire prevention systems in buildings and creating new automated ones, allowing faster identification of an incipient fire, considering the specific character of the fire hazard source: open flame, heating equipment, electrical overload.

A video-based SlowFastMTB model for detection of small amounts of smoke from incipient forest fires

Abstract This paper proposes a video-based SlowFast model that combines the SlowFast deep learning model with a new boundary box annotation algorithm. The new algorithm, namely the MTB (i.e., the ratio of the number of Moving object pixels To the number of Bounding box pixels) algorithm, is devised to automatically annotate the bounding box that includes the smoke with fuzzy boundaries. The model parameters of the MTB algorithm are examined by multifactor analysis of variance. To demonstrate the validity of the proposed approach, a case study is provided that examines real video clips of incipient forest fires with small amounts of smoke. The performance of the proposed approach is compared with those of existing deep learning models, including convolutional neural network (CNN), faster region-based CNN (faster R-CNN), and SlowFast. It is demonstrated that the proposed approach achieves enhanced detection accuracy, while reducing false negative rates.

Проблема своевременного обнаружения и ликвидация лесных пожаров

Up to 35 thousand forest fires are registered in Russia annually, the area of fire of which is up to 2.5 million hectares. The use of unmanned aerial vehicles as one of the effective ways to detect and prevent forest fires. The use of UAVs has more advantages over other means of fire detection. In conclusion, the authors conclude that if only an incipient forest fire can be detected, it will prevent large economic and environmental losses.

Convolutional neural network for smoke and fire semantic segmentation

In recent decades, global warming has contributed to an increase in the number and intensity of wildfires destroying millions hectares of forest areas and causing many casualties each year. Firemen must therefore have the most effective means to prevent any wildfire from breaking out and to fight the blaze before being unable to contain and extinguish it. This article will present a new network architecture based on Convolutional Neural Network to detect and locate smoke and fire. This network generates fire and smoke masks in an RGB image by segmentation. The purpose of this work is to help firemen in assessing the extent of fire or monitor an incipient fire in real time with a camera embedded in a vehicle. To train this network, a database with the corresponding images and masks has been created. Such a database will allow to compare the performances of different networks. A comparison of this network with the best segmentation networks such as U-Net and Yuan networks has highlighted its efficiency in terms of location accuracy, reduction of false positive classifications such as clouds or haze. This architecture is also efficient in real time.

Initial growth of fires in eucalypt litter, from ignition to steady-state rate of spread: laboratory studies

As part of an investigation of wildfire growth and acceleration, the initial growth of incipient fires burning in uniform dry eucalypt forest (Eucalyptus rossii, E. macrorhyncha) litter fuel of 1.2 kg m−2 was studied in a combustion wind tunnel with a fuel bed width of 1.5 m. Fifty-eight fires of three ignition patterns (point, 400-mm line and 800-mm line) were carried out at two air speeds (1.25 and 2.0 m s−1) and two dead fuel moisture content (FMC) groups for each air speed (≤7.5% and >7.5% oven-dry weight for the low air speed and ≤5% and >5% for the high air speed). The fraction of steady-state rate of spread reached as a function of time was determined and fitted to two theoretical fire growth models from the literature. The best model suggests the times for a point ignition fire to reach steady-state spread rate were ~38 and 50 min under the higher FMC for 2.0 m s−1 and 1.25 m s−1 air speeds, respectively, and ~25 min under the lower FMC for 2.0 m s−1. Future work will extend these results to field-scale fire behaviour, which will help improve operational response to wildfire outbreaks and planning of ignition patterns for prescribed burning.

Fight or flight? Behaviour and experiences of laypersons in the face of an incipient fire

Within minutes, an incipient fire can develop into a life-threatening full fire. Consequently, it should be fought as early as possible. But are laypersons capable of doing this? In such a situation, how do they behave and feel? These questions are addressed in the current study. Persons without any professional firefighting training (N = 64) were confronted in two experimental runs with a real incipient fire in the form of a burning pillow. The results show that most participants were motivated and able to extinguish the fire successfully. However, most of them made a number of mistakes. Of central importance for extinguishing the fire was self-efficacy. Furthermore, participants improved greatly in the second round, especially regarding reaction time span and various psychological variables (e.g. stress, mood). Particularly on the basis of these exercise effects, we can derive a number of practical implications. Practitioner Summary: Laypersons are willing and able to successfully fight an incipient fire. Yet, their behaviour is not optimal and could lead to self-endangerment. Thus, it is critically important that people perform practical exercises as part of fire safety trainings and repeat them after some time. Abbreviatons: TPB: theory of planned behaviour; CIT: critical incident technique; MANOVA: multivariate analysis of variance

A bio-hybrid odor-guided autonomous palm-sized air vehicle

Biohybrid systems integrate living materials with synthetic devices, exploiting their respective advantages to solve challenging engineering problems. One challenge of critical importance to society is detecting and localizing airborne volatile chemicals. Many flying animals depend their ability to detect and locate the source of aerial chemical plumes for finding mates and food sources. A robot with comparable capability could reduce human hazard and drastically improve performance on tasks such as locating disaster survivors, hazardous gas leaks, incipient fires, or explosives. Three advances are needed before they can rival their biological counterparts: (1) a chemical sensor with a much faster response time that nevertheless satisfies the size, weight, and power constraints of flight, (2) a design, sensor suite, and control system that allows it to move toward the source of a plume fully autonomously while navigating obstacles, and (3) the ability to detect the plume with high specificity and sensitivity among the assortment of chemicals that invariably exist in the air. Here we address the first two, introducing a human-safe palm-sized air vehicle equipped with the odor-sensing antenna of an insect, the first odor-sensing biohybrid robot system to fly. Using this sensor along with a suite of additional navigational sensors, as well as passive wind fins, our robot orients upwind and navigates autonomously toward the source of airborne plumes. Our robot is the first flying biohybrid system to successfully perform odor localization in a confined space, and it is able to do so while detecting and avoiding obstacles in its flight path. We show that insect antennae respond more quickly than metal oxide gas sensors, enabling odor localization at an improved speed over previous flying robots. By using the insect antennae, we anticipate a feasible path toward improved chemical specificity and sensitivity by leveraging recent advances in gene editing.

Examination of radiative fraction of small-scale pool fires at reduced pressure environments

Determining flame radiative fraction is necessary in the estimation of radiation heat transfer of a fire, while the radiative fraction for a given fuel is usually regarded as constant, even at sub-atmospheric pressures. To ascertain the factors affecting this parameter, a series of small-scale n-heptane pool fires with diameters of 4–10 cm are performed at low pressures by means of an altitude chamber in this work. Based on a point source model, the radiative fractions of tested pool fires are determined, and coupled effects of pressure and pool diameter on this parameter are observed. A dimensional scaling theory is further proposed to interpret these effects, i.e. Xr~P0.3−0.175αD0.15, and the experimental results can be well correlated with this model. The findings will be helpful in evaluating the radiative heat fluxes of pool fires at reduced pressures more accurately, further providing guidance on the detection of incipient fires at sub-atmospheric pressure environments.

Risk-Reduction Credit for Very Early Warning Fire Detection at Nuclear Power Plants: From FAQ to Fiction

In 2004, the U.S. Nuclear Regulatory Commission (NRC), with support from the commercial nuclear power industry, adopted the 2001 Edition of National Fire Protection Association (NFPA) Standard 805, “Performance-Based Standard for Fire Protection for Light Water Reactor Electric Generating Plants,” as the means by which commercial nuclear power licensees could comply with Title 10 of the Code of Federal Regulations, Part 50.48(c), to replace deterministic fire protection licensing bases with ones that are risk-informed and performance-based. To facilitate licensee “transitions” to the new licensing bases via NFPA 805, a “Frequently Asked Questions” (FAQs) program, established early during the pilot-plant phase, was expanded to enable use of consensus technical “short-cuts” for fire probabilistic risk assessment (PRA) methods. These “Fire PRA FAQs” enabled licensees, with NRC approval, to bypass more traditional means of establishing acceptable PRA method enhancements on an interim basis, pending eventual confirmation by test programs and/or more detailed analyses. The NRC approved several, of which perhaps the most substantial in providing risk reduction benefits was FAQ 08-0046 on “Incipient Fire Detection Systems,” more accurately characterized as “Very Early Warning Fire Detection Systems” (VEWFDSs). Controversial from the start, the hidden story behind this FAQ’s initial adoption is relevant to examination of the NRC NUREG report that later replaced it and remains in effect today. This article examines this backstory, tracing recommendations that were proposed and bypassed, then examines alternatives to the current guidance. These alternatives, which maximize possible risk reduction credit for VEWFDSs at nuclear power plants, remain at least a factor of two less than the current peak NUREG-2180 risk-reduction factor even before the latter accounts for the possibility of fire pre-emption altogether.

Contemporary technologies of early detection of fire in space vehicles

A fairly reliable method of fire detection at an early stage is suggested and proved. The method is based on the monitoring of the air chemical composition, which changes strongly due to thermal decomposition (pyrolysis) of overheated combustible materials that are starting to smolder. It is at this stage of incipient fire that adequate measures can be taken for fire extinction with simple tools or, in the case of overheating of electrical equipment, for switching off this equipment automatically using a signal from the fire protection system thereby eliminating the fire hazardous situation. Fire detectors and microcontrollers are designed. These gas fire detectors are particularly suitable at the objects with active ventilation, fоr example, at space vehicles.

Multi-Stage Feature Selection Based Intelligent Classifier for Classification of Incipient Stage Fire in Building.

In this study, an early fire detection algorithm has been proposed based on low cost array sensing system, utilising off- the shelf gas sensors, dust particles and ambient sensors such as temperature and humidity sensor. The odour or “smellprint” emanated from various fire sources and building construction materials at early stage are measured. For this purpose, odour profile data from five common fire sources and three common building construction materials were used to develop the classification model. Normalised feature extractions of the smell print data were performed before subjected to prediction classifier. These features represent the odour signals in the time domain. The obtained features undergo the proposed multi-stage feature selection technique and lastly, further reduced by Principal Component Analysis (PCA), a dimension reduction technique. The hybrid PCA-PNN based approach has been applied on different datasets from in-house developed system and the portable electronic nose unit. Experimental classification results show that the dimension reduction process performed by PCA has improved the classification accuracy and provided high reliability, regardless of ambient temperature and humidity variation, baseline sensor drift, the different gas concentration level and exposure towards different heating temperature range.