Fire and Materials最新文献

The Brazilian Amazon Rainforest is home to a vast number of fauna and flora species and plays a crucial role in mitigating the effects of global climate change. Despite its importance, the biome has been severely impacted by wildfires for years. Fuels are the most critical element in wildfire management, and leaves are the combustible particles present in all potential layers of fire spread. This paper presents the flammability evaluation of oven-dried live leaves from 24 native tree species of the Brazilian Amazon Ombrophilous Dense Forest, using the mass-loss cone calorimeter (MLCC) at 50 kW/m². Additionally, through hierarchical clustering analysis, species were grouped into five flammability clusters. The interquartile range (IQR) of the cone calorimeter parameters—PHRR, THR, and TPHRR—was the difference between 67.90 and 61.03 kW/m²; 5.93 and 5.50 MJ/m²; and 33.67 and 29.58 s, respectively, showing a smaller variation than that reported in live leaf cone calorimeter test literature (both dry and fresh). A clear distinction was also observed between palms and other species with compound leaves. While palms—Leopoldinia piassaba, Oenocarpus bacaba, and Phytelephas macrocarpa—were classified into the flammable, highly flammable, and Extremely Flammable groups, respectively, other species with compound leaves were grouped into the low flammable (Pentaclethra macroloba) and very low flammable groups (Anadenanthera colubrina and Parkia pendula). Finally, the results have the potential to improve predictions of Brazilian Amazon wildfire behavior and inform the selection of less flammable species for green belts or reforestation projects.

The development of technology and newer requirements for materials leads to an increase in their production and storage. In the past, serious fires have occurred in public buildings, residential buildings, industrial halls, and warehouses where plastics were used or produced, resulting in disastrous consequences for the environment and human health. For this reason, it is important to examine the risks to people and the environment that arise during a fire in places where these materials are located and stored. Polyurethane foams (PUF) used in building insulation and the automotive industry have been analyzed to determine their flammability and smoke emission during combustion. The thermal stability of PUFs was assessed using simultaneous thermal analysis (STA). Released gases were identified using STA combined with FT-IR (STA/FT-IR). Fire resistance and smoke emission during combustion were evaluated using cone calorimetry and a smoke chamber. Differences in thermal decomposition and combustion characteristics, including smoke release, were observed. The combustion of semi-rigid foam was accompanied by the lowest total smoke release and the lowest total heat release. However, the combustion of flexible foam was characterized by the highest amount of smoke and a high rate of heat release, despite only a 5% weight loss at the highest temperature. In the case of rigid foam, a large residue in the form of a carbonized layer was observed.

For the safety of workers in the oil and gas field, flame-resistant clothing is recommended to reduce the risks of skin burns and fatalities resulting from heat and fire hazards. However, flame-resistant fabrics (FRFs) contaminated with flammable substances can compromise their flammability and heat transfer properties. Therefore, this study aims to evaluate the heat transfer performance (HTP) of the contaminated FRFs to improve workers' safety from burn injuries by understanding how contamination affects fabric thermal protection. The HTP in terms of second-degree burn time was evaluated and characterized by exposing the fabrics to 84 kW/m² mixed convective and radiant heat flux. The peak temperature and average heat release rate of the FRFs were also evaluated. Two levels of contamination, consisting of drilling mud and crude oil, were added to three FRFs: Meta-aramid/cotton, meta-aramid/para-aramid, and para-aramid/polybenzimidazole. The HTP of drilling mud-contaminated fabrics increased, while the HTP of crude oil-contaminated fabrics varied by fabric type and contamination level. This may be attributed to drilling mud's higher specific heat capacity and lower flammability than crude oil. Among the fabrics tested, meta-aramid/cotton fabric showed the best HTP with higher second-degree burn times of 9.63 s with drilling mud and 9.07 s with crude oil. The relationship among contamination level, fabric properties, and HTP was developed using a multiple linear regression statistical model. The fabric's properties, such as fabric weight and air permeability, significantly contributed to the HTP of the contaminated fabrics.

Combustible dust poses a hazard to industry in two ways, i.e., reactive as a cloud or reactive as a pile. This paper deals with the smouldering behaviour of wood dust deposits initiated by hot bodies. Effects of embedded depth and airflow condition are investigated. Two sizes of wood dust are selected as test samples, namely wood powder and wood chip. The results indicate that under the same hot bodies embedded depth, wood chip combustion propagates faster than wood powder in general due to its unique flocculent structure. Due to the increased insulation effect of the wood dust layer, the temperature at the same measuring point is higher than that of the wood chip layer. In addition, under airflow conditions, the smouldering propagation of wood deposits is significantly higher than that without airflow (2.42 and 4.34 m/s) for both wood powder and wood chip samples. However, the deposited wood powder has a lower minimum ignition temperature than wood chip. Accumulated wood dust in considered to have a greater fire risk in powder form.

Ornamental vegetation constitutes the main type of natural fuel within the wildland-urban interface. Its flammability is often the criteria that fire risk managers use to promote certain species against others, with direct repercussions in fire safety. Traditionally, flammability is assessed using four parameters (ignitability, combustibility, sustainability, and consumability), through small-scale laboratory tests. Although widely used, these techniques fail to reflect the actual burning behaviour of species when they ignite. In our study, we developed an experimental method to classify ornamental vegetation's flammability based on full-scale burning behaviour. We categorise flammability into three levels: low (resists ignition), medium (acts as ladder fuel), and high (spreads fire). To evaluate this, we model normalised weight loss rate curves as Gaussian functions (flammability bells) and use metrics from these functions (maximum weight loss per second, associated time and standard deviation) as combustibility indicators. Experimental results reveal that these metrics can effectively differentiate between low, medium and high flammability. The method was developed by burning ornamental trees from four typical Mediterranean species (Leyland cypress, Arizona cypress, Northern white-cedar, and Cherry laurel) with varying spatial configurations, physiological statuses and moisture content. Our method is straightforward, robust, and affordable for implementation in appropriately equipped fire laboratories, offering meaningful insights into vegetation flammability and fire behaviour in the wildland-urban interface. These experimental results can help reduce fire risk from ornamental vegetation in human settlements.

Grassy vegetation represents a significant fuel source in multiple fire-prone regions around the globe. These fuels are a major component of surface fuel beds and are therefore typically the first layer of the wildland fuel strata that ignites. Thus, understanding the drivers of successful grassy fuel ignition is key to developing a comprehensive description of the process leading to fire spread. In the wildland-urban interface, hot metal particles produced by powerline failures or mechanical equipment operations are a leading ignition source for these types of fuels. The goal of this study is to develop improved understanding of the role of fuel species and physical characteristics on the ignition behavior of grass fuels when exposed to hot metal particles. Three common California invasive grass species were studied in their natural configuration as well as in configurations in which fuel particles have been shredded or made into a powder. Overall, it was observed that the ignition temperature was lower in fuels in their powder form than fuels in a shredded or natural form. Furthermore, the findings here align with prior works, indicating that the presence of chemical compounds such as lignin and proteins may hinder ignition. Consequently, wheatgrass fuels, which have higher lignin and protein content, consistently required higher temperatures to ignite (460°C) compared to Avena (370°C), Bromus (348°C), and excelsior fuels (350°C), which contain these compounds in lower concentrations.

In recent years, wildfires in residential regions have increasingly inflicted significant economic and social losses. Preemptive measures can reduce the damage to public infrastructure and lessen these impacts. Rapid evaluation of residential structures after wildfire is crucial for investigating the overall scope of the damage and establishing an effective disaster mitigation strategy. However, conducting these assessments involves detailed on-site examinations, which require considerable time and workforce. Furthermore, these qualitative assessments can be subjective and prone to error. To overcome these shortcomings, this study suggests a practical methodology for performing damage assessments of housing after a wildfire using deep learning technology. The applications of deep learning to three different image sources for residential areas are analyzed and compared as follows: uncrewed aerial systems imagery, aerial imagery, and satellite imagery. Notably, combinations of these image sources were considered from the training stage, and the impact of changes in training data when applied to each image source was comprehensively investigated. Key results reveal achievable accuracies depending on the various remote sensing data sources used in the training and application phases. This study is expected to provide deep learning researchers working on wildfires with a fundamental resource for the comprehensive use of remote sensing data and to provide valuable insights into the decision-making process for wildfire responders.