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The surge in lithium-ion battery (LIB) use, essential for mass-scale renewable energy storage, raises concerns about fire hazards. However, to date, there is a lack of industry-wide understanding of large-scale LIB fire propagation. This paper suggests a translational forensic approach to promote fire safety awareness and introduces the cellular automata (CA) model coupled with the Monte Carlo (MC) approach to address the complex fire propagation simulation within an energy storage system (ESS). The objective is to demonstrate that the CA-MC model can provide a flexible and scalable connection for all levels of battery fire studies. The numerical model is coupled with experimental tests which have been performed to establish the actual timing of fire propagation from a single source. Cellular automata simulation, conducted through hybrid modeling and an applied risk analysis approach to evaluate fire hazards associated with LIBs, offers crucial insights into potential risks. The results demonstrate that, with fire incident initiation at a probability of 0.1 (10%), 33% of batteries will burn, and at a probability of 0.6 (60%) and beyond, the entire battery module will face complete burndown. Achieving full combustion of the entire module will take only approximately 42 timesteps on average, indicating rapid fire propagation. The actual time for a complete fire to occur in the battery module has been estimated to be 304 s per timestep, or 3.5 h total. Using this example, it is shown that the CA-MC approach can be extended to many other aspects of battery fire studies and is ideal as a translational tool, spanning all domains of the LIB industry.

Forest fires are becoming a more significant problem in Central Europe, but their danger is not as high as that in Southern Europe. The exception, however, is forest fires occurring in disturbed areas (windthrow and bark beetle outbreak areas), which are comparable in severity and danger to the most serious forest fires. In this study, we describe the current situation in Central European countries in terms of fire protection for disturbed areas in managed forests and forest stands left to spontaneously develop (secondary succession). If a country has regulations and strategies in this area, they are often only published in the local language. In this review, we combine information from all Central European countries and summarize it in a unified international language, provide an opportunity for local authorities to express their own experiences, and integrate data from worldwide scientific research. Thus, this paper may be considered a universal guide for managing fire protection and preparedness in disturbed areas and can serve as a reference for the establishment of strict legislative rules at the state level. These laws must be obligatory for all stakeholders in individual countries. The motivation for this study was two large forest fires in an area left to spontaneously develop in the Bohemian Switzerland National Park in the Czech Republic and Harz Mountains in Germany in the summer of 2022. These incidents revealed that fire prevention legislation was inadequate or nonexistent in these areas. The strategy of the European Union is to increase the size of protected areas and spontaneous development areas. Therefore, we consider it necessary to provide governments with relevant information on this topic to create conditions for better management of these destructive events.

High complexity and growing interdependencies of chemical and process facilities have made them increasingly vulnerable to domino effects. Domino effects, particularly fire dominoes, are spatial-temporal phenomena where not only the location of involved units, but also their temporal entailment in the accident chain matter. Spatial-temporal dependencies and uncertainties prevailing during domino effects, arising mainly from possible synergistic effects and randomness of potential events, restrict the use of conventional risk assessment techniques such as fault tree and event tree. Bayesian networks—a type of probabilistic network for reasoning under uncertainty—have proven to be a reliable and robust technique for the modeling and risk assessment of domino effects. In the present study, applications of Bayesian networks to modeling and safety assessment of domino effects in petroleum tank terminals has been demonstrated via some examples. The tutorial starts by illustrating the inefficacy of event tree analysis in domino effect modeling and then discusses the capabilities of Bayesian network and its derivatives such as dynamic Bayesian network and influence diagram. It is also discussed how noisy OR can be used to significantly reduce the complexity and number of conditional probabilities required for model establishment.

An analysis of a dataset (n = 58) of high-intensity wildfire observations in cured grasslands from southern Australia revealed a simple relationship suitable for quickly obtaining a first approximation of a fire’s spread rate under low dead fuel moisture contents and strong wind speeds. It was found that the forward rate of fire spread is approximately 20% of the average 10-m open wind speed. The data on rate of fire spread and 10 m open wind speed ranged from 1.6 to 17 and 20 to 62 km h−1, respectively. The validity of the resulting rule of thumb was examined across a spectrum of burning conditions and its performance was contrasted against that of established empirical-based fire spread models for three different grassland fuel conditions currently used operationally in Australia. The 20% rule of thumb for grassfires produced error statistics comparable to that of the fire spread rate model for grazed or cut grass fuel conditions as recommended for general use during the summer fire season in southern Australia.

This work provides a detailed overview of existing investigations into the fire–wind interaction phenomena. Specifically, it considers: the fanning effect of wind, wind direction and slope angle, and the impact of wind on fire modelling, and the relevant analysis (numerical and experimental) techniques are evaluated. Recently, the impact of fire on buildings has been widely analysed. Most studies paid attention to fire damage evaluation of structures as well as structure fire safety engineering, while the disturbance interactions that influence structures have been neglected in prior studies and must be analysed in greater detail. In this review article, evidence regarding the fire–wind interaction is discussed. The effect of a fire transitioning from a wildfire to a wildland–urban interface (WUI) is also investigated, with a focus on the impact of the resulting fire–wind phenomenon on high- and low-rise buildings.

Live Fuel Moisture Content (LFMC) contributes to fire danger and behavior, as it affects fire ignition and propagation. This paper presents a two layered Landsat LFMC product based on topographically corrected relative Spectral Indices (SI) over a 2000–2011 time series, which can be integrated into fire behavior simulation models. Nine chaparral sampling sites across three Landsat-5 Thematic Mapper (TM) scenes were used to validate the product over the Western USA. The relations between field-measured LFMC and Landsat-derived SIs were strong for each individual site but worsened when pooled together. The Enhanced Vegetation Index (EVI) presented the strongest correlations (r) and the least Root Mean Square Error (RMSE), followed by the Normalized Difference Infrared Index (NDII), Normalized Difference Vegetation Index (NDVI) and Visible Atmospherically Resistant Index (VARI). The relations between LFMC and the SIs for all sites improved after using their relative values and relative LFMC, increasing r from 0.44 up to 0.69 for relative EVI (relEVI), the best predictive variable. This relEVI served to estimate the herbaceous and woody LFMC based on minimum and maximum seasonal LFMC values. The understory herbaceous LFMC on the woody pixels was extrapolated from the surrounding pixels where the herbaceous vegetation is the top layer. Running simulations on the Wildfire Analyst (WFA) fire behavior model demonstrated that this LFMC product alone impacts significantly the fire spatial distribution in terms of burned probability, with average burned area differences over 21% after 8 h burning since ignition, compared to commonly carried out simulations based on constant values for each fuel model. The method could be applied to Landsat-7 and -8 and Sentinel-2A and -2B after proper sensor inter-calibration and topographic correction.

In this study, the characteristics of fires in case of two vehicles in a tunnel are analyzed by Computational Fluid Dynamics analysis for varying tunnel aspect ratios. Unsteady heat release rates over time are set as the input conditions of fire sources considering real phenomena. Unsteady heat release rate values are obtained from experiments. As a result, the smoke velocities above the fire source appear faster in the case of tunnels with a large aspect ratio because the higher the height of the tunnel, the faster the smoke velocity caused by buoyancy forces. The smoke velocity in the longitudinal direction increases quickly. However, the temperature distribution in the vicinity of the ceiling is low when the tunnel aspect ratio is large because the height of the tunnel is not directly affected by the flames. Also, the higher the height of the tunnel, the lower the visibility distance due to the heat and smoke coming down along the wall surface. However, in the tunnels represented in this study, it is considered that the visibility of evacuees is sufficiently secured.