Search Results (230)

During on-site welding operations, the sealant coated on anchor bolt surfaces can be ignited by hot particles or localized sparks, potentially triggering a fire hazard. This combustion process involves a complex multi-physics coupling among sealant combustion, convective and radiative heat transfer, and three-dimensional heat conduction in solids. To resolve this coupling, a simulation strategy is proposed that correspondingly integrates the Fire Dynamics Simulator (FDS, version 6.7.6) for modeling combustion and radiation with ABAQUS (2024) for simulating conductive heat transfer in solids. The proposed method is validated against experimental measurements, showing close agreement in temperature evolution. It also demonstrates robustness across varying geometric scales, thereby confirming its reliability for predicting thermal response. Using this validated method, simulations are performed to analyze the fire behavior of an anchor rod-sealant system. Results show that the burning sealant can raise anchor rod temperatures above 900 °C and lead to rapid flame spread between adjacent rods. Furthermore, a sensitivity analysis of thermophysical parameters identifies critical thresholds for fire safety optimization: sealants with an ignition temperature > 280 °C and thermal conductivity ≥ 0.26 W/(m·K) demonstrate effective self-extinguishing properties, while specific heat capacity can retard flame growth. These findings provide a robust numerical framework and quantitative guidelines for the fire-safe design of bridge anchorage systems. Full article

Wildfire spread prediction models, including even the most sophisticated coupled atmosphere–wildfire models, diverge from observed wildfire progression during multi-day simulations, motivating the need for measurement-based assessments of wildfire state and improved data assimilation techniques. Data assimilation in the context of coupled atmosphere–wildfire models entails estimating wildfire progression history from observations and using this to obtain initial conditions for subsequent simulations through a spin-up process. In this study, an approach is developed for estimating fire progression history from VIIRS active fire measurements, GOES-derived ignition times, and terrain height data. The approach utilizes a conditional Wasserstein Generative Adversarial Network trained on simulations of historic wildfires from the coupled atmosphere–wildfire model WRF-SFIRE, with corresponding measurements for training obtained through the application of an approximate observation operator. Once trained, the cWGAN leverages measurements of real fires and corresponding terrain data to probabilistically generate fire progression estimates that are consistent with the WRF-SFIRE solutions used for training. The approach is validated on five Pacific US wildfires, and results are compared against high-resolution perimeters measured via aircraft, finding an average Sørensen–Dice coefficient of 0.81. The influence of terrain data on fire progression estimates is also assessed, finding an increased contribution when measurements are uninformative. Full article

The Shenzhen–Zhongshan Link is a key cross-sea corridor in the Guangdong–Hong Kong–Macao Greater Bay Area. As a representative ultra-wide cross-section undersea tunnel, it exhibits smoke spread behaviors that differ fundamentally from those of traditional road tunnels. In particular, the radial flow region of fire smoke is more pronounced, resulting in substantial lateral variations in smoke dynamics parameters. These characteristics render classical one-dimensional ceiling jet temperature rise theories insufficient for capturing the multidimensional thermal behavior in such geometries. In this study, the immersed-tunnel section of the Shenzhen–Zhongshan Link was investigated through a combination of full-scale fire experiments and Fire Dynamics Simulator (FDS) simulations. The longitudinal attenuation and lateral distribution characteristics of hot smoke temperature rise during spread in an ultra-wide tunnel were systematically obtained. Based on a simplified one-dimensional ceiling jet concept, differences in hot smoke diffusion distance were employed to characterize the lateral temperature rise ratio at any longitudinal location, from which a lateral distribution model was developed. The classical one-dimensional average temperature rise decay model was further reformulated to derive a modified longitudinal decay model applicable to the tunnel centerline of ultra-wide cross-sections. By integrating these characteristic models, a two-dimensional longitudinal prediction framework for hot smoke temperature rise in ultra-wide tunnels was established. Validation against full-scale fire experiments demonstrates that the proposed model can predict the two-dimensional thermal field with an accuracy within 25%. The findings of this study provide a theoretical basis for fire scenario reconstruction in the Shenzhen–Zhongshan undersea tunnel and offer a technical foundation for optimizing emergency ventilation strategies during fire incidents. Full article

The dead fine fuel moisture content (DFFMC) directly affects forest fire occurrence and spread. Accurate DFFMC prediction is key to estimating forest fire risk and behavior. The well-fitting machine learning (ML)-based meteorological factor regression models are a focus of DFFMC prediction modeling. Nevertheless, this method’s reliance on a considerable amount of training data and limited extrapolation hinders its potential for extensive implementation in practice. To improve the prediction accuracy of the model in the context of limited training data volumes and interspecies and spatial extrapolated predictions, this study proposed a novel DFFMC prediction method based on a knowledge-embedded neural network (KENN). By integrating the partial differential equation (PDE) of the meteorological response of forest fuel moisture content into a multilayer perceptron (MLP), the KENN utilizes prior physical knowledge and posterior observational data to determine the relationship between meteorology and moisture content. Data from Mongolian oak, white birch, and larch were collected to evaluate model performance. Compared with three representative ML algorithms for DFFMC prediction—random forest (RF), long short-term memory networks (LSTM), and MLP—the KENN can efficiently reduce training data volume requirements and improve extrapolation prediction accuracy within the investigated fire season, thereby enhancing the usability of ML-based DFFMC prediction methods. Full article

Climate change is widely recognized as a significant contributor to both wildfire initiation and spread, conditions such as high temperatures and prolonged droughts facilitating the rapid ignition and propagation of fires. As a result, extreme weather events can trigger fires through lightning strikes with increases in frequency and severity. Despite this, we argue that it is important to distinguish and clarify the concepts of fire occurrence and fire spread, as these phenomena are not directly synonymous in the field of fire ecology. This review examined the published literature to determine if climate factors contribute to fire occurrence and/or spread, and evaluated how well the concepts are used when drawing connections between fire occurrence and fire spread related to climate variables. Using the PRISMA bibliographic analysis methodology, 70 scientific articles were analyzed, including reviews and research papers in the last 5 years. According to the analysis, most publications dealing with fire occurrence, fire spread, and climate change come from the northern hemisphere, specifically from the United States, China, Europe, and Oceania with South America appearing to be significantly underrepresented (less than 10% of published articles). Additionally, despite climatic variables being the most prevalent factors in predictive models, only 38% of the studies analyzed simultaneously integrated climatic, topographic, vegetational, and anthropogenic factors when assessing wildfires. Furthermore, of the 47 studies that explicitly addressed occurrence and spread, 66 percent used the term “occurrence” in line with its definition cited by the authors, that is, referring specifically to ignition. In contrast, 27 percent employed the term in a broader sense that did not explicitly denote the moment a fire starts, often incorporating aspects such as the predisposition of fuels to burn. The remaining 73 percent focused exclusively on “spread.” Hence, caution is advised when making generalizations as climate impact on wildfires can be overestimated in predictive models when conceptual ambiguity is present. Our results showed that, although climate change can amplify conditions for fire spread and contribute to the occurrence of fire, anthropogenic factors remain the most significant factor related to the onset of fires on a global scale, above climatic factors. Full article

Acoustic fire suppression is a novel and environmentally friendly fire-extinguishing method. Electrical wires, as an important material in electrical systems, are a major cause of fires when short-circuited. In this study, we conducted experimental research on the flame spread and extinguishing characteristics of polyethylene-insulated electrical wires under the action of transverse acoustic waves within a frequency range of 50–70 Hz. The study systematically investigated the changes in flame morphology during the spreading process under different acoustic wave conditions. It was found that the flame spread rate first decreases and then increases with the increase in sound pressure, and the higher the acoustic frequency, the higher the spread rate. This study focused on the effect of acoustic frequency and wire inclination angle (0°, 30°, 60°) on the critical sound pressure for flame extinction. The experimental results showed that the critical sound pressure for flame extinction increases with the increase in frequency and inclination angle, with measured values ranging from 0.11 to 0.36 Pa for horizontal wires. An empirical model for predicting the critical sound pressure of flame extinguishment of inclined wires under acoustic waves was established based on an analysis of the strain rate. Full article

Aerial retardant drops are widely used in wildfire suppression, yet their effectiveness in slowing fire spread remains difficult to quantify at scale. This study evaluates their impact on wildfire rate of spread (ROS) using a framework that combines observed and counterfactual (synthetic) drop locations from 62 Oregon wildfires. Synthetic drops were generated to simulate a no-suppression baseline, enabling comparison of ROS with and without suppression. We trained two random forest classifiers: one with real and synthetic drops (full model) and one with only synthetic drops. Both incorporated environmental and topographic features to predict whether spread slowed following a drop. While the full model performed well, the real-synthetic indicator had low feature importance, offering limited causal evidence that aerial suppression consistently reduced spread. The synthetic-only model produced similar performance, suggesting that drops with observed ROS reductions often coincided with favorable environmental and topographic conditions and may have occurred independent of suppression. These findings highlight the challenges of evaluating suppression at scale and emphasize the need for finer data, detailed operational records, and advanced modeling to better assess the role of aerial fire retardant drops in future wildfire management activities. Full article

The basic reproduction number ($R_0$) is an epidemiological metric that represents the average number of new infections caused by a single infectious individual in a completely susceptible population. The methodology for calculating this metric is well-defined for numerous model types, including, most prominently, Ordinary Differential Equations (ODEs). The basic reproduction number is used in disease modeling to predict the potential of an outbreak and the transmissibility of a disease, as well as by governments to inform public health interventions and resource allocation for controlling the spread of diseases. A Petri Net (PN) is a directed bipartite graph where places, transitions, arcs, and the firing of the arcs determine the dynamic behavior of the system. Petri Net models have been an increasingly used tool within the epidemiology community. However, no generalized method for calculating $R_0$ directly from PN models has been established. Thus, in this paper, we establish a generalized computational framework for calculating $R_0$ directly from Petri Net models. We adapt the next-generation matrix method to be compatible with multiple Petri Net formalisms, including both deterministic Variable Arc Weight Petri Nets (VAPNs) and stochastic continuous-time Petri Nets (SPNs). We demonstrate the method’s versatility on a range of complex epidemiological models, including those with multiple strains, asymptomatic states, and nonlinear dynamics. Crucially, we numerically validate our framework by demonstrating that the analytically derived $R_0$ values are in strong agreement with those estimated from simulation data, thereby confirming the method’s accuracy and practical utility. Full article

Prescribed fire is a critical land management practice in the Great Plains of North America, helping to maintain native rangelands and reduce wildfire risk. Barriers to prescribed fire practice remain due to concerns on potential fire escape and fire danger. A localized fire danger index can help address these concerns by providing clear, science-based guidance, encouraging safer and confident use of prescribed fire. Our goal is to support the development of a localized Grassland Fire Danger Index (GFDI) for prescribed fire management in the Great Plains. The specific objective of this study is to develop user-friendly sub-models for dead fuel moisture content (DFMC) and grass curing, which serve as components of the proposed GFDI. DFMC reflects short-term fuel moisture that affects ignition and fire spread, while grass curing represents seasonal drying that controls fuel availability. Both are critical for fire prediction and safe burns. Lower DFMC and higher grass curing levels are strongly associated with wildfire risks. Using Oklahoma Mesonet weather data, the DFMC sub-model improves the accuracy and sensitivity of existing models. The grass curing sub-model shows that 50% curing usually occurs around April 15–16, which matches the time for the most intensive prescribed fire activities in the region, indicating it as a safe and effective window for prescribed fire recognized by landowners. Our sub-models lay the foundation for development of GFDI in the region. Full article

This article presents a cooperative framework for multi-robot wildfire monitoring that integrates dynamic Voronoi partitioning with large language model (LLM)-enhanced nonlinear model predictive control (NMPC) to address challenges in dynamic unknown environments. Conventional methods, particularly fixed-weight NMPC, lack adaptability in scenarios with suddenly changing obstacles, such as spreading fire fronts. Our approach employs a hierarchical architecture. At the task allocation level, an enhanced dynamic Voronoi algorithm ensures robust and collision-free area partitioning. At the motion control level, we innovatively leverage the semantic reasoning capability of LLMs to dynamically adjust the cost function weights of the NMPC in real time based on environmental features, overcoming the parameter rigidity of traditional controllers. Extensive simulations in benchmark environments demonstrate the framework’s superior performance over deep deterministic policy gradient (DDPG) and fixed-weight NMPC baselines, showing significant improvements in exploration efficiency and obstacle avoidance success rate. This work provides a viable solution that bridges high-level semantic cognition with low-level optimal control for robust autonomous surveillance. Full article

Predicting wildland fire spread requires numerical schemes that can resolve sharp gradients at the fireline while remaining stable and efficient on practical grids. We develop a compact high-order finite-difference scheme for Hamilton–Jacobi level-set formulations of wildfire propagation, based on the anisotropic spread law of Mallet and co-authors. The spatial discretization employs a compact finite-difference derivative scheme to achieve spectral-like resolution with narrow stencils, improving accuracy and boundary robustness compared with wide-stencil ENO/WENO reconstructions. To control high-frequency artifacts intrinsic to non-dissipative compact schemes, an implicit high-order low-pass filter is incorporated and activated after each Runge–Kutta stage. Convergence is verified on the eikonal expanding-circle benchmark, where the method attains the expected high-order spatial accuracy as the grid is refined. The proposed scheme is then applied to wind-driven wildfire simulations governed by Mallet’s non-convex Hamiltonian, including a single ignition under moderate and strong wind. A complex topology test case is also considered, involving two ignitions that merge into a single front with the evolution of an internal unburnt island. The results demonstrate that the proposed method accurately reproduces fireline evolution even on coarse grids, achieving accuracy comparable to fifth-order WENO while maintaining superior fidelity in complex fireline topologies, where it better resolves multi-front interactions and topological changes in the fireline. This makes the method an efficient, accurate alternative for level-set wildfire modeling and readily integrable into existing frameworks. Full article

This study develops an explainable machine learning framework for wildfire prediction across Australia, integrating region-specific models and feature attribution to identify key environmental drivers. Three wildfire indicators, Estimated Fire Area (FA), Mean Fire Brightness Temperature (FBT), and Fire Radiative Power (FRP), were modeled using Lasso, Random Forest, LightGBM, and XGBoost. Performance metrics (RMSEC, RMSECV, RMSEP) confirmed strong calibration and generalization, with Tasmania and Queensland achieving the lowest prediction errors for FA and FRP, respectively. Feature importance and SHAP analyses revealed that soil moisture, solar radiation, precipitation, and humidity variability are dominant predictors. Extremes and variance-based measures proved more influential than mean climatic values, indicating that fire dynamics respond non-linearly to environmental fluctuations. Lasso models captured stable linear dependencies in arid regions, while ensemble models effectively represented complex interactions in tropical climates. The results highlight a hierarchical process where cumulative soil and radiation stress establish fire potential, and short-term meteorological variability drives ignition and spread. Projected climate shifts, declining soil water and increased radiative load, are likely to intensify these drivers. The framework supports interpretable, region-specific mitigation planning and paves the way for incorporating generative AI and multi-source data fusion to enhance real-time wildfire forecasting. Full article

The spread of contemporary artificial intelligence technologies, particularly machine learning, has significantly enhanced the capacity to predict asymmetrical natural disasters. Wildfires constitute a prominent example, as machine learning can be employed to forecast not only their spatial extent but also their environmental and socio-economic impacts, propagation dynamics, symmetrical or asymmetrical patterns, and even their duration. Such predictive capabilities are of critical importance for effective wildfire management, as they inform the strategic allocation of material resources, and the optimal deployment of human personnel in the field. Beyond that, examination of symmetrical or asymmetrical patterns in fires helps us to understand the causes and dynamics of their spread. The necessity of leveraging machine learning tools has become imperative in our era, as climate change has disrupted traditional wildfire management models due to prolonged droughts, rising temperatures, asymmetrical patterns, and the increasing frequency of extreme weather events. For this reason, our research seeks to fully exploit the potential of Principal Component Analysis (PCA), Minimum Redundancy Maximum Relevance (MRMR), and Grammatical Evolution, both for constructing Artificial Features and for generating Neural Network Architectures. For this purpose, we utilized the highly detailed and publicly available symmetrical datasets provided by the Hellenic Fire Service for the years 2014–2021, which we further enriched with meteorological data, corresponding to the prevailing conditions at both the onset and the suppression of each wildfire event. The research concluded that the Feature Construction technique, using Grammatical Evolution, combines both symmetrical and asymmetrical conditions, and that weather phenomena may provide and outperform other methods in terms of stability and accuracy. Therefore, the asymmetric phenomenon in our research is defined as the unpredictable outcome of climate change (meteorological data) which prolongs the duration of forest fires over time. Specifically, in the model accuracy of wildfire duration using Feature Construction, the mean error was 8.25%, indicating an overall accuracy of 91.75%. Full article

This study validates numerical models for mixed polymer combustion in a B-707 aircraft cargo compartment against Federal Aviation Administration test data. A simplified approach using a predefined mass loss rate was compared with a detailed model coupling in-depth heat transfer and pyrolysis kinetics based on the assumption of negligible co-pyrolysis effects. Both approaches reliably captured smoke dynamics and light transmission. The detailed model predicted the mass loss rate with high accuracy, matching the experimental value of 0.11 g/s at 200 s after the ignition. However, it significantly overpredicted the heat release rate with a peak value of 8 kW versus 5 kW in the experiment. This discrepancy was examined through a sensitivity analysis of key parameters: the radiative fraction, heat of combustion, turbulence model, and pyrolysis kinetics. The Smagorinsky model best captures the growth pattern of the heat release and mass loss rates, despite its larger deviation from the experimental data compared to other models. The analysis revealed that the radiative fraction and the activation energy of high heat-of-combustion materials like high-density polyethylene are the most influential parameters. One possible solution to the overestimation is the calibration of the activation energy and heat of combustion values for high-energy materials like HDPE. The results confirm the detailed model’s physical realism for fire spread modeling and highlight a path for improving its heat release rate predictions. Further investigation is required across a wider range of computational cases with varying sample mass fractions, compositions, geometries, and boundary conditions to establish the broader applicability of this approach. Full article

Fire spotting (FS), the process by which firebrands are lofted, transported, and ignite new fires ahead of the main flame front, plays a critical role in escalating extreme wildfire events. This systematic literature review (SLR) analyzes peer-reviewed articles and book chapters published in English from 2000 to 2023 to assess the evolution of FS models, identify prevailing methodologies, and highlight existing gaps. Following a PRISMA-guided approach, 102 studies were selected from Scopus, Web of Science, and Google Scholar, with searches conducted up to December 2023. The results indicate a marked increase in scientific interest after 2010. Thematic and bibliometric analyses reveal a dominant research focus on integrating the FS model within existing and new fire spread models, as well as empirical research and individual FS phases, particularly firebrand transport and ignition. However, generation and ignition FS phases, physics-based FS models (encompassing all FS phases), and integrated operational models remain underexplored. Modeling strategies have advanced from empirical and semi-empirical approaches to machine learning and physical-mechanistic simulations. Despite advancements, most models still struggle to replicate the stochastic and nonlinear nature of spotting. Geographically, research is concentrated in the United States, Australia, and parts of Europe, with notable gaps in representation across the Global South. This review underscores the need for interdisciplinary, data-driven, and regionally inclusive approaches to improve the predictive accuracy and operational applicability of FS models under future climate scenarios. Full article