Evolution Of Fire Front Research Articles

Analytical formulations with experimental validation for determining the incident heat flux from firefronts

Any target exposed to a heat source receives a certain amount of heat flux, depending on the distance, view factor, characteristics of the source, and environmental properties. Incident heat flux is mainly characterized by firefront properties, e.g., flame dimensions, and its thermal attributes by fuel properties, topography, and meteorological factors. Predicting the incident heat flux from the firefronts is crucial for fire risk management and analytical and numerical fire modelling. Based on this specification, safe zones can be defined for elements such as unprotected individuals, firefighters, and buildings. Since the contribution of radiation and convection varies with the fire evolution, it is essential to specify them as a function of distance and slope. This study elaborates determination of radiation and convection for firefronts. Sets of laboratory-scale experiments were conducted using fuel beds of straw with a load of 1.5 kg/m2 on the slopes of 0°, 20°, and 40° measuring the incident radiative and total heat fluxes. The proposed methodology was validated using available approaches and correlations between the flame length and the acceptable safety distance (ASD) were established. Unlike the existing models, a nonlinear correlation between convection and radiation during the firefront evolution is proposed.

Learning-based prediction of wildfire spread with real-time rate of spread measurement

A learning-based wildfire spread model was developed in this study to predict short-term wildfire spread. Real-time rate of spread (RoS) measurement was first conducted by calculating normal movements of fire fronts. Subsequently, machine learning was employed to correlate the local RoS and environmental parameters and predict the RoS in the unburnt area. After that, a narrow-band level-set method was utilized to simulate the evolution of fire front. RoS measurement, machine learning, and level-set method were individually verified with numerically generated fire fronts, and applied in a real scale shrubland fire scenario. Results show that the proposed learning-based method is capable of predicting short-term fire spread without employing an empirical RoS model, which is beneficial for modeling spreading of a real wildfire.

Application of envelope theory for 2D fire front evolution

Application of envelope theory for 2D fire front evolution

An anlytical model of the wind-induced spread of forest fires

This paper presents an analytical model for predicting the advance of a fire-front. The method is independent of an a priori fixed shape, and takes into account fuel composition, wind effects and, where applicable, the influence of topography. The proposed model leads to a set of non-linear differential equations which can be solved numerically. The result is a time-history of the fire-front evolution.