A Model for Predicting Ignition Potential of Complex Fuel in Diurnally Variable Environment

TL;DR

This paper introduces TAMEFOE, a computational model that predicts diurnal temperature and moisture evolution in complex fuels, aiding in fire ignition risk assessment in variable environmental conditions.

Contribution

The paper presents a novel high-resolution model for predicting ignition potential of complex fuels considering environmental variability and detailed energy and water balance analysis.

Findings

Model validated against analytical and measured data.

Provides high-resolution temperature and moisture data for fire risk assessment.

Can serve as boundary conditions for CFD simulations.

Abstract

Fuel ignition potential is one of the primary drivers influencing the extent of damage in wildland and wildland-urban interface fires. Determining fire and ember exposure of fuels that vary spatially and temporally will help to recognize necessary defensive actions and reduce damages. In this paper, the development of a new computational model, Temperature And Moisture Evolution predictor for complex Fuel in Open Environment (TAMEFOE), is presented. TAMEFOE predicts the diurnal temperature and moisture content evolution and vulnerability to flame ignition of objects/fuels with complex shapes or settings and materials under variable environmental conditions. The model is applicable to complex fuel scenarios (e.g., interface or intermix communities) composed of natural and manmade random-shaped objects in open atmosphere under the influence of local weather and diurnal solar radiation. The vulnerability of fuel to ember or fire ignition is determined by predicting the transient temperature and dryness of fuel in connection with the surrounding, local environment, and flame heat if any exists. In this regard, a detailed surface energy balance analysis, coupled with a water budget analysis, is performed in high spatiotemporal resolution. The model performance was validated against several existing analytical and measured data. The discrete, high-resolution surface temperature and moisture content information obtained from the model can also provide unsteady boundary conditions for computational fluid dynamics simulations when coupled physics is desired.