Turbulence and fire-spotting effects into wild-land fire simulators

Abstract

This paper presents a mathematical approach to model the effects of phenomena with random nature such as turbulence and fire-spotting into the existing wildfire simulators. The formulation proposes that the propagation of the fire-front is the sum of a drifting component (obtained from an existing wildfire simulator without turbulence and fire-spotting) and a random fluctuating component. The modelling of the random effects is embodied in a probability density function accounting for the fluctuations around the fire perimeter which is given by the drifting component. In past, this formulation has been applied to include these random effects into a wildfire simulator based on an Eulerian moving interface method, namely the Level Set Method (LSM), but in this paper the same formulation is adapted for a wildfire simulator based on a Lagrangian front tracking technique, namely the Discrete Event System Specification (DEVS). The main highlight of the present study is the comparison of the performance of a Lagrangian and an Eulerian moving interface method when applied to wild-land fire propagation. Simple idealised numerical experiments are used to investigate the potential applicability of the proposed formulation to DEVS and to compare its behaviour with respect to the LSM. The results show that DEVS based wildfire propagation model qualitatively improves its performance (e.g., reproducing flank and back fire, increase in fire spread due to pre-heating of the fuel by hot air and firebrands, fire propagation across no fuel zones, secondary fire generation, \dots). Though the results presented here are devoid of any validation exercise and provide only a proof of concept, they show a strong inclination towards an intended operational use. The existing LSM or DEVS based operational simulators like WRF-SFIRE and ForeFire respectively can serve as an ideal basis for the same.Comment: Accepted for publication in Commun. Nonlinear Sci. Numer. Simula

A High Resolution Coupled Fire–Atmosphere Forecasting System to Minimize the Impacts of Wildland Fires: Applications to the Chimney Tops II Wildland Event

“…Accounting for long-range spotting has been shown to be important, and we are exploring the implementation of appropriate spotting models in WRF-Fire to reduce human intervention in the fire forecast process. Although literature on the topic is extensive [31][32][33][34][35][36][37][38], it is still a challenge to realistically incorporate spotting phenomena in the models, while preserving computational efficiency required for operational purposes. The adequacy of spotting implementations needs to be systematically evaluated on wildland fires events where long-range spotting played a primary role in the fire spread, a good example of which is the Chimney Tops II fire.…”

Justification of the choice of wood pulp combustion parameters for calculation of running crowning forest fires impact on power engineering facilities in Vietnam

“…Ìàòåìàòè÷åñêîå ìîäåëèðîâàíèå ëåñíûõ ïîaeàðîâ ÿâëÿåòñÿ î÷åíü ñëîaeíîé, äî êîíöà íå ðåøåííîé, ìíîãîôàêòîðíîé è íåëèíåéíîé çàäà÷åé [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Íåîïðåäåëåííîñòü òåïëîôèçè÷åñêèõ è õèìè÷åñêèõ ñâîéñòâ ëåñíûõ ãîðþ÷èõ ìàòåðèàëîâ íå ïîçâîëÿåò ïðîâîäèòü äîñòîâåðíûé ðàñ÷åò òåïëîâîãî ïîòîêà îò ëåñíîãî ïîaeàðà, âîçäåéñòâóþùåãî íà ýíåðãåòè÷åñêèå îáúåêòû Âüåòíàìà, òàêèå êàê ýëåêòðîïîäñòàíöèè, ÒÝÑ, ÃÝÑ, ëèíèè ýëåêòðîïåðåäà÷ è ò. ä.  ñâÿçè ñ ýòèì èññëåäîâàíèå ïðîöåññà ãîðåíèÿ äðåâåñíîé ìàññû äåðåâüåâ Âüåòíàìà ïðåäñòàâëÿåò ñîáîé àêòóàëüíóþ íàó÷íóþ è ïðàêòè÷åñêóþ çàäà÷ó.…”

The Spotting Distribution of Wildfires

“…Since then other stochastic models have been developed, such as the augmentation of Discrete Event System Specification models (which employ novel Lagrangian point-advancement techniques), to include spotting [35]. This particular reference improved on the spotting distribution of [33], employing the more realistic log-normal distribution.…”

“…For example, the model in [35] uses an indicator function approach to describe the interface between burned and unburned regions. They include spotting in the form of a log-normal distribution, but other spotting distributions, such as computed here, could be included as well.…”