Predicting Fire Propagation across Heterogeneous Landscapes Using WyoFire: A Monte Carlo-Driven Wildfire Model

Ott, Cory W.; Adhikari, Bishrant; Alexander, Simon P.; Hodza, Paddington; Xu, Chen; Minckley, Thomas A.

doi:10.3390/fire3040071

Cited by 11 publications

(9 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some new technologies have been used to simulate the fire spread to get a better simulation result, and machine learning based models have been in use for a long time [26,27]. Milanović [28] determined the main explanatory variables for forest fire occurrence for Logistic Regression (LR) and Random Forest (RF), and they mapped the probability of forest fire occurrence in Eastern Serbia based on these models.…”

Section: Introductionmentioning

confidence: 99%

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

Zhang

et al. 2022

Fire

View full text Add to dashboard Cite

The simulation of forest fire spread is a key problem for the management of fire, and Cellular Automata (CA) has been used to simulate the complex mechanism of the fire spread for a long time. The simulation of CA is driven by the rate of fire spread (ROS), which is hard to estimate, because some input parameters of the current ROS model cannot be provided with a high precision, so the CA approach has not been well applied yet in the forest fire management system to date. The forest fire spread simulation model LSTM-CA using CA with LSTM is proposed in this paper. Based on the interaction between wind and fire, S-LSTM is proposed, which takes full advantage of the time dependency of the ROS. The ROS estimated by the S-LSTM is satisfactory, even though the input parameters are not perfect. Fifteen kinds of ROS models with the same structure are trained for different cases of slope direction and wind direction, and the model with the closest case is selected to drive the transmission between the adjacent cells. In order to simulate the actual spread of forest fire, the LSTM-based models are trained based on the data captured, and three correction rules are added to the CA model. Finally, the prediction accuracy of forest fire spread is verified though the KAPPA coefficient, Hausdorff distance, and horizontal comparison experiments based on remote sensing images of wildfires. The LSTM-CA model has good practicality in simulating the spread of forest fires.

show abstract

Section: Introductionmentioning

confidence: 99%

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

Zhang

et al. 2022

Fire

View full text Add to dashboard Cite

show abstract

“…It was shown in a study 25 that a 200 s simulation could take upto 6.7 h, while a 3D model 26 was found to take 3 weeks for 2 min of simulation time in a m domain. A Monte Carlo-based wildfire simulation module called WyoFire 27 , which was recently developed for predicting the growth of wildfires in Wyoming, USA, either overestimated or underestimated the wildfire boundaries in grassland fires. Other tools such as QUIC-Fire (a fast running tool that utilizes the phenomenological feature of fire behavior learned from FIRETEC) 28 are in relatively early stages of development.…”

Section: Introductionmentioning

confidence: 99%

Investigating the turbulent dynamics of small-scale surface fires

Desai

Goodrick

Banerjee

2022

Sci Rep

View full text Add to dashboard Cite

High frequency (30 Hz) two-dimensional particle image velocimetry data recorded during a field experiment exploring fire spread from point ignition in hand-spread pine needles under calm ambient wind conditions are analysed in this study. In the initial stages, as the flame spreads approximately radially away from the ignition point in the absence of a preferred wind-forcing direction, it entrains cooler ambient air into the warmer fire core, thereby experiencing a dynamic pressure resistance. The fire-front, comprising a flame that is tilted inward, is surrounded by a region of downdraft. Coherent structures describe the initial shape of the fire-front and its response to local wind shifts while also revealing possible fire-spread mechanisms. Vortex tubes originating outside the fire spiral inward and get stretched thinner at the fire-front leading to higher vorticity there. These tubes comprise circulation structures that induce a radially outward velocity close to the fuel bed, which pushes hot gases outward, thereby causing the fire to spread. Moreover, these circulation structures confirm the presence of counter-rotating vortex pairs that are known to be a key mechanism for fire spread. The axis of the vortex tubes changes its orientation alternately towards and away from the surface of the fuel bed, causing the vortex tubes to be kinked. The strong updraft observed at the location of the fire-front could potentially advect and tilt the kinked vortex tube vertically upward leading to fire-whirl formation. As the fire evolves, its perimeter disintegrates in response to flow instabilities to form smaller fire “pockets”. These pockets are confined to certain points in the flow field that remain relatively fixed for a while and resemble the behavior of a chaotic system in the vicinity of an attractor. Increased magnitudes of the turbulent fluxes of horizontal momentum, computed at certain such fixed points along the fire-front, are symptomatic of irregular fire bursts and help contextualize the fire spread. Most importantly, the time-varying transport terms of the turbulent kinetic energy budget equation computed at adjacent fixed points indicate that local fires along the fire-front primarily interact via the horizontal turbulent transport term.

show abstract

“…At the wildland-urban level, the current risk is found in fire that may spread through both wildland and community and threaten the natural environment, included glass-based building enclosures and components [8,9]. Static and dynamic wildfire models based on statistical analyses are also of support of fire engineering developments [10].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity to Input Parameters of Failure Detection Methods for Out-of-Plane Loaded Glass Panels in Fire

Kozłowski

Bedon

2021

Fire

View full text Add to dashboard Cite

In recent years, glass has been a largely used material for load-bearing or non-structural components in buildings and constructions. For this reason, dedicated calculation methods and approaches are required for the major loading and boundary conditions that are of technical interest for safe design purposes. Among others, the resistance and mechanical performance of glass elements under fire exposure still represents an open challenge. This paper elaborates on the failure detection methods for out-of-plane loaded glass panels that are subjected to fire loading and simultaneous mechanical loads. As known, the conventional method for thermal failure detection is based on the maximum temperature gradient in glass, and its comparison with a set of allowable standardized values. However, especially for ordinary glass components in buildings that are required to sustain combined mechanical loads, the overall structural performance is even more complex to predict. This design issue is given to a combination of pure mechanical aspects (i.e., sustained loads and corresponding stress–strain analysis) and thermo-physical phenomena, that depend on the progressive modification of material properties while increasing temperatures. This research study, accordingly, investigates the sensitivity of input parameters on the failure time of a given glass element under fire and sustained mechanical loads. A major advantage is taken from finite element (FE) numerical analyses and standardized failure detection methods of literature, that are selected for comparative purposes. Further, the paper also introduces the “stress approach” that can be used to quantify (in place of the conventional thermal gradient) the actual effects of assigned thermal exposure and mechanical loads.

show abstract

Predicting Fire Propagation across Heterogeneous Landscapes Using WyoFire: A Monte Carlo-Driven Wildfire Model

Cited by 11 publications

References 20 publications

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

Investigating the turbulent dynamics of small-scale surface fires

Sensitivity to Input Parameters of Failure Detection Methods for Out-of-Plane Loaded Glass Panels in Fire

Contact Info

Product

Resources

About