Existing Research and Mechanisms of Fire Spread

2015 ◽  
pp. 11-16
Author(s):  
Nathan White ◽  
Michael Delichatsios
Keyword(s):  
Fire Spread

RATES OF SURFACE FIRE SPREAD IN A YOUNG CALABRIAN PINE (Pinus brutia Ten.) PLANTATION

2012 ◽  
Vol 11 (8) ◽  
pp. 1475-1480 ◽  
Author(s):  
Omer Kucuk ◽  
Ertugrul Bilgili ◽  
Serkan Bulut ◽  
Paulo M. Fernandes
Keyword(s):  
Fire Spread ◽  
Surface Fire ◽  
Pinus Brutia ◽  
Calabrian Pine

scholarly journals Ignition Vulnerabilities of Combustibles around Houses to Firebrand Showers: Further Comparison of Experiments

2021 ◽  
Vol 13 (4) ◽  
pp. 2136
Author(s):  
Sayaka Suzuki ◽  
Samuel L. Manzello
Keyword(s):  
Experimental Data ◽  
Thermal Radiation ◽  
Driving Force ◽  
Fire Spread ◽  
Significant Problem ◽  
Next Generation ◽  
Similar Time ◽  
Comparison Of Experiments ◽  
Fire Front ◽  
Wui Fires

Wildland fires and wildland urban-interface (WUI) fires have become a significant problem in recent years. The mechanisms of home ignition in WUI fires are direct flame contact, thermal radiation, and firebrand attack. Out of these three fire spread factors, firebrands are considered to be a main driving force for rapid fire spread as firebrands can fly far from the fire front and ignite structures. The limited experimental data on firebrand showers limits the ability to design the next generation of communities to resist WUI fires to these types of exposures. The objective of this paper is to summarize, compare, and reconsider the results from previous experiments, to provide new data and insights to prevent home losses from firebrands in WUI fires. Comparison of different combustible materials around homes revealed that wood decking assemblies may be ignited within similar time to mulch under certain conditions.

scholarly journals A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Real Time ◽  
Forest Fires ◽  
Wildland Fire ◽  
Fire Spread ◽  
Low Earth Orbit ◽  
Practical Implementation ◽  
Spatiotemporal Resolution ◽  
Active Fire

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

Upward Fire Spread Rate Over Real-Scale EPS ETICS Façades

2021 ◽  
Author(s):  
Biao Zhou ◽  
Hideki Yoshioka ◽  
Takafumi Noguchi ◽  
Kai Wang ◽  
Xinyan Huang
Keyword(s):  
Fire Spread ◽  
Spread Rate ◽  
Real Scale

scholarly journals Determination of Critical Separation Distance Between Dwellings in Informal Settlements Fire

2021 ◽  
Author(s):  
Yu Wang ◽  
Lesley Gibson ◽  
Mohamed Beshir ◽  
David Rush
Keyword(s):  
Heat Flux ◽  
Informal Settlement ◽  
Informal Settlements ◽  
Fire Risk ◽  
Fire Spread ◽  
Separation Distance ◽  
Flame Length ◽  
Critical Distance ◽  
Fire Disaster ◽  
Critical Separation

AbstractApproximately one billion people across the globe are living in informal settlements with a large potential fire risk. Due to the high dwelling density, a single informal settlement dwelling fire may result in a very serious fire disaster leaving thousands of people homeless. In this work, a simple physics-based theoretical model was employed to assess the critical fire separation distance between dwellings. The heat flux and ejected flame length were obtained from a full-scale dwelling tests with ISO 9705 dimension (3.6 m × 2.4 m × 2.4 m) to estimate the radiation decay coefficient of the radiation heat flux away from the open door. The ignition potential of combustible materials in adjacent dwellings are analyzed based on the critical heat flux from cone calorimeter tests. To verify the critical distance in real informal settlement fire, a parallel method using aerial photography within geographic information systems (GIS), was employed to determine the critical separation distances in four real informal settlement fires of 2014–2015 in Masiphumelele, Cape Town, South Africa. The fire-spread distances were obtained as well through the real fires. The probabilistic analysis was conducted by Weibull distribution and logistic regression, and the corresponding separation distances were given with different fire spread probabilities. From the experiments with the assumption of no interventions and open doors and windows, it was established that the heat flux would decay from around 36 kW/m2 within a distance of 1.0 m to a value smaller than 5 kW/m2 at a distance of 4.0 m. Both experiments and GIS results agree well and suggest the ignition probabilities at distances of 1.0 m, 2.0 m and 3.0 m are 97%, 52% and 5% respectively. While wind is not explicitly considered in the work, it is implicit within the GIS analyses of fire spread risk, therefore, it is reasonable to say that there is a relatively low fire spread risk at distances greater than 3 m. The distance of 1.0 m in GIS is verified to well and conservatively predict the fire spread risk in the informal settlements.

scholarly journals Modeling Low Intensity Fires: Lessons Learned from 2012 RxCADRE

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 139
Author(s):  
Rodman R. Linn ◽  
Judith L. Winterkamp ◽  
James H. Furman ◽  
Brett Williams ◽  
J. Kevin Hiers ◽  
...  
Keyword(s):  
Prescribed Fire ◽  
Fire Spread ◽  
Lessons Learned ◽  
Field Sampling ◽  
Wind Fields ◽  
Low Intensity ◽  
Novel Method ◽  
Wind Input ◽  
Variable Wind ◽  
Local Variability

Coupled fire-atmosphere models are increasingly being used to study low-intensity fires, such as those that are used in prescribed fire applications. Thus, the need arises to evaluate these models for their ability to accurately represent fire spread in marginal burning conditions. In this study, wind and fuel data collected during the Prescribed Fire Combustion and Atmospheric Dynamics Research Experiments (RxCADRE) fire campaign were used to generate initial and boundary conditions for coupled fire-atmosphere simulations. We present a novel method to obtain fuels representation at the model grid scale using a combination of imagery, machine learning, and field sampling. Several methods to generate wind input conditions for the model from eight different anemometer measurements are explored. We find a strong sensitivity of fire outcomes to wind inputs. This result highlights the critical need to include variable wind fields as inputs in modeling marginal fire conditions. This work highlights the complexities of comparing physics-based model results against observations, which are more acute in marginal burning conditions, where stronger sensitivities to local variability in wind and fuels drive fire outcomes.

scholarly journals Reconstruction of Grenfell Tower fire. Part 3—Numerical simulation of the Grenfell Tower disaster: Contribution to the understanding of the fire propagation and behaviour during the vertical fire spread

2019 ◽  
Vol 44 (1) ◽  
pp. 35-57 ◽  
Author(s):  
Eric Guillaume ◽  
Virginie Dréan ◽  
Bertrand Girardin ◽  
Faiz Benameur ◽  
Maxime Koohkan ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Fire Spread ◽  
Fire Propagation

A case study on fractal simulation of forest fire spread

2000 ◽  
Vol 43 (S1) ◽  
pp. 104-112 ◽  
Author(s):  
Qijiang Zhu ◽  
Taizong Rong ◽  
Rui Sun
Keyword(s):  
Forest Fire ◽  
Fire Spread ◽  
Fractal Simulation
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