On the existence of a steady state regime for slope and wind driven fires

2004 ◽  
Vol 13 (1) ◽  
pp. 101 ◽  
Author(s):  
Domingos X. Viegas
Keyword(s):  
Steady State ◽  
Radiation Effects ◽  
Field Experiments ◽  
Blow Up ◽  
Atmospheric Conditions ◽  
Fire Propagation ◽  
Rate Of Spread ◽  
Fire Front ◽  
State Regime ◽  
Definition Of

Forest fire behaviour analysis and prediction is based on the assumption that for a given set of boundary conditions a steady-state of fire propagation exists with a well-defined rate of spread. The evolution of a fire front for linear and point ignited fires is analysed and it is shown that, even in nominally uniform and permanent conditions, the rate of spread of the head fire does not remain constant in the general case of slope- and wind-driven fires due to joint convection and radiation effects. The basic case of a linear fire front without slope and without wind is one of the few cases for which the rate of spread is well defined and remains constant. if there is slope or wind in point ignition fires, the rate of spread of the head fire tends to increase while for linear ignition fires the contrary happens. It is shown that convective effects induced by the fire for steep slope terrain can produce the so-called ‘blow-up’ effect even in the absence of any other special atmospheric conditions. Therefore the definition of rate of spread of a fire and its evaluation from laboratory and field experiments is strongly questioned.

scholarly journals Parameter Estimation for the Forest Fire Propagation Model

2020 ◽  
Vol 75 (1) ◽  
pp. 1-22
Author(s):  
Martin Ambroz ◽  
Karol Mikula ◽  
Marek Fraštia ◽  
Marián Marčiš
Keyword(s):  
Data Assimilation ◽  
Forest Fire ◽  
Hausdorff Distance ◽  
Small Time ◽  
Propagation Model ◽  
Model Parameters ◽  
Normal Velocity ◽  
Fire Propagation ◽  
Rate Of Spread ◽  
Fire Front

AbstractThis paper first gives a brief overview of the Lagrangian forest fire propagation model [Ambroz, M.—Balažovjech, M.—Medl’a, M.—Mikula, K.: Numerical modeling of wildland surface fire propagation by evolving surface curves, Adv. Comput. Math. 45 (2019), no. 2, 1067–1103], which we apply to grass-field areas. Then, we aim to estimate the optimal model parameters. To achieve this goal, we use data assimilation of the measured data. From the data, we are able to estimate the normal velocity of the fire front (rate of spread), dominant wind direction and selected model parameters. In the data assimilation process, we use the Hausdorff distance as well as the Mean Hausdorff distance as a criterion. Moreover, we predict the fire propagation in small time intervals.

The Speed of a Fire Front and Its Dependence on Wind-Speed

1993 ◽  
Vol 3 (4) ◽  
pp. 193 ◽  
Author(s):  
T Beer
Keyword(s):  
Wind Speed ◽  
Laboratory Tests ◽  
Field Data ◽  
Fire Spread ◽  
Measured Data ◽  
Rate Of Spread ◽  
Wind Rate ◽  
Fire Front ◽  
Analytical Formulae ◽  
Definition Of

The results of a number of laboratory tests of wind-driven fires indicate the existence of a characteristic wind speed, U'. The form of the fire spread (V) as a function of mid-flame wind speed (U) differs above and below this characteristic speed. The scatter in field data is so great that it is difficult to confirm this result for field data. However, expressions of the form: V/V0 -1 = α(U/U')0.5 U/U' < 1 and V/V0 -1 = α(U/U')3 U/U' > 1 with U' = 2.5 m s-1 perform in a similar manner to existing models. For many fuel types α = 15. A difficulty with existing fire spread models is the measurement and definition of V0, the no-wind rate of spread. It can hardly ever be measured in the field and has to be inferred from analytical formulae, or by extrapolating measured data. The value of a depends on the method used estimate V0.

scholarly journals The effect of ignition protocol on the spread rate of grass fires: a comment on the conclusions of Sutherland et al. (2020)

2020 ◽  
Vol 29 (12) ◽  
pp. 1133
Author(s):  
Miguel G. Cruz ◽  
Andrew L. Sullivan ◽  
Rachel Bessell ◽  
James S. Gould
Keyword(s):  
Steady State ◽  
Measurement Data ◽  
Fire Spread ◽  
Fire Behaviour ◽  
Spread Rate ◽  
Real World Data ◽  
Rate Of Spread ◽  
State Regime ◽  
Length Direction ◽  
Behaviour Data

Sutherland et al. (2020) used simulations from a physics-based numerical fire behaviour model to investigate the effect of the ignition protocol (namely length, direction and rate of ignition) on the spread rates measured in experimental fires. They concluded that the methods used by Cruz et al. (2015) were inadequate as the fires were not spreading at the pseudo-steady state when rate of spread measurements were made, thereby raising questions about the validity of several published experimental and modelling results. Fire spread measurement data from three different outdoor experimental burning studies conducted in grass fuels are used to show that, contrary to the claims of Sutherland et al. (2020), the fire behaviour data collected in Cruz et al. (2015) were from fires spreading in the pseudo-steady-state regime and thus are compatible with data from larger experimental plots. A discussion is presented addressing why Sutherland et al. (2020) simulations were unable to replicate real-world data.

On the Correlation of Analytical and Experimental Free Shear Layer Similarity Profiles by Spread Rate Parameters

1971 ◽  
Vol 93 (3) ◽  
pp. 377-382 ◽  
Author(s):  
H. H. Korst ◽  
W. L. Chow
Keyword(s):  
Similarity Solutions ◽  
Free Shear Layer ◽  
Spread Rate ◽  
Empirical Parameter ◽  
Jet Mixing ◽  
Free Jet ◽  
Rate Of Spread ◽  
Profile Matching ◽  
Entire Flow ◽  
Definition Of

Analysis of turbulent isobaric free jet mixing normally requires the introduction of suitably formulated viscosity models. Similarity solutions can then be established which contain one empirical parameter. Such a parameter, however, not only describes the rate of spread of the mixing region, but also determines in detail the structure of the entire flow field. It is pointed out that this “spread rate parameter” σ depends on the selected viscosity model, the method of theoretical analysis, and the definition of profile matching. A comparison of different theoretical profiles can only be accomplished after these factors are properly recognized. Any attempts to contribute to the rather incomplete knowledge of the spread parameter must be cognizant of its dependence on the theoretical mixing model employed. This paper also establishes theoretical relations which allow comparison and consolidation of information based on different analytical concepts.

scholarly journals Drone Swarms in Fire Suppression Activities: A Conceptual Framework

Drones ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 17
Author(s):  
Elena Ausonio ◽  
Patrizia Bagnerini ◽  
Marco Ghio
Keyword(s):  
Technological Development ◽  
Fire Suppression ◽  
Water Flow Rate ◽  
Cellular Automata Model ◽  
Fire Propagation ◽  
Active Fire ◽  
Fire Front ◽  
Main Factors ◽  
Mediterranean Scrub ◽  
In Fire

The recent huge technological development of unmanned aerial Vehicles (UAVs) can provide breakthrough means of fighting wildland fires. We propose an innovative forest firefighting system based on the use of a swarm of hundreds of UAVs able to generate a continuous flow of extinguishing liquid on the fire front, simulating the effect of rain. Automatic battery replacement and extinguishing liquid refill ensure the continuity of the action. We illustrate the validity of the approach in Mediterranean scrub first computing the critical water flow rate according to the main factors involved in the evolution of a fire, then estimating the number of linear meters of active fire front that can be extinguished depending on the number of drones available and the amount of extinguishing fluid carried. A fire propagation cellular automata model is also employed to study the evolution of the fire. Simulation results suggest that the proposed system can provide the flow of water required to fight low-intensity and limited extent fires or to support current forest firefighting techniques.

Dissipative and Generative Fractional Electric Elements in Modeling RC and RL Circuits

2021 ◽  
Author(s):  
Kristian Haška ◽  
Stevan Cvetićanin ◽  
Dušan Zorica
Keyword(s):  
Asymptotic Behavior ◽  
Steady State ◽  
Constitutive Equation ◽  
Magnetic Flux ◽  
Electromotive Force ◽  
Constitutive Models ◽  
Steady State Regime ◽  
Power Type ◽  
State Regime ◽  
Transient And Steady State

Abstract Generalized capacitor (inductor) is constitutively modeled by expressing charge (magnetic flux) in terms of voltage (current) memory as a sum of instantaneous and power type hereditary contributions and it is proved to be a dissipative electric element by thermodynamic analysis. On the contrary, generalized capacitor (inductor) as a generative electric element is modeled using the same form of the constitutive equation, but by expressing voltage (current) in terms of charge (magnetic flux) memory. These constitutive models are used in transient and steady state regime analysis of the series RC and RL circuits subject to electromotive force, as well as in the study of circuits' frequency characteristics including asymptotic behavior.

Fine-Scale Fire Spread in Pine Straw

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 69
Author(s):  
Daryn Sagel ◽  
Kevin Speer ◽  
Scott Pokswinski ◽  
Bryan Quaife
Keyword(s):  
Fire Spread ◽  
Small Scale ◽  
Natural Setting ◽  
Fine Scale ◽  
Prescribed Burn ◽  
Fire Dynamics ◽  
Rate Of Spread ◽  
Scale Models ◽  
Fire Front ◽  
Visible Images

Most wildland and prescribed fire spread occurs through ground fuels, and the rate of spread (RoS) in such environments is often summarized with empirical models that assume uniform environmental conditions and produce a unique RoS. On the other hand, representing the effects of local, small-scale variations of fuel and wind experienced in the field is challenging and, for landscape-scale models, impractical. Moreover, the level of uncertainty associated with characterizing RoS and flame dynamics in the presence of turbulent flow demonstrates the need for further understanding of fire dynamics at small scales in realistic settings. This work describes adapted computer vision techniques used to form fine-scale measurements of the spatially and temporally varying RoS in a natural setting. These algorithms are applied to infrared and visible images of a small-scale prescribed burn of a quasi-homogeneous pine needle bed under stationary wind conditions. A large number of distinct fire front displacements are then used statistically to analyze the fire spread. We find that the fine-scale forward RoS is characterized by an exponential distribution, suggesting a model for fire spread as a random process at this scale.

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