Generalization framework for varying characteristics of the firebrand generation and transport from structural fire source

Himoto, Keisuke; Iwami, Tatsuya

doi:10.1016/j.firesaf.2021.103418

Cited by 5 publications

(3 citation statements)

References 54 publications

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“…According to the authors, the four sub-models were found to be good in various degrees at predicting the transport of firebrand particles. Work in Japan has also used a modified firebrand generator to investigate firebrand transport (Himoto and Iwami, 2021).…”

Section: Firebrand Transport and Deposition Studiesmentioning

confidence: 99%

The world is burning: What exactly are firebrands and why should anyone care?

Manzello¹,

Suzuki²

2023

Front. Mech. Eng.

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Large outdoor fires have become commonplace all over the world. The International Organization for Standardization (ISO) defines large outdoor fires as an urban fire, tsunami-generated fire, volcano-generated fire, wildland-urban interface (WUI) fire, wildland fire, or informal settlement fire, where the total burnout area is significant. Perhaps of all the large outdoor fires, it is wildland fires that spread into urban areas, simply called WUI fires that attract the most attention. A glance at the recent headlines in the summer of 2022 reveals numerous catastrophic WUI fires all over Europe. Across the Atlantic Ocean in the USA, there is yet another destructive WUI fire raging in the USA state of California. With the increasing risks from a changing climate, these large outdoor fire disasters are only going to become more and more commonplace all over the world. More homes will be lost and more lives will be lost. It is the authors opinion that a targeted, multi-disciplinary approach is needed to address the large outdoor fire problem. In this short, invited paper to Horizons in Mechanical Engineering, it is argued that large outdoor fire problem is a fascinating and challenging research area and that engineers have the necessary skills and training to impact a problem that influences millions upon millions of people all over the world. An important danger, present in all large outdoor fires, are firebrands. Firebrands are introduced for non-specialist readers, and the most recent literature is reviewed. Several challenges are discussed, in particular, areas where engineers may help move the needle forward on this globally important topic.

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Section: Firebrand Transport and Deposition Studiesmentioning

confidence: 99%

The world is burning: What exactly are firebrands and why should anyone care?

Manzello¹,

Suzuki²

2023

Front. Mech. Eng.

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“…Yet, to be feasible for fire operation applications, firebrand spotting effects are vastly based on empirical models, typically based on spotting distance and probability of ignition Tarifa, 1967;Tarifa et al, 1965;Albini, 1979;Albini, 1983;Ellis, 2015;Woycheese et al, 1998)), or incorporated into a cellular automata fire-spread framework (Alexandridis et al, 2011;Freire & DaCamara, 2019). Firebrands from urban structures specifically can be modeled by the implementations of (Himoto & Iwami, 2021) and the SWUIFT model by (Masoudvaziri et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Modeling firebrand spotting in WRF-Fire for coupled fire-weather prediction

Frediani,

Shamsaei,

Juliano

et al. 2024

Preprint

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This study introduces the firebrand spotting parameterization implemented in WRF-Fire and applies it to the Marshall Fire, Colorado (2021) to demonstrate that, without fire spotting, wind-driven fire simulations cannot accurately represent the fire behavior. Spotting can be a dominant fire spread mechanism in wind-driven events, particularly those that occur in the wildland-urban interface (WUI), such as the Marshall Fire. To simulate these fires, the model’s ability to spot is critical, in that it accelerates the rate of spread and enables the fire to spread over streams and urban features such as highways. The firebrand spotting parameterization was implemented in WRF-Fire to improve simulations of wind-driven fires in a fire-atmosphere coupled system. In the parameterization, particles are generated with a set of fixed firebrand properties, from locations vertically aligned with the fire front. Firebrands are transported using a Lagrangian framework and firebrand physics are represented by a burnout (combustion) parameterization. Fire spots may occur when firebrands land on unburned grid points. The parameterization components are illustrated through idealized simulations and its application is demonstrated through simulations of a devastating real case - the Marshall Fire (Colorado, 2021). The simulations were verified using time of arrival and contingency table metrics. Our metrics show that when fire spots were included in the simulations, fire rate of spread and burn area consistently improved.

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“…The conducted research to understand this phenomenon has been done in two ways. On one hand, there are extensive experimental studies focused on the characterization of the firebrands generation and transport process (Manzello et al 2007(Manzello et al , 2008Suzuki et al 2012;Thomas et al 2017;Himoto and Iwami 2021;Wickramasinghe et al 2022). Unfortunately, the short scales of the experiments limit its application into the calculation of the landing distribution (Pérez et al 2011;Sullivan and Cruz 2015).…”

Section: Brief Introductionmentioning

confidence: 99%

Fire-spotting modelling: A comparative study of an Italian test case

López-De-Castro¹,

Trucchia²,

Cella³

et al. 2022

Advances in Forest Fire Research 2022

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Wildfire propagation is a non-linear and multiscale system in which there are involved multiple physical and chemical processes. One critical mechanism in the spread of wildfires is the so-called fire-spotting: a random phenomenon which occurs when embers are transported over large distances by the wind, causing the start of new spotting ignitions which jeopardize fire fighting actions. Due to its nature, fire-spotting is usually modeled as a probabilistic process. Three principal processes are involved during the fire-spotting: firebrands generation, transport and landing, and spot ignition. In this work, the physical parametrization of fire-spotting RandomFront (Trucchia et al. 2019) has been implemented into the operational wildfire spread simulator PROPAGATOR (Trucchia et al. 2020), that is based on a cellular automata approach. In the RandomFront parametrization the downwind landing distribution of firebrands is modeled by the means of a lognormal distribution, which is parameterized taking into account the physics involved in the phenomenon. The considered physical parameters are wind field, fire-line intensity, fuel density, firebrand radius, maximum loftable height, as well as factors related to atmospheric stability and flame geometry (Trucchia et al. 2019; Egorova et al. 2020,2022). We have reproduced the evolution of a wildfire occured in Italy, in which the fire-spotting effects played a critical role in its spread, to test how RandomFront is able to reproduce it accurately. In addition, we have already implemented some established fire-spotting empirical parametrizations for cellular automata-based wildfire models to compare also the performance between the three firebrand landings models. The results show that the RandomFront parametrization on the one hand reproduces the main spotting effects given by the available literature parametrizations (Alexandridis et al. 2011; Perryman et al. 2013), while, on the other hand, generates a variety of fire-spotting situations as well as long range fluctuations of the burning probability. The physical parametrization allows for complex patterns of fire spreading in this operational simulator context.

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Generalization framework for varying characteristics of the firebrand generation and transport from structural fire source

Cited by 5 publications

References 54 publications

The world is burning: What exactly are firebrands and why should anyone care?

The world is burning: What exactly are firebrands and why should anyone care?

Modeling firebrand spotting in WRF-Fire for coupled fire-weather prediction

Fire-spotting modelling: A comparative study of an Italian test case

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