Comparison of different geometry trees in fire simulation

Flóra Hajdu; Csaba Hajdu; László Környei; Dóra Beke; Rajmund Kuti

doi:10.1556/606.2024.01022

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Pollack Periodica

Volume/Issue:: Volume 19: Issue 3

Comparison of different geometry trees in fire simulation

Authors:

Flóra Hajdu

Flóra Hajdu Department of Machine Design, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University, Győr, Hungary

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hajdfl@sze.hu https://orcid.org/0000-0002-7252-0879

Csaba Hajdu

Csaba Hajdu Department of Informatics, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University, Győr, Hungary

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László Környei

László Környei Department of Mathematics and Computational Sciences, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University, Győr, Hungary

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Dóra Beke

Dóra Beke Department of Plant Sciences, Albert Kázmér Faculty, Széchenyi István University, Győr, Hungary

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, and

Rajmund Kuti

Rajmund Kuti Department of Automation and Mechatronics, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University, Győr, Hungary

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Pages:: 34–39

Online Publication Date:: 31 Jul 2024

Publication Date:: 16 Oct 2024

Article Category:: Research Article

DOI:: https://doi.org/10.1556/606.2024.01022

Keywords:: tree burning; numerical simulation; fire spread simulation; Hungarian tree species; air pollution

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Abstract

Wildfire simulations can help to better understand the dynamics and effects of forest fires. The basis of wildfire simulation is the tree-burning simulation. In this paper, the fire simulation of 7 different geometry Hungarian trees in the case of arson is presented. It was observed that the trees were burned down fast. The maximum mass loss rate and maximum heat release rate were larger as the tree was larger. The largest intensity fire could be observed in the case of the smallest tree. The maximum temperature was higher in the case of a large crown diameter. The maximum aerosol reached high pollutant concentrations. In the case of large crown height, the maximum CO₂ concentration was higher. The results presented in this paper can be the basis of the following forest fire simulations.

Abstract

1 Introduction

Tree fire and burning simulation is the basis of forest fire simulation, however in the literature there is only some examples of a single tree fire simulation. Most of the scientific literature deals with forest or grassland fire simulation [1].

Paper [2] presents a novel, interactive to model the combustion of botanical trees. Physical and biological properties are stored in connected particles. These properties include the dynamic branch motion and exothermic combustion reaction, which includes the evaporation of moisture, the formation of char, and the reduction of wood and material properties. For fire simulation the Navier-Stokes equations are approximated through a grid-based fluid solver. In [3] Wildland urban interface Fire Dynamics Simulator (WFDS) is presented. The model is based on the large-eddy simulation approach. The governing equations of heat transfer, fluid flow, combustion, and thermal degradation of vegetative fuels are solved numerically. The used model includes solid fuel pyrolysis and water evaporation. It was assumed that trees are modeled with simple geometric shapes with uniformly distributed bulk fuel. The simulation was validated with fire experiments with Douglas firs. In [4] the surface-to-crown transitions of the fire in the case of a single tree and 66 Douglas firs on grassland are studied. The developed model is compared to an experimental result of a single Douglas fir. With this research it was shown that physic-based models are useful for crown fire simulations. In [5] an inverse analysis is presented with physics-based simulations, which include a single Douglas fir burning simulation and a forest fire simulation. The aim of the research was to reproduce the generation and behavior of firebrands based on experiments. In [6] the one-at-a-time sensitivity study of a tree burning simulation was carried out. It was studied how variables like the maximum mass loss rate, maximum temperature at the top of the tree, maximum heat release rate, and the percent of the remaining tree are sensitive to the moisture content, the ambient temperature, the crown base diameter, and the tree height. The tree was a pine and the model was based on [3].

It can be seen from the literature review that only some paper deals with the numerical simulation of a single tree fire. The aim of the research is to simulate the burning of native Hungarian trees [7].

2 Materials and methods

For the simulations Computational Fluid Dynamics (CFD) software [8], Fire Dynamics Simulator (FDS) was used. The program includes a thermal degradation model developed specifically for vegetation fires. More details about the thermal degradation model can be found in [9] and in a previous paper [6]. The heat of combustion was 14,516 kJ kg⁻¹, which is already included in the software used.

The branches and foliage were modeled as particles. The trees were modeled as cylinders from the particles with the following parameters: crown diameter, trunk diameter, height, and crown base height (Fig. 1).

Fig. 1.

Tree parameters (Source: edited by Authors)

Citation: Pollack Periodica 19, 3; 10.1556/606.2024.01022

The parameters of the trees are collected from databases and from the internet [10–19]. In case of no data, the crown base height was taken as approximately 70% of the total height [20, 21]. The properties of the trees are summarized in Table 1.

Table 1.

Properties of the trees

Tree	Crown diameter (m)	Trunk diameter (m)	Height (m)	Crown base height (m)
Oak (Quercus robur)	30	1.5	24	15
Beech (Fagus sylvatica)	21	1.5	35	15
Ash (Fraxinus ornus)	6	0.3	7	2
Poplar (Populus tremula)	19	0.3	24	9
Maple (Acer platanoides)	22	1.5	18	13
Elm (Ulmus glabra)	13	2	20	14
Willow (Salix alba)	15	1	15	10.5

Source: edited by Authors from databases [10–19].

The modeled trees are shown in Fig. 2.

Fig. 2.

The modeled trees, a) oak, b) beech, c) ash, d) poplar, e) maple, f) elm, g) willow (Source: edited by Authors)

Citation: Pollack Periodica 19, 3; 10.1556/606.2024.01022

The simulation area was 40 × 40 × 100 m; the mesh size was 0.5 × 0.5 × 0.5 m. The simulation time was 10 min except in the case of the ash, where 6 min was sufficient [22]. The ignition source was a circular burner below the tree and a temperature of 1,500 °C to model arson. As the aim of the study was to investigate the geometrical properties of the trees, therefore the moisture content was 20% in each case and the thermal degradation model was also the same. The sensors were placed at the top of the trees. The sensors measured the temperature, the aerosol concentration, and the CO₂ concentration. Besides the mass loss, the Mass Loss Rate (MLR) and the Heat Release Rate (HRR) were calculated. The simulation was run on a Message Passing Interface (MPI) cluster using 5 nodes.

3 Results and discussion

The results are shown in Fig. 3.

Fig. 3.

a) Mass of the tree, b) MLR, c) HRR, d) temperature, e) aerosol concentration, f) CO₂ concentration ((red/dot: oak, cyan/square: beech, blue/star: ash, green/triangle: poplar, magenta/cross: maple, orange/X: elm, black/full line: willow) (Source: edited by Authors)

Citation: Pollack Periodica 19, 3; 10.1556/606.2024.01022

It can be seen that the mass decreases fast in all cases. The cause of it is that the ignition source was large. The mass of the trees started to decrease around 100–150 s, which means that the fire spread was intensive. In the case of the beech, oak, maple and elm not the entire tree burned down. The explanation of it that these trees have a large diameter trunk, their structure is different and therefore it did not burn entirely till the end of the simulation time. From the MLR diagram it can be seen that the MLR increased fast, then after reaching a peak it decreased fast. The largest MLR could be observed in the case of the oak, followed by the beech and the poplar. The ash reached the peak value the fastest, followed by the poplar and the willow. The explanation of it can be that the crown diameter was small compared to the overall size of the crown and fire could spread faster. Similarly the HRR value is the largest in the case of the oak tree, followed by the beech and the poplar. Its explanation is that the oak has the largest crown diameter. The HRR in the case of the beech and the poplar increased fast then reached a peak point then decreased continuously, but with a slower pace. The explanation of it can be that these 2 trees has higher ratio of crown base height and total height. This phenomenon is similar in the case of the willow. The explanation of it is that the drooping branches were also modeled. In the case of the maple and the elm the HRR first increased fast, then reached a peak point and decreased fast. The difference is that the peak HRR is larger in the case of the maple as it is a larger tree. The ash as the smallest tree reached the peak HRR value the fastest followed by the poplar, willow, the elm, and the maple. From the HRR the intensity of fire can be calculated with the following formula [23]:

I \approx \frac{H R R_{a v e r a g e}}{A},

(1)

where

I

is the intensity of the fire,

H R R_{a v e r a g e}

is the average HRR and

A

is the area, which is in this case the area of the tree crown. The fire intensity is shown in Table 2. It can be seen that the highest intensity fire occurred in the case of the ash. The explanation of it that the ignition source was large compared to the tree. It is followed by the oak, which was the largest diameter tree. The maple had also a high intensity fire. The explanation can be that the crown diameter was quite large compared to the size of the tree. All the other trees had similar intensity of fire. The elm had the smallest intensity of fire. This is because the crown was small compared to the trunk. It can be concluded that if the crown is large compared to the tree larger fire intensity occurs.

Table 2.

Maximum values of the variables

Tree	Max. MLR (kg s⁻¹)	Max. HRR (kW)	Intensity of fire (kW m⁻²)	Max. temperature (°C)	Max. aerosol concentration mol mol⁻¹	Max. CO₂ concentration mol mol⁻¹
Oak	468	6.37∙10⁶	9,015	2,010	1.06∙10⁻⁶	13,400
Beech	323	3.48∙10⁶	2,267	1,964	1.31∙10⁻⁶	14,200
Ash	26	3.83∙10⁵	13,541	1,754	1.16∙10⁻⁶	14,300
Poplar	245	3.11∙10⁶	1,778	2,212	1.37∙10⁻⁶	14,300
Maple	178	3.03∙10⁶	7,962	1,994	1.04∙10⁻⁶	12,900
Elm	98	1.56∙10⁶	930	1,913	1.1∙10⁻⁶	13,300
Willow	130	1.91∙10⁶	1,151	1,848	9.6∙10⁻⁷	14,100

Source: edited by Authors.

The temperature reached near 2,000 °C in the case of all trees. The temperature was highest in the case of the poplar followed by the oak and the maple. In the case of the oak, beech, elm and maple the temperature did not decrease back to 20 °C, because these trees has a large diameter trunk, therefore it ignited. The maximum aerosol concentration was more or less the same in every tree, which was over 10⁻⁶ almost all of the trees except the willow. It is a high pollutant concentration [24]. The highest concentration was in the case of poplar and beech followed by ash. The aerosol concentration reached a peak value fast then decreased in the case of most trees. In the case of the beech, oak, maple and elm it decreased, but not to zero. The explanation is that these trees have a large diameter trunk, which started to burn. The CO₂ concentration reached over 12,000 ppm in all cases. It had the highest value in the case of the ash, which is followed by the poplar and the beach. In the case of trees with narrow trunk (ash, poplar, willow) the CO₂ concentration increased then reached a peak, then decreased fast. In the case of the trees with large trunk diameter the CO₂ concentration decreased more slowly. In the case of the beech it increased, then became constant then increased again when the trunk ignited. The maximum values of the examined variables are summarized in Table 2.

It can be seen that the MLR was the largest in the case of the oak, as it was the largest tree and the smallest in the case of the ash as it was the smallest. It can be concluded that the MLR is larger as the tree is larger. The HRR was the largest in the case of the oak and followed by the beech and the poplar, as they were the largest trees. The next trees with the largest HRR are the maple and the willow. The smallest HRR could be observed in the case of the elm and the ash, as they were the smallest trees. It can be concluded that the HRR is larger as the tree is larger. The temperature was the highest in the case of the poplar, followed by the oak. These trees have a quite large crown diameter and quite a lot of branches and foliage. The beech, maple, elm, and willow have a similar maximum temperature. The ash as the smallest tree had the smallest temperature. It can be concluded that trees with a larger crown diameter had a larger maximum temperature. The maximum aerosol concentration was the largest in the case of poplar and beech. This is because these trees had a dense crown with a lot of branches and foliage. The other trees had similar value. The lowest value had the willow. The explanation of it is that the top of the tree was modeled as foliage and not as branches. It can be concluded that the maximum aerosol concentration depends on the branches at the top of the tree. The maximum CO₂ concentration was not surprisingly similar in all of the trees. The largest value occurred in the case of the ash and the poplar. The explanation is that these trees had a large crown height compared to the diameter of the crown. The smallest values occurred in the case of the maple, elm, and oak. These trees had a large crown diameter compared to the crown height. It can be concluded that the maximum CO₂ concentration was higher in the cases, when the tree crown was large in height compared to its diameter.

4 Conclusions

In this paper the fire simulation of different geometry Hungarian trees was carried out. Due to the large ignition source the mass of the trees decreases fast in all cases. In the case of trees with large trunk (oak, beech, maple, elm) not the entire tree burned down. The MLR increased fast, then after reaching a peak it decreased fast in all cases. The HRR was the largest in the case of the largest diameter tree (oak). In most cases it increased fast, then reached a peak point then decreased fast. The maximum MLR and HRR were larger as the tree was larger (oak, beech). The largest intensity fire occurred in the case of the smallest tree (ash), because the ignition source was large compared to the tree. The temperature reached near 2,000 °C in the case of all trees. Trees with a larger crown diameter and a dense crown (poplar, oak) had a larger maximum temperature. The maximum aerosol concentration was around 10⁻⁶ almost all of the trees, which is a high pollutant concentration. The maximum aerosol concentration depends on the density of the branches at the top of the tree. The aerosol concentration reached a peak value fast then decreased in the case of most trees. The CO₂ concentration reached over 12,000 ppm in all cases. The maximum CO₂ concentration was higher in the cases, when the tree crown was large in height compared to its diameter (ash, poplar). It can be concluded that most of the simulation results approximate the real processes, but there are some differences. Further research task is to develop a model, which takes into account the differences between the trees burning properties. Also the tree geometry is planned to be improved using BlenderFDS. A mesh sensitivity study is also planned. The main task of further research is to create forests from the examined trees and carry out the fire simulation of them.

Acknowledgements

The Author's would like to thank to Dr. William Mell for sharing FDS example files.

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Pollack Periodica

Print ISSN:: 1788-1994

Online ISSN:: 1788-3911

Editorial Board

Bálint Bachmann (Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Jeno Balogh (Department of Civil Engineering Technology, Metropolitan State University of Denver, Denver, Colorado, USA)
Radu Bancila (Department of Geotechnical Engineering and Terrestrial Communications Ways, Faculty of Civil Engineering and Architecture, “Politehnica” University Timisoara, Romania)
Charalambos C. Baniotopolous (Department of Civil Engineering, Chair of Sustainable Energy Systems, Director of Resilience Centre, School of Engineering, University of Birmingham, U.K.)
Oszkar Biro (Graz University of Technology, Institute of Fundamentals and Theory in Electrical Engineering, Austria)
Ágnes Borsos (Institute of Architecture, Department of Interior, Applied and Creative Design, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Matteo Bruggi (Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Italy)
Petra Bujňáková (Department of Structures and Bridges, Faculty of Civil Engineering, University of Žilina, Slovakia)
Anikó Borbála Csébfalvi (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Mirjana S. Devetaković (Faculty of Architecture, University of Belgrade, Serbia)
Szabolcs Fischer (Department of Transport Infrastructure and Water Resources Engineering, Faculty of Architerture, Civil Engineering and Transport Sciences Széchenyi István University, Győr, Hungary)
Radomir Folic (Department of Civil Engineering, Faculty of Technical Sciences, University of Novi Sad Serbia)
Jana Frankovská (Department of Geotechnics, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
János Gyergyák (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Kay Hameyer (Chair in Electromagnetic Energy Conversion, Institute of Electrical Machines, Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Germany)
Elena Helerea (Dept. of Electrical Engineering and Applied Physics, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, Romania)
Ákos Hutter (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technolgy, University of Pécs, Hungary)
Károly Jármai (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
Teuta Jashari-Kajtazi (Department of Architecture, Faculty of Civil Engineering and Architecture, University of Prishtina, Kosovo)
Róbert Kersner (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Rita Kiss (Biomechanical Cooperation Center, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary)
István Kistelegdi (Department of Building Structures and Energy Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Stanislav Kmeť (President of University Science Park TECHNICOM, Technical University of Kosice, Slovakia)
Imre Kocsis (Department of Basic Engineering Research, Faculty of Engineering, University of Debrecen, Hungary)
László T. Kóczy (Department of Information Sciences, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, University of Győr, Hungary)
Dražan Kozak (Faculty of Mechanical Engineering, Josip Juraj Strossmayer University of Osijek, Croatia)
György L. Kovács (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Balázs Géza Kövesdi (Department of Structural Engineering, Faculty of Civil Engineering, Budapest University of Engineering and Economics, Budapest, Hungary)
Tomáš Krejčí (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
Jaroslav Kruis (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
Miklós Kuczmann (Department of Automations, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University, Győr, Hungary)
Tibor Kukai (Department of Engineering Studies, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Maria Jesus Lamela-Rey (Departamento de Construcción e Ingeniería de Fabricación, University of Oviedo, Spain)
János Lógó (Department of Structural Mechanics, Faculty of Civil Engineering, Budapest University of Technology and Economics, Hungary)
Carmen Mihaela Lungoci (Faculty of Electrical Engineering and Computer Science, Universitatea Transilvania Brasov, Romania)
Frédéric Magoulés (Department of Mathematics and Informatics for Complex Systems, Centrale Supélec, Université Paris Saclay, France)
Gabriella Medvegy (Department of Interior, Applied and Creative Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Tamás Molnár (Department of Visual Studies, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Ferenc Orbán (Department of Mechanical Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Zoltán Orbán (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Dmitrii Rachinskii (Department of Mathematical Sciences, The University of Texas at Dallas, Texas, USA)
Chro Radha (Chro Ali Hamaradha) (Sulaimani Polytechnic University, Technical College of Engineering, Department of City Planning, Kurdistan Region, Iraq)
Maurizio Repetto (Department of Energy “Galileo Ferraris”, Politecnico di Torino, Italy)
Zoltán Sári (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Grzegorz Sierpiński (Department of Transport Systems and Traffic Engineering, Faculty of Transport, Silesian University of Technology, Katowice, Poland)
Zoltán Siménfalvi (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
Andrej Šoltész (Department of Hydrology, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
Zsolt Szabó (Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Hungary)
Mykola Sysyn (Chair of Planning and Design of Railway Infrastructure, Institute of Railway Systems and Public Transport, Technical University of Dresden, Germany)
András Timár (Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Barry H. V. Topping (Heriot-Watt University, UK, Faculty of Engineering and Information Technology, University of Pécs, Hungary)

POLLACK PERIODICA
Pollack Mihály Faculty of Engineering
Institute: University of Pécs
Address: Boszorkány utca 2. H–7624 Pécs, Hungary
Phone/Fax: (36 72) 503 650

E-mail: peter.ivanyi@mik.pte.hu

or amalia.ivanyi@mik.pte.hu

Indexing and Abstracting Services:

SCOPUS
CABELLS Journalytics

2023
Scopus
CiteScore	1.5
CiteScore rank	Q3 (Civil and Structural Engineering)
SNIP	0.849
Scimago
SJR index	0.288
SJR Q rank	Q3

Pollack Periodica
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