computational study of smouldering combustion of peat in … · 2017-09-18 · part ii fire 341...

Proceedings of the Eighth International Seminar on Fire and Explosion Hazards (ISFEH8), pp. 339-349 Edited by Chao J., Liu N. A., Molkov V., Sunderland P., Tamanini F. and Torero J. Published by USTC Press ISBN:978-7-312-04104-4 DOI:10.20285/c.sklfs.8thISFEH.035

339

Computational Study of Smouldering Combustion of Peat in Wildfires

Huang X. Y.1,2,*, Rein G.1

1Department of Mechanical Engineering, Imperial College London, UK 2Department of Mechanical Engineering, University of California, Berkeley, USA

*Corresponding author email: [email protected]

ABSTRACT In this work, a one-dimensional (1-D) model of a reactive porous media is developed to solve the heat and mass transfer equations with heterogeneous chemistry. The model first predicts the smouldering thresholds, relating to the critical moisture and inert contents. Modelling results show a good agreement with experiments for a wide range of peat types. Then, the model is optimized to investigate the in-depth spread of peat fire into layers of heterogeneous profiles of moisture, mineral and density. Modelling results reveal that smouldering combustion can spread over peat layers of very high moisture content (> 250%), and the critical moisture for extinction can be much higher than that for ignition. The predicted critical moisture values and depths of burn show a good agreement with measurements of 20-30 cm thick peat samples in the literature. Afterwards, the model reproduces the mass loss rate of a bench-scale peat fire under various oxygen concentrations, and predicts that the minimun oxygen concentration for smouldering of dry peat is around 11%, much lower than flaming wildfires. This is the first time that the in-depth spread of peat fires is systemically studied based on a computational model to bridge the experimental data in last decades, thus helping to understand this important natural and widespread phenomenon.

KEYWORDS: Wildfire, kinetics, moisture content, depth of burn, oxygen concentration.

NOMENCLATURE

c heat capacity dp characteristic pore size E activation energy h enthalpy hc convective coefficient hm mass-transfer coefficient H sample height ∆H heat of reaction k thermal conductivity K permeability m ′′& mass flux n reaction order P pressure

eq′′& heat flux R gas constant S particle surface area t time T temperature X volume fraction

2OX oxygen concentration Y mass fraction z distance ∆z cell size Z pre-exponential factor Greek γ radiative conductivity coefficient δ thickness ε emissivity ν viscosity/stoichiometric coefficient ρ bulk density (mass concentration) ρs solid density, 1sρ ρ ψ= − σ Stefan-Boltzmann constant χ fraction factor ψ porosity w& reaction rate Superscripts * critical

Proceedings of the Eighth International Seminar on Fire and Explosion Hazards (ISFEH8)

340

Subscripts 0 initial ∞ ambient α/αo α-char/α-char oxidation β/βo β-char/β-char oxidation a ash d/f destruction/formation dr drying ex extinction

g gas i condensed species number ig ignition j gaseous species number k reaction number p/po/pp peat/peat oxidation/peat pyrolysis sm smouldering w water/wet

INTRODUCTION

Smouldering combustion is the slow, low-temperature, flameless burning of porous fuels and the most persistent type of combustion, different from flaming combustion [1, 2]. Smouldering is the dominant phenomena in wildfires in natural deposits of peat which are the largest burning fires on Earth. These fires contribute considerably to annual greenhouse gas emissions (roughly equivalent to 15% of the man-made emissions [3, 4]), and result in the widespread destruction of ecosystems and regional haze events, e.g. recent mega-fires in Southeast Asia, North America, and Northeast Europe [5], as shown in Fig. 1.

Figure 1. Photos of peat fire in Indonesia in 2015 (left), the corresponding haze event in Singapore (center),

and the comparison of flaming and smouldering wildfires (right).

Compared to flaming combustion (see Fig. 1), smouldering combustion can be initiated with a much weaker ignition source, and provide a hazard shortcut to flaming [2]. Once ignited, smouldering wildfires are the most persistent type of fire phenomena, and can consume huge amount of earth biomass, and burn for very long periods of time (weeks, months and years) despite extensive weather and climate changes [5]. For the past few decades, many experimental studies have been conducted on small-scale smouldering peat fires. However, there has been very few computational work [6-11] to systematically study such emerging fire phenomena.

Thermogravimetric analysis (TGA) has been conducted for various types of peat under inert and oxidative atmospheres at the mg scale [12-14]. Frandsen [15, 16] conducted a series of bench-scale experiments on multiple peat and other organic soils, and measured the maximum moisture content (MC1) for initiating smouldering within the range 40%-150%. He also extrapolated a decreasing quasi-linear relationship between critical moisture content (MC*) and inert content (IC*): soil with a high IC can only be ignited at low MC. Inert mineral matter acts as a heat sink but also enhances the heat transfer via its higher heat conductivity. After moisture and inert contents, other important properties for smouldering are bulk density, porosity, flow permeability and organic composition [5]. More recently, Benscoter et al. [17] conducted a series of experiments for the in-depth spread over

1 Moisture content (MC) is defined here in dry basis as the mass of water divided by the mass of a dried soil sample, expressed as %. Inert content (IC) is defined here in dry basis as the mass of soil inorganic matter (minerals) divided by the mass of a dried soil sample, expressed as %.

Part II Fire

341

field samples with heterogeneous profiles of MC, IC and density. The depth of burn (DOB) was measured for each sample, and smouldering was found to spread at an extremely high MC (>250%). Belcher et al. [18] found that smouldering could not be sustained for a bench-scale dry moss peat (MC = 25%) below an oxygen concentration (

2OX , percentage by volume) of 16%. Hadden et al. [19, 20] further found the oven-dried moss peat (MC < 10%) could be ignited by an irradiation of 20 kW/m2 within 1 min under the

2OX as low as 11%.

In this paper, we will review our latest computational studies on peat fires [6-11] and explain these experiments from a fundamental viewpoint. The modelling results are compared with experiments introduced above with different peat types and under various scales and environments [12-20].

Figure 2. Illustrations of the 1-D computational domain of peat with moisture and density profiles for an in-depth spread of smouldering fire at (a) the beginning of ignition ( 0t = ), and (b) the self-sustained spread [8].

COMPUATIONAL MODEL

Fig. 2 shows a typical 1-D computation domain of peat with moisture and density profiles for an in-depth spread of smouldering fire at (a) the beginning of ignition ( 0t = ), and (b) the self-sustained spread. As the fire spread downwards, an ash layer and a char layer are yielded and at the same time the DOB increases.

Governing equations

The model is developed in an open-source code Gpyro [21] and solves the 1-D transient equations for both solid and gas phases. The gas-phase temperature is assumed to be the same as the condensed-phase temperature (i.e. in thermal equilibrium), and the Darcyʼs law is used to solved gas-phase momentum conservation equation. The details are reported in [21], only the essentials conservation equations of the model (1) condensed-phase mass, (2) condensed-phase species (3) condensed-phase energy, (4) gas-phase mass, (5) gas-phase species, and (6) gas-phase momentum are presented here. All symbols are explained in the nomenclature.

fgtρ

ω∂ ′′′= −∂

& , (1)

( )ifi di

Yt

ρω ω

∂′′′ ′′′= −

∂& & , (2)


342

( ) ( ),

1

Kg r

di k kk

h m h qTk Ht z z z z

ρω

=

′′∂ ∂ ′′∂∂ ∂ ′′′+ = + ∆ − ∂ ∂ ∂ ∂ ∂ ∑

&& , (3)

( )gfg

mt z

ρ ψω

∂ ′′∂ ′′′+ =∂ ∂

& & , (4)

( ) ( )g j j ig fj dj

Y m Y YD

t z z zρ ψ

ρ ψ ω ω′′∂ ∂ ∂∂ ′′′ ′′′+ = − + − ∂ ∂ ∂ ∂

&& & , (5)

K Pmzν

∂′′ = −∂

& ( )g sP R Tρ= . (6)

Each condensed-phase species is assumed to have constant properties (e.g. bulk density, specific heat, and porosity). All gaseous species have unit Schmidt number, and equal diffusion coefficient and specific heat. The averaged properties in each cell are calculated by weighting the appropriate mass fraction, Yi, or volume fraction, Xi [21], for example:

,1 1 1 1

, , ,M M M M

p i p i i i i i i ii i i i

c Y c h Y h X k X kρ ρ= = = =

= = = =∑ ∑ ∑ ∑ . (7)

At the free surface ( 0z = ), convective boundary conditions are imposed: ,0 10ch = W/(m2·K) with surface reradiation ( 0.95ε = ) and ,0 0.02mh = kg/(m2·s). The environmental pressure and temperature are assumed to be atmospheric and 300 K, respectively. For ignition, an external heat flux ( eq ′′& ) is applied as the ignition source. At z L= , the mass flux is set to be zero for in-depth spread, and heat loss to the bottom wall is set with , wall wall/ 3c Lh k δ≈ = W/(m2·K).

A fully implicit formulation is adopted for solution of all equations, and more details about numerical solution methodology are reported in [21]. Current simulations were run with an initial cell size of 0.2 mm, and initial time step of 0.02 s. Reducing the cell size and time step by a factor of 2 gives no significant difference in results, so the grid is sufficiently resolved.

Kinetic model

The heterogeneous reaction in mass basis is written as [21]:

, , ,1 1

N N

k j k B k k j kj j

A gas j B gas jν ν ν= =

′ ′′+ → +∑ ∑ , (8)

where , 1 ( 1)B k B A kν ρ ρ χ= + − ; and kχ quantifies the shrinkage or intumescence of the cell size.

The destruction rate of a condensed species A in a reaction k is expressed by the Arrhenius law:

( )( )

O ,2

O2

/( )k

kk

k

nnA E RT A

dA kA

Y z Y zZ e Yz Y z

ρ ρω

ρ−Σ

Σ

∆ ∆′′′ = ∆ ∆

& , (9)

( ) ( ) ( )0 0| d

t

A A t fiY z Y z zρ ρ ω τ τ=Σ′′′∆ = ∆ + ∆∫ & , (10)

where subscripts d and f represent destruction and formation, respectively. The formation rate of the condensed species B and all gases are ,k kfB B k dAω ν ω′′′ ′′′=& & and ( ),1

k kfg B k dAω ν ω′′′ ′′′= −& & , respectively. The

corresponding solid-phase heat of reaction is , ks k dA kQ Hω′′′ ′′′= − ∆& & .

Part II Fire

343

Various decomposition schemes are investigated using thermogravity analysis (TGA) of six different peat soils from Ireland, Scotland, Siberia, and China [6, 9]. The best kinetics scheme for smouldering peat is found to be 5-step: (1) Drying (dr), (2) Peat pyrolysis (pp), (3) Peat oxidation (po), (4) β-Char oxidation (βo), and (5) α-Char oxidation (αo) as

Peat ∙ H2O → Peat + H2O (g) (dr), (11) Peat → α-Char + Gas (pp), (12) Peat + O2 → β-Char + Gas (po), (13) β-Char + O2 → Ash + Gas (βo), (14) α-Char + O2 → Ash + Gas (αo), (15)

where subscripts w, p, α, β, and a represent five condensed species (water, peat, α-char, β-char, and ash), and four gaseous species are considered: oxygen, nitrogen, water vapour, and emission gases. Essentially, there are two decomposition paths: (a) Peat → α-Char → Ash, and (b) Peat → β-Char → Ash [6].

Parameter selection

The thermo-physical properties of each condensed-phase species (i) are assumed to be constant, listed in Table 1. The solid ( 0iψ = ) thermo-physical properties, ,s iρ , ,s ik , ,p ic of peat, char, and ash are selected from [22]. The porosity is calculated with the bulk density ( iρ ) as

,

1 ii

s i

ρψ

ρ= − . (16)

The average bulk density of peat can be easily measured in experiment.

Table 1. The physical parameters of condensed-phase species [19, 20, 22].

Species (i)

,s iρ (kg/m3)

,0iρ (kg/m3)

,0iψ (-)

,s ik (W/(m·K))

,p ic (J/(kg·K))

water 1000 1000 0 0.6 4186

peat 1500 200 0.867 1.0 1840

α-char 1300 185 0.962 0.26 1260

β-char 1300 185 0.962 0.26 1260

ash 2500 35 0.997 1.2 880

The effective thermal conductivity includes the radiation heat transfer across pores as

( ) 3, 1i s i i ik k Tψ γ σ= − + , (17)

where 4 310 ~ 10iγ − −= m depends on the pore size as ,3i p idγ = [23]. The average pore size relates to the particle surface area as , 1 / ( )p i i id S ρ= where 0.05p cS S= = m2/g and 0.2aS = m2/g [24]. The absolute permeability of soil can be estimated from an empirical expression [25]

4 2, 2

110 ~wi p i

i

K dg

νρ

= , (18)


344

which varies from 12 910 ~ 10− − m2, and decreases with the bulk density. Because of a high porosity ( 0.867pψ = ) and the low volumetric water content, e.g. VWC = 0.1 at MC = 250%~400% [17], water is assumed to stay in the pores of peat without the volume expansion. Thus, the bulk density of a moist peat is (1 ) pMCρ ρ= + . The properties of α-char and β-char are assumed to be the same.

RESULTS AND DISCUSSIONS

Modelling TGA and kinetic parameters

TGA is so far the best experimental technique to study apparent solid-phase kinetics. It provides an ideal environment of controllable atmosphere and heating rate, and negligible thermal gradient and transport effects during the degradation of the small solid samples (∼mg). Therefore, the small sample in TGA can be assumed as a 0D isothermal cell, so the normalized mass-loss rate is

( )1 1

dd d 1d d d

N Ni

fi dii i

mm tT T t

ω ωβ= =

= = −∑ ∑ & & , (19)

where β = dT/dt is the linear heating rate in TGA.

The recent developed Kissinger-Genetic Algorithm (K-GA) method [26] is used to quickly and accurately search for good kinetic parameters which well predict the TG data. First, Kissingerʼs method is used with TG data of multiple heating rates to find the approximate values of Zk and Ek for 1-step drying and 1-step pyrolysis. Then, these approximate values are used to narrow the search range in GA. GA is a heuristic search method, imitating the principles of biological adaption based on Darwinian survival-of-the-fittest theory. We apply the GA code in MATLAB to couple with the above mass-loss model to search for all kinetic and stoichiometric parameters. GA eventually gives the multiple groups of good solutions for the model to match all TG curves. Note that the optimization system is heuristic, so it does not have unique solution [30, 13].

Figure 3. Mass-loss rates (MLR) and reaction rates of the low-mineral moss peat in TG experiments modelled by the 5-step kinetic model under (a)

2OX = 0% (inert ambient), (b) 2OX = 10% and (c)

2OX = 21% (air) [9].

For MLR, symbols are from experiments and curves are from modelling.

Part II Fire

345

Table 2. Reaction parameters and gaseous yields of 5-step reactions for an Irish moss peat sample [9].

Parameter/k dr pp po αo βo

1lg (lg )kA s − 8.12 5.92 6.51 1.65 7.04

kE (kJ/mol) 67.8 93.3 89.8 54.4 112

kn (-) 2.37 1.01 1.03 0.54 1.85

,B kν (kg/kg) 0 0.75 0.65 0.03 0.02

kH∆ (MJ/kg) 2.26 0.5 -2.66 -14.6 -14.6

2O ,kν (kg/kg) 0 0 0.20 1.11 1.12

Fig. 3 shows the modelled mass loss rate and reaction rate curves of an Irish low-mineral moss peat, compared with TG experimental curves. In general, a good agreement with experiment is shown. The optimized kinetic and stoichiometric parameters are listed in Table 2.

Critical moisture content for ignition

Fig. 4 shows an example of modelled (a) temperature profile, (b) mass evolution, and (c) reaction profile of smouldering spread over a 4 cm sample [7]. The ignition protocol is set to be 30 kW/m2 for 3 min, which mimics Frandsenʼs experiments [15]. The modelled smouldering peak temperature during steady-state spread is around 650 oC, similar to experimental measurements [27]. Simulations also reveal three reaction subfronts, (1) peat drying, (2) peat pyrolysis, and (3) char oxidation, at the in-depth spread.

Figure 4. Modelled (a) temperature profile, (b) mass evolution, and (c) reaction profile of smouldering spread

over a 4 cm sample [7].

Then, the modelled smouldering threshold, relating to the critical moisture content ( igM C ∗ ) and inert content ( igIC ∗ ), can be found at the boundary of ignition and no-ignition [7]. The modelled and experimental smouldering threshold are compared in Fig. 5. In general, modelling results give good agreements with the experimental measurements. igM C ∗ is found to compensate igIC ∗ in a nonlinear manner, different from the linear correlation extrapolated by Frandsen [15]. The sensitivity analysis on peat physico-chemical properties shows a high reliability in modelling different types of peat soils from different geological locations.


346

Figure 5. Predicted and measured critical curves igM C ∗ vs. igIC ∗ for the smouldering ignition thresholds of

different soils (a) with different peat kinetic parameters, and (b) with different peat properties [7].

Critical moisture content for extinction and depth of burn

It has been found in field the smouldering fire can spread over some wet peat layers with MC higher than 250% [17, 28], which is much higher than previous found values of = 100%-150% [15, 16, 29]. Modelling results in Fig. 6(a) show that the critical MC for extinction ( exMC∗ ) can be substantially higher than that for ignition ( igM C ∗ ). In addition, exMC∗ is independent on the ignition condition, but depends on both upstream burning conditions and downstream peat properties [8].

Fig. 6(b) shows a modelled temperature and moisture profiles for fire spreading over a soil sample in field. It can be seen that smouldering fire is able to spread over a peat layer with MC = 229%, but extinguish at a layer with MC = 373%. At the same time, the DOB is found to be 11.5 cm, comparable to experimental measurement [17]. Fig. 6(c) compares modelled DOBs with experimental measurements for 18 peat samples [17]. Good agreements are shown with average R2 value of 0.87.

Figure 6. (a) correlation among DOB, upstream and downstream peat MCs, (b) an example of predicted temperature and moisture profiles, and (c) comparison between experimental [17] and modelled DOBs [8].

Part II Fire

347

Critical oxygen concentration

Fig. 7(a) and (b) show the comparison between experimental and modelled mass-loss rate curves for smouldering fire on a bench-scale peat sample under a continuous external irradiation of 20 kW/m2. The stochastic sensitivity analysis is applied to study the influence of peat physicochemical properties [9]. The modelling results show good agreements with experiments [19].

Fig. 7(c) further predicts the mass-loss-rate curves under different oxygen concentrations after applying 1 min external radiation of 20 kW/m2. The minimum oxygen concentration (

2OX ) for

ignition is found to be 11%, which is close to the experimental value found in [20]. More important, this found critical value for smouldering wildfire is much lower than flaming wildfires (~16%) [30], thus defining the lower threshold for current and historical wildfires.

Figure 7. Predicted mass loss rate under (a) inert ambient and continuous irradiation (20 kW/m2), (b) air and

continuous irradiation, and (c) different oxygen concentrations and 1 min irradiation for ignition [9], compared with experimental measurements [19, 20].

CONCLUSIONS

In this paper, we have review our recent development on modelling smouldering combustion of natural fuels. The developed 1D model solves the species, momentum, and energy conservation equations and includes heterogeneous chemical reactions. A 5-step kinetic scheme for peat smouldering is proposed to describe the drying, thermal and oxidative degradation during the smouldering combustion. This kinetics successfully explains the TG experiments of multiple peat soils. The roles of peat moisture content, inert content, and atmospheric oxygen concentration are investigated, and their critical values for ignition and extinction are predicted. Modelling results show good agreements with several bench-scale experiments in the literature. This is the first time that the in-depth spread of peat fires is systemically studied based on a computational model to bridge the experimental data in the last decades, thus helping to understand this important natural and widespread phenomenon.

ACKNOWLEDGEMENTS

The authors thank the financial support from Department of Mechanical Engineering at Imperial College, and travel support from Santander, Imperial Trust, Old Centraliansʼ Trust, Association for Fire Ecology (AFE), Combustion Institute, International Association of Wildland fire (IAWF), and Institution of Physics (IOP). Valuable comments from Professors Naian Liu, Haixiang Chen


348

(University of Science and Technology of China), Dr Rory Hadden (University of Edinburgh), Dr Chris Lautenberger (Reax Engineering), Dr Albert Simeoni (Exponent, Inc.), Dr Salvador Navarro-Martinez and Francesco Restuccia (Imperial College London) are acknowledged.

REFERENCES 1. Rein, G. Smoldering Combustion, SFPE Handbook of Fire Protection Engineering, Hurley, M. J., et al.,

Editors. Springer, New York, pp. 581-603, 2016. 2. Ohlemiller, T. J. Modeling of Smoldering Combustion Propagation, Progress in Energy and Combustion

Science, 11(4): 277-310, 1985. 3. Page, S. E., Siegert, F., Rieley, J. O., Boehm, H. D. V., Jaya, A., and Limin, S. The Amount of Carbon

Released from Peat and Forest Fires in Indonesia during 1997, Nature, 420(6911): 61-65, 2002. 4. Poulter, B., Christensen, N. L., and Halpin, P. N. Carbon Emissions from a Temperate Peat Fire and Its

Relevance to Interannual Variability of Trace Atmospheric Greenhouse Gases, Journal of Geophysical Research: Atmospheres, 111(D6): D06301, 2006.

5. Rein, G. Smouldering Fires and Natural Fuels, Fire Phenomena and the Earth System, Belcher, C. Editor, John Wiley & Sons, pp. 15-33, 2013.

6. Huang, X., and Rein, G. Smouldering Combustion of Peat in Wildfires: Inverse Modelling of the Drying and the Thermal and Oxidative Decomposition Kinetics, Combustion and Flame, 161(6): 1633-1644, 2014.

7. Huang, X. Y., Rein, G., and Chen, H. X. Computational Smoldering Combustion: Predicting the Roles of Moisture and Inert Contents in Peat Wildfires, Proceedings of the Combustion Institute, 35(3): 2673-2681, 2015.

8. Huang, X., and Rein, G. Computational Study of Critical Moisture and Depth of Burn in Peat Fires, International Journal of Wildland Fire, 24(6): 798-808, 2015.

9. Huang, X., and Rein, G. Thermochemical Conversion of Biomass in Smouldering Combustion across Scales: The Roles of Heterogeneous Kinetics, Oxygen and Transport Phenomena, Bioresource Technology, 207: 409-421, 2016.

10. Huang, X., and Rein, G. Interactions of Earth Atmospheric Oxygen and Fuel Moisture in Smouldering Wildfires: A Computational Approach, Science of the Total Environment, 572: 1440-1446, 2016.

11. Huang, X. Fundamental Study of Smouldering Combustion of Peat in Wildfires, in Department of Mechanical Engineering, Imperial College London: London, 2016.

12. Chen, H. X., Zhao, W. T., and Liu, N. A. Thermal Analysis and Decomposition Kinetics of Chinese Forest Peat under Nitrogen and Air Atmospheres, Energy & Fuels, 25(2): 797-803, 2011.

13. Zhao, W. T., Chen, H. X., Liu, N. A, and Zhou, J. J. Thermogravimetric Analysis of Peat Decomposition under Different Oxygen Concentrations, Journal of Thermal Analysis and Calorimetry, 117(1): 489-497, 2014.

14. Cancellieri, D., Leroy-Cancellieri, V., Leoni, E., Simeoni, A., Kuzin, A. Y., Filkov, A. I., and Rein, G. Kinetic Investigation on the Smouldering Combustion of Boreal Peat, Fuel, 93: 479-485, 2012.

15. Frandsen, W. H. The Influence of Moisture and Mineral Soil on the Combustion Limits of Smoldering Forest Duff, Canadian Journal of Forest Research, 17(12): 1540-1544, 1987.

16. Frandsen, W. H. Ignition Probability of Organic Soils, Canadian Journal of Forest Research, 27(9): 1471-1477, 1997.

17. Benscoter, B. W., Thompson, D. K., Waddington, J. M., Flannigan, M. D., Wotton, B. M., De Groot, W. J., and Turetsky, M. R. Interactive Effects of Vegetation, Soil Moisture and Bulk Density on Depth of Burning of Thick Organic Soils, International Journal of Wildland Fire, 20(3): 418-429, 2011.

18. Belcher, C. M., Yearsley, J. M., Hadden, R. M., McElwain, J. C., and Rein, G. Baseline Intrinsic Flammability of Earthʼs Ecosystems Estimated from Paleoatmospheric Oxygen over the Past 350 Million Years, Proceedings of the National Academy of Sciences, 107(52): 22448-22453, 2010.

19. Hadden, R. M., Scott, S., Lautenberger, C., and Fernandez-Pello, A. C. Ignition of Combustible Fuel Beds by Hot Particles: An Experimental and Theoretical Study, Fire Technology, 47(2): 341-355, 2011.

20. Hadden, R. M., Rein, G., and Belcher, C. M. Study of the Competing Chemical Reactions in the Initiation and Spread of Smouldering Combustion in Peat, Proceedings of the Combustion Institute, 34(2): 2547-2553, 2013.

21. Lautenberger, C., and Fernandez-Pello, C. Generalized Pyrolysis Model for Combustible Solids, Fire Safety Journal, 44(6): 819-839, 2009.

Part II Fire

349

22. Jacobsen, R. T., Lemmon, E. W., Penoncello, S. G., Shan, Z., and Wright, N. Thermophysical Properties of Fluids and Materials, in Heat Transfer Handbook, Bejan, A., and Kraus, A. D. Editors, John Wiley & Sons, pp. 43-159, 2003.

23. Yu, F., Wei, G. S., Zhang, X. X., and Chen, K. Two Effective Thermal Conductivity Models for Porous Media with Hollow Spherical Agglomerates, International Journal of Thermophysics, 27(1): 293-303, 2006.

24. De Jonge, H., and Mittelmeijer-Hazeleger, M. C. Adsorption of CO2 and N2 on Soil Organic Matter: Nature of Porosity, Surface Area, and Diffusion Mechanisms, Environmental Science & Technology, 30(2): 408-413, 1996.

25. Punmia, B. C., and Jain, A. K. Soil Mechanics and Foundations, Laxmi Publications Pvt Limited, 2005. 26. Li, K. Y., Huang, X. Y., Fleischmann, C., Rein, G., and Ji, J. Pyrolysis of Medium-Density Fiberboard:

Optimized Search for Kinetics Scheme and Parameters via a Genetic Algorithm Driven by Kissingerʼs Method, Energy & Fuels, 28(9): 6130-6139, 2014.

27. Huang, X., Restuccia, F., Gramola, M., and Rein, G. Experimental Study of the Formation and Collapse of an Overhang in the Surface Spread of Smouldering Peat Fires, Combustion and Flame, 168: 393-402, 2016.

28. Watts, A. C. Organic Soil Combustion in Cypress Swamps: Moisture Effects and Landscape Implications for Carbon Release, Forest Ecology and Management, 294: 178-187, 2013.

29. Rein, G., Cleaver, N., Ashton, C., Pironi, P., and Torero, J. L. The Severity of Smouldering Peat Fires and Damage to the Forest Soil, Catena, 74(3): 304-309, 2008.

30. Watson, A. J., and Lovelock, J. E. The Dependence of Flame Spread and Probability of Ignition on Atmospheric Oxygen, in Fire Phenomena and the Earth System, John Wiley & Sons, 273-287, 2013.

computational study of smouldering combustion of peat in … · 2017-09-18 · part ii fire 341...

Documents