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CFD simulation of wood chipcombustion on a grate using anEuler–Euler approachD. Kurz a , U. Schnell a & G. Scheffknecht aa Institute of Combustion and Power Plant Technology (IFK),University of Stuttgart , Pfaffenwaldring 23, 70569 , Stuttgart ,GermanyPublished online: 23 Sep 2011.

To cite this article: D. Kurz , U. Schnell & G. Scheffknecht (2012) CFD simulation of wood chipcombustion on a grate using an Euler–Euler approach, Combustion Theory and Modelling, 16:2,251-273, DOI: 10.1080/13647830.2011.610903

To link to this article: http://dx.doi.org/10.1080/13647830.2011.610903

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Combustion Theory and ModellingVol. 16, No. 2, 2012, 251–273

CFD simulation of wood chip combustion on a grate using anEuler–Euler approach

D. Kurz∗, U. Schnell and G. Scheffknecht

Institute of Combustion and Power Plant Technology (IFK), University of Stuttgart, Pfaffenwaldring23, 70569 Stuttgart, Germany

(Received 30 May 2011; final version received 27 July 2011)

Due to the increase of computational power, it is nowadays common practice to useCFD calculations for various kinds of firing systems in order to understand the internalphysical phenomena and to optimise the overall process. Within the last years, biomasscombustion for energy purposes has gained rising popularity. On an industrial scale,mainly grate firing systems are used for this purpose. Generally, such systems consistof a dense-packed fuel bed on the grate and the freeboard region above, where in thefield of numerical modelling, it is common practice to use different sub-models for bothzones. To avoid this, the objective of this paper is the presentation of a numerical modelincluding a detailed three-dimensional description of the fuel bed and the freeboardregion within the same CFD code. Because of the implementation as an Eulerianmultiphase model, both zones are fully coupled in terms of flow and heat transfer, andappropriate models for the treatment of turbulence, radiation, and global reactions arepresented. The model results are validated against detailed measurements of temperatureand gaseous species close to the bed surface and within the radiative section of a 240kW grate firing test facility.

Keywords: CFD; Euler–Euler; grate firing; wood combustion; biomass

Nomenclature

Latin symbols

av Specific contact area between gas and particle phase, 1/mAMag Magnussen coefficientcp Specific heat capacity, J/(kg·K)dp Particle diameter, mDi Diffusion coefficient of gas species, m2/sDp Particle-mixing coefficient, m2/sf Mechanism parameter of carbon oxidationg Gravitation constant, 9.81 m/s2

h Specific enthalpy, J/kg�hevap Evaporation enthalpy, J/kg�hb Bond enthalpy, J/kgH Enthalpy flow, J/s

∗Corresponding author. Email: kurz@ifk.uni-stuttgart.de

ISSN: 1364-7830 print / 1741-3559 onlineC© 2012 Taylor & Francis

http://dx.doi.org/10.1080/13647830.2011.610903http://www.tandfonline.com

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252 D. Kurz et al.

I Radiation intensity, W/(m2·sr)Ib Black body intensity, W/(m2·sr)k Turbulent kinetic energy, m2/s2

ka Absorption coefficient, 1/mlw Wake length, mm Mass, kgnp Number of particles per unit volumeNu Nusselt numberp Pressure, N/m2

r Rate coefficient, var.r Reaction rate, kg/sR Universal gas constant, 8314.47 J/(kmol·K)Pr Prandtl numberRe Reynolds numberRep Reynolds number of flow around particles�s Direction vector, msφ Source term of the transport variable φ, var.T Temperature, Ku Velocity, m/sVw Wake volume, m3

Y Mass fraction, kg/kg

Greek symbols

αgp Heat transfer coefficient between gas and particle phase, W/(m2·K)ε Dissipation rate of turbulent energy, m2/s3

ε Volume fraction, m3/m3

γgp Specific heat transfer coefficient between gas and particle phase, W/(m3·K)λ Thermal conductivity, W/(m·K)µg Effective dynamic viscosity of the gas phase, kg/(m·s)µg,l Laminar dynamic viscosity of the gas phase, kg/(m·s)µg,t Turbulent dynamic viscosity of the gas phase, kg/(m·s)ν Stoichiometric coefficient, kg/kgρ Physical density, kg/m3

σφ Turbulent Schmidt/Prandtl number for φ

τij Shear stress tensor, N/m2

Subscripts

a Ashbl Boundary layerc Chardw Dried woodF Fuelg Gas phasemw Moist woodp Particle phaseProd Products

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Combustion Theory and Modelling 253

Superscripts

ˆ Effective value, considering the local volume fraction

1. Introduction

The generation of heat and electricity by utilisation of biomass is becoming a majoralternative to fossil fuel combustion due to the possibility of CO2 neutral energy production.Grate firing systems are the most common way for the combustion of municipal solid wasteor different kinds of biomass in large-scale applications. The fuel is transported through thefurnace by a moving grate and primary air is supplied from underneath. The solid materialis partially combusted, producing mainly H2O, CO2 and unburned species like CO anddifferent hydrocarbons in the adjacent freeboard region. Burnout is assured by additionalsecondary air in the over-bed region. The remaining solid products are bottom ash from thegrate and fly ash from the flue gas filters.

The numerical description of grate firing systems, either for municipal solid wasteor different kinds of biomass, has gained some interest within the last years. Basicallycompletely different modelling approaches are used, as indicated by the literature survey in[1], whereas it is most common to separate the bed region from the freeboard modelling.The model of the bed region therefore acts as a preprocessor, supplying inlet conditions(e.g. velocity, temperature and/or gas species concentration along the surface of the bed)for the actual CFD simulation of the freeboard region.

The simplest way to describe the bed region is to apply experience or measurement basedcorrelations, partially linked with energy and mass balances, to determine combustion ratesand/or gas species release as a function of the position on the grate (e.g. [2–9]).

Another approach for the treatment of the fuel bed is a cascade of perfectly stirredreactors to describe the fuel conversion and gas species evolution in different zones of thegrate (e.g. [10–14]). In most of these approaches, one reactor per primary air zone is used.

The approaches mentioned above neglect the influence of flow phenomena and locallyvarying properties like temperature and concentrations within the packed bed, whereasseveral authors perform more detailed hydrodynamic simulations for the bed region as wellto account for these effects. To describe the movement and especially the heterogeneousreactions within the fuel bed, either a Lagrangian representation with discrete particles isconsidered (e.g. [15–18]), or the description of the solid fuel by means of an additional,continuous Eulerian phase (e.g. [19–24]) is applied.

Even for models with the same fundamental approach, huge differences exist in theprecision and even in the inclusion or neglect of specific physical phenomena. Despiteall these differences, the common core of most of the cited models is the disadvantageof independent bed and freeboard region in terms of flow, turbulence and heat transfer,due to the separation into two sub-models. As a consequence, these models have to applyfurther simplifying assumptions, for instance the temperature, velocity and/or turbulenceprofile on the bed surface or even the shape of the bed. Due to this partially empiricalcharacter, the resulting overall model is not generally applicable to different grate firingsystems. Additionally, those models are rarely validated against experimental data. If atall, the model results are in most cases only compared with measurements in the radiativesection or at the chimney. However, especially for the fuel bed model, a validation withmeasurements within or close to the bed surface is preferred.

The scope of the present paper is the presentation of a numerical model which ac-counts for the different interactions of bed and freeboard region in detail, by considering

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254 D. Kurz et al.

the whole combustion chamber in a single, three-dimensional CFD code. Therefore, amultiphase approach has to be used, considering the physics and reactions of solid andgaseous species simultaneously, as well as their multiple interactions. Despite the enor-mous increase in computational power within the last decade, Lagrangian approaches fora three-dimensional simulation of an industrial-scale grate firing system still exceed thecomputational resources [24]. Hence, for the description of wood chip combustion on amoving grate, the implementation as an Euler–Euler multiphase approach has been chosen.

The following sections introduce the overall mathematical model, and the comparisonof simulation results with detailed experimental measurements at different positions withinthe combustion chamber of a 240 kW test facility will be presented.

2. Governing equations

In the following, the governing transport equations for mass, species, momentum and heattransfer for two interpenetrating continua, namely the gas and the particle phase, in aCartesian coordinate system are introduced. In the bed region, only part of the volume isavailable for each phase, resulting in the usage of effective values, indicated by a circumflex.For example, the effective density of the gas phase is given by

ρg = ρg · εg εg + εp = 1 (1)

with the physical density ρg , calculated by the ideal gas law, and the local volume fractionof the gas phase εg . The closure constraint implies that the volume fractions of both phasessum up to unity.

As transient effects are not of interest for the investigation at hand, the time dependentterm is not considered in the transport equations.

2.1. Transport equations for the gas phase

Continuity

∂

∂xj

(ρgug,j ) = sm,g (2)

with the effective density of the gas phase ρg , the gas velocity ug,j in coordinate directionj and the source term sm,g due to conversion processes during the combustion of the solidphase.

Momentum

∂

∂xj

(ρgug,jug,i) = ∂τg,ij

∂xj

+ ρggi − εg

∂p

∂xi

+ F (ug,i) (3)

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Combustion Theory and Modelling 255

where the shear stress tensor for a Newtonian fluid in presence of a particle phase is givenby

τg,ij = εg · µg

(∂ug,i

∂xj

+ ∂ug,j

∂xi

− 2

3δij

∂ug,k

∂xk

)with µg = µg,l + µg,t (4)

with the effective viscosity µg of the turbulent flow. The last term on the right-hand side ofEquation (3) represents the resistance of fluid flow in a porous medium and is calculatedby Erguns’ equation. Here, a modified version is used with adapted coefficients valid forparticles with rough surfaces [25]:

−F (ug,i) = �p

�xi

= 180(1 − εg

)2

ε3g · d2

p

· µg,l · ug,i + 4(1 − εg

)ε3g · dp

· u2g,i . (5)

Species transport

∂

∂xj

(ρgug,jYgi) = ∂

∂xj

(µg

σY

∂Ygi

∂xj

)+ sYgi

(6)

where Ygi indicates the mass fraction of gas species and sYgiis the source/sink term account-

ing for concentration changes of each individual species during drying, devolatilisation andcombustion processes. In the present reaction model, seven different gas species are con-sidered: N2, O2, H2, H2O, CO, CO2 and CxHy .

Enthalpy

∂

∂xj

(ρgug,jhg) = ∂

∂xj

(µg

σh

∂hg

∂xj

)+ γgp(Tp − Tg) + sh,g (7)

with the specific enthalpy of the gas phase hg and the convective heat exchange between thegas and particle phases. The source term sh,g accounts for contributions from homogeneousreactions, mass transfer from the particle phase, and radiative heat exchange.

2.2. Transport equations for the particle phase

Continuity

∂

∂xj

(ρpup,j ) = sm,p (8)

with the effective density of the particle phase ρp, the particle velocity up and the sink termsm,p due to consumption of solid mass during drying, devolatilisation and char combustion.By means of the following relation, gas and particle continuity are linked:

sm,g = −sm,p. (9)

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256 D. Kurz et al.

Momentum

Analogous to the gas phase, the conservation of momentum for the particle phase can bedescribed in the following manner [21, 22, 23]:

∂

∂xj

(ρpup,jup,i) = ∂τp,ij

∂xj

+ ρpgi + AGrate. (10)

The last term accounts for random movement of the particles caused by mechanical distur-bances of the grate and other random sources. At present no accurate models are at hand,neither for the shear stress of the particles τp nor for AGrate. Therefore, it is common senseto use a constant velocity of the particle phase in the grate direction, whereas the verticalvelocity is calculated by Equation (8) [21–23, 26, 27]. The effect of enhanced mixing inthe bed due to grate movement is included by the particle-mixing model presented by thegroup at the Sheffield University Waste Incineration Centre (SUWIC) (e.g. [21–23, 27, 28])as shown in the following sections.

Species transport

∂

∂xj

(ρpup,jYpi) = ∂

∂xj

(Dp

∂ρpYpi

∂xj

)+ sYpi

(11)

where Ypi represents the mass fraction of solid species moist wood (mw), dry wood (dw),char (c) and ash (a) in the particle phase. The source term sYpi

comprises the change inconcentration due to the heterogeneous reactions drying, devolatilisation and char combus-tion.

In general, solid species are not subject to diffusional transport [29]. In order to accountfor riddling of the fuel bed, the particle-mixing model introduces diffusional transport ofsolid species by including an experimentally determined particle-mixing coefficient Dp.

Enthalpy

∂

∂xj

(ρpup,jhp) = ∂

∂xj

(λp

∂Tp

∂xj

)− γgp(Tp − Tg) + sh,p (12)

with the specific particle phase enthalpy hp, the convective heat transfer between the gasand particle phases γgp and the source term sh,p comprising radiative effects, heterogeneousreactions and enthalpy transfer via mass transfer to the gas phase. The combined thermalconductivity λp consists of two parts, the conductivity of the solid material λp0 and addi-tional thermal transport caused by random movement of the particles λpm according to theparticle-mixing approach:

λp = λp0 + λpm. (13)

The transport coefficients describing the particle-mixing µp, Dp and λpm in the fuel bedhave to be estimated. By assuming a ‘Particle Prandtl Number’ Prp and a ‘Particle Schmidt

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Combustion Theory and Modelling 257

Number’ Scp of unity Yang et al. [21, 22, 23] deduce the following correlations:

µp = ρpDp and λpm = µpcp,p = ρpcp,pDp. (14)

The particle-mixing coefficient Dp can be approximated by measurements and is affected bythe physical properties of the bed material, grate type and operating conditions of the furnace[21]. Yang et al. performed lab scale as well as full scale experiments for different gratesand bed materials [22]. For the lab scale (1:15), the deduced particle-mixing coefficientswere in the range of 3.3·10−8 to 6.0·10−6 m2/s, whereas in the full scale measurements,coefficients in the range of 6.9·10−6 to 1.8·10−4 m2/s have been reported. In their modelvalidation, Yang et al. utilised values for the particle-mixing coefficient in the range of1.0·10−6 to 5.5·10−5 m2/s [22, 27].

2.3. Convective heat transfer in the bed

The specific convective heat transfer coefficient γgp is calculated from the heat transfercoefficient αgp and the specific contact area av between the gas and particle phases:

γgp = av · αgp. (15)

Under the assumption of spherical particles, the surface area of the particle phase withrespect to the local bed volume is given by

av = 6(1 − εg)

dp

. (16)

The heat transfer coefficient αgp is calculated from an empirical correlation for the Nusseltnumber. Wakao and Kaguei evaluated numerous experimental results for the convectiveheat exchange of gas and particle phases within a fixed bed and deduced the followingformula [30]:

Nu = αgp dp

λg

= 2.0 + 1.1 · Pr1/3Re0.6p . (17)

Pr represents the Prandtl number of the gas and Rep the Reynolds number of the flowaround the particles of the bed, given as:

Rep = dp|�ug − �up|ρg

µg,l

. (18)

2.4. Radiative heat transfer

Numerous publications dealing with fixed bed combustion adapt the thermal conductivityto account for particle radiation within the bed (e.g. [10, 19, 28, 31–38]). Here, no use of thissimplification is made and radiation effects of both phases are explicitly modelled, whichis assumed to give more realistic results [39]. Furthermore, this is necessary to account forthe influences between the freeboard region and the fuel bed in terms of radiation effects,which is supposed to have a strong impact on the overall combustion process.

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258 D. Kurz et al.

Assuming that scattering effects can be neglected, the radiative transport equation canbe written as

dI (x, �s)

ds= ka,g(x)Ib,g(x) + ka,p(x)Ib,p(x) − (ka,g(x) + ka,p(x))I (x, �s) (19)

where ka,g(x) and ka,p(x) are the absorption coefficients and Ib,g(x) and Ib,p(x) representthe black body radiation of gas and particle phase, respectively.

The calculation of the gas phase absorption coefficient ka,g(x) is realised via a poly-nomial approach, depending on the local CO2 and H2O concentration [40]. The particlephase absorption coefficient ka,p(x) is determined by the use of the following correlationproposed in [39]:

ka,p = − 1

dp

ln (εg). (20)

2.5. Turbulence modulation

For the description of turbulence a modification of the widely used standard k, ε model ofLaunder and Spalding [41] is introduced. The adapted transport equations for the turbulentkinetic energy k and the dissipation rate ε for multiphase flow are [42, 43]:

∂

∂xj

(ρguj k) = ∂

∂xj

[(µg,t

σk

+ µg,l

)∂k

∂xj

]+ P − ρε + sk (21)

∂

∂xj

(ρguj ε) = ∂

∂xj

[(µg,t

σε

+ µg,l

)∂ε

∂xj

]+ Cε1

ε

kP − Cε2ρ

ε2

k+ sε (22)

where the production term P is given by

P = µg,t

[1

2

(∂ug,i

∂xj

+ ∂ug,j

∂xi

)2

− 2

3

(∂ug,k

∂xk

)2]

(23)

and the effective turbulent viscosity is calculated from:

µg,t = Cµ ρg

k2

ε. (24)

The source terms sk and sε account for the interaction of turbulent gas flow and thepresence of solid particles, known as turbulence modulation. It is generally accepted thatlarge particles augment the turbulence intensity of the gas phase, whereas small particlesattenuate it [44–46]. On the other hand, dense packing of solids is supposed to dampen theturbulence intensity [43].

Here, the approach of Bolio and Sinclair is used [47]. The model was invented toaccount for turbulence production due to wakes and vortex shedding downstream of largeparticles:

sk = np �k

τep

. (25)

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Combustion Theory and Modelling 259

It depends on the number of particles per unit volume np,

np = 6 εp

π d3p

(26)

and the eddy/particle-interaction time τep,

τep = MIN

(le

|�ug − �up| ,l2e ρg

µg,t

)(27)

where the characteristic eddy length-scale is given by:

le = C0.75µ

k1.5

ε. (28)

The change in turbulent kinetic energy per eddy/particle-interaction �k is given by

�k = 1

2ρgVw( �ug

2 − �up2) (29)

with the wake volume Vw taken as half of the volume of an ellipsoid, characterised by thewake length lw, which is taken from the correlation from Rimon and Cheng [48]:

Vw = π d2p

6lw with

lw

dp

= 1.41 log(Re) − 1.93. (30)

The additional term in the transport equation for the dissipation rate ε is calculated from:

sε = Cε3 sk

ε

k. (31)

The constants of the model are given in Table 1. Within the dense packed bed region, thedistance between adjacent particles is assumed to be too small to allow for the developmentof wakes inside the bed. Therefore, the above mentioned model of Bolio and Sinclair is onlyutilised in the uppermost region of the fuel bed, where wakes downstream of the particlesare likely to extend into the freeboard region.

2.6. Mathematical modeling of the combustion process

A global seven-step reaction model has been developed for the combustion of wood chips,as indicated in Figure 1.

Table 1. Constants of the k, ε model.

Cµ Cε1 Cε2 Cε3 σk σε

0.09 1.44 1.92 1.2 1.0 1.3

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260 D. Kurz et al.

Figure 1. Schematic representation of the reaction model.

Drying

It is assumed that moist wood is heated up, mainly by radiation from the freeboard region,and the resulting evaporation rate (reaction (s1)) is determined from an enthalpy balanceof the particle phase:

revap = Hp(Tp) − Hp(Tsat )

�hev + �hb

(32)

where Tsat is the saturation temperature, taken as 373.15 K, and hev and hb are the evapo-ration and bond enthalpy, respectively.

Devolatilisation

The mathematical description of the devolatilisation process of a solid fuel (reaction (s2))comprises two aspects. On the one hand, the net devolatilisation rate in terms of a massflux from the particle phase to the gas phase has to be modelled, and on the other hand, thecomposition of the released gas has to be determined as well.

The devolatilisation rate is described with a simple one-step model, using an Arrheniusexpression:

rdevol = rs2 · mdw with rs2 = k0,s2 · exp

(− Es2

R · Tp

). (33)

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Combustion Theory and Modelling 261

The kinetic parameters are taken from [49]:

k0,s2 = 5300 s−1 andEs2

R= 8567 K. (34)

The composition of the volatile gas is identified using proximate and elementary analysisof the fuel. Assuming that the fuel mainly consists of the chemical elements C, H andO, three mass balances can be formulated and the amount of three volatile species canbe calculated accordingly. Utilising the experimentally observed ratios of CO/CO2 andCxHy /CO2 published by [50], two additional volatile species can be determined. Thefollowing five values on a mass basis have been determined: CxHy 20.1%; CO 43.7%; CO2

18.2%; H2O 17.8% and H2 0.2%.In terms of the reaction enthalpy of the devolatilisation, large uncertainty prevails in

the literature, not only of the absolute value but even for the treatment as an exothermic orendothermic overall process. Comparison of published data is given for example in [51–53]. According to [53] and [54], the competing endothermic and exothermic subprocessesmore or less compensate each other for which reason several authors dealing with thermalbiomass conversion simply neglect the reaction enthalpy of the devolatilisation process [49,55, 56]. This assumption is made here as well.

Char reactions

Under the concept of char reactions, several processes are summarised. On the one hand,the oxidation of fixed carbon with oxygen is accounted for, whereas on the other hand,gasification reactions with carbon dioxide or water vapour need to be considered as well.

(s3) : (1 + f ) C + O2 → 2f CO + (1 − f ) CO2 (35)

(s4a) : C + CO2 → 2 CO (36)

(s4b) : C + H2O → CO + H2. (37)

The temperature dependent ratio of the resulting products from the char oxidation reactionis given by [57] as:

CO

CO2= 2f

1 − f= 2500 · exp

(−6240

Tbl

)(38)

where the temperature of the boundary layer Tbl is estimated by the arithmetic mean of thegas and particle temperatures.

The effective reaction rate for each char reaction is calculated as the harmonic mean ofthe limiting resistances, namely physical diffusion and chemical kinetics:

r =(

1

rph

+ 1

rch

)−1

. (39)

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262 D. Kurz et al.

According to [30] the physical diffusion rate of a gaseous species i within a packed bedcan be described as:

rph = Di

dp

(2 + 1.1 · Re0.6

p · Pr1/3). (40)

Instead of Di , for simplification the binary diffusion coefficient of oxygen in nitrogen isused [58]:

DO2 = 3.49 · 10−4

(Tbl

T0

)1.75p0

p(41)

which is based on reference conditions T0 (1600 K) and p0 (1bar).The chemical reaction rate rch is calculated via an Arrhenius expression:

rch = k0,ch · Tp · exp

(Ech

R · Tp

). (42)

The kinetic constants for the char reactions are given in Table 2. The rates are calculatedusing the specific surface area of the particle phase (Equation 16):

rchar = av · r · mc. (43)

Homogeneous reactions

A global model for the homogeneous reactions is used, as indicated in Figure 1. Forsimplicity, a two-step model for the oxidation of higher hydrocarbons, as presented in [61],is chosen. Hydrogen from the heterogeneous pyrolysis reaction is oxidised to water vapour.

(g1) : CxHy +(x

2+ y

4

)O2 −→ x CO + y

2H2O (44)

(g2) : CO + 1

2O2 −→ CO2 (45)

(g3) : H2 + 1

2O2 −→ H2O. (46)

Turbulence/chemistry interaction is described by the eddy dissipation model of Magnussenand Hjertager [62]:

rgj = AMag · ε

k· mF · MIN

(YF

|νF | ,YO2

|νO2 |, 0.5

YProd

|νProd |)

. (47)

Table 2. Kinetic parameters of char oxidation and gasification.

Reaction Reactand k0,i [m/(sK)] Ei/R [K] Source

(s3) O2 1.715 9000 [59](s4a) CO2 3.42 15600 [60](s4b) H2O 3.42 15600 [60]

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Combustion Theory and Modelling 263

When applying this model to the relatively slow carbon monoxide oxidation, additionally akinetic rate proposed by Dryer and Glassmann [61] is calculated, and the minimum of bothrates is taken as the effective one. For further details please refer to [63].

3. Numerical solution

The above mentioned transport equations are solved in the finite volume code AIOLOSwhich has been developed at the Institute of Combustion and Power Plant Technology(IFK), University of Stuttgart. The SIMPLE method, described by [64], is utilised forvelocity–pressure coupling, whereas the pressure interpolation scheme of [65] preventsdecoupling of velocities and pressure on the non-staggered grid. The calculation of thepressure correction equation was done using the SIP method. An upwind scheme is appliedfor the calculation of convective fluxes and the solution of all other transport equations wascarried out by a SOR solver. The radiation equation is solved using a discrete ordinatesmethod. Concerning implementation issues the reader is referred to [63] and [66]. Themodel parameters are compiled in Table 3.

4. Experimental set-up

For validation purposes of the presented numerical model, detailed measurements wereconducted at a 240 kW grate firing test facility. A schematic plot of the test facility is givenin Figure 2. The fuel is forwarded into the combustion chamber with a conveying screw.Primary air from two different wind boxes is supplied from underneath the grate. Transportand riddling of the fuel is accomplished by five rows of periodically pushing grate bars.Burnout is assured by means of six arrays of secondary air nozzles in the flue gas duct.Validation is carried out by means of temperature measurements in the central cross-sectionof the combustion chamber and the flue gas duct, as well as measurements of the gaseousspecies oxygen, carbon monoxide and carbon dioxide at four different positions above thefuel bed in different cross-sections as indicated in Figure 2. The fuel input consisted ofwood chips (spruce wood with bark) with a high moisture content of almost 50%. The netinput of fuel energy was 281 kW.

5. Results and discussion

The computational grid, consisting of about 150 000 cells, as well as the hydrodynamicboundary conditions are shown in Figure 3. The preset flows of primary air (PA) andsecondary air (SA) are assigned to their specific positions, whereas the calculated leakageair is assumed to enter the furnace mainly through the fuel inlet. Due to the refractory

Table 3. Model parameters.

Particle volume fraction within the bed εp 0.65 m3/m3

Particle mixing coefficient Dp 3.0·10−5 m2/sInitial particle diameter dp 0.02 mPhysical density of the particle phase ρp 770 kg/m3

Thermal conductivity of the particle phase λp,0 0.2 W/(m K)Evaporation enthalpy of water �hev 2257 kJ/kgBond enthalpy of water in wood �hb 400 kJ/kgMagnussen coefficient of homogeneous reactions AMag 1.0

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Figure 2. Schematic representation of the test facility with measurement positions.

lining, the walls of the combustion chamber are assumed adiabatic, with the exception ofthe uppermost duct which is water cooled.

Due to the strong influence of the particle phase on the overall system and especially thesevere impact on the local behaviour in the case of existence/non-existence of the particlephase, especially the particle phase enthalpy as well as the particle phase species neededto be under-relaxed rather strongly. After 50 000 iterations, the numerical solution of alltransport equations converged to normalised residuals of less than 10−5. In addition, theoverall balances of enthalpy and elements C, H and O was closed with an error of less than0.1% related to the inflowing values. As the implementation is highly optimised for parallelvector computers, a computational time of less than two hours on a NEC-SX8 platformusing eight CPUs could be achieved.

The computed temperature field in the centre section of the furnace is shown in Figure 4.One can easily distinguish the relatively cold zone close to the fuel inlet where drying of thefuel mainly by radiation from the surrounding hot freeboard region occurs. During pyrolysis,combustible gases and char are released which leads to an increasing temperature due tooxidation reactions. The maximum temperature is observed in the flue gas duct where thesecondary air supply results in the burnout of the gas, leading to an additional temperatureincrease. After two thirds of the grate length, the char content of the residual solids isdepleted and the resulting ash is cooled by radiative and convective processes, leading to atemperature decrease towards the ash outlet.

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Figure 3. Computational grid.

The results are validated against the four measurement points within the flue gas duct,as well as the six positions in the combustion chamber, referred as measurement line M200(see Figure 4). Comparison of simulated and measured temperatures on measurement lineM200 is given in Figure 5, where the temperatures are plotted against the horizontal distancefrom the left side of the furnace. For the experimental results, the range of the measuredvalues is given in terms of error bars. It is observed that simulated temperatures are slightlyhigher than the measured ones. This might be due to the assumption of adiabatic walls,

Figure 4. Simulated temperature profile in the centre cross-section.

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400

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Figure 5. Simulated and measured temperatures on M200.

whereas in reality some heat removal over the walls by radiation and convection is likelyto occur. Nevertheless, the overall temperature profile within the combustion chamber isdepicted quite well by the model. Simulated and measured temperatures within the flue gasduct are compared in Figure 6. Especially for the measurement position FG3, the simulatedtemperature is considerably higher than the measured counterpart. As indicated by thehorizontal cross-section through the measurement points in the flue gas duct (Figure 7),the measurement point FG3 is located in an area of large temperature gradients. Therefore,even a slightly different position of the thermocouple or unsteady phenomena will resultin a considerably lower temperature, which might explain part of the deviation from the

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Figure 6. Simulated and measured temperatures in the flue gas duct.

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Figure 7. Temperature profile in the cross-section of measurement points FG1 to FG3.

simulated value. Also the penetration depth of the secondary air nozzle flows might beunderestimated in the model, resulting in an overestimation of the gas temperature atmeasurement points FG1 to FG3. At position FG4 where strong mixing due the deflectionof the flue gas stream has occurred the simulated temperature corresponds well to themeasured one.

Validation of the reaction model is carried out by comparison of measured gas concen-trations at the points of measurement M1 to M4 with the simulation results (see Figure 2).The concentration plots of oxygen and carbon monoxide on a dry basis in the centre cross-section of the furnace, and the positions of the measurement points M1 to M4, are givenin Figures 8a and 8b. According to the oxygen profile, one can easily identify the section

Figure 8a. Simulated O2 profile in the centre cross-section.

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Figure 8b. Simulated CO profile in the centre cross-section.

of high fuel conversion in the second third of the grate. Due to the already mentionedlow penetration depth of the secondary air nozzles in the flue gas duct, the low oxygenconcentrations extend into the water cooled region of the flue gas duct. The high carbonmonoxide concentrations above the grate accrue from devolatilisation and char reaction

0

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Figure 9a. Comparison of simulated and measured O2 profiles.

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0

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Figure 9b. Comparison of simulated and measured CO profiles.

processes, whereas the carbon monoxide is depleted by the oxidation to carbon dioxide inthe course of the flue gas flow.

The simulated oxygen, carbon monoxide and carbon dioxide concentrations and theirmeasured counterparts are given in Figures 9a–9c, where the gas concentration for eachmeasurement point is plotted over the width of the combustion chamber.

As indicated by the error bars, experimental results are subject to considerable fluc-tuations, especially in the vicinity of the bed surface. Reasons for that are unavoidableunsteady phenomena, caused by riddling of the fuel due to the grate movement, and suddenchanges in the bed status like the appearance or destruction of channels.

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Figure 9c. Comparison of simulated and measured CO2 profiles.

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Regarding the oxygen concentration (Figure 9a), the model slightly underpredicts thevalue in the main reaction zone, indicating an overestimation of the reaction process. Closeto the fuel inlet, at measurement point M4, the simulated oxygen concentration is higherthan the measured one. The carbon monoxide (Figure 9b) and carbon dioxide (Figure 9c)profiles correspond reasonably well to the measured values, as the simulation results arelocated in the range of variations of the experimental data. Only at measurement point M4,the carbon dioxide concentration is underestimated by the model. One possible reason mightbe the above mentioned assumption for the leakage air. Assuming the origin of leakage airthrough the fuel inlet might lead to the overestimation of oxygen and enhanced dilution ofthe flue gas flow above the fuel inlet.

6. Conclusions

The governing equations for mass, species and momentum conservation and heat transferfor a multiphase system applicable to a grate firing arrangement have been presented.Mathematical models for drying, devolatilisation and char burnout of the solid fuel, aswell as combustion reactions in the gas phase are illustrated in detail, as well as modelsdescribing radiation and turbulence. Therefore, the whole combustion chamber, includingboth the fuel bed and the adjacent freeboard region, can be simulated within one CFDcode, enabling the coupled description of gas–solid interactions in terms of flow, speciestransport, turbulence, reactions and heat transfer by convection and radiation.

To validate the model results, detailed measurements within a 240 kW grate firing testfacility have been performed. Emphasis was put especially on the regions close to the bedsurface where strong interactions between solid and gas reactions, as well as flow andheat transfer phenomena are likely to occur. Experimentally observed temperatures andgas concentrations were compared with the corresponding numerical results. Reasonablygood agreement between experimental and numerical results has been observed. For mostof the measurement points, the numerical results lie within the range of deviations ofthe experimentally observed values. Also, the general trend of all measurements, both inparallel and in transversal directions of the grate, is depicted quite well by the model.Nevertheless, there is still room for improvement of various sub-models. Especially for theheterogeneous reaction model the consideration of an uneven particle size distribution needsto be implemented in order to accurately predict the burnout behaviour of the solid fuel.

To prove the general applicability, the sensitivity of the model regarding fuel propertieslike moisture content as well as operational parameters will be presented and compared toexperimental findings in a subsequent paper. Furthermore, the scale-up of the presentedmodel towards industrial-scale arrangements needs to be validated. Therefore, detailedmeasurements within a 60 MW wood chip combustion grate firing application close tothe bed surface and within the radiative section have been conducted, and comparison tonumerical results will be presented in future publications.

AcknowledgementsFunding of this work by Energie Baden-Wurttemberg (EnBW) is gratefully acknowledged. Specialthanks go to Sven Unterberger and Oliver Greißl from EnBW for their cooperation, as well as ourcolleagues Daniel Kilgus and Kevin Brechtel for their support and for conducting the experimentalmeasurements.

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