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Numerical investigation of the effects of fuel variability on the dynamics of syngas impinging jet flames
Fuel 103 (2013) 646–662
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Numerical investigation of the effects of fuel variability on the dynamics of syngas impinging jet flames D. Mira Martinez a,⇑, X. Jiang a, C. Moulinec b, D.R. Emerson b a b
Engineering Department, Lancaster University, Lancaster LA1 4YR, UK STFC Daresbury Laboratory, Warrington, Cheshire WA4 4AD, UK
a r t i c l e
i n f o
Article history: Received 5 April 2012 Received in revised form 4 June 2012 Accepted 6 June 2012 Available online 25 June 2012 Keywords: Large-eddy simulation Syngas Impinging flame Chemical kinetic mechanism Vortical structure
a b s t r a c t Numerical simulations using the Large-eddy simulation technique is presented to study the effects of fuel variability on the dynamics of hydrogen and syngas impinging flames. The compositions of CO and H2 are varied in a syngas mixture, including a pure H2 case as the baseline Case 1, 20% CO with 80% H2 for Case 2, 40% CO with 60% H2 for Case 3, and 20% CO with 20% CO2 and 60% H2 for Case 4. The impinging flame configuration has a distance to nozzle diameter ratio of H/d = 20 and the inlet velocity of the fuel is 27 m/s. The fuel is issued from a circular nozzle and mixes with air in a non-premixed configuration. The results show that the flames develop vortical structures in the primary jet associated with the buoyancy and shear layer instability, and the wall jet progresses parallel to the impinging plate forming large-scale vortex rings at different locations and strengths as a consequence of the fuel compositions. A comprehensive analysis of vortical structures in the primary and secondary jet streams, along with a description of their effects on the near-wall heat transfer and instabilities of syngas flames is presented here. Pollutant emissions and species formations are also investigated in order to gain further insight into the syngas burning characteristics for future cleaner combustion systems. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Current combustion systems are moving towards more efficient low carbon technologies in order to reduce greenhouse gas emissions to the atmosphere. Hydrogen- and hydrogen-enriched fuels such as syngas are becoming a real option for future power generation as demonstrated in practical syngas-fired gas turbines [1–3]. However, the dynamics of syngas or synthetic gas flames is not well understood yet due to the differences in the transport and chemical properties of the mixtures with different compositions as well as the interaction of the different chemical kinetics of the species with the flow structures. One of the main concerns in practical applications using syngas is the fuel composition, since the synthetic gas is produced by gasification of different carbon-compounds such as coal, biomass or general waste allowing the existence of fuels with different compositions according to the selected process [3]. Syngas is mainly composed of H2, CO and CO2, together with small amounts of other species such as CH4, N2 or H2O, which introduce some variation in the fuel mixture altering the combustion dynamics [4]. Integrated Gasification Combine Cycle (IGCC) power plants permit limited range of fuel variability because of the in situ fuel ⇑ Corresponding author. E-mail address: [email protected] (D. Mira Martinez). 0016-2361/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.fuel.2012.06.025
gasification. Syngas fuels can be considered as appropriate for this technology although further insight into their burning dynamics needs to be gained. One of the major problems of IGCC plants operating with syngas is the ratio between H2 and CO in the fuel stream, since this parameter can vary from 0.33 up to 2.36 by volume [4], leading to substantial differences and undesired phenomena such as flashback or combustion instabilities [5]. The aim of the present work is to study the effects of fuel variability on a syngas impinging flame configuration corresponding to a laboratory setup. Impinging flows have been studied over the years because of their broad-ranged industrial use for cooling and heating purposes, since the heat and mass transfer rates are enhanced in this configuration [6–10]. The flow develops complex fluid dynamic features involving a stagnation region, a wall boundary layer, small-scale turbulent mixing, jet deflection along with heat transfer to the target plate. Therefore, this configuration provides a useful platform for comparing the dynamics of syngas flames with different H2/CO ratios and to gain better understandings on syngas burning characteristics that might be used for practical burners of future IGCC plants. Numerical simulations of combustion processes are nowadays essential to overcome the limitation of experimental measurement techniques. Advanced numerical techniques such as Direct Numerical Simulation (DNS) or Large-Eddy Simulation (LES) provide comprehensive information about the physical problems and are
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Nomenclature IGCC DNS LES SGS Re u0 d H D Prt Sct St h kc ksgs
Integrated Gasification Combine Cycle Direct Numerical Simulation Large-Eddy Simulation subgrid scale Reynolds number inlet velocity jet nozzle diameter domain height domain diameter turbulent Prandtl number turbulent Schmidt number Strouhal number heat transfer coefficient heat conductivity turbulent kinetic energy in the subgrid scale
viewed as an alternative to experimental analysis for complex fluid dynamic problems. DNS solves directly all the relevant time and length scales of the flow without requiring any model, but it involves such a large amount of computational resources that turbulent flows simulated by DNS in complex geometries are out of reach for many applications. On the other hand, LES only resolves the large scales of the flow, while it models the small scales using closure rules since the small eddies are more isotropic and dissipative [6]. The scale-separation is accomplished using a spatial filter that separates the grid and the subgrid scales, but it leads to the creation of subgrid scale terms in the governing equations. LES requires not only modeling for the subgrid terms due to both momentum and scalar transport [6], but also chemical kinetics of the species involved in the reaction process. It is very important to precisely describe the chemistry taking place in the combustion process, since the flame is largely influenced by the interaction of turbulence and chemical kinetics. As a large number of chemical kinetic mechanisms for syngas and H2/CO mixtures exist in the literature, only a short overview is given here. A more comprehensive analysis can be found in [11–15]. A well-established sub-mechanism with 30 reversible reactions for H2/CO mixtures comprising 11 species is available from the so-called San Diego mechanism [16]. This mechanism has been extensively used for a wide range of combustion applications and it has been reduced by the same authors leading to 16 and 4 steps reduced mechanisms [17]. This reduction process relies on neglecting the reactions that play a minor role in the combustion process along with assuming steady state of some species. A mechanism involving 44 reversible reactions and 13 reactive species has also been developed in order to account for the effects of methane addition on the syngas flame dynamics [18]. This mechanism was validated against experimental data for ignition delay time, laminar flame propagation velocity and species mole fraction in shock tubes and flow reactors. Another mechanism was also developed to incorporate current thermodynamic and transport data. It is based on an extensively used syngas kinetic model of 30 reactions and 14 species [19] but with different rate coefficients and third body efficiencies [20]. Details of different syngas kinetic mechanisms can be found in the aforementioned literature. This work is intended to provide some understanding of the effects of fuel variability on the dynamics of syngas flames. LES of three-dimensional syngas flames impinging on a solid wall was performed in order to study the near-wall heat transfer, vortical structures and combustion instabilities of syngas with different compositions. The next section details the mathematical modeling, governing equations, boundary conditions, numerical approach
T
q t
l I Ym Dm q
mt ~i u xi LETOT FFT
temperature density time dynamic viscosity specific internal energy mass fraction of species – m diffusion coefficient of species –m heat conduction eddy viscosity velocity component – i physical coordinate – i large eddy turnover time fast Fourier transform
and computational domain. Results are then described for both instantaneous and time-averaged quantities. A heat transfer analysis is also performed and a discussion of the instabilities in the flow field is presented, finally some conclusions are drawn. 2. Mathematical modelling 2.1. Governing equations The equations governing chemically reactive flows in combustion problems correspond to the conservation of mass, momentum and energy along with the conservation of the chemically reactive species. In the LES approach, the large eddies containing the major fraction of energy are resolved by the governing equations, while the small eddies that are more isotropic are represented by modeling approaches such as subgrid scale models [6]. This approach follows the Kolmogorov inter-scale energy transfer in which it is assumed that the small scales of the flow have a more universal behaviour and therefore, can be modelled using general assumptions. Modeling these small scales is known as subgrid scale (SGS) modeling and LES accuracy relies on closing the system of governing equations properly. The spatial filter considered in this LES implementation is a box filter D defined by the length scale of each computational cell. Thus, the LES equations are obtained by filtering the full compressible Navier–Stokes equations using the spatial Favre filter.
~j @ q @ qu ¼0 þ @t @xj
ð1Þ
~i @ qu ~j u ~i @ qu @ ¼ ðsij ssgs þ ij Þ þ ðq q1 Þg @xj @t @xj
ð2Þ
~j Y~ m @ qY~ m @ qu @ @ Y~ ¼ qDm m þ @xj @t @xj @xj
!
@/sgs j;m @xj
þ q_ c
ð3Þ
sgs
~ j @qj @ u ~ i sij @hj ~ j~I @ q~I @ qu @u þ ¼ p þ Hsgs þ Q c @t @xj @xj @xj @xj @xj
ð4Þ
In the above equations, the bar denotes the standard LES filtering and variables q, tildeuj , t, xj, s, q1, g, Ym, Dm, q, p, I, Qc denote the density, velocity component, time, spatial coordinate, stress, ambient density, gravity, mass fraction, diffusion coefficient, heat conduction, pressure, internal energy and heat of combustion respectively. The indices i and j go from 1 to 3 and m representing the chemical species go from 1 to 11. Due to the filtering process,
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D. Mira Martinez et al. / Fuel 103 (2013) 646–662 Table 1 Computational cases. Name
Fuel composition (%)
Case Case Case Case
100% H2 80% H2 + 20% CO 60% H2 + 40% CO 60% H2 + 20% CO + 20% CO2
1 2 3 4
some new terms arise from the governing equations representing the effects of the subgrid scale field on the resolved field. These terms are denoted by the superscript SGS, including the subgrid scale stress ssgs in the momentum equation (Eq. (2)), the subgrid mass flux of species Usgs in the species conservation equation j sgs (Eq. (3)) and the subgrid enthalpy flux hj and subgrid dissipation H in the energy equation (Eq. (4)) respectively. Further details about the filtering process can be found in [21]. Note that buoyancy effects have been included to account for the effects of density inhomogeneity on the momentum transport.
pffiffiffiffiffiffiffi
mt ¼ C m ksgs D sgs
@ qk @t
2.2. Subgrid scale modelling Subgrid scale terms account for the effects of the unresolved eddies on the momentum transport, the heat conduction on the subgrid scale and the viscous work, along with the mass flux and mass diffusion of species. The purpose of the SGS models is to express the above terms by means of the resolved fields. The first term that needs to be modelled is the subgrid scale stress term that arises after filtering the Navier–Stokes equation represented by ssgs in the present work.
ssgs ¼ qðu~i u~j u~i u~j Þ
This term corresponds to the stress in the subgrid scale and accounts for the contribution of the unresolved scales to the resolved field. Generally, the net kinetic flux goes from the resolved scales to the small eddies and is associated with the dissipation from the large eddies to the SGS eddies at some specific rates. The most broadly used model for closing the momentum transport in the SGS is the eddy viscosity model or the so-called Smagorinsky model, in which a balance between energy production and dissipation is assumed. This model may not be able to predict the SGS contributions accurately since at high Reynolds numbers, the flow develops a large dynamic range of scales that might be unresolved especially when coarse meshes [22] are used. In this work, a model including the turbulent kinetic energy in the subgrid scale called k D model has been employed in order to account for the local non-equilibrium between the subgrid energy production and dissipation rate [22]. The eddy viscosity mt is obtained using the turbulent kinetic energy in the subgrid scale and thus, a new conservation equation is needed in order to close the system.
ð5Þ
þ
~ j ksgs @ qu @ m @ksgs ¼ Psgs Dsgs þ q t @xj @xj Prt @xj
Psgs ¼ ssgs ij
Dsgs ¼
~i @u @xj
sgs 3=2 Ceq k D
ð6Þ ð7Þ
ð8Þ
ð9Þ
In Eqs. (8) and (9), Psgs and Dsgs denote the production of subgrid turbulent kinetic energy and its dissipation rate respectively, while Cv and Ce are model constants set as 0.067 and 0.916 [6].
Fig. 1. Instantaneous isosurfaces of gas temperature over the computational domain at t = 0.275 s for Cases 1 (a) to 4 (d) respectively.
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Fig. 2. Instantaneous cross-sectional gas temperature plots at two time instants t = 0.250 s (left column), 0.275 s (right column) for Cases 1 (a) to 4 (d) respectively.
Scalar transport in the subgrid scale is modelled using the eddy diffusivity model following a similar approach to the eddy viscosity method already mentioned. In this approach, a gradient diffusion model is used for the subgrid mass and enthalpy fluxes, given as
/sgs i;m ¼ q sgs
hj ¼ q
mt @ Ye m Sct @xi
mt cp @ Te Prt @xj
ð10Þ
ð11Þ
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In the above equations, Prt and Sct represent the turbulent Prandtl and Schmidt number respectively, while T stands for the temperature and cp is the specific heat of the gas. 2.3. Finite-rate chemistry Finite-rate chemical kinetics for syngas flames is included in the LES governing equations for the reaction rates in the species governing equations. Since a governing equation for each species in each computational cell has to be solved, large number of reactive species leads to large amount of computational costs. To avoid the prohibitive computational costs, reduced mechanisms are used in this work to solve this three-dimensional (3D) reacting flow problem. A well-established 4-step mechanism for syngas combustion [17] was considered in the current application. This mechanism involves 11 species from which 7 are considered reactive (O2, H2, H2O, H, HO2, CO, CO2), 3 species (O, OH and HCO) are considered reactive but steady state is assumed for them, and finally N2 that is used as an inert non-reactive species. The seven reactive species taking part in the 4 steps reversible mechanism are described by Eqs. (12.a), (12.b), (12.c), (12.d).
2.4. Numerical approach LES was performed using a parallel unstructured finite-volume code that solves the time-dependent governing equations. The time integration incorporates an automatic timestep control that calculates the minimum timestep required by the chemistry in which the heat release should not exceed a fraction of the internal energy of the cell, diffusion stability, and rate of strain tensor to limit the cell distortion to some extent. The spatial differencing is computed in two stages defined as Lagrangian and Eulerian phases [23]. The Lagrangian phase includes the computation of the pressure in which a Poisson equation is solved, diffusion of mass, momentum and energy, and the equation of state by the modified conjugate residual method following a modified SIMLE algorithm. Consecutively, the convective transport of the scalar variables is computed in the Eulerian phase of the method using a quasi-second order upwind differencing approach. No stability restrictions, but numerical accuracy is considered in time-marching the solution, since the diffusion calculation is performed implicitly while the convection is subcycled.
3. Physical problem I
3H2 þ O2 $ 2H2 O þ 2H
ð12:aÞ 3.1. Computational domain and boundary conditions
I
2H þ M $ H2 þ M III
H2 þ O2 $ HO2 þ H IV
CO þ H2 O $ CO2 þ H2
ð12:bÞ ð12:cÞ ð12:dÞ
This reduced model gives the global reaction rates wI to wIV from a combination of elementary reaction rates of the San Diego kinetic mechanism and includes a correction for autoignition. Further details can be found in the literature [16].
The computational setup corresponds to a laboratory burner located at Sheffield University, UK and joint efforts are being made to compare the experimental and numerical results. The application addressed here is a non-premixed syngas jet flame with different compositions impinging on a solid plate. The geometry of the burner is characterized by one single round nozzle of d = 0.46 cm and an impinging distance measured from nozzle to plate of 10 cm. The jet issues at 2700 cm/s with a Reynolds number Re = 1300 based on the inflow conditions. The computational domain is setup as a three-dimensional cylindrical region above the jet nozzle plane with 10 cm length in the streamwise direction,
Fig. 3. Instantaneous cross sectional velocity vector field at t = 0.250 s for Cases 1 (a) to 4 (d) respectively.
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Fig. 4. Gas temperature profiles at three axial locations Z = 6 cm (left column), 8 cm (middle column), 9 cm (right column) and at two time instants t = 0.250 s (a–c) and 0275 s (d–f).
whereas the diameter of the computational domain is defined as D = 30 cm, allowing the jet to spread out in the radial direction. A grid-dependence test analysis was carried out using three different meshes of 0.8, 1.2 and 1.4 million hexahedral cells. No substantial differences were observed between the results obtained with the
intermediate and finest grid and therefore, all the results shown in the present study correspond to the 1.2 million grid points. No-slip boundary conditions were employed on the solid walls, whereas open boundary conditions were considered in the radial direction of the domain.
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Fig. 5. Axial velocity profiles at three axial locations Z = 6 cm (left column), 8 cm (middle column), 9 cm (right column) and at two time instants t = 0.250 s (a–c) and 0.275 s (d–f).
3.2. Computational cases The computational cases considered represent 4 different fuels in which the concentration changes from Case 1 to Case 4. Case 1 corresponds to a pure hydrogen flame, whereas Cases 2–4 represent
syngas mixtures with different compositions. All four cases were setup under the same inflow conditions in order to perform a fair comparison. The cases were selected in order to analyse the effects of the H2/CO ratio of the fuel stream on the flame dynamics. The four computational cases are summarised in Table 1.
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Fig. 6. Axial velocity profiles along the centerline (a) and shear layer X = 0.5 cm (b) at t = 0.275 s.
Fig. 7. Instantaneous contour plots of H2O Mass Fraction at t = 0.275 s for Cases 1 (a) to 4 (d) respectively.
4. Results 4.1. Instantaneous results Sample instantaneous three-dimensional results of the four impinging flames are presented in Fig. 1 by isosurfaces of temperature up to 600 K (this low temperature range was chosen to show clearly the near-wall structures in the graph) at the time instant t = 0.275 s in one half of the computational domain and the velocity vector field over the cutting plane. This figure shows very clearly the existence of vortex rings in the near-wall region after the jet is deflected by the solid wall. These vortex rings are formed parallel to the plate and develop downstream in the radial direction. The 3D results presented here show the two main regions formed in the flow field: the primary jet region before the jet flow
touches the impinging wall and the wall jet region, in which vortex rings are created and a boundary layer appears as the flow develops in the radial direction after the impingement. Instantaneous cross-sectional snapshots of gas temperature at two different time instants are shown in Fig. 2 for the four computational cases. These two plots correspond to two different stages in which the flame jet is already developed. The instants represented by labels (a.1–d.1) at t = 0.250 s are representative of the stage in which the flame develops a Kevin-Helmholtz type shear layer instability in the primary jet region because of the jet momentum and buoyancy acceleration, while the instant t = 0.275 s, labeled as (a.2–d.2), is representative of the stage when the flames are developing or about to spread along the surface of the solid plate. The figure shows that as the jet issues from the nozzle, all the flames develop a primary jet with some bulges
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Fig. 8. Radial OH radical profiles at three axial locations Z = 6 cm, 8 cm, 9 cm at t = 0.250 s.
before touching the plate. In this region, the flames develop vortical structures in the shear layer that are convected downstream. However, as the jet approaches the impinging wall, the axial velocity is reduced and the static pressure increases in this region leading to the deflection of the jet in the radial direction. A wall boundary layer is formed in the near-wall region that will be analysed in more detail subsequently. From Fig. 2, it is seen that the overall structures of the flames are similar although some differences can be appreciated. As the hydrogen concentration of the fuel is changed, the flame thickness, surface area and flame propagation speed all vary slightly, which is associated with the effects of fuel variability. The instantaneous cross-sectional velocity vector field for the four computational cases is presented in Fig. 3 at t = 0.250 s. In order to trace the movements of fluid particles in the flow field and to locate more precisely the vortical structures, streamlines are plotted on the half-plane X > 0 of Fig. 3. The thick red lines indicate the starting points of the streamtraces. These plots, together with Fig. 2, show that the increase of CO with respect to H2 leads to more strained and vortical flames. A change in the hydrogen composition of the syngas fuel also slightly changes the flame burning speed, which causes different spreading of the jet as observed in the vortex ring displacement in the radial direction. The movement of the jet in the streamwise direction is affected by the buoyancydriven entrainment that squeezes and expands the flame at different axial locations [21]. In the wall boundary layer, the impinging
flow progresses into a radial wall-jet formation as a consequence of the jet deflection and the axial momentum of this secondary jet decay as it spreads radially. In order to further analyse the differences in flame dynamics between the four cases, radial profiles of different scalars and velocity components are presented subsequently. The gas temperature profiles at different axial locations at two time instants are firstly displayed in Fig. 4. The plot shows that the four fuel mixtures have similar overall trend with different values of peak temperature according to the mixture composition. The highest temperature peaks are reached in Case 1 with the maximum H2 content. As the mixture becomes H2-leaner, the temperature peaks are reduced and the lowest temperature peak is observed in Case 4, in which part of CO was substituted by CO2. The reason for the lower burning temperature of this case is associated with the low specific heat and high molecular weight of CO2, leading to the lower burning speed of the mixture [24]. It can also be noticed that when CO2 is increased in the syngas mixture (Case 4), the peak of temperature drops slightly compared to Case 3, but not as much as it would be expected because CO2 acts as a diluent in the mixture and it is a highly radiation-participating species [25]. As radiation effects were not considered in the simulations, the radiative heat losses that might reduce the flame temperature and burning speed were not taken into account. It is evident that the differences between the cases are larger at the downstream location Z = 9 cm where the vortical structures formed in the nearwall region have perturbed the temperature distributions. These
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Fig. 9. Radial H radical profiles at three axial locations Z = 6 cm, 8 cm, 9 cm at t = 0.250 s.
structures enhance the air entrainment and mixing affecting the local heat transfer and flame dynamics as seen in the plots. Fig. 5 shows axial velocity profiles in the radial direction at different streamwise locations for all the cases. This plot shows how the axial velocity changes as a consequence of variation in fuel compositions. Unlike the temperature distribution, the maximum axial velocity appears around the jet centerline and then decays in the radial direction, where the shear layer of the primary jet stream does not exhibit peaks as those shown in the temperature profiles. Note that the dissimilarities in the axial velocity component between the syngas mixtures are not very significant, especially for the peak values in the centerline. The axial velocity of the jets is controlled mainly by the momentum inertia and buoyancy induced by density inhomogeneity which are similar for all cases, where the differences in chemical reactions and transport associated with different fuel compositions are not playing a major role. This effect can be more clearly seen in Fig. 6, in which the axial velocity profile along the centerline and its profile at X = 0.5 cm where the reactive shear layer is formed are presented for further comparison. The profiles presented in Fig. 6 indicate that the flame axial velocity is strongly affected by the combustion heat-release and flow vortical structures in the jet shear layers, which also affect the velocity along the centerline but to a less extent. The axial velocity pattern along the centerline is similar for all the fuel mixtures and it is characterized by velocity decreases in the streamwise
direction with small variations near the jet nozzle exit and large decreases near the impinging wall. The differences in the centreline axial velocity profiles seen in Fig. 6a might be associated with the different diffusivity and viscosity of the fuel mixtures. Although buoyancy accelerated the flow in the shear layer up to the location of Z = 6 cm, it did not lead to an acceleration of flow speed in the jet centreline. As the jet approaches the impinging wall, the velocity reduces sharply to zero. For the axial velocity of the flame jet 0.5 cm away (X = 0.5 cm) from the jet centerline, it presents some interesting features. At this location, the velocity is very low at upstream locations (Z < 0.4 cm) and a small recirculation zone is created due to the jet expansion at the nozzle exit and the presence of the nozzle wall. Slightly further downstream at Z = 0.4 cm, the velocity starts increasing because of the acceleration associated with the buoyancy induced by the heat-release in the shear-layer. The jet also spreads from the axial location Z > 0.5 cm onwards and the velocity is largely affected by the vortical structures formed in this region as seen in the large velocity variations shown in Fig. 6b. These velocity variations which are associated with the Kevin-Helmholtz type-shear layer and buoyancy of the flow are highly dynamic and correspond to the formation of large budges in the primary jet stream as seen in Fig. 3. These profiles show that the axial velocity along the shear-layer is increased by combustion heat-release and the H2-richer flames experience higher acceleration because of the larger buoyancy effects and the lower viscosity of the mixture.
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Fig. 10. Radial H2O species profiles at three axial locations Z = 6 cm, 8 cm, 9 cm at t = 0.250 s.
The interaction between the large vortical structures in the primary jet stream and chemical kinetics is now examined. Fig. 7 shows contour plots of the mass fraction of H2O that can be used as an indicator of regions where chemical reactions take place. The shear layers correspond to regions of intense chemical reactions and therefore regions with large heat release, especially at upstream locations as those observed by the proximity of the lines of the contour plots in that region. As the jet develops downstream, most of the fuel is already consumed and the intensity of the chemical reactions is reduced in the developing zone of the flow (see from Z = 6 cm to 9 cm). The plots also reveal some interesting near-wall features. A second region of high chemical activity takes place near the vortex ring located at the wall boundary layer. This region with intense combustion reactions is caused by the transport of fuel from the fuel jet by vortical structures in the near-wall region. In syngas mixtures, when hydrogen content is reduced, the fuel mixture becomes less diffusive. Comparing the different cases shown in Fig. 7, it can be seen that the flames tend to be more elongated as the fuel mixture becomes H2-leaner. It can also be observed that when the hydrogen content is high, large amounts of H2O are formed slightly further away from the jet centreline near the stagnation zone in the shear layer as a consequence of the diffusive characteristics of the hydrogen compound. Fuel variability not only affects flame dynamics but also leads to different species and pollutant formations. Figs. 8–11 show radial profiles of radicals H and OH, as well as radial profiles of pollutant CO2 and the formation of H2O respectively. The radical OH that acts
as an intermediate species in syngas and hydrogen combustion is shown in Fig. 8. At upstream locations where the flame is not largely affected by vortical structures and air entrainment, the radical OH profile peaks at the stoichiometric region in which the flame is located. Its formation occurs in regions of high temperature as seen in the plot. Further downstream, the syngas mixtures (Cases 2–4) attain higher concentration of OH than that for Case 1. The reasons for this difference are associated with the unsteady behaviour of these flames in this region and the fact that the burning speed of the hydrogen flame is higher than the syngas mixtures, leading to a different stage of development for the H2 flame. This effect can be seen in Fig. 8c where the peak of Case 1 flame is displaced radially away from the geometric centerline. The radical OH is an intermediate species of great importance in the branching process [26] and it is directly involved in the production of H and O radicals as well as in the production of CO2 from CO. This species is assumed to be in steady state in the finite-rate mechanism considered in the present study and it might have been under-predicted [11]. Fig. 9 shows the distributions for radical H at three axial locations. The H content is lower when the CO content is increased as that occurred for the OH radical. This is due to the mechanisms governing the formation of H, OH and O. These radicals are mainly produced from the dissociation of H2. As the mixture becomes H2leaner, a reduction in the formation of these radicals is achieved decreasing the flame temperature. Plots for radical O also have similar behaviour but are not presented here for brevity. Profiles
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Fig. 11. Radial CO2 species profiles at three axial locations Z = 6 cm, 8 cm, 9 cm at t = 0.250 s.
Fig. 12. SGS wall turbulent kinetic energy profiles at two time instants t = 0.250 s and 0.275 s.
of the contents of products H2O and CO2 are shown in Figs. 10 and 11. Fig. 10 demonstrates that the formation of water vapour follows a similar trend for the four fuel compositions under investigation. However, it can be seen that the formation of H2O is enhanced in the pure hydrogen flame but not as much as it would be expected, since this species is produced by reactions involving OH and HO2
radicals interacting with HCO or H2. The figure indicates that the reaction involving HCO becomes predominant when the H2 content is low. It is shown that the H2O mass fraction is not reduced dramatically when the H2 mass is reduced from the fuel stream. Fig. 11 shows the CO2 mass fraction at three axial locations. The figure reveals some interesting features. Firstly, it is seen that the CO2
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Fig. 13. Radial profiles of RMS of the axial velocity fluctuations at three axial locations Z = 6 cm, 8 cm, 9 cm.
Fig. 14. Mean wall normal and shear stresses for Cases 1–4.
formation increases as the CO concentration is increased in the fuel stream, since CO2 is produced in the oxidation of CO or comes as an unburnt species in the case of the diluted fuel (Case 4). Moreover, the profiles show that the diluted fuel mixture (Case 4) produces less CO2 than the non-diluted syngas mixtures. The addition of CO2 in the fuel stream not only reduces the flame burning speed and gas temperature but also affects the chemical kinetics of the
reaction. The chemical reactivity of CO2 in the 4-step reduced mechanism considered in the simulations is taken into account in a backward reaction (reaction 22 of the San Diego mechanism [16] CO + OH CO2 + H) used to compute step IV of the finite-rate chemical kinetic mechanism. However, results of Fig. 11 suggest that this step is less important than the combination of the same forward step with the remaining reactions of CO conversion leading
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Fig. 15. Mean radial velocity of the wall-jet at different radial locations for Cases 1–4.
Fig. 16. Instantaneous t = 0.250 s and time-averaged Nusselt number distribution at the solid wall for Cases 1–4.
to the current results in which the production of CO2 is reduced by diluting the fuel with carbon dioxide. The difference in the formation of the pollutant CO2 is not large and it is around 5% at all axial locations. Fig. 12 shows the subgrid scale turbulent kinetic energy (ksgs) at the wall for the four computational cases considered at two time instants. From Fig. 12, it is evident that the hydrogen flame (Case 1) produces less SGS turbulent kinetic energy than the three syngas flames due to the fact that the syngas flames are more vortical and more wrinkled (see Figs. 2 and 3). This can be mainly attributed to the high diffusivity of hydrogen. As the hydrogen content is reduced, the flame becomes more vortical and the SGS turbulent kinetic energy increases (see Fig. 12). The plot shows that Cases 2–4 develop higher peaks of SGS turbulent kinetic energy along with extended region of high SGS turbulent energy at the wall. 4.2. Time-averaged results Time-averaged results were calculated after 20 LETOT or large eddy turnover times in order to eliminate the effect of the initial conditions on the flow field. The flow field was averaged for an additional 40 LETOTs [27]. The averaging interval selected LETOT is defined as the ratio between the inlet velocity and the domain
diameter (LETOT = D/u0) and provides a reasonable estimation on the statistical quantities and Reynolds stresses. As this work is focused on the near flow field, 40 LETOTs are considered sufficient although some variables might not be fully converged. Fig. 13 shows the root mean square (RMS) of the axial velocity fluctuations at three axial locations. The profiles indicate that the hydrogen flame (Case 1) is less vortical than the syngas flames (Cases 2– 4) and therefore it develops less fluctuation even though the axial velocity is slightly higher than the other cases. Mean normal and shear stresses of the flow at the wall also provide important information about the flow in the wall boundary layer (see Fig. 14). These profiles show that as the flames become H2-leaner (Cases 1–3), the magnitude of the stresses decreases because of the larger acceleration of the secondary jet in the boundary layer experienced by fuels with high hydrogen content. The presence of CO and CO2 reduces the stresses of the wall-bounded jet as seen in the figure. In order to further analyse the fluid dynamic behaviour of the wall-jet in these flames, mean radial velocity profiles are presented in Fig. 15 at different radial locations of the boundary layer for the all flames. This allows an appreciation of the development of the secondary jet and the effects of fuel variability on the flow development in this region. This figure shows that the mixture containing larger amounts of H2 in the fuel stream
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Fig. 17. Fourier spectra of the axial velocity at axial locations Z = 5 cm, 7 cm and 9 cm for Cases 1–4.
develops a secondary jet with larger acceleration than the mixtures with lower amounts of H2 or the one including CO2. The wall-jet acceleration comes from the expansion of the fluid due to heat release, and radial jet momentum. Syngas mixtures with low hydrogen content demonstrate lower flame temperatures because of the lower heat released from the combustion process, leading to secondary jets with smaller radial acceleration. This reduction in the acceleration of the wall-jet is enlarged by the higher molecular weight of the mixtures with carbon compounds. 4.3. Heat transfer analysis Heat transfer is of crucial importance in practical applications especially for gas turbines and combustion engines. The dynamics of impinging flames are strongly affected by the presence of the solid wall while the heat flux from the flame to the wall affects the performance of the wall materials. This flame/wall interaction could lead to overheating of the burner and a reduction of lifespan or even damage of the device. Heat transfer can be measured by the Nusselt number, which gives a quantification of the temperature gradient by fluid properties only, decoupling the problem from solid properties. The Nusselt number is defined by the convective heat transfer coefficient, the jet nozzle diameter and the gas thermal conductivity as follows:
Nu ¼
hd kc
ð13Þ
The heat transfer coefficient ‘h’ in the Fourier form is written as:
h¼
kc @T T jet T wall @~ n
ð14Þ
where ~ n represents the wall normal direction. Instantaneous and time-averaged Nusselt number profiles at the solid wall are shown in Fig. 16 for the four cases. From the Nusselt number that measures the heat transfer from the fluid to the wall, it can be observed that increasing the CO content reduces the heat transfer of the flame in this configuration. However, it is worth noting that the four flames corresponding to the four fuel mixtures have different thermal loads, since only composition was changed from one to another while the flow conditions were not adjusted to maintain a constant thermal load. Therefore, absolute values in this figure cannot be strictly compared in terms of flame/wall interactions. On the other hand, the Nusselt profiles present some patterns that are worth analysing. Fig. 16a shows that the Nusselt profile develops several peaks related to vortical structures shedding from the primary jet, one main peak that remains near the stagnation zone and a second peak nearby with higher value of Nusselt number for Cases 1 and 2, together with a peak downstream in the radial direction [11]. Spatial variations in the instantaneous Nusselt number distribution are evident. For Cases 1 and 2, three peaks can be observed. As the concentration of CO is increased in the fuel mixture, the second peak begins to reduce its intensity and to merge into the main peak near the stagnation zone until only one large peak is present in Cases 3 and 4. The effect of CO2 is almost negligible because the thermal load for these two mixtures (Cases 3 and 4) is practically the same. As Case 4 is obtained by replacing the amount of CO by CO2, this dilution does
D. Mira Martinez et al. / Fuel 103 (2013) 646–662
not lead to substantial changes on the heat transfer profiles at the wall as seen in the plots of Fig. 16. 4.4. Flame jet instabilities In order to further analyse the flame dynamics, several monitoring points are defined within the computational domain to record the velocity signals. These time traces are used to identify the frequency of the vortical structures caused by buoyancy and Kevin-Helmholtz type shear layer instabilities of these flames. The frequency is obtained from the time history of the simulation results via the fast Fourier transform (FFT) for a statistically stationary random process [28] and is displayed in Fig. 17 for a spectral analysis. The FFT results show that all the flames experience periodic and oscillatory behaviour as seen by the peaks of the spectrum. At upstream locations, all the flames have a characteristic frequency of 24 Hz (Strouhal number St = 0.4) which corresponds to the frequency of the instability in the jet shear-layer. This periodic behaviour is caused by the Kevin-Helmholtz shearlayer instability of the jet momentum and it is not related to the fuel composition as shown in Fig. 17a. However, it is worth noting that the instability is affected by the H2 content, since the buoyancy forces acting on the jet are greater for low density fuels because of the large density inhomogeneity. This enhances the effects of the instability. Downstream at Z = 7 cm and 9 cm, the spectra for all the cases contain not only the main peak at 24 Hz, but also peaks corresponding to both higher and lower frequencies. These peaks are associated with the vortex shedding in the nearwall region interacting with the large-scale vortices in the jet shear-layer. The plots (b) and (c) show that the amplitudes of instability of the structures in the wall region are almost as large as those of the structures in the jet shear layer described above. The results shown in Fig. 17 indicate that the fuel variability in this application only affects the amplitudes of the instabilities without affecting the characteristic frequencies, since all the syngas mixtures present the same characteristic frequencies. 5. Conclusions LES of four impinging jet flames of hydrogen and syngas fuels with different compositions was carried out in the present work for a laboratory setting-up configuration. A well-established four step reduced mechanism is used to account for the finite-rate chemistry of the syngas flames, whereas the k-D model is employed to compute the subgrid scale stress in the momentum equation. The results reveal that the flames develop large-scale vortical structures in the shear-layer of the primary jet, independent from the fuel composition. These structures are shed from the jet shear-layer and impact the impinging wall affecting the behaviour of the near-wall flame and its heat transfer characteristics. This work provides some understanding on the burning characteristics of syngas flames and the effects of fuel variability on the dynamics of impinging jet flames. Syngas mixtures with lower H2/CO ratio develop more unsteady and vortical flow in the wall region with smaller flame thickness. The flame surface is reduced due to the low diffusivity and relatively high viscosity of the fuel, leading to more strained and vortical flames. The flame burning speed is reduced for H2-leaner mixtures due to the difference in transport properties of the mixture. Time-averaged radial profiles of the secondary jet also confirmed the same trend. The temperature and velocity field are strongly affected by fuel variability. For H2-richer mixtures, larger temperature peaks and larger acceleration by heat-release are observed, especially in the jet shear-layers. Regions with lower chemical activity show similar characteristics in both temperature and
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axial velocity, since these differences are only associated with the physical properties of the mixtures. An analysis of the species formation was performed to further understand the flame dynamics under mixtures with different compositions. It is shown that the formation of radicals is reduced for H2-leaner mixtures leading to lower temperature peaks, while the formation of water vapour is not associated with the H2 content only, since fuels with lower hydrogen lead to similar amount of H2O formation. Besides, it is also presented that the pollutant CO2 can be reduced by diluting the fuel with CO2. An analysis of the turbulence characteristics suggests higher production of subgrid scale turbulent kinetic energy in the wall boundary layer for flames with lower H2 content, which are more vortical and wrinkled. Further investigations should be performed on this issue to fully understand the performance of the subgrid scale models as well as the effects of fuel compositions on the turbulence characteristics. The Nusselt number analysis shows that the H2/CO ratio affects the heat transfer performance of the flames, altering the wall heat-transfer characteristics. Finally, the analysis of the flow instabilities indicates that all flames have a characteristic frequency of 24 Hz (St = 0.4) corresponding to the frequency of the instability in the jet shear-layer. The amplitude of the instability is magnified with the H2 content, while the characteristic frequency remains unchanged. This work provides some understanding on the burning characteristics of syngas flames and the effects of fuel variability on the dynamics of impinging jet flames. References [1] Kim YS, Lee JJ, Kim TS, Sohn JL. Effects of syngas type on the operation and performance of a gas turbine in integrated gasification combined cycle. Energy Convers Manage 2011;52(5):2262–71. [2] Littlejohn D, Cheng RK, Noble DR, Lieuwen T. Laboratory investigations of lowswirl injectors operating with syngases. J Eng Gas Turbines Power 2010; 132(5):1–8. [3] Dam B, Corona G, Hayder M, Choudhuri A. Effects of syngas composition on combustion induced vortex breakdown (CIVB) flashback in a swirl stabilized combustor. Fuel 2011;90(11):3274–84. [4] Natarajan J, Lieuwen T, Seitzman J. Laminar flame speeds of H2/CO mixtures: effect of CO2 dilution, preheat temperature, and pressure. Combust Flame 2007;151:104–19. [5] Dam B, Love N, Choudhuri A. Flashback propensity of syngas fuels. Fuel 2011;90(2):618–25. [6] Menon S, Yeung P-K, Kim W-W. Effect of subgrid models on the computed interscale energy transfer in isotropic turbulence. Comput. Fluids 1996;25: 165–80. [7] Zuckerman N, Lior N. Jet impingement heat transfer: physics, correlations, and numerical modeling. Adv Heat Trans 2006;39:565–631. [8] Hadziabdic M, Hanjalic K. Vortical structures and heat transfer in a round impinging jet. J Fluid Mech 2008;596:221–60. [9] Mishra DP. Emission studies of impinging premixed flames. Fuel 2004;83(13): 1743–8. [10] Jiang X, Zhao H, Luo KH. Direct computation of perturbed impinging hot jets. Comput Fluids 2007;36:259–72. [11] Mira Martinez D, Jiang X. Numerical investigations of a hydrogen impinging flame with reduced and detailed chemical kinetics. In: International conference on applied energy (ICAE), Suzhou, China; 2012. p. A10185. [12] Gokulakrishnan P, Kwon S, Hamer AJ, Klassen MS, Roby RJ. Reduced kinetic mechanism for reactive flow simulation of syngas/methane combustion at gas turbine conditions’. Combust Fuels Educ 2006;1:513–21. [13] Chaos M, Burke MP, Ju Y, Dryer FL. Syngas chemical kinetics and reaction mechanisms. In: Lieuwen TC, Yang V, Yetter RA, editors. Shynthesis gas combustion: fundamentals and applications. Taylor and Francis; 2009. p. 29–70 [chapter 2]. [14] Chaos M, Dryer FL. Syngas combustion kinetics and applications. Combust Sci Technol 2008;180(6):1053–906. [15] Marzouk OA, Huckaby ED. A comparative study of eight finite-rate chemistry kinetics for CO/H2 combustion. Eng Appl Comput Fluid Mech 2010;4(3): 331–56. [16] Mechanical and Aerospace Engineering (Combustion Research). University of California at San Diego. Chemical-kinetic mechanisms for combustion applications, San Diego mechanism.. [17] Boivin P, Jiménez C, Sánchez AL, Williams FA. A four step reduced mechanism for syngas combustion. Combust Flame 2011;158:1059–63. [18] Starik AM, Titova NS, Sharipov AS, Kozlov VE. Syngas oxidation mechanism. Combust Explos Shcok Waves 2010;46(5):491–506.
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[19] Mueller MA, Yetter RA, Dryer FL. Flow reactor studies and kinetic modeling of the H2/O2/NOX and CO/H2O/O2/NOX reactions. Int J Chem Kinet 1999;31: 705–24. [20] Davis SG, Joshi AV, Wang H, Egolfopoulos F. An optimized kinetic model of H2/ CO combustion. Proc Combust Inst 2005;30:1283–93. [21] Mira Martinez D, Jiang X. Large-eddy simulations of unsteady hydrogen annular flames. Comput Fluids. doi: 10.1016/j.compfluid.2012.01.013. [22] Fureby C. On subgrid scale modeling in large eddy simulations of compressible fluid flow. Phys Fluids 1996;8:1301–11. [23] Torres DJ, Trujillo MF. KIVA-4: An unstructured ALE Code for compressible gas flow with sprays. J Comput Phys 2006;219:943–75. [24] Berta P, Aggarwal SK, Puri IK. An experimental and numerical investigation of n- heptane/air counterflow partially premixed flames and emission of NOx and PAH species. Combust Flame 2006;145:740–64.
[25] Kim JS, Park J, Bae DS, Vu TM, Ha JS, Kim TK. A study on methane-air premixed flames interacting with syngas-air premixed flames. Int J Hydrogen Energy 2010;35(3):1390–400. [26] Saxena P, Williams FA. Testing a small detailed chemical-kinetic mechanism for the combustion of hydrogen and carbon monoxide. Combust Flame 2006;145:316–23. [27] Ciofalo M. Large-eddy simulations of turbulent flow with heat transfer in simple and complex geometries using Harwell-FLOW3D. Appl Math Model 1996;20:262–71. [28] Pope SB. Turbulent flows. Cambridge University Press; 2000. p. 683–696.
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Fuel journal homepage: www.elsevier.com/locate/fuel
Numerical investigation of the effects of fuel variability on the dynamics of syngas impinging jet flames D. Mira Martinez a,⇑, X. Jiang a, C. Moulinec b, D.R. Emerson b a b
Engineering Department, Lancaster University, Lancaster LA1 4YR, UK STFC Daresbury Laboratory, Warrington, Cheshire WA4 4AD, UK
a r t i c l e
i n f o
Article history: Received 5 April 2012 Received in revised form 4 June 2012 Accepted 6 June 2012 Available online 25 June 2012 Keywords: Large-eddy simulation Syngas Impinging flame Chemical kinetic mechanism Vortical structure
a b s t r a c t Numerical simulations using the Large-eddy simulation technique is presented to study the effects of fuel variability on the dynamics of hydrogen and syngas impinging flames. The compositions of CO and H2 are varied in a syngas mixture, including a pure H2 case as the baseline Case 1, 20% CO with 80% H2 for Case 2, 40% CO with 60% H2 for Case 3, and 20% CO with 20% CO2 and 60% H2 for Case 4. The impinging flame configuration has a distance to nozzle diameter ratio of H/d = 20 and the inlet velocity of the fuel is 27 m/s. The fuel is issued from a circular nozzle and mixes with air in a non-premixed configuration. The results show that the flames develop vortical structures in the primary jet associated with the buoyancy and shear layer instability, and the wall jet progresses parallel to the impinging plate forming large-scale vortex rings at different locations and strengths as a consequence of the fuel compositions. A comprehensive analysis of vortical structures in the primary and secondary jet streams, along with a description of their effects on the near-wall heat transfer and instabilities of syngas flames is presented here. Pollutant emissions and species formations are also investigated in order to gain further insight into the syngas burning characteristics for future cleaner combustion systems. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Current combustion systems are moving towards more efficient low carbon technologies in order to reduce greenhouse gas emissions to the atmosphere. Hydrogen- and hydrogen-enriched fuels such as syngas are becoming a real option for future power generation as demonstrated in practical syngas-fired gas turbines [1–3]. However, the dynamics of syngas or synthetic gas flames is not well understood yet due to the differences in the transport and chemical properties of the mixtures with different compositions as well as the interaction of the different chemical kinetics of the species with the flow structures. One of the main concerns in practical applications using syngas is the fuel composition, since the synthetic gas is produced by gasification of different carbon-compounds such as coal, biomass or general waste allowing the existence of fuels with different compositions according to the selected process [3]. Syngas is mainly composed of H2, CO and CO2, together with small amounts of other species such as CH4, N2 or H2O, which introduce some variation in the fuel mixture altering the combustion dynamics [4]. Integrated Gasification Combine Cycle (IGCC) power plants permit limited range of fuel variability because of the in situ fuel ⇑ Corresponding author. E-mail address: [email protected] (D. Mira Martinez). 0016-2361/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.fuel.2012.06.025
gasification. Syngas fuels can be considered as appropriate for this technology although further insight into their burning dynamics needs to be gained. One of the major problems of IGCC plants operating with syngas is the ratio between H2 and CO in the fuel stream, since this parameter can vary from 0.33 up to 2.36 by volume [4], leading to substantial differences and undesired phenomena such as flashback or combustion instabilities [5]. The aim of the present work is to study the effects of fuel variability on a syngas impinging flame configuration corresponding to a laboratory setup. Impinging flows have been studied over the years because of their broad-ranged industrial use for cooling and heating purposes, since the heat and mass transfer rates are enhanced in this configuration [6–10]. The flow develops complex fluid dynamic features involving a stagnation region, a wall boundary layer, small-scale turbulent mixing, jet deflection along with heat transfer to the target plate. Therefore, this configuration provides a useful platform for comparing the dynamics of syngas flames with different H2/CO ratios and to gain better understandings on syngas burning characteristics that might be used for practical burners of future IGCC plants. Numerical simulations of combustion processes are nowadays essential to overcome the limitation of experimental measurement techniques. Advanced numerical techniques such as Direct Numerical Simulation (DNS) or Large-Eddy Simulation (LES) provide comprehensive information about the physical problems and are
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Nomenclature IGCC DNS LES SGS Re u0 d H D Prt Sct St h kc ksgs
Integrated Gasification Combine Cycle Direct Numerical Simulation Large-Eddy Simulation subgrid scale Reynolds number inlet velocity jet nozzle diameter domain height domain diameter turbulent Prandtl number turbulent Schmidt number Strouhal number heat transfer coefficient heat conductivity turbulent kinetic energy in the subgrid scale
viewed as an alternative to experimental analysis for complex fluid dynamic problems. DNS solves directly all the relevant time and length scales of the flow without requiring any model, but it involves such a large amount of computational resources that turbulent flows simulated by DNS in complex geometries are out of reach for many applications. On the other hand, LES only resolves the large scales of the flow, while it models the small scales using closure rules since the small eddies are more isotropic and dissipative [6]. The scale-separation is accomplished using a spatial filter that separates the grid and the subgrid scales, but it leads to the creation of subgrid scale terms in the governing equations. LES requires not only modeling for the subgrid terms due to both momentum and scalar transport [6], but also chemical kinetics of the species involved in the reaction process. It is very important to precisely describe the chemistry taking place in the combustion process, since the flame is largely influenced by the interaction of turbulence and chemical kinetics. As a large number of chemical kinetic mechanisms for syngas and H2/CO mixtures exist in the literature, only a short overview is given here. A more comprehensive analysis can be found in [11–15]. A well-established sub-mechanism with 30 reversible reactions for H2/CO mixtures comprising 11 species is available from the so-called San Diego mechanism [16]. This mechanism has been extensively used for a wide range of combustion applications and it has been reduced by the same authors leading to 16 and 4 steps reduced mechanisms [17]. This reduction process relies on neglecting the reactions that play a minor role in the combustion process along with assuming steady state of some species. A mechanism involving 44 reversible reactions and 13 reactive species has also been developed in order to account for the effects of methane addition on the syngas flame dynamics [18]. This mechanism was validated against experimental data for ignition delay time, laminar flame propagation velocity and species mole fraction in shock tubes and flow reactors. Another mechanism was also developed to incorporate current thermodynamic and transport data. It is based on an extensively used syngas kinetic model of 30 reactions and 14 species [19] but with different rate coefficients and third body efficiencies [20]. Details of different syngas kinetic mechanisms can be found in the aforementioned literature. This work is intended to provide some understanding of the effects of fuel variability on the dynamics of syngas flames. LES of three-dimensional syngas flames impinging on a solid wall was performed in order to study the near-wall heat transfer, vortical structures and combustion instabilities of syngas with different compositions. The next section details the mathematical modeling, governing equations, boundary conditions, numerical approach
T
q t
l I Ym Dm q
mt ~i u xi LETOT FFT
temperature density time dynamic viscosity specific internal energy mass fraction of species – m diffusion coefficient of species –m heat conduction eddy viscosity velocity component – i physical coordinate – i large eddy turnover time fast Fourier transform
and computational domain. Results are then described for both instantaneous and time-averaged quantities. A heat transfer analysis is also performed and a discussion of the instabilities in the flow field is presented, finally some conclusions are drawn. 2. Mathematical modelling 2.1. Governing equations The equations governing chemically reactive flows in combustion problems correspond to the conservation of mass, momentum and energy along with the conservation of the chemically reactive species. In the LES approach, the large eddies containing the major fraction of energy are resolved by the governing equations, while the small eddies that are more isotropic are represented by modeling approaches such as subgrid scale models [6]. This approach follows the Kolmogorov inter-scale energy transfer in which it is assumed that the small scales of the flow have a more universal behaviour and therefore, can be modelled using general assumptions. Modeling these small scales is known as subgrid scale (SGS) modeling and LES accuracy relies on closing the system of governing equations properly. The spatial filter considered in this LES implementation is a box filter D defined by the length scale of each computational cell. Thus, the LES equations are obtained by filtering the full compressible Navier–Stokes equations using the spatial Favre filter.
~j @ q @ qu ¼0 þ @t @xj
ð1Þ
~i @ qu ~j u ~i @ qu @ ¼ ðsij ssgs þ ij Þ þ ðq q1 Þg @xj @t @xj
ð2Þ
~j Y~ m @ qY~ m @ qu @ @ Y~ ¼ qDm m þ @xj @t @xj @xj
!
@/sgs j;m @xj
þ q_ c
ð3Þ
sgs
~ j @qj @ u ~ i sij @hj ~ j~I @ q~I @ qu @u þ ¼ p þ Hsgs þ Q c @t @xj @xj @xj @xj @xj
ð4Þ
In the above equations, the bar denotes the standard LES filtering and variables q, tildeuj , t, xj, s, q1, g, Ym, Dm, q, p, I, Qc denote the density, velocity component, time, spatial coordinate, stress, ambient density, gravity, mass fraction, diffusion coefficient, heat conduction, pressure, internal energy and heat of combustion respectively. The indices i and j go from 1 to 3 and m representing the chemical species go from 1 to 11. Due to the filtering process,
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D. Mira Martinez et al. / Fuel 103 (2013) 646–662 Table 1 Computational cases. Name
Fuel composition (%)
Case Case Case Case
100% H2 80% H2 + 20% CO 60% H2 + 40% CO 60% H2 + 20% CO + 20% CO2
1 2 3 4
some new terms arise from the governing equations representing the effects of the subgrid scale field on the resolved field. These terms are denoted by the superscript SGS, including the subgrid scale stress ssgs in the momentum equation (Eq. (2)), the subgrid mass flux of species Usgs in the species conservation equation j sgs (Eq. (3)) and the subgrid enthalpy flux hj and subgrid dissipation H in the energy equation (Eq. (4)) respectively. Further details about the filtering process can be found in [21]. Note that buoyancy effects have been included to account for the effects of density inhomogeneity on the momentum transport.
pffiffiffiffiffiffiffi
mt ¼ C m ksgs D sgs
@ qk @t
2.2. Subgrid scale modelling Subgrid scale terms account for the effects of the unresolved eddies on the momentum transport, the heat conduction on the subgrid scale and the viscous work, along with the mass flux and mass diffusion of species. The purpose of the SGS models is to express the above terms by means of the resolved fields. The first term that needs to be modelled is the subgrid scale stress term that arises after filtering the Navier–Stokes equation represented by ssgs in the present work.
ssgs ¼ qðu~i u~j u~i u~j Þ
This term corresponds to the stress in the subgrid scale and accounts for the contribution of the unresolved scales to the resolved field. Generally, the net kinetic flux goes from the resolved scales to the small eddies and is associated with the dissipation from the large eddies to the SGS eddies at some specific rates. The most broadly used model for closing the momentum transport in the SGS is the eddy viscosity model or the so-called Smagorinsky model, in which a balance between energy production and dissipation is assumed. This model may not be able to predict the SGS contributions accurately since at high Reynolds numbers, the flow develops a large dynamic range of scales that might be unresolved especially when coarse meshes [22] are used. In this work, a model including the turbulent kinetic energy in the subgrid scale called k D model has been employed in order to account for the local non-equilibrium between the subgrid energy production and dissipation rate [22]. The eddy viscosity mt is obtained using the turbulent kinetic energy in the subgrid scale and thus, a new conservation equation is needed in order to close the system.
ð5Þ
þ
~ j ksgs @ qu @ m @ksgs ¼ Psgs Dsgs þ q t @xj @xj Prt @xj
Psgs ¼ ssgs ij
Dsgs ¼
~i @u @xj
sgs 3=2 Ceq k D
ð6Þ ð7Þ
ð8Þ
ð9Þ
In Eqs. (8) and (9), Psgs and Dsgs denote the production of subgrid turbulent kinetic energy and its dissipation rate respectively, while Cv and Ce are model constants set as 0.067 and 0.916 [6].
Fig. 1. Instantaneous isosurfaces of gas temperature over the computational domain at t = 0.275 s for Cases 1 (a) to 4 (d) respectively.
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Fig. 2. Instantaneous cross-sectional gas temperature plots at two time instants t = 0.250 s (left column), 0.275 s (right column) for Cases 1 (a) to 4 (d) respectively.
Scalar transport in the subgrid scale is modelled using the eddy diffusivity model following a similar approach to the eddy viscosity method already mentioned. In this approach, a gradient diffusion model is used for the subgrid mass and enthalpy fluxes, given as
/sgs i;m ¼ q sgs
hj ¼ q
mt @ Ye m Sct @xi
mt cp @ Te Prt @xj
ð10Þ
ð11Þ
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In the above equations, Prt and Sct represent the turbulent Prandtl and Schmidt number respectively, while T stands for the temperature and cp is the specific heat of the gas. 2.3. Finite-rate chemistry Finite-rate chemical kinetics for syngas flames is included in the LES governing equations for the reaction rates in the species governing equations. Since a governing equation for each species in each computational cell has to be solved, large number of reactive species leads to large amount of computational costs. To avoid the prohibitive computational costs, reduced mechanisms are used in this work to solve this three-dimensional (3D) reacting flow problem. A well-established 4-step mechanism for syngas combustion [17] was considered in the current application. This mechanism involves 11 species from which 7 are considered reactive (O2, H2, H2O, H, HO2, CO, CO2), 3 species (O, OH and HCO) are considered reactive but steady state is assumed for them, and finally N2 that is used as an inert non-reactive species. The seven reactive species taking part in the 4 steps reversible mechanism are described by Eqs. (12.a), (12.b), (12.c), (12.d).
2.4. Numerical approach LES was performed using a parallel unstructured finite-volume code that solves the time-dependent governing equations. The time integration incorporates an automatic timestep control that calculates the minimum timestep required by the chemistry in which the heat release should not exceed a fraction of the internal energy of the cell, diffusion stability, and rate of strain tensor to limit the cell distortion to some extent. The spatial differencing is computed in two stages defined as Lagrangian and Eulerian phases [23]. The Lagrangian phase includes the computation of the pressure in which a Poisson equation is solved, diffusion of mass, momentum and energy, and the equation of state by the modified conjugate residual method following a modified SIMLE algorithm. Consecutively, the convective transport of the scalar variables is computed in the Eulerian phase of the method using a quasi-second order upwind differencing approach. No stability restrictions, but numerical accuracy is considered in time-marching the solution, since the diffusion calculation is performed implicitly while the convection is subcycled.
3. Physical problem I
3H2 þ O2 $ 2H2 O þ 2H
ð12:aÞ 3.1. Computational domain and boundary conditions
I
2H þ M $ H2 þ M III
H2 þ O2 $ HO2 þ H IV
CO þ H2 O $ CO2 þ H2
ð12:bÞ ð12:cÞ ð12:dÞ
This reduced model gives the global reaction rates wI to wIV from a combination of elementary reaction rates of the San Diego kinetic mechanism and includes a correction for autoignition. Further details can be found in the literature [16].
The computational setup corresponds to a laboratory burner located at Sheffield University, UK and joint efforts are being made to compare the experimental and numerical results. The application addressed here is a non-premixed syngas jet flame with different compositions impinging on a solid plate. The geometry of the burner is characterized by one single round nozzle of d = 0.46 cm and an impinging distance measured from nozzle to plate of 10 cm. The jet issues at 2700 cm/s with a Reynolds number Re = 1300 based on the inflow conditions. The computational domain is setup as a three-dimensional cylindrical region above the jet nozzle plane with 10 cm length in the streamwise direction,
Fig. 3. Instantaneous cross sectional velocity vector field at t = 0.250 s for Cases 1 (a) to 4 (d) respectively.
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Fig. 4. Gas temperature profiles at three axial locations Z = 6 cm (left column), 8 cm (middle column), 9 cm (right column) and at two time instants t = 0.250 s (a–c) and 0275 s (d–f).
whereas the diameter of the computational domain is defined as D = 30 cm, allowing the jet to spread out in the radial direction. A grid-dependence test analysis was carried out using three different meshes of 0.8, 1.2 and 1.4 million hexahedral cells. No substantial differences were observed between the results obtained with the
intermediate and finest grid and therefore, all the results shown in the present study correspond to the 1.2 million grid points. No-slip boundary conditions were employed on the solid walls, whereas open boundary conditions were considered in the radial direction of the domain.
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Fig. 5. Axial velocity profiles at three axial locations Z = 6 cm (left column), 8 cm (middle column), 9 cm (right column) and at two time instants t = 0.250 s (a–c) and 0.275 s (d–f).
3.2. Computational cases The computational cases considered represent 4 different fuels in which the concentration changes from Case 1 to Case 4. Case 1 corresponds to a pure hydrogen flame, whereas Cases 2–4 represent
syngas mixtures with different compositions. All four cases were setup under the same inflow conditions in order to perform a fair comparison. The cases were selected in order to analyse the effects of the H2/CO ratio of the fuel stream on the flame dynamics. The four computational cases are summarised in Table 1.
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Fig. 6. Axial velocity profiles along the centerline (a) and shear layer X = 0.5 cm (b) at t = 0.275 s.
Fig. 7. Instantaneous contour plots of H2O Mass Fraction at t = 0.275 s for Cases 1 (a) to 4 (d) respectively.
4. Results 4.1. Instantaneous results Sample instantaneous three-dimensional results of the four impinging flames are presented in Fig. 1 by isosurfaces of temperature up to 600 K (this low temperature range was chosen to show clearly the near-wall structures in the graph) at the time instant t = 0.275 s in one half of the computational domain and the velocity vector field over the cutting plane. This figure shows very clearly the existence of vortex rings in the near-wall region after the jet is deflected by the solid wall. These vortex rings are formed parallel to the plate and develop downstream in the radial direction. The 3D results presented here show the two main regions formed in the flow field: the primary jet region before the jet flow
touches the impinging wall and the wall jet region, in which vortex rings are created and a boundary layer appears as the flow develops in the radial direction after the impingement. Instantaneous cross-sectional snapshots of gas temperature at two different time instants are shown in Fig. 2 for the four computational cases. These two plots correspond to two different stages in which the flame jet is already developed. The instants represented by labels (a.1–d.1) at t = 0.250 s are representative of the stage in which the flame develops a Kevin-Helmholtz type shear layer instability in the primary jet region because of the jet momentum and buoyancy acceleration, while the instant t = 0.275 s, labeled as (a.2–d.2), is representative of the stage when the flames are developing or about to spread along the surface of the solid plate. The figure shows that as the jet issues from the nozzle, all the flames develop a primary jet with some bulges
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Fig. 8. Radial OH radical profiles at three axial locations Z = 6 cm, 8 cm, 9 cm at t = 0.250 s.
before touching the plate. In this region, the flames develop vortical structures in the shear layer that are convected downstream. However, as the jet approaches the impinging wall, the axial velocity is reduced and the static pressure increases in this region leading to the deflection of the jet in the radial direction. A wall boundary layer is formed in the near-wall region that will be analysed in more detail subsequently. From Fig. 2, it is seen that the overall structures of the flames are similar although some differences can be appreciated. As the hydrogen concentration of the fuel is changed, the flame thickness, surface area and flame propagation speed all vary slightly, which is associated with the effects of fuel variability. The instantaneous cross-sectional velocity vector field for the four computational cases is presented in Fig. 3 at t = 0.250 s. In order to trace the movements of fluid particles in the flow field and to locate more precisely the vortical structures, streamlines are plotted on the half-plane X > 0 of Fig. 3. The thick red lines indicate the starting points of the streamtraces. These plots, together with Fig. 2, show that the increase of CO with respect to H2 leads to more strained and vortical flames. A change in the hydrogen composition of the syngas fuel also slightly changes the flame burning speed, which causes different spreading of the jet as observed in the vortex ring displacement in the radial direction. The movement of the jet in the streamwise direction is affected by the buoyancydriven entrainment that squeezes and expands the flame at different axial locations [21]. In the wall boundary layer, the impinging
flow progresses into a radial wall-jet formation as a consequence of the jet deflection and the axial momentum of this secondary jet decay as it spreads radially. In order to further analyse the differences in flame dynamics between the four cases, radial profiles of different scalars and velocity components are presented subsequently. The gas temperature profiles at different axial locations at two time instants are firstly displayed in Fig. 4. The plot shows that the four fuel mixtures have similar overall trend with different values of peak temperature according to the mixture composition. The highest temperature peaks are reached in Case 1 with the maximum H2 content. As the mixture becomes H2-leaner, the temperature peaks are reduced and the lowest temperature peak is observed in Case 4, in which part of CO was substituted by CO2. The reason for the lower burning temperature of this case is associated with the low specific heat and high molecular weight of CO2, leading to the lower burning speed of the mixture [24]. It can also be noticed that when CO2 is increased in the syngas mixture (Case 4), the peak of temperature drops slightly compared to Case 3, but not as much as it would be expected because CO2 acts as a diluent in the mixture and it is a highly radiation-participating species [25]. As radiation effects were not considered in the simulations, the radiative heat losses that might reduce the flame temperature and burning speed were not taken into account. It is evident that the differences between the cases are larger at the downstream location Z = 9 cm where the vortical structures formed in the nearwall region have perturbed the temperature distributions. These
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Fig. 9. Radial H radical profiles at three axial locations Z = 6 cm, 8 cm, 9 cm at t = 0.250 s.
structures enhance the air entrainment and mixing affecting the local heat transfer and flame dynamics as seen in the plots. Fig. 5 shows axial velocity profiles in the radial direction at different streamwise locations for all the cases. This plot shows how the axial velocity changes as a consequence of variation in fuel compositions. Unlike the temperature distribution, the maximum axial velocity appears around the jet centerline and then decays in the radial direction, where the shear layer of the primary jet stream does not exhibit peaks as those shown in the temperature profiles. Note that the dissimilarities in the axial velocity component between the syngas mixtures are not very significant, especially for the peak values in the centerline. The axial velocity of the jets is controlled mainly by the momentum inertia and buoyancy induced by density inhomogeneity which are similar for all cases, where the differences in chemical reactions and transport associated with different fuel compositions are not playing a major role. This effect can be more clearly seen in Fig. 6, in which the axial velocity profile along the centerline and its profile at X = 0.5 cm where the reactive shear layer is formed are presented for further comparison. The profiles presented in Fig. 6 indicate that the flame axial velocity is strongly affected by the combustion heat-release and flow vortical structures in the jet shear layers, which also affect the velocity along the centerline but to a less extent. The axial velocity pattern along the centerline is similar for all the fuel mixtures and it is characterized by velocity decreases in the streamwise
direction with small variations near the jet nozzle exit and large decreases near the impinging wall. The differences in the centreline axial velocity profiles seen in Fig. 6a might be associated with the different diffusivity and viscosity of the fuel mixtures. Although buoyancy accelerated the flow in the shear layer up to the location of Z = 6 cm, it did not lead to an acceleration of flow speed in the jet centreline. As the jet approaches the impinging wall, the velocity reduces sharply to zero. For the axial velocity of the flame jet 0.5 cm away (X = 0.5 cm) from the jet centerline, it presents some interesting features. At this location, the velocity is very low at upstream locations (Z < 0.4 cm) and a small recirculation zone is created due to the jet expansion at the nozzle exit and the presence of the nozzle wall. Slightly further downstream at Z = 0.4 cm, the velocity starts increasing because of the acceleration associated with the buoyancy induced by the heat-release in the shear-layer. The jet also spreads from the axial location Z > 0.5 cm onwards and the velocity is largely affected by the vortical structures formed in this region as seen in the large velocity variations shown in Fig. 6b. These velocity variations which are associated with the Kevin-Helmholtz type-shear layer and buoyancy of the flow are highly dynamic and correspond to the formation of large budges in the primary jet stream as seen in Fig. 3. These profiles show that the axial velocity along the shear-layer is increased by combustion heat-release and the H2-richer flames experience higher acceleration because of the larger buoyancy effects and the lower viscosity of the mixture.
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Fig. 10. Radial H2O species profiles at three axial locations Z = 6 cm, 8 cm, 9 cm at t = 0.250 s.
The interaction between the large vortical structures in the primary jet stream and chemical kinetics is now examined. Fig. 7 shows contour plots of the mass fraction of H2O that can be used as an indicator of regions where chemical reactions take place. The shear layers correspond to regions of intense chemical reactions and therefore regions with large heat release, especially at upstream locations as those observed by the proximity of the lines of the contour plots in that region. As the jet develops downstream, most of the fuel is already consumed and the intensity of the chemical reactions is reduced in the developing zone of the flow (see from Z = 6 cm to 9 cm). The plots also reveal some interesting near-wall features. A second region of high chemical activity takes place near the vortex ring located at the wall boundary layer. This region with intense combustion reactions is caused by the transport of fuel from the fuel jet by vortical structures in the near-wall region. In syngas mixtures, when hydrogen content is reduced, the fuel mixture becomes less diffusive. Comparing the different cases shown in Fig. 7, it can be seen that the flames tend to be more elongated as the fuel mixture becomes H2-leaner. It can also be observed that when the hydrogen content is high, large amounts of H2O are formed slightly further away from the jet centreline near the stagnation zone in the shear layer as a consequence of the diffusive characteristics of the hydrogen compound. Fuel variability not only affects flame dynamics but also leads to different species and pollutant formations. Figs. 8–11 show radial profiles of radicals H and OH, as well as radial profiles of pollutant CO2 and the formation of H2O respectively. The radical OH that acts
as an intermediate species in syngas and hydrogen combustion is shown in Fig. 8. At upstream locations where the flame is not largely affected by vortical structures and air entrainment, the radical OH profile peaks at the stoichiometric region in which the flame is located. Its formation occurs in regions of high temperature as seen in the plot. Further downstream, the syngas mixtures (Cases 2–4) attain higher concentration of OH than that for Case 1. The reasons for this difference are associated with the unsteady behaviour of these flames in this region and the fact that the burning speed of the hydrogen flame is higher than the syngas mixtures, leading to a different stage of development for the H2 flame. This effect can be seen in Fig. 8c where the peak of Case 1 flame is displaced radially away from the geometric centerline. The radical OH is an intermediate species of great importance in the branching process [26] and it is directly involved in the production of H and O radicals as well as in the production of CO2 from CO. This species is assumed to be in steady state in the finite-rate mechanism considered in the present study and it might have been under-predicted [11]. Fig. 9 shows the distributions for radical H at three axial locations. The H content is lower when the CO content is increased as that occurred for the OH radical. This is due to the mechanisms governing the formation of H, OH and O. These radicals are mainly produced from the dissociation of H2. As the mixture becomes H2leaner, a reduction in the formation of these radicals is achieved decreasing the flame temperature. Plots for radical O also have similar behaviour but are not presented here for brevity. Profiles
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Fig. 11. Radial CO2 species profiles at three axial locations Z = 6 cm, 8 cm, 9 cm at t = 0.250 s.
Fig. 12. SGS wall turbulent kinetic energy profiles at two time instants t = 0.250 s and 0.275 s.
of the contents of products H2O and CO2 are shown in Figs. 10 and 11. Fig. 10 demonstrates that the formation of water vapour follows a similar trend for the four fuel compositions under investigation. However, it can be seen that the formation of H2O is enhanced in the pure hydrogen flame but not as much as it would be expected, since this species is produced by reactions involving OH and HO2
radicals interacting with HCO or H2. The figure indicates that the reaction involving HCO becomes predominant when the H2 content is low. It is shown that the H2O mass fraction is not reduced dramatically when the H2 mass is reduced from the fuel stream. Fig. 11 shows the CO2 mass fraction at three axial locations. The figure reveals some interesting features. Firstly, it is seen that the CO2
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Fig. 13. Radial profiles of RMS of the axial velocity fluctuations at three axial locations Z = 6 cm, 8 cm, 9 cm.
Fig. 14. Mean wall normal and shear stresses for Cases 1–4.
formation increases as the CO concentration is increased in the fuel stream, since CO2 is produced in the oxidation of CO or comes as an unburnt species in the case of the diluted fuel (Case 4). Moreover, the profiles show that the diluted fuel mixture (Case 4) produces less CO2 than the non-diluted syngas mixtures. The addition of CO2 in the fuel stream not only reduces the flame burning speed and gas temperature but also affects the chemical kinetics of the
reaction. The chemical reactivity of CO2 in the 4-step reduced mechanism considered in the simulations is taken into account in a backward reaction (reaction 22 of the San Diego mechanism [16] CO + OH CO2 + H) used to compute step IV of the finite-rate chemical kinetic mechanism. However, results of Fig. 11 suggest that this step is less important than the combination of the same forward step with the remaining reactions of CO conversion leading
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Fig. 15. Mean radial velocity of the wall-jet at different radial locations for Cases 1–4.
Fig. 16. Instantaneous t = 0.250 s and time-averaged Nusselt number distribution at the solid wall for Cases 1–4.
to the current results in which the production of CO2 is reduced by diluting the fuel with carbon dioxide. The difference in the formation of the pollutant CO2 is not large and it is around 5% at all axial locations. Fig. 12 shows the subgrid scale turbulent kinetic energy (ksgs) at the wall for the four computational cases considered at two time instants. From Fig. 12, it is evident that the hydrogen flame (Case 1) produces less SGS turbulent kinetic energy than the three syngas flames due to the fact that the syngas flames are more vortical and more wrinkled (see Figs. 2 and 3). This can be mainly attributed to the high diffusivity of hydrogen. As the hydrogen content is reduced, the flame becomes more vortical and the SGS turbulent kinetic energy increases (see Fig. 12). The plot shows that Cases 2–4 develop higher peaks of SGS turbulent kinetic energy along with extended region of high SGS turbulent energy at the wall. 4.2. Time-averaged results Time-averaged results were calculated after 20 LETOT or large eddy turnover times in order to eliminate the effect of the initial conditions on the flow field. The flow field was averaged for an additional 40 LETOTs [27]. The averaging interval selected LETOT is defined as the ratio between the inlet velocity and the domain
diameter (LETOT = D/u0) and provides a reasonable estimation on the statistical quantities and Reynolds stresses. As this work is focused on the near flow field, 40 LETOTs are considered sufficient although some variables might not be fully converged. Fig. 13 shows the root mean square (RMS) of the axial velocity fluctuations at three axial locations. The profiles indicate that the hydrogen flame (Case 1) is less vortical than the syngas flames (Cases 2– 4) and therefore it develops less fluctuation even though the axial velocity is slightly higher than the other cases. Mean normal and shear stresses of the flow at the wall also provide important information about the flow in the wall boundary layer (see Fig. 14). These profiles show that as the flames become H2-leaner (Cases 1–3), the magnitude of the stresses decreases because of the larger acceleration of the secondary jet in the boundary layer experienced by fuels with high hydrogen content. The presence of CO and CO2 reduces the stresses of the wall-bounded jet as seen in the figure. In order to further analyse the fluid dynamic behaviour of the wall-jet in these flames, mean radial velocity profiles are presented in Fig. 15 at different radial locations of the boundary layer for the all flames. This allows an appreciation of the development of the secondary jet and the effects of fuel variability on the flow development in this region. This figure shows that the mixture containing larger amounts of H2 in the fuel stream
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Fig. 17. Fourier spectra of the axial velocity at axial locations Z = 5 cm, 7 cm and 9 cm for Cases 1–4.
develops a secondary jet with larger acceleration than the mixtures with lower amounts of H2 or the one including CO2. The wall-jet acceleration comes from the expansion of the fluid due to heat release, and radial jet momentum. Syngas mixtures with low hydrogen content demonstrate lower flame temperatures because of the lower heat released from the combustion process, leading to secondary jets with smaller radial acceleration. This reduction in the acceleration of the wall-jet is enlarged by the higher molecular weight of the mixtures with carbon compounds. 4.3. Heat transfer analysis Heat transfer is of crucial importance in practical applications especially for gas turbines and combustion engines. The dynamics of impinging flames are strongly affected by the presence of the solid wall while the heat flux from the flame to the wall affects the performance of the wall materials. This flame/wall interaction could lead to overheating of the burner and a reduction of lifespan or even damage of the device. Heat transfer can be measured by the Nusselt number, which gives a quantification of the temperature gradient by fluid properties only, decoupling the problem from solid properties. The Nusselt number is defined by the convective heat transfer coefficient, the jet nozzle diameter and the gas thermal conductivity as follows:
Nu ¼
hd kc
ð13Þ
The heat transfer coefficient ‘h’ in the Fourier form is written as:
h¼
kc @T T jet T wall @~ n
ð14Þ
where ~ n represents the wall normal direction. Instantaneous and time-averaged Nusselt number profiles at the solid wall are shown in Fig. 16 for the four cases. From the Nusselt number that measures the heat transfer from the fluid to the wall, it can be observed that increasing the CO content reduces the heat transfer of the flame in this configuration. However, it is worth noting that the four flames corresponding to the four fuel mixtures have different thermal loads, since only composition was changed from one to another while the flow conditions were not adjusted to maintain a constant thermal load. Therefore, absolute values in this figure cannot be strictly compared in terms of flame/wall interactions. On the other hand, the Nusselt profiles present some patterns that are worth analysing. Fig. 16a shows that the Nusselt profile develops several peaks related to vortical structures shedding from the primary jet, one main peak that remains near the stagnation zone and a second peak nearby with higher value of Nusselt number for Cases 1 and 2, together with a peak downstream in the radial direction [11]. Spatial variations in the instantaneous Nusselt number distribution are evident. For Cases 1 and 2, three peaks can be observed. As the concentration of CO is increased in the fuel mixture, the second peak begins to reduce its intensity and to merge into the main peak near the stagnation zone until only one large peak is present in Cases 3 and 4. The effect of CO2 is almost negligible because the thermal load for these two mixtures (Cases 3 and 4) is practically the same. As Case 4 is obtained by replacing the amount of CO by CO2, this dilution does
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not lead to substantial changes on the heat transfer profiles at the wall as seen in the plots of Fig. 16. 4.4. Flame jet instabilities In order to further analyse the flame dynamics, several monitoring points are defined within the computational domain to record the velocity signals. These time traces are used to identify the frequency of the vortical structures caused by buoyancy and Kevin-Helmholtz type shear layer instabilities of these flames. The frequency is obtained from the time history of the simulation results via the fast Fourier transform (FFT) for a statistically stationary random process [28] and is displayed in Fig. 17 for a spectral analysis. The FFT results show that all the flames experience periodic and oscillatory behaviour as seen by the peaks of the spectrum. At upstream locations, all the flames have a characteristic frequency of 24 Hz (Strouhal number St = 0.4) which corresponds to the frequency of the instability in the jet shear-layer. This periodic behaviour is caused by the Kevin-Helmholtz shearlayer instability of the jet momentum and it is not related to the fuel composition as shown in Fig. 17a. However, it is worth noting that the instability is affected by the H2 content, since the buoyancy forces acting on the jet are greater for low density fuels because of the large density inhomogeneity. This enhances the effects of the instability. Downstream at Z = 7 cm and 9 cm, the spectra for all the cases contain not only the main peak at 24 Hz, but also peaks corresponding to both higher and lower frequencies. These peaks are associated with the vortex shedding in the nearwall region interacting with the large-scale vortices in the jet shear-layer. The plots (b) and (c) show that the amplitudes of instability of the structures in the wall region are almost as large as those of the structures in the jet shear layer described above. The results shown in Fig. 17 indicate that the fuel variability in this application only affects the amplitudes of the instabilities without affecting the characteristic frequencies, since all the syngas mixtures present the same characteristic frequencies. 5. Conclusions LES of four impinging jet flames of hydrogen and syngas fuels with different compositions was carried out in the present work for a laboratory setting-up configuration. A well-established four step reduced mechanism is used to account for the finite-rate chemistry of the syngas flames, whereas the k-D model is employed to compute the subgrid scale stress in the momentum equation. The results reveal that the flames develop large-scale vortical structures in the shear-layer of the primary jet, independent from the fuel composition. These structures are shed from the jet shear-layer and impact the impinging wall affecting the behaviour of the near-wall flame and its heat transfer characteristics. This work provides some understanding on the burning characteristics of syngas flames and the effects of fuel variability on the dynamics of impinging jet flames. Syngas mixtures with lower H2/CO ratio develop more unsteady and vortical flow in the wall region with smaller flame thickness. The flame surface is reduced due to the low diffusivity and relatively high viscosity of the fuel, leading to more strained and vortical flames. The flame burning speed is reduced for H2-leaner mixtures due to the difference in transport properties of the mixture. Time-averaged radial profiles of the secondary jet also confirmed the same trend. The temperature and velocity field are strongly affected by fuel variability. For H2-richer mixtures, larger temperature peaks and larger acceleration by heat-release are observed, especially in the jet shear-layers. Regions with lower chemical activity show similar characteristics in both temperature and
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axial velocity, since these differences are only associated with the physical properties of the mixtures. An analysis of the species formation was performed to further understand the flame dynamics under mixtures with different compositions. It is shown that the formation of radicals is reduced for H2-leaner mixtures leading to lower temperature peaks, while the formation of water vapour is not associated with the H2 content only, since fuels with lower hydrogen lead to similar amount of H2O formation. Besides, it is also presented that the pollutant CO2 can be reduced by diluting the fuel with CO2. An analysis of the turbulence characteristics suggests higher production of subgrid scale turbulent kinetic energy in the wall boundary layer for flames with lower H2 content, which are more vortical and wrinkled. Further investigations should be performed on this issue to fully understand the performance of the subgrid scale models as well as the effects of fuel compositions on the turbulence characteristics. The Nusselt number analysis shows that the H2/CO ratio affects the heat transfer performance of the flames, altering the wall heat-transfer characteristics. Finally, the analysis of the flow instabilities indicates that all flames have a characteristic frequency of 24 Hz (St = 0.4) corresponding to the frequency of the instability in the jet shear-layer. The amplitude of the instability is magnified with the H2 content, while the characteristic frequency remains unchanged. This work provides some understanding on the burning characteristics of syngas flames and the effects of fuel variability on the dynamics of impinging jet flames. References [1] Kim YS, Lee JJ, Kim TS, Sohn JL. Effects of syngas type on the operation and performance of a gas turbine in integrated gasification combined cycle. Energy Convers Manage 2011;52(5):2262–71. [2] Littlejohn D, Cheng RK, Noble DR, Lieuwen T. Laboratory investigations of lowswirl injectors operating with syngases. J Eng Gas Turbines Power 2010; 132(5):1–8. [3] Dam B, Corona G, Hayder M, Choudhuri A. Effects of syngas composition on combustion induced vortex breakdown (CIVB) flashback in a swirl stabilized combustor. Fuel 2011;90(11):3274–84. [4] Natarajan J, Lieuwen T, Seitzman J. Laminar flame speeds of H2/CO mixtures: effect of CO2 dilution, preheat temperature, and pressure. Combust Flame 2007;151:104–19. [5] Dam B, Love N, Choudhuri A. Flashback propensity of syngas fuels. Fuel 2011;90(2):618–25. [6] Menon S, Yeung P-K, Kim W-W. Effect of subgrid models on the computed interscale energy transfer in isotropic turbulence. Comput. Fluids 1996;25: 165–80. [7] Zuckerman N, Lior N. Jet impingement heat transfer: physics, correlations, and numerical modeling. Adv Heat Trans 2006;39:565–631. [8] Hadziabdic M, Hanjalic K. Vortical structures and heat transfer in a round impinging jet. J Fluid Mech 2008;596:221–60. [9] Mishra DP. Emission studies of impinging premixed flames. Fuel 2004;83(13): 1743–8. [10] Jiang X, Zhao H, Luo KH. Direct computation of perturbed impinging hot jets. Comput Fluids 2007;36:259–72. [11] Mira Martinez D, Jiang X. Numerical investigations of a hydrogen impinging flame with reduced and detailed chemical kinetics. In: International conference on applied energy (ICAE), Suzhou, China; 2012. p. A10185. [12] Gokulakrishnan P, Kwon S, Hamer AJ, Klassen MS, Roby RJ. Reduced kinetic mechanism for reactive flow simulation of syngas/methane combustion at gas turbine conditions’. Combust Fuels Educ 2006;1:513–21. [13] Chaos M, Burke MP, Ju Y, Dryer FL. Syngas chemical kinetics and reaction mechanisms. In: Lieuwen TC, Yang V, Yetter RA, editors. Shynthesis gas combustion: fundamentals and applications. Taylor and Francis; 2009. p. 29–70 [chapter 2]. [14] Chaos M, Dryer FL. Syngas combustion kinetics and applications. Combust Sci Technol 2008;180(6):1053–906. [15] Marzouk OA, Huckaby ED. A comparative study of eight finite-rate chemistry kinetics for CO/H2 combustion. Eng Appl Comput Fluid Mech 2010;4(3): 331–56. [16] Mechanical and Aerospace Engineering (Combustion Research). University of California at San Diego. Chemical-kinetic mechanisms for combustion applications, San Diego mechanism.
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