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Simulation of turbulent combustion in the
distributed reaction regime
Christophe Duwig1,2*, Matthew J. Dunn3
1
Haldor Tospøe A/S
Nymøllevej 55
Lyngby
Denmark
chdu@topsoe.dk
2
3
Lund University
Lund
Sweden
Sandia National Laboratories
Livermore,
California, USA
* corresponding author
ABSTRACT
Large eddy simulation that incorporates complex chemistry is a promising tool for
understanding, designing and simulating large industrial reformers. A typical industrial
reformer operates under oxyfuel conditions at very high Reynolds numbers, these
conditions pose a significant challenge for many state of the art combustion modelling
tools. The present work focuses on the simulation of a laboratory scale burner that
operates at high Karlovitz number in the distributed reaction regime, hence featuring
realistic reforming conditions. To simulate this burner an implicit large eddy simulation
method coupled with a skeletal chemistry oxidation scheme (20 species, 82 reactions) is
utilised. Two cases with increasing jet velocity are considered, these cases are denoted as
PM1-50 and PM1-150. For the PM1-50 case, the simulation results are compared to
experimental data and it is shown that the mean and RMS fields of temperature, CO and
OH are accurately predicted. For the high jet velocity case PM1-150, it is shown that
qualitative agreement is obtained by the prediction of the apparent extinction region
above the pilot. Conclusions are drawn on future strategies for the accurate simulation of
industrial reactors.
Keywords: Large Eddy Simulation, Auto-thermal reforming, detailed chemistry,
turbulent combustion.
1. INTRODUCTION
1.1
About combustion in reforming processes
In many industrial applications such as ammonia or methanol production, low heating
value fuels (also referred to as syngas) are produced as intermediates; the composition of
the syngas consists mainly of hydrogen and CO. Beside chemicals, syngas is also a
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starting point for the synthesis of synthetic fuels such as diesel or gasoline. A key step in
the conversion of natural gas or methane containing syngas (medium heating value fuel)
into CO and H2 is to prevent excessive production of CO2 [1, 2]. Since the water-gas-shift
reaction reacts CO and steam to produce CO2, it is desirable to utilise a very dry process
by keeping the steam partial pressure low to minimizing the conversion of CO to CO2.
The oxygen source required for conversion of methane to carbon monoxide is introduced
in the form of pure oxygen [1, 2]. When dealing with large plant capacities, this solution
is referred as auto-thermal reforming (ATR) and offers a cost-effective and compact
solution [1-3]. Figure 1 presents a schematic of the layout for the conversion of natural
gas, steam and oxygen to syngas. It features a unique reactor where natural gas or
possibly methane rich syngas, steam and oxygen are introduced. The oxidation of
methane to CO follows, with the oxygen flow kept low (about half a mole O2 per mole of
methane) in order to avoid further conversion to carbon dioxide. The steam flow is also
kept low (about half a mole H2O per mole of methane) but sufficient to avoid the
formation of soot. The exit of the ATR is set to about 1270K when operating at pressures
in the range of 20-30 bars. Downstream of the ATR, the syngas is cooled and the excess
heat is used for steam generation. Figure 1 also shows an alternative layout where the hot
syngas is used to power steam reforming in a downstream reactor, hence producing more
syngas instead of steam, providing a higher overall thermal efficiency.
In both cases, the ATR operates with two distinct zones. In the top zone, the oxygen
reacts with a mixture of methane and steam. This is a very exothermic flame with
temperatures potentially up to 3000K. In a second stage, the products of the flame pass
over a reforming catalyst, and the gas undergoes endothermic catalytic conversion of
methane and water into hydrogen and carbon monoxide. The heat of reaction is provided
by the initial heat release of the flame and operating conditions are set such that the
methane is consumed.
Focusing on combustion predictions in the top part of the ATR, a highly preheated and
diluted oxy-flame at Reynolds number over 106 is considered to be the modelling target.
The combination of the thermo-chemistry and fluid mechanics results in very steep
temperature gradients and thin flame fronts distributed by small-scale turbulence, making
an accurate prediction in these conditions exceptionally challenging.
Figure 1: Schematic figure of two syngas preparation methods for gas to liquid (GTL) and methanol
plants. WHB and HTER stand for waster-heat-boiler and Haldor Topsøe exchange reformer,
respectively.
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1.2
Reforming: a challenge in turbulent combustion
Despite of more than 60 years of research, a comprehensive and predictive description of
turbulent combustion remains elusive. The interaction between turbulent structures and
chemical reactions are a multi-scale and non-linear problem. In the limit of an infinitelyor very-thin reaction layer, the flamelet concept has been shown to be successful.
However, this case is not always valid in industrial combustion devices, which operate at
very high Reynolds numbers (Re), and often in the distributed reaction regime. One
reason for the lack of detailed knowledge about this combustion regime is the lack of
experimental studies at high Karlovitz number (Ka). While flamelet like flames are
relatively easy to operate in laboratories, conducting experiments at high Ka is more
challenging.
Recent advances in laser diagnostics combined with recently developed experimental
burners have opened new insight into the distributed regime with great prospects to
improve the understanding and simulation of turbulent combustion at both high Ka and
Re. Among the recently developed experiments, this paper focuses on the piloted
premixed jet burner (PPJB) [4-5], greater discussion of this burner will be presented in
the next section. This burner features a flame well in the distributed regime (Ka>1000),
exhibits steep temperature gradients (temperature increase of about 2000K within few
millimetres) and is well characterised with to extensive velocity and scalar laser
diagnostic measurements. The PPJB is a very relevant, but also extreme test for any
modelling approach to be used for ATR simulations. After the presentation of the PPJB
and the flame challenges, the modelling approach is described before presenting the
results.
Figure 2. Cross-sectional view of the PPJB, illustrating the flow passages and flow conditioning for
the coflow, pilot and central jet streams before their respective exit planes [4].
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2.
PILOTED PREMIXED JET BURNER - PPJB
2.1
Description of the burner
The PPJB geometry, flame characterization and basic flame structure have been
previously been reported [4-5] and only a brief overview of the relevant geometric
parameters relevant to the modelling of the PPJB is given below. A schematic cutaway
drawing of the PPJB is shown in figure 2 illustrating the flow paths for the central jet,
pilot and coflow through the burner. The central jet tube is a 4mm inside diameter smooth
bored pipe that is unobstructed for more than 100D upstream from the jet exit plane. The
pilot premixture is fed through the burner in an annular passageway around the outside of
the central jet tube, the pilot flow is conditioned in the pilot shroud by passing through a
column of packed spheres to ensure an even flow before flowing through the 44 x 1.3mm
diameter holes in the pilot base plate. The burner configuration is effectively a central jet
(fresh methane/air mixture) discharging into concentric annular jets of combustion
products from the pilot and coflow.
2.2
Flame characteristics and available data
The PPJB PM1 flame series is well characterised with advanced laser-diagnostic
measurements performed at both University of Sydney (UoS) and Sandia National
Laboratories (SNL). The operating conditions for the PPJB PM1 flame series is given
below in table 1, further details of the experiments and the experimental uncertainties are
beyond the scope of this paper and can be found in other publications [4-6].
Table 1: Operating conditions for the PPJB PM1 flame series.
Central jet
Pilot
Coflow
Ka
PM1-50
600
Φ=0.5 – U=50m/s Upilot=0.8m/s* Ucoflow=0.7m/s*
T = 300 K
PM1-100 Φ=0.5 – U=100m/s
1600
Φ=1.0
Φ=0.43
T = 300 K
(methane/air)
(hydrogen/air)
PM1-150 Φ=0.5 – U=150m/s
T ~ 2230K
T~1500K
3000
T = 300 K
*All velocities are taken to be the bulk flow values at 300K prior to combustion.
Images of the mean flame chemiluminescence for the four PPJB PM1 flame series flames
are shown in figure 3. In all cases the pilot flame region is highly luminous and almost
white in color. For all flames the combustion of the central jet reactants corresponds to a
long blue flame brush. At low velocities the flame is an elongated conical shape and
extends up to x/D~40. At higher velocities, the flame brush has a long inverted conical
shape with a reduced luminosity downstream. For the 150 m/s case, the flame is barely
visible at x/D~30, although it apparently “re-ignites” downstream at x/D~40. It is noted
that for the highest velocity PM1-200 case the flame brush is barely visible beyond
x/D~10.
For the composition of the central jet, the chemical time scale for a freely propagating
premixed methane/air flame with equivalence ratio Φ=0.5 is τc=δL/SL=0.028s. Based on
these figures, the global Damköhler numbers are Da~2·10-2 (PM1-50) and Da~6·10-3
(PM1-150) while the global Karlovitz number is Ka~600 for the PM1-50 flame and
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Ka~3000 for the PM1-150 flame. This indicates that both the large and intermediate size
turbulent structures can disturb and distribute the reaction zone [4-6].
Although the complete PM1 flame series is of interest, simulations and results have
already been reported for the PM1-100 flame [6] and are not repeated here. Instead, this
paper focuses the simulation of two of the PM1 series flames, PM1-50 and PM1-150. The
PM1-50 results are compared to experimental measurements in terms of the mean and
rms fields, whilst the PM1-150 results are evaluated on a qualitative basis focusing on the
intermediate region with low light emission.
Figure 3. Mean flame chemiluminescence images for the PPJB PM1 flame series. From A to D, the
jet velocity is increased from 50, 100, 150 to 200 m/s.
3. MODELLING HIGH KARLOVITZ FLAME WITH
LARGE-EDDY-SIMULATION
3.1
Governing equations
Simulation of turbulent combustion implies solving concurrently a fluid dynamics
problem (turbulent stirring and mixing) and a chemical problem (oxidation). When these
problems are coupled in a non-linear manner, they pose a challenge to Computational
Fluid Dynamics (CFD) in requiring adequate spatial and temporal resolution of both the
physical and the chemical processes. The recent increase of computer power has allowed
the use of Large Eddy Simulation (LES) to study combustion problems of engineering
interest. The idea behind LES is to explicitly simulate the large energetic scales of the
flow and the thermochemistry, directly affected by boundary conditions while modelling
the small subgrid scales [7].
The basic equations describing the motion of a reacting fluid are the transport equations
of momentum, species mass fraction, and energy. This system of equations is closed with
the addition of an equation of state. By utilising a low Mach number formulation, density
is a function of temperature and gas composition only. In LES, the dependent variables
are decomposed into resolved and unresolved, or subgrid, components using spatial
~
~
filtering of length ∆, f = f + f ′′ with f = ρf / ρ , where overbars denote spatial filtering.
Applying filtering to the reactive Navier-Stokes Equations and the state equation yields
[7]:
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
∂


∂



∂


∂


 ρ


t
ρ + ∇ ⋅(ρ u ) = 0,
t
ρ Y%i + ∇ ρ u Y%i = ∇ ⋅ ( ρ u Y%i − ρ u Y i + ρ D i ∇ Y%i ) + w& i ,
t
ρ u% + ∇ ρ u u% = − ∇ p + ∇ ⋅ ( ρ u u% − ρ u u + τ% ) ,
1c
t
ρ h% + ∇ ρ u h% = ∇ ⋅ ( ρ u h% − ρ u h + λ ∇ T% ) ,
1d
 p0 
= 

 RT 
1e
1a
i= 1 , … ,N ,
1b
Where: u is the velocity vector, h the total enthalpy, p0 the reference pressure, ρ the
density, µ the viscosity, τ the viscous stress tensor, λ the thermal conductivity, R the
specific ideal gas constant, Di the diffusivity of species i, Yi the mass fraction of species i
and w& i the reaction term of species i. Diffusive transport models are included; Fick’s law
for species diffusion, Sutherland’s law for mixture viscosity, and Fourier’s law for
thermal conduction. The transport subgrid terms arising on the right-hand-side in
equations (1a-e) are modelled using a classical gradient type with an effective ‘eddy’
viscosity computed using the classical Smagorinsky model. The analogy between the
unresolved transport of momentum and scalar are used to extend the effective viscosity
for computing the effective diffusivity and conductivity using effective Schmidt and
Prandtl numbers taken to be Sc∆ =1 and Pr∆ =1 respectively [6].
3.2
Chemical kinetic modelling and Implicit LES closure
Incorporating combustion chemistry into LES involves finding a suitable reaction mechanism and solving the filtered species equations (1b). Appropriate reaction mechanisms
may involve tens or hundreds of species with hundreds or thousands of reaction steps, but
are often drastically reduced to avoid solving a large system of stiffly coupled equations.
However, if practical aspects limit the use of large mechanisms, adequate description of
the oxidation chemistry may still be needed. Typically, more than ten species are required
for accurately describing non-equilibrium kinetic effects in reformers. In addition, the
present experimental data-set includes semi-quantitative measurements of OH radical
which is informative and a severe check for the simulation models. Therefore, a skeletal
mechanism for methane oxidation [8] that consists of 20 species and 82 reactions is
employed.
An additional modelling issue lies in the species equations (1b) which contain the filtered
reaction rates, w& i . The reaction rates are non-linear functions of species mass fraction
and temperature. Different strategies have been used for modeling of the filtered reaction
rate, starting by extending Reynolds Averaged Navier-Stokes (RANS) combustion
models to LES applications. Recently modern methods have been proposed that are
specifically designed for the LES framework [6-7]. The combustion regime of interest is
characterized by a relatively high Karlovitz number and is designed to highlight the
effects of finite-rate chemistry. From a balance point of view, it implies that the small
eddies are energetic enough to distribute the chemical reactions. In other words, the
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subgrid mixing acts faster than any chemical reaction. The flame structure corresponds,
locally, to a perfectly stirred reactor with a homogenous subgrid species and temperature
field. For a species j, the ILES closure [6] gives:
~ ~
w& j (Yi , T ) = w& j (Yi , T ) ,
(2)
where the reaction rate is obtained as a sum of Arrhenius expressions. A detailed
discussion of this choice is available in [6] and will not be repeated presently.
3.3
Numerical methods
The CFD solver used in this paper is based on the OpenFOAM object-oriented library [9]
and was written to feature a reacting low Mach number formulation of equation (1). The
spatial discretisation uses an unstructured finite volume method, [9], based on Gauss’s
theorem. The reconstruction of convective fluxes by central differencing gives second order accuracy in space and second order accurate backward discretisation is used in time.
The convective terms in the scalar equation (1b,d) are treated using a total variation
diminishing scheme, which maintains high resolution and second order accuracy while
preserving boundedness [10]. Time stepping is done implicitly, the pressure-velocity
coupling is done with the PISO method [11] and spurious oscillations are avoided using
the Rhie-Chow interpolation. A solver strategy, based on a judicious blend of conjugategradient and multi-grid methods, is used to speed-up the pressure-velocity coupling steps.
The equations are solved sequentially using explicit source terms to obtain rapid convergence. A maximum Courant-Fredrich-Levy number of 0.4 is enforced corresponding to a
physical time step ∆t<10-7s. The present solver settings are similar to previous works [6,
12].
3.4
Computational domain and boundary conditions
The computational domain is a frustum, consisting of a disc-shaped base with a diameter
15D located at the exit of the high speed jet nozzle. It expands in the streamwise direction
to a diameter of 30D after a length 50D. The base of the frustum is divided into three
concentric sections corresponding to the central jet, the annular pilot and the external
annular coflow. Since the physical external annular coflow is larger than the
computational domain, the side of the frustum is included in the coflow. The downstream
face of the frustum is modelled as outflow.
The computational grids used in the present study are similar to the grid used by Duwig
et al. [6]. The grids consist of unstructured hexahedral cells with refinement toward the
central jet shear-layer and close to the jet nozzle outlet. The high speed jet nozzle region
is captured on the finest cells, ensuring that the smaller cells are located where the
variable gradients are expected to be large. On averaged, 47 points are used to describe
the jet diameter giving a total number of cells of 8×105 when focussing on the near field
only (PM1-50) and 1.5×106 when the fine region is extended in the downstream direction
(PM1-150). It is worth noting that a grid sensitivity study was presented in [6] showing
that the present grid resolution is adequate. In addition, the grid used here is comparable
or finer to the ones usually reported in the literature for reacting jets (see [6] for further
discussion).
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Boundary conditions, in particular inlet boundary conditions are important in the
framework of LES. The case of a round free jet has been studied in many investigations
and significant differences have been found in the literature for different experiments
having the same inflow conditions [13-14]. This experimental sensitivity causes great
difficulty for LES of these flows. Typically, an exact reconstruction of the inflow
boundary conditions is typically not feasible [15], instead a compromise is made so that
the inflow boundary conditions are a reasonable approximation of the experimental
measurements at an acceptable computational cost. In the present case, the LES
computations are started at the end of the central jet nozzle. The turbulent inflow velocity
profile is obtained by superposing synthetic turbulent fluctuations onto the mean profile
obtained from LDV experimental data. The synthetic fluctuation is generated using a sum
of vortex pairs enabling to maintain the integral length scale at D/5, while ensuring
divergence-free signal [16].
At the inflow boundaries the major species mass fractions are approximated assuming
chemical equilibrium, as well as CO, OH, and H2. The other intermediate and radical
species are not measured and the mass fractions are set to zero at the inlets, which is close
to their equilibrium values. The temperature of the coflow and pilot are set to the
equilibrium temperature of the corresponding mixture. This assumes that the pilot and
coflow do not exchange heat (separate adiabatic flames), which is not the case in practice.
In particular, the temperature measurements show levels exceeding the adiabatic flame
temperature, hence suggesting that the pilot gases are preheated by the coflow before
combustion. However, this effect has not been thoroughly quantified and it cannot be
included in the boundary condition modelling. The outflow boundary is modelled using a
flux corrected zero-gradient for velocity, a zero gradient condition for scalars, and a
constant total pressure.
It is stressed that the extreme sensitivity to boundary conditions and the relative
uncertainty regarding the experimental operating conditions (temperature profile, velocity
profile, and radical species) limit the prediction capabilities. For example, a different of
100K in the pilot temperature makes a significant change in the level of CO and OH at
the boundary. Further Improvements in simulation tools (including detailed chemistry)
can only proceed if the experimental databases evolve accordingly and include necessary
details. At present, such detailed boundary condition specification is not the case even in
the more comprehensive databases. This limitation should indeed be kept in mind when
comparing simulation results to experimental measurements.
4. RESULTS
4.1
PM1-50 flame
The first case examined is the PM1-50 flame. As shown on figure 3, the PM1-50 is
characterised by conical shaped flame with apparent strong influence of the pilot up to
x/D~25. Figure 4 shows the mean and RMS temperature profiles from the measurements
and LES. Both the LES and experimental data depict a low temperature jet with sharp
temperature gradients close to the nozzle. These high temperature gradients are up to
1800K over D/2 (2 mm) close to the jet exit and gradually dissipate downstream. The
influence of the pilot is also seen with temperature regions above 2000K apparent even at
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x/D=15. In general the agreement between the LES and experimental data for the
temperature field is good with discrepancies on the order of the experimental uncertainty.
Figure 4. Radial profiles of Mean and RMS temperature.
Figure 5. Radial profiles of mean and RMS CO mass fraction.
Figure 6. Radial profiles of Mean and RMS OH mass fraction.
It is noted that the experimental data exhibits a higher temperature in the pilot region.
This can be explained by the preheating of the pilot gases before combustion. The heattransfer from the coflow to the pilot results in higher pilot temperatures and lower coflow
temperature, which can be seen in figure 4. In line with this observation, the CO and OH
profiles are examined in figures 5 and 6. Again, the main discrepancies are seen in the
pilot region for the mean fields close to the inflow boundary. Indeed, CO and OH are
very sensitive to the temperature and under-estimating the pilot temperature, results in
setting values for CO and OH that are too low at the pilot inflow boundary. The
sensitivity of OH to the inflow temperature is larger than that of CO, indeed such an
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increased sensitivity for OH can indeed be seen in the results in figure 5. Except for the
differences in the near pilot region, both data sets agree well with the experimental
measurements. In particular, the fluctuation levels in the shear-layer are captured
accurately, even for OH. The spreading rate of the jet, hence the reacting layer is also
well captured as verified by the CO mass fraction mean and RMS profiles.
Figure 7 presents LES snapshots of the temperature, OH and CO fields. The temperature
field displays the cold jet surrounded by the hot coflow and pilot gases. It can be seen that
the influence of the pilot gases extends almost to the tip of the jet. Typical jet instabilities
are seen in the shear-layer suggesting vortex interaction with the flame. The OH and CO
fields follow the same description although the OH field is only present in the region of
very high temperature. The OH levels are particularly high in the pilot region where
temperatures are elevated. The high OH layer envelops the jet and does not exhibit
interaction with medium scale turbulence, though it does suggest an interaction with a
helical large scale coherent structure. The layer of maximum CO is located differently,
and predominately occurs in the shear-layer where methane oxidation takes place. The
CO field also exhibits local maxima and pockets in accordance with the local vortex
dynamics. Hence, the CO field is stirred and significantly influenced by medium scale
turbulence. The maxima increase in level downstream as the effect of the pilot decreases.
While OH depends strongly on the pilot influence, CO is produced in the flame and is a
maximum in the shear-layer due to fuel oxidation. This explains why it is less sensitive to
the pilot inflow conditions than OH. Indeed, it can be concluded that for the present flame
and more generally, when high temperature gases are present, CO is a better marker for
the reaction zone than OH. This aspect will be exploited in the next section to exclude
unburnt CO in low temperature regions.
Figure 7. LES snapshots of the PM1-50 flame. From left to right: temperature, OH mass fraction and
CO mass fraction.
Regarding the modelling capabilities, it is shown that ILES together with detailed
chemistry gives an accurate picture of the reaction layer and its dynamics. The major
source of uncertainty lies in the inflow boundary conditions, especially for the
temperature and OH mass fraction in the pilot region.
4.2
PM1-150 flame
As the quality of the ILES prediction tool is established, it is tempting to study a case that
is even more challenging than the PM1-50 flame (higher Ka) and try to reproduce the
extinction seen in figure 3 for the PM1-150 flame. Such a simulation of the PM1-150
flame is examined here and Figure 8 shows snapshots of the temperature and OH fields.
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In comparison with figure 7, the effect of the pilot much smaller in the PM1-150 flame
compared to PM1-50 flame. This is a result of the faster entrainment rate due to the
higher jet central velocity in the PM1-150 case. In other words, the pilot gases are mixed
with the jet in a shorter distance and at faster rate when the central jet velocity is
increased. The PM1-150 flame structure is dramatically different to the PM1-50 flame
structure, in that already at x/D=7.5 the pilot effect is barely visible in the temperature
and OH fields. Also the PM1-150 flame differs from the PM1-50 flame structure with a
more wrinkled surface indicating that the increased entrainment also moves the OH
layers closer to the shear-layer. At x/D=7.5, the OH brush forms a crown surrounding the
jet due to the pilot, further downstream this thick OH layer is replaced by a low intensity,
discontinuous ensemble of pockets.
Figure 8. LES snapshots of the PM1-150 flame - longitudinal and stream-normal cuts. From left to
right: temperature and OH mass fraction.
The location of the sudden decrease in the OH levels corresponds to the regions of
decreased visible light can be observed in Figure 3 for PM1-150. As noticed previously,
the OH mass fraction levels are very sensitive to the pilot inflow temperature and do not
necessarily correlate with a variation of the reaction rate. Instead, the product YOH×YCO
(referred as the indicator) which uses the OH signal to “filter out” the low temperature
CO fraction which is unlikely to react. It, indeed is correlated to the forward reaction rate
for the reaction OH+CO→H+CO2, which is known to play a significant role in the heatrelease at moderate temperatures (i.e. in the reaction layer). Further, this indicator has
been shown to correlate in the qualitatively in the mean to the mean chemiluminescence
of the PM1 flames [5]. This indicator is presented as a single representative instantaneous
snapshot in figure 9 to compare the LES reaction rate fields for both PM1-50 and 150
flames. The snapshot of the PM1-50 flame indicates that the reaction layer of the PM1-50
flame resembles a long conical shape with wrinkling typically occurring only at large
scales. The blue layer representing the reaction zone indicator is thin and relatively
intense and features only a moderate decrease in intensity downstream. Generally the
reaction indicator for the PM1-50 flame is a continuous layer around the flame brush with
few holes or regions of reduced intensity. The PM1-150 snapshot shows an intense blue
colour in the piloted region (0<x/D<~5) followed by a thinner, but equally intense, region
in the flame brush region of ~5<x/D<~12. Further downstream, the indicator level drops
dramatically with fading blue colour with only pockets of medium and low indicator
levels. The persistence of these intermittent pockets of medium reaction rate indicator
levels at x/D>~12 gives rise to the hypothesis that these pockets may be key to the
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prediction of the subsequent re-ignition process, clearly the prediction such intermittent,
highly non-equilibrium effects can only be captured with LES utilizing complex
chemistry. Interestingly, the existence of highly intermittent medium reaction rate
pockets has also been previously found experimentally [4]. The three consecutive zones
just outlined are qualitatively consistent with the picture displayed for the PM1-150 flame
in Figure 3.
Figure 9. LES snapshots of the PM1-50 (top) and PM1-150 (bottom) flames.
To complement the qualitative descriptions and comparisons based on the reaction rate
indicator levels presented in figure 9, the reaction rate indicator levels are examined on a
quantitative basis and also validated with laser diagnostic measurements. Figure 10
presents the resulting scatter plots of the reaction rate indicator in the temperature space
at three axial locations (x/D=2.5, 7.5 and 15). In regions close to the nozzle, the reaction
rate indicator is particularly high; this is due to the large amounts of enthalpy and
intermediates provided by the high temperature pilot gases that strongly promote the
vigorous initial reaction of the central jet premixture. Both the LES and the experimental
data depict the same vigorous levels of reaction at x/D=2.5, particularly at temperatures
>1200K. Typically for all of the experimental data the scatter of the data is larger than the
LES, to some degree this can be attributed to measurement noise, however the level of
influence of noise vs. true scatter is yet to be quantified. Also in the experimental data the
non-zero values for T<800K indicates the existence of a biasing influence at these
temperatures, as the mass fraction of OH is expected to be virtually zero in this region.
Further downstream at x/D=7.5 and 15, the temperature dependence of the reaction rate
indicator is well captured by the LES when compared to the experimental data.
Downstream at x/D=15, a dramatic drop in the values of the indicator is observed, the
indicator maxima is reduced by a factor 4-5 compared to the maxima at the previous
location (x/D=7.5). This dramatic reduction which is captured by the LES corresponds to
the apparent extinction region observed in figure 3 for PM1-150. It is stressed that
although the apparent extinction corresponds to a decrease in chemiluminescence, the
oxidation reactions are still proceeding, although these oxidation reactions are low, as
seen by figure 10. The region at x/D=15 of very low reaction rate and chemiluminescence
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production corresponds to a very distributed regime of combustion, indeed nearly a
flameless mode of combustion.
Figure 10. Scatter plots of the product YCO×YOH×106 in temperature space for the PM1-150 flame,
top: experiments, bottom: LES. Left column x/D=2.5, middle column x/D=7.5, right column x/D=15.
5. CONCLUSIONS
A simulation methodology based on large eddy simulation that includes detailed
chemistry, has been presented and validated with experimental measurements. The
characteristics of the problem were chosen to emulate the challenges present in a
reformer reactor, namely finite-rate chemistry effects, very high Karlovitz numbers and
broadband chemistry/turbulence interactions. The simulation methodology was shown to
successfully predict the temperature fields as well as the mass fraction fields of the
intermediate species. The model has been shown to be capable of capturing complex
turbulence-chemistry interactions, including the spectacular apparent extinction for the
PM1-150 case. The complexity of such phenomena highlights the need for a relatively
detailed chemistry representation, which was utilized in the present model simulations
with only moderate computational cost.
The results also indicated that increasing the complexity of the chemical scheme
improves the simulation capabilities but also magnifies the sensitivity to the boundary
conditions. The critical boundaries feature high temperatures where some intermediate
species and radicals are not negligible and need to be specified precisely. It follows that
future experiments intended for model validation should place significant effort to
quantifying the boundary conditions in the best manner possible. However, industrial
reformers are not subject to such complicated thermo-chemical boundary conditions, as
the composition of the inflow streams are relatively well known and cold, making all
radical mass fractions close to zero. This leaves the challenge of boundary condition
specification to be primarily in terms of the flowfield. With the relatively simple
boundary conditions found in a turbulent auto-thermal reformer combined with the model
approach outlined in this paper, the accurate prediction of turbulent auto-thermal
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The Swedish and Finnish National
Committees of the International Flame
Research Foundation – IFRF
reforming with a detailed chemistry representation is now considered to be a realistic
prospect.
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7. ACKNOWLEDGEMENTS
The computations were performed on Haldor Tospøe’s super-computing facilities thanks
to the efforts of Dr. B. Hinnemann, which are greatly appreciated and acknowledged.
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