modeling the transient structure of reacting diesel jets ...technique is used to computationally...
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Paper # 070IC-0085 Topic: Internal Combustion Engines and Gas Turbine Engines
* Corresponding author: [email protected]
8th
U. S. National Combustion Meeting
Organized by the Western States Section of the Combustion Institute
and hosted by the University of Utah
May 19-22, 2013
Modeling the Transient Structure of Reacting Diesel Jets using
Large Eddy Simulation Muhsin M Ameen
1* Vinicio Magi
1,2 John Abraham
1,3
1 School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907-2088, USA
2 School of Engineering, University of Basilicata, 85100 Potenza, Italy
3School of Mechanical Engineering, University of Adelaide, Adelaide, South Australia 5005,
Australia
Accurate modeling of the transient structure of reacting diesel jets is important as transient features
like autoignition, flame propagation, and flame stabilization have been shown to correlate with
combustion efficiency and pollutant formation. In this work, the large eddy simulation (LES)
technique is used to computationally model a lifted jet flame at conditions representative of those
encountered in diesel engines. An unsteady flamelet progress variable (UFPV) model is used for
turbulence/chemistry interactions. The UFPV model has been proposed for predicting the
averaged/filtered chemistry source terms when modeling turbulent non-premixed combustion. In the
model, a look-up table of reaction source terms is generated as a function of mixture fraction Z,
stoichiometric scalar dissipation rate χst, and progress variable Cst by solving the unsteady flamelet
equations. In the present study, the progress variable is defined based on the sum of the major
combustion products. A 37-species reduced chemical reaction mechanism for n-heptane is used to
generate the UFPV libraries. The results show that ignition initiates at multiple points in the mixing
layer around the jet, towards the edges of the jet, where the mixture fraction is rich, and the strain
rates are within the ignition limits. These ignition kernels grow in time and merge to form a
continuous flame front. Lift-off height is determined by the minimum axial distance from the orifice
below which the local scalar dissipation rate does not favor ignition. The LES results are compared
with Reynolds Averaged Navier-Stokes (RANS) simulation results from prior work. This comparison
shows that though there are noticeable differences in the transient phenomena, lift-off heights
predicted by both methods are within 25% and the predicted mechanism of lift-off is related to
ignition in both cases.
1. Introduction
Reducing emissions of particulate matter (PM) and nitrogen oxides (NOx) from diesel
engines is a continuing challenge that faces heavy-duty diesel engine manufacturers who, in turn,
have invested significant resources to address it. Increasingly stringent regulations force engine
designers to search for innovative ways to cut down emissions. Exhaust aftertreatment devices which
remove the pollutants in the exhaust are effective and increasingly deployed by manufacturers.
Nevertheless these devices add to the cost and size of the engine package and are, hence, not the
preferred means of achieving emissions goals. Advanced combustion engines, such as
homogeneous-charge compression-ignition (HCCI) engines, are promising but have not reached a
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stage of development where they are practical. While progress is being made in the areas of exhaust
aftertreatment and advanced combustion engines, it is imperative that any gains that can be achieved
through improvements in conventional diesel engine combustion are exploited. Making
improvements in conventional diesel engine combustion can be accelerated by improving the
understanding of the in-cylinder fuel/air mixing and combustion processes.
In recent years, it has been shown through experimental studies that flame lift-off in reacting
diesel jets is related to soot concentration in the jets (Siebers and Higgins, 2001; Pickett and Siebers,
2004). The suggestion is that the higher the lift-off, the greater the mixing upstream of the lift-off
height which results in lower soot formation downstream in the jet. If this is indeed the case,
predicting lift-off in reacting diesel jets is important in the context of multidimensional modeling of
the jets. Accurate modeling, however, requires an understanding of the physics of lift-off. This can
be achieved through experimental and computational studies. It is important to point out that while
the interest in flame lift-off in diesel engines is relatively more recent, it has been the subject of
study in turbulent reacting jets for over 30 years (Pitts, 1989; Peters, 2000; Venugopal and Abraham,
2007b). There have been several efforts to computationally model lift-off in flames. Most of these
studies employed the Reynolds Averaged Navier-Stokes (RANS) approach.
Chomiak and Karlsson (1996) employed a partially stirred reactor (PaSR) model in
combination with multi-step chemical kinetics in a RANS code to model lift-off height in diesel jets
for varying conditions of injection pressure, orifice diameter, and ambient temperature and density.
In the model, the computational cell is divided into reacting and non-reacting zones. The reacting
zone is considered as the perfectly stirred reactor. It is coupled to the non-reacting zone through
mass and energy transfer. Defining the volume fraction of these two regions in a given cell requires
careful consideration. Time scales of turbulence and kinetics are employed in the model to estimate
the volume fractions. It was shown that the computed results are in good agreement with the
measurements of Winklhofer et al. (1992). Tao and Chomiak (2002) employed the PaSR model to
numerically investigate flame lift-off in diesel sprays and reaction zone structure in the lift-off
region. The computed lift-off heights were in reasonable agreement with the measurements of
Siebers and Higgins (2001) when changes in chamber pressure and temperature were considered.
Kärrholm et al. (2008) evaluated the PaSR model against the measurements of Siebers and Higgins
(2001) and Siebers et al. (2002) under high EGR and varying ambient temperature conditions.
Senecal et al. (2003) adopted an approach where each computational cell in a RANS
simulation was considered as a perfectly stirred reactor (PSR). In other words, multistep kinetics
were directly implemented in the RANS simulation. The numerical results were in good agreement
with the measured results of Siebers and Higgins (2001) when an iso-line of 2200 K was employed
to identify the lift-off height. Tap and Veynante (2005) employed a generalized flame surface
density (GFSD) approach to model flame lift-off in diesel sprays. Their computed trends agreed well
with the measured trends of Siebers and Higgins (2001) for varying orifice diameters and chamber
densities.
Errico et al. (2008) assessed two models of different complexity for lift-off predictions. One
model was an extension of the eddy dissipation model (EDM) (Magnussen and Hjertager, 1976) in
which the ignition delay is obtained from a tabulated database created by complex chemistry
calculations and the other was the perfectly-stirred reactor (PSR) model which computes multistep
chemical kinetics in each computational cell making use of an in-situ adaptive tabulation (ISAT)
method (Pope, 1997) to reduce the computational time. Lift-off heights were compared with the
results of Idicheria and Pickett (2006). The lift-off was identified by the 2200 K iso-line. It was
shown that both models were able to predict the lift-off dependency on oxygen concentration and
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mixture temperature. However, quantitative differences exist. With both models, the lift-off height
was over-predicted. In the case of the EDM model, the differences were large and attributed to the
inadequacies of the ignition model employed in the work.
Venugopal and Abraham (2007a) modeled lift-off in diesel jets using diffusion flamelet
extinction as the criterion for identifying lift-off. Two chemical kinetic mechanisms, a 37-species
56-step mechanism (Peters et al., 2002) and a 159-species 1540-step mechanism (Seiser et al., 2000)
were employed to model kinetics. The computed results across the range of conditions considered
were in agreement (in trends) with experimental results (Siebers and Higgins, 2001; Siebers et al.,
2002). Also the measured extinction scalar dissipation rate correlated well with scalar dissipation
rate at the measured lift-off height. Not surprisingly, the results were sensitive to the kinetic
mechanism employed. Venugopal and Abraham (2007b) employed the representative interactive
flamelet (RIF) approach of Pitsch et al. (1995), with multiple flamelets, to compute the lift-off height
for varying conditions of injection pressure, ambient temperature and oxygen concentration. They
showed that with multiple flamelets the lift-off height could be predicted. The approach is, however,
computationally expensive since multiple flamelets have to be interactively solved.
Bajaj et al. (2013) employed the unsteady flamelet progress variable (UFPV) model (Pierce
and Moin, 2004; Ihme et al., 2005; Ihme and Pitsch, 2008; Ihme and See, 2010) to model ignition
and flame lift-off in diesel jets. In this model, all thermochemical quantities are parameterized by
mixture fraction, reaction progress parameter, and stoichiometric scalar dissipation rate by the
solution of unsteady flamelet equations (Peters, 2000). A presumed PDF closure model was
employed to evaluate Fávre-averaged thermochemical quantities. For this, a beta-distribution was
used for the mixture fraction, and Dirac-delta function distributions for the reaction progress
parameter and the stoichiometric scalar dissipation rate. These Fávre-averaged thermochemical
quantities were tabulated in UFPV libraries and were used as the turbulent combustion model for the
RANS simulations. Numerical simulations were conducted for a wide range of parameters including
variations in chamber temperature, pressure, density, and oxygen fraction, and nozzle diameters. The
computed ignition delay and flame lift-off heights agree within about 25% of the measured values
(Pickett et al., 2005, http://www.ca.sandia.gov/ECN). This model also allows the use of detailed
chemical kinetics through tabulation without considerably increasing the computational time.
From this discussion, it can be concluded that if the only objective of the simulations is to
predict ignition delay and model flame lift-off height, RANS models are adequate. The more
important objective is, however, to also predict soot and NOx emissions accurately. The prediction of
soot and NOx is likely to be dependent on the highly transient nature of the reacting turbulent jet.
Furthermore, large scale turbulent structures and unsteady effects (e.g. extinction, re-ignition, flame
weakening) are likely to influence mixing and subsequently soot and NOx formation. In fact, these
structures have also been suggested to influence flame lift-off (Broadwell et al., 1984). The RANS
models are unable to represent these effects. Large-eddy simulation (LES) is potentially a powerful
tool to study the mechanism(s) of flame lift-off and the major factors affecting it because large-scale
mixing and transient effects, which are believed to play an important role in flame dynamics near the
lift-off height, are resolved in LES. The effect of unsteady flame dynamics and large structures on
soot and NO formation can also be studied.
In the present study, LES is carried out of a jet generated by injecting n-heptane vapor at 373
K into air at temperature of 1000 K and a pressure of 40 bar with a velocity of 150 m/s
(corresponding to Re=250,000) through an orifice diameter of 200 µm. While the pressure,
temperature, and orifice diameter and representative of diesel engine conditions, the injection
velocity is about a factor of 4 smaller. It is selected to reduce the computational overhead. The
4
computational domain, and the subgrid models used to model the turbulence and turbulence-
chemistry interactions are discussed next. Results and discussion follow. The paper will end with
summary and conclusions.
2. Computational Model
The computations are performed in a three-dimensional domain which extended 150
diameters in the axial direction and 75 diameters in the radial direction. Figure 1 shows the
computational domain and the boundary definitions. The computational grid consists of
approximately 7.9 million grid points (350 x 150 x 150). The grid is stretched in both the axial and
radial directions with the maximum resolution located along the jet centerline. The grid spacing in
the axial direction varies from 0.25 jet diameters near the inlet boundary to 0.50 jet diameters near
the outlet boundary, and the grid spacing in the radial direction varies from approximately 0.10 jet
diameters at the jet axis to 1.70 jet diameters at the side boundaries. Except for the inlet boundary,
all of the domain boundaries are implemented as subsonic non-reflecting outflow conditions. The
implementation details of these boundary conditions were discussed in Abraham and Magi (1997)
and Anders et al. (2007). Due to the presence of the higher velocity, temperature, and density
gradients, the Artificial Diffusivity Scheme (ADS) sub-grid scale model introduced by Kawai and
Lele (2010) was employed to obtain stable results.
Figure 1. Computational domain and boundary conditions for LES.
The UFPV model is used as the turbulence-chemistry interaction model. Note that this model
has been employed by Bajaj et al. (2013) within the context of RANS simulations to model ignition
and flame lift-off. n-Heptane is employed as the surrogate fuel as in the work of Bajaj et al. (2013).
A 37-species chemical reaction mechanism developed by Peters et al. (2002) is employed to
generate the UFPV libraries. In this model, the reaction rates are tabulated as a function of 3
independent variables - mixture fraction Z, scalar dissipation rate χst, and the progress variable C.
For the tabulation, 51 points are used in the Z coordinate, 10 points in χst coordinate and 21 points in
the C coordinate. The accuracy of the resolution adopted has been assessed by refining the number
5
of points and repeating the computations. Additional details about the modeling approach can be
found in Bajaj (2012) and Bajaj et al. (2013).
3. Results and Discussion
The LES of the jet was carried out until 0.83 ms; the lift-off height reaches a steady value at
approximately 0.65 ms. Figures 2 (a) - (f) show the transient evolution of the mixture fraction and
temperature profiles in the central X-Y plane at several instants after start of injection (ASI). The
left-hand side column shows the mixture fraction and the right-hand side column shows the
temperature. The different stages of ignition, flame development and flame stabilization are evident
in these figures. When the fuel is injected into the domain, it penetrates into the chamber while
entraining the hot surrounding air. The mixing of the fuel and hot air is followed by the formation of
product radicals and rise in temperature. It is found that the ignition delay is about 0.28 ms based on
the criterion of when the temperature first reaches 1500 K.
(a)
(b)
(c)
6
(d)
(e)
(f)
Figure 2. Transient evolution of the mixture fraction (LHS) and temperature (RHS) profiles in the
central X-Y plane at (a) 0.29 ms, (b) 0.33 ms, (c) 0.36 ms, (d) 0.42 ms, (e) 0.59 ms and (f) 0.83 ms
ASI.
Figure 2 (a) shows the mixture fraction and temperature profiles at a time of 0.29 ms.
Ignition is noticeable at the leading edge of the jet. This ignition kernel grows with time as evident
by comparing Fig. 2 (a) with Fig. 2 (b). Meanwhile additional ignition kernels are noticeable in the
jet. These ignition kernels develop spatially in time, and then merge to form a continuous flame (see
Fig. 2 (e)). Figure 2 (f) shows that the flame stabilizes at the lift-off location. The lift-off height for
this case is seen to be at the approximate axial distance of x/D = 40, i.e. 8 mm. There is no
noticeable propagation of the flame upstream and the stabilization occurs at approximately the
distance where the farthest upstream ignition occurs. Figures 2 (c) to (e) also show the processes of
local ignition and extinction at various locations upstream of the lift-off height. For example, it is
seen that ignition occurs at an axial location of about 30D, upstream of the lift-off height, but the
local strain rates which evolve in time extinguish these ignition kernels. This is discussed in more
detail later in the section.
Figure 3 shows the dynamics of the flame near the lift-off height. Figure 3 (a) shows the
temperature contours and iso-lines of mixture fraction (Z=0.05 and Z=0.08), overlaid with the
7
velocity vectors, at a time of 0.65 ms. It is seen that the maximum temperatures are observed in
regions where the mixture fraction lies between 0.05 and 0.08. This is not surprising as the
stoichiometric mixture fraction for n-heptane is 0.062. There are regions of the jet where the mixture
fraction lies in this range but high-temperature reactions do not occur because the strain rates are
large. The flame stabilization location is seen to be strongly affected by the local flow field (Figs. 3
(b) to (e)). Another interesting aspect of the flame is the presence of a large vortex at the flame
stabilization location, as seen in Fig. 3 (e). These vortices may play a role in stabilizing the flame,
but this needs further investigation.
(a) (b)
(c) (d)
8
(e)
Figure 3. Flame dynamics near the lift-off height (a) 0.65 ms, (b) 0.70 ms, (c) 0.75 ms, (d) 0.80 ms,
(e) 0.83 ms ASI. The dimensions on the axes are in m. Orifice diameter is 0.0002 m.
When discussing the results of Fig. 2, the occurrence of local extinction at locations upstream
of the lift-off location was pointed out. Figure 4 shows the flooded temperature contours and the iso-
contour of scalar dissipation rate of value 500 s-1
, i.e. close to the extinction scalar dissipation rate of
n-heptane, in the central X-Y plane at different time instants between the formation and extinction of
these ignition kernels. The ignition kernels are circled in the figure. At t=0.53 ms, ignition kernels
appear at an axial location of about 7 mm. As seen in Figure 4 (a), the iso-contour of extinction
scalar dissipation rate (χe) is located close to the ignition kernel. Also note that this kernel is very
close to the edge of the potential core of this jet. With increasing time, the turbulent velocity field
causes the χe contour to start engulfing the ignition kernel as shown in Figs 4 (b) to (d). At a time of
0.545 ms, the ignition kernel is seen to be completely extinguished. This discussion highlights that
the processes near the lift-off height are highly unsteady. Furthermore, it shows that while flame
stabilization is primarily controlled by the ignition scalar dissipation rate, extinction plays a role
further upstream. In fact, the gradients in strain rates are relatively sharp near the lift-off height and
the extinction and ignition scalar dissipation rates do not occur far apart. This may explain why lift-
off heights computed on the basis of extinction scalar dissipation rates (Venugopal and Abraham,
2007a) also give reasonable agreement with measured values.
(a)
(c)
Figure 4. Local extinction of ignition
(b) 0.535 ms, (c) 0.54 ms and (d) 0.545 ms
Figure 5 shows the transient development of the flame at a location downstream of the lift
off height. As in Fig. 3, the maximum temperature lies between the mixture fractions of 0.05 and
0.08. At a time of 0.65 ms, the ignition kernels are still growing and not
As these kernels grow, they are stretched and
and (c). To show the growth and stretch of the kernels more clearly, one of the developing kernels is
circled in Figs. 5 (a) to (c). At a time of 0.70 ms (Fig.
from its initial location at 0.65 ms. The shape of the kernel is highly stretched by the local flow
The high strain rates near the kernel also cause it to split into
kernels then grow in time and merge to form a single kernel at 0.75 ms (Fig.
front is seen after time of 0.80 ms
1700 K. The development of the flame is seen to be highly unsteady. The flame thickness and the
location of the maximum temperature vary strongly with time.
mixture fraction contour values is indicative of the strain. Shorter distances c
strain. In general, no flames or (only) weak flames are observed when the distances are short, i.e.
higher strain. In most cases, it appears as if ignition does not occur in these regions or the flame does
not propagate into these regions. There are instances where an existing flame is weakened by
increasing strain. These dynamics are also evident near the lift
that a lifted flame results from a complicated interplay of ignition, kernel growth, flame
and local extinction processes which determine the transient flame structure.
9
(b)
(d)
. Local extinction of ignition kernels at locations upstream of the lift off height (a) 0.53 ms,
(b) 0.535 ms, (c) 0.54 ms and (d) 0.545 ms ASI.
shows the transient development of the flame at a location downstream of the lift
, the maximum temperature lies between the mixture fractions of 0.05 and
ms, the ignition kernels are still growing and not connected (see
As these kernels grow, they are stretched and advected by the local flow-field as seen in Figs.
To show the growth and stretch of the kernels more clearly, one of the developing kernels is
(a) to (c). At a time of 0.70 ms (Fig. 5 (b)), the kernel has convected downstream
from its initial location at 0.65 ms. The shape of the kernel is highly stretched by the local flow
The high strain rates near the kernel also cause it to split into a large and a small kernel. These two
kernels then grow in time and merge to form a single kernel at 0.75 ms (Fig. 5 (c)).
front is seen after time of 0.80 ms (Figs. 5 (d) and (e)). Notice that the minimum contour value is
opment of the flame is seen to be highly unsteady. The flame thickness and the
location of the maximum temperature vary strongly with time. The normal distance between the two
mixture fraction contour values is indicative of the strain. Shorter distances c
strain. In general, no flames or (only) weak flames are observed when the distances are short, i.e.
higher strain. In most cases, it appears as if ignition does not occur in these regions or the flame does
ons. There are instances where an existing flame is weakened by
These dynamics are also evident near the lift-off height (see Fig. 3).
a complicated interplay of ignition, kernel growth, flame
and local extinction processes which determine the transient flame structure.
at locations upstream of the lift off height (a) 0.53 ms,
shows the transient development of the flame at a location downstream of the lift-
, the maximum temperature lies between the mixture fractions of 0.05 and
onnected (see Fig. 5 (a)).
field as seen in Figs. 5(b)
To show the growth and stretch of the kernels more clearly, one of the developing kernels is
(b)), the kernel has convected downstream
from its initial location at 0.65 ms. The shape of the kernel is highly stretched by the local flow-field.
a large and a small kernel. These two
(c)). A merged flame
Notice that the minimum contour value is
opment of the flame is seen to be highly unsteady. The flame thickness and the
The normal distance between the two
mixture fraction contour values is indicative of the strain. Shorter distances correspond to greater
strain. In general, no flames or (only) weak flames are observed when the distances are short, i.e.
higher strain. In most cases, it appears as if ignition does not occur in these regions or the flame does
ons. There are instances where an existing flame is weakened by
off height (see Fig. 3). This shows
a complicated interplay of ignition, kernel growth, flame weakening
10
(a) (b)
(c) (d)
11
(e)
Figure 5. Flame dynamics downstream of the lift-off height (a) 0.65 ms, (b) 0.70 ms, (c) 0.75 ms, (d)
0.80 ms, (e) 0.83 ms ASI.
Figure 6 shows the vorticity (flooded contours) and temperature (line contours) distributions
near the lift-off height. The vorticity is seen to have a patchy distribution, with very large values of
vorticity interspersed between low values of vorticity. One noticeable aspect of Fig. 6 is that the lift-
off height (X = 8mm) is characterized by the presence of large patches of high vorticity at nearby
upstream locations. It can also be noted that the high temperature regions are all located at the edge
of the jet, where the vorticity is relatively low (< 20,000 s-1
). At the centre of the jet, the vortices are
strong enough to prevent the formation or growth of ignition kernels. Of course, the fact that the
mixture at the centre of the jet is rich can also be a reason for the absence of flames at the centre of
the jet.
Figure 6. Vorticity (flooded) and temperature (lines) distribution near the lift-off height at 0.83 ms
ASI.
12
Figure 7 shows the evolution of the scalar dissipation (χ) contours with time near the lift-off
height. Also shown are the iso-lines of temperature (1700 K, 1900 K, 2100 K and 2300 K). In the
figure, white color indicates regions of low scalar dissipation rate (χ < 5 s-1
). Similar to the
distribution of the vorticity, χ is also seen to have a patchy distribution where regions of high χ are
interspersed within regions of low χ. The high temperature regions are all seen to be located at
regions where χ is low as is evident in Figs. 7 (a) to (e). To show the evolution of χ with time and its
effect on the flame structure, one of the regions of low χ is circled in Figs. 7 (a) to (e). For
convenience, this island of low χ is denoted as A in the following discussion. At a time of 0.65 ms
(Fig. 7 (a)), the island A is located at a region of high temperature. In time, the island A grows in size
and also gets advected away from its initial location (Figs. 7 (b) to (d)). Since, low values of χ favor
the formation of ignition kernels, the iso-lines of temperature follow the motion of island A.
(a) (b)
(c) (d)
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(e)
Figure 7. Scalar dissipation rate and temperature evolution near the lift-off height (a) 0.65 ms, (b)
0.70 ms, (c) 0.75 ms, (d) 0.80 ms, (e) 0.83ms ASI.
To compare the mechanism of flame development predicted by LES and RANS, a RANS
simulation was performed for the same computational domain and boundary conditions. The same
code and models employed by Bajaj et al. (2013) are employed for this simulation. Figure 8 shows
the transient development of the flame predicted by RANS. The ignition delay predicted by RANS
simulation is about 0.75 ms, as opposed to the value of 0.28 ms predicted by LES. One of the
reasons for the difference in the predicted ignition delay is thought to be due to the differences in the
definition of the scalar dissipation rate between the two methods. The RANS simulation also shows
a single ignition kernel forming at the leading edge of the jet where the mixture is rich, as opposed to
the formation of multiple ignition kernels in the case of the LES. An ignition front is then seen to
propagate from the initial ignition location to the stoichiometric location. Then, a flame front
propagates upstream until it stabilizes at the lift-off location. This is the same mechanism discussed
by Bajaj et al. (2013), who also argued that the lift-off height is at the location where the local scalar
dissipation rate is equal to the ignition scalar dissipation rate. The steady lift-off height predicted by
RANS is found to be 10 mm, which is 25% higher than that predicted by LES. Although the
mechanisms of flame development predicted by LES and RANS are different, the mechanism which
leads to flame stabilization is related to ignition scalar dissipation rate in both cases.
Figure 8. Transient development of temperature conto
4. Conclusions
In the present study, LES
computational domain at high temperature and pressure conditions
engines is performed. It is seen that ignition occurs at multiple locations a
The ignition kernels grow in time
determined by the minimum axial distance below which the local flow conditions do not favor the
formation or growth of ignition kernels. The flame structure is seen to be highly unsteady, and
affected strongly by the local flow
14
. Transient development of temperature contours for the vapor jet using RANS simulations.
In the present study, LES of a lifted n-heptane turbulent reacting
at high temperature and pressure conditions representative of those in diesel
. It is seen that ignition occurs at multiple locations along
The ignition kernels grow in time and merge to form a continuous flame front. The lift
the minimum axial distance below which the local flow conditions do not favor the
formation or growth of ignition kernels. The flame structure is seen to be highly unsteady, and
affected strongly by the local flow-field. The unsteady flow-field leads to large
urs for the vapor jet using RANS simulations.
jet injected into a
representative of those in diesel
the edges of the jet.
to form a continuous flame front. The lift-off height is
the minimum axial distance below which the local flow conditions do not favor the
formation or growth of ignition kernels. The flame structure is seen to be highly unsteady, and
rge variations in the
15
vorticity and scalar dissipation rate fields in the domain, which influence the processes of kernel
growth, flame weakening and local extinction.
The mechanism of ignition and flame development predicted by LES is seen to be different
from that predicted by RANS. In RANS, ignition occurs at a location toward the leading edge of the
jet and a flame propagates from the leading edge until it stabilizes at the lift-off height where the
local scalar dissipation rate is equal to the ignition scalar dissipation rate. To the extent that the lift-
off height is determined by the ignition scalar dissipation rate in both cases, there is agreement
between RANS and LES.
Acknowledgements
The authors would like to thank the National Institute of Computational Sciences (NICS) and
eResearch SA (eRSA) for providing the computing resources for this work. Financial support for
this work was provided by Caterpillar, Inc.
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