International journal of automotive technology, tập 10, số 6, 2009

Copyright © 2009 KSAE
1229−9138/2009/049−01

International Journal of Automotive Technology, Vol. 10, No. 6, pp. 645−652 (2009)

DOI 10.1007/s12239−009−0076−3

HCCI COMBUSTION CHARACTERISTICS DURING OPERATION
ON DME AND METHANE FUELS
Y. TSUTSUMI , A. IIJIMA , K. YOSHIDA , H. SHOJI and J. T. LEE
1)*

1)

1)

1)

1)

2)

Department of Mechanical Engineering, College of Science and Technology, Nihon University,
1-8-14 Kanda-Surugadai, Chiyoda-gu, Tokyo 101-8308, Japan
School of Mechanical Engineering, Sungkyunkwan University, Gyeonggi 440-746, Korea
2)

(Received 28 July 2008; Revised 19 December 2008)

ABSTRACT−The Homogeneous Charge Compression Ignition (HCCI) engine has attracted much interest because it can

simultaneously achieve high efficiency and low emissions. However, the ignition timing is difficult to control because this
engine has no physical ignition mechanism. In addition, combustion proceeds very rapidly because the premixed mixture
ignites simultaneously at multiple locations in the cylinder, making it difficult to increase the operating load. In this study, an
HCCI engine was operated using blended test fuels comprised of dimethyl ether (DME) and methane, each of which have
different ignition characteristics. The effects of mixing ratios and absolute quantities of the two types of fuel on the ignition
timing and rapidity of combustion were investigated. Cool flame reaction behavior, which significantly influences the ignition,
was also analyzed in detail on the basis of in-cylinder spectroscopic measurements. The experimental results revealed that
within the range of the experimental conditions used in this study, the quantity of DME supplied substantially influenced the
ignition timing, whereas there was little observed effect from the quantity of methane supplied. Spectroscopic measurements
of the behavior of a substance corresponding to HCHO also indicated that the quantity of DME supplied significantly
influenced the cool flame behavior. However, the rapidity of combustion could not be controlled even by varying the mixing
ratios of DME and methane. It was made clear that changes in the ignition timing substantially influence the rapidity of
combustion.

KEY WORDS : Internal combustion engine, Combustion, HCCI, DME, Methane, Spectroscopic measurement

1. INTRODUCTION

et al., 2006; Sato et al., 2006). This study examined the

method of using a blend of two types of fuel. The test fuels
used were dimethyl ether (DME), which tends to autoignite
easily because of its active low-temperature oxidation reactions, and methane, which does not autoignite readily, as it
has no low-temperature oxidation reaction mechanism. The
heat release rate was analyzed to investigate the influence
of each type of test fuel on combustion behavior when the
fuel mixing ratios were varied. Spectroscopic techniques
(Shoji et al., 1994, 1996) were used to measure the light
emission intensity and absorbance of HCHO, which is

rapidly produced in cool flame reactions.

The Homogeneous Charge Compression Ignition (HCCI)
engine (Thring, 1989) can simultaneously reduce nitrogen
oxide (NOx) and particulate matter (PM) emissions (Aoyama
et al., 1996) because, among other factors, the air and fuel
are premixed homogeneously and operation is possible in
the lean mixture region. Another reason for the increased
interest the HCCI combustion process is that it achieves
thermal efficiency on par with that of diesel engines. However, it is difficult to control the ignition timing of HCCI
combustion because the fuel is ignited by the temperature
rise resulting from compression. Furthermore, the fact that
combustion occurs simultaneously throughout the combustion
chamber causes the pressure to rise too quickly.
Various methods of controlling HCCI combustion have
been proposed, including varying the compression ratio
(Hyvonen, 2005), varying the intake air temperature (Yoshida
et al., 2005), applying exhaust gas recirculation (EGR)
(Urushihara et al., 2003; Urata et al., 2004; Persson et al.,
2004; Iijima et al., 2007), and using two types of fuel
having significantly different ignition characteristics (Ozaki

2. TEST FUELS
2.1. Characteristics of DME and Methane
The properties of DME and methane are shown in Table 1
(Glassman, 1996). DME has drawn interest as an alternative
fuel for compression ignition engines because its high
cetane number allows for compression ignition. It also has
a negative temperature coefficient region in which the
ignition delay is not shortened even though the mixture

reaches a higher temperature due to compression. For that
reason, it displays a multi-stage heat release pattern attribut-

*Corresponding author. e-mail:
645


646

Y. TSUTSUMI et al.

Table 1. Properties of test fuels.
Fuel
DME
Methane
Molecular Formula
CH3OCH3
CH4
Cetane Number
>55
0
Auto Ignition Temperature [K]
623
905
ed to low-temperature and high-temperature oxidation reactions.
Methane has vastly different ignition characteristics from
DME. It does not autoignite easily because it has a cetane
number of zero and displays only a single-stage heat release
pattern ascribable to high-temperature oxidation reactions.
2.2. DME and Methane Reaction Mechanisms

Figure 1 shows the oxidation reaction process of a blended
DME and methane fuel (Pilling
., 1997; Konno, 2003).
DME reactions (denoted as A in the figure) are divided into
two processes. One reaction process (1) begins from the
first O2 addition and, depending on the temperature region,
follows a path to a second O2 addition; the other reaction
process (2) proceeds without any addition of O2. At low
temperatures below 800 K, reaction (1) takes place and is
accelerated by a chain-branching reaction (cool flame region).
As the temperature rises further, the process switches to
reaction (2), which is a chain propagation reaction, such
that acceleration of the reaction ceases (negative temperature coefficient (NTC) region (Leppard, 1998; Shoji
.,
1992)) in spite of the temperature rise. A subsequent increase
in temperature induces reaction (3), resulting in excessive
production of OH radicals and causing an acceleration of
the reaction, leading to autoignition. In relation to the
temperature rise, two-stage ignition (Koyama
., 2001)
occurs owing to the progression from a cool flame through
the NTC region to autoignition as the reactions proceed
from (1) to (3).
In the case of a blended DME and methane fuel, the OH
radicals produced by the reaction of DME are consumed by
et al

et al

et al

Figure 1. Oxidation reaction process of blended DME and
methane fuels.

the initial H-atom abstraction reaction (5) of methane. This
is said to influence the progress of the oxidation reaction of
DME. In the cool flame region of DME, rapid production
of HCHO occurs, and therefore attention was focused on
HCHO in this study in order to investigate cool flame
behavior.

3. EXPERIMENTAL PROCEDURE
3.1. Experimental Equipment
Specifications for the test engine are given in Table 2, and
the configuration of the test equipment is shown schematically in Figure 2. A 4-cycle air-cooled single-cylinder diesel
engine was used as the test engine. The engine inducted a
premixed mixture that was ignited by compression to accomplish HCCI combustion. Mass flow controllers (denoted as
(C) in the figure) were used to control the respective supply
of DME and methane. The cylinder pressure was measured
with a crystal pressure transducer (P). In order to investigate the engine operating condition, K-type sheath thermocouples were used to measure the combustion chamber
wall temperature and the intake air temperature.
The equipment shown in Figure 3 was attached between
the cylinder head and the cylinder as well as to the piston
crown for measurement of light emission and absorption.
Flame light was extracted through a quartz window and
introduced into a spectroscope via an optical fiber cable.
Light was separated at a wavelength of 395.2 nm, corresponding to the light emission wavelength of HCHO. The
inside of the combustion chamber was also irradiated with
light from a xenon lamp and the transmitted light was introduced through an optical fiber cable into the spectroscope.
Light was separated at a wavelength of 293.1 nm correTable 2. Specifications of test engine.

Number of cylinders
1
Bore×Stroke
76×66 mm
Displacement
299 cm3
Compression ratio
12:1
Intake valve close
54 deg. ABDC
Exhaust valve open
56 deg. BBDC

Figure 2. Configuration of test equipment.


HCCI COMBUSTION CHARACTERISTICS DURING OPERATION ON DME AND METHANE FUELS

Figure 3. Schematic of spectroscopy system.
sponding to the absorption wavelength of HCHO (Gaydon,
1974). The wavelength resolution of the spectroscope used
in the light emission and absorption measurements was 4.0
nm in terms of the half-bandwidth value. The separated light
in each case was input into a photomultiplier for conversionto an electric signal. The output voltage of the photomultiplier was regarded as the emission intensity of the
flame light. For the transmitted light from the xenon lamp,
absorbance AHCHO was calculated using Equation (1) below,
where E0 denotes the baseline output voltage of the photomultiplier at bottom dead center and E denotes the output
voltage at each crank angle.
E0 – EAHCHO= -----------(1)
E0

In the experiments, the test engine was operated at 1400
rpm, and the intake air temperature and the combustion
chamber wall temperature were controlled to 313 K and
353 K, respectively. The quantity of fuel supplied was kept
within the range where misfiring and knocking did not
occur.
3.2. Method of Calculating Heat Release Rate
In a combustion process with a fast burning velocity, the
rate of change in the specific heat ratio influences the
calculated heat release rate. Accordingly, it is important to
take into account that rate of change when calculating the
heat release rate (HRR) for HCCI combustion, which proceeds extremely rapidly. Therefore, in this study, the change
in the in-cylinder gas composition and the temperaturerelated change in the specific heat ratio were factored into
the HRR calculation (Shudo et al., 2000; Muto et al.,
2006). The specific heat ratio κ (n , T) was calculated based
on the in-cylinder gas composition ni and average gas
temperature T at crank angle θ. Taking into account the rate
of change in the specific heat ratio dκ /dθ, the HRR was
calculated with Equation (2) below.
PV - ⋅ d-----κdV- ⎞ − -------------1 ⎛ -----dQ
------- =---------V dP + κ P -----(2)
dθ ⎠ ( κ – 1) dθ
dθ κ – 1 ⎝ dθ

647

In calculating the HRR, the composition and number of
moles of the gaseous body filling the cylinder were determined from the intake air mass and quantity of DME and
methane consumed. The change in the number of moles of
the fuel was calculated, under the assumption of complete

combustion, by finding the cumulative heat release from
the measured cylinder pressure data. Using the change in
the number of moles of the fuel, the respective change in
the number of moles of O2, CO2, and H2O was found. In the
case of a blended DME and methane fuel, the autoignition
temperature of methane is higher than that of DME, as
indicated by the fuel properties in Table 1. Therefore, the
change in the in-cylinder gas composition was calculated
on the assumption that methane burned after the DME had
burned. The average temperature of the in-cylinder gas was
calculated using the equation of state for an ideal gas. The
specific heat of each component was calculated at that
temperature (Prothero, 1969; Fujimoto et al., 2006) and
then the average specific heat ratio of the working gas was
found.
3.3. Experimental Conditions
Figure 4 shows the range of the injected heat value of the
fuel per cycle. Experiments were conducted under the
conditions defined in the four cases below in order to
investigate in detail how changes in the injected heat value
of DME and methane influenced combustion.
Case 1: Only DME was supplied and the injected heat
value of DME QDME was varied. This condition
was used to investigate the basic combustion
characteristics when DME was supplied as a
single component fuel.
Case 2: Both DME and methane were supplied. The
injected heat value of methane QCH4 was varied
while keeping that of DME QDME constant. This
condition was designed for investigating the combustion characteristics of methane as a single

component fuel. However, the test engine could
not be operated under this experimental condition

i

Figure 4. Operating map.


648

Y. TSUTSUMI et al.

because the high autoignition temperature of
methane gave rise to misfiring. Therefore, a constant amount of DME was injected.
Case 3: Both DME and methane were supplied. The
methane share of the injected heat value γCH4
(=QCH4/Qin) was varied while keeping the total
injected heat value Qin (=QDME+QCH4) constant.
Because the injected heat value of the fuel has a
large influence on ignition characteristics, the
influence of the mixing ratio of DME and methane
was investigated while keeping the quantity of
fuel injected constant.
Case 4: Under the conditions of Case 3, the intake air
temperature was adjusted so that the ignition timing for each level of the methane share of the
injected heat value γCH4 was 10 degrees or 2 degrees
before top dead center (BTDC). The influence of
the ignition timing was excluded in this case
because of its large influence on combustion
characteristics.

4. RESULTS AND DISCUSSION
4.1. Investigation of Separate Control of Ignition Timing
and Operating Load
Figure 5 shows the HRR results for Case 1. With only
DME as the test fuel, heat release of the high-temperature
oxidation reactions increased as the injected heat value was
increased. Simultaneously, the ignition timing was advanced
considerably to an earlier crank angle (X in the figure).

These results indicate that the load and ignition timing
cannot be varied independently with a single-component
fuel of DME. Additionally, increasing the injected heat
value of DME results in extremely rapid combustion.
The indicated mean effective pressure (IMEP) relative to
the injected heat value is compared in Figure 6 for Cases 1
and 2. For Case 1, the IMEP increased due to the increase
in heat release until the injected heat value reached point A.
However, it was observed that IMEP stopped increasing
after point A because the ignition timing advanced too far.
Even though the injected heat value was increased, it did
not increase the load owing to the advance of the ignition
timing.
The HRR results for Case 2 are shown in Figure 7. The
ignition timing (Y in the figure) did not change appreciably
even though the injected heat value of methane was
increased. It was also seen that the heat release of the hightemperature oxidation reactions increased. These results
indicate that varying the injected heat value of methane
alone can change the load, without changing the ignition
timing. As is also clear from the IMEP graph in Figure 6,

the IMEP continued to increase because the ignition timing
did not change even though the injected heat value was
increased. Furthermore, the knock limit was higher compared with Case 1 (i in Fig. 6) because combustion did not
become extremely rapid owing to the fact that the ignition
timing did not change.
An investigation was made of the ignition timing θ ign and
the interval τ from the occurrence of a cool flame until
ignition, under a condition where the quantities of fuel supplied were varied. The definitions of θ ign and τ are shown in
Figure 8. The fuel supply conditions were those of Case 1
with only DME as the fuel, Case 2 in which the injected

Figure 5. Influence of QDME on HRR in Case 1.
Figure 7. Influence of QCH4 on HRR in Case 2.

Figure 6. Injected heat value (Qin) vs. IMEP in Case 1 and
Case 2.

Figure 8. Definitions of cool flame used for analysis.


HCCI COMBUSTION CHARACTERISTICS DURING OPERATION ON DME AND METHANE FUELS

649

HRRcool as a function of the injected heat value of DME. It
can be seen that the plots of HRRcool are arranged along the
same line in relation to the increase in the injected heat
value of DME under all of the conditions examined.
Accordingly, the following reason can be inferred for the
dependence of τ and θ ign on the injected heat value of

DME, as shown in Figure 10. This is attributed to the fact
that the level of cool flame activity is strongly dependent
on the injected heat value of DME and is little influenced
by that of methane.

Figure 9. Influence of QDME on ignition timing (θ ign) and
ignition delay after occurrence of a cool flame (τ ).
heat value of methane was varied (while keeping that of
DME constant at values of QDME=240, 260, 278, and 297 J/
cycle), and Case 3 in which the mixing ratios of DME and
methane were varied while keeping the total injected heat
value Qin constant at 357, 387 and 417 J/cycle, respectively.
Figure 9 shows θ gn and τ in relation to the injected heat
value of DME as the parameter. The results in this figure
show that the plots of θ ign and τ continued along the same
line even though the injected heat value of methane differed, indicating that θ ign and τ were dependent on the injected
heat value of DME. This result suggests that, under the
condition used in this study (γCH4 <50%), the cool flame
reaction was not influenced by the H-atom abstraction
reaction of methane, which consumes the OH radicals
produced by the reaction of DME in cool flame (reaction
(5) in Figure 1). This indicates that the ignition timing can
be varied independently by changing the injected heat
value of DME. The results show that t became shorter and
θ ign was advanced as the injected heat value of DME was
increased.
Figure 10 shows the maximum HRR of the cool flame
i

4.2. Influence of DME and Methane Mixing Ratios on

Rapidity of Combustion Following Ignition
Figure 11 shows the HRR results for Case 3. Increasing the
methane share of the injected heat value while keeping the
total injected heat value constant had the effect of retarding
the ignition timing considerably (Z in the figure). It also
shows that combustion proceeded gradually even though
the injected heat value was constant. Figure 12 shows the
HRR results for Case 4 in which the ignition timing was
controlled to 10 degrees BTDC. The same heat release
pattern is seen following ignition irrespective of the methane
share of the injected heat value. Figure 13 shows the
maximum HRR and combustion duration as a function of
the ignition timing in Cases 3 and 4. It can be noted that the
results shown for Case 4 are for ignition timing control to
10 degrees BTDC and 2 degress BTDC, respectively. The
maximum HRR values decreased and the combustion
duration became longer as the ignition timing was retarded,
indicating that the rate of combustion was moderated. This
is probably attributable to a drop in the in-cylinder temperature commensurate with the increase in the cylinder
volume due to the faster descent speed of the piston during

Figure 11. Influence of γCH4 on HRR in Case 3.

Figure 10. Influence of QDME on maximum HRR of cool
flame.

Figure 12. Influence of γCH4 on HRR in Case 4.


650

Y. TSUTSUMI et al.

Figure 13. Ignition timing vs. maximum HRR and combustion duration.
the combustion period, when ignition timing is retarded.
Moreover, since the plots are arranged nearly along the
same line under all of the conditions, this indicates that the
rapidity of combustion is strongly dependent on the ignition
timing.

began to decline sharply. At the onset of heat release from
the cool flame (denoted as “Occurrence of Cool Flame” in
the figure), absorbance at the wavelength corresponding to
HCHO began to rise (point a). In the interval between
points “a” and “b”, where the absorbance waveform has a
steep slope, the light emission intensity at the wavelength
corresponding to HCHO shows a peak (denoted as “Light
Emission” in the figure). This behavior is assumed to indicate rapid production of HCHO by the cool flame reactions
in the a-b interval. Subsequently, HCHO was not produced
in the b-c interval because it was in the NTC region and
absorbance remained relatively flat. It is also clear that
point “c” occurred near the time of ignition (denoted as
“Ignition” in the figure). These results are thought to reflect
the rapid production of HCHO by the cool flame and then
its decomposition owing to the temperature rise induced by
ignition. The large increase seen in the light emission intensity after point “b” is attributed to the light emission of a
continuous spectrum resulting from the recombination
reaction of CO and O, and not to light emission from
HCHO. This suggests that the recombination reaction of

4.3. Light Emission Intensity and Light Absorbance of
HCHO
Figure 14 shows a typical example of the experimental
light emission and absorption results for HCHO. From the
top, the figure shows the cylinder pressure P, HRR, light
emission intensity EHCHO at a wavelength of 395.2 nm, and
absorbance AHCHO at a wavelength of 293.1 nm. In the
absorbance waveform, point “a” is where the absorbance
began to rise, point “b” is where the increase subsequently
started to become more moderate, and point “c” is where it

Figure 15. Experimental results for Case 1 (QCH4=0 J/cycle
constant).

Figure 14. Typical experimental results.

Figure 16. Experimental results for Case 2 (QDME=297 J/
cycle constant).


HCCI COMBUSTION CHARACTERISTICS DURING OPERATION ON DME AND METHANE FUELS

Figure 17. Experimental results for Case 3 (Qin=417 J/cycle
constant).
CO and O produces a strong light emission at wavelengths
between 250 nm and 500 nm (Iijima and Shoji, 2007).
These features were observed for all of the fuel supply and
injected heat value conditions used in this study.
Figures 15, 16, and 17 present the measured light emission
and absorption waveforms for the conditions of Cases 1

through 3, respectively. The results in Figure 15 for Case 1
show that the ignition timing advanced and absorbance
increased (i in the figure) as the injected heat value of DME
was increased. The results in Figure 16 for Case 2 indicate
that absorbance did not change appreciably in the interval
to ignition even though the injected heat value of methane
was varied. The results in Figure 17 for Case 3 reveal that
the ignition timing was retarded and absorbance decreased
(d in the figure) as the methane share of the injected heat
value was increased. These results suggest that the ignition
timing was advanced in proportion to the quantity of HCHO
produced.
Absorbance Ab at point “b”, as defined in Figure 18, was
examined in order to investigate the influence of HCHO
produced by the cool flame. Figure 19 shows Ab as a
function of the injected heat value of DME in Case 1 with
only DME as the test fuel, in Case 2 where the injected heat
value of methane was varied while keeping that of DME
constant at QDME=240, 260, 278, and 297 J/cycle, and in
Case 3 in which the mixing ratios of DME and methane
were varied while keeping the total injected heat value Qin
constant at 357, 387, and 417 J/cycle, respectively. The
figure shows that Ab increased linearly as the injected heat

Figure 19. Influence of QDME on Ab.
value of DME was increased. This makes it clear that the
quantity of HCHO produced by the cool flame was largely
dependent on the injected heat value of DME and was not
influenced by the injected heat value of methane. Within
the scope of the conditions used in this study, the quantity

of HCHO produced increased in proportion to the increase
in the injected heat value of DME. Accordingly, the HCHO
behavior as measured with absorption spectroscopy revealed that cool flame behavior was substantially influenced
by the change in the injected heat value of DME.
5. CONCLUSIONS

The experimental results measured under the conditions
used in this study for HCCI engine operation with blended
DME and methane fuels made the following points clear:
(1) The interval from the appearance of the cool flame until
ignition as well as the ignition timing were strongly
dependent on the injected heat value of DME and were
little influenced by that of methane. Accordingly, the
ignition timing can be varied independently by the
quantity of DME supplied, while adjusting the quantity
of methane supplied makes it possible to vary the load
independently.
(2) Under a condition where the total injected heat value
was kept constant, varying the mixing ratios of DME
and methane did not suppress the rapidity of combustion. This suggests that the rapidity of combustion is
strongly influenced by the ignition timing. As the ignition timing was retarded, the maximum HRR decreased and the combustion duration became longer.
(3) The results of in-cylinder spectroscopic measurements
showed that the quantity of substance corresponding to
HCHO produced during cool flame reactions was strongly dependent on the amount of DME supplied and little
influenced by the amount of methane supplied.
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Copyright © 2009 KSAE
1229−9138/2009/049−02

International Journal of Automotive Technology, Vol. 10, No. 6, pp. 653−662 (2009)

DOI 10.1007/s12239−009−0077−2

MODEL-BASED CONTROL SYSTEM DESIGN IN A UREA-SCR
AFTERTREATMENT SYSTEM BASED ON NH SENSOR FEEDBACK
3

M. DEVARAKONDA , G. PARKER , J. H. JOHNSON and V. STROTS

1)*

1)

1)

2)

ME-EM Department, Michigan Technological University, Houghton, MI 49931, USA
Advanced Aftertreatment Technologies, Navistar Inc, Melrose Park, IL 60160, USA

1)

2)

(Received 25 November 2008; Revised 1 February 2009)

ABSTRACT−This paper presents preliminary control system simulation results in a urea-selective catalytic reduction (SCR)

aftertreatment system based on NH3 sensor feedback. A four-state control-oriented lumped parameter model is used to analyze
the controllability and observability properties of the urea-SCR plant. A model-based estimator is designed via simulation and
a control system is developed with design based on a sliding mode control framework. The control system based on NH3
sensor feedback is analyzed via simulation by comparing it to a control system developed based on NOx sensor feedback.
Simulation results show that the NH3 sensor-based strategy performs very similarly in comparison to a NOx sensor-based
strategy. The control system performance metrics for NOx index, urea index, urea usage, and NH3 slip suggest that the NOx
sensor can be a potential alternative to a NOx sensor for urea-SCR control applications.

KEY WORDS : Urea-SCR catalyst, Model-based estimation, Observer, Control system design, NH sensor, Sliding mode
3

control

1. INTRODUCTION

sensitivity to NH , which is a limitation for accurate NO
feedback. This limitation can be overcome to certain extent
through a NO sensor model with the objective of determining the components of the NO sensor signal. To
implement this strategy on a vehicle, for example, using an
FTP (Federal Transient Procedure) cycle, an accurate NO
sensor model is needed to reduce NO and NH , a topic
which is not addressed in the literature.
Another approach used to overcome this limitation is to
use an NH sensor, developed by Delphi (Wang et al.,
2007) and in the process of testing for SCR control applications. NH sensors, which are relatively new to automotive applications, have been researched from a materials
standpoint in Europe in order to meet the NO emission
regulations (Moos et al., 2002). Additionally, Wingbrant et
al. developed a MISiC-FET (Metal Insulated Silicon Carbide
Field Effect Transistor) for detection of NH in SCR
systems (Wingbrandt et al., 2005). The authors concluded
that the presence of water vapor was shown to have the
largest effect on the sensors at low levels. Because the NO
sensors are limited for closed loop SCR control applications
because of the sensor’s cross-sensitivity towards NH , NH
sensors are being explored as an alternative (Wang, 2007).
This gives the motivation for the study and analysis of the
NH sensor in simulation for possible SCR control applications. This paper focuses on the development of a modelbased estimator and control strategy based on NH sensor
feedback and compares its control system performance in
simulation to a control strategy based on NO sensor feedback.
3

Urea-SCR catalysts are regarded as the leading NO aftertreatment technology for compliance with the 2010 NO
emission standards set by the US EPA (Environmental
Protection Agency) for heavy duty diesel engines. SCR
catalysts have long been used for NO reduction in
stationary applications such as power plants and industrial
reactors (Tronconi et al., 1996). In such applications, NH
is introduced directly into the catalyst, which reduces the
NO in the flue gases. With regards to mobile sources,
urea-SCR catalysts are a proven technology in Europe for
meeting Euro III and Euro IV diesel engine NO standards
(Schar et al., 2006). A urea solution spray is injected into
the exhaust gas upstream of the SCR catalyst. At sufficiently high exhaust gas temperature, the urea droplets
evaporate and mix with the exhaust gas. NH is formed as a
result of urea decomposition and HNCO hydrolysis reactions in the exhaust pipe and in the SCR catalyst. NO is
reduced to N via several SCR reactions aided by the
catalyst.
The urea-SCR catalyst must be actively controlled to
ensure high NO reduction, low NH slip, and low urea
consumption. NO sensors are placed downstream of the
SCR catalyst to provide NO feedback to the closed loop
control system in order to determine the urea injection rate
necessary to minimize NH slip and maximize NO conversion efficiency. The state-of-the-art NO sensors have crossx

x

x

x

x

x

x

3

3

x

x

3

3

x

2

x

3

3

x

x

3

3

3

x

x

x

3

x

3

x

x

*Corresponding author. e-mail:
653

3


654

M. DEVARAKONDA, G. PARKER, J. H. JOHNSON and V. STROTS

The paper is organized as starting with a brief description of the four-state model with parameter identification,
and validation is then presented followed by a linear systems
analysis of the model based on NH3 sensor feedback. The
estimator design is discussed next, followed by the control
system design. Sample simulation results analyzing the
control system performance based on NOx and NH3 sensor
models are discussed, and then a summary of conclusions
is presented.

oxidation reaction. NH3 adsorption and desorption reactions
are included in the models as shown in Equation (5) and
Equation (6).
NH3 +S → NH3*
(5)
NH3* → NH3 +S
(6)
For detailed information on the reaction rates included in
the four-state model, the reader is referred to (Devarakonda,
2008a).

2. FOUR-STATE MODEL

2.1. Assumptions and Equations
A high level illustration of a urea-SCR aftertreatment
system is shown in Figure 1. As the exhaust gas moves
through the SCR catalyst, molecules of NO, NO2, and NH3
are transported to the stagnant thin layer at the surface of

the monolith wall and then take part in the catalytic reactions.
Reaction products are desorbed back into the thin layer,
then are transported into the bulk flow, and to the monolith
exit. The major assumptions in the four-state model (in
addition to the assumptions listed in Devarakonda et al.
(2008a)) are:
(1) Mass transfer is neglected in the model, meaning that
the chemical kinetics in the catalyst are reaction-controlled.
(2) Surface phase concentrations of the species are neglected.
(3) Reaction rates are defined as a function of ammonia
storage and gas phase concentrations of NO and NO2.
The four-state model contains the gas phase concentrations of NO, NO2, and NH3 and ammonia storage as the
states, shown in Equation (7). All of the six reactions considered in the higher order model discussed in Devarakonda
(2008b) are considered in the four-state model.
C· NO=−QCNO−k1ΩθCNOCNO −k2ΩθCNOCO +QCNO,
C· NO =−QCNO −k1ΩθCNOCNO −k3ΩθCNO +QCNO ,
θ· =− ( k6 + k4 )θ+k5CNH − k1CNOCNO θ

Upadhyay et al. proposed a three-state control-oriented
lumped parameter model that contains the gas phase concentrations of NOx and NH3 and ammonia storage as the
states (Upadhyay and van Nieuwstadt, 2006, 2002). Incorporating NO and NO2 individually into the model enables the
tracking of NO2 slip from the tailpipe, which is a major
concern for many environmental agencies, such as CARB
and MSHA, and a recent reference shows concern about
the increase of NO2 levels in the atmosphere while NO
levels decrease (Czerwinski, 2007). A higher order model
with detailed modeling methodology and assumptions is
discussed by Devarakonda et al. (Devarakonda et al.,
2008a) and is not discussed here for compactness. Also, the
four-state model is selected for this work because it has

been shown that a strategy based on individual NO-NO2
concentrations performs better than the NOx based strategy
(Devarakonda et al., 2008b). The chemical reactions relevant
to the four-state model are shown here.
The three main SCR reactions used in the model are the
‘fast’ SCR, the ‘standard’ SCR, and NO2 based SCR (‘slow’
SCR), respectively shown in Equations (1) to (3):
4NH3 +2NO+2NO2 → 4N2 +6H2O
(1)
(2)
4NH3 +4NO+O2 → 4N2+6H2O
8NH3 +6NO2 → 7N2 +12H2O
(3)
The authors in references (Krocher et al., 2006) and
(Devadas et al., 2006) report that the rate of reaction shown
in Equation (3) is comparable to the fast SCR reaction rate
and is greater than the standard SCR reaction rate on Fezeolite catalysts at high temperatures. Only oxidation of
NH3 to N2 is used in the model given by Equation (4).
4NH3 +3O2 → 2N2 +6H2O
(4)
The authors in references (Tronconi et al., 2007) and
(Devadas et al., 2005) report 100% selectivity of NH3
oxidation towards N2 up to T=600oC, which is an advantage in Fe-zeolite catalysts. The NO formed from NH3
oxidation can be considered as an intermediate species that
participates in the SCR reaction, and therefore, NH3 oxidation to NO can be neglected. Fe-zeolites also exhibit distinct NO oxidation to NO2 capability as reported in (Devadas
et al., 2006) and (Devadas et al., 2005). This reaction is
also neglected in the model because NO2 is reduced by NH3
through the SCR reactions, even when produced by the NO

2

2

2

2

3

in

2

2

2

2

−k2CNOCO θ−4/3k3CNO θ−k5θCNH
·CNH =k6Ωθ−(k5Ω + Q)CNH +k5ΩθCNH +QCNH ,
2

3

2

3

(7)

3

3

3

in

in

The reaction rate constants ki are defined using the
Arrhenius expression shown in Equation (8).
E

– -----i

(i =1...6)
(8)
Here, Ai are the pre-exponential factors, Ei are the
activation energies of the reactions, and R is the universal
gas constant. A total of 13 parameters need to be identified
in this reduced order model, which includes the pre-exponk =A e
i

Ki

i

Figure 1. High level illustration of the urea-SCR aftertreatment system.

MODEL-BASED CONTROL SYSTEM DESIGN IN A UREA-SCR AFTERTREATMENT SYSTEM

655

Table 1. Reaction rate parameters identified for the fourstate model.
Reaction
A
E (kJ/mol)
m 4.50E14 ---------------------Fast SCR
100
mol – sec
m 3.50E5 -------------------Standard SCR
75
mol – sec
6

2

Figure 2. Schematic of aftertreatment setup used to conduct
tests for parameter identification exercise.
ential factors and activation energies of the six reactions
considered in the model and the total NH adsorption capacity.
3

3

NO SCR

m 2.83E8 -------------------mol – sec

85

NH Oxidation

14.44E6 -----sec

106

3

2

3

2.2. Parameter Identification and Model Validation
Experiments were conducted on a Navistar I6 7.6-L engine.
The experimental setup with a Horiba emission bench and
an MKS FTIR analyzer at locations denoted as a and b is
shown in Figure 2.
Tests were designed to facilitate parameter identification
and validate the model. The urea flow rate was manually
set to cover a wide range of NH /NO ratios in the test. The
experiments were conducted at various steady-state engine
operating points. The step changes from point to point were
used to capture the transient effects of temperature and
mass flow rates on catalyst dynamics. The parameter identification exercise was formulated as an optimization problem and Matlab’s simplex method-based optimization
function fminsearch is used to identify the parameters
while minimizing the cost function in the species concentrations. The optimization problem is defined as:

Find the model parameters (x ) where x are the preexponential factors and activation energies of the reactions
which minimize the cost function in Equation (9).
3

1

∑ (C
N

i

i, s

–C

) , i=NO, NO , NH
2

(9)

3

C and C in Equation (9) refer to the simulated and test
i,s

i,t

concentrations of the species. The reaction rate parameters
identified (calibrated) for the four-state model are tabulated
in Table 1.

The total NH adsorption capacity, Ω, is identified as 157
moles of NH per m of exhaust gas volume. Figure 3
shows the validation of the four-state model based on the
test data in NO, NO , and NH concentrations.
A detailed analysis demonstrating the adequacy of reduced order models is shown in Devarakonda, 2008a. The
adequacy is demonstrated based on the RMS error in the
concentrations of species and NO and NH conversion
efficiencies.
3

3

3

2

47

NH Desorption

13.26E9 -----sec

114

3

3

i

2

i, t

=1

m 5.24E6 -------------------mol – sec

3

x

i

J = ---N

NH Adsorption

Figure 3. Four-state model validation using data from the
tests on the engine-aftertreatment set-up shown in Figure 2.
states required to design the control strategy.
A detailed observability analysis is done based on NH
feedback from the NH sensor. To design a model-based
estimator based on the NH sensor, the linear system must
be observable.
3

3

3

3.1. Observability and Controllability Analysis
The linear system of equations is shown in Equation (10).

3

x

3

3. SIMULATION-BASED ANALYSIS OF NH
SENSORS

3

A high level illustration of the control strategy based on the
four-state model is shown in Figure 4. The estimator is
based on the NH concentration from a NH sensor downstream of SCR and estimates the concentrations of the four
3

3

Figure 4. Model-based control system with a state estimator and a control law based on the four-state model.


656

M. DEVARAKONDA, G. PARKER, J. H. JOHNSON and V. STROTS

⎧

⎫
⎪ C· NO ⎪ A A
11 12
⎪
⎪
⎪ C· NO2 ⎪ A21 A22
⎨
⎬=
⎪ θ· ⎪ A31 A32
⎪
⎪
⎪ C· NH3 ⎪ 0 0
⎩
⎭

A13
A23
A33
A43

⎧ CNO ⎫
⎪
⎪
⎪C ⎪
x=⎨ NO2 ⎬,
⎪ θ ⎪
⎪C ⎪
⎩ NH3 ⎭
⎧ CNO,
⎪

⎪C
u=⎨ NO2,
⎪ θ
⎪C
⎩ NH3,

in

in

in

linear system formulated based on NH3 sensor feedback is
observable and controllable. This indicates that, based on
NH3 measurement downstream of the SCR catalyst, a
model-based estimator can be designed to estimate the
unmeasurable state θ in the catalyst.

⎧ ⎫
0
⎪ 1⎪
0 x+⎪ 1 ⎪ u
⎨ ⎬
A34 ⎪ 0 ⎪
A44 ⎪⎩ 1 ⎪⎭

(10)

est

est

C = [ 0 0 0 1 ] , x ε R4 , u ε R3 , y ε R 2
Here, u is the input vector, x represents the dynamic state
vector, and c represents the output matrix. The elements of
the A matrix remain the same as shown in Appendix A.
The observability matrix O is then formulated using the

expression shown in Equation (11).

…

(11)

n

resulting in

0
O= 0

0 0 1
0 A43 A44

E11 E12 E13 E14
F11 F12 F13 F14

(12)

The details of the observability matrix are shown in

Appendix A. The rank of the observability matrix is then
determined and is found to be equal to the rank of the
linearized plant, thus indicating that the system is observable. The observability matrix might lose rank (full rank=
4) at certain conditions, and the system can become
unobservable. There is one such instance when this can
happen theoretically, but it is not true physically. When A43
=0, the observability matrix loses its rank (rank=2). Physically, this is not possible, as the imposed condition results
in a relation that should satisfy
(13)
k5 Ω ( 1 – θ 0 ) = − Q
which ends up in a relation shown in Equation (14).
Q
θ0 =1+ --------k5 Ω

3

est

3

est

T

⎫
⎪
⎪
⎬,
⎪
⎪

⎭

⎧
⎫
⎪ C ⎪
⎪ CA ⎪
⎪
⎪
ρ ( O ) =⎨ CA2 ⎬=n
⎪
⎪
⎪
⎪
⎪ CA – 1 ⎪
⎩
⎭

3.2. Model Based Estimator Design
A linear estimator of the form shown in Equation (15) is
proposed.
(15)
x· = f (x , u, t)+L(CNH – CNH , )
where
x =[CNO, CNO , θ CNH , ] denotes the estimated states,
f indicates the nonlinear reduced order model given in
Equation (7), L is the estimated gain vector, and CNH3 is the
measured NH3 sensor reading.
In this work, the NH3 sensor reading is assumed to be the
NH3 concentration measured by FTIR analyzer. The value
of L must be chosen such that the estimator is stable. Since

the linear portion of the nonlinear model is stable, this is
possible. The estimator gains are tuned in the simulation
and are obtained as L=[−5; −5; −1; −5]T. With these gains,
the error in the true and estimated states are found. The
percentage absolute error between the true and estimated
states falls to 0.03% within 1.2 seconds of simulation time.
This indicates that the estimator gains yield a faster
convergence to the true states.

(14)

which can never happen as θ0 cannot be greater than 1. The
controllability matrix is shown in Appendix A and the
matrix remains at its full rank at all conditions. Thus, the

2

est

est

3

est

est

3.3. Model-Based Control System Design
The control objective is to minimize the NO, NO2, and NH3
slip from the SCR catalyst. A modified conversion efficiency (based on the definition in Upadhyay and van

Nieuwstadt (2006) in reference to NO, NO2, and NH3) is
defined in Equation (16).
η

CNO, + CNO2, – CNO, – CNO2, – λCNH3,
= ----------------------------------------------------------------------------------------------CNO, + CNO2,
in

T

in

out

CNH ,

out

in

out

in

=ηNOx−λ --------------------------------CNO, + CNO2,
3

(16)

out

in

in

This definition is used in defining the response goal,
which can be expressed as ep=e· p=0 where ep=pdes−p and
p is a linear combination of the four-state model states
CNO, CNO2, and CNH3 shown in Equation (17).
p=CNO+CNO +λCH3
(17)
Pdes is the sum of the desired NO, NO2, and NH3 concentrations coming out of the catalyst, which is set to zero in
this work. Substituting the model equations into the response
goal, the dynamic portion of the control law is obtained as
shown in Equation (18).
2

CNH ,
3

,

in dyn

1
=CNH3, +--- (CNO, + CNH3, – CNO, – CNO2, )
est

λ

est

est

in

in

1
+ --Q- (k5Ω(1 – θ )CNH3, − k6Ωθ )
est

est

est

1- ·
+λ-----Q p +2 Ωθ k1CNO, CNO2,
des

est

est

est

+Ωθ k2CNO, CO2+Ωθ k3CNO2,
est

est

est

est

(18)


MODEL-BASED CONTROL SYSTEM DESIGN IN A UREA-SCR AFTERTREATMENT SYSTEM
The NO and NO concentrations (C , C ) can be
obtained using a NO sensor or an engine NO emissions
model in conjunction with models for aftertreatment components upstream of the SCR catalyst, such as DOC and/or
CPF. The models should incorporate reversible NO-NO
oxidation in both DOC and CPF as well as the NO reduction by PM.
The complete control law is created by appending a
correction term that penalizes deviations from the objective
of ep=0 , as shown in Equation (19).
NO,in

2

x

NO2,in

x

2

2

(19)

CNH3,in =CNH3,in,dyn −Γ sgn ( ep )

Here, Γ is a control variable that can be tuned in the
simulation to meet the control objective. Based on the sign
of sgn (ep) , the sign of the sgn function changes. The sgn
function is defined as:
⎧ –1 : x < 0 ⎫
⎪
⎪
0:x=0 ⎬
⎪
⎪
⎩ 1:x > 0 ⎭

(20)

sgn ( x ) =⎨

Ensuring stability in the presence of the model, measurement and disturbance uncertainties place constraints on
the design parameter. These constraints are developed
using Lyapunovs’ direct method illustrated below. A
candidate Lyapunov function, as shown in Equation (21), is
created.
1
2

V = --- ep

2

(21)

If V· <0 for the four-state model dynamics, then the
closed loop system is asymptotically stable.
(22)
V· = ep e· p= −Γ ep
Thus, Γ >0 guarantees closed loop stability.

4. MODEL REDUCTION FOR REAL TIME
IMPLEMENTATION

2

The quadratic equation is solved to obtain C . C is then
obtained using the expression shown in Equation (24).
NO2

CNO =

3

2

NO

QC
---------------------------------------------------------Q + k Ωθ C + k Ωθ C

(24)

NO, in

1

NO2

2

O2

The value of C is solved by setting the time derivative to
zero and is shown in Equation (25).
NH3

CNH3 =

Q CNH3,

+ k6 Ω θ

----------------------------------k ( 1 – θ )Ω + Q
in

(25)

5

For control system performance analysis and sensor

related studies, the experimental setup shown in Figure 5 is
used. In accordance with the setup, two catalyst models are
used in series.
NH storage is the only state estimated in both models,
and the concentrations of the species are calculated as
steady-state expressions shown in Equations (23)~(25).
Based on the corresponding sensor signals (NO sensor or
NH sensor), model-based estimators are designed based
on the plant and their respective sensor models, which are
explained in the next section.
3

x

3

5. SENSOR MODELS AND EXPERIMENTAL
VALIDATION
The NO sensor model is developed based on the NO
sensor data and the species concentrations from the FTIR
downstream of the second SCR catalyst (SCR2 in Figure
5). The NO sensor signal with cross-sensitivity towards
NH can be represented as a function of NO, NO , and NH
concentrations, as shown in Equation (26).
(26)
S = A1CNO +A2 CNO + A3( α ) CNH
where S is the NO sensor signal in ppm, and A , A , and
A (α) are the coefficients to be obtained from the NO
sensor model. The variable α is known as the Normalized
Stoichiometric Ratio (NSR) and is defined as the concentration of NH in ppm to the concentration of NO in ppm in

the exhaust gas, as given by Equation (27).
x

x

x

3

2

2

3

3

x

1

2

3

x

3

The time constants associated with the concentrations in

the four-state model are on the order of micro-seconds, and
hence the four-state model cannot be used for control
strategy implementation on a vehicle. Therefore, the fourstate model is reduced to a one-state model with θ as the
only state in the model, with the concentrations of NO,
NO , and NH species calculated as steady-state expressions.
Setting the time derivatives of C· NO and C· NO shown in
Equation (7) to zero, a quadratic equation in C is obtained as shown in Equation (23).

657

x

CNH

α = ----------3
CNO

(27)

x

NH concentration at the inlet of the catalyst is not
measured and is calculated from the urea injection flow
rate as shown in Equation (28). It is based on the assumption that one mole of urea forms two moles of NH and is
available for NO conversion in the catalyst.
3

3

x

NO2

aCNO2 +bCNO2 +c=0
2

where a=k ΩθQ+k k Ω θ
and b=Q +k Ωθ QC +k Ωθ QC
1

2

1 3

2

O2

+ k2 k 3 Ω θ
2

and

2

2

2

1

2

NO, in

CO2 − k1Ωθ QC NO2,

+ k3 Ω θ Q

in

c = − Q CNO2,in− k2Ω θ QCNO2,in CO2

(23)
Figure 5. Schematic of aftertreatment test setup for control
system performance studies.


658

M. DEVARAKONDA, G. PARKER, J. H. JOHNSON and V. STROTS

Figure 6. Coefficient of NH3 component in the NOx sensor
signal as a function of α using Test 2 data as input.

Figure 7. Experimental validation of NOx sensor model
using Test 2 data as input.

· urea MWexh- × 1E6
---------CNH =2.0 × m

m· exh --------------MW

estimator is intended for real time implementation, the onestate model is used. A linear NH3 storage estimator based
on NOx sensor feedback is shown in Equation (30).
θ· est= f (C C θ C C C
C )

3

(28)

urea

Here, CNH3 is the concentration of NH3 in ppm. m· urea is the
mass flow rate of urea in kg/sec, and MWurea is the molecular weight of urea in gm/gm-mole (MWurea=60 gm/gmmole). MWexh is the molecular weight of exhaust gas in gm/
gm-mole (MWexh=28.8 gm/gm-mole).
For experimental validation, CNO, CNO2, and CNH3 are the
concentrations obtained from the FTIR analyzer at the SCR
2 outlet (Γ). A1 and A2 are obtained when no urea was
injected in the aftertreatment system. The coefficients are
determined as A1=1.0 and A2=0.95 with a mean of 1 ppm
and a standard deviation of 2 ppm. The coefficient A3 is
determined as a function of a for various test cases and is
shown in Figure 6. The functional relationship between A3
and α is used in the NOx sensor model. The sensor model is
validated using two different sets of test data. Exhaust gas
temperature and α are also shown on the figures to illustrate
the effect of these variables on the NOx sensor signal.
Figure 7 shows the validation of the NOx sensor model
using the data from Test 2 as input. The details of the test

are shown in Appendix B.
The NH3 sensor is assumed not to have a cross-sensitivity towards NO and NO2 species. Here, the NH3 concentration from the FTIR analyzer is assumed to be the NH3
sensor signal. The NOx sensor model given in Equation
(26) is slightly modified to obtain the NH3 sensor model
and is shown in Equation (29).
(29)
S1=A3(α)CNH3
For further details about the NH3 sensor, the reader is referred
to (Devarakonda, 2008c) and (Devarakonda et al., 2008d).

NO ,

L(C

NO2 ,

est ,

C

NH3 ,

NO,in ,

NO2 ,in ,

NH3 ,in

(30)
CNO, CNO2, and CNH3 are the steady-state concentrations of

the species calculated in Equations (23)~(25), respectively.
CNOx,meas is the measured NOx concentration from the sensor,
and CNOx,est is the estimated NOx concentration calculated
from the estimator with the NOx sensor model shown in
Equation (26). L is the scalar estimator gain tuned in
simulation. The estimator with the NOx sensor model is
tested in simulation and the concentrations are compared
using the test data discussed in Appendix B, as shown in
Figure 8. The tuned estimator gain is 1E-4.
A linear NH3 storage estimator based on NH3 sensor
feedback is shown in Equation (31).
θ· est f (CNO CNO2 θest CNH3 CNO,in CNO2,in CNH3,in)
+

=

NOx ,meas –

,

+

L(C

,

NH3 ,meas –

NOx ,est

)

,

,

C

NH3 ,est

)

,

,

(30)

6. CONTROL SYSTEM PERFORMANCE BASED
ON NO SENSOR AND NH SENSOR MODELS
x

3

An estimator is designed based on the two catalyst model
to compare the downstream NOx concentrations from the
model-based estimator and the test data. As the designed

Figure 8. Test and estimated NOx sensor signal comparison
using Test 2 data as input.

MODEL-BASED CONTROL SYSTEM DESIGN IN A UREA-SCR AFTERTREATMENT SYSTEM

659

Here, m· NO , is defined based on the US EPA’s approach
of defining NOx regulation (x=2) where the total NOx at the
inlet and outlet of the catalyst is calculated as an equivalent
of NO2. Also, such an approach has been suggested in
Czerwinski, 2007. Hence, m· NO , is calculated from the
total concentration of NOx in ppm as a function of the
molecular weight of NO2 and is defined in Equation (33).
2

eq

2

t

m· NO2,

f

∫0

eq,in

=

CNO , dt
MW

-------------------------- MWNO m·
t

eq,in

x in

2

exh

(33)

exh

is calculated in the same manner and is defined in
m· NO ,
Equation (34).
2

eq,out

t

Figure 9. Test and estimated NH3 sensor signal comparison
using Test 2 as input.

CNH3,meas is the measured NH3 from the NH3 sensor (FTIR
concentration) and CNH3,est is the estimated NH3 concentration calculated from the estimator with the NH3 sensor
model shown in Equation (29). L is the scalar estimator
gain tuned in simulation. The estimator with the NH3
sensor model is tested in simulation and the concentrations
are compared using the test data discussed in Appendix B
as shown in Figure 9. The tuned estimator gain is 1E-3.
The closed loop control strategies with their respective
model-based estimators and sensor models are compared.
The control system based on the NOx sensor model is
hereby denoted as ‘NOx sensor’ and the control system
based on the NH3 sensor model is hereby termed as ‘NH3
sensor’. The closed loop control strategies are compared
based on the NOx index, urea index, NH3 index, and urea
usage. All indices are calculated based on a lumped quantity NOx rather than individual NO and NO2 concentrations.
The NOx index is defined as an NO2 equivalent, as shown
in Equation (32).
·
·
∫ m NO , , dt ∫ m NO , , dt
-------------------------------------------------------------------(32)
·
m
dt
,
∫
t

t

t

f

0

2

eq in
t

t

–

t

f

0

2

eq out

m· NO2,

eq,out

CNO , dt
MW

---------------------------- MWNO m·
t

=

f

∫0

x out

2

exh

(34)

exh

For this analysis, the urea index is defined as a function
of the overall NOx quantity reacted and is shown in
Equation (35). As the urea index is defined in NOx, 1:1
stoichiometry between NOx and NH3 is assumed.
( m· NO , − m· NO , )dt
---------------------------------------------------------------------------------------------------·
∫ m , dt
t

f

∫0

x in

t

x out

(35)

2 + 46

t

t

f

ure a in j

0

The total slip from the catalysts is calculated from both
strategies using the equation shown in Equation (36).
·
∫ CNH , dt
(36)

m· NH , ---------------------------- MWNH m·
t

t

3

out

=

f

0

3

MW

out

3

exh

exh

Here, MWNH3 is the molecular weight of NH3 (MWNH3=
17 grams/gm-mole). For both the strategies, λ is set to 0.1,
Γ is set to 0.06, and p· is set to 0.0. The performance

comparison of both the strategies in the performance
metrics mentioned is shown in Table 2. From Table 2, it
can be observed that the control strategy based on the NOx
sensor model shows a better performance than the control
des

f

0

urea inj

Table 1. Performance comparison in various metrics using NOx sensor- and NH3 sensor-based control strategies.
Urea Index
Urea
Total NH3 Slip
Strategy
NOx Index
gm
reacted
gm
urea
reacted
------------------------------------------------------------------------------------------------Unit
kg
kg
gm urea injected
gm urea injected
0.42
0.27

0.99
0.0289
NOx sensor based
NH3 sensor based
0.40
0.26
1.04
0.0315
% Change
4.7↑
4.7↑
5.3↓
9.1↓
of NOx

of

of

of

Table 2. Performance comparison in various metrics using the control strategies and the test data.
Urea Index
Urea
Strategy
NOx Index
gm
reacted
gm urea reacted----------------------------------------------------------------------------------------------Unit
kg

gm urea injected
gm urea injected
0.42
0.27
0.99
NOx sensor based
NH3 sensor based
0.40
0.26
1.04
Test data
0.43
0.28
1.01
of NOx

of

of

of

Total NH3 Slip
kg
0.0289
0.0315
0.0162


660

M. DEVARAKONDA, G. PARKER, J. H. JOHNSON and V. STROTS

strategy based on the NH sensor model in all of the
performance metrics. Though the percent improvement is
approximately 5% in NO index, urea index, and urea
usage, the control strategy based on the NO sensor model
controls the NH slip out of the SCR2 better than the NH
sensor.
Table 3 shows the comparison in NO index, urea index,
urea usage, and NH slip between the control strategies and
the test data. The NO sensor-based control strategy uses
less urea while obtaining an approximately similar NO
index and urea index, which makes it a better candidate
than the NH sensor-based control strategy whose indices
are slightly less. Both of the sensor-based control strategies
exhibit higher NH slip than the test data. This might be
due to linear approximation of the dependency of α on the
NH concentration in the NO sensor signal. An interesting
task for the future will be to study whether the NH
concentration in the NO sensor signal is dependent on á in
a polynomial formulation.
The concentrations of NO, NO , and NH species from
both sensor-based control strategies are compared to the
concentrations recorded by FTIR at the SCR2 in Figure 10.
Both strategies show similar trends in NO, NO , and
NH concentrations except at high temperatures, approximately between 200 minin exhaust gas temperatures results in slight NH slip and
thus a discrepancy in NO output. Figure 11 shows a comparison in urea injection rate from the closed loop controllers and from the test data. The estimated NH storage
curves from both control strategies are also shown in the

figure.
From Figures 10 and 11, it is observed that though the
state-of-the-art NO sensor has cross-sensitivity towards
NH , the control strategy based on a NO sensor model
shows better catalyst performance than the strategy based
on an assumed NH sensor model. One important observation from this simulation-based analysis of the NH sensor
3

x

x

3

3

x

3

x

x

3

3

3

x

3

x

2

3

2

3

3

3

x

3

x

3

3

Figure 11. Comparison of urea injection rates and estimated NH storage curves from the control strategies based on
sensor models.

3

is that the sensor can be used for model-based control by
measuring NH at SCR out. Linear systems theory showed
that the system is observable and controllable at all practical operating conditions and can used for model-based
SCR control applications as a potential alternative to NO
sensors.
3

x

7. RESULTS AND DISCUSSION
State-of-the-art NO sensors are cross-sensitive to NH and
are a drawback for real time NO control if the crosssensitivity is not compensated. A NO sensor model is
developed based on the test data and is tested in simulation
using a two catalyst model. A single state is used in the two
catalyst model and an estimator is developed in conjunction with the NO sensor model. NH sensors (which do not
exhibit cross-sensitivity according to the literature) are
analyzed in simulation and an estimator is developed based
on the sensor model. Though the two catalyst model is not
perfectly validated in simulation, it is used to study the
control strategy performance based on the sensor models.
The control strategies based on the two sensor models are
compared using the performance metrics in NO index,
urea index, NH slip, and urea usage. The control performance analysis showed that the strategy based on a NO
sensor model performed slightly better than the NH sensorbased strategy and is close to the performance metrics
calculated based on experimental data. One important outcome of the simulation-based analysis of the NH sensor is
that, in the absence of an NO sensor model, model-based
SCR control systems can be developed in conjunction with
an NH sensor and can be implemented in real time.

x

3

x

x

x

3

x

3

x

3

3

x

3

8. CONCLUSION
Figure 10. Comparison of NO, NO , and NH species
concentrations from the control strategies based on sensor
models and the test data.

2

3

An NO sensor model based on experimental data is
developed and validated using various sets of test data. The
sensor model is then tested in simulation using a one-state
x


MODEL-BASED CONTROL SYSTEM DESIGN IN A UREA-SCR AFTERTREATMENT SYSTEM
model by considering the two catalysts in series. An NH
sensor assuming no cross-sensitivity towards any other
species is analyzed using linear systems theory for observability and controllability. Sensor models and model-based
estimators based on the two catalyst models are developed
and tuned in simulation. The control strategies based on the
sensor models are then compared based on the performance metrics. The outcome of the control systems performance analysis is that the control strategy in conjunction
with the NO sensor model performs slightly better than the
NH sensor model. One important conclusion from the
analysis is that the NH sensor model, from its simulationbased performance, can be regarded as a potential candidate for SCR control applications in the absence of an
accurate NO sensor model. An interesting observation
from the analysis is that the estimated NH storage and urea
injection flow rate from the strategy based on the NH
sensor match within 2~5% of those obtained from a strategy
based on the NO sensor.
3

x

3

3

x

3

3

x

ACKNOWLEDGEMENT−The authors would like to thank
Navistar Inc for their financial support throughout the project.

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Mayer, A. (2007). Diesel NO/NO /NO emissions – New
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N., Pfeifer, M., Demel, Y. and Mussmann, L. (2006).
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Devarakonda, M. N., (2008c). Dynamic Modeling, Simu2

x

x

3

2

3

lation and Development of Model Based Control Strategies in a Urea-SCR Aftertreatment System for Heavy
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University.
Devarakonda, M., Parker, G., Johnson, J. H. and Strots, V.
(2008d). Simulation based control system analysis of a
urea SCR aftertreatment system based on NH sensor
feedback. Cross-cut Lean Exhaust Emissions Reduction
3

Simullation (CLEERS) Workshop. www.cleers.org.

Devarakonda, M., Parker, G., Johnson, J. H., Strots, V. and
Santhanam, S. (2008a). Adequacy of reduced order
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661

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Wang, D., Tao, S., Cabush, D. and Racine, D. (2007).
Ammonia sensor for SCR NOx reduction. Diesel
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Wingbrant, H., Svenningstorp, H., Salomonsson, P., Kubinski,
D., Visser, J., Lofdahl, M. and Spetz, A. (2005). Using a
MISic-FET sensor for detecting NH in SCR systems.
IEEE Sensors J., 5, 1099−1105.
3

Appendix A: Linear Systems Analysis for NH3
Here, Sensor Feedback
The elements of the A matrix linearized about the equilibrium point (C , C , θ , C ) are shown in Equation
(37).
NO,0

NO2,0

0

NH3,0

A11=– (Q + k CNO2,0Ωθ0 + k CO2θ0Ω)
A12=–k CNO,0θ0Ω
A13=– (k CNO,0CNO2,0Ω + k CNO,0CO2Ω)

A21=–k CNO2,0θ0Ω
A22=– (Q + k CNO,0Ωθ0 + k θ0Ω)
A23=– (k CNO,0CNO2,0Ω + k CNO2,0Ω)
A31=– (k CNO2,0θ0 + k CO2θ0)
A32=– (k CNO,0θ0 + k θ0)
A33=– (k CNH3,0+k + k CNO,0CNO2,0)
fst

std

fst

fst

std

fst

fst

stdsl

fst

sl

fst

fst

ads

std

sl

des

fst

(37)


662

M. DEVARAKONDA, G. PARKER, J. H. JOHNSON and V. STROTS
+k

std

Appendix B: Input Data for Test 2

CNO,0 CO2 +ksl CNO2,0 +ksox

A34=kads – θ0kads

(

A43 = – Q + kads
A44 =kads

Ω+k

Ω−k Ωθ0)
CNH ,0 Ω

ads

ads

3

The observability matrix based on NH3 sensor feedback is
shown here in Equation (38) to explain the elements of the
matrix.
0
O= 0

0 0 1
0 A43 A44

(38)

E11 E12 E13 E14

F11 F12 F13 F14

Here,

E11 =A43 A31

Table 4. Steady state data points in test 2.
Data
point Speed Load

mexh

Units RPM M-m

kg
hr

A1B
B1B
C1B
D1B
E1B
F1B

E12 =A43 A32

E13 =A43 A33 +A44 A43
2

E14 =A34 A43 +A44

(
)
(
)

F13 =A43 A31 A13 +A43 A32 A23 +( A43 A33 + A44 A43 ) A33
2
F14 =( A34 A43 + A44 )A43

The steady state conditions used in test 2 are shown in
Table 4. The exhaust temperature, urea injection flow rate,
engine variables such as speed and load, mass flow rate at
various conditions are shown in Figure 12. The
concentrations of species NO, NO2 and NH3 at the inlet of
the catalyst are shown in Figure 13.

F11 =A43 A31 A11 +A43 A32 A21 + A43 A33 + A44 A43 A31
F12 =A43 A31 A12 +A43 A32 A22 + A43 A33 + A44 A43 A32

1400
2100
1700
2100
1700
2200

·

Texh

murea

-----

o

C

206
225
306
330
409
419

169 264
136 463
410 463
546 782
956 785
819 1018

·

Pexh

-------

kg
sec

kPa PPM PPM

38
42

87
98
143
143

101
102
102
105
106
109

NO
47
39
37
48
107
111

NO2
110
63
73
73
43
48

(39)

A. Controllability Matrix
The controllability matrix is shown here in Equation (40) to
explain the elements of the matrix.
1
C= 1
0
1

A11 +A12
A21 +A22

M13 N14
M23 N24

A31 + A32 + A34 M33 N34
A44

(40)

M43 N44

M13=A211 +A12A21+A13 A31+A11A12 +A22A12+A13A32+A13A34

+A222 +A23 A32+A23A34
M33=A31 A11 +A32 A21 +A33 A31 +A31 A12 +A32 A22 +A33 A32 +A33 A34
+A34 A44
2
M34=A43 A31 +A43 A32 +A34 A43 +A44
2
N14 =A11( A11 + A12 A21 + A13 A31 ) +A21 ( A11 A12 + A12A22 + A13 A32 )

+A31 (A11 A13 + A12A23 + A13 A33 ) +A12( A211 + A12A21 + A13A31 )
+A22 (A11 A12 + A12A22 + A13 A32 ) +A32( A11A13 + A12A23 + A13A33 )
+A34 (A11 A13 + A12A23 + A13 A33 ) +A13A34A44
2
N24 =A11( A11 A11 + A22 A21 + A23 A31 ) +A21 ( A12 A21 + A22 + A23 A32 )
+A31 (A21 A13 + A22A23 + A23 A33 ) +A12( A21A11 + A22A21 + A23A31 )
+A22 (A12 A12 + A222 + A23 A32 ) +A32( A21 A13 + A22A23 + A23A33)
+A34 (A21 A13 + A22A23 + A23 A33 ) +A23A34A44
N34 =A11( A31 A11 + A32 A21 + A33 A31 ) +A21 ( A31 A12 + A32A22 + A33 A32 )
+A31 (A13 A31 + A23A32 + A233 + A34A43 )
+A12 (A31 A11 + A32A21 + A33 A31 )
+A22 (A31 A12 + A32A22 + A33 A32 )
+A32 (A13 A31 + A12A32 + A233 + A34A43 )
+A34 (A13 A31 + A23A32 + A233 + A23A43 ) +A44 (A33 A34 + A34A44 )
N44 =A43A31A11 +A43 A32 A21 +A21 ( A43 A33 + A44 A43 )
+A43 A31 A12 +A43A32A22 +A32 (A43A33 + A44A43 )
(41)
+A34 (A43 A33 + A44A43 ) +A44 (A34A43 + A244 )
M23=A21 A11 +A22 A21 +A23 A31 +A12 A21

Figure 12. Input profiles of exhaust gas temperature, urea
injection flow rate, speed, load, and mass flow rate during
Test 2.

Figure 13. Concentrations of NO, NO2, and NH3 species at
the inlet of the SCR catalyst during Test 2.


Copyright © 2009 KSAE
1229−9138/2009/049−03

International Journal of Automotive Technology, Vol. 10, No. 6, pp. 663−668 (2009)

DOI 10.1007/s12239−009−0078−1

OH-RADICAL BEHAVIOR OF UNSTEADY LIFTED FLAME BASED ON
INSTANTANEOUS CHANGE OF THE EQUIVALENCE RATIO
S. H. JUN , T. K. KIM , J. Y. JANG and Y. KIDOGUCHI
1)*

2)

3)

1)

Department of Ecosystem Engineering, Tokushima University, Tokushima 770-8506, Japan
School of Mechanical and Automotive Engineering, Keimyung University, Daegu 704-701, Korea
The Center for Automotive Parts Technology, Keimyung University, Daegu 704-701, Korea
1)

2)

3)

(Received 16 September 2008; Revised 1 June 2009)

ABSTRACT−In an earlier study, the current authors showed that an unsteady-state lifted flame generated by an equivalence

ratio conversion system for a given fuel, was similar to a steady-state lifted flame in terms of the change characteristics from

a premixed flame to a critical flame and then to a triple flame with a diffusion flame positioned in the middle according to the
concentration difference. Therefore, this study used an OH-PLIF method to investigate the characteristics of a steady-state
lifted flame and an unsteady-state lifted flame created under conditions identical to the flames in the preceding study. PLIF
(Planar laser induced fluorescence) is practically effective for visualizing the concentration fields within a flame. The resulting
OH-radical measurements showed that an unsteady-state lifted flame created under the specific conditions used in this study
showed similar tendencies in terms of OH-radical distribution, fluorescence intensity, and liftoff height, to a steady-state lifted
flame, thereby confirming that the behavior of an unsteady-state lifted flame can be effectively predicted based on the behavior
of a steady-state lifted flame.

KEY WORDS : Lifted flame, Premixed flame, Triple flame, Unsteady flame, OH-PLIF

1. INTRODUCTION

studied the concentration of chemical species and their
temperature distribution, and noted that flame stability is
governed by the local stoichiometry and turbulence characteristics through the PLIF method. Muóniz and Mungal
(1997) studied flame propagation speed using the liftoff
height and particle image velocimetry (PIV) as the jet exit
velocity and coflow velocity varied, while supplying
methane and ethylene as fuel to the nozzle and air with a
coflow. They showed that the mean liftoff height of the
flame increases when the jet exit velocity and coflow
velocity increase, and that the flame stabilizes itself when
the local gas velocity is close to the premixed laminar
flame speed and does not exceed 3SL. Schefer and Goix
(1998) extended Muñiz and Mungal (1997)’s experiment
and carried out PIV and OH-PLIF measurements for a
turbulent lifted flame over a range of Reynolds numbers
from 7,000 to 19,500. Consequently, they found that the
mean axial velocity at the stabilization point was about five

times below the laminar burning velocity at the lowest
Reynolds number; however it was nearly 20% higher than
the laminar burning velocity when the Reynolds number
increased. Pressing et al. (1998) studied the characteristics
of the liftoff of a triple flame experimentally and analytically, while adjusting the flow velocity and the diluted
fuel and lean fuel concentration based on supplying diluted
fuel, lean fuel, and air using a three-stream coflow nozzle.
Kioni et al. (1999) studied the velocity field inside a flame
based on the PIV and OH radical distribution in a laminar

A lifted flame provides important information for a
turbulent nonpremixed flame model. The characteristics of
a lifted flame, such as the liftoff height and flame shape,
change depending on the flow velocity of the fuel and the
equivalence ratio. Thus, various studies have been carried
out to understand the stabilization mechanism of a lifted
flame. The most representative stabilization model is a
premixed combustion model. Because the fuel and air are
well mixed upstream of the leading edge of a flame and the
region of the leading edge of a flame becomes a premixed
flame, the premixed combustion model predicts that the
leading edge is stabilized in the region where the burning
velocity at that edge coincides with the velocity of the flow
supply. Recently, a triple flame (Lee and Seo, 2005) model
has also been studied as an important feature of lifted flame
stabilization (Kioni et al., 1993, 1999, Azzoni et al., 1999).
In addition, Dold (1989) suggested that the mixture fraction is an important clue when determining the structure
and propagation speed of a triple flame.
A combustion diagnostic and measurement method
using a laser (Park et al., 2002) is primarily utilized to

obtain information about the velocity field and concentration field in a combustion field and also the lifted flame
and triple flame characteristics. Schefer et al. (1994)
*Corresponding author. e-mail:
663


664

S. H. JUN, T. K. KIM, J. Y. JANG and Y. KIDOGUCHI

triple flame using PLIF. From simultaneous measurements
of the PIV, CH-PLIF, and OH-PLIF of lifted flames (Kim
., 2006), Watson
(1999) discovered that a triple
flame stabilizes in the region where the incoming gas speed
is low and close to the laminar burning velocity. Jang
(2005) and Kim and Jang (2005) studied the behavioral
characteristics of a premixed flame, critical flame, and
triple flame as function of the concentration difference
using a lifted flame stabilization model. Moreover, they
installed a direct sampling probe inside the combustion
field, measured the local concentration, and compared the
characteristics of each flame. However, they did not
measure the radical generated in the combustion process
using an optical method. Thus, in a previous study (Jun
., 2008), the current authors investigated the behavioral
characteristics of an unsteady-state lifted flame that was
varied from a premixed flame to a triple flame based on
instantaneous change of the equivalence ratio using an
equivalence ratio conversion system, and compared the

results with those for a steady-state lifted flame. While
direct photographs showed similarity between the behavioral characteristics of an unsteady-state lifted flame and a
steady-state lifted flame, an analysis of the major reactions
inside the flame was not conducted. Therefore, to confirm
the concentration characteristics of the major reactions
inside the flames, this study used an OH-PLIF method to
investigate the characteristics of the concentration fields of
a steady-state lifted flame and an unsteady-state lifted
flame.
et al

et al.

et al.

et

al

2. EXPERIMENTAL SETUP AND PROCEDURE
2.1. Experimental Setup
The experimental equipment was the same as that used in
our preceding study (Jun
., 2008) except for the addition of an Nd:YAG laser, a Dye laser, sheet beam optics, a
reflector, and a narrowband pass filter. Commercial LPG
was used as the fuel, and high purity air (99.99% purity)
comprised of 79% nitrogen and 21% oxygen was used as
the oxidant. To ensure the gases were supplied at a constant
pressure, each gas was passed through a regulator, and the
flow rate was controlled by flow meters (Matheson 602,

604) that were adjusted to a precise flow rate using a
bubble meter. After the flow meter, the gases were passed
through a mixing chamber before being supplied to the slot
burner. The mixing chamber was cylindrical in shape, 160
mm in length, 314 ml in volume, and had an inside
diameter of 50 mm. Thus, the fuel and oxidant, set at a
certain equivalence ratio, flowed into the mixing chamber
and became homogeneous as a result of the swirl flow
inside.
The slot burner, designed to alter the gas concentration,
included 4-four slots with a 10 mm width inside, 40 mm
width, and 700 mm length, which were made of a 10 mmthick acrylic plate and 0.5 mm-thick stainless steel plate
combined with bolts. Inside the slot burner, a vinyl pipe (5
et al

mm diameter and 200 mm length) and ceramic honeycomb
(1.5 mm width, 1.5 mm length, and 250 mm height) were
installed to ensure uniform velocity for the flow field. The
premixture was supplied to the two slots in the center,
while ambient nitrogen was supplied at an ambient flow to
the other two slots at the edge. The nitrogen increased the
exit velocity of the mixture to prevent any inflow of an
external oxidant as well as disruption from an outside flow.
After passing through the ceramic honeycomb, the premixture entered a contraction nozzle, and a flame was
generated due to the concentration difference. The exit of
the contraction nozzle was 21 mm wide, 30 mm long, and
made using a plaster mold based on a 3rd order polynomial
fitting of the inside shape used in the previous study conducted by Morel (1975); its rectangular shape minimizes
the three dimensional influence on a flame as pictures are
taken.

The equivalence ratio conversion system consisted of a
solenoid valve to change the equivalence ratio of each slot
of the slot burner from the condition for the generation of a
premixed flame to that of a triple flame, hardware (computer and PCI-MIO-16EI board) and software (LabVIEW).
In the slot burner, the premixture was supplied to the 2 slots
in the center, while ambient nitrogen was supplied to the
other two slots at the edges. In this study, the focus was to
measure the OH radicals in a steady-state lifted flame and
unsteady-state lifted flame, as in our preceding study (Jun
., 2008).
Figure 1 shows the experimental setup of the OH-PLIF. A
laser beam with a 532 nm wavelength was generated by an
Nd:YAG laser (Lee and Nishido, 2008), then passed
through a dye laser to form a beam with a 566 nm wavelength, and finally passed through a UVT (UV Tracker)
containing a double crystal. The result was a beam with a
283 nm wavelength that is appropriate for measuring OH
radicals. Rodamine 590 was used as the dye for the dye
laser to create the excitation wavelength. The laser beam
changed into a sheet beam after passing through the optics;
the sheet beam then passed through the upper part of the
contraction nozzle of the slot burner. The laser beam was
emitted at the moment the flame was changed by the
et al

Figure 1. Schematic diagram of experimental setup.


OH-RADICAL BEHAVIOR OF UNSTEADY LIFTED FLAME BASED ON INSTANTANEOUS CHANGE
equivalence ratio conversion system; moreover, to take a
picture, the laser and operation signal of the ICCD camera

were synchronized and triggered by a signal emitted from
the conversion system. The resolution of the ICCD camera
was 1,024×1,024 pixels, and the exposure time and gain
were set to 50 ns and 250, respectively. A narrow band pass
filter (WG-305) and UG-11 filter were also included for the
OH-radical measurement.
2.2. Experimental Procedure
The mixture and ambient flow supplied at a flow velocity
of 1.1m/s generated a lifted flame without blowing out or
flashing back to the exit of the burner. For the steady-state
lifted flame, the fluorescence intensity of the OH radicals
was measured under the conditions created by the equivalence ratio (φ ) in the center right slot in the slot burner,
which was fixed at 1.2, and the equivalence ratio (φ )
created using the center left side slot, changed stepwise
from 1.2 to 0.4. Meanwhile, for the unsteady-state lifted
flame, the fluorescence intensity of the OH radicals was
measured under the φ conditions fixed at 1.2, and φ
instantly changed from 1.2 to 0.4. As previously reported
(Jun
., 2008), the unsteady-state lifted flame stayed for
1.6 seconds, which was 1.2 to 2.8 seconds after activating
the solenoid valves, and then turned into a steady-state
lifted flame. Therefore, because the concentration changed
with the passage of time, the suggested data are expressed
according to time.
R

L

R

L

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3. RESULTS AND DISCUSSION

665

For the steady-state lifted flame at φ =1.2 and φ =1.2, there
was a thick distribution of OH radicals around the leading
edge of the flame, while the distribution downstream in the
flame was thin. As previously reported (Jun
., 2008),
the flame at φ =1.2 and φ =1.2 was a premixed flame with
the same equivalence ratio and was very round and showed
a semi-spherical shape. The shape formed by the OH
radicals was similar to the flame shape taken by the direct
photograph. As φ decreased, the OH-radical distribution
for the flames at φ =0.8, 0.7, 0.5, and 0.4 was quite different from that at φ =1.2. A decrease in φ generated a triple
flame due to the concentration difference of the mixture
supplied to the center slots in the slot burner. A triple flame
is generated because of a concentration difference when the
composition of the fuel and oxidant from the two slots are
different from each other. When a laminar flame is formed
by a partial premixed mixture, a rich mixture with more
fuel than the stoichiometric equivalence ratio is formed in
the region near the leading edge; meanwhile, a lean mixture with less fuel than the stoichiometric equivalence ratio
is formed in the other region. As the residual fuel spreads
from the rich premixed flame and the oxidant spreads from

the lean premixed flame, a diffusion flame with a stoichiometric equivalence ratio is then generated at the center,
the key feature of a triple flame. These three branches were
previously confirmed based on a direct photograph of a
triple flame (Jun
., 2008).
From the OH-radical distribution in the triple flame at
φ =0.7, 0.5, and 0.4, shown in Figure 2, the fluorescence
intensity was high around the leading edge, at the left side
of the slot burner, and around the middle. As such, a lean
R

L

et al

R

L

L

L

L

L

et al

L

During the combustion reaction, the OH radicals are
generated by the reaction of hydrogen and oxygen and then
destroyed by the combustion reaction with CO. Thus, a
thick distribution of OH radicals appeared in the region of
the lean premixed flame that contain a significant amount
of hydrogen and the diffusion flame. Furthermore, in the
region of the diffusion flame, the distribution of OH
radicals increased when they approached the slightly lean
premixed flame, yet almost disappeared when approaching
the rich premixed flame.
Figure 2 shows the shape of the OH-radical distribution
in the steady-state lifted flames, where φ was 1.2, 1.0, 0.8,
0.7, 0.5, and 0.4, while φ was 1.2. The black arrow at the
bottom of the photos identifies the slot burner nozzle exit.
As previously reported (Jun
., 2008), when changing
the concentration difference, the steady-state lifted flame
was altered from a premixed flame to a critical flame, and
then to a triple flame with a diffusion trailing flame in the
middle. Thus, the steady-state lifted flames created under
the specific conditions in this study were classified into
three groups according to the distribution shape and fluorescence intensity of the OH radicals: φ =1.2-1.0, φ =1.00.8, and φ =0.7-0.4, where φ =1.2-1.0 represents the premixed flame region, φ =0.7-0.4 represents the triple flame
region, and φ =1.0-0.8 represents the critical flame region.
L

R

et al

L

L

L

L

L

L

Figure 2. OH radical of steady state lifted flame (φ =1.2).
R


666

S. H. JUN, T. K. KIM, J. Y. JANG and Y. KIDOGUCHI

premixed flame was formed around the left side of the slot
burner by the lean mixture, while a diffusion flame was
formed in the middle due to the concentration difference
between the rich mixture and the lean mixture. The
fluorescence intensity was high in the middle of the flames
because the diffusion flame had a stoichiometric equivalence ratio (Kim
., 2006).
Based on the OH-PLIF of a triple flame, Kioni
(1999) reported that the concentration of hydroxyl radicals
was high around the leading edge and the diffusion flame

with a stoichiometric equivalence ratio. They also found
that the OH-radical concentration decreased rapidly when
moving away from the leading edge and approaching the
rich premixed flame rather than the lean premixed flame.
The presented results also showed a very high OH-radical
concentration in the middle of the flame at the location of
the diffusion trailing flame.
With the use of a gas chromatograph and sampling
probes, Kim and Jang (2005) compared the concentration
of the reactant and product inside a flame to analyze the
concentration fields of a premixed flame and triple flame.
They measured the concentrations of hydrogen and oxygen
as the reactant and carbon monoxide as the product. In the
premixed flame region, the concentrations of hydrogen and
oxygen maintained a constant value in the flame-center, yet
the triple flame region displayed a distinctive difference
that decreased rapidly, as the excess oxidant from a lean
region and hydrogen from a rich region diffused toward the
centerline downstream, resulting in a very active diffusion
combustion reaction. Similarly, in Figure 2 the premixed
flame showed an almost even OH-radical distribution
throughout the flame, whereas the fluorescence intensity of
the OH radicals in the triple flame was high due to the
active diffusion combustion reaction of hydrogen and
oxygen around the flame center.
In contrast, Kioni
. (1999) measured a thin distribution of CO in the diffusion flame, while the CO distribution was thicker in the rich premixed flame than in the
lean premixed flame. Kim and Jang (2005) also found that
the density of CO was thin in the region of the lean
premixed flame with abundant oxidants, yet increased

rapidly when approaching the region of the rich premixed
flame. Thus, because this increase of CO is a major reaction source of OH-radical extinction, the hydroxyl radical
distribution is expected to be thin in the region of the rich
premixed flame.
Figure 3 shows the shape of the OH-radical distribution
for the unsteady-state lifted flame. Because the unsteadystate lifted flames remained for only 1.6 seconds, the OHradical distribution in the unsteady-state lifted flame
appeared slightly thinner than that in the steady-state lifted
flame. However, the change trend was similar in both
flames. The unsteady-state lifted flame was created through
an instant change of the equivalence ratio from φ R=1.2 and
φ L=1.2 to φ R=1.2 and φ L=0.4 using the equivalence ratio
conversion system. As previously reported (Jun
.,
et al

et al.

et al

et

al

Figure 3. OH radical of unsteady state lifted flame.
2008), after activating the solenoid valve of the equivalence ratio conversion system, the unsteady-state flame
began to be created after t=1.2 sec and ended after t=2.8
sec. The unsteady-state lifted flame changed from a semispherical shape similar to the steady-state lifted flame to a
streamline shape that became sharper with the passage of
time. At t=1.2 sec, the flame shape was the same as that of
the steady state flame at φ R=1.2 and φ L=1.2; then, from

t=1.8-2.8 sec, the flame exhibited a diffusion trailing flame
in the middle that was similar to the steady-state flame at
φ R=1.2 and φ L=0.8-0.4. After t=2.8, the flame became the
same as the steady-state lifted flame at φ R=1.2 and φ L=0.4.
With respect to the distribution of hydroxyl radicals, the
unsteady-state lifted flame at t=1.2 sec exhibited a very
similar distribution to that of the steady-state flame at
φ L=1.2 in Figure 2. The density of hydroxyl (OH) radicals
appeared to be thick around the leading edge, and then
gradually thinned farther from the leading edge.
Over time, the fluorescence intensity of the OH radicals
became high around the leading edge of flame, at the left


OH-RADICAL BEHAVIOR OF UNSTEADY LIFTED FLAME BASED ON INSTANTANEOUS CHANGE

667

Figure 4. Intensity of OH radicals.

Figure 5. Lift-off height.

side of the slot burner, and around the middle of the flame.
After t=2.8 seconds, the flame showed a high fluorescence
intensity around the center due to the diffusion flame.
Thus, when comparing the distribution of OH radicals in
the unsteady-state lifted flame and steady-state lifted flame,
the two flames showed a similar change progression and
similar results, as previously reported based on the flameshape changes (Jun
., 2008).

Figure 4 shows a comparison of the fluorescence
intensity of the OH radicals along the horizontal direction
of a 28-mm position from the leading edge of the lifted
flames in Figures 2 and 3. In our previous study (Jun
.,
2008), the luminescence intensity was obtained at a height
of 28.9 mm from the leading edge of the lifted flames using
a direct photograph. Based on the central area of the picture
(x=0, flame center), the right area (x>0) was the region
where the flame of φ R appeared, while the left area (x<0)
was the region where the flame of φ L appeared. In addition,
the intensity at the center was greatly increased because of
the diffusion flame. Similarly, the steady-state lifted flame
and unsteady-state lifted flame in Figure 4 exhibited the
same trend for the fluorescent intensity, i.e., the gradient of
the intensity increased greatly because of the influence of
the diffusion flame in the central area; the left side of the
flames was relatively stronger in intensity than the right

side.
Figure 5 shows the liftoff height of the steady-state lifted
flame and unsteady-state lifted flame determined on the
basis of the lifted flame shape as well as the distribution of
OH radicals. The equivalence ratio, φ L is indicated on the
horizontal axis of the steady-state lifted flame, while the
time (sec) is indicated on the horizontal axis of the unsteady-state lifted flame. As previously reported (Jun
.,
2008), the liftoff height changed depending on the concentration difference of the mixture, and the liftoff height of the
unsteady-state lifted flame exhibited a similar change to
that seen with the steady-state lifted flame. The liftoff

height according to the distribution of hydroxyl radicals
also showed a similar result to that of the flame shape. The
lower sections in Figure 5 show the three regions of the
steady-state and unsteady-state lifted flame, which are
classified according to the OH-radical distribution, gradient
of the liftoff height and fluorescence intensity.
The results in Figures 2-5 and our preceding study show
that the characteristics of an unsteady-state lifted flame,
such as the liftoff height, fluorescence intensity, and OHradical distribution, are similar to those of a steady state
lifted flame. Moreover, the behavior of an unsteady-state
lifted flame involves the same phenomena as a steady-state
lifted flame, i.e., it changes from a premixed flame, to a

et al

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668

S. H. JUN, T. K. KIM, J. Y. JANG and Y. KIDOGUCHI

critical flame region, and then to a triple flame with a
diffusion trailing flame, depending on the concentration
difference.
Therefore, it is concluded that the behavior of an unsteady-state lifted flame created under the specific conditions in this study, can effectively forecast the behavior of a
steady-state lifted flame.
4. CONCLUSIONS

In a previous study, the current authors investigated the
similarity of the behavioral characteristics of an unsteadystate lifted flame and steady-state lifted flame, yet there
was no analysis of the major reactions occurring inside the
two flames. Thus, using OH-PLIF to analyze the concentration fields, experiments were performed to understand
the concentration fields of an unsteady-state lifted flame
and steady-state lifted flame with the following results:
(1) For a steady-state lifted flame, the hydroxyl radicals in
the premixed flame are mainly distributed around the
leading edge and thin out farther from the leading edge.
In addition, when increasing the concentration difference, high fluorescence intensity occurs around the
leading edge, the region of the lean premixed flame
and the central region. It appears that the central region
is the diffusion trailing flame region of a triple flame,
and the high intensity of OH radicals is due to an active
diffusion combustion reaction of hydrogen and oxygen.
(2) For an unsteady-state lifted flame, the OH radicals are
thickly distributed around the leading edge during the
initial stage of t=1.2 sec. Over time, the fluorescence
intensity increases around the leading edge, the left
side of the slot burner and around the center. Thus, the
characteristics of the unsteady-state lifted flame, including the distribution of hydroxyl (OH) radicals, liftoff
height, fluorescence intensity, and three classified
regions, showed a similar tendency to the characteristics of the steady-state lifted flame.
In conclusion, the behavior of an unsteady-lifted flame
created under the specific conditions used in this study, can
be effectively predicted based on the behavior of a steadylifted flame, as reported in our previous study.

ACKNOWLEDGEMENT−The present research has been
partially conducted by the Bisa Research Grant of Keimyung

University in 2005.
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