International journal of automotive technology, tập 12, số 2, 2011

Current Diesel engine fuel injection quantity control algorithms are either based on pre-calibrated tables or injector models, which may not adequately handle the effects of disturbances

International Journal of Automotive Technology , Vol 12, No 2, pp 149 − 157 (2011)

149

COMMON RAIL INJECTION SYSTEM ITERATIVE LEARNING

CONTROL BASED PARAMETER CALIBRATION FOR

ACCURATE FUEL INJECTION QUANTITY CONTROL

F YAN and J WANG *

Department of Mechanical Engineering, The Ohio State University, Columbus, Ohio 43210, USA

(Received 17 May 2010; Revised 9 July 2010)

ABSTRACT− This paper presents an accurate engine fuel injection quantity control technique for high pressure common rail (HPCR) injection systems by an iterative learning control (ILC)-based, on-line calibration method Accurate fuel injection quantity control is of importance in improving engine combustion efficiency and reducing engine-out emissions Current Diesel engine fuel injection quantity control algorithms are either based on pre-calibrated tables or injector models, which may not adequately handle the effects of disturbances from fuel pressure oscillation in HPCR, rail pressure sensor reading inaccuracy, and the injector aging on injection quantity control In this paper, by using an exhaust oxygen fraction dynamic model, an on-line parameter calibration method for accurate fuel injection quantity control was developed based on an enhanced iterative learning control (EILC) technique in conjunction with HPCR injection system A high-fidelity, GT-Power engine model, with parametric uncertainties and measurement disturbances, was utilized to validate such a methodology Through simulations at different engine operating conditions, the effectiveness of the proposed method in rejecting the effects

of uncertainties and disturbance on fuel injection quantity control was demonstrated.

KEY WORDS : Common rail injection system, Iterative learning control, Fuel injection quantity control, Diesel engines

NOMENCLATURE

: piston surface area effective parameter

: piston surface area effective parameter

combustion

in-cylinder pressures

A cyl : area of the total outflow section

c d,cyl : fuel flow discharge coefficient

∆ m restV : mass from exhaust manifold to cylinder caused by

the volume change

∆ m restB : mass from exhaust manifold to cylinder caused by

the pressure difference

150 F YAN and J WANG

economy and pollutions worldwide, precise engine control

is becoming imperative for improving engine combustion

al., 2010; Wang, 2008b; Seykens et al., 2005; Tsutsumi, et al,

2009; Lee and Huh, 2010) The engine combustion process is

mainly determined by the in-cylinder conditions (ICCs) and

fuel injection strategies To achieve the desired combustion on

a cycle-by-cycle basis, seamless combinations of advanced

air-path control techniques and precise fuel injection control

are critical (Wang and Chadwell, 2008; Wang 2008a; Wang

2008b) As the control through fuel-path is much faster

than that of the air-path and the combustion process is very

sensitive to the fuel injection, accurate fueling control is

High pressure common rail (HPCR) fuel injection systems,

typically employed in light- and medium-duty Diesel engines,

provide an effective way in fuel injection quantity and

injection timing control primarily due to their high rail

pressure (Huhtala and Vilenius, 2001; Stumpp and Ricco,

1996) Through HPCR systems, typically, fuel injection per

cycle per cylinder can be controlled based on a pre-calibrated

table or injector models However, HPCR pressure oscillation,

HPCR pressure sensor reading inaccuracy, and the injector

aging can all cause fuel injection quantity error (Alzahabi and

Schulz, 2008; Baumann and Kiencke, 2006) It is therefore

desirable to have some on-line adaptive correction methods

for reducing such effects

To a large extent, the imprecise of the fuel injection are

caused by periodical disturbance (the oscillation effect of

HPCR pressure) and quasi-constant inaccuracy (HPCR

pressure sensor reading inaccuracy and the injector aging)

This type of disturbance can be effectively rejected by iterative

2007) By combining the information of previous control

signal and the feedback error, an updated control law can be

generated to reduce the effect of system variations/

uncertainties without exactly knowing the system dynamics

(Norrlof and Gunnarsson, 2001; Norrlof, 2004) In this paper,

an ILC-based HPCR injection system on-line parameter

calibration algorithm is presented To generate the error used

in the ILC algorithm, an oxygen mass fraction model, based

on the engine breathing process, is introduced An ILC on-line

calibration control law can then be devised to reduce the effect

of the periodical HPCR pressure disturbance and slowly

varying uncertainties (pressure sensor reading inaccuracy and

fuel injector parameter uncertainties) The algorithm can help to

achieve accurate injection quantity control without additional

hardware Such an algorithm can also be applied for injection

system on-board fault diagnosis purposes

The arrangement of the rest of this paper is as follows In

section II, the oxygen mass fraction model is presented

Section III describes a fuel injection on-line parameter

calibration algorithm based on the enhanced ILC (EILC)

method and the convergence criterion In section IV, the

validation of the on-line calibration method is shown by

applying it to a high-fidelity GT-Power engine model, which is

an industrial standard simulation package and is able tosimulate the one-dimensional wave dynamics and heattransfer throughout the engine systems Conclusive remarksare provided in the end

2 OXYGEN MASS FRACTION MODEL

The aim of this model is to generate a nominal exhaust oxygenmass fraction with respect to the desired fuel injection Bycomparison between the nominal value and the one measuredfrom a lambda sensor installed in the exhaust manifold of areal engine, a base error can be derived for disturbancerejection purpose in ILC

Here a single-input-single-output (SISO) dynamic model isproposed through the engine breathing and gas exchangingprocess (Yan and Wang, 2010) Respectively, the input is thefuel injection quantity and the output is the exhaust oxygenmass fraction For lean-burn engines such as Diesel engines,the oxygen mass fraction in exhaust gas is considerable Thecombustion is assumed complete, i.e the fuel injected into thecylinder is completely burned Only the in-cylinder oxygenmass fraction at the crank angle of intake valve closing (IVC),which is before the occurrence of combustion for eachcylinder/cycle, is considered as the other state

The dynamic models were developed based on the massconservation and are described by the following differenceequations as:

where k is the index of engine cycle, m c(k) and are the mass

of charge in the cylinder and in the exhaust manifold at the

kth IVC, respectively m ic(k), m ec(k), and m ce(k) are the mass

of charge from intake manifold to cylinder, from exhaustmanifold to cylinder, from cylinder to exhaust during theperiod right after the kth IVC F i(k), F e(k), F c(k) and F eo(k)are the oxygen fractions of the gases in intake manifold, inexhaust manifold, in cylinder, and out of cylinder aftercombustion at or right after the kth IVC, respectively m f(k) is

illustrates the evolving process

The oxygen fraction of the gas after combustion can bederived by:

COMMON RAIL INJECTION SYSTEM ITERATIVE LEARNING CONTROL BASED PARAMETER 151

It is assumed that the mass of inlet gas equals to that of

outlet gas for both the cylinder and the exhaust manifold in

So,

, (6)and also

As illustrated above, the oxygen fraction can be described

as a discrete linear parameter-varying system with

With the assumption that the density of in-cylinder charge

at IVC is considered the same as the one in intake manifold

can be approximated, by the ideal gas law, as below:

By the speed-density equation, the mass flow rate into

In what follows, the mass flow from exhaust manifold tocylinder during intake and exhaust valve overlapping periodare derived by using the model developed in (Koehler andBargende, 2004):

Figure 1 Engine breathing and gas exchanging process from

the kth IVC to (k+1)th IVC

152 F YAN and J WANG

volume change and pressure difference, respectively The

two terms can be written as:

overlapping, and can be approximated by the intake

manifold charge density calculated through ideal gas law

ABS(p e -p i) denotes the absolute value of pressure difference

effective parameters is the crank angle

The intake and exhaust manifold signals (including

pressures, temperatures, and oxygen fractions) can be

obtained by sensors and/or air-path observers (Wang,

2008a; Wang, 2008b) for calculating the predicted exhaust

manifold oxygen fraction based on the desired

(commanded) fuel injection quantity Such intake and

exhaust manifold sensors are available on some new

engine platforms As the effectiveness of the method

proposed in this paper relies on the accuracy of the exhaust

oxygen mass fraction model, the parameters in the model

need to be carefully calibrated Figure 2 illustrates the

comparison of the foregoing discrete dynamic model with

a high-fidelity, one-dimensional computational, GT-Power

engine model As it indicates, the dynamic model can well

predict the exhaust oxygen fractions in both the

steady-state and transient conditions with varying oxygen

fractions and engine speed

3 HPCR FUEL INJECTION PARAMETER ON-LINE CALIBRATION

3.1 Injector ModelThe predicted exhaust manifold oxygen fraction can begenerated by the oxygen fraction model presented insection 2 based on the desired fuel injection amount and thesignals measured on the engine Thus, the differencebetween the predicted exhaust manifold oxygen fractionand the one measured from the engine can be chosen as thebase error in the injector model parameter on-linecalibration algorithm

The HPCR injection mass quantity can be modeled by(Lino et al., 2005, 2007):

where A cyl is the area of the total outflow section, c d,dyl is the

difference between common rail and in-cylinder pressures

ρ fuel is the fuel density

The injection model (31) can be used to generate theinjection duration with the information of the desired fuelquantity and the pressure difference between HPCR andcylinder pressures However, the pressure differencereading may not be accurate due to the sensor bias, and theinjector parameters may change with injector aging andenvironmental conditions These uncertainties/variationswill affect the actual fuel injection quantity To ensure theinjection quantity control accuracy, an uncertainty

will be calibrated on-line Thus, the injection model (31)can be modified as:

where, the nominal value of θ(k) is 1.0

3.2 ILC and EILCHere, ILC and Enhanced ILC algorithms are briefly

2007) can be written as:

Figure 2 Comparison of the exhaust manifold oxygen fraction

model with a high-fidelity GT-Power engine model

COMMON RAIL INJECTION SYSTEM ITERATIVE LEARNING CONTROL BASED PARAMETER 153

desired and system outputs at time n in iteration i G j and L j

denote the forgetting factor and learning gain operator In

the basic ILC, G j is chosen as 1 e i(n) is the current tracking

conditions Essentially, the ILC includes the information

from previous control signal (feed-forward) and the

feedback signal So, by choosing m=i −1, G j= 1, r(·) = 0,

and s(·) =K p, in (34)~(36), one of the basic controller,

In ILC, the control law includes the control signal and the

error signal in the last iteration However, the convergence

of the ILC in (37) requires identical initial condition, which

may not be satisfied in the highly nonlinear engine systems

(Chien, 1998) Thus, in this paper, the enhanced ILC (Chien

et al., 2007) is employed to release the identical initial

condition requirement The control law is given as:

Here K is the compensation gain and K[e i(n)− e i-1(n)]

term compensates the state difference between two

same initial conditions for all iterations as that in normal

ILC (Chien, 1998)

3.3 EILC-based On-line Fuel Injection Parameter Calibration

In the EILC algorithm (38), the was chosen by the

difference of the oxygen fraction ∆ F e, and the ∆θ=θ(k)−1

cycles To keep consistent with the EILC algorithm, we use

∆ F e,i(z), ∆θ i(z), to represent ∆ F e(z) in i th iteration Thus,

the EILC algorithm in (38) can be written as

(39)

As can be seen from the simulation scheme in Figure 3,

the input signal is the desired fuel injection quantity The

nominal values of the injector model parameters can be

obtained by injector calibration and measurement from therail pressure sensor By applying the EILC, a compensating

model to generate the adjusted injection duration signalsfor delivering accurate injection quantity to the cylinders.3.4 Convergence Criterion and Parameters Selection

chosen by the following convergence criterion, which issimilar to the one for linear time-invariant (LTI) systems as

disturbance caused by fuel pressure oscillation and etc.The dynamics of oxygen fraction generated by thecomparison model is:

154 F YAN and J WANG

in i th and (i−1) th iterations in one expression, we use G i

∆F eas ∆F e,i in the th iteration lead to:

(50)

By rearrangement of (50), one can get

(51)Consequently, by considering Equation(48), one can

have:

(52)Evaluating (49) on the unit cycle z=e jω leads to:

Thus, the convergence criterion is derived as:

By choosing and satisfying Equation (54), as indicated in

of ∆F e will lead to the correction of fuel injection amount.Here we do not provide the proof of the detectability It can beeasily realized that, in real engineering practice, with the otherconditions being the same, the injection fuel amount andexhaust oxygen mass fraction are one-to-one correspondence,i.e., when error of the latter converges to zero, the former can

GT-is able to simulate the one-dimensional wave dynamics andheat transfer throughout the engine systems In the GT-Power combustion model, the fuel injection quantity wasassumed to be precise

4.2 Case 1-Fuel Injection Parameter Uncertainty andDisturbance Rejection

4.2.1 Simulation conditions

To evaluate the parameter calibration algorithm, a “real”engine, including injection system, was constructed with thetypical parameters: HPCR high-frequency pressure oscillation

as shown in Figure 4 (one periodical disturbance was set 8times of fuel injection frequency to simulate thecircumstance of a 8-cylinder engine, i.e 80 Hz with amagnitude of 15 MPa for 1200 rpm engine speed; anotherlow frequency disturbance was added with 2 Hz frequencyand 15 MPa magnitude); the fuel flow discharge coefficient

c d,cyr= 0.75; the fuel density ρ fuel= 850 kg/m2; the area ofinjection section A cyl= 2×10-8m2 Whereas, the parameters

of injector model with the uncertainty and variations were

= γLzG i ( ) γKG z – i ( ) z

Figure 4 HPCR pressure variation

COMMON RAIL INJECTION SYSTEM ITERATIVE LEARNING CONTROL BASED PARAMETER 155

the fuel flow charge coefficient c d,cyi= 0.71; the fuel density

ρ fuel= 870 kg/m2; the area of injection section A cyl= 1.9×10

-8m2

Thus, without the active fuel injection system parameter

on-line calibration, the actual fuel injection quantity delivered into

GT-Power combustion model will be different from the

desired one due to the unknown uncertainties To evaluate the

developed injection system parameter on-line calibration

algorithm, co-simulations within Matlab/SIMULINK and

GT-Power were conducted The parameters in the controller were

chosen as: L= -1.2, K=-1.2

4.2.2 Simulation results

Figure 5, was modeled based on the desired injection

quantity and measured intake and exhaust manifold signals

according to the oxygen mass fraction model in section II

The difference between this predicted exhaust manifold

oxygen fraction and the measured one is related to the

injection quantity error Therefore, such an oxygen fraction

difference can provide an error signal in EILC algorithm

calibration was initiated at the 110th engine cycle As can be

seen from Figure 5, the error between the predicted andmeasured exhaust manifold oxygen fractions was rendered

to zero after the algorithm was applied

Figure 6 shows that the uncertainty parameter of the

EILC-based parameter calibration algorithm was activated Figure 7 shows that the desire fuel injection amount is

20 mg, whereas the actual injection amount before EILCalgorithm correction was around 22.3 mg, with oscillation.After parameter on-line calibration was started, the actualfuel injection quantity was adjusted to the desired value(20 mg) by updating the uncertainty parameter in the injectormodel (32) and thus the corresponding injection duration forthe injector

4.3 Case 2-Effects of Engine Signal Measurement Inaccuracy4.3.1 Simulation conditions

The proposed on-line parameter calibration method is based

on signals (temperature, pressure, and oxygen fraction)measured in intake and exhaust manifolds The accuracies ofsuch measurements will affect the oxygen fraction modelFigure 5 Error convergence

Figure 6 Model uncertainty parameter adjustment

Figure 7 Desired and actual fuel injection quantities duringthe injection model parameter on-line calibration

Figure 8 HPCR pressure variation

156 F YAN and J WANG

and therefore the performance of the on-line parameter

calibration To show the effects by inaccurate measurements,

another simulation was conducted at a different operation

condition (different EGR opening and engine speed, i.e

1800 rpm) with measurement signal uncertainties/noise

The parameters in the “real” engine, including injection

system, were set as follows: the fuel flow discharge

coefficient c d,cyl= 0.8; the fuel density ρ fuel= 835 kg/m2; the

pressure is indicated as Figure 8 Comparing to the

previous case, two periodical disturbances (one is with a

frequency of 60 Hz and a magnitude of 12 MPa; the other

is with 3 Hz frequency and a magnitude of 16 MPa) for

1800 rpm engine speed were added to the HPCR pressure

The parameters of injector model with the uncertainty and

variations were assumed as: HPCR pressure sensor reading

section A cyl= 1.9×10-8m2 To be noted, comparing to the

sensor pressure reading 150 MPa, the real HPCR pressure

has a mean value of 135 MPa, i.e 15 MPa bias value wasincluded

To evaluate the influence of inaccurate manifold signals,

a measurement uncertainty with a bias value of -0.02 wasadded to the actual intake manifold oxygen fraction (Figure9) and a noise signal was added to the exhaust manifoldpressure as indicated in Figure 10

4.3.2 Simulation results

By the same on-line calibration design method, thesimulation result is shown in Figure 11 As it indicates, theinaccurate measurement signals affect the correction offuel injection quantity to a certain extent This simulationshows that the performance of the proposed method isrelated to engine measurement signal accuracies

5 CONCLUSIONS

In this paper, a HPCR injection system on-line parametercalibration method based on the EILC algorithm wasdeveloped for accurate fuel injection quality control ofDiesel engines Such an algorithm can significantly reducethe effects of periodical disturbance and uncertainties (such

as the HPCR pressure sensor uncertainty and variationsassociated with injector aging and fuel properties) on thefuel injection quantity control accuracy Simulations using

a high-fidelity GT-Power engine model with addeddisturbance and uncertainties were utilized to demonstratethe effectiveness of the developed algorithm It wasobserved that, by the on-line calibration, the actual HPCRfuel injection quantity can be precisely controlled aroundthe desired value However, the effectiveness of theproposed method relies on accurate engine signalmeasurements as indicated in simulation case 2

Figure 9 Intake manifold oxygen fraction signals

Figure 10 Exhaust manifold pressure signals

Figure 11 Desired and actual fuel injection quantities duringthe injection model parameter on-line calibration withmeasurement inaccuracies

COMMON RAIL INJECTION SYSTEM ITERATIVE LEARNING CONTROL BASED PARAMETER 157

REFERENCES

Ammann, M., Fekete, N P., Guzella, L and lattfelder, A H G

(2003) Model-based control of the VGT and EGR in a

turbocharged common-rail diesel engine: Theory and

2003-01-0357

Alzahabi, B and Schulz, K (2008) Analysis of pressure

3, 223−246

Baumann, J and Kiencke, U (2006) Practical feasibility of

measuring pressure waves in common rail injection

2006-01-0891

Benajes, J., Molina, S., Novella, R., Amorim, R., Hamouda,

H B H and Hardy, J P (2010) Comparison of two

injection systems in an HSDI diesel engine using split

injection and different injector nozzles Int J Automotive

Technology 11, 2, 139−146

Chien, C J (1998) A discrete iterative learning control for

Automatic Control 43, 5, 748−752

Chien, C J., Lee, F and Wang, J (2007) Enhanced iterative

learning control for a piezoelectric actuator system using

wavelet transform filtering J Sound and Vibration, 299,

605−620

Huhtala, K and Vilenius, M (2001) Study of a common

Killingsworth, N J., Aceves, S M., Flowers, D L and Krstic,

2006 IEEE Int Conf Control Applications, 2424−2430

Koehler, U and Bargende, M (2004) A model for a fast

Lee, Y J and Huh, K Y (2010) Simulation of HCCI

combustion with spatial inhomogeneities via a locally

deterministic approach Int J Automotive Technology 11, 1,

19−26

Lino, P., Maione, B and Rizzo, A (2007) Nonlinear

modeling and control of a common rail injection system

Artificial Intelligence, 14, 87−94

Norrlof, M (2004) Disturbance rejection using an ILC

SAE Paper No. 960870

Tsutsumi, Y., Iijima, A., Yoshida, K., Shoji, H and Lee, J T.(2009) HCCI combustion characteristics during operation

10 , 6, 645−652

Wang, J and Chadwell, C (2008) On the advanced path control for multiple and alternative combustion

Wang, J (2008) Air fraction estimation for multiplecombustion mode diesel engines with dual-loop EGRsystems Control Engineering Practice 16, 12, 1479−1486.Wang, J (2008a) Hybrid robust air-path control for dieselengines operating conventional and low temperature

International Journal of Automotive Technology , Vol 12, No 2, pp 159 − 171 (2011)

159

INSTANTANEOUS 2-D VISUALIZATION OF SPRAY COMBUSTION AND FLAME LUMINOSITY OF GTL AND GTL-BIODIESEL FUEL BLEND

UNDER QUIESCENT AMBIENT CONDITIONS

U B AZIMOV 1) , K S KIM 1)* , D S JEONG 2) and Y G LEE 2)

Korea Institute of Machinery and Materials, 171 Jang-dong, Yuseong-gu, Daejeon 305-343, Korea

(Received 18 September 2009; Accepted 14 February 2010)

ABSTRACT− An experimental study has been performed on spray combustion and two-dimensional soot concentration in diesel (ULSD), GTL and GTL-biodiesel fuel jets under high-pressure, high-temperature quiescent conditions Instantaneous images of the fuel jets were obtained with a high-speed camera It was confirmed that by blending GTL with 20% rapeseed biodiesel, certain fuel properties such as kinematic viscosity, density, surface tension, volatility, lower heating value and others may be designed and improved to be more like those of conventional diesel fuel but with considerable decrease in the amount

of sulfur, PAH, cold filter plugging point, etc The results showed that the spray tip penetration increased and the spray cone angle decreased when 20% biodiesel fuel was added to GTL fuel Autoignition of the GTL-biodiesel blend occurred slightly earlier than that of diesel fuel Experiments under high-pressure, high-temperature conditions showed that higher injection pressure induced a lower soot formation rate The integrated flame luminosity, which serves as an indicator of soot concentration in the fuel jet, was slightly higher for the GTL-biodiesel blend than for pure GTL fuel due to the slightly higher sulfur content of pure biodiesel fuel

KEY WORDS : Diesel spray combustion, Flame luminosity, GTL, GTL-biodiesel blend, Soot formation

1 INTRODUCTION

In the transportation sector, the diesel engine is the most

widely used power source because of its fuel efficiency and

specific power However, the diesel engine also emits large

amounts of NOx and particulate matter Increasingly strict

pollutant emission regulations have forced manufacturers

to integrate more complex engine systems, which have to

be designed and calibrated to work with a designated fuel

It is important to note that the exact combinations of diesel

engine operating parameters that will lead to the best

performance and the lowest levels of pollutant formation

depend highly on the nature of the fuel, and therefore

blends of clean alternative fuels with properties similar to

those of conventional diesel fuel will be the focus of future

engine research Alternative fuels with properties defined

for diesel engines must be utilized to meet emission targets

and to reduce dependency on crude oil Currently, four

important types of alternative liquid fuels are used in diesel

engines: 1) fuel synthesized from fossil or biogenic gas

(gas-to-liquid, known as GTL, or biomass-to-liquid, known

as BTL) produced with the Fischer-Tropsch process, 2)

fatty acid methyl ester, or biodiesel, made from different

vegetable oils, 3) neat vegetable oil, and 4) recycled waste

oil from fossil or biogenic sources

As long as some important fuel properties, such ascetane number, density, viscosity, heating value, lubricity,boiling point, etc., remain similar to the correspondingproperties of crude oil diesel fuel, alternative fuels can beburned without or with only small modifications to theengine However, these four types of alternative fuels havedifferent chemical and physical properties than crude oildiesel fuel, which can significantly affect the spray com-bustion process For example, the disadvantages of biodieselcompared to diesel fuel include marginal cetane number,fuel density that exceeds the recommended density, ex-cessive viscosity, measurably lower calorific value, andmarginal properties of the cloud point and final boilingpoint (Carraretto et al., 2004; Demirbas, 2007; Yoon et al.,

2009) In general, the higher density hydrocarbon fuelshave greater volumetric heats of combustion and lowervolumetric fuel consumptions However, Table 1 showsthat biodiesel has a lower heating value, also known as theenergy density In addition, fuel viscosity influences theinjection fuel spray in a diesel engine Highly viscous fuelcan reduce fuel flow rates, resulting in poor combustion,loss of efficiency and increased CO and hydrocarbonemissions On the other hand, if the fuel viscosity is toolow, the injection spray is too soft and will not penetrate far

*Corresponding author. e-mail: sngkim@chonnam.ac.kr

160 U B AZIMOV, K S KIM, D S JEONG and Y G LEE

enough into the cylinder, resulting in loss of power

Ade-quate atomization enhances mixing and complete combustion

in a direct injection engine and is therefore an important

factor in engine emission and efficiency According to

Lefebvre (1989), the physical properties of a liquid fuel

that affect its atomization in a diesel engine are viscosity,

density and surface tension Therefore, a fuel with higher

viscosity would delay atomization by suppressing the

instabilities that lead to the breakup of the fuel jet An

increase in fuel density adversely affects atomization,

whereas higher fuel surface tension opposes the formation

of droplets from the liquid fuel (Lefebvre, 1989) Hence, a

suitable diesel fuel for a diesel engine requires the

visco-sity, density and surface tension to be balanced to ensure

1999b) predicted the viscosity and surface tension of pure

compositions Their results showed that the viscosity and

surface tension of the biodiesels could be up to 120% and

22% higher, respectively, than those of No.2 diesel Table 1

shows that the density of biodiesel could be up to 6%

higher than that of diesel Allen and Watts (2000) presented

the Sauter Mean Diameter (SMD) of some biodiesels at

was a function of viscosity and surface tension only Their

results showed that the SMDs of biodiesels could be

showed analytically that the SMD of a pure biodiesel could

(2007) performed an analytical study of the effects of

characteristics of pure biodiesel and its blends They

show-ed that B20 biodiesel blends yieldshow-ed smaller drop sizes

than B100, and that the biodiesel from rapeseed oil

pro-duced the largest drop sizes

Fuel blends of synthetic GTL and renewable biodiesel

may be options for gaining environmental benefits and

reducing dependence on fossil fuel By blending GTL and

biodiesel fuels, it is possible to design a fuel with the

properties required for use in conventional diesel engines

Blending GTL fuel with biodiesel has synergetic benefits,

which are obtained from the combined beneficial properties

contain very low quantities of aromatics and sulfur, which

would normally limit the blending ratios with conventional

diesel GTL fuel improves the cold flow properties and

decreases the viscosity that is associated with biodiesel On

the other hand, biodiesel increases the GTL fuel density

without weakening its energy density Biodiesel is prone to

gel formation in cold weather GTL fuel, on the other hand,

has good cold flow properties and a high cetane value

because of the predominantly methyl branching in the

terminal positions of the paraffinic chains during the

iso-merisation process This type of branching prevents wax

crystallization while maintaining a high cetane number GTL

fuel has excellent thermal stability that exceeds premiumdiesel requirements Biodiesel also has a low flash point,which makes it a safe fuel to use Both GTL and biodieselfuels are readily biodegradable and non-toxic if spilt The main objective of this study was to qualitativelycompare the spray combustion characteristics and flameluminosity of diesel, GTL and GTL-biodiesel fuel blend.The viscosity, density and cetane number of GTL changewhen pure GTL is blended with pure biodiesel producedfrom rapeseed oil The flame luminosity can be interpreted

as a qualitative indicator of in-cylinder soot formation (Siebers

et al., 2002; Muller and Martin, 2002) Researchers analyzedtemporal sequences of flame and soot images acquired byapplying natural flame luminosity and LII (Nakayama andTanida, 1996) They showed that the first soot was detect-able at the peak heat release rate Once luminous flame wasestablished, LII soot images revealed that soot was distri-buted throughout the cross-section of the combusting fueljet, and that the extent of the soot distribution was wellcorrelated with the extent of the luminous flame region

profile was in good agreement with the temporal profile ofnatural flame luminosity obtained from high-speed direct

signal at various in-cylinder pressures and injection ssures first appeared slightly earlier at higher injectionpressures and lasted as long as the natural flame luminositylasted

pre-2 EXPERIMENTAL

2.1 Fuel Properties

In this study, three different types of fuel were compared:ultra-low sulfur diesel (ULSD), gas-to-liquid (GTL) and ablend of 80% GTL to 20% biodiesel Throughout the text,ULSD will be denoted as diesel fuel and GTL 80%-bio-diesel 20% as G80-B20 Figure 1 shows the fuel types used

in this study

ULSD is a diesel fuel that is defined to have a maximumsulfur content of 15 ppm, and it will eventually replacetoday’s on-highway diesel fuel known as conventional low

Figure 1 Fuels used in this study: (A) ULSD; (B) GTL; (C)biodiesel; (D) G80-B20

INSTANTANEOUS 2-D VISUALIZATION OF SPRAY COMBUSTION AND FLAME LUMINOSITY OF GTL 161

sulfur diesel (LSD), which may contain as much as 350

ppm sulfur ULSD will facilitate the next generation of

advanced emission control devices, which cannot tolerate a

high level of sulfur in the fuel There are several diesel fuel

properties other than sulfur content that will change as a

result of reducing the sulfur content in the fuel The

pro-cessing required to reduce sulfur to very low level also

removes naturally-occurring lubricity agents in diesel fuel

and reduces the aromatics content and density of diesel

fuel, resulting in the reduction of energy content In

addi-tion, the reduction of the aromatic content results in an

increase of the cetane number

GTL is synthetic fuel produced from natural gas throughthe Fischer-Tropsch process GTL is a liquid at normaltemperature and pressure and can therefore be utilized in

less than 0.1 wt.% aromatics With a higher carbon (H/C) ratio, paraffins have lower densities than otherhydrocarbon types Therefore, the density of GTL is lowerthan that of diesel fuels derived from conventional crudeoil GTL has a higher cetane number and lower auto-igni-

hydrogen-to-Table 1 Fuel properties

162 U B AZIMOV, K S KIM, D S JEONG and Y G LEE

tion temperature than diesel fuel More importantly, GTL

fuel has an extremely low sulfur content Because the

Fischer-Tropsch synthesis catalyst is poisoned by sulfur,

sulfur components in the synthesis gas are reduced to very

low levels in the syngas preparation process Previous results

have shown that a higher proportion of GTL contributes to

lower sulfur content in blends A similar trend was observed

in a work showing the relation between aromatics and GTL

fraction It was shown that aromatics decrease gradually as

2007)

Biodiesel is a renewable fuel that consists of mono-alkyl

esters of long chain fatty acids usually derived from the

transesterification of vegetable oils with an alcohol using

an appropriate catalyst In this study, rapeseed methyl ester

(RME) biodiesel was used RME is a biodiesel fuel

pro-duced from rapeseed oil and is one of the feedstocks for

which fuel producers must compete with the food industry

RME and other types of biodiesel fuels have been

exten-sively tested by engine manufacturers and academics

have been tested in engines with no modifications (Demirbas,

2005) Combustion has been found to result in a slight

increase of NOx emissions but lower CO and PM emissions

than combustion of conventional diesel fuel (Schumacher

et al., 1996; Canaki and Van Gerpen, 2003; Rudolph and

the rapeseed cultivation capacity is insufficient to cover the

needs of the transport industry, use of a blend of 5 to 20%

RME in conventional diesel has been proposed for economic

and environmental reasons (US Dept Energy, 2004; Boehman

et al., 2005; Coronado et al., 2009) Several studies show

that a conventional diesel engine can run for an extended

time with biodiesel Researchers have run diesel test engines

and diesel engines in city buses and trucks on various

2004; Lin et al., 2006; Krahl et al., 2007; Durbin et al.,

B2%-D98%, B20%-D80% and B100% (Bozbas, 2008) A

standard diesel engine might operate on 100% biodiesel;

however, biodiesel begins to cloud and thicken at about

272 K, a warmer temperature than diesel fuel However,

additives are available that can lower the pour point

Mix-ing biodiesel with diesel fuel can also lower the pour point

Installing an in-tank or fuel line heater may also be necessary

to keep the fuel flowing in cold weather A blend of

diesel/diesel fuel has a lower pour point than 100%

bio-diesel, but gelling may still occur without care (Hofman,

2003; Joshi and Pegg, 2007) A comparison of the

proper-ties of the fuels used in this study is shown in Table 1

2.2 Experimental Conditions and Setup

Two types of experiments were performed under quiescent

ambient conditions: 1) shadowgraph visualization was

per-formed to investigate the effect of fuel type on spray

penetration and spray angle at ambient temperature (298 K)

and autoignition at 820 K, 870 K and 920 K at differentinjection pressures; 2) instantaneous two-dimensional visuali-zation of combustion and flame luminosity were performed

to investigate soot formation in the fuel jets The mental conditions are presented in Table 2 The fuel masswas measured by using a Bosch common-rail diesel fuelinjector with a single 0.163 mm diameter orifice and adigital microscale The average injected fuel mass wascalculated from the data of ten experiments, taking intoaccount the cycle-to-cycle variations in the fuel injections.Diesel, as a reference fuel, was injected for 1.2ms into theconstant volume chamber by the same common-rail systemsetup The fuel injector was activated by the TDA-3200Hdiesel common-rail injector driver and a delay generator.The injector driver is a control unit that allows two types ofcontrol: current control and command control In currentcontrol mode, it controls the current profile characteristics,and in command control mode, it controls characteristicssuch as the main injection, pilot injection, pilot advance,etc The injector was activated 76 ms earlier for GTL fuelthan for diesel fuel, and 45 ms earlier for G80-B20 Thisallowed same amount of each tested fuel to be injected.The ambient gas pressure was 4 MPa, ambient gas temper-atures were 820 K, 870 K and 920 K, and the fuel injectionpressures were 90, 135 and 150 MPa These ambient gasvalues were chosen to provide measurement conditions

The combustion chamber has two round quartz windows

ensure optical access for visualization of the combustionand illumination of the light source The operation of theconstant volume chamber has been explained elsewhere

acquiring shadowgraph spray combustion images and

mea-Table 2 Experimental conditions

Ambient gas temperature 298 K, 820 K, 870 K, 820 K

Figure 2 Optical setup

INSTANTANEOUS 2-D VISUALIZATION OF SPRAY COMBUSTION AND FLAME LUMINOSITY OF GTL 163

suring the flame luminosity To measure the spray

pene-tration and autoignition, the chamber was illuminated by a

light source To detect the luminosity from flames, the

same setup was maintained, but the light source was turned

off Sequential spray and flame images were acquired by a

Photron FASTCAM Ultima APX 36000 fps high speed

color digital camera at a resolution of 512×128 pixels with

an attached 700 nm (10 nm FWHM) bandpass filter to

measure the flame luminosity from the soot particles

3 RESULTS AND DISCUSSION

3.1 Spray Penetration

The speed and extent to which the fuel spray penetrates in

the combustion chamber has an important influence on air

utilization and the fuel-air mixing rate The effects of the

characteristics of diesel, GTL and G80-B20 on the spray tip

penetration and cone angle were investigated in a constant

volume chamber at an ambient gas pressure of 4 MPa and

an ambient temperature of 298 K Fuels were sprayed into

the quiescent gas at 90 MPa, 135 MPa and 150 MPa

injection pressures Spray tip penetration was measured as

the fuel penetration distance from the nozzle to the tip of

the jet downstream, and the spray cone angle was defined

at 60d 0 (Arai et al., 1984), where d 0 is the nozzle orifice

diameter Data for each of the presented curve in Figures 3,

4 and 5 are the fitted average data of 10 experiments,

taking into account the cycle-to-cycle variations in the

spray In the direct comparison among the fuels shown in

Figures 3 and 4, moderately higher penetrations and lower

spray angles were observed for G80-B20, suggesting a

more compact spray

The spray tip penetration of GTL fuel has been presented

in previous studies, although a clear trend was not observed

Kim et al (2007) showed that GTL had a longer

penet-ration than diesel fuel at ambient gas temperature and

pressure of 300 K and 4 MPa, respectively They found that

GTL fuel had a 12% larger mean drop size than diesel fuel

Therefore, they concluded that the larger drop size of GTL

fuel caused the longer spray penetration, due to greater

momentum

were no significant differences in spray penetration andcone angle between standard diesel and Sasol-GTL fuel.However, their results showed large temporal fluctuations

(2005) showed slightly shorter spray penetrations fordifferent types of GTL fuel compared to diesel fuel Upon evaluation of the influence of physical properties

on spray penetration, we would conclude that a decrease infuel density results in a decrease in the mass injection rateand an increase in the jet velocity The microscopic behavior

2006, 2007), and the momentum is not affected by the twoeffects described above On the other hand, viscosity andsurface tension have significant effects on spray dynamics

related to fuel viscosity The spray angle is widened, and

SMD also decreases and the spray angle increases Figure 5 shows that the spray tip penetration graduallyincreases with increasing injection pressure The temporalevolution of the spray penetration was investigated at lower

Figure 3 Effect of fuel on spray tip penetration for diesel,

penet-164 U B AZIMOV, K S KIM, D S JEONG and Y G LEE

injection pressures in experiments by Hiroyasu and Arai

obtained with the direct photographic method are described

very well by the Hiroyasu and Arai correlation, regardless

showed that changing the mixing ratio of biodiesel caused

little difference in the spray tip penetration

Under non-evaporative conditions, the penetration and

the spray shape are affected by the ambient gas pressure

Figure 5 shows that in the early stages of injection, the

spray penetrates at a straight and high slope, indicating a

weak interaction with the gas in the chamber Later, a

decline in the bend penetration emerges due to a decrease

in the kinetic energy of the jet

The complexity of the observed phenomena seems to

indicate that the actual behavior of alternative fuel sprays

can be quite different from that of diesel fuel, depending on

the physical characteristics of the fuel used Furthermore,

results obtained in the present experimental work also

indicate that engine performance could be at least partially

diminished by the use of unconventional fuels

3.2 Autoignition

Autoignition and the ensuing combustion are very sensitive

to many parameters but are especially sensitive to the fuel’s

chemical and physical properties The difference in the

autoignition delays of diesel and G80-B20 fuel is depicted

in Figure 6 The auto-ignition delay of the G80-B20 blend

is about 55 ms shorter than that of diesel fuel The decrease

in the G80-B20 auto-ignition delay time is due to the

higher cetane number of the blend, even after 20% of lower

cetane number biodiesel was added to the higher cetane

number GTL Previous work showed that the cetane number

increases linearly with the biodiesel fraction in a

pressure of GTL is higher than that of diesel fuel, theevaporation becomes more intense at high temperatures,and a spray of G80-B20 appears to be thinner

The effect of the ambient gas temperature and fuel tion pressure on autoignition is depicted in Figure 7 Theresults show that the auto-ignition delay increases withdecreasing ambient gas temperature and decreases withincreasing injection pressure

injec-For example, when the ambient gas temperature isdecreased by 100 K at 135 MPa injection pressure, theauto-ignition delay time of diesel fuel is prolonged by about

500 ms This extension of the auto-ignition delay period issupposed to provide better mixing of fuel and air, and totherefore induce soot reduction The trend in the ignitiondelay under the conditions in this paper is consistent with

biodiesel fraction in a fuel blend led to a gradual reduction

Figure 7 Autoignition delay for different experimentalconditions

INSTANTANEOUS 2-D VISUALIZATION OF SPRAY COMBUSTION AND FLAME LUMINOSITY OF GTL 165

shown that the cetane number increased linearly with the

3.3 Soot Formation

The time-resolved narrow-band flame luminosity emitted

by a burning fuel jet was measured with a high-speed

camera positioned to view the entire chamber through a

concave mirror, as shown in Figure 2 To a certain extent,

the flame luminosity can be interpreted as a qualitative

indicator of in-cylinder soot formation (Siebers et al., 2002;

Muller and Martin, 2002) The flames produced during

diesel combustion emit light in the visible spectral region

One source of this light is the photons emitted when

molecules in an excited electronic state return to a lower

state (ground state) Another source is the “black body”

radiation from the soot particles that form when

hydro-carbons burn The amount of soot formed is dependent on

the flame temperature, as is the wavelength distribution of

the light

Although acquiring light emissions from a burning jet is

straightforward, it is important to consider the factors that

might affect the outcoming signals During combustion,

strong band emissions from gaseous species can be present,

as well as thermal radiation from soot particles In the

reaction zones of the flames, many radicals, such as OH,

in the visible and near ultraviolet regions In the infrared

and fuel vapor must be avoided Thus, this optical setup

was adapted considering the maximal spectral emissive

power of soot particles as black bodies (Zhao and

Ladommatos, 1998) A band-pass filter at 700 nm (10 nm

FWHM) was used to measure the radiation emitted from

incandesced soot particles within this band, blocking the

emission due to scattered light from fuel drops,

chemilumine-scence and infrared thermal radiation

To check the validity of this approach, the flame

lumino-sity detected with the 700 nm band-pass filter was

com-pared with the luminosity obtained by using a sharp-cut red

filter The relative light transmission with the red filter

starts at 500 nm and reaches 90% at 600 nm It is shown in

Figure 8 that the flame luminosity (dashed ovals) appears

earlier through the red filter than through the 700 nm filter

During the initial combustion period, no soot was detected

in the flame because it takes some time before soot starts to

form Soot was observed for the first time immediately

after the start of the diffusion combustion period, and the

sooting zone rapidly expanded its volume toward the flame

lumino-sity starts appearing, the heat release rate almost reaches its

peak In general, the heat release rate peaks earlier than the

flame luminosity This means that soot formation occurs at

a certain temperature, developing from the released heat

over time Vattulainen (1998) showed that the light emitted

by a diesel flame is dominated by soot incandescence, and

that the maximum emission intensity occurs at a

wave-length of about 700 nm As shown in Figure 8, the flameluminosity at 700 nm, which we believe mostly consists ofblackbody radiation from soot particles, first appears whenthe flame luminosity through the red filter begins to rapidlyincrease After the red filter flame luminosity reaches itspeak, soot oxidation dominates and the flame luminositydecreases Therefore, we believe that the first occurrence offlame luminosity in the 700 nm filter is the formation ofsoot during the diffusion combustion period

As shown in Figure 9, the flame front propagation isadvanced at higher injection pressures; at the same pressurevalues, it is slightly more advanced for GTL than for diesel.The flame front propagation of G80-B20 was similar tothat of GTL fuel The effect of the injection pressure onadvancing the flame front propagation is probably due tothe higher droplet momentum, which causes more intenseevaporation and mixing at higher injection pressures (Gill

et al., 2005) However, in terms of the effect of fuel type,the GTL spray penetrates in a similar way as the dieselspray with a slightly advanced flame front propagation.This similarity may be due to the fact that GTL fuel has ahigher vapor pressure and therefore higher volatility thandiesel fuel, contributing to a certain extent to better mixtureformation, increased combustion speed and further flame

Figure 8 Comparison of flame luminosity of diesel fuelwith 700 nm filter and sharp cut red filter

Figure 9 2-D visualization of flame luminosity with 700

nm band pass filter

166 U B AZIMOV, K S KIM, D S JEONG and Y G LEE

The flame luminosity integrated over the cross-sectional

area is selected as the qualitative representation of the

amount of soot in the whole flame It is evident that the

amount of soot increases with time after fuel ignition and

reaches a peak, after which it decreases to very low values

approaching zero The spatial distribution of the flame

luminosity was determined using a numerical integral

f(x,y), (x,y)∈σ, can be computed as:

Using a numerical integration method, (1) can be written

as:

(2)

matrix, respectively In the actual process of computing,

is used instead of The image intensity before injection was sub-

tracted from the image intensity at any time during thecombustion This eliminated the background light, so thatonly the magnitude of the light emitted from flame wasplotted

Comparing Figures 9(D), 10 and 11, the effect of theinjection pressure on soot formation can be observed Theincrease in injection pressure results in a decrease in thepeak amount of soot The influence of injection pressurehas previously been investigated, and it was found that athigher injection pressures, soot was usually located further

of injection pressure on soot formation They found that inall instances, the first appearance of the LII signal seemed

to occur slightly earlier at higher injection pressures Therefore, it was confirmed that higher injection pre-ssures produce smaller quantities of soot This can be ex-plained by the fact that higher injection pressures aregenerally believed to enhance atomization, promoting the

1995) which in turn result in reduced local fuel vapor

INSTANTANEOUS 2-D VISUALIZATION OF SPRAY COMBUSTION AND FLAME LUMINOSITY OF GTL 167

concentrations and decreased ignition delays In addition, a

higher injection pressure enhances the mixing of fuel and

ambient gas, which promotes the formation of smaller soot

particles that more rapidly oxidize It can be seen in Figure

9(D) that flames lasted longer at lower injection pressures;

this allows more time for oxidation at the flame sheath

Since lower fuel pressures are known to result in higher

concluded that the extended flame duration is not sufficient

to oxidize the excess soot production

Figure 12 shows that for the injection pressures tested,

the highest injection pressure exhibited the lowest flame

luminosity peak Since the maximum amount of soot that

can be produced is directly related to the amount of

injected fuel, and higher injection pressures result in higher

injection rates, it was expected that an increase in injection

pressure would be associated with an increase in the soot

production rate However, the results show that as the

injection pressure increased from 90 MPa to 150 MPa, the

integrated flame luminosity decreased A similar trend was

an increase in injection pressure from 100 MPa to 160 MPa

resulted in a 24% increase in injection rate, increased soot

production was not observed They showed that for all cylinder pressures tested, the highest injection pressuresexhibited the lowest peaks in LII signal intensity The effects of ambient gas temperature on the sootdistribution within diesel, GTL and G80-B20 fuel jets areshown in Figures 13, 14 and 15 The ambient gas temper-atures considered were 820, 870 and 920 K, while the

Figure 12 Effect of injection pressure on flame luminosity

at Tamb-920 K

168 U B AZIMOV, K S KIM, D S JEONG and Y G LEE

ambient gas pressure and injection pressure were fixed at 4

MPa and 150 MPa, respectively

Figure 9 shows that the peak flame luminosity decreases

with decreasing ambient temperature; the flame luminosity

is at a minimum at 820 K, and therefore the soot formation

is low These results are in good agreement with the results

of other researchers Pickett and Siebers (2004a) showed

that the peak in the axial profile of KL increased

signifi-cantly with increasing ambient gas temperature They

sug-gested that the factors contributing to the increase in peak

soot level with the increase of ambient temperature are the

decrease in lift-off length and the concurrent increase in the

equivalence ratio at the lift-off length Pickett and Siebers

(2004b) employed planar laser-induced incandescence (PLII)

soot visualization and indirect measurement of luminous

soot spatially integrated over the photodiode field of view,

commonly referred to as spatially-integrated natural

lumino-sity (SINL) They showed that as the ambient gas

temper-ature decreases, the soot concentration also decreases The

time-resolved SINL was consistent with the observations

from PLII imaging The SINL decreased as the PLII soot

levels decreased, and the SINL at 850 K was negligible

throughout the entire diesel combustion event They

show-ed that the SINL decreasshow-ed to zero as the equivalence ratio

at the lift-off length decreased below a value of

approxi-mately two This observation was also confirmed by direct

PLII soot measurements

An interesting trend in the flame luminosity of G80-B20was observed At all ambient gas temperatures in this study,the flame luminosity of this blend was slightly higher thanthat of pure GTL fuel This may be because the sulfurcontent of the pure biodiesel fuel used in this study was alittle higher than that of GTL, as shown in Table 1 Thefuel-bond sulfur is believed to be a source of PolycyclicAromatic Hydrocarbons (PAHs) (Gulder and Glavincevski,

et al., 1998; Vaaraslahti et al., 2004; Lim et al., 2005,2007), and PAHs are soot precursors (Heywood, 1989) Asshown in Figures 13~15, the GTL and G80-B20 curvescoincide before the flame luminosity reaches its peak Whenthe flame luminosity and therefore the soot concentrationreach their maxima, the soot is oxidized and decreased, but

a slight difference between GTL and G80-B20 is observed.This means that the soot precursor has a stronger influ-ence than the available oxygen during the soot formation-oxidation process, i.e., the soot formation rate due to thepresence of the precursor outweighs the soot oxidation ratedue to the presence of oxygen, although extra oxygen be-comes available in the fuel blend due to the addition of20% biodiesel fuel In addition, GTL has a lower boilingpoint, and biodiesel has a higher boiling point We maysuppose that blending 20% biodiesel and 80% GTL fuelmay affect the evaporation of the fuel blend and, as a result,affect the mixing of the fuel vapor with the ambient gas

the fuel jet and found that evaporation and air-fuel mixingare slower for biodiesel than for diesel Insufficient air-fuelmixing may cause local high-concentration fuel zones,which induce higher rates of soot formation

4 CONCLUSIONS

Spray combustion and flame luminosity were visualized atvarious ambient gas temperatures and injection pressuresunder quiescent ambient gas conditions using diesel, GTLand GTL-biodiesel fuel The main results are summarized

as follows:

INSTANTANEOUS 2-D VISUALIZATION OF SPRAY COMBUSTION AND FLAME LUMINOSITY OF GTL 169

(1) By blending 80% GTL with 20% rapeseed biodiesel,

fuel properties such as the kinematic viscosity, density,

surface tension, volatility, lower heating value and others

may be designed and improved to be similar to those of

conventional diesel fuel, but with considerable decreases

in the amount of sulfur, PAH, cold filter plugging point,

etc in the fuel blend

(2) The results showed that the spray tip penetration

in-creases and the spray cone angle dein-creases if 20%

biodiesel fuel is added to GTL fuel The spray tip

penetration of GTL fuel was shorter and that of

G80-B20 was longer than that of diesel fuel Despite the

different physical properties, there is little difference in

the spray tip penetration Based on these results, it can

be said that G80-B20 can be used in compression

combustion engines with few modifications

(3) When higher-cetane number GTL fuel was blended

with lower-cetane number biodiesel, the cetane number

of the blend decreased to a level that caused a

sub-stantial difference in the ignition delays of GTL and

G80-B20, and autoignition occurred slightly earlier for

GTL-biodiesel blend than for diesel fuel

(4) Experiments under high-pressure, high-temperature

condi-tions showed that the injection pressure had a

consi-derable effect on soot reduction At higher injection

pressures, fuel atomization was improved, increasing

evaporation, allowing better mixing of the fuel and the

ambient gas and reducing the amount of soot formed

Reduction of the amount of soot at higher injection

pressures clearly observed when the flame luminosity

was integrated across two-dimensional instantaneous

images of the jet flame It was shown that soot oxidizes

much more quickly at an injection pressure of 150 MPa

than at 135 MPa

(5) The integrated flame luminosity of G80-B20 was

slight-ly higher than that of GTL due to the slightslight-ly higher

sulfur content in the pure biodiesel fuel The fuel-bond

sulfur is a source of PAHs, which are soot precursors

Therefore, the soot formation rate due to the presence

of the precursor dominates the soot oxidation rate due

to the presence of oxygen, although extra oxygen

becomes available in the fuel blend due to the addition

of 20% biodiesel fuel

ACKNOWLEDGEMENTS− This work was supported by the

project “Development of Partial Zero Emission Technology for

Future Vehicle” funded by the Ministry of Commerce, Industry

and Energy, Republic of Korea.

REFERENCES

Ahmed, M A., Ejim, C E., Fleck, B A and Amirfazli, A

(2006) Effects of biodiesel fuel properties and its blends

Alleman, T L., Clark, R., Nine, R., Wayne, S., Lansing, R.,

Jacobs, T., Eudy, L., Miyasoto, M., Oshinuga, A., Allison,

S., Corcoran, T., Chatterjee, S., Cherrillo, R A and Virrels,

I (2004) Fuel property, emission test, and operabilityresults from a fleet of class 6 vehicles operating on gas-

Paper No 2004-01-2959

Allen, C A W., Watts, K C R., Ackman, G and Pegg, M

J (1999a) Predicting the viscosity of biodiesel fuels

Allen, C A W and Watts, K C (2000) Comparative analysis

of the atomization characteristics of fifteen biodiesel fueltypes Am Soc Agric Engrs 43, 2, 207−211

Arai, M., Tabata, M., Hiroyasu, H and Shimizu, M (1984).Disintegrating process and spray characterization of fuel

Azimov, U B., Roziboyev, E A., Kim, K S., Jeong, D S.,Lee, Y G and Yun, J E (2008) Investigation of sootformation in diesel-GTL fuel blends under quiescentconditions Int J Automotive Technology 9, 5, 523−534.Basha, S A., Gopal, K R and Jebaraj, S (2009) A review

on biodiesel production, combustion, emissions and

13, 1628−1634

Benjumea, P., Agudelo, J and Agudelo, A (2008) Basic

Boehman, A L., Song, J and Alam, M (2005) Impact ofbiodiesel blending on diesel soot and the regeneration ofparticulate filters Energy & Fuels, 19, 1857−1864.Bougie, B., Tulej, M., Dreier, T., Dam, N J., Ter Meulen, J

J and Gerber, T (2005) Optical diagnostics of dieselspray injections and combustion in a high-pressure high-temperature cell J Appl Phys., B80, 1039−1045.Bozbas, K (2008) Biodiesel as an alternative motor fuel:

and Sustainable Energy Reviews, 12, 542−552

Bruneaux, G., Verhoeven, D and Baritaud, T (1999) pressure diesel spray and combustion visualization in a

1999-01-3648

Canaki, M and Van Gerpen, J H (2003) Comparison ofengine performance and emissions for petroleum dieselfuel Yellow grease biodiesel and soybean oil biodiesel

Trans ASAE 46, 4, 937−944

Candeia, R A., Silva, M C D., Carvalho Filho, J R.,Brasilino, M G A., Bicudo, T C and Santos, I M G.(2009) Influence of soybean biodiesel content on basicproperties of biodiesel-diesel blends Fuel, 88, 738−743.Carraretto, C., Macor, A., Mirandola, A., Stoppato, A andTonon, S (2004) Biodiesel as alternative fuel: Experi-

Cheng, A S., Upatnieks, A and Mueller, C J (2006)

170 U B AZIMOV, K S KIM, D S JEONG and Y G LEE

Investigation of the impact of biodiesel fuelling on NOx

emissions using an optical direct injection diesel engine

Int J Engine Research, 7, 297−318

Coronado, C R., de Carvalho Jr, J A., Yoshioka, J T and

Silveira, J L (2009) Determination of ecological

effici-ency in internal combustion engines: The use of biodiesel

Applied Thermal Engineering, 29, 1887−1892

Crua, C., Kennaird, D A and Heikal, M R (2003)

Laser-induced incandescence study of diesel soot formation in

Com-bustion and Flame, 135, 475−488

Dec, J E., Espey, C., Zur Loye, A O and Siebers, D L

(1992) Symposium on mechanisms and chemistry of

pollutant formation and control from internal combustion

Delacourt, E., Desmet, B and Besson, B (2005)

Characteri-zation of very high diesel sprays using digital imaging

techniques Fuel, 84, 859−867

Demirbas, A (2007) Importance of biodiesel as

transpor-tation fuel Energy Policy, 35, 4661−4670

Demirbas, A (2009) Progress and recent trends in

14−34

Demirbas, A (2005) Biodiesel production from vegetable

oils via catalytic and non-catalytic supercritical methanol

31, 466−487

Desantes, J M., Payri, R., Salvador, F J and Gil, A (2006)

Development and validation of a theoretical model for

diesel spray penetration Fuel, 85, 910−917

Desantes, J M., Payri, R., Garcia, J M and Salvador, F J

(2007) A contribution to the understanding of isothermal

diesel spray dynamics Fuel, 86, 1093−1101

DiGeorgio, F., Laforgia, D and Damiani, V (1995)

Investi-gation on drop size distribution in the spray of a

Dobbs, H H., Villahermosa, L A., Stavinoha, L L and

Heywood, J B (2000) Alternative fuels: Gas to liquids

2000-01-3422

Durbin, T D., Cocker III, D R., Sawant, A A., Johnson,

K., Miller, J W., Holden, B B., Helgeson, N L and Jack,

J A (2007) Regulated emissions from biodiesel fuels

41, 5647−5658

Ejim, C E., Fleck, B A and Amirfazli, A (2007)

Analy-tical study for atomization of biodiesels and their blends

in a typical injector: Surface tension and viscosity effects

Fuel, 86, 1534−1544

Fang, T., Coverdill, R E., Lee, C F and White, R A

(2005) Low temperature combustion within a small bore

Paper No 2005-01-0910

Fukumoto, M., Oguma, M and Goto, S (2003)

Experi-mental investigation of lubricity improvements of to-liquid (GTL) fuels with additives for low sulphur

Gill, K., Marriner, C., Sison, K and H Zhao (2005) cylinder studies of multiple diesel fuel injection in a single

Gulder, O L and Glavincevski, B (1991) Influence offuel-bound sulfur on soot formation in laminar diffusion

Technology, 77, 337−343

Fund-amentals McGraw-Hill New York 539−541

Hiroyasu, H and Arai, M (1990) Structures of fuel sprays

North Dakota State University of Agriculture AppliedScience and US department of Agriculture Cooperating.Fargo, North Dacota

Inagaki, K., Takasu, S and Nakakita, K (1999) In-cylinderquantitative soot concentration measurement by laser-

Johansen, K., Gabrielsson, P., Stavnsbjerg, P., Bak, F.,Andersen, E and Autrup, H (1997) Effect of upgradeddiesel fuels and oxidation catalysts on emission properties,

Joshi, R M and Pegg, M J (2007) Flow properties ofbiodiesel fuel blends at low temperatures Fuel, 86, 143−

Kitano, K., Sakata, I and Clark, R (2005) Effects of GTL

2005-01-3763

Kosaka, H., Nishigaki, T., Kamimoto, T and Harada, S.(1995) A study on soot formation and oxidation in anunsteady spray flame via laser induced incandescence

Krahl, J., Munack, A., Grope, N., Ruschel, Y., Schroder, O.and Bunger, J (2007) Biodiesel, rapeseed oil, gas-to-liquid, and a premium diesel fuel in a heavy duty diesel

35, 5, 417−426

Krahl, J., Knothe, G., Munack, A., Ruschel, Y., Schroder,O., Hallier, E., Westphal, G and Bunger, J (2009) Com-parison of exhaust emissions and their mutagenicityfrom the combustion of biodiesel, vegetable oil, gas-to-liquid and petrodiesel fuels Fuel, 88, 1064−1069.Lapuerta, M., Armas, O and Rodrigues-Fernandez, J (2008)

Pro-gress in Energy and Combustion Science, 34, 198−223.Lee, C S., Park, S W and Kwon, S I (2005) An experi-mental study on the atomization and combustion charac-

19, 2201−2208

INSTANTANEOUS 2-D VISUALIZATION OF SPRAY COMBUSTION AND FLAME LUMINOSITY OF GTL 171

Publishing Corporation New York

Lim, M C H., Ayoko, G A., Morawska, L., Ristovski, Z

D and Jayaratne, R (2005) Effect of fuel composition

and engine operating conditions on polycyclic aromatic

hydrocarbon emissions from a fleet of heavy-duty diesel

Lim, M C H., Ayoko, G A., Morawska, L., Ristovski, Z

D and Jayaratne, E R (2007) The effect of fuel

charac-teristics and engine operating conditions on the elemental

composition of emissions from heavy duty diesel busses

Fuel, 86, 1831−1839

Lin, Y C., Lee, W J., Wu, T S and Wang, C T (2006)

Comparison of PAH and regulated harmful matter

emi-ssions from biodiesel blends and paraffinic fuel blends on

engine accumulated mileage test Fuel, 85, 2516−2523

Mitchell, K., Steere, D E., Taylor, J A., Manicom, B.,

Fisher, J E., Sienicki, E J., Chiu, C and Williams, P

(1994) Impact of diesel fuel aromatics on particulate,

Muller, C J and Martin, G C (2002) Effects of oxygenated

compounds on combustion and soot evolution in a DI

diesel engine: Broadband natural luminosity imaging

SAE Paper No. 2002-01-1631

II CRC Press, Inc

Payri, F., Arregle, J., Fenollosa, C., Belot, G., Delage, A.,

Schaberg, P., Myburgh, I and Botha, J (2000)

Charac-terisation of the injection-combustion process in a

com-mon-rail DI diesel engine running with sasol

Pickett, L M and Siebers, D L (2004a) Soot in diesel fuel

jets: Effects of ambient temperature, ambient density, and

injection pressure Combustion and Flame, 138, 114−135

Pickett, L M and Siebers, D L (2004b) Non-sooting, low

flame temperature mixing-controlled DI diesel

Rudolph, V and He, Y (2004) Research and development

461−474

Sakai, A., Takeyama, H., Ogawa, H and Miyamoto, N

(2004) Improvements in premixed charge compression

ignition combustion and emissions with lower distillation

temperature fuels Int J Engine Research, 6, 433−442

Schumacher, L G., Borgelt, S C., Fosseen, D., Goetz, W

and Hires, W G (1996) Heavy-duty engine exhaust

emission tests using methyl ester soybean oil/diesel fuel

blends Bioresour Technol., 57, 31−36

Senda, J., Choi, D., Iwamuro, M., Fujimoto, H and Asai,

G (2003) Experimental analysis on soot formation

pro-cess in DI diesel combustion chamber by use of optical

Senda, J., Ikeda, T., Haibara, T., Sakurai, S., Wada, Y and

Fujimoto, H (2007) Spray and combustion

characteri-stics of reformulate biodiesel with mixing of lower boiling

Siebers, D., Higgins, B and Pickett, L (2002) Flame off on direct-injection diesel fuel jets: Oxygen concent-

Soltic, P., Edenhauser, D., Thurnheer, T., Schreiber, D andSankowski, A (2009) Experimental investigation ofmineral diesel fuel, GTL fuel, RME and neat soybeanand rapeseed oil combustion in a heavy duty on-roadengine with exhaust gas aftertreatment. Fuel, 88, 1−8.Szybist, J P., Song, J., Alam, M and Boehman, A L (2007).Biodiesel combustion, emissions, and emission control

Fuel Processing Technology, 88, 679−691

Tanaka, S., Takizawa, H., Shimizu, T and Sanse, K (1998).Effect of fuel compositions on PAH in particulate matter

Tsolakis, A., Megaritis, A., Wyszynski, M L and Theinnoi,

K (2007) Engine performance and emissions of a dieselengine operating on diesel-RME (rapeseed methyl ester)

Turrio-Baldassarri, L., Battistelli, C L., Conti, L., Crebelli,R., De Berardis, B., Iamiceli, A L., Gambino, M andIannaccone, S (2004) Emission comparison of urbanbus engine fueled with diesel oil and biodiesel blend

Science of the Total Environment, 327, 147−162

Use Guidelines DOE/GO-102004-1999 Revised November2004

Vaaraslahti, K., Virtanen, A., Ristimaki, J and Keskinen, J.(2004) Nucleation mode formation in heavy-duty diesel

and Technology 38, 18, 4884−4890

Vattulainen, J (1998) Experimental determination ofspontaneous diesel flame emission spectra in a largediesel engine operated with different diesel fuel qualities

SAE Paper No. 981380

Verhoeven, D D., Vanhemelryck, J L and Baritaud, T A.(1998) Macroscopic and ignition characteristics of high-

Wu, T., Huang, Z., Zhang, W., Fang, J and Yin, Q (2007).Physical and chemical properties of GTL-diesel fuelblends and their effects on performance and emissions of

and Fuels, 21, 1908−1914

Yamane, K., Uera, A and Shimamoto, Y (2001) Influence

of physical and chemical properties of biodiesel fuels oninjection, combustin and exhaust emission characteristics

International Journal of Automotive Technology , Vol 12, No 2, pp 173 − 182 (2011)

173

MODEL-BASED DESIGN OF A VARIABLE NOZZLE TURBOCHARGER CONTROLLER

H G ZHANG 1) , E H WANG 1)* , B Y FAN 1) , M G OUYANG 2) and S Z XIA 3)

(Received 12 January 2010; Revised 20 July 2010)

ABSTRACT− Variable Nozzle Turbocharger (VNT) was invented to solve the problem of matching an ordinary turbocharger with an engine VNT can harness exhaust energy more efficiently, enhance intake airflow response and reduce engine emissions, especially during transient operating conditions The difficulty of VNT control lies in how to regulate the position

of the nozzle at different engine working conditions The control strategy designed in this study is a combination of a loop feedback controller and an open-loop feed-forward controller The gain-scheduled proportional-integral-derivative (PID) controller was implemented as the feedback controller to overcome the nonlinear characteristic As it is difficult to tune the parameters of the gain-scheduled PID controller on an engine test bench, system identification was used to identify the plant model properties at different working points for a WP10 diesel engine on the test bench The PID controller parameters were calculated based on the identified first-order-plus–dead-time (FOPDT) plant model The joint simulation of the controller and the plant model was performed in Matlab/Simulink The time-domain and frequency-domain performances of the entire system were evaluated The designed VNT control system was verified with engine tests The results indicated that the real boosting pressure traced the target boosting pressure well at different working conditions.

closed-KEY WORDS : Variable nozzle turbocharger, System identification, PID controller design, Modeling, Diesel engine

1 INTRODUCTION

Variable Nozzle Turbocharger (VNT) is a good solution to

overcome the shortcomings of an ordinary turbocharger

matched with a vehicle engine The use of the VNT will

become more advantageous as engine-emission legislation

becomes stricter The nozzle geometry and area cannot be

changed in an ordinary turbocharger; accordingly, the

intake flow of the compressor cannot be adapted for the

engine when the engine is working at low-speed

condi-tions, and the amount of the intake air is insufficient for the

requirements of large torque output When the engine is

working at high-speed conditions, the waste-gated valve

has been adopted to bypass the exhaust gas to avoid

over-speed operation of the ordinary turbocharger, but the

exhaust energy cannot be fully utilized this way

The VNT resolves the above constraints by reducing the

nozzle flow area at low engine speeds This action results

in an increased turbine speed, and the amount of intake air is

accordingly increased The VNT increases the nozzle flow

area at high engine speed This maintains a sufficient amount

of intake air, while preventing damage to the turbocharger from

overspinning With the ordinary turbocharger, the response

time of the intake air system lags significantly behind the injection system when the engine is working in accelerating

transient-response performance of the intake-air system byregulating the nozzle position, thereby improving accelerationperformance and reducing exhaust emission Such areduction in the transient-state emission of diesel engines,especially the particulate emissions, could be a remarkableadvantage of the VNT

A schematic of a diesel engine matched with a VNT isshown in Figure 1 Fresh air is compressed by the compressorthen cooled in the intercooler The cooled air flows throughthe intake manifold pipes and enters the cylinders Theexhaust gas is discharged into the VNT through theexhaust-manifold pipes after being burned with the fuel in thecylinders The exhaust gas expands in the turbine, andmechanical work is produced Here differing from an ordinaryturbocharger, there is a unison ring in the VNT, whichregulates the nozzle in unison (Steve et al., 2002) The nozzlecan be rotated by its axle with a tab-and-slot mechanismconnected to the unison ring; as a result, the regulation ofthe cross-sectional area of the airflow can be realized Therotation of the unison ring is controlled by the actuatorassembled on the turbine housing

The boosting pressure in the intake manifold can be used

to control the amount of intake air to the engine cylinders

*Corresponding author. e-mail: enhua.wang@yahoo.com

174 H G ZHANG et al.

VNT is used, the boosting pressure can be controlled by

regulating the nozzle position, which is in turn regulated by

the VNT control system The key point of VNT control is

to implement the regulation of the nozzle position quickly

to reduce the response time of changes in intake airflow

Many vehicle manufacturers have developed VNT control

systems including Nissan (Takashi et al., 2001; Ogawa et al.,

main control methods used in the current control units The

first is feed-forward control (Takashi et al., 2001; Ogawa et

working point is obtained through engine tests and is then

implemented in the control program as a lookup table mapped

to the engine speed and load The second method is

proportional-integral-derivative (PID) feedback control (Ogawa

et al., 1998; Riccardo et al., 1997; Ammann et al., 2003; He et

al., 2006) The control strategy determines the target

boosting-pressure value by looking up in a map table The boosting-pressure

difference between the target boosting pressure and the real

boosting pressure is calculated The output value to drive the

actuator is then computed according to the PI or PID control

equations The nozzle position can be readily achieved in

steady-state conditions using the first method, but the

performance is poor during transient conditions Using the

second method, the real boosting pressure can trace the target

boosting pressure; however, while the effect is improved, the

performance remains inadequate Because the system

characteristics over the working range are nonlinear, one set of

PID controller parameters cannot satisfy the requirements for

the whole range Furthermore, a method for tuning the PID

controller parameters has not been presented to date

In this study, a model-based method was introduced for

VNT controller design First-order-plus-dead-time (FOPDT)

models were estimated at different working points To

overcome the system nonlinearity, a gain-scheduled PID

controller was studied and a solution was developed

2 VNT CONTROL SYSTEM DESIGN

The scheme of the designed VNT control system is shown in

Figure 2 The system consisted of the battery, the engine-speed

sensor, the accelerator-pedal-position sensor, the pressure sensor, the intake-air-temperature sensor, theambient-pressure sensor, the ignition switch, the diagnosticswitch, the control module, the electro-hydraulic solenoid andthe VNT When the ignition switch is set on the “ON” position

boosting-or the “Start” position, a voltage (24 V) is applied to thecontrol unit The control program samples the signals from thesensors and computes the output signal

The requirements for the VNT control system are that thereal boosting pressure must be regulated to the target boostingpressure within two seconds in any engine working conditionand the percent overshoot can be no more than 30% Thedesigned control strategy is shown in Figure 3 as a combina-tion of the gain-scheduled PID controller and the feed-forwardcontroller The control object of the feedback control is theboosting pressure The control object of the feed-forwardcontrol is the optimal nozzle position The feedback control isresponsible for keeping the real boosting pressure at the targetboosting pressure in both steady and transient states The feed-forward control is responsible for providing a plausiblefloating base point for the PID controller

Figure 1 Scheme of the VNT system

Figure 2 Scheme of the VNT control system

Figure 3 Designed control strategy of the VNT control system

MODEL-BASED DESIGN OF A VARIABLE NOZZLE TURBOCHARGER CONTROLLER 175

accelerator pedal position p The target boosting pressure p t

boosting-pressure sensor The boosting-pressure error ∆P = P t −P r is computed

and input to the PID controller The transfer function of the

PID controller G c(s) is expressed as:

(1)where K p is the proportional coefficient, T i is the integral

time constant, T d is the derivative time constant and Y 1 is

the output value of the PID controller K i and K d were used

where K i = K p/T i and K d= K p T d

The gain-scheduled PID control strategy is used to adapt

the parameters of the controller for different operating

conditions Here, K p, K i and K d are computed from three

respective maps stored in the control software as 3D tables

The Lagrange linear interpolation method is used to

calculate the output values according to the input values of

n and d The output value of the feed-forward control Y 2 is

The map data were obtained from the engine-calibration

Y 1 and Y 2

3 PLANT MODEL IDENTIFICATION

The parameters in the gain-scheduled PID controller must be

calibrated using an appropriate method The usual tuning

method is empirical critical proportioning tuning (Haugen,

2004) Herein, the critical unattenuated oscillation curve is

measured and stored to calculate the controller parameters

However, it is difficult to determine such a critical curve on

the engine test bench Therefore, the empirical critical

pro-portioning tuning method was unsuitable for the VNT

controller Accordingly, a tuning method based on the

identified plant model was applied here This method avoids

the need for many engine tests and provides sufficient

accur-acy and response times

There are two kinds of modeling approaches for control

purposes: physical modeling and system identification The

plant model can be set up according to the working principles

of physics, mechanics and chemistry It is difficult to build

such a physical plant model for a VNT control system because

the parameters are difficult to measure and errors are often

large in a lumped-parameters model Hence, the first approach

was not utilized here In the system-identification approach,

the plant model is treated as a black box and the designer only

focuses on the data taken directly from the engine tests by

exciting the plant and measuring its response The process of

constructing a model from experimental data is the main work

of system identification In the plant model for VNT control,

the data input is the driving voltage applied to the actuator and

the data output is the real boosting pressure The

driving-voltage and the real boosting-pressure data were measured onthe engine test bench The relationship between the drivingvoltage and the real boosting pressure was found to benonlinear over the entire engine operating range As a result, auniform linear plant model would not be accessible however,when the engine is running steadily at one point in the range,this relationship can be approximated as linear The pertinentPID controller parameters can then be tuned based on theidentified linear model

The classical approaches for acquiring the data are thestep-response method and the impulse-response method

step-response method was used to obtain the data in thiswork; the main stages are listed in the following First, the feed-forward controller map was calibrated on theengine test bench The nozzle position was adjusted from themaximum open position to the minimum open position, whilekeeping both the engine speed and the accelerator pedalposition stable The real boosting pressure was recorded withthe nozzle at different positions and the target boostingpressure was thus determined The driving voltage corres-ponding to the target boosting pressure was set as the targetvalue in the map

Second, the target value of the driving voltage was set asthe base voltage A certain deviation voltage was added to

or subtracted from the base value to excite the plant Thereal boosting pressure was recorded during this process.Third, the recorded data were filtered and input to thesystem-identification toolbox in Matlab (Lennart, 1999,2008) The process model was estimated in the GUI interface.Different configurations were tried to yield satisfactory results.The FOPDT plant model was estimated The transfer function

of the FOPDT model is written as:

(2)

and τ is the time delay

Operating points based on different engine speeds and loadswere selected across the entire working range Their relativeplant models were estimated, and all the correspondingparameters of the PID controller were tuned based on theseplant models The gain-scheduled PID controller was thenused to organize all the PID controller parameters

The VNT control system was assembled and installed on

a WP10 common-rail turbocharged diesel engine (WeichaiPower Co., Ltd., China) The engine is a water-cooled,four-stroke, EVB-brake, direct-injection and intercooledunit The detailed specifications of the WP10 diesel engineare listed in Table 1

The typical results from the tests are presented in Figure

4 The data were measured at the engine working point of2,200 r/min and 45% load The output data of the realboosting pressure (upper part) were in response to theincrement of the driving voltage (lower part)

The time-domain data were imported into the graphical

176 H G ZHANG et al.

user interface (GUI) shown in Figure 5 The data were split

into two parts; one was used to estimate the plant model,

and the other was used to validate the plant model The

FOPDT plant model was estimated as:

(3)

where the low-frequency gain is 21.1887, the time constant

is 0.852 s, and the time delay is 0.268 s

The measured signal and the simulated signal arecompared in Figure 6 The measured boosting pressure isshown as the black line, and the simulated pressure is theblue line The simulated line predicted the experimentalline well The difference between these output signals ispresented in Figure 7 Significant differences were found inthe intervals from -1 kPa to 1 kPa, so this result could meetthe requirements for engine control The cross-correlationbetween the input signal and the output residuals is shown

in Figure 8 The results are within the confidence regions,indicating there was no significant correlation between pastinputs and the residuals

The absolute stability of the FOPDT model was alsoanalyzed The results are listed in Table 2 The Matlab code

Figure 4 Measured data at 2,200 r/min and 45% load

Figure 5 GUI interface used for data processing

Figure 6 Comparison of the measured and simulated modeloutputs

Figure 7 Residuals of the measured and simulated modeloutputs

MODEL-BASED DESIGN OF A VARIABLE NOZZLE TURBOCHARGER CONTROLLER 177

The results shown in the command window of Matlab

The estimated plant models at six selected working

points and their stabilities are listed in Table 2 The engine

speed and the engine load in the first two columns were

used to define the working point The FOPDT models are

listed in the third column The absolute stability of the

corresponding model is listed in the fourth column The

first crossover frequency and corresponding stability

margin are given in the last two columns The results

indicate that the absolute stability changed from stable to

unstable as the engine speed and load were increased The

crossover frequency increased as the engine load rose whenthe engine speed remained stable The corresponding gainmargin decreased at low and medium engine speeds butincreased at high engine speed

4 PID CONTROLLER TUNING

The PID controller has been the heart of engineering controlpractices for seven decades However, implementation ofcontrol, in particular, how to tune the controller, remains anactive research area The great majority of PID tuning rulesassume that an FOPDT model of the process is available.Many tuning rules have been put forward based on theFOPDT model (Aidan, 2006) However, their application toengine control has not been reported In this study, theFOPDT plant models at different working points wereestimated based on the results in the previous section Theparameters of the corresponding PID controller were tunedaccording to the system-identification results in the Matlabenvironment

Zhuang and Atherton proposed a PID controller-tuningrule based on the integral squared time-weighted error(ISTE) criterion for the FOPDT model (Zhuang and

as:

K p = 0.509K c

T d = 0.125T cwhere κ = KKc is the normalized gain The results at 2,200 r/min and 45% load according to Equation 4 determined as

K p= 0.1357 V / kPa, T i= 0.9651 s, and T d = 0.1204 s.The performance of the controller at the above working

Figure 8 Cross-correlation between the input signal and

the output residuals

Table 2 Identified FOPDT models at different working points

178 H G ZHANG et al.

point was analyzed in the time domain The simulation

model, including the controller and the plant model, was

elaborated in the Matlab/Simulink environment, as shown

Here, it is a step-stimulation signal with the same shape as

disturbance, as presented in Figure 10 The configuration

parameters in Matlab/Simulink are shown in Figure 11

The output of the simulation is shown in Figure 12; here,

the blue line is the input of u, the black line is the output of

show the 5% limit for the settling time The results reveal

that the settling time to within 5% of the final value was

less than 1.2 s and the percent overshoot was about 20%

The magenta line is the data with disturbance, showing a

very similar outcome as long as the white-noise disturbance

good time response The settling time was enhanced from 2

s, as shown in Figure 4, to 1.14 s

The stability of the above-defined PID controller was then

analyzed in the frequency domain The open-loop

log-magnitude-phase diagram (Bode diagram) is shown in Figure

13; the red line is the plant model G(s) plot, the blue line is the

PID controller Gc(s) plot and the black line is the open-loop

plot of the feedback system G c(s)G(s) The red line shows that

the plant model is unstable according to the Nyquist stability

criterion but the feedback system of G c(s)G(s) is stable The

relative stability can also be analyzed by the phase margin in

the open-loop Bode diagram The phase margin was

computed as 64.78 degrees at 3.17 rad/s It is generally found

that gain margins of three or more combined with phase

margins between 30 and 60 degrees result in reasonable

trade-offs between bandwidth and stability In addition, if the

second-order approximation is used, the damping ratio is

0.648 according to the approximation relationship: ζ = 0.01φ m

(Richard and Robert, 2001) Therefore, the percent overshootcan be evaluated as:

(5)

σ e = – ζπ ⁄ 1 ζ –2× 100%

Figure 9 Simulation model for the time-domain performance analysis at 2,200 r/min and 45% load

Figure 10 White-noise disturbance signal

Figure 11 Parameters configuration in simulink

MODEL-BASED DESIGN OF A VARIABLE NOZZLE TURBOCHARGER CONTROLLER 179

and the calculated percent overshoot is 7%, which is also

less than the allowed value

Table 3 lists the tuning results according to the FOPDT

models in Table 2 K p, T i and T d at each working point were

tuned based on Equation 4 The settling time and the percent

overshoot of each point were also computed using the

simulation model in Figure 9 The results indicate that the

time-domain performance of the controllers was good and

within our desired limiting values

5 WP10 ENGINE VALIDATION TESTS

The maps for the gain-scheduled PID controller were

constructed based on the method presented in Sections 3 and

4 There were only six points included in Tables 2 and 3

However, more appropriate working points had to be selected

at each calibrated engine-speed line Because the identifiedworking points were not situated only at the nodes of themaps, interpolation processes were employed to calculate thevalues for the maps, and the refined calibration of thecalculation results was then performed The control strategywas realized in the control software

The entire system including the WP10 engine and the VNTcontrol module was verified on the engine test bench shown inFigure 14 First, the dynamometer was configured for theconstant-speed control mode The accelerator pedal wasregulated from the minimum position to the maximumposition The relevant data were collected by the calibrationtool The results at 2,200 r/min are presented in Figure 15: (a)

is the engine speed variation curve during testing; (b) is theaccelerator pedal position variation curve; (c) are the VNTcontrol values denoted by voltages; (d) are the variation curves

of the real boosting pressure and the target boosting pressure

In addition, Figure 16 shows the results at 1,400 r/min.Subsequently, the dynamometer was configured for theconstant-torque control mode The accelerator pedal wasregulated from the minimum position to the maximumposition The corresponding data were measured The results

at 700 Nm and 600 Nm are displayed in Figures 17 and 18,respectively The results indicate that the real boostingpressure followed the target boosting pressure very well whenthe accelerator pedal was adjusted While the engine wasoperating at high speed, the VNT control value was relativelysmaller Thus, the open area of the VNT vanes was larger than

Figure 12 Simulation results with and without disturbance

input

Figure 13 Open-loop Bode diagram of the results at 2,200

r/min and 45% load

Table 3 Parameters of the PID controllers and their domain performances

Figure 14 Validation of the VNT control system with WP10diesel engine

180 H G ZHANG et al.

at low speed

6 CONCLUSIONS

The application of VNT to a diesel engine can improve the

power output performance and reduce emissions The

demonstrated dynamic response indicates that the VNT is

suitable for a heavy-duty diesel engine

The main research results reported herein are as follows

First, the VNT control system was successfully developed

according to the design strategy, which consisted of a

feed-forward controller and a gain-scheduled feedback PID

controller Second, the FOPDT plant models were estimated at

different work points The results indicated that the absolute

stability changes from stable to unstable as the engine speed

and the engine load are increased The crossover frequencyincreases as the engine load rises when the engine speed isstable The corresponding gain margin decreases at low andmoderate engine speeds but increases at high engine speed.Third, the parameters of the PID controller were tunedaccording to ISTE criteria The time-domain performance andthe frequency-domain stability were analyzed The resultsindicated that the setting time and the percent overshootsatisfied the requirements for the VNT controller Finally, thecomplete control system was verified on the engine test benchwith transient-state tests The results indicated that theboosting pressure control could be achieved under both steadyFigure 15 Results of speed control mode at 2,200 r/min

Figure 16 Results of speed control mode at 1,400 r/min

MODEL-BASED DESIGN OF A VARIABLE NOZZLE TURBOCHARGER CONTROLLER 181

states and transient states for the designed VNT control

system The real boosting pressure traced the target boosting

pressure very well Accordingly, the results indicate that a

model-based design method for a VNT control system is

feasible and convenient

REFERENCES

Tuning Rules.2nd Edn Imperial College Press.London

154−235

Ammann, M., Fekete, N P., Guzzella, L and Glattfelder,

A H (2003) Model-based control of the VGT and EGR

in a turbocharged common-rail diesel engine: Theory

Elbert, H and Spencer, C S (1990) Mean value modeling

182 H G ZHANG et al.

He, Y S., Lin, C C and Anupam, G (2006) Integrated

simulation of the engine and control system of a

2006-01-0439

Jensen, J P., Kristensen, A F., Spencer, C S., Houbak, N

and Elbert, H (1991) Mean value modeling of a small

538

User’s Guide Mathworks Co Ltd

Ogawa, H., Hayash, M and Yashiro, M (1998)

Develop-ment of the continuous and feedback controlled variable

nozzle turbine turbocharger system for heavy-duty

trucks (In Chinese) Vehicle Engine,1,9−15

Riccardo, B., Alessandro, C., Enrico, L and Alberto, P.(1997) DI diesel engine with variable geometryturbocharger (VGT): A model based boosting pressurecontrol strategy Meccanica,32,409−421

Systems 9th Edn Prentice Hall New Jersey.406-519 Steve, A., Mark, G., Shahed, S M and Kevin, S (2002).Advanced variable geometry turbocharger for diesel

Identification and PID Control.Wiley.Singapore.151−

316

Takashi, S., Hiroyuki, I and Hiromichi, M (2001).Study ofstrategy for model-based cooperative control of EGRand VGT in a diesel engine JSAE Review, 22,3−8

Feedback Control: Analysis and Design with MATLAB

Zhuang, M and Atherton, D P (1993) Automatic tuning

216−224

International Journal of Automotive Technology , Vol 12, No 2, pp 183 − 191 (2011)

183

EXPERIMENTAL AND NUMERICAL INVESTIGATION OF FUEL MIXING EFFECTS ON SOOT STRUCTURES IN COUNTERFLOW

DIFFUSION FLAMES

B C CHOI 1) , S K CHOI 2) , S H CHUNG 2) , J S KIM 3) and J H CHOI *3)

Thuwal 23955-6900, Saudi Arabia

(Received 3 November 2009; Revised 9 July 2010)

ABSTRACT− Experimental and numerical analyses of laminar diffusion flames were performed to identify the effect of fuel mixing on soot formation in a counterflow burner In this experiment, the volume fraction, number density, and particle size

of soot were investigated using light extinction/scattering systems The experimental results showed that the synergistic effect

of an ethylene-propane flame is appreciable Numerical simulations showed that the benzene (C 6 H 6 ) concentration in mixture flames was higher than in ethylene-base flames because of the increase in the concentration of propargyl radicals Methyl radicals were found to play an important role in the formation of propargyl, and the recombination of propargyl with benzene was found to lead to an increase in the number density for cases exhibiting synergistic effects These results imply that methyl radicals play an important role in soot formation, particularly with regard to the number density.

KEY WORDS : Soot, Counterflow, Light extinction/scattering, Synergistic effects, Number density

1 INTRODUCTION

Soot is an aggregate of carbonaceous particles produced

during the incomplete combustion or high-temperature

pyrolysis of hydrocarbon fuels (Glassman, 1988) Soot

particles can increase heat transfer in practical combustors

through thermal radiation Soot, along with several types of

polycyclic aromatic hydrocarbon (PAH) species known as

soot precursors, can cause health hazards These PAH

species have recently been reported to be mutagenic and

The effect of fuel structure on the formation of soot and

PAHs, which are considered to be soot precursors, has been

found to be significant from studies on diffusion flames

The species generated from fuel pyrolysis lead to incipient

et al., 1984)

Many studies have been conducted on soot inception and

particle size in premixed ethylene flames and laminar

studies on fuel mixing and doping have been conducted to

investigate the effects of fuel structure on PAH and soot

formation mechanisms in non-premixed flames (McEnallyand Pfefferle, 2007; Kang et al., 1997; Hwang et al., 1998)

A synergistic effect was observed when the mixture fuelsproduced more PAHs and soot than the respective pure fuels,e.g., the case of propane added to ethylene fuel Thissynergistic effect has been explained based on the interactionbetween acetylene and propargyl species Acetylene isknown to be an important species for the hydrogen-

and Warnatz, 1987; Frenlach, 1988), which influences theoverall soot formation process Propargyl plays an importantrole in incipient ring formation through a propargyl

D’Anna et al. (2000) and Anderson et al (2000) investigated

addition of a small amount of propane in ethylene fuel couldenhance the production of soot and PAHs by providingextra propargyl radicals to the already abundant acetylene

Recently, experimental and numerical studies on the role

*Corresponding author. e-mail: choi_jh@hhu.ac.kr

184 B C CHOI et al.

of odd-carbon chemistry in soot formation in ethylene-base

diffusion flames with fuel mixing have been conducted

generated in the PAH formation region during the

decomposition reaction of ethane and propane However,

in that study, only soot volume fraction was measured,

using the laser-induced incandescence (LII) technique, to

explain the synergistic effect; detailed measurements of

soot number density and soot size were not performed

The purpose of the present study was to obtain

quantitative data on the volume fraction, number density,

and diameter of soot particles in a counterflow burner for

various mixture flames, including mixing methane, ethane,

and propane fuels to ethylene-base flames A light

extinction/scattering system was used to measure the

volume fraction, number density, and diameter of soot

particles To further explain the effects of fuel mixing, a

numerical study on the role of fuel mixing was performed

2 EXPERIMENT AND NUMERICAL

SIMULATION

The experimental apparatus consisted of a counterflow

burner, flow controllers, and a laser light extinction/scattering

setup, shown schematically in Figure 1 The counterflow

burner contained a pair of convergent-divergent nozzles with

an area contraction ratio of 80:1 The nozzle exit diameter, as

well as the separation distance between the two nozzles, was

14.2 mm The fuels were commercially pure grade methane

(>99.9%), ethane (>99.9%), and propane (>99.9%) mixed

with the ethylene-base flame The volume of the fuel mixed

was 5% of the volume of the ethylene-base flame

The nozzle exit velocities of the fuel and oxidizer

streams were maintained at 19.5 cm/s to maintain the flame

stretch constant Each nozzle had a concentric slit through

which nitrogen was supplied to shield the ambient air

Mass flow controllers, calibrated with a wet-test gas meter,

were used for flow control A 0.5-W, 514.5-nm Ar-ion laser

was used to obtain the volume fraction, particle size, andnumber density of the soot particles through laser lightscattering and extinction techniques A photodiode(Hamamatsu, S1227) monitored the extinction intensityand the scattering signal was measured by a photomultipliertube (Hamamatsu, R928) placed at a right angle through a514.5-nm narrow band-pass filter having 1-nm full-width-half-maximum (FWHM) During the measurement of thescattering signal, a half-wave plate and a polarization filterwere used to control the orientation of the polarization axis forthe incident laser and scattered light, respectively Amechanical chopper (1100 Hz) and a lock-in amplifier(Standard Research, SR 530) were used to enhance thesignal-to-noise ratio Details of these techniques have been

To better understand and interpret the experimentalresults, a numerical simulation was conducted using the

Thermodynamic properties were calculated using

the transport interpreter and subroutine library of

calculate transport properties because the reaction modeluses an updated transport database for calculating diffusioncoefficients The governing equations and numericalschemes have been presented in other studies (Lee andChung, 1994 Smooke, 1982)

3 RESULTS AND DISCUSSION

3.1 Effects of Fuel Mixing on Soot

To further understand the effects of fuel structure on sootformation, the characteristics of methane, ethane, and propanemixed in ethylene diffusion flames were investigated Sootformation characteristics in counterflow diffusion flames can

be categorized into soot formation (SF) flame and sootformation/oxidation (SFO) flame characteristics, based onthe relative positions of the flame and the particle stagnationplane (Kang et al., 1997) In the present study, we consideredthe characteristics of soot formation flames, which occurwhen the flame is located on the oxidizer side of the particlestagnation plane

Figure 2 shows a schematic of a soot zone structure and aphotograph of the soot formation flame in counterflow Due

to the stoichiometric fuel to oxidizer mass ratio, the flame islocated on the oxidizer side of the counterflow Thus, sootparticles formed on the fuel side of the flame are transportedaway from the flame and toward the stagnation plane,following the streamlines reasonably Soot particles do notexperience oxidation in hydrocarbon fuels Therefore, theflame is categorized as soot formation flame

In the photograph (Figure 2), the yellow luminous zone

of the soot is located on the fuel side of the flame Adetailed flow visualization characterizing such flames has

Figure 1 Schematic of the experimental setup for light

extinction/scattering measurements

EXPERIMENTAL AND NUMERICAL INVESTIGATION OF FUEL MIXING EFFECTS 185

Figure 3 shows the profiles of soot volume fractions as a

function of the distance from the fuel nozzle In this figure,

each value shows the soot volume fraction for a pure

ethylene flame and the mixture flames of methane/ethane/

propane whose volume is 5% of ethylene The soot volume

fraction was calculated from laser extinction data The

ethylene-base flame and 1.32×10-6, 1.78×10-6, and 2.19×10-6

for the flame mixed with methane, ethane, and propane,

respectively It is interesting to note that the mixture flames

of ethane and propane have enhanced soot volume fractions

as compared to the ethylene-base flame, whereas the

methane-ethylene flame has a decreased soot volume

fraction The synergistic effect on the soot formation in the

mixture fuel of ethylene and propane has been explained

can also be explained by the role of propargyls in the

formation of the incipient ring of benzene

For all flames, the soot volume fractions have maximum

sooting zones (5 mm < Z < 7 mm) are on the oxidizer side

of the stagnation plane The flow field in the sooting zone

is directed toward the fuel side in the region between theflame and particle stagnation plane Therefore, the maxima

of soot volume fractions for all flames appear near theparticle stagnation plane because the transportation andgrowth of soot particles is toward the direction of the fuel.The axial profiles of the scattering signal along thecenterline of the counterflow are plotted in Figure 4 for amixing ratio of 5% in volume The measured scatteringsignals exhibit a skewed shape because soot particles begin

to form on the fuel side of the soot formation flame andgrow as they migrate toward the particle stagnation plane.Note that the particle stagnation plane is located close to Z

simulation by considering the thermophoretic effect andwill be elaborated later As compared to the case ofethylene-base flame, in the figure, the scattering signal ofthe methane mixing flame decreased, whereas the propanemixing flame shows increased signal intensity Ethanemixing was comparable to the ethylene-base flame case

To further clarify the soot formation processes for themixture flames, soot size (D 63) and number density (N) were

1984, 1990) From the quasi-one-dimensional characteristics

of counterflow flames, the extinction coefficient (K ext) can beexperimentally calculated using (Bohren and Huffman,1983):

(1)

intensity after extinction, and are the corresponding

the test section The volumetric differential cross section

K ext = – ∆ X 1 log W e ⁄ W i = – ∆ X 1 log I e ⁄ I i

I˜ i I˜ e

Figure 2 Schematic of the soot zone structure for soot

formation flames in counterflow and direct photograph

Figure 3 Profiles of soot volume fractions as a function of

distance from the fuel nozzle

Figure 4 Scattering signals as a function of distance fromthe fuel nozzle

186 B C CHOI et al

(2)

For spherical soot particles, which are small compared to

K ext and Q vv as follows (Dobbins et al., 1984):

(3)(4)(5)with

(6)

of soot particles, I is the irradiance of light, m is the

determined from the optical scattering cross-section and

extinction coefficient (Dobbins et al., 1984, 1990) E( ) and

index of the soot particles was assumed to be = 1.57

-0.56i for the present experiment using Ar-ion laser with λ =

cross section, Q vv, and extinction coefficient, K ext

The profiles of number density and size of soot particles

obtained from Eqations (1) ~ (6) are shown in Figures 5 and

6, respectively Figure 5 shows the profiles of soot number

density as functions of the distance from the fuel nozzle for

the ethylene-base flame and the mixture flames with 5% in

volume In the figure, the maxima of the number densities

flame zone, and decrease while moving away from this

zone toward the fuel side This indicates that the beginning

of the formation of soot particles is likely to occur in the

high-temperature region near the flame zone The soot

number density decreased rapidly as Z decreased in the

direction of soot particle transport, toward the fuel side for

all the cases, whereas the soot particle size, as shown in

Figure 6, increases as the particles are transported toward

the stagnation plane These trends suggest that soot

density decreases as Z decreases in the direction of soot

particle transport due to coalescence At the same time, the

size of soot particles increases due to coalescence and the

surface growth of soot

Figure 6 shows the profiles of soot particle size as a

function of distance from the fuel nozzle for based flames and mixture flames with 5% volume ofmethane, ethane, or propane The soot particles had a

grew as they moved toward the low-temperature region

the ethylene-base flames This result clearly indicates thatsoot particle size for the methane, ethane, or propanemixing flames are nearly the same, although there is smalldecrease in size compared to ethylene-base flames Thiscan be explained by the HACA mechanism, which plays adominant role in soot surface growth Both temperatureand acetylene species play crucial roles in the HACAmechanism Note that the adiabatic flame temperature is

for the mixture fuels than for the ethylene-base flame From the results in Figures 5 and 6, the synergistic effect

in the mixture fuels of propane/ethylene and ethane/ethylene, which had a larger soot volume fraction compared

to the ethylene-base flame, can be attributed to the higher

D 303( ) 2 -

= ,

= ,

=

m

m m

EXPERIMENTAL AND NUMERICAL INVESTIGATION OF FUEL MIXING EFFECTS 187

soot number density of the mixture flames, especially in the

soot inception region To enhance understanding of the

possible mechanism and for future modeling purposes, we

have further investigated this point through a numerical

simulation in the following subsection

3.2 Numerical Simulation

The experimental results showed that the volume fraction

and number density of soot particles increased for ethane

and propane mixture flames, whereas the volume fraction

for methane flames decreased Moreover, the soot particle

size for the mixture flames was slightly smaller than that of

the ethylene flame The mechanism of soot formation

could be influenced by variations in fuel structure due to

mixing Therefore, numerical calculations were performed

to better understand the soot zone structure with respect to

these characteristics

Figure 7 shows the calculated profiles of the temperatures

(T), the particle velocity (V p), and the concentrations of C2H2

for the ethylene-base flame and the mixture flames of

ethylene with methane, ethane, or propane as a function of

the distance from the fuel nozzle The calculated adiabatic

flame temperature was highest for the ethylene-base flame

The differences in temperatures of the mixture flames were

within approximately 10 K of the temperature for ethylene

These characteristics are also exhibited in the temperature

profiles, such that the profiles are very similar Maximum

8.1 mm The local temperatures in the soot region of 5 mm

< Z < 7.2 mm observed during the experiment were in the

range of 1000 – 1750 K

Due to the appreciable temperature gradient near the

flame zone, particle velocity may be affected by the

thermophoretic force, which arises from uneven molecular

2006) indicated that it is essential to consider the

thermophoretic effect when determining the behavior of

soot, especially in the soot deposition process

In Figure 7(a), the calculated particle velocity (V p = V g +

V t) is plotted by considering the thermophoretic velocity V t

velocity in the free molecular limit is expressed as

(Waldmann and Schmitt, 1996):

(7)

accommodation coefficient (assumed to be unity) The

particle velocities (V p) of all the cases show similar behavior

due to the temperature characteristics The particle stagnation

The formation and growth of PAHs and soot have been

explained by the HACA reactions where acethylene plays

an important role, especially in the growth process of soot

particles To illustrate this, the concentration profiles of

region (5 mm < Z < 7.2 mm), and the concentrations of

ethylene-base flame is the largest The results and thetemperature characteristics imply that the growth rate ofsoot by the HACA mechanism is expected to be the highestfor ethylene-base flames This is consistent with theexperimental results for soot particle size shown in Figure

6, wherein the soot particle size is the highest for theethylene-base flame

3.3 Surface Growth of Soot ParticlesThe surface growth rate of soot particles was furtheranalyzed based on the experimental results In thecalculation, particle velocity is required such that thecalculated velocity was used Hwang (1999) confirmed that

diffusion flames of ethylene and propane at various fuel/oxygen mole fractions are in good agreement with thevelocities measured using a laser Doppler velocimeter

mass growth rate per unit surface area of soot particles, has

Figure 7 Calculated temperatures and particle velocities:

distance from the fuel nozzle

188 B C CHOI et al.

been determined experimentally using (Hwang and Chung,

2001)

(8) (9)(10)

volume, A s [cm2/cm3] is the integrated surface area per unit

volume, ρ p is the density of soot particles (≈2 g/cm3), P(D)

1984), D is the particle size, and D 20 is the area-averaged

optical scattering cross-section and extinction coefficient

Figure 8 shows the surface growth rate of soot particles

(ω G [g/cm2/s]) as a function of distance from the fuel nozzle

for the ethylene-base flame, as well as the mixture flames

with methane, ethane, or propane The soot surface growth

rates for the mixture flames of methane, ethane, or propane

slightly decreased compared to the ethylene-base flame

This result is consistent with the results of the soot particle

values of all the flames are positive in the sooting zone (5.0

mm < Z < 7.6 mm)

and then decreased as it approached the particle stagnation

plane It is interesting to note that the decrease was mitigated

for Z < 6 mm and that a slightly increasing trend near Z ≈ 5.6

mm was observed This region is away from the maximum

temperature region; therefore, the HACA mechanism for

soot growth cannot be effective here This behavior of soot

surface growth in the low-temperature region of 1000 < T <

1500 K has previously been attributed to soot-PAH

coagulation (Hwang and Chung, 2001)

Furthermore, the soot number densities for all the flames

in Figure 5 increase slightly and show local maxima near Z

temperature is expected to be too low for soot formationthrough the growth of PAHs to soot by the temperaturesensitive HACA reaction (Frenlach and Warnatz, 1987).This result may be attributed to the role of PAHs in sootinception via the PAH agglomeration process (Hwang,1999), which deserves further consideration in future.3.4 Synergistic Effect

The present experimental results show that the soot volumefractions for the mixture flames, except for the methanemixture, are higher than that of the ethylene-base flame.This result suggests that the explanation of formation and

inappropriate

Z < 7 mm, which corresponds to the soot zone The

sooting zone (5 mm < Z < 7 mm) as opposed to the

mixture was the highest in the low-temperature region

Figure 8 Rates of soot surface growth as a function of

distance from the fuel nozzle Figure 9 Concentrations of CHfunction of the distance from the fuel nozzle.3 (a) and C3H3 (b) as a

EXPERIMENTAL AND NUMERICAL INVESTIGATION OF FUEL MIXING EFFECTS 189

methane mixture in the low-temperature region of Z < 5.5

mm is relatively low, whereas in the high-temperature

This behavior can be partly understood based on the bond

methane is relatively high (438 kJ/mol); therefore, it is

to break he C-C bonds in ethane and propane Thus, the

low-temperature region, where soot number densities are high,

is relatively low, such that the synergistic effect may be less

pronounced than for other fuels This behavior can be

al., 2000)

order of propane, ethane, methane, and ethylene-base

flame This order is the same as the soot number density

result in this behavior Miller and Melius(1992) and e Cole

et al (1992) explained the synergistic effect based on the

characteristics of propargyl, and we have further analyzed

the behavior of benzene and its formation pathways

ethylene-base and mixture flames The mole fractions of

benzene for all the mixture flames increase and are higher

than in the ethylene-base case In particular, the maximum

propane-ethylene flame is 1.34 times higher than that of the

ethylene-base flame Considering that incipient benzene

ring formation is a key step in the formation of PAHs and

soot inception, this result is in accordance with the results

of the soot number density in Figure 5 An interesting

feature is that the mole fraction of benzene for the methane

mixture is larger than that of the ethylene-base flame,

whereas the soot volume fraction for the methane mixture

flame is lower The higher mole fraction for the

methane-ethylene flame as compared to the methane-ethylene-base flame can

be partially attributed to the fact that the methane case

exhibits relatively higher propargyl concentration, which

leads to the incipient benzene ring formation As shownpreviously, the temperature and velocity profiles of both

mainly attributed to the chemical effects of fuel mixing andnot thermal or residence time effects Although themethane case shows a higher concentration of benzene

lower, which could have influenced the inception andgrowth of soot

the reaction path diagram has been introduced Figure 11shows the results for the (a) ethylene-base and (b) propanemixture flames The calculations were performed using thefull chemistry mechanism, and the integrated productionrates in the region of 5 mm < Z < 7 mm are represented bythe relative thickness The number in the parenthesisindicates the reaction number All reaction steps are coupledand interrelated; therefore, the reaction path diagram ishelpful for understanding C6H6 formation characteristics forvarious mixture flames

The reaction path diagrams demonstrate that C1 – C4chemistries are interrelated for benzene formation In the

produced and increased in concentration due to theproduction of pC3H4 + H = C3H3 + H2 (R317) and pC3H4 +

distance from the fuel nozzle

propane mixture (b) flames