ANALYSIS AND DESIGN OF A CAN COMBUSTOR A THESIS FOR MASTER OF SCIENCE IN CHEMICAL ENGINEERING By Sanjoy Kumer Bhattacharia Student No: 04020201 IF DEPARTMENT OF CHEMICAL ENGINEERING BANGLADESH UNIVERSITY OF ENGINEERING AND TECHNOLOGY DHAKA, BANGLADESH ,
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ANALYSIS AND DESIGN OF A CAN COMBUSTOR
A THESIS FOR
MASTER OF SCIENCE IN CHEMICAL ENGINEERING
BySanjoy Kumer BhattachariaStudent No: 04020201 IF
DEPARTMENT OF CHEMICAL ENGINEERINGBANGLADESH UNIVERSITY OF ENGINEERING AND TECHNOLOGY
DHAKA, BANGLADESH ,
ANALYSIS AND DESIGN OF A CAN COMBUSTOR
BySanjoy Kumer BhattachariaStudent No: 04020201 IF
A thesis submitted to the Department of Chemical Engineering, Bangladesh University ofEngineering and Technology, Dhaka, in partial fulfillment of the requirement for the
degree of Master of Science in Chemical Engineering.
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DEPARTMENT OF CHEMICAL ENGINEERINGBANGLADESH UNIVERSITY OF ENGINEERING AND TECHNOLOGY
DHAKA, BANGLADESH
June 2006
BANGLADESH UNIVERSITY OF ENGINEERIN.G AND TECHNOLOGYDEPARTMENT OF CHEMICAL ENGINEERING
•CERTIFICA nON OF THESIS WORK
We, the undersigned, certify that Sanjoy Kumer Bhattacharia, candidate for thedegree of Master of Science in Engineering (Chemical), has presented his thesis onthe subject "Analysis and Design of A Can Combustor" that the thesis is acceptablein form and content, and that the student demonstrated a satisfactory knowledge of thefield covered by this thesis in the oral examination held on the June 7, 2006.
-JJnAD~ ~~Du4~;~n-dr~- Nath MondalAssociate ProfessorDepartment of Chem ical EngineeringSUET, Dhaka
~v::sDr Ijaz Hossain.ProfessorDepartment of Chemical EngineeringBUET, Dhaka
En"r. Rashed Maksud KhChairmanBengal Fine Ceramics td52/1 New Eskaton, 0 aka-IOOO.
Chairman
Member
Member
Member (External)
".
DECLARATION
I do hereby declare that this thesis or any part of it has not been submitted elsewhere forthe award of any degree or diploma.
Velocity vector colored by velocity magnitude at y=0 for the 30angle of rotation ono'.
Velocity vector colored by velocity magnitude at y=0 for the 30angle of rotation of 45'.
Velocity vector colored by velocity magnitude at y=0 for the 31angle of rotation of 60'.
Velocity vector colored by velocity magnitude at y=0 for the 31angle of rotation of 90'.
Contour of static temperature at the plane y=o for the angle of 32rotation 30'.
Contour of static temperature at the plane y=o for the angle of 32rotation 45'.
Contour of static temperature at the plane Y=O for the angle of 32rotation 60'.
Contour of static temperature at the plane y=o for the angle of 32rotation 90'.
Contour of the concentration of methane at the plane Y~O for the 34angle of rotation 30'.
Contour of the concentration of methane at the plane y=o for the 34angle of rotation 45'.
Contour of the concentration of methane at the plane y=o for the 34angle of rotation 60'.
Contour of the concentration of methane at the plane y=o for the 34angle of rotation 90'.
Contour ofthe concentration of NO x at the plane Y=O for the 35angle of rotation 30'.
Contour ofthe concentration of NO x at the plane Y=O for the 35angle of rotation 45'.
Contour of the concentration of NO x at the plane y=o for the 36angle of rotation 60'.
Contour of the concentration of NOx at the plane y=o for the 36angle of rotation 90'.
Fig 4.5(a) Contour of the concentration of CO2 at the plane y=o for the 37angle of rotation 30°.
Fig 4.5(b) Contour of the concentration of CO2 at the plane y=o for the 37angle of rotation 45°.
Fig 4.5(c) Contour of the concentration of CO2 at the plane y=o for the 37angle of rotation 60°.
Fig 4.5(d) Contour of the concentration of CO2 at the plane Y=O for the 37angle of rotation 90°.
Fig 4.6(a) Contours of Static Temperature at the plane y=0 when 50% 39Excess air is used in the primary air inlet.
Fig 4.6(b) Contours of Static Temperature at the plane y=0 when 75% 39Excess air is used in the primary air inlet.
Fig 4.6(c) Contours of Static Temperature at the plane y=0 when 100% 39Excess air is used in the primary air inlet.
Fig 4.6(d) Contours of Static Temperature at the plane y=0 when 125% 39Excess air is used in the primary air inlet.
Fig 4.7(a) Contour of the concentration of methane at the plane y=0 when 4050% Excess air is used in the primary air inlet.
Fig 4.7(b) Contour of the concentration of methane at the plane y=0 when 4075% Excess air is used in the primary air inlet.
Fig 4.7(c) Contour ofthe concentration of methane at the plane y=0 when 41100% Excess air is used in the primary air inlet.
Fig 4.7(d) Contour of the concentration of methane at the plane y=0 when 41125% Excess air is used in the primary air inlet.
Fig 4.8(a) Contour of the mass fraction of NOx at the plane y=0 when 50% 42Excess air is used in the primary air inlet.
Fig 4.8(b) Contour of the mass fraction of NOx at the plane y=0 when 75% 42Excess air is used in the primary air inlet.
Fig 4.8(c) Contour of the mass fraction of NOx at the plane y=0 when 100% 42Excess air is used in the primary air inlet.
Fig 4.8(d) Contour of the mass fraction of NOx at the plane y=0 when.125% 42Excess air is used in the primary air inlet.
Fig 4.9(a) Contour of mass fraction of CO, for 50% excess air. 43
Fig 4.9(b) Contour of mass fraction of CO, for 75% excess air. 43
Fig4.9(c) Contour of mass fraction of CO, for 100% excess air. 44
Fig 4.9(d) Contour of mass fraction of CO2 for 125% excess air. 44
. Fig 4.1O(a) Velocity vector colored by velocity magnitude at x=O, when 45secondary air is Off.
II
Fig 4.1O(b) Velocity vector colored by velocity magnitude at x=O, when 45secondary air is off
Fig 4.11(a) Contours of Static Temperature at the plane y=0, when secondary 46air is off.
Fig 4.11(b) Contours of Static Temperature at the plane y=0 with secondary 46Air.
Fig 4.12(a) Velocity vector colored by Static Temperature at the plane y=0, 46when secondary air is off.
Fig4.12(b) Velocity vector colored by Static Temperature at the plane y=0 46with secondary air.
Fig 4.13(a) Contours of mass fraction of methane at the plane y=0, when 47secondary air is off.
Fig 4.13(b) Contours of Contours of mass fraction of methane at the plane 47y=0 with secondary air.
Fig 4.14(a) Contours of mass fraction of CO, at the plane y=0, when 48secondary air is off.
Fig 4.14(b) Contours of Contours of mass fraction of CO, at the plane y=0 48with secondary air
Fig 4.15(a) Contours of mass fraction of NOx at the plane y=0, when 49secondary air is off.
Fig 4.15(b) Contours of Contours of mass fraction of NO x at the plane y=0 49with secondary air.
Fig 4.16(a) Velocity vector for the secondary air at point 2. 50
Fig 4.16(b) Velocity vector for the secondary at point I. 50
Fig4.17(a) Contour of static temperature at the plane y=0 for secondary air at 51point 2.
Fig 4.17(b) Contour of static temperature at the plane x=O for secondary air at 51point 2.
Fig 4.18(a) Contour of static temperature at the plane y=0 for secondary air at 51point I.
Fig 4.18(b) Contour of static temperature at the plane x=O for secondary air at 51point 1
Fig4.19 Contour of mass fraction of Methane at y=0 for secondary air at 52point 2.
Fig 4.20(a) Contour ofthe mass fraction of NOx at the plane y=0 for 53secondary air at point 2.
Fig 4.20(b) Contour of the mass fraction of NOx at the plane y=0 for 53secondary air at point I.
III
Fig 4.21 Secondary air is introduced at three different position at the plane 53x=O.
Fig 4.22(a) Contour of secondary air at the plane y=0 for three secondary air 54inlet.
Fig 4.22(b) Contour of secondary air at the plane x=O for three secondary air 54inlet.
Fig 4.23 Contour of mass fraction of methane for three secondary air inlet. 55
Fig 4.24 Contour of mass fraction of NOx at the plane x=O for secondary 56air inlet.
Fig 4.25 Contour of static temperature at the plane x=O for secondary air 57inlet point 3 at x=O
Fig 4.26 Contour of static temperature at the plane x=O for secondary air 57inlet point 3 at x=O
Fig 4.27(a) Contours of Static Temperature at the plane y=0 for Radiation Off 58
Fig 4.27(b) Contours of Static Temperature at the plane y=0 for Radiation On 58
Fig 4.28(a) Contour of mass fraction of NOx for Radiation Off 58
Fig 4.28(b) Contour of mass fraction of NOx for Radiation Off 58
Fig 4.29(a) Contour of static temperature at the plane y=0 for two step 60reaction.
Fig 4.29(b) Contour of static temperature at the plane y=0 for single step 60reaction.
Fig 4.30(a) Contour of mass fraction of methane at the plane y=0 for two step 60reaction.
Fig 4.30(b) . Contour of mass fraction of methane at the plane y=0 for single 60step reaction.
Fig 4.31 (a) Contour of static temperature at the plane y=0 for constant Cpo 61
Fig4.31(b) Contour of static temperature at the plane y=0 for Cp (piece wise 61polynomial).
Fig 4.32(a) Contour of mass fraction of NOx for at the plane y=0 for constant 62Cpo
Fig 4.32(b) Contour of mass fraction of NOx for at the plane y=o for Cp 62(piecewise polynomial).
Fig 4.33(a) Contour of mass fraction of CO, for at the plane y=O for constant 62Cpo
Fig 4.33(b) Contour of mass fraction of CO, for at the plane y=0 for Cp 62(piecewise polynomial).
Fig C.I Iteration Curve 88
IV
Table A.I Wall Temperature for Different Rotation of Primary Air Inlet 69
Table A.2 Wall Temperature for the variation % Excess Air 69
Table A.3 Effect of Secondary Air on Wall Temperature- I69
Table AA Effect of Secondary Air on Wall Temperature- 1169
Table A.5 Effect of Secondary Air on Wall Temperature- III 70
Table A.6 Effect of Radiation heat transfer on wall temperature 70
v
NOMENCLATURE
Cp -Heat capacity at constant pressure, (J/kg-K, Btullbm-F)Gk -Generation of turbulence. kinetic energy due to the mean velocity
gradientsCh, -Generation of turbulence kinetic energy due to buoyancyN -Total number of fluid phase chemical species present in the systemu -Velocity magnitude (mis, fils)v -Velocity vectorT -Temperature (OCor"K)k -Turbulence kinetic energy (J/kg, Btullbm)%, -Material derivativeR; -Net rate of production of specie 'i' by chemical reactionS; -Rate of creation by addition from the dispersed phase.Sk -User defined source termsS, -User defined source termsYM -Contribution of fluctuating dilation in compressible turbulence to the
The author acknowledges with thanks and gratitude the encouraging advice and helpful
co-operation he received from Dr. Harendra Nath Modal, Associate Professor,
Department of Chemical Engineering, Bangladesh University of Engineering and
Technology (BUET), under whose supervision the research work was carried out. The
author acknowledges his gratitude to the Head, Department of Chemical Engineering,
BUET for providing required facilities. The author also extends his thanks to Mr.
Satyajit Roy, graduate student of Department of Chemical Engineering, BUET, for his
extensive help in the work.
VII
ABSTRACT
A Can combustor is a feature of gas turbine engine. Prediction of the performance of
combustor becomes an integral part for the development of efficient combustor. Primary
design objectives are to bum the fuel efficiently, keep the wall temperature as low as
possible and minimize emissions such as NOx, Unburnt Hydrocarbon, etc. Some of the
parameters that controls the performances of a combustor are fuel/air ratio, degree of
turbulence, geometry of the primary air, flow rate of secondary air, etc. Efficient burning
depends on how well the fuel and air are mixed before ignition which in tum depends on
the degree of turbulence. To keep the wall temperature as low as possible, excess air with
higher volume plays an important role, which affects the burning and the process becomes
further complicated. To enhance the turbulence, different air injection patterns of primary
air inlet are studied and the effect of secondary air injection is investigated. Some
theoretical aspects are investigated using different reaction steps and ways of heat transfer.
Fluent, a CFD software, is used for the simulation of the combustor applying k-c model for
turbulence computation and eddy-dissipation model for studying reaction dynamics.
Investigation revealed some important features of the performance of a Can combustor.
Investigation revealed that increasing the angle of rotation of primary air inlet and
percentage of excess air could reduce wall temperature as well as increase NOx production.
It is found that injection of secondary air inlet reduces the wall temperature significantly.
Applying the secondary air inlet in different position together reduces wall temperature
more effectively along with efficient burning of methane in the combustor. It is also found
that wall temperature was drastically reduced when radiation heat transfer is off and
variation of reaction steps makes a very little effect on the performance.
vm
CONTENTS
List of Figures and Tables
Nomenclature VI
Acknowledgements VII
Abstract VIII
CHAPTER 1. Introduction 1-2
1.1 Motivation I
1.2 Objectives 2
CHAPTER 2. Literature Review 3-252.1 General 4
2.1.1 The process of Combustion 52.1.2 Stationary films 62.1.3 Combustion Fundamentals 62.1.4 Mechanism of Combustion 72.1.5 Elementary Carbon 82.1.6 Combustion of Methane 82.1.7 Flame Propagation 92.1.8 Fonnation of NOx 102.1.9 Thennal NOx 10
2.2 Computational Fluid Dynamics II2.2.1 Benefits of CFD II2.2.2 Methods of Prediction 122.2.3 Choice of Prediction method 142.2.4 Nature of Numerical Methods 14
2.3 Review of Previous work on modeling of Combustor 16
CHAPTER 3. Description of the Modeling Procedure 26-293.1 Methodology 26
3.1.1 Assumptions 263.2 Model Calculation 273.2.1 The Standard k-E Model 27 •3.2.2 Eddy dissipation Model 28
3.3 Description of the Unit 283.4 Description of the software 29
CHAPTER 4. Results and Discussion 30-624.1 Variation of Geometry of Primary air inlet 304.1.1 Effect of Rotation of Primary air inlet on the wall 31
temperature4.1.2 Concentration of Methane in the combustor 334.1.3 NOx concentration in the combustor 354.1.4 CO, concentration in the combustor 36
CHAPTER
APPENDIXAPPENDIXAPPENDIX
4.24.2.14.2.24.2.34.2.4
4.34.3.14.3.24.3.34.3.4
4.44.4.14.4.24.4.3
4.54.5.14.5.2
4.64.7
5.
ABC
Influence of Excess Air on can CombustorEffect of Excess Air on the Wall TemperatureConcentration of Methane in the combustorProduction of NOxCO, Concentration in the combustor
Effect of Secondary AirEffect of Secondary Air
Concentration of Methane in the CombustorProduction of CO, in combustorNOx Production
Secondary Air Inlet at different PositionSecondary Air Inlet below the reference pointSecondary Air inlet at three placesSecondary Air Inlet at Two different position
Radiation Off and OnWall TemperatureNOx Production
Reaction StepsHeat CapacityConclusion and RecommendationREFERENCESAPPENDICESWall Temperature for different mode of operationModel specification and Material PropertiesCalculation using the Software
1.1 MotivationThis study is related to the main component of a gas turbine engines, and more particularly
to a compact annular Can combustor, which provides enhanced performance in gas turbine
engines having gaseous oxidant delivered to the gas turbine engine via a conduit or duct
from a pressurized source. It is well known that, in order to maximize fuel efficiency and
power output from such a gas turbine engine, the engine should be operated with a
combustion temperature and a turbine inlet temperature, which are as high as possible. As a
practical malter, however, the maximum temperatures which may be utilized are determined
by the ability of materials used in fabricating components of the engine, such as the
combustor, turbine wheel, nozzle, and shroud, to withstand extended exposure to elevated
temperatures. While it is not possible to overcome the limitations on combustion and turbine
inlet temperatures which are imposed by the materials, it is well known in the art that an
acceptable balance between power output, reliability, and life of the engine may be achieved
by utilizing a relatively high combustion temperature and providing means within the engine
for utilizing a portion of the compressed oxidant either as a diluent injected just upstream of
the nozzle for reducing the temperature of the hot gases, or for convectively cooling engine
components exposed to the hot gases.
In technical process, combustion nearly always takes place in the turbulent rather than a
laminar flow field. The reason is two fold: First, turbulence increases the mixing process and
thereby enhances combustion. Combustion releases heat and thereby generates flow
instability by buoyancy and gas expansion, which then enhance transition to turbulence.
Some of the parameters playa major in a combustion reactor. Air fuel ratio, primary air
inlet, secondary air inlet, fuel inlet, turbulence and wall temperature are the important
variable which should be optimized to get maximum efficiency from combustion reactor.
Control over the production of pollutants like NOx, SOz, CO2, CO, Soot and unbumt
Hydrocarbon (HC) is another concern of a combustion process. Researchers used different
computational model to study the effect of various parameter for the enhancement of the
performance of different combustor where they have shown that prior modeling is one of the
most effective ways to predict the performance of a combustor. But no work was reported
about the optimization of the performance of a Can combustor studying its controlling
parameter. In this study, performance of a Can combustor will be evaluated using a
Computational Fluid Dynamics tool.
1.2 ObjectivesPrimary design objectives are to bum the fuel efficiently, keep the wall temperature as low
as possible and minimize emissions. Efficient burning depends on how well the fuel and air
are mixed before combustion which in tum depends on the degree of turbulence. To enhance
the turbulence, different fuel and air injection patterns will be studied. To maintain the wall
temperature and minimize emissions, different fuel to air ratios will be investigated, and
different geometric arrangements for primary and secondary air will also be studied. Hence
simulation will be carried out to study the following parameter to enhance combustion and
reduce emission:
~ Effect of degree of swirl of the primary air
~ Variation percentage of excess air
~ Effect of secondary air on the performance and finding out the optimal
location for injection of secondary air.
}> Effect of Radiation heat transfer on wall temperature
}> Effect of reaction steps of the conversion of methane.
}> Effect of heat capacity on the wall temperature.
2
Chapter 2
2. Literature Review
2.1 General
Man has been fascinated by fire from earliest existence on earth, but a quantitative
understanding of the combustion process was not achieved until about the year 1880. Prior
to that date, one can trace the development of many hypotheses concerning the nature and
properties of fire, including some that were expressed in supernatural terms of fear and
awe. However, even the existence of the now discredited phlogiston theory of combustion
did not prevent enterprising engineers from designing and constructing boilers to generate
steam for the earliest steam engines.
Phlogiston was a hypothetical mysterious substance which sometimes was presumed to
have the property of negative weight and which combined with a body to render it com-
bustible. First proposed by G. E. Stahl in 1697, the phlogiston theory dominated the
chemical thought of the 18th century. Even such a perceptive observer as Joseph Priestly,
who in 1774 discovered the unique power of oxygen for supporting combustion, accepted
the phlogiston theory. In the years between 1775 and 1781, Antoine L. Lavoisier
substituted for it the theory of oxygenation and provided experimental evidence that
combustion was the union of the substance burned with the oxygen of the atmosphere.
In 1755 Joseph Black discovered carbon dioxide, and in 1781 Henry Cavendish demon-
strated the compound nature of water. At about this same time Lavoisier made the precise
measurements and formulated the volume and weight relationships that underlie the
modem theory of combustion. Beyond this, in 1811 Amedeo Avogadro established that the
number of molecules in a unit volume under standard conditions is the same for all gases.
During this same period John Dalton articulated the law of partial pressures, and in 1803
3
his study of the physical properties of gases led to formulation of the atomic theory,
including the law of combining weights. A related observation was made by Gay-Lussac in
1808 that gases always combine in volumes that bear simple ratios to each other. Volume
under standard conditions is the same for all gases. During this same period John Dalton
enunciated the law of partial pressures, and in 1803 his study of the physical properties of
gases led to formulation of the atomic theory, including the law of combining weights. A
related observation was made by Gay-Lussac in 1808 that gases always combine in
volumes that bear simple ratios to each other.
2.1.1 The Process of CombustionAt one time, it was thought there were only four elements, which composed all nature: fire,
water, air, and earth. In fact, fire has been regarded with fear by men throughout history, for
the useful effects it could perform as well as for the terrible destruction it might cause.
Researchers have spent hundreds of many years studying the effects of the numerous
variables on burning. Nevertheless, many aspects of combustion are still only partly
understood [Beer & Chigar, 1972].
Combustion is the rapid, high-temperature oxidation of fuels. Since most fuels used at
present consist almost entirely of carbon and hydrogen, burning involves the rapid
oxidation of carbon to carbon dioxide, or carbon monoxide, and of hydrogen to water
vapor. The combustion reaction takes place in the gaseous phase, except for the burning of
the fixed carbon in solid fuels. Even in the latter case, the oxygen and the combustion
products exist as gases, and only the fixed carbon itself is present as a solid. Flame may be
defined as gas rendered luminous by the liberation of chemical energy.
The flame front is the surface or area between the luminous region and the dark region of
unburned gas, which exists in all combustion reactions in the gaseous phase. Since the
gases may not become luminous instantly, it is expedient to visualize the burning zone as
consisting of a luminous zone and a reaction zone [Toong, 1983].
Ignition and most of the oxidation occur in the latter zone, while completion of burning and
emission of light take place in the luminous zone. Generally, the locally available supply of
oxygen is consumed in the reaction zone. It is difficult to make a clear distinction between
these regions because the total thickness of the burning zone may vary from a few
4
thousandths of an inch to an indefinite thickness, depending upon the turbulence and the
homogeneity of the gases.
These definitions provide for the persistence of flame until it remains luminous and ceases
radiation, even after the chemical reaction has proceeded to equilibrium. The continuance
of luminosity, called after burning, is evident in various types of combustion, especially in
spark ignition engines. A fast-burning mixture has a very thin reaction zone, and ignition,
combustion, and luminescence occur almost simultaneously.
2.1.2 Stationary Flames
A stationary flame is one in which the flame front is more or less stationary in space; the
unburned gases flow toward the reaction zone at the propel speed to maintain the position;
of the flame. This type of flame may be further classified as combustion in which the fuel is
premixed with air or in which the fuel and air enter the combustion area separately. The
latter is called a diffusion flame as it becomes necessary for the oxygen to be diffused into
the reaction zone and mixed with the fuel before burning can occur.
Stationary flames are utilized at atmospheric pressure or at other pressures, higher or lower.
Gas burners, pulverized-coal burners employ this type of flame. The flow of mixture to
these flames may be either laminar or turbulent. If there is a great deal of turbulence, the
reaction zone and resulting flame front may be irregular and rather unsteady. Such
turbulence may create what appears to be a solid cone of flame in the vicinity of the
combustion. Some oil and gas burners operate with a diffusion flame. In these cases
considerable turbulence of the air and fuel in the combustion chamber is employed to
ensure fairly rapid mixing. A true diffusion flame creates a much longer zone of reaction,
and there are comparatively few applications where diffusion of the gases alone is relied
upon to provide mixing of the fuel and oxygen.
2.1.3 Combustion Fundamentals
To the engineer concerned with boiler design and performance, combustion may be
considered as the chemical union of the combustible of a fuel and the oxygen of the air,
controlled at such a rate as to produce useful heat energy. The principal combustible con-
stituents are elemental carbon, hydrogen, and their compounds. In the combustion process,
the compounds and elements are burned to carbon dioxide and water vapor. Small quan-
5
tities of sulfur are present in most fuels. Although sulfur is a combustible and contributes
slightly to the heating value of the fuel, its presence is generally detrimental because of the
corrosive nature of its compounds.
Air, the usual source of oxygen for combustion in boilers, is a mixture of oxygen, nitrogen
and small amounts of water vapor, carbon dioxide, argon and other elements. In an ideal
situation, the combustion process would occur with the exact proportions of oxygen and a
combustible that are called for in theory as the stoichiometric quantities. But it is
impractical to operate a boiler at the theoretical level of zero percent excess oxygen. In
practice, this condition is approached by providing an excess of air varies with fuel, boiler
load and type firing equipment.
2.1.4 Mechanism of CombustionThe term mechanism of combustion refers to the reactions by which fuel is transformed
chemically to combustion products. A self-sustaining chemical process which consists of a
series of different reactions in which intermediate products are formed in one step and
destroyed in a succeeding step is known as a chain reaction. The intermediate products
formed are known as chain carriers since they help to carry the reaction to completion.
Chain carriers may be free atoms of diatomic gases such as Hydrogen (H) and Oxygen (0),
free radicals (like OH, CHO, CH, etc.), or some organic compound (such as formaldehyde,
HCHO). A free radical is a group of atoms, which carries one unpaired electron. In other
words, a free radical has a free valence bond. The hydroxyl free radical like OH unites with
a free hydrogen atom to form a water molecule, H20, or it may enter into many other
reactions. A chain carrier may exist only a minute fraction of a second. Billions of chain
carriers are formed and instantly destroyed during the course of a chain reaction.
Any chain reaction consists of an initiation phase, a propagation phase, .and a termination
phase. In the first phase the chain carriers are formed which promote the propagation phase.
Combustion may be terminated by a chain breaking reaction in which some of the chain.
carriers are taken out of play by another substance, which reacts with or adsorbs the chain
carriers. A cold combustion chamber wall in an oil burner, for instance, apparently adsorbs
enough of the chain carriers to stop the combustion of fuel oil near the surface. As a result
there is a strong tendency to deposit partially burned fuel, or soot, on combustion chamber
6
walls when they are cold. Some intermediate reactions occur in such a manner that several
chain carriers are formed with each step. Such a reaction is known as a chain branching
reaction. Each of these new chain carriers may then branch out and start a new series of
reactions of its own.
2.1.5 Elementary CarbonThe combustion mechanism for elementary carbon in the solid state, as it appears in the
fixed carbon in coal, coke, or charcoal, not thoroughly understood, and various
investigators have arrived at different conclusion as to the predominant reactions. The basic
mechanism involve the diffusion of gaseous oxygen to the surface of the solid carbon
where the oxygen molecules react to form a primary product, which may be either carbon
monoxide or carbon dioxide. This gaseous product must then diffuse from the surface to
allow more oxygen to contact the surface molecules of carbon. It is thought that two
distinctly different types of reaction are involved, the one mechanism prevailing at
temperatures over 1800 F and the other at lower temperatures. In either case, the speed of
the actual chemical reaction is so great, when compared to the rate of diffusion of gases to
and from the carbon surface, that diffusion control the rate of burning almost entirely.
Temperatures between about 1650 and 2000 F the reaction rate increases with temperature,
and above 2000 F it appears to remain fairly constant. Some C02 may be detected as a
primary product at the surface, but the concentration of the C02 is low as compared with
the CO at all times. Evidently the oxidation reaction at the surface proceeds only to the
formation of CO, and the gaseous CO oxidized at some point beyond the carbon surface.
2.1.6 Combustion of MethaneThe combustion of methane whereby methane and air can unite, but that only a branched-
chain mechanism produces active combustion, as follows
Oz + CH3 -+ CH300 .
CH4+ CH300' -+ CH3' + CH300HCH300H -+ CO + 2Hz + 0 .CH4+ O' -+ CH3' + ORThe net effect of the above series of reactions may be represented by the following
equation, which sums up the action of the primary chain:
7
CH4 + O2 -+CO + 4H2
The primary products CO and H2 are then oxidized to CO2 and H20 by the secondary
mechanisms:
CO+OH -+ H'+C02
02 + CO + H -+ OR + C02
H2 + OH -+ H' + H20O2+ H2 + H -+ OH' + H20The CH)OOH (methylhydroperoxide) is termed a propagating center, which serves as a
carrier to promote the chain, It normally breaks up into two primary products and an
oxygen atom, thus branching the chain. To ignite a mixture of methane and air, it seems
necessary to produce a few CH)OOH molecules to act as propagating centers. This may be
accomplished by heating the mixture to the ignition temperature or by some auxiliary
means. However, it appears quite certain that the critical factor in ignition is the production
of propagating centers by inducing reactions and not the temperature of the mixture.
2.1. 7 Flame PropagationIn burner flames, the flame is propagating against the flow of the reagents of the reagents
and its position is stationary. Variation in input condition such as fuel flow rate, air/fuel
ratio or preheat can cause these flame to become non stationary or unstable. A flame is
considered to be stable over a range of an input parameter if variation of such a parameter
within this range does not cause the flame to blow off or to flashback into the burner tube.
One of the basic concept in flame theory is that of flame propagation. This refers to the
propagation of the zone of burning or of the combustion wave through a combustible
mixture. It is generally appreciated that the ignition source is a source of heat. It also
produces atoms and free radicals, which may act as chain carrier in the chemical reaction.
Once the heat flow and the diffusion of these active species have initiated chemical reaction
in the adjacent layer of the combustible medium, this layer becomes the source of heat and
'of chain carriers and is capable of initiating reaction in the next layer. A quantitative theory
of flame propagation is based on the transfer of heat and mass from the reaction zone to the
unburned mixture,
8
2.1.8 Formation of NOxNitrogen monoxide (NO) and nitrogen dioxide (N02) are byproducts of the combustion
process of virtually all fossil fuels. The quantity of these inorganic compounds in the
products of combustion was not sufficient to affect boiler performance, and their presence
was largely ignored. In recent years, oxides of nitrogen have been shown to be. key
constituents in the complex photochemical oxidant reaction with sunlight to form smog.
Today, the presence of N02 and NO (collectively referred to as NOx) is regulated by the
authorities and has become an important consideration in design of fuel firing equipment.
2.1.9 Thermal NOxThe formation of NOx in the combustion process is often explained in terms of the source
of nitrogen required for the reaction. The N2 can originate from the atmospheric air, in
which case the product is referred to as thermal NOx or from the organically bound
nitrogen components found in all coals and fuel oils that are termed fuel NOx. 11 is;
important to note that even though NOx consists usually of 9S percent NO and only S
percent N02, the normal practice is to calculate concentrations of NOx as 100 percent N02.
The mechanisms involving thermal NOx were first described by Zeldovich and later
modified to what is referred to as the extended Zeldovich mechanism.
~O-+NO+N
N+02 -+ NO+O
N +OH -+NO+H
As the equilibrium values predicted by this mechanism are higher than those actually
measured, it is generally assumed that first reaction is rate determining due to its high
activation energy of 317 kllmo!.
Although the kinetics involved in the conversion of organically bound nitrogen compounds
found in fossil fuels are not yet well understood, numerous investigators have shown fuel
NOx to be an important mechanism in NOx formation from fuel oil, and the dominant
mechanism in NOx generated from the combustion of coa!. A most significant property of
fuel nitrogen conversion that affects the design of fuel-firing equipment relates to the
availability of oxygen to react with the fuel-nitrogen compounds in their gaseous state.
Simply stated, the compounds that evolve from a coal particle such as NCH and NH), are
9
relatively unstable and will reduce to harmless N2 under fuel-rich conditions, or to NO
under air-rich conditions.
2.2 Computational Fluid Dynamics (CFD)A Working Definition of CFD: computation Fluid Dynamics - the dynamics of things that
flow. CFD - a computational technology that enables to study the dynamics of things that
flow. It is mathematical prediction method. Using CFD, a computational model can be built
that represents a system or device. Then the fluid flow physics can be applied to this virtual
prototype, and the software will give a prediction of the fluid dynamics. CFD is a
sophisticated analysis technique. It not only predicts fluid flow behavior, but also the
transfer of heat, mass, phase change, chemical reaction such as combustion, mechanical
movement such as an impeller turning, and stress or deformation of related solid structures
such as a mast bending in the wind.
2.2.1 Benefits of CFDBasically, the compelling reasons to use CFD are these three:
Insight: There are many devices and systems that are very difficult to prototype. Often,
CFD analysis shows the parts of the system or phenomena happening within the system that
would not otherwise be visible through any other means. CFD gives a means of visualizing
and enhanced understanding of the designs.
Foresight: Because CFD is a tool for predicting what will happen under a given set of
circumstances, it can answer many 'what if?' questions very quickly. Effects of the
variation of different variables could be found out easily. As a result, performance
prediction of a design can be carried out in a short time. All these prediction can be made
before the physical proto typing, which helps to design better and faster.
Efficiency: Better and faster design or analysis leads to shorter design cycles. Time and
money are saved. Products get to market faster. Equipment improvements are built and
installed with minimal downtime. CFD is a tool for compressing the design and
development cycle.
10
2.2.2 Methods of PredictionPrediction of heat transfer and fluid-flow processes can be obtained by two main methods:
experimental investigation and theoretical calculation. A comparison between the methods
is discussed in the following.
2.2.2.1 Experimental InvestigationThe most reliable information about a physical process is often gIven by actual
measurement. An experimental investigation involving full-scale equipment can be used to
predict how identical copies of the equipment would perform under the same conditions.
Such full-scale tests are in mosCcases, prohibitively expensive and often impossible. The
alternative then is to perform experiments on small-scale models. The resulting
information, however, must be extrapolated to full scale, and general rules for doing this
are often unavailable. Further, the small-scale models do not always simulate all the
features of the full-scale equipment; frequently, important features such as combustion or
boiling are omitted from the model tests. This further reduces the usefulness of the test
results. Finally, it must be remembered that there are serious difficulties of measurement in
many situations, and that the measuring instruments are not free from errors.
2.2.2.2 Theoretical CalculationA theoretical prediction works out the consequences of a mathematical model, rather than
those of an actual physical model. For the physical processes of interest here, the
mathematical model mainly consists of a set of differential equations. If the methods of
classical mathematics were to be used for solving these equations, there would be little
hope of predicting many phenomena of practical interest. A look at a classical text on heat
conduction or fluid mechanics leads to the conclusion that only a tiny fraction of the range
of practical problems can be solved in closed form. Further, these solutions often contain
infinite series, special functions, transcendental equations for Eigen values, etc., so that
their numerical evaluation may present a formidable task. Development of numerical
methods and the availability of large digital computers hold the promise that the
. implications of a mathematical model can be worked out for almost any practical problem.
11
2.2.2.3 Advantages of a Theoretical CalculationA theoretical calculation offers following advantages over a corresponding experimental
investigation
Low cost: The most important advantage of a computational prediction is its low' cost. In
most applications, the cost of a computer run is many orders of magnitude lower .than the
cost of a corresponding experimental investigation. This factor assumes increasing
importance as the physical situation to he studied becomes larger and more complicated.
Whereas the prices of most items are increasing, computing costs are likely to be even
lower in the future.
Speed: A computational investigation can.be perfomled with remarkable speed: A designer
can study the implications of hundreds of different configurations in less than a day and
choose the optimum design. On the other hand, a corresponding experimental investigation,
it is easy to imagine, would take a very long time.
Complete ill/ormatioll: A computer solution of a problem gIves detailed and complete
information. It can provide the values of all the relevant variables (such as velocity,
pressure, temperature, concentration, turbulence intensity) throughout the domain of
interest. Unlike the situation in an experiment, there are few inaccessible locations in a
computation, and there is no counterpart to the flow disturbance caused by the probes.
Obviously, no experimental study can be expected to measure the distributions of all
variables over the entire domain. For this reason, even when an experiment is performed,
there is great value in obtaining a companion computer solution to supplement the
experimental information.
Ability to simulate realistic cOllditiolls: In a theoretical calculation, realistic conditions can
be easily simulated. There is no need to resort to small-scale or cold-flow models. For a
computer program, there is little difficulty in having very large or very small dimensions, in
treating very low or very high temperatures, in handling toxic or flammable substances, or
in following very fast or very slow processes.
Ability to simulate ideal conditions: A prediction method is sornetimes used to study a
basic phenomenon, rather than a complex engineering application. In the study of a
phenomenon, one wants to focus attention on a few essential parameters and eliminate all
irrelevant features. Thus, many idealizations are desirable-for example, two-dimensionality,
12
constant density, an adiabatic surface, or infinite reaction rate. In a computation, such
conditions can be easily and exactly set up. On the other hand, even a very careful
experiment can barely approximate the idealization.
2.2.3 Choice of Prediction MethodAn appreciation of the strengths and weaknesses of both approaches is essential to the
proper choice of the appropriate technique. There is no doubt that experiment is the only
method for investigating a new basic phenomenon. In this sense, experiment leads and
computation follows. It is in the synthesis of a number of interacting known phenomena
that the computation performs more efficiently. Even then, sufficient validation of the
computed results by comparison with experimental data is required. On the other hand, for
the design of experimental apparatus, preliminary computations are often helpful, and the
amount of experimentation can usually be significantly reduced if the investigation is
supplemented by computation [Patankar 1980]. An optimal prediction effort should thus be
a judicious combination of computation and experiment. The proportions of the two
ingredients would depend on the nature of the problem, on the objectives of the prediction,
and on the economic and other constraints of the situation.
2.2.4 Nature of Numerical MethodsA numerical solution of a differential equation consists of a set of numbers from which the
distribution of the dependent variable <Dcan be constructed. In this sense, a numerical
method is akin to a laboratory experiment, in which a set of instrument readings enables us
to establish the distribution of the measured quantity in the domain under investigation. The
numerical analyst and the laboratory experimenter both must remain content with only a
finite number of numerical values as the outcome, although this number can, at least in
principle, be made large enough for practical purposes.
<P can be represented by a polynomial in x like following:~ 23m'fI = Go + a,x + G]X + a4x + + amx
and employ a numerical method to find the finite number of coefficients au,al,a, am•
This will enable to evaluate <P at any location x by substituting the value of x and the values
13
of a's into equation. This procedure is, however, somewhat inconvenient if our ultimate
interest is to obtain the values of <P at various locations. The values of a's are not
particularly meaningful, and the substitution operation must be carried out to arrive at the
required values of <P. Numerical method treats as its basic unknowns the values of the
dependent variable at a finite number of locations, which are call~d the grid points, in the
calculation domain. The method includes the tasks of providing a set of algebraic equations
for these unknowns and of prescribing an algorithm for solving the equations.
2.2.4.1 DiscretizetionA discretization equation is an algebraic relation connecting the values of <P for a group of
grid points. Such an equation is derived from the differential equation governing <P and
thus expresses the same physical information as the differential equation. That only a few
grid points participate in a given discretization equation is a consequence of the piecewise
nature of the profiles chosen. The value of <P at a grid point thereby influences the
distribution of <P only in its immediate neighborhood. As the number of grid points
. becomes very large, the solution of the discretization equations is expected to approach the
exact solution of the corresponding differential equation. This follows from the
consideration that, as the grid points get closer together, the change in <P between
neighboring grid points becomes small, and then the actual details of the profile assumption
become unimportant. For a given differential equation, the possible discretization equations
are by no means unique, although all types of discretization equations are, in the limit of a
very large number of grid points, expected to give the same solution. The different types
arise from the differences in the profile assumptions and in the methods of derivation.
2.2.4.2 Control volume FormulationThe discretization equation obtained in this manner expresses the conservation principle for
<Dfor the finite control volume, just as the differential equation expresses it for an
infinitesimal control volume. The most attractive feature of the control-volume formulation
is that the resulting solution would imply that the integral conservation of quantities such as
mass, momentum, and energy is exactly satisfied over any group of control volumes and, of
course, over the whole calculation domain. This characteristic exists for any number of grid
points-not just in a limiting sense when the number of grid points becomes large. Thus,
14
even the coarse-grid solution exhibits exact integral balances. When the discretization
equations are solved to obtain the grid-point values of the dependent variable, the result can
be viewed in two different ways. In the finite-element method and in most weighted-
residual methods, the assumed variation of <Dconsisting of the grid-point values and the
interpolation functions between the grid points is taken as the approximate solution. In the
finite-difference method, however, only the grid-point values of <Dare considered to
constitute the solution, without any explicit reference as to how <Dvaries between the grid
points. This is similar to a laboratory experiment where the distribution of a quantity is
obtained in terms of the measured values at some discrete locations without any statement
about the variation between these locations.
2.3 Review of Previous works on modeling of Combustor
Combustion is a mass energy conversion process during which chemical bond energy is
converted into thermal energy. Combustion is the dominant technology in energy sector.
Combustion and its control are very essential and it has been said that approximately 80
percent of the energy in the world came from combustion sources. Fossil fuel, still, remains
the main source of energy for domestic heating, power generation and transportation.
Combustion of fossil fuels continues to provide most of the energy required for
transportation and for stationary power generation. Combustion of fossil fuel, being
humanity's oldest technology, remains a key technology today and for the foreseeable
future. Industrial processes rely heavily on combustion. Iron, steel, aluminum, and other
metal refining industries employ furnaces for producing the raw products, while heat
treating and annealing furnaces or ovens are used down-stream to add value to the raw
material as it is converted into a finished product. Other industrial combustion devices
include boilers, refinery and chemical fluid heaters, glass melters, solid dryers, orgamc
fume incinerators etc. can be cited to give just a few examples. The cement manufacturing
industry is a heavy user of heat energy delivered by combustion. So it can be generalized
that great energy savings could be made by improving combusting devices. Combustion
requires that fuel and oxidizer to be mixed at the molecular level. Molecular mixing of fuel
and oxidizer, as a prerequisite of combustion, therefore takes place at the interface between
15
small eddies. Chemical reaction consumes the fuel and oxidizer at the interface and will
thereby steepen gradients even further. The downside issue associated with combustion is
directly associated with environmental pollution. It is well known that combustion, not only
generates heat, but also produces pollutants like NOx, SOz, COz, CO, Soot and unbumt
Hydrocarbon.
The recent development in computer hardware and numerical methods raises the possibility
to use more complex combustion models in three-dimensional predictions of combustor. In
most three-dimensional simulation codes of combustion for practical systems, suitable
assumption reaction chemistry is used to model the gas phase combustion. Chemical
kinetics, however, have a major influence on pollutant formation, especially in combustion
systems equipped with air or fuel staging. Although the use of a detailed description of
turbulent combustion would be extremely time and memory consuming, it paves the way of
better prediction of the performance. The primary objectives in the design of the next
generation gas turbine engines are to enhance combustion efficiency, reduce pollutant
emissions and maintain stable combustion in the lean limit. Techniques such as the lean
premixed pre-vaporized combustion process are being explored to achieve low emission
combustion. A side effect of lean combustion is that the combustion process can go unstable
leading to large-amplitude, low frequency pressure oscillation that can result in system
failure. Active and passive control methods are being studied to suppress this type of
instability. However, it is difficult to isolate and differentiate between the various system
parameters that control the combustion dynamics. Numerical simulation of combustion
instability is even more difficult since the process is highly time-dependent and unsteady
and is a result of coupling between unsteady. heat release and acoustic modes in the
combustor. Proper resolution in space and time of the pressure oscillation and heat release is
required to predict the instability process accurately. Fortunately, the instability is due to the
low frequency, long wavelength disturbance that can be resolved in a simulation approach
such as large-eddy simulation (LES). In LES modeling of the momentum transport scales
larger than the grid size are computed using a time and space accurate scheme, while the
effect of the unresolved smaller scales (assumed to be mostly isotropic) on the resolved
motion is modeled using an eddy viscosity based subgrid model. This approach is acceptable
16
for momentum transport since all the energy containing scales are resolved and all the
unresolved scales that primarily provide for dissipation of the energy transferred in large
scales can be modeled by using an eddy dissipation sub grid model. However, these
arguments cannot be extended to reacting flows since, for combustion to occur, fuel and
oxidizer species must first mix at the molecular level. Since, this process is dominated by the
mixing process in the small-scales, ad hoc eddy diffusivity concepts cannot be used except
under very specialized conditions. To deal with these distinctly different modeling
requirements, a new sub grid mixing and combustion model has been developed that allows
for proper resolution of the small scale scalar mixing and combustion effects within the
framework of a conventional LES approach. The earlier studies [Kim & Menon 1999; Kim,
Menon & Mongia 1999] have established the ability of the LES model in premixed
combustion and in fuel-air mixing. To reduce the computational cost, the past calculations
employed flame let models for premixed combustion or simulated fuel-air mixing without
detailed chemical kinetics. However, for realistic simulations for the reacting flow,
especially to predict pollutant emission, detailed finite-rate kinetics must be included. The
computational effort involved when using detailed kinetics is so large as to make LES of
even a simple configuration computationally infeasible. Typically, global kinetics is
employed to reduce the computational cost. However, such kinetics is not able to deal with
ignition and extinction processes and is also unable to predict the pollutants (NOx, CO and
UHC) formation accurately. Recent development of skeletal mechanisms has provided an
opportunity to address these issues. Skeletal mechanisms are derived from the full
mechanisms using sensitivity analysis and have been shown to be reasonably accurate over a
wide range of equivalence ratio. Although typical skeletal mechanism is much smaller than
a full mechanism, the computational cost is still exorbitant for LES application. Menon et al
carried out a study on the development of the simulation methodology and investigates
issues related to the integration of detailed finite-rate kinetics into the LES solver [Menon
& Stone 1999]. The use of in-situ adaptive tabulation to calculate multi-species finite-rate
kinetics is demonstrated Application of global kinetics to study fuel-air mixing and
combustion in a Trapped Vortex Combustor is also discussed and analyzed. It was shown
that a LES methodology could be used to simulate complex reacting flows in gas turbine
17
engines. Prediction of utility boiler performance becomes an important tool for the
development of new combustion methods [Bray 1978; Gorres, Schnell & Hein 1994;
Howard Williams & Fine 1994]. But advanced combustion modifications require more
detailed modeling of turbulent combustion when the formation and destruction processes of
carbon monoxide are to be predicted in order to reduce harmful concentrations near the
furnace walls. Recent development in computer hardware and numerical methods raises the
possibility to use more complex combustion models in three-dimensional predictions of
utility boilers. In most three-dimensional simulation codes of pulverized coal combustion
for practical systems the infinite-fast-chemistry assumption is used to model the gas phase
combustion [Libby & Williams 1980]. Chemical kinetics, however, have a major influence
on pollutant formation, especially in combustion systems equipped with air or fuel staging.
The use of a detailed description of turbulent combustion, even if available, would be
extremely time and memory consuming and therefore not be applicable to practical three-
dimensional calculations. Thus simplifications in the description of the turbulence behavior
and the chemical reaction mechanisms are necessary [Magel el al 1995].
Numerical simulation of utility boilers was reported [Magnussen & Hjertager 1976]. This
study presented calculations of a pulverized coal flame and a coal-fired utility boiler with
advanced combustion technologies. A combustion model based on an extended Eddy
Dissipation Concept combined with finite rate chemistry was described. A domain
decomposition method was used to introduce locally refined grids. Validation and
comparison of both combustion models were made by comparison with measurement data
of a swirled flame with air staging in a semi-industrial pulverized coal combustion facility.
The application of three-dimensional combustion systems was demonstrated by the
simulation of an industrial coal-fired boiler. It was shown that the inclusion of chemical
kinetics in the combustion model could achieve significant improvement in comparison to a
combustion model that assumes infinite fast chemistry. The EDC combined with finite rate
chemistry is a promising concept to calculate the near burner field of swirled flames.
Reacting computational fluid dynamics (CFD) models have been shown to be useful in
evaluating and optimizing performance of these new technologies and operating conditions
[Adams & Smith 1993]. These CFD models have traditionally used equilibrium chemistry
18
models to predict specie concentrations throughout the combustor, however equilibrium
assumptions for CO oxidation at lower temperatures is inaccurate. Performance of
industrial and utility combustion systems is becoming increasingly affected by limits on
pollutant emissions such as NOx and CO [O'Connor, Himes & Facchiano 1999]. CO
emissions impact design and operation of combustion systems, particularly when coupled
with NOx reduction technologies that involve lower temperature operation or staged firing.
Lower combustion temperatures or delayed mixing of fuel and air helps minimize NOx
formation, but can increase CO concentrations and minimize CO oxidation rates [Miller &
Bowman 1989]. CO oxidizes rapidly at high temperatures in the presence of oxygen, but
does not oxidize as well at the cooler temperatures or less mixed conditions common with
some in-furnace NOx control technologies. Reacting Computational Fluid Dynamics (CFD)
tools can be used to evaluate NOx reduction technologies and their impact on CO
emissions, provided the chemistry in the combustion model is sufficiently accurate to
represent the actual system behavior. CFD models for chemically reacting flows commonly
use an equilibrium chemistry approach to compute the chemical reactions in the
combustion or reaction process [Adams & Smith 1995]. This is based on the fact that in
diffusion flames, the fuel and oxidizer are initially separated in different streams, which
must be intimately contacted on a molecular level before reaction can occur. The
assumption is made that this micro-mixing process is what controls the rate at which
chemical reactions proceed. This allows the chemistry to be computed from equilibrium
considerations. Only one differential equation is required to describe the degree of
mixedness between fuel and oxidant at a point, a great simplification compared to the
immense system of equations required for a detailed chemical kinetic scheme. This
improves computational times without compromising accuracy and allows chemistry
calculations to be coupled with fluid flow, heat transfer and particle phase calculations.
Alternative techniques that focus on detailed chemistry require tracking of multiple species
and significantly greater computational effort, making it difficult to couple with full fluid
flow and heat transfer calculations for complex geometries typical of actual combustion
systems. Adams et al studied the development of a non-equilibrium CO model and
integration with a reacting CFD model [Adams, Cremer & Wang 2000]. The use of the
19
resulting model is illustrated on two combustion systems - a waste gas incinerator and a
cyclone-fired utility boiler. Results showed that low temperature CO oxidation can be
accurately predicted with the use of the nonequilibrium CO model. Modeling of various
aspects of methane-air combustion were reported [Chen 1988]. A general procedure for
constructing QSSA based reduced mechanisms through automatic matrix operation by a
computer was developed to study the methane air combustion. An interactive code (CARM:
Computer Assisted Reduction Mechanism) for automatic generation of reduced chemistry
was also developed for the same purpose [Chang 1996]. This code produces FORTRAN
source codes needed for computing the chemical sources, which can be linked to Chemkin
[Kee, Rupley & Miller 1989]. Homma and Chen proposed new mechanism for methane-air
combustion [Homma & Chen 1999]. Two new 14-step and 16-step reduced mechanisms for
methane-air combustion were developed with the emphasis on their capabilities to predict
N02 formation with the help of CARM code. The systematic reduction was carried out by
assuming the quasi-steady state for 26-28 species in the starting mechanism with the help of
a automatic mechanism reduction code. The two reduced mechanisms reproduce N02
formation behaviors obtained with the starting mechanism both in post flame region and in
opposed diffusion flames. The promotion of N02 formation by hydrocarbon additive was
also successfully predicted by the reduced chemistry. In addition, the reduced chemistry is
accurate in predicting the diffusion flame structure and the ignition delay time.
Ravary and Johnsen developed a 2D modeling of the combustion in a furnace. They carried
a preliminary numerical study of the combustion of CO/Si02 gas and NOx formation in a
Silicon furnace [Ravary & Johansen 1999]. A preliminary numerical study of the
combustion of CO/Si02 gas and NOx formation, in a Ferro Silicon furnace was conducted.
Two compositions of the process gas have been considered: only CO and a mixture of CO
and Si02. Inputs to this model are either physical data or, parameters that are deduced from
former measurements and observations in a Ferro Silicon plant. The calculated distributions
of velocity, temperature and species seem physically correct. In the case of the upper intake
of air, the jet of air results in a peculiar flow field with gas sucked towards the air inlet. It
was shown that NOx formation depends mainly on the temperature distribution in the
20
furnace, which in tum depends on its geometry and in particular the design of air intakes. It
was also found that when Si02 is introduced, NOx formation is increased. This is due to the
higher heat of reaction for Si02 combustion. The comparison with measurements of NOx
concentration and off gas temperature showed a fair agreement.
Kidiguchi et at investigated the reduction mechanism of NOx in diesel combustion using a
mathematical model [Kiduguchi, Miwas & Mohamrnadi 2001]. Rich and high turbulence
combustion was formed experimentally using a rapid compression machine with changed
swirl velocity and equivalence ratio, and transient concentrations of NO and lower
hydrocarbons were measured at each stage of combustion by a total gas-sampling method.
High-speed photography and CFD computation were also employed for the analysis of the
flame behavior and NO formation. Results show that the heat release rate is proportional to
the concentration of light hydrocarbons produced by the thermal cracking of fuel. NO
concentration gradually increase at the initial combustion stage and, at the end of diffusion
combustion, the concentration keeps maximum level. However, on the rich and high swirl
condition, NO concentration decreases during the diffusion combustion. Analysis of the
flame behavior shows that, under the rich and high swirl condition, a ring flame is formed
inside the periphery of the chamber and the flame keeps the ring structure until the end of
the combustion. In the ring flame region, rich and high temperature mixture is formed. A
large amount of thermally cracked hydrocarbons is confined in the flame and NO formation
rate decreases. It was shown that, in the local rich and high turbulence region, NOx emission
should be reduced by a chemically reduction mechanism. The mechanism is caused by some
chemical species formed through the fuel decomposition. Reduction of NOx emission from
direct-injection diesel engines is of urgent necessity from a standpoint of preserving the
environment. NOx emission from a direct-injection diesel engine mainly comes from
thermal NO that is described by Zeldovich mechanism. The previous works for NOx
reduction have been conducted mainly to control the formation of Zeldovich NO, namely,
reducing initial combustion by reducing combustion temperature and oxygen concentration.
Injection timing retard, two stage combustion and EGR have been employed to reduce NOx
emission during combustion process [Konno, Chikahisa & Murayama 1993; Baert,
Beckman & Verbeek 1996; Kidoguchi, Yang & Miwa 1999]. However, it is necessary to
2\
find restoration mechanism for the further NOx reduction. In regard of NOx restoration,
after treatment is employed [Myerson 1975; Chandker et at 2000]. While, Myerson has
reported NO reduction mechanism caused by hydrocarbons, and the authors reported that
NO could be reduced by thermal cracked hydrocarbons using a flow reactor [Ikeda, Nakami,
Kidoguchi & Miwa 1998]. It is suggested that NO may be restored during diesel
combustion.
An investigation of in-furnace DeNOx technologies usmg a mathematical model was
reported [Magel et at 1996]. Two 'global' NO models were used to calculate the fuel
nitrogen conversion in pulverized coal combustion with DeNOx technologies. The
investigated models showed good agreement of predicted effluent NO emissions with
measured trends for the change of unstaged to fuel-staged combustion. Further investigation
was carried out including detailed reaction mechanism based on eddy dissipation concept to
describe the interaction between chemistry and turbulence. Preliminary results show that
this approach accounts for all major trends that are observed in the experiments. It was
shown that turbulence has a major effect on NO chemistry. Magel et at showed a
combustion model that is able to explain finite rate chemistry in turbulent combustion
[Magel et at 1995]. Predictions of pulverized coal combustion systems with air staging were
presented. It was shown that the inclusion of chemical kinetics in the combustion model can
achieve significant improvement in comparison to a combustion model which assumes
infinite fast chemistry.A series of investigations were reported on combustion in a diesel engme. A newly
developed "conceptual diesel model" proposed by Dec [Dec 1997] represents the status in
which the overall diesel process is described as a cold fuel spray entraining hot ambient air
and supplying hydrocarbon fragments to a lifted diffusion combustion flame. Contrary to
classical spray models, in which soot is assumed to be formed along the stoichiometric
surface on the rich side of a Burke-Schumann diffusion flame, Dec's conceptual model
locates the soot cloud formation downstream of the fuel jet, prior to the main combustion
zone. The difference between the earlier theoretical predictions and Dec's experimental
observation reveals a weak point of classical diesel combustion models, although many
22
authors declared that the overall thermodynamic performance of their models is in good
agreement with experimental measurements. The inaccurate predictions of the diesel flame
structure can often be traced to the fact that many numerical approaches use Magnussen
and Hjertager' eddy-dissipation concept [Magnussen & Hjertager 1989], in which the
complexity of chemical reactions is eliminated by replacing it with the fast chemistry limit.
As in the diesel combustion process there exists a full spectrum of chemical and turbulence
time scales from slow, distributed chemistry limit to turbulent mixing-controlled fast
chemistry limit, both mixing and chemical time scales are crucial to the diesel modeling. A
few attempts to improve the numerical predictions have been made. The flame-let model
[Peters 1984] suggests that reactions occur in wrinkled turbulent flames, which can be
considered as a collection of laminar flame-lets and, thus, the chemical reactions and
molecular transport are approximated by means of a laminar flame structure. However, the
use of flame1et models requires the separation between chemistry and turbulence time
scales in the inertial sub-range. This limitation restricts the applicability of flame-let models
particularly when the auto-ignition, extinction and stabilization of diesel sprays are
concemed. Another modification suggested by Abraham and Bracco [Abraham & Bracco
1993] is to replace the controlling time scale in the Magnussen and Hjertager's model by
the slowest one of the mixing time and the chemical time. However, this modification
improves the eddy dissipation concept model only to a minor extent because it just includes
the time scales from the limiting ends of the diesel combustion time scale spectrum. In a
series of recent publications [Chomiak & Karlsson 1996; Golovitchev, Tao & Chomiak
The intrinsic idea behind the eddy dissipation model is that the rate of combustion is
determined by the rate at which percent of unburned gas are broken down into smaller ones,
such that there is sufficient interfacial area between the unburned mixer and hot gases to
permit reaction. The implication of this is that chemical reaction rates play no role in
determining the burning rates, but, rather, turbulent mixing rates completely control
combustion. The model assumes that the reaction rate may be related directly to the time
required to mix reactants at the molecular level. Since in turbulent flows, the mixing rate is
dominated by the eddy properties, the rate is proportional to a mixing time defined by k IE.
Multiple simultaneous reactions are modeled with reactions occurring in the bulk phase or
on wall or particle surfaces. It is modeled with the mixing and transport of chemical species
by solving conservation equation describing convection, diffusion and reaction sources for
each component species. Conservation equation takes the following general form for i-th
species:
Where R; is the net rate of production of specie; i' by chemical reaction and S; is the rate of
creation by addition from the dispersed phase. An equation of this form will be solved for N-
I species where N is the total number of fluid phase chemical species present in the system.
In addition to the transport equation of all the species, a transport equation for mixer fraction
is also solved to deduce the product and oxidizer mass fractions.
3.3 Description of The Unit
The basic geometry is shown in Fig I with a section of the outer wall cut away. There are
six secondary air inlets, each with a surface area of 2 cm2 The outlet has a surface area of
ISO cm'. There are six small fuel inlets, each with a surface area of 0.14 cm2 Primary air
inlet is guided by vanes to give the air a swirling velocity. Its total surface area is 57 cm'.
Input parameters are velocity of fuel, primary air and secondary air.
28
)
I"r20 em
+10cm.!.
Ol"dletSurhilce are:a:150 cJ
<Eo-- CombustorRadius=11 .5 em
+-- Secondary Air Inlets (6)Surface area =2.0 crJ (each)
___ PrimM.,. Air Inlets (6)Tolal suface area=57 err(
Fig 1 : Model of the Can Combustor
3.4 Description ofthe Software
FLUENT version 6.122 has been used for modeling. It has been configured to run in
parallel network of personal computers, having Intel Pentium 4 (2.6 GHz) processors
running Windows 2000 server. The free Message Passing Interface (MPI) software called
MPICH from www-unix.mcs.anl.gov/mpilmpich will be used for cluster configuration. The
model of the Can Combustor has been built on the software Gambit 2.1.6.
Fig 3 .2(a) Discretised Model
29
Fig 3.2(b) Discretised Model
Chapter 4
4. Results and Discussion
4.1 Variation in Geometry of primary air inlet
Different parameters are varied to study the performances of the combustor. Effect of the
geometry of the primary air inlet on the performance of the Can combustor has been
investigated. Variation of geometry for primary air inlet produces different swirling pattern
in the combustor. Effects of swirl velocity on combustion process are investigated by
varying the pattern of rotation of the primary air inlet. Investigation has revealed significant
changes of the distribution of static temperature, NOx production. Angles of rotation that
has been used for the investigation are 30°, 45°, 60° and 90°. Fuel flow rate, flow rate of
primary air and secondary air for this investigation are 40 mis, 20 mls and 6 mlsrespectively. Predicted flow fields in the combustor are shown on figure 4.1(a), 4.1(b),
4.I(c) and 4.1 (d) for 30°, 45°, 60° and 90° respectively.
Contours of Static Temperature ~kJ Sep DEl,2004FLUENT6.1 (3d. segregated. spe5. !ike)
Contours of Static Temperature lkJ Sep 06, 2004FLUD.jf S.l [3d. segregated, spe5. !ike)
Fig 4.31 (a) Contour of static temperatureat the plane y=O for constant Cp
Fig 4.31 (b) Contour of static temperatureat the plane y=O for Cp (piece wisepolynomial)
61
Contours of M8!1!1fr8ctl0l'\ of NO Sep 06. 200~FLUENT6.1 (3d. segregated, spe5. skel
Fig 4.32(a) Contour of mass fraction ofNOx for at the plane y=O for constantCp
I;ontcurs ct Mass tractlcn Qt NU Sep Ul::i.,UU4:FLUENT6.1 13d. ~gregated. spe5. ekeJ
Fig 4.32(b) Contour of mass fmction ofNOx for at the plane y=O for Cp(piecewise polynomial)
C""luur,; ur M"",,,, rrCl,-,UUJl"r '-'u2 s •••.•00. 20 D.FLUENT6.t {3d, segregated. spe5. skel
Fig 4.33(a) Contour of mass fraction ofC02 for at the plane y=O for constantCp
Contours of Mas'S fractltm of co2 _ Sep 06. 200~rUA:NI 0.1 IJd. s"'9regated. spe~. skil'l
Fig 4.33(b) Contour of mass fmction ofC02 for at the plane y=O for Cp(piecewise polynomial)
62
Chapter 5
5. Conclusions and Recommendations
This study was conducted with a vIew to predicting the performance of Can type
combustor using the CFD software. This study revealed some important features, which
can be considered for better design of a Can combustor. It provided a general view of the
performance of the combustor with a specific combination of performance parameter.
Although some of the investigation like consideration of heat transfer without radiation,
is completely theoretical, but this provided an interesting aspect of the performance of
the combustor. Performance of the combustor was monitored by varying swirling pattern
of the primary air inlet, Primary air for combustion, secondary air, location of the
secondary air, radiation heat transfer, steps ofreaction and constant heat capacity. Based
on the investigation, following inference could be drawn
1. It showed that higher swirl velocity by increasing the degree of rotation of the
primary air inlet reduced the wall temperature, but produced more NOx.
2. The wall temperature is reduced by higher volume of excess air. But NOx
production is higher at higher percentage of excess air
3. It also clearly revealed that the injection of secondary air helped to maintain wall
temperature lower and the absence of secondary air increased the wall
temperature drastically, and reduced overall efficiency of the combustor.
4. Secondary air at three different position (according to the investigation) produced
better performance in terms of reduction of wall temperature, burning of
methane, production ofNOx.
5. Wall temperature was drastically reduced when radiation heat transfer was off.
6. Variation of Reaction steps didn't produce any significant change in the
performance.
63
Considering the results of the investigations in terms of the temperature near the wall,
burning of methane and NOx production, 90° rotation of primary air inlet, 125% excess
air and injection secondary air at three different positions together would produce
optimum performance of a Can combustor. As mentioned earlier that this work is a
prediction of the performance of a Can type combustor, an experimental investigation
would ensure how the designed combustor work in specific combination of variables. So
future work must be focused on the experimental investigation with full-scale equipment
to monitor the performance an identical unit. Continuation of this present work should
be leading to develop an experimental model for investigating how it performs in
identical conditions as the mathematical model. Moreover, more theoretical works
should be carried out by changing the scale of the combustor, fuel flow rate and flow
rate of primary and secondary air.
64
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Magel H. C., Schneider R., Risio B., Schnell u., Hein K.R.G. "Numerical simulation ofutility boilers with advanced combustion technologies" Eighth International Symposiumon Transport Phenomena in Combustion, San Francisco, (1995).
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67
APPENDIX A
Wall Temperature for different mode of operation
Table A 1: Wall Temperature for Different Rotation of Primary Air Inlet
Degree of Wall temperature caC)Rotation30° 827-125745° 767-113760° 690-99790° 557-927
Table A 2 Wall Temperature for the variation % Excess Air
Excess Air Wall temperature (OC)50% 777-121775% 767-1197100% 646-1137125% 576-757
Table A 3 Effect of Secondary Air on Wall Temperature- I
Mode of operation Wall temperature (0C)Without secondary air 1057-1787
With secondary air 646-1137
Table A 4 Effect of Secondary Air on Wall Temperature- II
Mode of operation Wall temperature(DC)
Secondary air at Point I (30 cm above the 646-1137bottom of the CombustorSecondary air at Point 2 (20 cm above the 827-1237bottom of the Combustor
69
Table A 5 Effect of Secondary Air on Wall Temperature- III
Mode of operation Wall temperature(0C)
Secondary air at Point I (30 cm above 646-1137the bottom of the Combustor)Secondary air injected through three 601-1077different point (20 cm, 30 cm and 40 cmabove the bottom of the Combustor)
Table A 6 Effect of Radiation heat transfer on wall temperature
Mode of operation Wall temperature (0C)Radiation heat transfer Off 271-638
Radiation heat transfer On 644 -1137
70
APPENDIXBModel specification and Material Properties
Name of the Software: FLUENTRelease: 6.1.22Model: 3d, Segregated.
Models
Model Settings------~-------------------------------------------------~-------SpaceTimeViscousWall TreatmentHeal TransferSolidlficationand MeltingRadiationSpecies TransportCoupled DispersedPhaseSoot
Standard State Entropy j/kg-mol-k Constant 188696.44Reference Temperature k Constant 298.15L-J Characteristic Length Angstrom Constant 2.605L-J Energy Parameter k Constant 572.4Absorption Coefficient lim Constant 0.54Scattering Coefficient 11m Constant 0Scattering Phase Function IsotropicThermal ExpansionCoefficient Ilk Constant 0Degrees of Freedom Constant 0
Carbon Dioxide
Property Units Method Value--------------------P-----.---.----------------------- ________________
Density kg/m' Constant 1.7878Thennal Conductivity w/m-k Constant 0.0145Viscosity kg/m-s Constant 1.37X I0.0;Molecular Weight kg/kg-mol Constant 44.00995Standard StateEnthalpy j/kg-mol Constant -3.9353235X I0'Standard State Entropy j/kg-mol-k Constant 213715.88Reference Temperature k Constant 29815L-J CharacteristicLength Angstrom Constant 3.941L-J Energy Parameter k Constant 1952Absorption Coefficient 11m Constant 0.43Scattering Coefficient 11m Constant 0Scattering Phase Function IsotropicThermal ExpansionCoefficient Ilk Constant 0Degrees of Freedom Constant 0
74
Oxygen
Property Units Method Value
Constant 0Constant 0
DensityThermal ConductivityViscosityMolecular WeightStandard State EnthalpyStandard State EntropyReference TemperatureL-] Characteristic LengthL-] Energy ParameterAbsorption CoefficientScattering CoefficientScattering Phase FunctionThermal ExpansionCoefficientDegrees of Freedom
Property Units Method Value----------------------------------------------------------------_.---------.-----DensityThermal ConductivityViscosityMolecular WeightStandard State EnthalpyStandard State EntropyReference TemperatureL-J Characteristic LengthL-J Energy ParameterAbsorption CoefficientScattering CoefficientScattering Phase FunctionThermal ExpansionCoefficientDegrees of Freedom