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Development of a computational fluid dynamics model for combustion
of fast pyrolysis liquid (bio-oil)
by
Arran Thomas McGrath
A thesis submitted in conformity with the requirements
for the degree of Master of Applied Science
Graduate Department of Mechanical and Industrial Engineering
University of Toronto
© Copyright by Arran Thomas McGrath 2011
ii
Development of a computational fluid dynamics model for combustion of
fast pyrolysis liquid (bio-oil)
Master of Applied Science 2011
Graduate Department of Mechanical and Industrial Engineering
University of Toronto
Abstract
A study was carried out into the computational fluid dynamic simulation of bio-oil combustion.
Measurements were taken in an empirical burner to obtain information regarding the flow behaviour.
A surrogate fuel was developed to mimic the unique chemical and physical properties of bio-oil
combustion. The resulting computational model of the burner domain and surrogate fuel was
compared with empirical data. The bio-oil model displayed a good agreement with the data in terms
of the combustion behaviour, but was limited by the uncertain flow solution associated with the
burner used.
iii
Acknowledgements
I greatly appreciate the advice, support and assistance of the bio-oil team, in particular Tommy
Tzanetakis and Sina Moolodi. Professor M.J. Thomson deserves thanks because of his valued
supervision, and the unerring ability to provide excellent advice and support. The team at HPCVL
deserve a special thanks due to their consistent ability to answer an unending stream of reasonable
and unreasonable questions, as well as providing consideration for the needs of this project as it
evolved over time. This research was made possible by the financial support from Agriculture and
Agri-food Canada and the Natural Sciences and Engineering Research Council of Canada. Finally, I
would like to thank my family and friends for their perpetual encouragement and Olivia Poremba in
particular for donating her computer to the bio-oil cause, and her unwavering support. And of course
the dogs, who made sure I never ran out of steam by getting me out at least three times a day for a
walk.
iv
Table of Contents
Abstract ........................................................................................................................................................ ii
Acknowledgements ..................................................................................................................................... iii
Table of Contents ........................................................................................................................................ iv
List of Figures ........................................................................................................................................... viii
List of Tables ............................................................................................................................................... xi
Chapter 1 Introduction ...............................................................................................................................1
1.1 Motivation .....................................................................................................................................1
1.2 Objectives ......................................................................................................................................3
Chapter 2 Literature Review ......................................................................................................................5
2.1 Bio-oil Chemistry ..........................................................................................................................5
2.2 Bio-oil Combustion .......................................................................................................................7
2.3 Turbulent Combustion Modelling .................................................................................................9
2.3.1 Turbulence Modelling .................................................................................................... 9
2.3.1.1 Reynolds-Averaged Navier-Stokes (RANS) ............................................................ 10
2.3.1.1.1 Algebraic (Zero-Equation) ................................................................................. 11
2.3.1.1.2 One-Equation ...................................................................................................... 11
2.3.1.1.3 Two-Equation ..................................................................................................... 12
2.3.1.1.4 Reynolds Stress Models (RSM) ......................................................................... 14
2.3.1.2 Large Eddy Simulation (LES) .................................................................................. 14
2.3.1.3 Direct Numerical Simulation (DNS) ........................................................................ 14
v
2.3.1.4 Swirl ......................................................................................................................... 15
2.3.2 Combustion Modelling ................................................................................................. 16
2.3.2.1 Finite Rate ................................................................................................................ 16
2.3.2.2 Mixture Fraction ....................................................................................................... 19
Chapter 3 Methodology............................................................................................................................23
3.1 Bio-oil Fuel Properties ................................................................................................................23
3.1.1 Base Case...................................................................................................................... 24
3.1.1.1 Distillable Phase ....................................................................................................... 25
3.1.1.2 Char Phase ................................................................................................................ 26
3.1.1.3 Summary................................................................................................................... 28
3.1.2 Low Char Case ............................................................................................................. 28
3.2 Bio-oil Combustion .....................................................................................................................30
3.2.1 Distillable Phase ........................................................................................................... 32
3.2.1.1 Devolatilization Rate Calibration ............................................................................. 32
3.2.2 Char Phase .................................................................................................................... 36
3.2.2.1 Burnout Rate Calibration .......................................................................................... 36
3.2.3 Bio-oil Gas Phase Reactions ........................................................................................ 37
3.2.4 Summary....................................................................................................................... 38
3.3 CFD Modelling ............................................................................................................................39
3.3.1 Domain and Grid Design .............................................................................................. 39
3.3.2 Solver ............................................................................................................................ 41
vi
3.3.3 Turbulence Model ........................................................................................................ 41
3.3.4 Droplet Simulation ....................................................................................................... 43
3.3.5 Pilot Flame.................................................................................................................... 43
3.3.6 Boundary Conditions .................................................................................................... 44
3.3.7 Simulation Procedure ................................................................................................... 46
3.4 Flow Validation ...........................................................................................................................50
3.4.1 Experimental Setup ...................................................................................................... 53
3.4.1.1 Sampling Probe ........................................................................................................ 53
3.4.1.2 Sample Line .............................................................................................................. 56
3.4.1.3 Oxygen Sensor.......................................................................................................... 56
3.4.1.4 FTIR ......................................................................................................................... 56
3.4.1.5 Pump ......................................................................................................................... 57
3.4.1.6 Ethanol Combustion Chemistry................................................................................ 57
3.4.2 Experimental Methodology .......................................................................................... 57
Chapter 4 Results and Discussion ............................................................................................................62
4.1 Fluid Mechanics Analysis ...........................................................................................................62
4.1.1 Experimental CO2 Measurements ................................................................................ 62
4.1.2 CFD Simulation ............................................................................................................ 66
4.1.2.1 Simulation Results .................................................................................................... 66
4.1.2.2 RNG k-ε Failure ....................................................................................................... 72
4.2 Bio-oil Combustion .....................................................................................................................77
vii
4.2.1 Base Case...................................................................................................................... 77
4.2.1.1 Atomizing Air ........................................................................................................... 78
4.2.1.2 Ignition Source Energy ............................................................................................. 83
4.2.2 Low Char Case ............................................................................................................. 85
Chapter 5 Closure .....................................................................................................................................87
5.1 Conclusions .................................................................................................................................87
5.2 Recommendations for Future Work ............................................................................................88
5.2.1 Turbulence Model ........................................................................................................ 88
5.2.2 Ethanol Vaporization .................................................................................................... 89
5.2.3 Diesel Validation .......................................................................................................... 89
5.2.4 Flow Validation ............................................................................................................ 90
5.2.5 Swelling ........................................................................................................................ 90
5.2.6 Grid Design .................................................................................................................. 91
5.2.7 Bio-oil Model Validation ............................................................................................. 91
Appendix A – Varying Case Parameters .....................................................................................................92
Appendix B – Supplemental Thermal Boundary Conditions ......................................................................94
Appendix C – User Defined Functions .......................................................................................................95
Swirl100Pilot100.c ......................................................................................................................................95
Swirl50Pilot75.c ..........................................................................................................................................96
References ...................................................................................................................................................99
viii
List of Figures
Figure 1 – Combustion phases of a bio-oil droplet as observed by Wornat et al. .................................. 7
Figure 2 – Trace of the mixture fraction for a fluid parcel and the resulting PDF [52] ....................... 21
Figure 3 – Bio-oil combustion model design and implementation process. ......................................... 30
Figure 4 – Cenosphere captured by a particulate filter from 80/20 bio-oil/ethanol blend combustion
[60] ....................................................................................................................................................... 31
Figure 5 – Viewport of bio-oil burner during operation ....................................................................... 34
Figure 6 – Experimental TGA mass-loss curve for bio-oil normalized to the estimated devolatilization
time and compared with the kinetic model ........................................................................................... 35
Figure 7 – Diagram of burner setup ..................................................................................................... 39
Figure 8 – Burner geometric features (not to scale) [66] ..................................................................... 39
Figure 9 – Non-dimensionalized axial velocity measurements for a movable block swirl burner from
[70] and the fitted equation used for a primary air boundary condition for the bio-oil base case. ....... 44
Figure 10 – Non-dimensionalized tangential velocity measurements for a movable block swirl burner
from [70] and the fitted equation used for a primary air boundary condition for the bio-oil base case 45
Figure 11 – Measured wall temperature profile along the burner for bio-oil and the fitted curves used
as boundary conditions in the simulations for bio-oil (the termination of the diffuser is considered the
origin) ................................................................................................................................................... 46
Figure 12 – Some regions of mass imbalance calculated by ANSYS Fluent during the solution
procedure. ............................................................................................................................................. 48
Figure 13 – Comparison of a mildly swirling flow with no CTRZ and a swirl flow with a CTRZ [66]
.............................................................................................................................................................. 51
Figure 14 – Schematic of probe setup .................................................................................................. 53
Figure 15 – Visualization of the probe within the burner chamber ...................................................... 53
Figure 16 – In situ probe arrangement ................................................................................................. 55
ix
Figure 17 – Probe setup and insertion at the burner flange .................................................................. 55
Figure 18 – Connections of the exhaust and probe sample lines .......................................................... 56
Figure 19 – CO2 response time at each data point for 100% swirl and 100% pilot flame ................... 59
Figure 20 – CO2 response time after probe move at each data point for 50% swirl and 75% pilot flame
.............................................................................................................................................................. 60
Figure 21 – Borescope image looking up at the nozzle showing the probe, pilot flame and ethanol
flames. .................................................................................................................................................. 60
Figure 22 – CO2 profile across the burner for 100% swirl and 100% pilot flame ................................ 63
Figure 23 – CO2 profile across the burner for 50% swirl and 75% pilot flame .................................... 64
Figure 24 – Simulation and experimental comparison of %CO2 for 100% swirl. The simulated probe
measurements are shown along with those for radii to either side of the actual probe................. 66
Figure 25 – Modelled ethanol molar percentages in the sampling plane for 100% swirl, 100% pilot
flame. .................................................................................................................................................... 67
Figure 26 – Modelled CO2 contour in the probe sampling plane for 100% swirl, 100% pilot. Also
shown is the probe radii . ............................................................................................................... 67
Figure 27 – Simulation and experimental comparison of %CO2 for 50% swirl. The simulated probe
measurements are shown along with those for radii to either side of the actual probe................. 69
Figure 28 – Comparison of the velocity fields (in-plane) for the 100% and 50% swirl cases for
complete cases, including pilot flame, atomizing air and combustion. ................................................ 70
Figure 29 – Temperature contour in-plane with fuel jets for 100% swirl, 100% pilot flame ............... 71
Figure 30 – Temperature contour in-plane with fuel jets for 50% swirl, 75% pilot flame ................... 71
Figure 31 – Temperature contours for 100% (left) and 50% (right) simulations downwind of the jet
plane. .................................................................................................................................................... 71
Figure 32 – Diffuser section vector plot of the 100% swirl ethanol simulation with pilot flame and no
atomizing air immediately before the addition of combustion ............................................................. 73
x
Figure 33 – Diffuser section vector plot of the 100% swirl ethanol simulation prior to the addition of
atomizing air. The high speed jets are due to drag from the droplets and density changes from heat
release. .................................................................................................................................................. 74
Figure 34 – Diffuser section vector plot of the 100% swirl ethanol simulation with 100% atomizing
air. ......................................................................................................................................................... 75
Figure 35 – Vector plot of the deflection of the atomizing air. The original trajectory at the boundary
matched that of the droplet. .................................................................................................................. 76
Figure 36 – Experimental particulate matter measurements from [55] compared with the droplet mass
remaining at 5cm below the diffuser. The masses have been normalized to the basepoint to aid
comparison. .......................................................................................................................................... 79
Figure 37 – Temperature history of the droplets along the burner axis for different atomizing air
flowrates. .............................................................................................................................................. 80
Figure 38 – The temperature, volatile mixture fraction and char mixture fraction contour plots for the
Base Case. ............................................................................................................................................ 81
Figure 39 – Temperature contour for High Atomizing Air Case. ........................................................ 82
Figure 40 – Vector plot of the High Atomizing Air Case with the particle trajectories superimposed.
.............................................................................................................................................................. 82
Figure 41 – Temperature contour for the No Pilot Case. ..................................................................... 83
Figure 42 – Vector plot comparison between the Base Case and the No Pilot Flame Case ................. 84
Figure 43 – Low Char Case temperature contour plot ......................................................................... 85
Figure 44 – Mass loss curves for the Base Case and the Low Char Case ............................................ 86
xi
List of Tables
Table 1 – Base Case distillable phase composition .............................................................................. 25
Table 2 – Char atomic composition for four different bio-oil producers [22] ...................................... 26
Table 3 – Complete bio-oil atomic composition for four different bio-oil producers [22] .................. 27
Table 4 – Base Case char atomic composition ..................................................................................... 27
Table 5 – Empirical and model bio-oil properties for the Base Case ................................................... 28
Table 6 – Low Char Case distillable phase composition ...................................................................... 29
Table 7 – Low Char Case char atomic composition ............................................................................. 29
Table 8 – Empirical and model bio-oil properties for the Low Char Case ........................................... 30
Table 9 – Single kinetic rate model constants for lignite coal and bio-oil ........................................... 36
Table 10 – Diffusion-limited model constants for lignite coal and bio-oil .......................................... 37
Table 11 – Difference in iteration times for the three grid densities tested, as well as the % change in
radial location of the streamline at the diffuser exit ..................................................................... 40
Table 12 – Selection of grid metrics ..................................................................................................... 41
Table 13 – RNG-k-ε transport equation parameter definitions ............................................................ 42
Table 14 – RNG-k-ε constants ............................................................................................................. 42
Table 15 – Simulated residence times for all cases considered in the current study ............................ 78
Table 16 – Base Case ........................................................................................................................... 92
Table 17 – Low Atomizing Air Case ................................................................................................... 92
Table 18 – High Atomizing Air Case ................................................................................................... 92
Table 19 – 100% Swirl, 100% Pilot ..................................................................................................... 92
Table 20 – 50% Swirl, 75% Pilot ......................................................................................................... 93
Table 21 – Ethanol Thermal BCs ......................................................................................................... 94
Table 22 – Bio-Oil Thermal BCs ......................................................................................................... 94
1
Chapter 1 Introduction
1.1 Motivation
A growing body of evidence indicates that anthropogenic use of fossil fuels is having a negative
impact on the planet’s equilibrium. Greenhouse gases (GHG) are contributing to this shift by
affecting the balance of radiation heat transfer between the earth, sun and space. GHG such as H2O
(water), CO2 (carbon dioxide), CH4 (methane), N2O (nitrous oxide), chlorofluorocarbons (CFCs) and
aerosols trap radiation in the atmosphere, raising average global temperatures [1]. It is estimated that
the atmospheric CO2 could increase from the current level of 390 parts per million (ppm) to 900-
1100ppm by the end of the twenty-first century mainly due to fossil fuel consumption [2]. It took the
Earth 30-100 million years to reduce such a high CO2 concentration down to modern levels [2].
In 2004, Pacala and Socolow proposed several mitigation “wedges” to be used to maintain current
CO2 emissions levels until 2050 by utilizing existing technologies [3]. One of the proposed wedges is
the substitution of biomass – or carbon neutral – fuels in place of fossil derived fuels. Government
regulation has already begun the process of implementing fuels and power generated from biomass,
such as Ontario’s Regulation 535/05 which requires gasoline retailers to average 5% ethanol content,
and the Feed-in-Tariff program, designed to encourage power generation from renewable sources [4]
[5]. However, in order to achieve the reduction in carbon release required, a comprehensive biomass
fuel portfolio will need to be implemented. Due to the absence of a single uniform biomass source of
sufficient quantity, finding a solitary bio-fuel capable of significantly displacing fossil fuel in the
short- and medium-term is unlikely. If all roundwood (the most abundant) timber harvested in Canada
was used as a fuel feedstock it would only be capable of accounting for 14% of all of Canada’s
energy needs [6]. Differences in regional biomass sources, conversion processes and energy needs
will play a role in how the shift to biomass fuels develops [7].
2
Canada is particularly well-placed for exploitation of biomass as a fuel due to large forestry and
agriculture industries. These industries generate residual waste streams which would provide both
environmental and economic benefits were they to be converted to usable fuels. Woody biomass
available from harvesting and urban (residential) sources are capable of providing 5% of the energy
provided in Canada by fossil fuels [6]. By utilizing forestry, agriculture and municipal residual
streams as well as introducing ethanol (or other additives) and biodiesel into transportation fuels,
Canada can develop a holistic biofuel economy.
The difficulty lies in turning those residual streams into energy. Residual biomass, especially timber,
is often found in remote locations and possesses a low energy density. Studies have shown that as
much as 59% of available forestry residual biomass remains unused because it is not economically or
technically feasible to transport and process [8] [9]. As a result, there is considerable interest in
increasing the energy density of the biomass prior to transportation and/or use. Currently, the routes
available through thermal or thermo-chemical processing are combustion, gasification, pyrolysis,
liquefaction and supercritical fluid extraction [10] [11].
Pyrolysis is the process of thermally decomposing a substance in the absence of oxygen. Pyrolysis
forms a gaseous fraction, a char fraction and a condensable fraction made up of vapours and aerosols.
By altering the residence times and temperatures a wide distribution of these fractions can be
produced [10]. Short vapour residence times and moderate temperatures maximize the liquid yield
from the biomass and is termed “fast pyrolysis”. The resulting liquid fraction is called fast pyrolysis
liquid, or bio-oil. This liquid can have a gravimetric heating value just over one third of fuel oil No6.
Ensyn Corporation has pursued bio-oil as a profitable approach to residue processing and is now
marketing a renewable fuel oil [12]. Agri-therm, based out of London Ontario, has developed a
mobile processing unit [13]. While pyrolyzing raw biomass can increase the heating density, making
transportation and handling profitable, only a mobile unit can eliminate the problem of distributed
biomass sources.
3
Bio-oil can be combusted “as-is” in stationary burners, or partially upgraded to improve fuel quality.
Research into combustion characteristics of bio-oils is growing to accommodate a desire to use bio-
oil in an increasing number of applications including small- and large-scale stationary power plants,
gas turbines and internal combustion engines [14] [15] [16] [17] [18]. Although there has been much
attention paid to the combustion behaviour of bio-oil, no effort has been made to develop a predictive
tool for use in designing for, or understanding, the comprehensive combustion of bio-oil. In
particular, a considerable body of work has been generated by the experimental work of Tzanetakis et
al. into the combustion behaviour of bio-oil [14] [15]. Extensive diagnostics were available for
exhaust gas speciation, particulate matter characterization and flame visualization. However, no
methods were available to characterize the combustion itself, and the resulting fluid behaviour within
the burner. Many questions remain that a reliable predictive tool may be able to answer regarding the
experimental results. If a model can be developed that is able to duplicate the trends observed by
Tzanetakis et al, then extrapolation to industry may be possible. This is critical as in order to
encourage the adoption of bio-oil as an alternative carbon-neutral energy source, industry must be
given the tools necessary to understand the fuel. An immediate need exists for a comprehensive
combustion model that can be used in designing for the adoption of bio-oil in existing or proposed
combustion systems.
1.2 Objectives
The first objective of this work is to develop a predictive combustion model for bio-oil. This model
must preserve bio-oil’s unique chemical and physical properties while duplicating its unique
combustion behaviour. The model must be useable by industry with regards to both the software used
and the data a user might possess.
The second objective is to validate the model with the extensive experimental data collected on the
combustion of bio-oil at the University of Toronto. Key issues are whether the model can duplicate
the qualitative trends observed in the empirical results, and can the model help explain those trends.
4
This validation will also attempt to determine the fluid mechanic properties of the empirical burner,
such as the effect of swirl.
5
Chapter 2 Literature Review
The computational modelling of bio-oil combustion has been minimal, and largely restricted to the
micro scale. This is likely due to the general complexity of bio-oil chemistry and the unique
combustion behaviour of bio-oil. These two problems are discussed here, along with efforts that have
been made in the research community to overcome them.
2.1 Bio-oil Chemistry
Bio-oils contain more than 300 identified species which can vary significantly depending on the
feedstock, residence times, temperatures, filtration and phase separation used during the pyrolysis
process [19]. A study carried out by Moloodi et al. at the University of Toronto on effects of changes
in bio-oil properties on combustion behaviour encountered water content ranging from 8.91% to
26.66% by weight [20]. Due to bio-oil’s high water content it is not technically an oil, and is thus also
known by several other names such as fast pyrolysis liquid, liquid smoke, liquid wood, pyroligneous
tar, wood distillates, etc. [10].
The multitude of chemicals present in bio-oil is a result of the depolymerisation and fragmentation of
the cellulose, hemicelluloses and lignin in biomass during pyrolysis [21]. This is a rearrangement of
the elemental composition of biomass so that the final composition of bio-oil correlates with the
original biomass with some changes occurring due to char and gaseous products removal. A
consequence of this rearrangement is a large amount of oxygen remaining in the fuel, which along
with the high water content lower the heating value. Such high concentrations of water are possible
without separation due to polar hydrophilic compounds present in bio-oil [21]. Although the water
will lower the heating value and increase ignition times, it also improves atomization and fuel
handling properties because of a reduction in viscosity.
6
The unique chemical composition of bio-oil includes sugars and oligomeric phenolics and several
other compounds which are non-distillable [21]. There are also reactive species which will undergo
polymerization and pyrolysis at elevated temperatures. During the distillation of bio-oil, it will
therefore form a residual which cannot be evaporated. This must be treated separately from the liquid
phase as the elemental composition of this residual has been shown to differ from that of the liquid
phase [22].
Hallet and Clark classified the components of bio-oil into four categories when modelling the
evaporation of a single droplet. Water, aldehydes & ketones, organic acids and pyrolytic lignin were
used to represent the full bio-oil mixture [23]. This is a continuous mixture model, where each group
was given a distribution function for dictating physical and chemical properties. Branca and Di Blasi
have carried out considerable studies into evaporation and combustion of bio-oil which they have
used to develop kinetic models. The light fraction of bio-oil, termed “Fraction A” by Branca and Di
Blasi uses a lumped mechanism approach. The fraction is separated into three parts where all
compounds which get lumped together possess similar devolatilization properties [24]. Specific
attention was given to the residual char formed by distillation of bio-oil, referred to as secondary char.
Currently, their approach has been to separate the char into four component groups depending on
their tendency to devolatilize. While three groups could be devolatilized, the fourth group was the
char material which could only be consumed through surface oxidation [25]. Hristov and Stamatov
utilized a different approach to represent bio-oil as they were interested in modelling the unique
devolatilization behaviour only, and so modelled the substance as a single medium, but represented
the bulk chemical properties in the droplet devolatilization behaviour [26].
Work reported in the literature indicates that using a grouping or lumped approach to representing
bio-oil chemistry is favourable. This is because the bulk properties of the fuel can be preserved, while
simplifying the over 300 compounds into a more manageable number.
7
2.2 Bio-oil Combustion
Bio-oil is unique in that it does not combust in a manner similar to either distillable fuels such as
diesel or solid fuels like coal. A bio-oil droplet passes through four stages during its combustion.
These stages are shown in Figure 1 as captured by Wornat et al. and are described as quiescent
burning, microexplosion, coalescence and burnout [27].
Quiescent Burning Microexplosion Coalescence Burnout
Figure 1 – Combustion phases of a bio-oil droplet as observed by Wornat et al.
During the quiescent burning phase components within the droplet begin to devolatilize and burn in
the gas phase surrounding the droplet. This loss of volatile matter increases the viscosity of the
droplet while increasing the surface temperatures. As a result, reactive compounds polymerize and
pyrolyze which resists the escape of volatiles remaining within the droplet [26]. This resistance
results in an increase in diameter and eventual microexplosions as vaporized material escapes the
droplet skin. Once the viscosity of the droplet reaches a certain level, microexplosions are no longer
possible and the material coalesces. The final stage of the combustion is burnout.
Referring to the stages just described it can be concluded that bio-oil combustion is considerably
more complex than many traditional fuels. Parallels with distillable fuels such, as diesel, can be made
during the quiescent phase. Similarities exist with solid fuels, such as coal, during the slight
devolatilization in the coalescent phase and char burnout. Water-in-oil emulsification droplets
experience a similar microexplosion as water is vaporized within the emulsification, causing
secondary atomization [28].
8
Several approaches have been taken to modeling bio-oil droplet combustion. Hristov and Stamatov
developed a bubble shell model which focused on simulation of the microexplosion [26]. The main
objective in their work was to simulate the heating period leading up to, and the occurrence of, a
microexplosion. For this case, an assumption was made that volatile matter in the bio-oil vaporized
within the droplet (bubble) and low-volatile compounds form a membrane (shell) around this bubble.
As the focus was on predicting microexplosion times and critical temperatures, the chemistry of the
bio-oil within a combustion environment was essentially ignored. The model is an excellent tool for
analyzing microexplosion phenomena and superheating properties but neglects the burnout phase and
has not been sufficiently validated. Branca et al. developed kinetic rates for both the distillable and
residual phases [24]. The distillable phase components were lumped into three groups as described in
2.1 to reduce complexity, each group possessing its own rate. Residual material, or secondary char,
was modelled separately by Branca and Di Blasi with four kinetic rates [25]. Secondary char is
defined by Branca and Di Blasi as the material remaining after the sample temperature has reached
550K. As shown by Tzanetankis et al. devolatilization is still not complete when temperatures exceed
800K, though evaporation rates are very low, and slowing [14]. Thus, Branca and Di Blasi use four
parallel rates for combustion of secondary char, three for continued devolitilization and one for char
burnout. These rates were extensively validated against thermogravemetric analyses (TGA) results.
TGA is a method of determining a material’s evaporation and combustion behaviour by gradually
heating a sample in a reactive or inert environment and monitoring the mass loss. It is also possible to
attach gaseous species diagnostics to the exhaust to monitor the products. The kinetic models
developed by Brance et al. and Branca and Di Blasi for both the volatile and non-volatile phases were
designed for agreement with the TGA curves. Model behaviour can be altered by changing the
relative contribution each lumped group (rate) makes to the overall bio-oil sample. A sample with a
larger fraction of light volatiles will favour rates that govern the evaporation of those volatiles.
Though extensively validated via TGA, the time scales are not representative of those experienced in
a turbulent flame, nor is swelling and microexplosion considered.
9
The final model considered in this report is that of Hallett and Clark. As discussed earlier, their bio-
oil was separated into four parts, water, aldehydes & ketones, organic acids and pyrolytic lignin. The
model utilized what Hallett and Clark term “continuous thermodynamics” due to the use of
distribution functions instead of discrete properties for the four chemical groups [23]. Distribution
parameters are solved for in the transport and balance equations. Similar to Branca et al. this approach
is not able to accommodate microexplosion. Model results were compared with droplet combustion
experiments with 20s residence times. Though shorter than the Branca et al. residence times (4000s),
they are still long compared to spray combustion. A detailed comparison of the results with
experiments was not published. Phases of the combustion process were compared between the model
and the droplet experiments, such as the onset of microexplosion dampening, and droplet
solidification into a carbon cenosphere.
All of the models discussed were developed to represent a single droplet, with either TGA or droplet
combustion in a quiescent atmosphere as the standard for comparison. No effort has been made to
develop a model for the time scale encountered in a full-scale turbulent combustor with an eye on
what the engineering community requires as a design tool. Currently, there does not exist a means for
a design engineer to model bio-oil combustion utilizing existing design tools.
2.3 Turbulent Combustion Modelling
2.3.1 Turbulence Modelling
Fluid flow is described by the Navier-Stokes (NS) (2.1) and continuity (2.4) equations shown below
for an incompressible fluid [29] [30]. The solution of these equations are impractical to solve for
nearly all cases:
(2.1)
where (2.2)
10
(
)
(2.3)
(2.4)
The solution to this problem has been to simplify the resulting system of equations. Doing this results
in an un-closed system. Turbulence models are an effort to close the system in a manner which
reduces the introduced error to a minimum.
2.3.1.1 Reynolds-Averaged Navier-Stokes (RANS)
The most popular method for approximating the NS equations is the Reynolds-Averaged approach.
The concept is based on the idea of defining a flow as possessing mean flow variables and
fluctuations from these means, first proposed by Reynolds in a reading to the Royal Society of
London in 1894 [31]. This averaging can be carried out temporally, spatially and in ensemble. As the
turbulence is assumed to be temporally constant in the case considered in this study, such a method
will be discussed here. The Cartesian velocities and temperature are then represented by [30]:
(2.5)
where is the mean velocity and the fluctuation from that mean. The key foundation for the
RANS approach is the substitution of the above definitions into the continuity and NS equations,
leading to the generalized equations [32]:
(2.6)
( (
)
) (2.7)
The term that has appeared is called the Reynolds stress tensor and represents the transport of
momentum via turbulent fluctuations. It is this term that will perpetually add more unknowns than
equations to a turbulent flow problem.
11
The Reynolds Stress Model directly solves the transport equations for these stresses. The zero-, one-
and two- equation models utilize the Boussinesq hypothesis, whereby the turbulent momentum
transfer can be modelled using a turbulent eddy viscosity [33]. For one- and two-equation models the
hypothesis allows the relation of the mean velocity gradients to the Reynolds stresses via:
(
)
(
) (2.8)
where is the turbulent eddy viscosity, the turbulent kinetic energy and the Kronecker delta.
The differences between the available one- and two-equation models lie in how they define the first
two properties. The following is a brief discussion of some available closure models.
2.3.1.1.1 Algebraic (Zero-Equation)
These are the most simplified turbulence models which utilize the eddy viscosity hypothesis of
Boussinesq where no differential transport equations (zero-equation) are used for the convection or
diffusion of turbulence [33]. The Reynolds stresses are described by one scalar value, or the turbulent
eddy viscosity, which is derived from the mixing length ( ) particular to that flow [32]. As the
parameters for deriving the stresses must be identified beforehand, the model is considered to be
incomplete. Restrictions exist due to the availability of empirical correlations for mixing length and as
turbulence transport is not present in the model it is not capable of accurately predicting flow
phenomena where hysteresis is an issue [30] [32] [33]. As a result, fluid flow simulations where
separation may occur should not use this model.
2.3.1.1.2 One-Equation
A one-equation model represents the eddy viscosity as:
⁄ (2.9)
where is a constant and k the specific turbulent kinetic energy [30]. The most popular one-equation
model is the Spalart-Allmaras model which is both numerically stable and easy to implement.
12
Moderate accuracy is possible with this model for turbulent flow solutions facing adverse pressure
gradients, but the user should keep it in mind that the model was calibrated using wakes and flat plate
boundary layers [33].
2.3.1.1.3 Two-Equation
Two differential equations are used to describe the turbulence, while the eddy viscosity is generally
written as:
(2.10)
where is the turbulent dissipation rate [30]. Both and are obtained from differential equations in
this case. An alternative to the use of turbulent dissipation is to employ the specific dissipation [34]:
(2.11)
The choice between these two approaches resulted in the - and - models. Unlike the models
previously discussed, two-equation models allow for the calculation of a turbulent length scale and
are thus considered complete. Therefore, they can be used to predict a turbulent flow structure
without requiring previous knowledge of the flow [32].
The three most popular two-equation models are the - , - and Shear Stress Transport (SST). The
- model has proven fairly accurate and stable over a large range of scenarios. It is more difficult to
implement numerically than the one-equation models, encounters problems with large or adverse
pressure gradients and requires dampening or wall functions to deal with boundary layers. The -
model has proven accurate in some recirculation cases and is superior to the - model near solid
boundaries and the boundary layer. The - has proven to have a greater insensitivity to freestream
turbulence values whereas complex systems can result in freestream ambiguities for the - model
[32] [35].
13
An effort has been made to combine the benefits of both models in the SST model. SST utilizes the -
model in the near-wall region avoiding the need for damping functions and the - in the
freestream region resulting in greater numerical stability [33]. Other variations include the
Renormalized Group (RNG) and Realizable - models. Yakhot et al. utilized a statistical technique
(renormalization group theory) to renormalize the NS equations to include the effects of smaller-scale
turbulence [36]. RNG is an effort to eliminate the restriction of a single length scale being used to
calculate the eddy viscosity present in the standard - model. All length scales in reality contribute
in some manner to the diffusion of turbulence in a flow. The resulting transport equations for
turbulent energy and dissipation have additional terms and functions to account for these additional
length scales, as well as changes to the constants in the N-S equations [37]. The consideration of a
wider range of length scales may increase the accuracy of the RNG model with regards to swirling
flows where small scale turbulence can be significant. Due to this the CFD software package used in
this study, ANSYS Fluent, specifically recommends the RNG model for use in swirling cases, and
has included proprietary alterations to the model that can be activated when a user specifies they
intend to model a swirling flow [38]. The resulting modified turbulent viscosity is then written:
(
) (2.12)
where is the viscosity without any consideration for swirl, is a swirl constant which indicates if
the flow is swirl-dominated or simply mildly swirling, and is the characteristic swirl number
calculated by Fluent [39]. Greater detail into the concept of swirl is provided in 3.4.
The realizable model was developed to adhere to mathematical constraints on the Reynold stresses
which are consistent with the physical reality of turbulence. The model as first developed by Shih et
al. contains a unique model dissipation rate equation and realizable eddy viscosity formulation [40].
Jets, both planar and round, are more accurately predicted and other possible improvements may
occur in rotational flows, boundary layers with adverse pressure gradients, separation and
14
recirculation [41]. Mean rotational flow is considered in the definition of turbulent viscosity which
can potentially cause problems where there is both a rotating and non-rotating frame of reference in a
particular flow solution. Additionally, the model is not as thoroughly validated as RNG.
2.3.1.1.4 Reynolds Stress Models (RSM)
Under certain circumstances, especially in three-dimensions, the eddy viscosity assumption may no
longer be valid [42]. The Reynolds Stress Model avoids this problem by directly solving for the
Reynolds stresses. As the model determines the stresses directly, considerably more accurate
solutions have been obtained for flows with strong swirl, recirculation, stagnation and separation [42].
However, in some cases an identical degree of accuracy to the standard - model is the possible
outcome. This is unfortunate considering that to solve for the stresses explicitly seven equations must
be handled. The solution thus requires considerably more computational effort, while generally being
stiffer than the two-equation models. A stiff equation being an equation which is numerically unstable
if insufficiently dense discretization is used.
2.3.1.2 Large Eddy Simulation (LES)
The premise of this model is that the smaller the turbulent structures, the more homogeneous their
character. This implies that a numerical solution which explicitly solves for the larger eddies while
describing these small turbulent structures via a model could be more accurate than a corresponding
RANS solution. For certain flows, the characteristics of the flow are of the same scale as the turbulent
fluctuations which will be lost if solved utilizing a RANS approach [33]. The major downside of LES
is the considerable computational effort required due to the high density grids necessary to capture the
eddies of interest [30].
2.3.1.3 Direct Numerical Simulation (DNS)
Direct Numerical Simulation (DNS) is the exact solution to the NS equations. This is extremely CPU
intensive as the largest eddies in the flow, the integral scale (L), and the smallest eddies, the
15
Kolmogorov scale (η), must be captured by the problem grid [43]. To effectively capture the integral
scale, the domain should be several multiples of this value. The consequence of capturing both scales
is that the linear dimensions of the domain scale with the Reynolds Number evaluated at the integral
scale, ⁄ . Considering a three-dimensional problem with an unsteady solution, the computational
effort will scale with making high Re flows extremely expensive to solve [42].
The effort required to solve a flow problem using DNS reduces its applicability to mostly research
topics. Simple geometries and low Re flows are solvable but tend not to cover common engineering
applications. The volume of information that a DNS solution can provide on a particular flow can
allow scientists to study flow patterns that are difficult to capture empirically. Compressibility effects
in turbulent flows, turbulent energy generation and its dissipation, and turbulent combusting systems
are some areas where DNS is playing an active role [42].
2.3.1.4 Swirl
The high shear and pressure gradients introduced by swirl are often difficult to model accurately. Of
particular concern in this study is the size and existence of any recirculation zone, and the mass that is
being recirculated. This information can help determine the degree of mixing that is taking place
within the burner.
A large body of work into isothermal swirling flow modelling has been carried out. In the majority of
cases the two-equation models, specifically the k-ε, were not able to capture the flow [44] [45]. In the
work of [45] it was found that the size and shape of the recirculation zone was not properly predicted
by the k-ε model. The experimental measurements indicated a long central toroidal recirculation zone
(CTRZ) which the RSM model was able to predict, while the k-ε model predicted a CRTZ at least 20-
30% shorter. In the case of [44], the physical size of the RSM CTRZ was 106% of that of the
experimental data, while that of the k-ε model was 58%. It was found that the first-order model had
been unable to properly predict the viscous terms of the NS equations because of significantly higher
16
turbulence viscosities [44]. However, there is a belief that the ε equation may be another cause for k-
ε’s difficulty in predicting swirling flows. Some effort has been made to improve the eddy dissipation
using a correction factor, but the attempt did not significantly correct the prediction [46]. Once
combustion is included in a swirling flow the improvements in the prediction capabilities are not as
clear. In fact, some observed no significant increase in accuracy with RSM [47].
2.3.2 Combustion Modelling
Many different methods of representing combustion are available to a modeller, such as finite rate,
equilibrium and flamelet chemistry. The descriptions presented here will only include the models
used in the current study. Specifically, the laminar finite-rate, eddy-dissipation and mixture fraction
models will be discussed.
2.3.2.1 Finite Rate
This approach requires the solution of N-1 conservation equations of the form:
( ) ( ) (2.13)
where N is the number of species included in the simulation, is the net rate of production of
species i via chemical reactions, and any additional source or sink. is the diffusion flux of species
i which for turbulent flows can be written as:
( ) (2.14)
is the diffusion coefficient of the species within the mixture, the turbulent viscosity and
the turbulent Schmidt number. The reaction source terms in the conservation equation are calculated
via one of three models available in ANSYS Fluent: laminar finite-rate, eddy-dissipation and the
eddy-dissipation-concept.
17
The laminar finite-rate model does not consider turbulence fluctuations, making it exact for laminar
flames, but reducing its applicability in cases of fast chemistry and large turbulent fluctuations [48].
The laminar finite-rate model uses an Arrhenius expression to describe the chemical kinetics. The
reaction term in the conservation equation is written as:
∑
(2.15)
where is the molecular weight of species i, and its molar rate of creation or destruction in
reaction r for reactions. The reaction can be described as:
∑
⇔ ∑
(2.16)
where and are the forward and backward reaction rates, and
the stoichiometric
reactant and product coefficients of species . Therefore, the reaction rate is:
(
) . ∏[ ]
∏[ ]
/
(2.17)
where [ ] is the molar concentration of species j in reaction r, and the rate exponent. The effect
of third-bodies on the reaction rate is represented by:
∑
(2.18)
where is the third-body efficiency of species j on reaction r. The calculation of the forward and
backward rates is of the Arrhenius form:
⁄ (2.19)
18
where is the pre-exponential factor, the temperature exponent and the activation energy for
the reaction. The backward rate can then be calculated via the equilibrium constant, , using the
relation:
(2.20)
where
(
)(
)∑ (
)
(2.21)
∑(
)
(2.22)
∑(
)
(2.23)
and
are the standard-state entropy and enthalpy of species i,
representing the change
in Gibbs free energy.
As stated earlier, the applicability of the laminar finite-rate model is reduced in cases of fast
chemistry, or heavy turbulent mixing. Additionally, chemical kinetic mechanisms tend to be highly
non-linear and stiff, making convergence difficult [48].
The eddy-dissipation model is based on separating the chemical and turbulent time scales [49]. This is
necessary because many fuels burn rapidly and are restricted by the turbulent mixing of fuel and
oxidant. In this model, derived from the work first presented by Magnussen and Hjertager, the
reaction rate is represented by the smaller of the two following expressions [50]:
(
) (2.24)
∑
∑
(2.25)
19
where and are the mass fractions of the products and reactants, and and empirical
constants.
is the mixing time scale of the turbulence and determines the magnitude of the reaction.
Equation 2.22 uses the smallest concentration from all the reactants to control the rate ( ). Any
locations without turbulence will undergo no reactions. As the model no longer requires an ignition
source to begin combustion, modelling premixed fuel inlets can be problematic. To overcome this
issue Fluent allows the combination of the laminar finite-rate and eddy-dissipation models by using
the slower of the two computed rates. Chemical kinetics rates will usually be slower until ignition, at
which point the chemistry will become mixing-limited.
A weakness with the eddy-dissipation model lies in the assumption of fast chemistry, which can lead
to an over-prediction of the formation of products in some cases, resulting in an excessive heat
release. This can be a concern when species dependent on local temperatures, such as oxides of
nitrogen, are being predicted [51]. The model also cannot predict intermediate species such as
radicals. As all reactions are limited by the same mixing time, one- or two-step global reactions
should be used. Utilizing more than two steps is likely to provide inaccurate results.
2.3.2.2 Mixture Fraction
The mixture fraction is a conserved scalar quantity which can become a powerful approach to
characterizing a reacting flow when certain assumptions can be employed. The mixture fraction is
written:
(2.26)
where is the mass fraction of element i, and ox and fuel subscripts indicate the mass fractions of
element i in the oxidizer and fuel at the inlets. In the case where the diffusion coefficients for all
elements are the same, the mixture fraction is the elemental fraction that was introduced via the fuel
20
stream. A second mixture fraction can also be included for cases where two fuels are present.
Therefore there would be three mixture fractions which must sum to 1:
(2.27)
The assumption of identical diffusivities permit the species equations to be represented by the mixture
fraction only. This assumption is valid when considering a turbulent flow as convection is much
greater than molecular diffusion. Both a Favre averaged mean mixture fraction ( ) and the mixture
fraction variance ( ) are solved for. The mixture fraction variance is necessary for the closure of
the turbulence-chemistry interactions. Their expressions are:
( ) ( ) (
) (2.28)
( ) ( )
( ) ( )
(2.29)
where is a source term for fuel entering the domain through evaporation of a droplet or any other
unique source; , and are constants.
With the assumption of thermodynamic equilibrium, all species properties can be linked to the
mixture fraction. So all species properties, such as mass fraction, density and temperature, in a non-
adiabatic system take the form:
( ) (2.30)
where H is the instantaneous enthalpy.
In order to model the turbulence-chemistry interactions, Fluent utilizes the assumed shape probability
density function (PDF) approach. The PDF, ( ), is a description of the quantity of time that a fluid
parcel is at a particular state . Figure 2 illustrates the PDF concept.
21
Figure 2 – Trace of the mixture fraction for a fluid parcel and the resulting PDF [52]
The true shape of the PDF is not known so experimental results are used to predict possible shapes.
( ) can identify the time-fluctuating behaviour of the mixture fraction in a turbulent flow allowing
the mean values of species properties to be calculated:
∫ ( ) ( )
(2.31)
The shape of ( ) can be computed via the double-delta or β-functions. The latter tends to provide a
better match with experimental results, but requires greater computing effort. The β-function PDF is
provided by the function:
( )
( )
∫ ( ) (2.32)
where
[
( )
] (2.33)
( ) [
( )
] (2.34)
22
Some complications arise during the consideration of non-adiabatic cases. The ideal solution is to
generate a joint PDF which includes enthalpy. Due to heavy computational requirements, an alternate
solution is frequently used which assumes the enthalpy fluctuations in a turbulent flow are
independent of the total enthalpy. By including a transport equation for , a joint PDF is avoided and
the property equations then become:
∫ ( ) ( )
(2.35)
These integrals can be calculated before an actual flow solution has been generated and used as a
look-up table. These equations create 2D surfaces in non-adiabatic, single mixture fraction scenarios
that are functions of the mixture fraction and its variance. For the cases where two mixture fractions
are used, some integration takes place during the simulation as generating 4D tables ahead of time
would be too time consuming. Non-adiabatic cases generate 3D tables which consider the maximum
heat loss or gain and a discrete number of enthalpy slices between these two extremes.
The strength of the mixture fraction model is in its inclusion of chemical equilibrium for species
concentrations. This allows for dissociation of primary species in the flame into intermediate species
which can in certain circumstance achieve considerable concentrations [53]. However, it should be
taken into consideration that equilibrium methods rest on the assumption of infinitely fast chemistry,
and tend to over-predict temperatures.
23
Chapter 3 Methodology
3.1 Bio-oil Fuel Properties
The ultimate goal for this work is to create a predictive design tool for use in industry or the research
community for turbulent bio-oil combustion. When creating this tool, the final user or client must be
kept in mind. An example of a potential user might be a design engineer working on retrofitting a
stationary power plant for use with bio-oil. Key questions to consider with regards to the target
audience are: What information would they have, or be able to easily obtain regarding the bio-oil?
What key characteristics regarding a fuel are important to them? What current tools (software) are
they likely to be familiar with? What level of experience do they have regarding modelling unique
fuels?
Bio-oil is considered an excellent candidate for stationary heat and power so a potential user in the
energy sector is likely. Consequently, the heating value will be a property quantified through standard
techniques, such as ASTM D240 (American Society for Testing and Materials). The growing
importance of emissions reduction, in particular carbon, and smog producing compounds, requires the
atomic composition of the fuel. ASTM 5291 is one method of obtaining the C-H-O-N weight percent
balance on a dry basis for a fuel. Ash and solids can play a role in both particulate emissions and
coatings of heat transfer surfaces and must be quantified. Water and acidity are likely to be tested as
they are important for fuel system design. Other properties such as density or pour point are either not
relevant to a potential combustion model, or are accommodated in the model via other means.
Four fuel properties were selected as the necessary criteria to design the surrogate fuel: lower heating
values (LHV), C-H-O-N balance, water percentage and distillation residual. These points cover items
critical to the intended client while including the properties which change between various bio-oils.
The bio-oil compositions considered are the Base Case and the Low Char Case. The motivation for
the Low Char Case is discussed in section 3.1.2.
24
3.1.1 Base Case
Bio-oil is a complex mixture that contains more than 300 identified compounds [54]. Duplicating this
complexity is not feasible as data regarding all compounds and quantities would be required. As
already noted, most researchers grouped the chemicals present in bio-oil into groups based on
physical or chemical criteria. This permits the user some flexibility in which characteristics of the fuel
they wish to capture. The benefit of the approach of [25] and [23] is that the appropriate components
devolatilize when the droplet reaches certain temperatures. This may not be a critical point however,
as during microexplosion several compounds may be released concurrently as the droplet passes
through a temperature range while the vapours are trapped within the skin.
With consideration given to potential combustion models to be used it was decided to design a
surrogate fuel from a select number of components commonly found in bio-oil. The bio-oil was
divided into two parts: a distillable phase, and a distillation residue or char phase. As discussed in
section 2.2, a bio-oil droplet undergoes devolatilization and microexplosion, and then coalescence and
sooty burnout. This final stage is made possible by the creation of a non-distillable fraction,
analogous to coal burnout. Thus the char phase should be treated differently in terms of combustion
phenomena and separating the fuel into two parts allows for this.
TGA was carried out in a TA Instruments model Q50 by Tzanetakis et al. on the bio-oil samples used
in the experiments to determine their mass-loss characteristics [14]. The analyses carried out raised
the sample temperature from 22.1 C to 5 5.68 C over 58 minutes. The amount of char phase is
determined from the TGA residue at the end of the test, 20.25 weight percent of the original sample.
The relative quantities of each phase of the fuel determined from the TGA is assumed to be similar to
that in a combusting droplet. This satisfies the design criteria for matching surrogate residual
quantities with the bio-oil sample. This topic will be discussed in greater detail in 3.1.1.1 and 3.1.1.2.
25
3.1.1.1 Distillable Phase
Although discussed further in 3.2.3 it is necessary to identify at this time that the mixture fraction
equilibrium model has been selected for bio-oil combustion in this study. The reasons for this choice
are outlined in the relevant sections. In order to be used in an equilibrium model certain chemical
properties must be available to calculate the Gibbs function of change. Specifically, they must be
available to ANSYS Fluent users, who are the intended audience for this work.
Several hundred species found in bio-oil were identified in [22] by analyzing TGA exhaust data.
These identified compounds were selected as a pool from which to draw compounds for the surrogate
fuel. The use of the mixture fraction equilibrium model narrows down the available compounds for
the distillable phase to glyoxal, formaldehyde, acetaldehyde, methanol, water, formic acid and
phenol. These compounds are available for use in Fluent’s equilibrium model and are representative
of bio-oil.
The final seven compounds and their relative weight
percentages are indicated in Table 1. The largest
single component is water at 28.5 weight percent,
which is set to the same quantity as that measured in
the original bio-oil sample. This satisfies one of the
four properties for the surrogate fuel. The remaining
two criteria, atomic balance and LHV require a compromise between the two. Amounts of each
component were determined so as to obtain as close an agreement as possible for both the heating
value and atomic balance. An exact match is impossible because of the variation in elemental make-
up of each component and its associated heat release. The LHV for the distillable phase is calculated
via the enthalpies of the reactants and products:
(3.1)
Table 1 – Base Case distillable phase composition
Compound wt%
Glyoxal 10.75
Formaldehyde 5.00
Acetaldehyde 8.75
Methanol 5.00
Water 28.50
Formic Acid 10.75
Phenol 11.00
Total 79.75
26
∑
∑
(3.2)
where , and are the number of moles, molar weight and enthalpy for reactants i and products
j. Both the atomic balance and the LHV includes the char phase which should be explored before the
entire bio-oil surrogate’s properties are discussed.
3.1.1.2 Char Phase
Very little data is available regarding the chemistry of the residual char from bio-oil. Branca et al.
have analyzed what they term “secondary char” in order to avoid confusion with the char present in
bio-oil from the production process [22]. Samples from several different companies were tested for
the C-H-O-N balance, summarized in Table 2.
Table 2 – Char atomic composition for four different bio-oil producers [22]
wt%
Element BTG Dynamotive Ensyn Pyrovac
C 72.53 71.99 70.93 71.91
H 4.81 4.39 3.90 5.09
O 24.55 24.58 24.82 22.16
N 0.49 0.10 0.35 0.84
Among all four samples the largest variation was 2.66wt% between the Pyrovac and Ensyn oxygen
content. Between BTG, Dynamotive and Ensyn the largest variation is 1.6wt%. This is interesting
because the atomic composition of the entire bio-oil, reproduced in Table 3, varies significantly
between producers. Differences in carbon content span 14.3wt%, oxygen 13.5wt%. This implies that
as bio-oil samples devolatilize they become more uniform, regardless of the original process and
feedstock. This permits the char composition to be held constant by the user, and only change the
total residual weight percent when modifying the surrogate fuel for a new bio-oil. Therefore, the
assumption that the char composition from Branca et al. is the same as that contained in the droplets
of Tzanetakis et al. is reasonable.
27
Table 3 – Complete bio-oil atomic composition for four different bio-oil producers [22]
wt%
Element BTG Dynamotive Ensyn Pyrovac
C 37.1 44.7 47.2 51.4
H 7.6 7.2 6.9 7.0
O 55.1 48.1 45.6 41.6
N 0.1 0.1 0.1 0.3
The composition of the char phase from Branca et al. was used as a guide for the surrogate fuel.
Minor alterations had to be made to accommodate the nitrogen present in the bio-oil used by
Tzanetakis [55]. No nitrogen-containing compounds
were used in the surrogate for the distillable phase so all
nitrogen measured in the bio-oil had to be placed into
the char phase. The final char composition is presented
in Table 4.
The LHV for the char phase is calculated using the Dulong formula for solid fuels [56]:
( ⁄ )
(3.3)
( ⁄ ) (3.4)
There are concerns regarding this formula’s accuracy with increased oxygen content. Therefore, the
result (
) was compared with work carried out on correlations for lignocellulosic fuels with
higher oxygen contents. Their previous studies into the higher heating values (HHV) for
lignocellulosic fuels were used by [57] to develop correlations based on the carbon and hydrogen
content. A large number of data points were available for the correlation, resulting in a correlation
coefficient of 0.9814 for the following:
( ) (3.5)
where C and H are the weight percentages of carbon and hydrogen, respectively. The HHV for the
surrogate char phase based on the Dulong equation is
whereas the same char has a HHV of
Table 4 – Base Case char atomic composition
Element wt%
C 14.11
H 0.85
O 4.94
N 0.36
Total 20.25
28
when calculated using the correlation above. This difference in heating value is only 2.6%
when considering the entire surrogate fuel, assuming the total difference in HHV contributes to the
LHV. The difference between the Dulong equation and that of a correlation designed for
lignocellulosic fuels is negligible. Therefore, the ubiquitous Dulong equation is used here to calculate
the heating value for the char.
3.1.1.3 Summary
The four design criteria for the complete surrogate fuel and the original bio-oil sample can be found
in Table 5. The water content and distillation residue may be considered trivial as they were set
during the design phase, but the atomic balance and LHV show an excellent approximation of the
original bio-oil.
Table 5 – Empirical and model bio-oil properties for the Base Case
Property Bio-oil Model
C-H-O-N (wt% wet) 39-7.6-53-0.36 38-7.3-54-0.36
Water (wt%) 28.5 28.5
LHV (MJ/kg) 14.5 14.7
Distillation Residue (wt%) 20.25 20.25
3.1.2 Low Char Case
It was stated previously that during the design of the Base Case surrogate fuel the assumption was
made that a bio-oil droplet undergoing combustion experiences the same behaviour as a TGA sample.
However, bio-oil contains many reactive compounds which can polymerize or pyrolyze over time in
elevated temperatures. Therefore, it is likely that a droplet experiencing heating rates several orders of
magnitude higher than a TGA sample will evolve differently. Given the shorter timespan bio-oil will
have to polymerize and pyrolyze both the atomic balance and quantity of the resulting char are likely
to be different from TGA results. This is supported by the literature which has found that droplets of
the same order of magnitude in diameter to those considered in this study and at heating rates similar
to those experienced in a flame, converted only 8wt% to char [58]. At lower TGA heating rates up to
30wt% of the same sample was converted to char. This behaviour is also in agreement with findings
29
regarding coal devolatilization and char formation. In these cases, higher heating rates resulted in
greater volatile creation, while the atomic composition of the remaining char also changed [59]. With
higher heating rates a larger proportion of the carbon in coal was released in the volatiles than with
heating rates used in a conventional TGA.
The implications here are that the assumption of 20.25wt% char may not reflect actual conditions
experienced by a bio-oil droplet. Therefore, a second surrogate fuel with a reduced char yield is
proposed here. The distillation residue in this case is determined via the TGA sample residue and the
reduction in char observed by van Rossum et al:
(3.6)
To the author’s knowledge, no data regarding the atomic composition for this high heating rate char is
available. To utilize the TGA residue composition is unlikely to be accurate due to polymerization,
pyrolysis and previous coal studies indicating
heating rates change the atomic balance. However,
due to the lack of available data it is necessary to use
the TGA results for this Low Char Case. Due to this
introduced error the Low Char Case is not be used as
the base Case for the study.
The reduction of the high heating value char in the
surrogate required significant changes to the component
fractions in the distillable phase. Components with
higher heating values were increased in concentration to
make up for the lower char, shown in Table 6. The
entire nitrogen content of the surrogate fuel is found in the char phase and is set by the amount
measured in the bio-oil sample. As this value does not change, the LHV of the char phase is reduced
Table 6 – Low Char Case distillable phase composition
Compound wt%
Glyoxal 11.00
Formaldehyde 11.75
Acetaldehyde 14.00
Methanol 3.00
Water 28.50
Formic Acid 10.35
Phenol 16.00
Total 94.60
Table 7 – Low Char Case char atomic composition
Element wt%
C 3.58
H 0.21
O 1.25
N 0.36
Total 5.40
30
to
as a larger relative percentage of the char is made up of nitrogen. The atomic composition
of the char for this case is presented in Table 7. A summary of the Low Char Case surrogate design
criteria are presented next to those of the original bio-oil in Table 8.
Table 8 – Empirical and model bio-oil properties for the Low Char Case
Property Bio-oil Model
C-H-O-N (wt% wet) 39-7.6-53-0.36 37-7.7-55-0.36
Water (wt%) 28.5 28.5
LHV (MJ/kg) 14.5 14.7
Distillation Residue (wt%) 20.25 5.4
3.2 Bio-oil Combustion
Figure 3 – Bio-oil combustion model design and implementation process.
The complete bio-oil combustion model design and implementation process is illustrated in Figure 3.
The details of the model are described sequentially in the following sections as per the above
diagram.
As discussed in 2.2, bio-oil droplets undergo devolatilization, microexplosion, coalescence, final
devolatilization and burnout. A broad analogy can be drawn between this behaviour and that of coal,
31
which also experiences a devolatilization phase, and then burnout. If a coal combustion model were to
be used, it would neglect the microexplosion, and any possible oscillatory swelling that it undergoes.
The thermochemical effects of microexplosion
during a combustion case have not been studied.
The skin formation around a droplet resists the
release of volatiles until sufficient pressure builds
up to breach the skin. Energy transfer to the droplet
will continue during this time, vaporizing
compounds over a temperature range. Each
microexplosion is likely to release several
vaporized compounds at once, as well as providing secondary atomization. The overall effect of these
two phenomena is that the compounds released are more likely a mixture of boiling points rather than
in the case of pure distillation without microexplosion.
The TGA results of the bio-oil samples showed a large degree of swelling. Additionally, the
cenospheres captured during combustion of bio-oil also indicated a significant degree of swelling.
Some cenospheres captured on the filter were of diameters over five multiples larger than the
estimated average droplet size generated by the fuel nozzle. An example of one of these cenospheres
is presented in Figure 4, showing the unique structure of the cenosphere. The carbonaceous material
captured on the filter during combustion is observed to be in the range of of the original fuel
[15]. Although significant quantities of material may be deposited inside the burner and exhaust
system before the filter, it is inappropriate to draw conclusions regarding the swelling behaviour from
such a small example. The combustion studies of [27] which observed the droplet throughout its
combustion recorded a return of the droplet to its original diameter following microexplosion [27].
Due to the contradictory nature of the evidence regarding swelling, it is not included in the current
model.
Figure 4 – Cenosphere captured by a particulate filter
from 80/20 bio-oil/ethanol blend combustion [60]
32
3.2.1 Distillable Phase
The previous work presented in the literature into modelling bio-oil combustion or evaporation has
favoured kinetic rates of the Arrhenius form. This is in agreement with well-established models used
for coal combustion, incorporated in ANSYS Fluent. Fluent supports three rate models: the constant
rate; the single kinetic rate; two competing rates, which can be used when the devolatilization of a
particle has two distinct regimes. The single kinetic rate was selected for this study based on the
discussion in the previous section, its superior accuracy over the constant rate and TGA
devolatilization behaviour which does not call for the competing rate model.
In the single kinetic rate model the mass-loss behaviour of the particle is described by:
[ ( ) ] (3.7)
where is the initial mass of the droplet (particle), the kinetic rate and the initial mass
fraction of volatiles in the droplet. This model is based on the work by Badzioch and Hawksley and
owes its increased accuracy to accounting for the volatiles remaining in the droplet [59]. The kinetic
rate is of the form:
( ⁄ ) (3.8)
where is the pre-exponential factor, the activation energy, the universal gas constant and the
droplet temperature. is the property that can be manipulated to obtain the desired devolatilization
behaviour.
3.2.1.1 Devolatilization Rate Calibration
In order to determine an appropriate devolatilization rate it is necessary to develop an estimate for the
residence time. Several studies have been carried out into bio-oil droplet combustion but at
significantly larger diameters than is estimated to exist in the studies carried out by Tzanetakis et al.
For this study, the calculation for the mean Sauter mean diameter from [61] was used:
33
, *
( )
+
(
)-( )
(3.9)
where and represent the density and mass flow rate, respectively, of the liquid or air, the
surface tension, the relative air-liquid velocity and the liquid viscosity. For a discussion
regarding the choice of correlation, the reader is referred to [55]. Under Base Case operating
conditions for a 100% bio-oil droplet the correlation generates an SMD of .
In the extensive combustion studies carried out by Wornat et al. droplet diameters of were
studied [27]. The droplets studied by Shaddix and Hardesty were in diameter [19]. However,
the data they collected was used to develop a law analogous to that used for distillable fuels. The
general form for such a relation is:
(3.10)
where is the initial diameter, the combustion rate constant and the time into the droplet`s life.
From their observations, the portion of the residence time during which behaviour is displayed is
very short [19]. However, it was proposed that smaller droplet sizes and higher Reynolds numbers
would increase the proportion of the droplet`s residence time which exists in the regime [19] [62].
Therefore, it was decided to apply the law throughout the devolatilization, microexplosion and
coalescence phases.
A residence time of for a droplet of diameter was observed by [27] of which
was ignition and burnout. The remainder, , was taken up by the devolatilization phase.
Applying the law for both data from [27] and the droplet studied here, the equations simplify to:
(3.11)
34
where the time and diameter refer to either those in this study or [27]. This yields the devolatilization
time of for a diameter of .
It is necessary to discuss the possible errors associated with the estimated residence time. Both studies
used to develop the estimate carried the experiments out under laminar flow conditions. In the burner
that is simulated in this study, convection will play a far more significant role in the transport of both
species and heat to and from the droplet. This may increase the droplet burning rate, reducing the
residence time. However, studies carried out on single droplet combustion of hydrocarbon fuels has
found that the burning rate may increase negligibly with increased turbulence but increasing
turbulence may also periodically extinguish the flame [63]. Once the flame is extinguished,
vaporization is no longer assisted by the flame and the burning rate is reduced. They concluded that
the deviation from the stagnant case was negligible, and thus the use of the laminar rate for this case
is appropriate. The presence of a CH4/O2 pilot flame, and possible exhaust gas recirculation may also
contribute to an increased burning rate, though the experiments of [27] [19] used near-flame
temperatures for the surrounding air. Also, the droplets considered here are significantly smaller than
those used to develop the estimate, though the reduced time for pyrolysis and polymerization may
increase the applicability of the law to this scenario [62]. Video footage and photography of the
bio-oil burner in operation is not able to provide the necessary insight into what the mean residence
time might be. Figure 5 illustrates the confused
situation inside the burner in which it is
impossible to track a single droplet. As a
consequence of this the above method for
providing an estimate for a bio-oil droplet
residence time must be employed here.
Figure 5 – Viewport of bio-oil burner during operation
35
In order to characterize the mass-loss behaviour of the droplet during devolatilization, it is assumed to
behave similarly to a sample undergoing a TGA. The TGA data for the bio-oil used by Tzanetakis
was scaled to the calculated devolatilization time of and a mass of . Figure 6
compares the scaled experimental TGA mass-loss curve to the mass-loss curve for the single kinetic
rate model using the final values for the pre-exponential factor and the activation energy.
Figure 6 – Experimental TGA mass-loss curve for bio-oil normalized to the estimated devolatilization time and
compared with the kinetic model
The first is ignition at the end of which the model and TGA slope, or
, is dissimilar,
which is interesting as the CFD model produced a residence time less than that predicted by the
solution of equation 3.7. This will be due to differences between the model and the TGA in the
transport of heat and mass to and from the droplet.
The calibration of was an iterative process. An initial guess was provided by directly matching the
single kinetic rate model to the TGA curve. This was then used in ANSYS Fluent to determine the
modelled distillation time. If the simulated droplet residence time was either too long or too short,
and from equation 3.8 were adjusted appropriately. This process continued until a satisfactory
0.0E+00
5.0E-11
1.0E-10
1.5E-10
2.0E-10
2.5E-10
3.0E-10
3.5E-10
4.0E-10
4.5E-10
5.0E-10
0.0E+00 2.0E-03 4.0E-03 6.0E-03 8.0E-03 1.0E-02
Par
ticl
e M
ass
(kg)
Elapsed Time (s)
TGA Model
36
residence time was reached. The final pre-exponential factor and activation energy for the bio-oil
model are presented in Table 9, alongside the values corresponding to lignite coal.
Table 9 – Single kinetic rate model constants for lignite coal and bio-oil
Constant Lignite Coal Bio-oil
Pre-exponential Factor 1.8E-07 1.45E+03
Activation Energy 1.17E+08 5.15E+06
The pre-exponential factor has increased by nearly ten orders of magnitude. This is not surprising
when considering the differences between the release of volatiles in a solid porous media and a liquid
droplet which can undergo internal mixing.
3.2.2 Char Phase
The diffusion-limited model was selected for the burnout phase. This model was selected because the
time necessary for burnout has already been estimated (3.2.2.1), and mass consumption data similar
to that collected for the volatile phase was not available for this bio-oil.
The model as used by Fluent is derived from the work of Baum and Street and has the form:
( ) (3.12)
where is the diffusion coefficient of the oxidant in the mixture, the local mass fraction of the
oxidant and the stoichiometric coefficient for the oxidant on a mass basis [64]. The stoichiometry
was given by the atomic composition of the char. is the property that can be adjusted to obtain
the desired burnout time.
3.2.2.1 Burnout Rate Calibration
From [27], approximately of the droplet`s residence time is burnout, thus for this case
of the total . The diffusion coefficient was changed to obtain this desired burnout time in a
37
similar calibration process to that described for the distillable phase. The final values are shown in
Table 10 along with the original values for lignite coal.
Table 10 – Diffusion-limited model constants for lignite coal and bio-oil
Constant Lignite Coal Bio-oil
Diffusion Coefficient 4.0E-5 1.5
Stoichiometric Coefficient 2.67 3.9
Although these values provide the burnout time that is desired, the physical reality of the diffusion
coefficient was lost. The magnitude of the diffusion coefficient of the bio-oil is 37 500 times larger
compared to the lignite coal. One possible cause for this is the error involved in using the 20.25wt%
TGA residual as the quantity of char remaining after devolatilization. Recall that based on the
literature [58], the actual char may be as low as 5.4wt%. If this is the case, the diffusion coefficient
necessary to burn the droplet out in the given time would be considerably lower, though it is not
likely to reduce it to the magnitude of lignite. The remaining difference may be due to the high
porosity of the bio-oil char, illustrated by Figure 4, as burnout is proportional to the particle surface
area.
3.2.3 Bio-oil Gas Phase Reactions
A two mixture fraction PDF non-adiabatic model is used to model the bio-oil, with the volatiles and
char each having a mixture fraction. The char phase is considered the primary fuel following Fluent`s
standard approach for coal modelling. The atomic composition and LHV of the char phase is
sufficient information for Fluent to define an empirical fuel. The secondary stream, the distillable
phase, is defined by setting the proper species compositions. 30 species are included in the
equilibrium calculations, a sufficient number to include the major dissociating species. Fluent selects
the species with the largest molar concentrations to include in the look-up tables. The PDF tables are
defined by 21 mixture fraction and 21 secondary mixture fraction points and 45 enthalpy slices. The
resulting tables possess 19845 points which can be loaded onto a login node at the High Performance
38
Computing Virtual Laboratory (HPCVL) in 0.5-2 hours. A large set of tables can improve accuracy,
but 29 000 point tables can take up to 8 hours or more to load, which is not feasible.
The mixture fractions are introduced into the gas phase by the models described in 3.2.1and 3.2.2.
The single kinetic rate model describing the devolatilization behaviour of the droplet corresponds
with the secondary mixture fraction, or the distillable phase. The diffusion-limited rate controls the
release of the primary mixture fraction, or the char phase. The combustion is then determined based
on the distribution of the two mixture fractions in the gas phase using chemical equilibrium.
3.2.4 Summary
The final model is made of two parts, a single kinetic rate for governing the devolatilization phase,
and a diffusion-limited model to dictate the droplet burnout. The model went through an iterative
process to match as closely as possible, the estimated residence time of . Subsequently, poor
model convergence during cases with particularly long residence times, discussed in 3.3.7, required
that the total droplet lifespan be reduced to . Inputs for the distillable and char phases
provided in this chapter reflect this constraint. Results will be further discussed in 3.4 regarding
specific model accuracy and behaviour, however, there are some questions that emerge because of the
application of this model in a complete turbulent case, rather than an isolated simulation: What is the
dominant fluid mechanic behaviour inside the burner? What are the implications of that behaviour on
the bio-oil model? Are there any trends in the empirical data that could not be explained by bio-oil
combustion which may be because of the fluid mechanical properties of the burner? 3.4 is devoted to
an effort to answer these questions.
39
3.3 CFD Modelling
3.3.1 Domain and Grid Design
The burner setup is illustrated in Figure 7 to the left. Air enters
the system through the air box where resistant heaters add heat
when necessary, and via the swirl generator enter into the burner
itself. The nozzle is a six jet air-blast atomizer with a half-angle
of though visual inspection of photographic and video
footage indicates the effective spray half-angle is in the area of
depending on burner conditions. Immediately below
the nozzle is the CH4/O2 pilot flame mentioned in 3.2, which
provides both a source of ignition and stabilizes the flames. After
the pilot flame the chamber opens into a diffuser section with a
half-angle of to encourage the development of a CTRZ [65].
The remainder of the chamber is a cylinder of diameter
with breaks in the walls to accommodate the viewports (four) and a longitudinal viewport fitted with
a series of sampling ports near the bottom of the burner. The internal geometry of the burner is
illustrated in Figure 8.
In order to reduce computational effort the
viewports identified in Figure 8 are not included
in the model. As a result the burner is divided
into six 60° identical segments. Complete
axisymmetry is not possible due to the six jet
nozzle. The resulting mesh contains 2.8x106
tetrahedral cells generated in ANSYS Modeller.
A very high cell density is necessary near the fuel nozzle in order to resolve the fuel jet and obtain
Figure 7 – Diagram of burner setup
Figure 8 – Burner geometric features (not to scale) [66]
40
satisfactory convergence behaviour, partly contributing to the high element count. Growth functions
were used to smooth the increase in element size away from the nozzle, and control the element size
in the diffuser. Preliminary, non-reacting flows without the energy equation were used to help guide
grid design. The simulations were used in an effort to reduce changes in residuals between two
adjacent cells below 10%. Grid independence was also tested using these simulations. This study is
designed to capture the qualitative trends observed in experiments, so the three grids were tested for
qualitative agreement. The existence of a CTRZ is a key question in this study and thus the grid
independence was checked via the CTRZ shapes and velocity magnitudes between the three grids. It
was found that an increase in grid size did produce slightly different results, detailed in Table 11, but
at an increased cost in computing power. A reduction in grid density introduced significant changes in
the shape of the CTRZ, and thus the current grid was used. It should be noted that the element count
was derived from obtaining good convergence behaviour and residual gradients, thus a reduced grid
size is not actually feasible.
Table 11 – Difference in iteration times for the three grid densities tested, as well as the % change in radial location of the
streamline at the diffuser exit
Grid Type
Seconds per Iteration
per 2 CPUs
% Change in Streamline
Radial Location
Low Density 0.70 21.7
Medium Density (final type used) 1.02 0.0
High Density 1.16 10.3
Grid quality is tested by Modeler’s internal Mesh Metrics: Element Quality, Aspect Ratio, Jacobian
Ratio, Warping Factor, Parallel Deviation Maximum Corner Angle and Skewness. Aspect ratio and
skewness were the most heavily used metrics, though the Element Quality metric is a composite
metric which provides a ratio of volume to edge length which is also useful. The aspect ratio is a
quantification of the degree of stretch an ideal tetrahedral underwent to make the current shape.
Mathematically, it is the ratio of the greatest distance between a cell’s centroid and face centroids to
the shortest distance between cell nodes. This value should generally be kept between 1-10, although
Fluent recommends aspect ratios of less than 5 [67] [68]. Skewness is a measure of the difference in
41
shape between an equilateral tetrahedral and the current cell. For a tetrahedral all angles should be as
close to 60° as possible. Some examples of the grid metrics are displayed in Table 12.
Table 12 – Selection of grid metrics
Minimum Average Maximum Standard Deviation
Element Quality 0.173293 0.843152 0.999986 0.095793
Aspect Ratio 1.159 1.818706 12.531 0.463717
Skewness 2.54E-05 0.218778 0.961179 0.118797
3.3.2 Solver
ANSYS Fluent 6.3.26 is used in this study. A pressure based solver is employed in conjunction with
an implicit formulation. Due to the variation in element sizes a double precision solver is used,
although this requires a greater amount of memory, the increase in accuracy usually justifies the
added demand [69]. Green-Gauss cell based gradient evaluation is used to determine the face scalar
values necessary for identifying the gradient of that particular scalar.
3.3.3 Turbulence Model
Fluent’s RNG k-ε turbulence model is used to model the burner’s internal flow for reasons discussed
in 2.3. The flow is considered to be swirl-dominated, with full buoyancy effects and standard wall
functions. The specific transport equations for and for an incompressible case are:
( )
( )
(
) (3.16)
( )
( )
(
)
( )
(3.17)
where
( ⁄ )
(3.18)
|
|
42
Table 13 – RNG-k-ε transport equation parameter definitions
Parameter Definition
, Inverse effective Prandtl numbers.
Effective viscosity. Modelling the effective viscosity is how the RNG model is
better equipped to deal with low-Reynolds-number and near-wall flows.
Generation of turbulent kinetic energy due to mean velocity gradients.
Generation of turbulent kinetic energy due to buoyancy.
, Source term for turbulent kinetic energy and its dissipation.
Mathematically defined above. The addition to the model which improves
turbulence prediction for strained flows.
Measure of local strain.
, Flow velocities perpendicular and parallel to the gravitational vector, respectively.
Swirl constant (not explicit in equations 3.16-18). 0.07 for 50% and 0.11 for 100%
The PREssure Staggering Option (PRESTO!) is used to discretize the pressure. The method is
analogous to using a staggered mesh in the cases of structured grids. The SIMPLE method is used for
pressure-velocity coupling. Second order upwind discretization is used throughout.
Radiation was not included in the model. The effects of radiation on atomizing air, fuel preheating
and wall temperatures were included as thermal boundary conditions. Energy balances carried out by
Tzanetakis discovered an approximate 10% difference in energy between bio-oil and diesel at the
exhaust heat exchanger [55]. This is largely attributed to the difference in luminosity between bio-oil
and diesel, which can be confirmed by visual inspection. Measured wall temperatures for diesel, bio-
oil and ethanol show the latter two to be cooler, reducing the radiative effects of the walls. Radiation
was therefore not included in the final model.
Table 14 – RNG-k-ε constants
Constant Value
Cμ 0.0845
C1ε 1.42
C2ε 1.68
4.38
β 0.012
Prwall 0.85
Scpdf 0.85
43
3.3.4 Droplet Simulation
Fluent’s Discrete Phase Model (DPM) was used to simulate the fuel jet. Particle boundary conditions
are based on temperature and mass flow rate observations carried out during the experimental tests.
SMDs are estimated as outlined in 3.2. The jet trajectory was determined using manufacturer
literature, physical measurement of the nozzle and video camera footage taken during combustion.
Droplet velocities are estimated to be identical to the atomizing air velocity which is calculated from
the known nozzle orifice diameter. Specific parameters for the DPM can be found in Appendix A –
Varying Case Parameters. The Discrete Random Walk model is used to duplicate the random effects
of turbulent eddies on a droplet. During the simulation the high velocities and small diameters
minimizes this effect. A maximum of 15000 steps for each droplet is allowed, however evaporation or
burnout takes place before this limit is reached. Fluent calculates the number of steps by:
(3.19)
where is the estimated element transit time for the droplet, the time step and the step length
factor which is 12 for bio-oil simulations and 15 for ethanol. The smaller number for bio-oil is to
reduce computational effort. Fluent recommends a step length of 10 or greater. The equations of
motion for the droplet are solved using a trapezoidal (semi-implicit) integration where stable, and an
implicit integration otherwise. A accuracy tolerance of 1E-4 is applied, with Fluent limited to 17 re-
calculations of the integration procedure if the tolerance is exceeded. Upon the 18th try Fluent chooses
the 17th solution as a best guess. The frequency of DPM iterations is reduced as the solution begins to
stabilize until 50 flow iterations per DPM iteration has been reached.
3.3.5 Pilot Flame
The high speed of the pilot flame visibly introduces an asymmetric element to the flame, however this
aspect is impossible to duplicate due to the partial axisymmetric assumption made for the model
44
geometry. The energy added by the pilot flame is modelled as a spherical region immediately below
and encapsulating the fuel nozzle with a constant volumetric heating rate.
3.3.6 Boundary Conditions
The fuel jet is divided into a particle injection for fuel and a mass flow inlet at the mesh wall for
atomizing air. The swirl block boundary is defined as a velocity inlet. Air volumetric flow rates
measured during the experiments dictate the bulk axial flow rate, while the mean angular velocity is a
result of the swirl number corresponding to each test. The literature [70] suggests that the velocity
profiles across the exit of a swirl burner are not uniform in axial or angular velocity. Non-
dimensionalized velocity profiles from a moveable-block swirl generator study was used to create
appropriate axial and tangential velocity profiles based on the mean values [70]. The data used and
the fitted curves are shown in Figure 9 and Figure 10 for axial and tangential velocities, respectively,
for the Base Case. The burner exhaust was modelled as a pressure outlet.
Figure 9 – Non-dimensionalized axial velocity measurements for a movable block swirl burner from
[70] and the fitted equation used for a primary air boundary condition for the bio-oil base case.
R² = 0.9981
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.00 0.02 0.04 0.06
Axia
l V
eloci
ty (
m/s
)
Radius (m)
Leuckel
Fit
45
Figure 10 – Non-dimensionalized tangential velocity measurements for a movable block swirl burner
from [70] and the fitted equation used for a primary air boundary condition for the bio-oil base case
Temperature profiles were measured along the outside of the burner for bio-oil combustion at the
Base Case. This temperature profile is used for all bio-oil combustion simulations. Similar profiles
were measured for both 100% and 50% swirl ethanol experiments. The thin walls of the burner result
in a negligible temperature gradient between the inside and outside surfaces of the burner. Curves
fitted to that data is used for those simulations. The remaining geometric features and boundary
conditions, such as primary inlet air, use the temperatures listed in Appendix B – measured by
thermocouples attached to, or inside, the burner.
An elastic boundary condition was used to control the collision behaviour between droplet and wall
throughout. Ideally, the Weber number, , should be used to determine the collision behaviour:
(3.20)
where is the velocity normal to the boundary, the droplet diameter, the liquid density and
the surface tension. The first two values are known at the time of collision, but no data exists
regarding the last two during the combustion process, making the use of the Weber number
R² = 0.9975
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.00 0.02 0.04 0.06
Tan
gen
tial
Vel
oci
ty (
m/s
)
Radius (m)
Leuckel
Fit
46
impossible in this scenario. In its place a 60% retention in normal and 80% in tangential momentum
is employed based on video footage.
Figure 11 – Measured wall temperature profile along the burner for bio-oil and the fitted curves used as boundary
conditions in the simulations for bio-oil (the termination of the diffuser is considered the origin)
3.3.7 Simulation Procedure
All simulations were initialized at the primary air inlet temperature and no flow. The solution is then
found for the flow driven by the primary air only to a convergence on 10-3
. It is also necessary in the
case of a swirling flow to check the progression of the tangential velocity profile. The nature of the
solver causes the axial velocities to transit the entire domain in a considerably shorter time than the
tangential velocities. Not providing enough iterations for the completion of this progression will
create an inaccurate solution due to the hysteresis effect of the non-physical flow pattern.
The pilot is then turned on and the fuel released into the domain via a single DPM and flow iteration.
The DPM frequency is then reduced to 25 until the flame is stable after which one DPM iteration is
called every 50 flow iterations. After the solution is showing a confortable degree of stability and the
residuals are between 10-2
and 10-3
or less, the atomizing air is gradually introduce. It is extremely
0
100
200
300
400
500
600
700
800
900
-0.1 0.1 0.3 0.5 0.7 0.9
Tem
per
atu
re (
K)
Altitude (m)
Chamber Upper
Diffuser
Chamber lower
Fit - Upper Chamber
Fit - Diffuser
Fit - Lower Chamber
47
easy to blow out the ethanol flame because of the selection of the lowest of the finite-rate and eddy-
diffusivity terms so the introduction of atomization has to be carried out gradually. The discretization
order of the residuals is increased to 2nd
order upwinding schemes.
Default criteria for convergence in Fluent is considered to be 10-4
for all residuals except energy,
which is preferred to be below10-6
. A residual is defined by Fluent as:
∑ |∑
|
(3.21)
This is a measure of the inequality of the equation:
∑
(3.22)
where ∑
(3.23)
is the residual of interest, the coefficient for the element, the coefficient for the
neighbouring cells. The residual limits presented above refer to scaled residuals. The motivation
behind the use of scaled residuals is to place the residual value into context. The aggregate nature of
the residual can make identification of convergence difficult in some cases. Thus a scaled residual can
be used:
∑ |∑ |
∑ | | (3.24)
The continuity residual varies from this approach by being scaled by the largest absolute value
residual from the first five iterations of the simulation. Due to this, the scaled residual for continuity
may not be appropriate for judging convergence if the initial guess was good. This is the case with
most of the tests considered in this study as the iterations were started from previous solutions.
Indeed, the residuals can be excellent tools for monitoring convergence, but it is the items of interest
in any particular study which should dictate when convergence has been achieved.
48
The grid was designed using cold flow solutions and preliminary
ethanol combustion. As a result, the long residence times that were
necessary for bio-oil were not considered during the design process.
In particular, grid densities were lowered below the diffuser and
along the center axis due to low anticipated recirculating velocities.
The consequence of this is poor convergence behaviour for all bio-oil
tests below residuals of 10-3
. Examples of regions where large mass
imbalances exist are shown in Figure 12. The residuals used in this
study were therefore adjusted appropriately in order to ensure a
comparable degree of accuracy between tests.
Due to the size of the grid it can take up to eight hours for ANSYS
Modeller to calculate a new mesh. Therefore, the grid design process
is quite lengthy and a new iteration consumes several days. A redesign of the mesh, including testing,
would have taken considerable time away from the bio-oil model development during an advanced
stage of the project. The bio-oil model is the primary goal of this work, thus the necessary
modifications were made to complete the study with the current grid.
A second change was a limit on the residence times as longer residence times were creating larger
instabilities as the droplets progressed further down the burner. The Base Case average residence
times had to be reduced from to to ensure tests with longer residence times (Low
Atomizing Air) would be capable of converging. This was done by increasing the aggressiveness of
the devolatilization model and the diffusion coefficient for the char burnout.
The simulations are run in parallel on the HPCVL Enterprise M9000 servers. These are shared-
memory servers with 8TBytes of memory [71]. This is useful here as the large memory requirements
of the mesh and droplet tracking exceeded the capabilities of a 64-bit desktop. Each simulation
Figure 12 – Some regions of mass
imbalance calculated by ANSYS
Fluent during the solution
procedure.
49
utilizes 32 processors and can take an average of 9-30 seconds per iteration depending on the case
under consideration and the temperature range the PDF must span.
50
3.4 Flow Validation
Turbulence modelling is an extremely complex task, without including combustion. Many deviations
from the physical situation are possible due to the assumed boundary and symmetry conditions, mesh
quality, model choice and many other factors. The most popular turbulence models are the RANS
models discussed earlier. These models make an explicit assumption as to the character of the
turbulence being modelled. Because of this, important features such as radial pressure gradients,
severe mixing via turbulent eddies and reversed flow can be altered or lost completely. In order to
certify the validity of the model it is necessary to compare the results with known physical cases.
The flow patterns inside the burner being studied are extremely complex. Included in this problem
are high speed jets, a highly swirling bulk flow, possible recirculation and large density changes. In
order to provide some validation material for the modeling of the burner, and shed further light on the
experimental results, a probe is used for taking measurements inside the burner.
Previous work by the research group discovered that the dynamic pressures within the burner were
low enough to be in the same order of magnitude as the measurement error, making direct
measurement of local velocities impractical. Another alternative was to measure gaseous species
concentrations at different locations within the burner. This can be achieved using FTIR (Fourier
Transform Infrared) spectroscopy to measure CO2 concentrations. The high burn-out efficiency of
ethanol results in CO and UHCs (Unburned Hydrocarbons) in the parts per million range making it an
ideal fuel for this purpose. The error due to the calibration of the FTIR was 0.3% and thus for CO or
UHCs to be significant they would have had to add up to 3000ppm, which was an order of magnitude
higher than the peak values measured with the probe. Thus, the experimental CO2 readings could be
compared directly to model results.
51
During axial flow with 0% swirl the exhaust gases
from the ethanol flames would be carried down the
burner chamber. This scenario would include high
CO2 gradients below the flames and regions devoid of
CO2 at the center of the burner. Once swirl is
introduced, regions of recirculated mass appear with
increased mixing, resulting in higher CO2
concentrations and no areas of 0% CO2 [65]. The
explanation for this is that as tangential momentum is
added to a flow, radial pressure gradients increase.
Essentially, the fluid wants to move out from the centre of rotation. As shown in Figure 13, these
radial pressure gradients generate a low axial pressure zone which then draws material back up into a
CTRZ. The transition between a recirculating and a non-recirculating flow generally occurs in the
swirl number range of 0.4-0.6 [65] [72]. The swirl number is a non-dimensional measure of the
degree to which tangential momentum is dominating the flow. Mathematically, the swirl number is
the ratio of the axial flux of tangential momentum to the axial flux of axial momentum [65]:
(3.25)
∫
(3.26)
∫
∫
(3.27)
where and are the axial fluxes of tangential and axial momentum, respectively. and are the
axial and tangential velocity components, the radius of the inlet, or containing vessel, and the
static pressure. Presented in this way the swirl number is conserved, although in some cases the
pressure term may be neglected which produces a varying swirl number. The swirl can also be
Figure 13 – Comparison of a mildly swirling flow
with no CTRZ and a swirl flow with a CTRZ [66]
52
described in terms of percentage based on a particular geometry, where 100% swirl corresponds to a
swirl number of 5.4 for the movable block generator used on this burner. This is the maximum
amount of swirl possible for this given geometry. Detailed information on the derivation of a swirl
number for a movable block swirl generator is available here [55].
Swirl can be an important issue because it is often used to generate a CTRZ to improve the
combustion properties of a flame. If a recirculation zone forms then exhaust gases will be returned
back to the flame which can aid in ignition, help stabilize the flame and complete the combustion of
any unburned species [65]. Mixing of the fuel and oxidizer is also improved by the turbulence
introduced by swirl [65]. Due to the impact swirl can have on a flame, it is important to be able to
characterize any recirculation. If it is not known whether a CTRZ exists then it is difficult to attribute
combustion characteristics specifically to bio-oil combustion behaviour. Developing a probe to
determine the CO2 profile across the burner radius is an effort to answer the question of whether or
not swirl is present. The tests are simulated to indicate whether or not the turbulence model can
duplicate the fluid mechanics of the burner, and possibly shed further light on the flow.
53
3.4.1 Experimental Setup
Figure 14 – Schematic of probe setup
3.4.1.1 Sampling Probe
The probe is designed to
minimize the invasive nature of
the sampling. Two main issues
due to the probe’s presence in the
burner are the fluid mechanical
effect of the probe on the internal
flow, and the volume of material
the probe is removing from the
burner, discussed in 3.4.1.6.
To minimize the drag due to the probe, diameter tubing with an internal diameter of
is used for the horizontal sliders and vertical segment. The first probe used tubing with a
larger internal diameter, however this proved to be undesirable due to the low rigidity of the tubing.
Figure 15 – Visualization of the probe within the burner chamber
54
The cutting and installation process easily introduced deflections in the tube which hampered the
probe in its translation across the burner radius. The possibility also existed for further deflection
during operation due to sticky sliding and elevated temperatures which would make identifying the
location of the probe tip difficult. The second probe was constructed with thicker walls that proved
easier to use and resisted bending.
The probe consists of two horizontal segments and one vertical segment. The two horizontal segments
of and were designed to allow the vertical segment to scan the
diameter of the burner. As shown in Figure 15, the horizontal tubing is required to span the burner
diameter as well as the ports on either side, a total distance of . One of the segments is
pinched on one end as it does not carry the sample. The pinch was leak tested with compressed
building air to ensure its effectiveness.
Preliminary model results indicated that close to the diffuser exit the gradients in CO2 were
significant, and the possibility existed that recirculated exhaust gas could be identified. Higher in the
diffuser the concentrations would be stronger as the probe nears the fuel inlet, however there are
several problems with increasing the probe altitude. Visually inspecting the flame length indicates
that much of the chemistry was still ongoing within the diffuser. In the case of low swirl the flames
extend outside the diffuser. Not allowing for complete or close to complete combustion before
sampling will lower the levels of CO2 and possibly cause clogging problems if droplets enter the
probe. A Flame Ionization Detector (FID) unit was available but using both the FID and the FTIR to
calculate the mixture fraction would introduce errors due to the different flow rates. A test conducted
with the sample taken beneath a long flame during a poor burning case experienced a maximum
concentration of UHCs of 300ppm, or 0.03%. This was considered negligible compared to 6-10%
CO2 measured via FTIR spectroscopy.
55
In order to control the probe location two rails were constructed from slotted steel visible in Figure 16
and Figure 17. One rail provides support when the probe arm is projecting far out from the burner. A
second rail is placed under the probe angle control arm to ensure the probe angle remains unchanged
as the probe transits the burner radius. The control arm is designed to control and measure the angle
Figure 16 – In situ probe arrangement
Figure 17 – Probe setup and insertion at the burner flange
of the probe inside the burner. When assembled the angle between the vertical probe segment and the
control arm was set at 90°. Measuring any deviation from the horizontal position of the control arm
provided the probe’s angle away from the vertical axis of the burner. Due to time constraints this
feature of the probe was never used for an official test.
56
3.4.1.2 Sample Line
The sample line following the probe is heated using a -wide McMaster-Carr 288W heating
tape to prevent condensation. A needle valve is placed at the termination of this segment of the line
followed by a tee connection. One branch continues on to the oxygen sensor and FTIR in another
heated line as described in Figure 14. The second branch of the tee leads to a tap in the exhaust line in
order to sample the fully-mixed exhaust as shown in Figure 18. A second needle valve is located
immediately before the oxygen sensor and FTIR unit.
Figure 18 – Connections of the exhaust and probe sample lines
3.4.1.3 Oxygen Sensor
An ECM (Engineering Control and Monitoring) model OXY6200 (ZrO2) Zirconia oxygen sensor was
placed immediately after the 2nd
needle valve. The oxygen sensor was calibrated for equivalence
ratios of 0.3-1 which were expected in the region of interest. The line split at this point so that the
sample could continue on to the FTIR system while the oxygen sensor was running when sample flow
rates were sufficiently high.
3.4.1.4 FTIR
A Nicolet 380 FTIR was used to measure the concentration of CO2 in the sample. The FTIR scans in
the range of 500-4000cm-1
, or the mid-infrared region. The unit compares the absorbance spectrum
for a gas sample with a set of characterized spectra. Eight spectra for equivalence ratios of 0.3-1 are
57
used as the standard spectra in this case. Spectra consisted of 24 scans taken over one minute at a
resolution of 1cm-1
.
A classical least squares (CLS) model was employed to analyze the spectra. The error for the
calibration is 0.3% based on the eight spectra used, which is considered insignificant.
3.4.1.5 Pump
A Varian IDP3 vacuum pump is used to place the FTIR and sample line under vacuum. A rotameter
measured the flow rate at the exhaust.
3.4.1.6 Ethanol Combustion Chemistry
The finite-rate/eddy-dissipation model is used to simulate the ethanol tests. Test results are compared
with CO2 percentages measured within the burner, which FID measurements show to be nearly 100%
conversion. It is not necessary to model dissociation for comparison with the probe, thus the finite-
rate/eddy-dissipation model was selected because of its reduced computational effort.
When in the finite-rate regime the conversion of ethanol to CO2 is modelled by a single-step reaction
with a kinetic rate with a pre-exponential factor of 8.435E9 and an activation energy of 1.256E8
.
Fluent’s standard mixing parameters are used for when the reaction takes place in the eddy-
dissipation regime.
3.4.2 Experimental Methodology
Two tests were completed to provide CO2 profiles across one radius of the burner. Five data points
were taken for each test. The two tests chosen were 100% swirl and 100% pilot flame (1st tests) and
50% swirl and 75% pilot flame (2nd
test). Identifying the change in flow conditions when swirl was
introduced was key to this study. 100% and 0% swirl would have been the ideal points to consider in
this case but stability issues make this impossible. Below 50% swirl the flames become unstable,
58
frequently experiencing a blow-out. It is likely that such behaviour would compromise any CO2
sampling, eliminating from possible study any operating points below 50%.
The pilot flame has a significant impact on the flame characteristics as it imparts considerable radial
momentum to the flow immediately below the nozzle in an extremely non-axisymmetric way.
Eliminating this effect is of great interest but introduces instabilities similar to the low swirl cases.
75% pilot flame is used because it is able to maintain a stable flame at 50% swirl while providing
75% of the base case energy but only 56.25% of the momentum.
Prior to each test the peristaltic fuel pump flow rate is calibrated and atmospheric air drawn into the
burner to allow the oxygen sensor to reach equilibrium and settle on a base voltage. The sample line
is attached to the exhaust of the burner to begin each test as shown in Figure 18. This is to ensure the
oxygen sensor would be sampling fully-mixed exhaust gases once the test began.
Once the oxygen sensor was getting a stable reading the CH4/O2 pilot flame is ignited and the fuel
pump turned on. The ethanol flame is then ignited and allowed to burn for 10-15min after which the
fan is varied according to the oxygen sensor to provide an equivalence ratio of 0.6. This warm-up
phase continues until at least 20min from ignition and the thermocouple reading for a burner flange is
in excess of 300°C. During this time the background spectrum for the FTIR are taken.
After the warm-up period the diagnostics sample line is switched to the probe and the oxygen sensor
turned off and completely disconnected from the sample line. Utilizing the 1st and 2
nd needle valves
the pressure in the FTIR cell was maintained at 86.3kPa while minimizing the flow rate through the
probe. The temperature of the cell was kept between 115°C and 120°C.
59
Figure 19 – CO2 response time at each data point for 100% swirl and 100% pilot flame
At the beginning of the test the probe was placed at “Point 1” at the wall of the burner. The remaining
four points are evenly distributed between this point and the centre of the burner (Point 5). The
response of the probe over time at each point during the two tests is illustrated in Figure 19 and
Figure 20. Point 1 samples for an extended period of time because of the wait necessary for the FTIR
cell to reach the necessary pressure. Due to this, in the 1st test the values for Point 1 as a function of
time are not recorded, however they are recorded for the 2nd
test as shown in Figure 20. The stability
of the 2nd
test Point 1 sample confirmed that the sample taken in the 1st test is stable as well.
0
1
2
3
4
5
6
7
8
9
10
0 5 10 15 20
CO
2 [
%]
Time From Probe Move [min]
Point 2
Point 3
Point 4
Point 5
60
Figure 20 – CO2 response time after probe move at each data point for 50% swirl and 75% pilot flame
Once a spectrum is taken the probe is then moved to the next point. The moment the probe is in the
next position the timer is reset and spectra taken every two minutes. The exception to this is Point 3
during the 2nd
test. No spectra exist for 10 and 12 minutes after the probe move as the pressure in the
cell moved from 86.3kPa and had to be corrected in order for the spectra to be valid.
Sample flow rates remain fairly constant during testing. For
the 1st test the flow rates remained between 47.72 and
53sccm while during the 2nd
tests those values were 41.92
and 46.67sccm.
To determine the angular location of the probe in relation to
the nozzle a borescope image is taken of the probe during
combustion, as shown in Figure 21. The angle between the
probe and the flames can be measured to identify where the
0
1
2
3
4
5
6
7
8
9
10
0 5 10 15 20
CO
2 [
%]
Time From Probe Move [min]
Point 1
Point 2
Point 3
Point 4
Point 5
Figure 21 – Borescope image looking up at
the nozzle showing the probe, pilot flame and
ethanol flames.
61
probe is sampling. Due possible inaccuracies in the borescope placement, camera focus and complex
lighting, the estimated angle between the flame axes and the probe is not likely to be more accurate
than .
62
Chapter 4 Results and Discussion
The CO2 probe successfully measured profiles across the burner for 100% swirl and pilot flame and
50% swirl with 75% pilot flame. These were then compared with CFD simulations to test the validity
of the RNG k-ε model for use in this highly-swirling turbulent case, and possibly shed additional light
on the nature of the flow. Unfortunately, the model proved unable to duplicate the experimental
results. Many different approaches were taken in an effort to produce similar CO2 profiles, without
success. The CFD simulation was hoped to shed light on some of the trends observed by Tzanetakis
et al, but this is no longer possible. As such, the ethanol experimental results and CFD simulations are
discussed first before moving on to the bio-oil combustion model.
4.1 Fluid Mechanics Analysis
4.1.1 Experimental CO2 Measurements
The CO2 profile measured by the probe in the burner for 100% swirl and pilot flame (1st Test) is
reproduced in Figure 22, below. The measurements were taken on a radial profile just over
below the diffuser exit. The errors bars on the data points on Figure 22 and Figure 23 are the error
associated with the FTIR method for interpreting the collected spectra. The values are an average of
the last three samples taken at that point and so the standard deviation for these averages has also
been included in the error bars.
The ethanol flame is anchored directly to the atomizer jet and combusts rapidly. The low FID reading
and visual inspection of the flame both indicate that the combustion for both cases is complete by the
time the flow reaches the probe altitude. It is possible that fuel-derived compounds not detected by
the FID may still be present at the probe location near the wall in the wake of the flame. The final
FTIR model used was derived specifically for CO2 cannot shed any light on this. However,
preliminary tests carried out before the new model was introduced measured negligible quantities of
63
formaldehyde and the formyl radical. These tests were carried out before the final test procedures
where finalized and suffer from higher flow rates, reducing measurement resolution. They do imply
complete, or near-to-complete combustion, however.
Figure 22 – CO2 profile across the burner for 100% swirl and 100% pilot flame
Also present in the figure is the percentage of CO2 assuming complete mixing. A considerable
amount of CO2 is present at the centreline of the burner. This indicates that one of two possible flow
patterns exist within the burner. Rapid and thorough mixing of the incoming primary air, atomizing
air and flame products could produce this result. This would imply that there is no recirculation
present in the burner as the products must be transported down and in towards the axis of the burner.
The higher than fully-mixed percentages are likely because of the probe sampling in the wake of a
flame. There is a slight increase in CO2 towards into the burner, but otherwise the profile is
essentially flat across the burner.
The second possible explanation for the existence of CO2 at the centreline is a recirculation of exhaust
products. As discussed in section 3.4, the introduction of swirl to a flow can trigger the recirculation
of mass towards the flame. If this is taking place then exhaust products, such as CO2 would travel up
0
1
2
3
4
5
6
7
8
9
10
0.00 2.00 4.00 6.00 8.00 10.00
CO
2 [
%]
Distance from right edge of burner [cm]
Experimental
Fully-Mixed
BU
RN
ER C
ENTER
LINE
64
the centreline towards the flame. Due to the flat nature of the profile it is impossible to choose
between the two possibilities with any certainty based on the one test.
The results for the 50% swirl 75% pilot flame (2nd
Test), along with the simulation results are
presented in Figure 23.
Figure 23 – CO2 profile across the burner for 50% swirl and 75% pilot flame
Again, the CO2 percentages exceed the fully-mixed value, indicating that the probe is still in the wake
of a flame even with lower swirl. The well-mixed level will vary slightly between the two tests as the
fuel flow rate differed by 0.05ml/min. The percent CO2 at Point 1 for both tests was nearly identical
at 8.06% and 8.07% for the 1st Test and 2
nd Test, respectively. Point 2 was within identical within two
significant digits, however the levels of CO2 rose higher in the 2nd
Test for all the remaining points.
The two datasets are still within the uncertainty but the 2nd
Test clearly trends higher. The flame wake
with 100% swirl was likely just past the probe, which migrated counter to the direction of rotation
with reduced swirl, bringing the probe deeper into the wake. Significantly, the levels of CO2 present
at the centreline are higher than those of the higher swirl test: 0.41% and 0.17% higher at Point 4 and
Point 5, respectively. The implication of this observation is that it is unlikely that recirculation is the
0
1
2
3
4
5
6
7
8
9
10
0.00 2.00 4.00 6.00 8.00 10.00
CO
2 [
%]
Distance from right edge of burner [cm]
Experimental
Fully-mixed
BU
RN
ER C
ENTER
LINE
65
source of the CO2 at the center of the burner. With a reduction in 50% of the swirl, the CTRZ should
have been significantly reduced in strength, or lost completely. CO2 levels should decrease with a
weaker CTRZ. Additionally, as discussed earlier, if no recirculation is present then there should only
be uncombusted air passing through the centreline. In this case, CO2 levels have remained the same
on the centreline between 100% and 50% swirl, discounting the likelihood of a CTRZ, but neither
does it agree with the alternative originally discussed. That is, where no CO2 should be present as
exhaust products are carried to the outer radii by the droplet trajectories and atomizing air and carried
straight down the burner, leaving only unreacted air at the centerline. Therefore, it is likely that the
third possibility applies here, where fast mixing is occurring between the primary air passing down
the burner past the nozzle and down the centreline with products from droplet combustion close to the
pilot flame.
Another possibility that would not be captured by the model is an asymmetric effect introduced by the
pilot flame. The pilot flame introduces a considerable amount of radial momentum, offset from the
probe by approximately 40°. This may introduce horizontal recirculation, causing the uniform CO2
levels across the burner. The author does not believe this is as likely as a CTRZ or fast axial mixing
due to the similarities between the 100% and 75% pilot flame tests. The 25% reduction in energy
resulted in a drop in momentum from the pilot flame down to 56.25%. The author feels that if the
high amounts of CO2 present in the centerline were due to horizontal recirculation from the pilot
flame then a greater change in measured CO2 would have been observed between the two tests.
Unfortunately, as 0% swirl is unstable that case could not be studied. 0% swirl would have been able
to prove conclusively whether CO2 at the centreline was due to recirculation or heavy mixing. Also,
the test results do include some uncertainty that may lead to the similarity in the data collected
between the two tests. An effort was made to utilize the lowest flow rates possible within the
constraints of the sampling system (FTIR pressures), but probe tip bulk velocities exceeded
for all tests. Velocities within the burner will be much larger than this value, but if there is any
66
recirculation then the probe velocities will be much larger than burner velocities near the
streamline. Therefore, the possibility still exists for the contamination of probe samples between one
point to another. The probe may also be removing sufficient matter to alter the flow patterns within
the burner. The complexity of the fluid flow inside the burner, as well as the changing burner
operation points make the use of an isokinetic probe impossible. The reduction in sampling flow rates
is an effort to reduce the impact of this fact. Any conclusions drawn from the data collected from this
probe must consider these possible issues.
In the case of both experiments, the model is unable to quantitatively or qualitatively capture the
experimental trends, which will be discussed in 4.1.2.
4.1.2 CFD Simulation
4.1.2.1 Simulation Results
Figure 24 – Simulation and experimental comparison of %CO2 for 100% swirl. The simulated probe measurements are
shown along with those for radii to either side of the actual probe.
Figure 24 presents the CO2 profiles taken during the experimental work with the probe, and the
accompanying simulation. The CO2 profiles for 5° to either side of the calculated probe position are
also shown in case the estimation of the probe position is incorrect, and there may be better
0
2
4
6
8
10
12
14
0.00 2.00 4.00 6.00 8.00 10.00
CO
2 [
%]
Distance from right edge of burner [cm]
ExperimentalFully-MixedModelProbe -5Probe +5
BU
RN
ER C
ENTER
LINE
67
agreement with the experiment. Figure 26 illustrates the probe location, and the 5° offset to either
side. However, the simulation did not quantitatively or qualitatively agree with the probe
measurements. Very high levels of CO2 are predicted at the outer radius of the burner, while
uncombusted air is predicted in the center core of the burner, displayed as a contour in Figure 26. The
low CO2 levels are axisymmetric, and thus the high center CO2 readings taken during the experiment
could not be due to the angle of the probe.
The ethanol contour plot presented in Figure 25 can partly explain the higher levels of exhaust
products predicted in the outer radii. There remains a large amount of ethanol in the sampling plane,
indicating that the chemistry is not yet complete. This is due to the long droplet residence time of
. The droplets reach an average distance of below the diffuser before evaporating
completely. This is below the sampling plane, and in disagreement with the FID testing
carried out during the experiments which indicated negligible quantities of UHCs. During FID
sampling the probe was used to scan tangentially as well as radially, so the probe would not have
missed the locations with ethanol. Therefore, the combustion products have less time to mix in the
model, resulting in higher local levels of CO2.
Figure 25 – Modelled ethanol molar percentages in the
sampling plane for 100% swirl, 100% pilot flame.
Figure 26 – Modelled CO2 contour in the probe sampling
plane for 100% swirl, 100% pilot. Also shown is the probe
radii .
68
The flame is not anchored to the nozzle as well as in the experiments. This can be explained by the
possible absence of recirculation in the simulation. Although the experimental results do not
conclusively prove the existence of a recirculation zone, it would significantly improve both the
anchoring of the flame, and the stability of the flame. This is confirmed by simulations discussed in
4.1.2.2. Instead of recirculation, the atomizing air is proceeding down the vertical axis of the burner,
lowering the concentration of CO2 in that region. This is surprising considering the momentum of the
axial jet, and is discussed further in 4.1.2.2.
Other possibilities for the retarded ignition are the absence of a radiation model and partial
vaporization of ethanol in the atomizer. Although the radiative properties of ethanol are not as
considerable as diesel, resulting lower wall temperatures, the additional heat transfer to the droplets
was not included in the model. Occasional blow-off occurring during ethanol tests, and the anchoring
of the flame immediately at the atomizer orifice imply that a fraction of the ethanol is being vaporized
within the atomizer. This effect allows for the immediate mixing of fuel with air without the
absorption of heat for vaporization. Though the model is capable of duplicating this boundary
condition there is no way of predicting what the fraction of vaporized ethanol might be.
The predicted CO2 profile for the 50% swirl 75% pilot flame case is similar to that of the 100% case,
shown in Figure 27. The predicted peak CO2 is again in the outer radii, as with the 100% swirl case.
The profiles to either side of the probe vary less for the 50% swirl case than the 100%. This may
be due to the difference in residence times and evaporation altitudes. For the 2nd
Test the residence
time was reduced from that in the 100% swirl down to from . The droplets complete
their evaporation higher in the burner as well. In this case the average droplet has completely
vaporized below the diffuser. While this is still below the probe sampling height, the more
rapidly evaporating droplets may allow the CO2 gradients to decrease more than in the case for 100%
swirl.
69
Figure 27 – Simulation and experimental comparison of %CO2 for 50% swirl. The simulated probe measurements are shown
along with those for radii to either side of the actual probe.
The profile is also level for a larger portion of the burner radius than for 100% swirl. This may be due
to the decrease in swirl reducing the radial pressure gradients, minimizing the tendency for material to
accumulate closer to the wall. The predicted profile is also in agreement with the measured CO2
values for a third of the radius, from Point 2 to Point 3. With consideration given to the potential for
error in the sampling system due to the flow rates used and the non-isokinetic conditions, the
simulation could be predicting the experimental conditions. However, the velocities in the center
region are in excess of those inside the probe tip, therefore the low CO2 measurements should have
been captured if they are real. The flow solution is discussed in greater detail in 4.1.2.2.
0
2
4
6
8
10
12
14
0.00 2.00 4.00 6.00 8.00 10.00
CO
2 [
%]
Distance from right edge of burner [cm]
ExperimentalFully-mixedModelProbe -5Probe +5
BU
RN
ER C
ENTER
LINE
70
Figure 28 – Comparison of the velocity fields (in-plane) for the 100% and 50% swirl cases for complete cases, including
pilot flame, atomizing air and combustion.
The difference in residence times between the 100% and 50% swirl cases is difficult to explain.
Referring to the velocity fields for both cases, shown in Figure 28, no major differences in flow
patterns can be found between the two in the axial plane. It is significant that the RNG k-ε turbulence
model generated very similar solutions for the two cases when the input swirl differs so greatly. In
addition to the similarities, only 75% of the pilot flame energy is being inputted, reducing energy
available for evaporation of the droplet. The 50% swirl case experiences fewer regions of peak
temperatures, displaying a more even heat release in Figure 29 and Figure 30 in the jet plane.
Temperature contour plots for downwind of the jet plane are shown in Figure 31 for both
simulations. A greater number of high temperature regions are present in the 100% swirl while the
50% swirl has only increased slightly.
71
Figure 31 – Temperature contours for 100% (left) and 50% (right) simulations downwind of the jet plane.
Figure 29 – Temperature contour in-plane with fuel
jets for 100% swirl, 100% pilot flame
Figure 30 – Temperature contour in-plane with fuel
jets for 50% swirl, 75% pilot flame
72
The ethanol entering the vapour phase in the 100% swirl simulation is being rapidly carried
downwind of the droplet where it is reacting with the air. This reduces the density of the heat released
in the area immediately surrounding the droplet. This effect is reduced in the case of 50% swirl, hence
the shorter residence time for the case with half the swirl.
Approximately one third of the way down the burner chamber a heart-shaped zone of cooler
temperatures is formed in both tests. This is an annular recirculation zone ringing the central core.
The 100% swirl test experiences stronger recirculation in this region, bringing a greater quantity of
cooler air up the burner. This is the most explicit example of a difference in swirl intensities between
the two tests.
The literature predicted that a CTRZ would develop in the burner due to the high swirl and diffuser.
In an effort to verify this two experiments at two different swirl numbers is used beside numerical
simulations of those experiments. The simulations predicted considerable recirculation, but not
centrally located. Two annular recirculation regions formed around the diffuser exit, and surrounding
the atomizing air jet. Both of these recirculation regions, along with several other minor regions,
return exhaust products to the flame increasing CO2 levels, but not to the extent observed during the
experiments. The flat CO2 profiles and flames anchored to the nozzle are two features the model
failed to duplicate. Section 4.1.2.2 is a discussion of the model’s performance.
4.1.2.2 RNG k-ε Failure
The simulations run using the RNG k-ε model were not able to duplicate the CO2 levels found in the
experiment. An extensive series of tests was carried out in an attempt to obtain a solution which
agreed with the experimental results.
The similarity in centreline CO2 mole fractions between the 100% and 50% swirl cases imply that
mixing, and not recirculation is the cause. However, the literature overwhelmingly points to the likely
existence of a recirculation zone in a case with such a high swirl (S=5.4) and a conic rapid expansion
73
(diffuser) [65] [70]. Therefore, the focus of the CFD testing on the two ethanol cases was to generate
a stable recirculation zone. Consideration was also given to solutions where rapid mixing capable of
explaining the experimental results existed.
Three methods of preparing the flow solution for combustion are used. The first is described in 3.3.7
where the pilot flame and combustion are introduced concurrently, and the atomizing air is introduced
gradually after combustion has been established. The next method is to introduce the pilot flame
during the flow solution to take into account the buoyancy effects from the high temperature flame.
The final approach requires the solution of the entire flow including the atomizing air prior to the
introduction of combustion. The solution for the second is shown in Figure 32 immediately before the
introduction of the fuel droplets. The swirling flow creates a CTRZ as expected. Interestingly, the
introduction of the pilot flame has caused the recirculated mass along the centreline to increase
in speed and squeeze the diameter of the CTRZ in the diffuser region. An annular recirculation zone
has also formed in the elbow of the diffuser. This second recirculation zone is not present in any of
the other flow solutions before combustion. With combustion this annular recirculation zone
Figure 32 – Diffuser section vector plot of the 100% swirl ethanol simulation with pilot flame and no atomizing air
immediately before the addition of combustion
74
disappears and the CTRZ widens and shortens in length. The vector plot for an established ethanol
flame is shown in Figure 33, illustrating this point. The recirculation is stabilizing the flame and
providing an ignition source, as expected from the literature.
Figure 33 – Diffuser section vector plot of the 100% swirl ethanol simulation prior to the addition of atomizing air. The high
speed jets are due to drag from the droplets and density changes from heat release.
Figure 34 illustrates the effect of the atomizing air on the flow patterns inside the burner. Atomizing
air contributes approximately only 10% of the total flow through the burner but has a large impact on
flow behaviour. The CTRZ disappears and is replaced by the atomizing air which forms a column of
unreacted air travelling straight down the burner centreline. The flame remains in its original position,
but the flow has reversed, travelling counter to the droplets.
75
Figure 34 – Diffuser section vector plot of the 100% swirl ethanol simulation with 100% atomizing air.
The atomizing jet loses its coherence extremely rapidly, changing its trajectory downwind. The
annular recirculation zone is reformed and collides with the atomizing air as it exits the nozzle. The
radial momentum of the atomizing air is completely lost, and it proceeds down the centerline of the
burner shown in Figure 35. This is interesting because as shown in Figure 33, the initial guess when
the atomizing air is introduced includes negative axial velocities around the nozzle, and extremely
low bulk velocities compared to the atomizing jet. This may be an example of k-ε`s weakness in
predicting jet spreading rates, an area models such as the Realizable k-ε were designed to improve.
The introduction of atomizing air has slowed the onset of combustion, causing the flame to move
farther downwind (Figure 29, Figure 30). In the case of low or no pilot flame the reactions are too
slow to account for the heat lost to the atomizing air and the flame is extinguished. This is because of
the annular recirculation zone in the diffuser transporting cool atomizing air to the flame low in the
chamber. This is also visible in Figure 29 and Figure 30 as the cool sheath surrounding the initial
ignition.
76
Figure 35 – Vector plot of the deflection of the atomizing air. The original trajectory at the boundary matched that of the
droplet.
Also, the dominant axis for discretization is the vertical axis of the burner, while the centerline is the
area of lowest pressure. This may create a bias towards the centre axis when solving the discretized
problem. Numerical diffusion can also be a concern in convection dominated simulations such as in
this study. However, the use of a high density mesh in the region surrounding the nozzle, and second
order discretization should eliminate this problem. A run carried out where the atomizing air was
introduced first, followed by the primary swirling air may be able to shed light on these possibilities,
but was not carried out in this study due to time constraints.
77
All approaches to solving the flow solution resulted in the atomizing air jet diversion to the centre
axis. The most extreme effort made to retain the CTRZ was to employ hysteresis. The volume
containing the heat addition used to simulate the pilot flame was also used for one test to introduce
negative axial momentum to the flow. By adding negative momentum it is possible to retain the
CTRZ up to 100% of the atomizing air. Once the momentum addition was removed, however, the
atomizing jet gradually lost coherence and converged upon the solution already discussed.
During this simulation the droplets vaporize sooner, and the flame is anchored closer to the nozzle
with the forced recirculation. A converged case requires three times the pilot power to anchor the
flame at the nozzle than the comparable experiment. When a forced CTRZ exists this power
requirement for an anchored flame can be reduced. This finding implies that the actual experiment
could be experiencing some recirculation.
4.2 Bio-oil Combustion
4.2.1 Base Case
Tzanetakis et al. considered several common parameters used in burner design: swirl, atomization,
ignition source energy, air/fuel preheat and equivalence ratio [14] [15]. The first three are considered
in this study, with swirl the subject of section 4.1. The viability of the bio-oil model is studied by
comparison of the model results with those of Tzanetakis et al.
The basis for comparison between the experimental results and the numerical for atomization quality
is the normalized droplet mass below the diffuser exit. This altitude was selected in order to
ensure droplets continued to exist in the study plane for all cases, and to maximize the length of time
available to the droplet for combustion before comparison. The ignition source energy and low char
cases are compared with the base case in terms of residence time and mass loss behaviour. A
summary of the simulated residence times for all cases considered in this study is presented in Table
78
15. These residence times are provided by the DPM for the droplet travelling through the domain. All
data regarding droplet behaviour, such as mass loss is provided by the DPM.
Table 15 – Simulated residence times for all cases considered in the current study
Case
Average Residence
Time (ms)
Base Case 8.80
Low Atomizing Air 12.29
High Atomizing Air 6.76
No Ignition Source 8.53
Low Char 8.42
4.2.1.1 Atomizing Air
Two cases were designed to simulate the effects of atomization quality compared with the Base Case.
The conditions for the two cases are based on the two operating points used in [14], with specific
boundary conditions and parameter data available in Appendix A – Varying Case Parameters. The
low Atomizing Case uses while the high Atomizing Air Case utilizes .
are used in the Base Case.
The purpose of the atomizing air is to provide sufficient turbulence in the nozzle mixing chamber to
break up the fuel stream into droplets. The atomizing air used in the nozzle for the burner has a
traceable impact on the SMD, more air providing improved atomization via smaller SMDs [55]. The
simulation boundary conditions will include appropriate SMDs depending on the atomizing air as
well as altered air velocities.
The three cases are compared with one another, and with the experimental measurements in Figure
36. The average mass of the tracked particles in the observation plane for all three cases is normalized
to their initial mass, and then to the Base Case mass. This is then compared with the particulate
material measured on the exhaust stream of the burner, normalized to the experimental base case.
79
Figure 36 – Experimental particulate matter measurements from [55] compared with the droplet mass remaining at 5cm
below the diffuser. The masses have been normalized to the basepoint to aid comparison.
Only part of the experimental trend is captured by the model. The Low Atomizing Case observed a
decrease in mass consumption compared to the Base Case, indicating poorer combustion
characteristics. This is expected as the heat transfer to the droplets is related to the available surface
area, which is reduced when the droplet size is increased and the number of droplets decreased. The
lower temperatures further down the burner axis are also likely to play a role in droplet behaviour
later in their lifespan. At the difference in consumption efficiency between the Base Case and
the Low Atomizing Air Case is only as opposed to at which extrapolated implies that
an analysis of the end of the lifespans for both cases would lead to a better agreement between the
experiment and the model. It must be remembered that due to stability reasons, the full estimated
residence time could not be tested with the current grid. The Low Atomizing Air Case is likely to
perform much poorer as the residence time would be extended by . This hypothesis is supported
by the temperature history presented in Figure 37 for the three cases in terms of travel distance into
the burner.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 5 10 15 20 25 30
No
rmal
ized
Par
ticu
late
Mat
ter
Atomizing Air (SLPM)
Experimental
Model
80
Figure 37 – Temperature history of the droplets along the burner axis for different atomizing air flowrates.
The slope of the temperature curve for the Low Atomizing Air Case levels off after dropping
into the burner. It is reasonable to assume that if the droplet life were extended to match the original
estimate this drop would become more pronounced.
0.0E+00
2.0E+02
4.0E+02
6.0E+02
8.0E+02
1.0E+03
1.2E+03
1.4E+03
1.6E+03
1.8E+03
-20 -10 0 10 20 30 40
Dro
ple
t T
emp
erat
ure
[K
]
Axial Position within Burner (Diffuser exit at 0) [cm]
Base Case
Low Atomizing Air
High Atomizing Air
81
Figure 38 – The temperature, volatile mixture fraction and char mixture fraction contour plots for the Base Case.
In Figure 38, the temperature plot for the Base Case is displayed alongside the mixture fraction plots
for both the volatile and char phases of the bio-oil. It can be seen how the two phases of the bio-oil
contribute to the overall combustion behaviour. The Low Atomizing Air Case burns in a similar
manner, but with a longer trajectory. The High Atomizing Air Case differs from these two because of
its rapid release of volatile matter. The large heat release is transported back to the flame via
recirculation as shown in Figure 39 and Figure 40. This process accelerates the consumption of the
droplet which magnifies the drop seen in Figure 36.
82
Figure 39 – Temperature contour for High
Atomizing Air Case.
Figure 40 – Vector plot of the High Atomizing Air Case with the
particle trajectories superimposed.
The error for the High Atomization Air Case is larger than for the other two cases because some of
the droplets have begun the char burnout phase of their combustion which significantly increases the
mass loss. This contributes an increase in variation from the mean.
The experimental results for higher amounts of atomization air were affected by a decrease in
combustion quality due to the proximity to the lean blow-out limit. The use of the equilibrium model
eliminates the simulation of strain in the flame. As a result, the model did not encounter a lean blow-
out limit, instead seeing an improvement in combustion quality due to improved atomization.
83
4.2.1.2 Ignition Source Energy
The absence of a pilot flame has done very little to change
the way the bio-oil droplet combusts. Ignition does not
appear to have been slowed by the absence of the pilot
flame. In all cases a small recirculation zone has appeared
above the flame at the diffuser neck. When the pilot flame
was removed in the simulation, the ignition quality
remained unchanged. This implies that the recirculation
above the flame is acting as the main ignition energy
source, not the pilot flame.
The experiments discovered that the flame experienced an
instability when the pilot flame was removed [55]. This is
not in agreement with the simulation results which have
estimated an improvement in combustion quality. The mass
loss curves between the two cases are nearly identical
during the devolatilization phase. Differences in residence
times seem to originate from more rapid char burnout
without the pilot flame. The residence time for the case
without a pilot flame has dropped to from the
of the Base Case. The likely explanation for this behaviour is a restructuring of the fluid
mechanics in the nozzle region. As illustrated by Figure 40 the fluid flow between the two cases is
quite similar. The recirculation immediately above the droplet trajectory is more pronounced without
the pilot flame, and a greater number of streamlines proceed directly to the fuel jet. The high
temperature region surrounding the nozzle is thus increased in size, as can be seen by comparison of
Figure 41 and Figure 38. The most plausible cause of this is the reduction in density change
Figure 41 – Temperature contour for the No
Pilot Case.
84
associated with the gasses passing through the pilot flame region. In the Base Case, atomizing air and
primary swirl air are drawn into the pilot flame region. The energy added to the flow then increases
the gas temperature, and thus reduces the density. The removal of the pilot flame thus causes a void
which draws in a greater amount of exhaust products from farther down the flame. The primary swirl
air is also completely blocked from entering the nozzle region. This is a significant difference as the
swirl air possesses the lowest temperatures in the burner. The resulting higher temperatures
surrounding the droplet as it first enters the domain appears to have a greater impact than the pilot
flame itself. Figure 42, below, graphically illustrates the difference between the two cases.
Figure 42 – Vector plot comparison between the Base Case and the No Pilot Flame Case
Recirculation in the middle portion of the burner is returning exhaust products from the char burnout
to the droplet at the end of the devolatilization phase. This could be the cause for the acceleration in
char burnout which accounts for of the change in residence time. As this significant a
downstream recirculation is not duplicated in any of the other simulations it is likely the pilot flame
was repressing that recirculation. As the experiments recorded a degradation in combustion quality,
rather than an improvement, it is possible this recirculation is another artefact of the turbulence
model’s difficulty in solving this flow, as discussed in 4.1.2.2.
85
4.2.2 Low Char Case
The Low Char Case is similar to the High Atomizing Air
Case. The positive feed-back loop created by the rapid
release of volatile material due to smaller diameters is
replicated in this case due to a higher volatile content in the
fuel. The negative velocity region surrounding the centre
positive velocity core is carrying heated exhaust gases from
the lower flame to the nozzle as shown in Figure 43,
improving overall combustion. The longer portion of the
residence time devoted to devolatilization causes some of
the volatile mixture fraction to enter the periphery of the
center jet. This is then carried downstream to the same
region as the char burnout, producing a larger flare than at
the same location for the Base Case.
The mass loss behaviour of the two cases is also similar.
The higher concentration of volatiles in the droplet
produces a larger initial rate of mass loss. As a
consequence more fuel is available for combustion, which
raises the droplet temperature and further increases the
evaporation rate. Thus the mass loss curves for the two cases, shown in Figure 42, display a slightly
different curve. The Low Char Case losses its mass more rapidly until the concentration of volatiles is
quite low and the masses of the two particles converge. The smaller residence time of the Low Char
Case is a result of this larger mass loss rate, plus less char requiring burnout. The burnout phase
requires of the time of the Base Case, even though it only possesses of the char. This is
Figure 43 – Low Char Case temperature
contour plot
86
attributable to the higher energy density of the char which, when present in larger quantities, will
release significantly more heat, speeding up the burnout process.
Figure 44 – Mass loss curves for the Base Case and the Low Char Case
The purpose of the Low Char case was to investigate the possible implications of an inaccurate
assumption regarding the char content of the fuel. It is significant that the residence time was reduced
by from this change. Therefore, accurately identifying the quantity of char in a given bio-oil
sample when rapidly heated will be important to anyone interested in designing a burner for a specific
residence time.
0.0E+00
5.0E-11
1.0E-10
1.5E-10
2.0E-10
2.5E-10
3.0E-10
3.5E-10
4.0E-10
4.5E-10
5.0E-10
0.0E+00 2.0E-03 4.0E-03 6.0E-03 8.0E-03 1.0E-02
Dro
ple
t M
ass
[kg]
Residence Time [s]
Base Case
Low Char Case
87
Chapter 5 Closure
5.1 Conclusions
The purpose of this study was to develop a computational fluid dynamics model for bio-oil
combustion. The model is intended for eventual use by the engineering community as a design tool
for bio-oil, and as a complementary source of insight into the working of bio-oil research combustors.
In developing a model, the question is raised as to what effects can be attributed to bio-oil or the
burner fluid mechanics. The problem was thus divided into fluid mechanics and bio-oil combustion.
The fluid mechanics of the burner were analyzed by taking CO2 readings from within the burner for
an ethanol flame to be compared with the simulation. The CO2 readings imply that there is fast
mixing within the burner due to identical CO2 levels for both 100% and 50% swirl. Only rapid radial
mixing of the fuel and the primary swirl air is likely to produce identical CO2 readings for both cases.
The numerical simulations were not able to quantitatively or qualitatively duplicate the trends from
the experiments. The turbulence model predicted a large number of small recirculation zones and a
high speed central jet travelling down the burner, originating from the atomizer. Recirculating
material collides with the atomizing air immediately upon introduction into the burner, causing it to
lose all radial momentum. The flame was also anchored far from the nozzle, with very long droplet
residence times. Both of these features are contrary to those observed in the experiments. Simulations
run with forced central recirculation reduced the residence times and properly anchored the flames.
The fluid mechanics study has thus run into two bodies of contradictory evidence supporting the
existence of a CTRZ and disputing it.
The bio-oil combustion model successfully duplicated the atomizing air trend observed by Tzanetakis
et al. The absence of a chemistry model incorporating strain results in a disagreement at the lean
blow-out limit between the empirical and numerical results which was expected. The unique flow
88
solution generated by the RNG k-ε model for the No Pilot Case created a recirculation region above
the nozzle which effectively stabilized the flame when the experimental flames were not. This adds to
the concern over the applicability of the turbulence model to this burner arrangement. The Low Char
Case displayed a reduction in residence time due to its increased volatility. This will have serious
implications for design applications where residence time is an important design parameter as it has
been shown to be related to the char fraction.
The bio-oil model has shown it can duplicate the behaviour of actual bio-oil in a turbulent combustion
environment. Cases where deviations were observed between the experimental and numerical results
are largely attributed to the inability of the turbulence model to accurately predict the flow structures
inside the burner. Model trends have been validated against empirical results, indicating that the
model can be used as a qualitative design tool for the prediction of bio-oil combustion. Further work
described in 5.2 can produce a second generation model which will provide even greater predictive
ability.
5.2 Recommendations for Future Work
5.2.1 Turbulence Model
The RNG k-ε turbulence model was not able to obtain a stable physical solution in agreement with
the experimental results in the scenario simulated in this study. The RNG model is capable of
modelling mildly swirling flows, but encounters difficulty with the complex stresses involved in a
highly swirling flow. If it is desired to properly address the questions regarding the flow patterns
within the burner, then the issue should be addressed by using a stress model. The model is
computationally intense and stiff however, so the value of an accurate solution must be weighed
carefully before employing an RSM.
89
Alternatively, it has been suggested by researchers at Pratt & Whitney Canada that the standard k-ε
may in fact be better equipped to model the flow encountered in this study. Specifically, it has been
suggested to analyze the appropriateness of the coefficients used, particularly for ε, such as C1ε [73].
A complete 3D transient or steady simulation of the burner could also be of value in identifying any
flow behaviour which is not captured due to the symmetry conditions. A transient simulation will be
useful in determining if a precessing vortex core is the cause for the unique flow solution. Simulations
including atomizing air, but not combustion and introducing atomizing air before any other flow
features are two tests that are encouraged as well.
5.2.2 Ethanol Vaporization
It has been suggested that partial evaporation of ethanol is taking place within the atomizing chamber
of the nozzle, as well as flash vaporization when the fuel exits the nozzle. This vaporized fuel may be
the reason for better overall mixing within the experimental burner and improved flame anchoring.
This feature was not included in the model because of an absence of known boundary conditions, but
is recommended as an area for further study. The model is able to include a fraction of the fuel in the
atomizing air inlet. Several cases ranging from 100% vaporization to the current 0% base case should
be run to explore the effect on C02 levels within the burner. If a better agreement between the
experimental results and the model is possible it may shed some insight into the validity of the flow
solution provided by the RNG k-ε turbulence model.
5.2.3 Diesel Validation
The asymmetric fluid mechanic and thermal effects of the pilot flame could not be thoroughly studied
in this test because of the instabilities experienced by ethanol when pilot flame levels are reduced
below 75% energy output. Diesel has proven to be a more stable flame in the burner and can be
combusted without the use of a pilot flame. Repeating the swirl tests carried out in this study with
90
diesel without the pilot flame will shed light on the impact of the pilot flame on flow symmetry, as
well as provide another basis for comparison between the model and the empirical burner.
5.2.4 Flow Validation
The uncertain flow solution is a major obstacle for the development of this bio-oil model. The
disagreement between the literature, experimental measurements and the numerical model make it
very difficult to ascertain what is taking place within the burner. It is suggested that additional
diagnostics be employed to obtain measured data for flow velocities within the burner. Particle image
velocimetry (PIV) can be used to obtain instantaneous velocity measurements for within the burner.
This would be extremely beneficial as it will provide an answer to what is the internal flow structure
of the burner and what is the nature of any recirculation zones at the measurement location. Such
information can be used to explain trends in the experimental bio-oil combustion data, as well as
facilitate the convergence of the turbulence model upon the correct solution. Having the correct
solution to the flow problem will permit a more comprehensive comparison of the bio-oil model to
the experimental data.
5.2.5 Swelling
The model was not able to duplicate the droplet entrainment that is present in video footage of the
burner under operation. During actual combustion most visible droplets were entrained in the bulk
flow, and some were recirculated up to the flame. Near the end of the droplet`s life, during char
burnout, the droplet became somewhat affected by the bulk flow, but not entrained and underwent no
cases of recirculation.
It is the author`s opinion that the cause of this discrepancy is that none of the droplets undergo
swelling, whereas it has been shown via particulate filter samples that a number of droplet do undergo
an increase in diameter. If a droplet were to undergo an increase in diameter that was experienced by
one of the droplets captured by the filter, the density would decrease by a factor of 125. Such swelling
91
would significantly increase the coefficient of drag for the droplet, increasing the influence of the
bulk flow on its trajectory, without any increase in droplet momentum. The speed of the droplet is
two orders of magnitude larger than the swirl velocities so the low drag calculated by Fluent without
swelling is reasonable. Although combustion swelling is not as prevalent as during TGA [22] [27]
[19], the effect of swelling on droplet behaviour should be assessed as it may be a weakness in the
model.
5.2.6 Grid Design
The grid was designed prior to the beginning of the determination of the possible residence times for
the bio-oil droplets and with the expectation of entrainment, as seen in the experiments.
Consequently, the grid was optimized for recirculation and the atomizer jet in the upper regions of the
burner. Average element sizes were increased in the lower regions of the burner due to lower levels of
turbulence, and to reduce computational effort. The long residence times of the bio-oil droplets meant
the transfer of momentum and mass to the continuous phase occurs lower in the burner than was
anticipated. It is not possible to meet the residual convergence limits with this mesh with such long
residence times and penetration into the burner. It is recommended that a new grid be generated to
accommodate the longer residence times.
5.2.7 Bio-oil Model Validation
The application of data gathered from the literature to a turbulent combustion case relied on several
assumptions [19] [27]. A great deal of information can be gained by simulating the laminar flow
reactor and droplet of [27]. This will provide a validation point for the model that can be compared
directly. This simulation will be able to shed some light on the applicability of the chosen distillation
and burnout models to other combustion cases. It will also decouple the test from the uncertain flow
solution provided by the RNG model in this study.
92
Appendix A – Varying Case Parameters
Table 16 – Base Case
DPM Property Value
X-Position(m) 0.0022913
Y-Position(m) 0.0013273
Z-Position(m) -0.095924
X-Velocity(m/s) 54.31
Y-Velocity(m/s) 31.35
Z-Velocity(m/s) 80.40
Diameter(m) 9.0742E-5
Fuel Temperature(K) 363
Flow Rate(kg/s) 1.1308E-4
Property Value
Swirl Number 5.41
Swirl Air Temp (K) 531
Atomizing Air(kg/s) 6.0976E-5
Atomizing Temp (K) 373
Pilot Power (W/m3) 1.104E+8
Table 17 – Low Atomizing Air Case
DPM Property Value
X-Position(m) 0.0022913
Y-Position(m) 0.0013273
Z-Position(m) -0.095924
X-Velocity(m/s) 43.24
Y-Velocity(m/s) 24.97
Z-Velocity(m/s) 62.04
Diameter(m) 1.5135E-4
Temperature(K) 363
Flow Rate(kg/s) 1.1308E-4
Property Value
Swirl Number 5.41
Swirl Air Temp (K) 531
Atomizing Air(kg/s) 4.7604E-5
Atomizing Temp (K) 373
Pilot Power (W/m3) 1.104E+8
Table 18 – High Atomizing Air Case
DPM Property Value
X-Position(m) 0.0022913
Y-Position(m) 0.0013273
Z-Position(m) -0.095924
X-Velocity(m/s) 82.28
Y-Velocity(m/s) 47.50
Z-Velocity(m/s) 118.05
Diameter(m) 4.2858E-5
Temperature(K) 363
Flow Rate(kg/s) 1.1308E-4
Property Value
Swirl Number 5.41
Swirl Air Temp (K) 531
Atomizing Air(kg/s) 9.0662E-5
Atomizing Temp (K) 373
Pilot Power (W/m3) 1.104E+8
Table 19 – 100% Swirl, 100% Pilot
DPM Property Value
X-Position(m) 0.0022913
Y-Position(m) 0.0013273
Z-Position(m) -0.095924
X-Velocity(m/s) 52.54
Y-Velocity(m/s) 30.33
Z-Velocity(m/s) 82.30
Diameter(m) 3.8986E-5
Temperature(K) 363
Flow Rate(kg/s) 6.1575E-5
Property Value
Swirl Number 5.41
Swirl Air Temp (K) 338
Atomizing Air(kg/s) 6.1511E-5
Atomizing Temp (K) 363
Pilot Power (W/m3) 5.52E+7
93
Table 20 – 50% Swirl, 75% Pilot
DPM Property Value
X-Position(m) 0.0022913
Y-Position(m) 0.0013273
Z-Position(m) -0.095924
X-Velocity(m/s) 52.08
Y-Velocity(m/s) 30.07
Z-Velocity(m/s) 82.30
Diameter(m) 3.9687E-5
Temperature(K) 363
Flow Rate(kg/s) 6.16735E-4
Property Value
Swirl Number 1.46
Swirl Air Temp (K) 338
Atomizing Air(kg/s) 6.1511E-5
Atomizing Temp (K) 363
Pilot Power (W/m3) 5.52E+7
94
Appendix B – Supplemental Thermal Boundary Conditions
Table 21 – Ethanol Thermal BCs
Geometric Feature Temperature (K)
Primary Swirl Air 348.7
Atomizing Air 363.0
Nozzle 355.7
Swirl Box Wall 400.1
Swirl Box Bottom 451.6
Wall Solid 351.0
Diffuser Neck 503.0
Diffuser
Appendix C –
User Defined
Functions
Chamber Wall
Appendix C –
User Defined
Functions
Chamber Bottom 451.5
Exhaust Wall 572.7
Table 22 – Bio-Oil Thermal BCs
Geometric Feature Temperature
Primary Swirl Air 513.0
Atomizing Air 363.0
Nozzle 544.7
Swirl Box Wall 544.7
Swirl Box Bottom 576.3
Wall Solid 544.7
Diffuser Neck 608.0
Diffuser
Appendix C –
User Defined
Functions
Chamber Wall
Appendix C –
User Defined
Functions
Chamber Bottom 502.0
Exhaust Wall 543.0
95
Appendix C – User Defined Functions
/**************************************************************** Swirl100Pilot100.c
For use with Swirl100Pilot100
UDF for specifying the thermal boundary conditions at the walls UDF for specifying the velocity boundary conditions at the inlet
****************************************************************/
#include "udf.h"
DEFINE_PROFILE(chamber_wall, thread, index)
{
real x[ND_ND]; /*position vector*/
real z;
face_t f;
begin_f_loop(f,thread)
{ F_CENTROID(x, f, thread);
z=x[2];
if (z < 0.407) F_PROFILE(f, thread, index)=-82353.*pow(z,4.)+88374.*pow(z,3.)-30595.*pow(z,2.)+3152.7*z+699.58;
else
F_PROFILE(f, thread, index)=2020.2*pow(z,3.)-4589.9*pow(z,2.)+2972.2*z+27.076; }
end_f_loop(f, thread)
}
DEFINE_PROFILE(diffuser, thread, index)
{
real x[ND_ND]; /*position vector*/
real z; face_t f;
begin_f_loop(f,thread) {
F_CENTROID(x, f, thread);
z=x[2]; F_PROFILE(f, thread, index)=576644*pow(z,3)+14219*pow(z,2)+289.68*z+727.24;
}
end_f_loop(f, thread) }
DEFINE_PROFILE(axial, thread, index) {
real p[ND_ND]; /*position vector*/ real x,y,q,r;
face_t f;
begin_f_loop(f,thread)
{
F_CENTROID(p, f, thread); x=p[0];
y=p[1];
q=pow(x,2)+pow(y,2); r=sqrt(q);
F_PROFILE(f, thread, index)=-2636.7*pow(r,3)+251.99*pow(r,2)-0.1147*r+0.0342;
} end_f_loop(f, thread)
}
DEFINE_PROFILE(tangen, thread, index)
{
96
real p[ND_ND]; /*position vector*/
real x,y,q,r; face_t f;
begin_f_loop(f,thread) {
F_CENTROID(p, f, thread);
x=p[0]; y=p[1];
q=pow(x,2)+pow(y,2);
r=sqrt(q); F_PROFILE(f, thread, index)=4039.7*pow(r,3)-39.381*pow(r,2)-47.989*r+0.0418;
}
end_f_loop(f, thread) }
/****************************************************************
Swirl50Pilot75.c For use with Swirl50Pilot75 ONLY
UDF for specifying the thermal boundary conditions at the walls
UDF for specifying the velocity boundary conditions at the inlet ****************************************************************/
#include "udf.h"
DEFINE_PROFILE(chamber_wall, thread, index) {
real x[ND_ND]; /*position vector*/ real z;
face_t f;
begin_f_loop(f,thread)
{
F_CENTROID(x, f, thread); z=x[2];
if (z < 0.407)
F_PROFILE(f, thread, index)=-82353.*pow(z,4.)+88374.*pow(z,3.)-30595.*pow(z,2.)+3152.7*z+699.58; else
F_PROFILE(f, thread, index)=2020.2*pow(z,3.)-4589.9*pow(z,2.)+2972.2*z+27.076;
} end_f_loop(f, thread)
}
DEFINE_PROFILE(diffuser, thread, index)
{
real x[ND_ND]; /*position vector*/
real z;
face_t f;
begin_f_loop(f,thread)
{
F_CENTROID(x, f, thread);
z=x[2];
F_PROFILE(f, thread, index)=576644*pow(z,3)+14219*pow(z,2)+289.68*z+727.24; }
end_f_loop(f, thread)
}
DEFINE_PROFILE(axial, thread, index)
{
real p[ND_ND]; /*position vector*/
real x,y,q,r; face_t f;
begin_f_loop(f,thread)
97
{
F_CENTROID(p, f, thread); x=p[0];
y=p[1];
q=pow(x,2)+pow(y,2); r=sqrt(q);
F_PROFILE(f, thread, index)=-2572.2*pow(r,3)+245.82*pow(r,2)-0.1119*r+0.0334;
} end_f_loop(f, thread)
}
DEFINE_PROFILE(tangen, thread, index)
{
real p[ND_ND]; /*position vector*/
real x,y,q,r;
face_t f;
begin_f_loop(f,thread)
{ F_CENTROID(p, f, thread);
x=p[0];
y=p[1]; q=pow(x,2)+pow(y,2);
r=sqrt(q);
F_PROFILE(f, thread, index)=1063.8*pow(r,3)-10.37*pow(r,2)-12.637*r+0.011; }
end_f_loop(f, thread) }
/****************************************************************
boundary.c UDF for specifying the thermal boundary conditions at the walls
UDF for specifying the velocity boundary conditions at the inlet
****************************************************************/
#include "udf.h"
DEFINE_PROFILE(chamber_wall, thread, index)
{
real x[ND_ND]; /*position vector*/
real z;
face_t f;
begin_f_loop(f,thread)
{ F_CENTROID(x, f, thread);
z=x[2];
if (z < 0.407) F_PROFILE(f, thread, index)=-82353.*pow(z,4.)+88374.*pow(z,3.)-30595.*pow(z,2.)+3152.7*z+699.58;
else
F_PROFILE(f, thread, index)=2020.2*pow(z,3.)-4589.9*pow(z,2.)+2972.2*z+27.076;
}
end_f_loop(f, thread)
}
DEFINE_PROFILE(diffuser, thread, index)
{
real x[ND_ND]; /*position vector*/
real z; face_t f;
begin_f_loop(f,thread) {
F_CENTROID(x, f, thread);
z=x[2];
98
F_PROFILE(f, thread, index)=576644*pow(z,3)+14219*pow(z,2)+289.68*z+727.24;
} end_f_loop(f, thread)
}
DEFINE_PROFILE(axial, thread, index)
{
real p[ND_ND]; /*position vector*/
real x,y,q,r;
face_t f;
begin_f_loop(f,thread)
{ F_CENTROID(p, f, thread);
x=p[0];
y=p[1]; q=pow(x,2)+pow(y,2);
r=sqrt(q);
F_PROFILE(f, thread, index)=-4093.7*pow(r,3)+391.23*pow(r,2)-0.1782*r+0.0531; }
end_f_loop(f, thread)
}
DEFINE_PROFILE(tangen, thread, index)
{
real p[ND_ND]; /*position vector*/ real x,y,q,r;
face_t f;
begin_f_loop(f,thread)
{
F_CENTROID(p, f, thread); x=p[0];
y=p[1];
q=pow(x,2)+pow(y,2); r=sqrt(q);
F_PROFILE(f, thread, index)=6272*pow(r,3)-61.143*pow(r,2)-74.508*r+0.0649;
} end_f_loop(f, thread)
}
99
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