Detailed modeling of CO2 addition effects on the evolution of soot particle size distribution functions in premixed laminar ethylene flames

Combustion and Flame 183 (2017) 75–87

Contents lists available at ScienceDirect

Combustion and Flame journal homepage: www.elsevier.com/locate/combustflame

Detailed modeling of CO2 addition effects on the evolution of soot particle size distribution functions in premixed laminar ethylene flames Ali Naseri, Armin Veshkini, Murray J. Thomson∗ Combustion Research Laboratory, Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, ON M5S 3G8, Canada

a r t i c l e

i n f o

Article history: Received 13 January 2017 Revised 10 February 2017 Accepted 24 April 2017 Available online 19 May 2017 Keywords: CO2 addition Sooting behavior Premixed laminar ethylene flame Sectional aerosol dynamics Particle size distribution

a b s t r a c t This study investigates computationally the influence of CO2 addition on the sooting behavior in premixed laminar ethylene/ oxygen/ argon burner stabilized stagnation (BSS) flames at the atmospheric pressure. The discrete sectional aerosol dynamics method combined with a reversible nucleation model and a novel model of reversible polycyclic aromatic hydrocarbon (PAH) condensation were employed to predict the size evolution of the particle size distribution (PSD) function. The predicted temperature profiles and PSD functions are in reasonably good agreement with the experimental data for nascent soot measured in the BSS configuration. The evolution of the PSD functions shows that CO2 addition reduces the soot nucleation and mass growth rates, consequently lowering the soot yield. The addition of CO2 reduces the concentrations of H, C2 H2 , C6 H6 , and large PAHs (e.g., pyrene) which all suppress the soot formation process through a chemical effect; the thermal effect of CO2 is not as strong as the chemical effect. © 2017 Published by Elsevier Inc. on behalf of The Combustion Institute.

1. Introduction Energy is essential for modern society. Currently, fossil fuels supply about 80% of the global energy consumption [1]. The overall liquid fuels consumption of the world is predicted to expand to 30% of the current consumption for the duration of thirty years [2]. The generation of energy from fossil fuels is mostly produced by combustion which is a source of atmospheric emissions such as NOX , green house gases (GHGs), and particulate matter (PM). Formation of condensed-phase materials is present in many flames. These materials form generally as nanoparticles suspended in combustion products [3]. Combustion derived nano-particles known as soot are emitted from various combustion processes, mostly the incomplete rich combustion of fossil fuels, biofuels, and biomass[4]. The adverse effects of soot particles on public health and climate change have been well recorded. Time-series studies of the shortterm effects of air pollutants, conducted around the world, have described a noticeable connection between daily mortality and daily exposure to PM2.5 [4]. Soot particle aerosols have significant impacts on climate change through several mechanisms: absorp-

∗

Corresponding author. E-mail addresses: [email protected], [email protected] (M.J. Thomson). http://dx.doi.org/10.1016/j.combustflame.2017.04.028 0010-2180/© 2017 Published by Elsevier Inc. on behalf of The Combustion Institute.

tion of solar radiation; influence on cloud formation; and deposition on the snow and ice [4–6]. The development of effective technologies to reduce soot emissions from combustion applications has been an active research area over the past years [7]. The dilution of the fuel and/or oxidizer mixture stream is an established method (e.g., exhaust gas recirculation (EGR)) to develop low temperature combustion in order to prevent both soot and NOX formation [7]. CO2 is one of the major components of combustion products; understanding how the addition of CO2 affects the flame properties and soot formation requires attention. According to [7,8], CO2 addition suppresses soot formation in most of the flame conditions; however, there are unanswered questions regarding the role of CO2 in the soot formation process. There is evidence which suggests the suppression of soot is due to the thermal effects [9,10], while the majority of the literature posits that the observed phenomenon is mostly due to chemical effects. Schug et al. [9] and Abhinavam et al. [10] advocate the thermal effects of CO2 in soot suppression. Abhinavam et al. [10] measured the concentrations of soot precursor species including C2 H2 and C6 H6 for different diluents such as argon, helium, and carbon dioxide. The different levels of soot precursors produced in the diluted flames are attributed to the differences in the transport properties of the diluents, where the thermal diffusivities cause the temperature difference between the helium flame (hottest flame) and the

76

A. Naseri et al. / Combustion and Flame 183 (2017) 75–87

carbon dioxide-diluted flame (coolest flame). This feature makes carbon dioxide a better suppressant among the diluents tested. Du et al. and Zhang et al. [11,12] found that CO2 hinders the soot formation chemically; they measured concentrations of species to investigate the dilution effect. Liu et al. [13] took advantage of a numerical model, and determined that CO2 addition suppresses the soot nucleation by lowering the acetylene and enhancing the concentration of OH radicals; reactions CO2 +H → CO+OH and CO2 +CH → HCO+CO were found to be responsible for the chemical effects of CO2 addition. In a more recent paper, Liu et al. [7] studied the effects of fuel dilution by CO2 and N2 on soot formation and the flame structure in laminar coflow C2 H4 /air diffusion flames both experimentally and numerically including soot simulation; according to their study, CO2 ’s role in the soot suppression process is mainly due to its chemical effects. According to Guo et al. [14], CO2 addition reduces the H radical formation which consequently results in lower pyrene concentrations. Less polycyclic aromatic hydrocarbon (PAH) reduces the inception rate. The chemical effects of CO2 on soot formation are more complex in premixed flames [7], and the soot formation process differs significantly between premixed and diffusion combustion. In the premixed flames soot starts to form after passing the flame front and in the post flame region, while in the diffusion flames soot forms in a fuel rich heated zone before reaching the flame front. Zhang et al. [15], in a numerical simulation of soot precursors in a plug flow reactor, insisted on the chemical effect of CO2 addition. They determined that the reaction CO2 +H + CO+OH is pretty fast at intermediate temperatures; the mentioned reaction competes with the reaction O2 +H + O+OH in depleting the H radicals. Tang et al. [8] did a thorough experimental investigation and a chemistry simulation of species to understand how CO2 addition affects the particle size distribution (PSD) function evolution. They concluded that that CO2 addition hinders particle inception, and thermal effect plays a minor role. Tang et al. took advantage of a burner stabilized stagnation (BSS), which minimizes the problem of probe perturbation in experiments. This burner was used earlier by Abid et al. and Camacho et al. [16,17] to follow the evolution of PSD function of nascent soot. Up to here, there is a consensus among different researchers about chemical effects of CO2 addition on soot formation; however, disagreement still exists on how the added CO2 affects the soot formation chemically, even via inception or surface growth. In addition, some studies suggest CO2 addition does not always suppress soot formation [18,19]; however, inclusion of CO2 in the combustion reactants seems a pragmatic solution to prevent soot formation in the contemporary devices. Utilizing this capability requires a better understanding of the morphology in different conditions. A detailed soot model accompanied with a rich experimental data which includes soot volume fraction, particle diameter, and number density could be helpful to find out the reasons which accounts for the observed phenomenon. The detailed soot models take advantage of cutting edge aerosol dynamics prediction tools to calculate accurate solutions for a wide range of aggregate structures. The most recent chemical and physical kinetic mechanisms describing PAH [20–23] and soot formation/oxidation are embodied into the detailed models. These models can yield comprehensive information about the factors influencing particles for a broad range of conditions. This characteristic enables them to investigate the fundamentals of soot formation. The proper aerosol dynamics models for studying soot comprises sectional methods [24–29], Galerkin methods [30,31], stochastic methods [32,33], and moment methods [34,35]. These algorithms can capture the majority of the particle properties with moderate computational resources; however, modifications to these models to extract additional information, dramatically increase their complexity and computational cost.

Table 1 Flame compositions in mole fraction. There is no CO2 in flame A1, but 12% and 18% of argon have been replaced with CO2 in flames A2 and A3, respectively [8]. Flame

A1 (%)

A2 (%)

A3 (%)

CO2 C2 H 4 O2 Ar

0.00 0.16 0.24 0.60

0.12 0.16 0.24 0.48

0.18 0.16 0.24 0.42

An advanced sectional aerosol dynamics model [28,36] is used in this work that can provide soot morphology in addition to mean soot properties and the size distribution of particles. Two equations, number densities of aggregates and primary particles, are solved per section which allows resolving the formation and coagulation of the fractal-like soot aggregates as well as soot polydispersity. Abilities of the sectional soot model to successfully simulate soot formation has been demonstrated in plug flow reactors [36], shock tubes [37], BSS flames [27–29] and coflow diffusion flames [38–40]. The main goal of this paper is to answer the following questions regarding the influence of CO2 addition: Is the suppression effect of CO2 due to thermal or chemical factors; what are the chemical reactions involved in this soot reduction process; is the nucleation or condensation more affected? The influence of the chemical kinetic file on the evolution of PSDs will be studied as well. This work is the first one to use a BSS flame soot model to investigate the CO2 addition effect; thus, we are able to see the influence of dilution on different factors in the soot morphology. 2. Setup and computational model 2.1. Burner stabilized stagnation (BSS) flame The descripion of the burner is summarized here. Frame (A) of Fig. 1 shows the schematic of a BSS flame. Laminar premixed flat ethylene flames with an unburned composition of 16%(mol) ethylene, 24% (mol) oxygen and 60% (mol) argon were generated by a commercial McKenna burner with a stainless steel outer layer and a 6 cm-diameter-bronze water-cooled porous sintered plug. The unburned fuel and oxidizer mixture leaves the plug with an equivalence ratio, ϕ , of 2 and a cold gas velocity of 8 cm/s (298 K and 1 atm); these values have been used for the velocity and temperature inlet boundary conditions. A shroud of nitrogen isolates the flame from the surrounding air to keep the flame stabilized. An S-type thermocouple was used to measure the flame temperature. For more details, the reader is directed to [8]. A flat plate, stagnation plate, is located at a distance to the burner and parallel to it to stabilize the stagnation flame. The particles have been measured at the location of the stagnation wall. The sample was extracted through an orifice which is drilled into the stagnation wall. For the details of the sampling line the reader can refer to [8]. A type-K thermocouple embedded in the stagnation plate to measure the orifice temperature. The orifice temperature was about 465 ± 30 K during sampling; the stagnation wall temperature boundary conditions were adopted from the measured temperature profiles of [8]. The particle size distribution was measured by a Scanning Mobility Particle Sizer (SMPS, Model 3936)[8]. Three flames with different fuel composition were examined (see, Table 1). Flame A1 has no CO2 content in the unburned fuel mixture, and it is very similar to the flame studied by Abid et al. [16] and Camacho et al. [17] (ethylene C3 flame). In flames A2 and A3, 20% and 30% of argon were replaced by CO2 , respectively [8]. The work by Tang et al. [8] has been selected for this study because it contains rich measurements of soot volume fraction (fv ),

A. Naseri et al. / Combustion and Flame 183 (2017) 75–87

77

Fig. 1. Schematic representation of a burner stabilized stagnation flame, including coordinate orientation.

particle number density, and particle diameters. Most of the experimental data in the literature provide only soot volume fraction. The modeling tool that is explained in the coming section will be validated against fv , soot number density, and particle diameters measured by Tang et al. [8]. 2.2. Computational model Conservation of mass and momentum (Navier–Stokes), conservation of energy, and conservation of species compose the gas-phase governing equations. The solution of these equations describes the flow field, pressure, temperature, and gas mixture compounds. In order to solve all the equations, species production rate, transport properties, and thermodynamics properties have to be evaluated to calculate the source terms. Illustrated in Fig. 1, the flow field of this problem can be assumed two dimensional due to the axial symmetry. Using a similarity solution, by introducing two new variables as G(z ) = −ρv and F (z ) = ρ2u , the 2D domain is r transformed into a 1D domain [41]. The schematic of the 1D grid is depicted in frame (B) of Fig. 1. This capability reduces the computational burden significantly. Although there is an orifice drilled into the stagnation wall which affects the flow field, the model used in this work does not consider the existence of the orifice at the wall boundary. The OPPDIF code has been designed to simulate a premixed/diffusion counter-flow flame in a 1D domain [42]. This code can be modified to a burner stabilized stagnation using the non-slip boundary condition at the wall boundary [16,17,28]. The soot aerosol dynamics model has been coupled with the OPPDIF code to predict the soot desired properties. The code is able to generate the PSD profiles throughout the domain, but since the experimental measurements provides the PSD at the stagantion location, the numerical results will be also studied at the same location. A radiation model based on the optically thin assumption has been implemented into the energy equation to account for the particles and gas species radiation [43]. Three species including CO,CO2 , and H2 O as well as soot particles are considered for the radiation. The Plank mean absorption coefficient for gas phase species and soot particles are taken from [44] and [45], respectively. In addition to the bulk velocity of the gas phase, the ordinary diffusion and thermal diffusion (thermophoresis) velocies are also included in the model [43]. The governing equations of the stagnation

reacting flow and the modified Newton method used to solve these equations are described in [39,42,46,47]. Two chemical kinetics mechanisms, which both take advantage of recently advanced PAH formation pathways, have been utilized to describe the gas-phase reaction kinetics. The first chemical mechanism has been developed by the Clean Combustion Research Centre at King Abdullah University of Science and Technology (KAUST) [23] which comprises of 202 species and 1351 reactions. The other chemical kinetics mechanism used in this work has been developed by CRECK Modelling Group at Polytechnic University of Milan, and it will be referred as CRECK mechanism [22]. This mechanism contains 249 species and 8153 reactions. In addition to the gas phase chemistry, a detailed sectional model is incorporated to calculate the soot particle size distributions. The spacing between the sections have been kept the same for all the simulation cases. The soot particle mass range is divided into 55 sections that cover the soot particle diameter range between 1 and 120 nm. For each section two conservation equations are solved for soot aggregate and primary particle number densities, respectively. The soot sectional model used in this work includes particle nucleation, PAH surface condensation, chemical surface growth, coagulation, oxidation, and fragmentation. Frame (A) of Fig. 1 summarizes the soot formation steps considered in this study. In PAH-based soot formation models, the generation and growth of aromatic species connects the gas phase and solid phase (particles). Evidence shows that the formation of small soot particles depends on the existence of PAH species [48]. Thus, the dimerization of a pair of PAH molecules is considered as the nucleation model. The dimer formation rate is proportional to the rate of collision of PAH species [20]. According to Sabbah et al. [49], Wang [3], and Eaves et al. [40,50], the pair of PAH molecules constructing a dimer can separate due to the thermodynamic condition of the flames; thus, the presence of efficiencies in the nucleation models is necessary to account for the dimer dissociation.

PAHi + PAH j ←→ Dimer

1 ≤ i, j ≤ 3

(1)

In order to represent the nucleation process with a more fundamental model, the nucleation process has been allowed to be reversible. The nucleation model used in this work has been adopted from [40,50]. The forward rate of dimerization (kFWD ) is

78

A. Naseri et al. / Combustion and Flame 183 (2017) 75–87 Table 2 HACA–based soot surface growth and oxidation reactions[43], k = AT b e−Ea /RT .

Fig. 2. Nucleating species chemical structure for KAUST mechanism.

determined by the rate of physical collision of the nucleating PAH molecules in the free-molecular regime. Following the work by Eaves et al. [40,50], the reverse rate coefficient (kREV ) is calculated from the relationship between the dimerization equilibrium constant and rate coefficients. According to Eq. (1), the dimerization of three PAHs are allowed in the model. As a result six different dimers can form. The relation that calculates the nucleation efficiency is a function of PAH-PAH binding energy E0 and vibrational frequency. The values 69.2 KJ/mol [28] and 18 cm−1 [50] were used for binding energy and vibrational frequency, respectively. The nucleating species used for the dimerization are the largest polycyclic molecules available in the chemical kinetic mechanisms. Figure 2 depicts the molecular structure of the nucleating species. In the original KAUST mechanism, coronene is the largest PAH; however, since it is missing the oxidation reactions, it has been completely deleted from the mechanism; thus, the largest specie is anthanthrene. Once particles are formed, they can grow in mass and size through various processes. The adsorption of large gas-phase species to the particle’s surface is one of the heterogeneous gasto-particle conversions known as condensation. Similar to nucleation, the collision of condensing species and the surface of the particles forms the basis of the condensation model [51]. The PAHs that are allowed to condensate are the same as those in the nucleation process. In previously developed soot models, a 100% sticking probability upon collision is a common assumption [28]; however, based on the flame thermodynamics conditions the freshly attached PAHs can evaporate from the surface of the soot particles [3]; hence, the condensation model needs to be reversible. The condensation model that is used in the current work has been adopted from Veshkini et al. [28]. This novel model is based on an equilibrium stand point. In their study, soot particles in each section are assumed to be unique species with properties of a large PAH molecule with the same mass. The mass growth via PAH addition will transform a soot particle to a particle with larger mass. The Veshkini et al. [28] condensation model works very similar to the reversible nucleation model explained earlier. The relation which calculates the condensation efficiency is a function of binding energy and vibrational frequency. A similar binding energy as nucleation process is considered; however, according to Veshkini et al. [28], there is a lack of theoretical studies for the estimation of vibrational frequencies. Eaves et al. [50] have mentioned that for PAH stacks, e.g., an octamer, the condensation vibrational frequency ranges from 50 to 1.8 cm−1 . In general, as the PAH stack sizes increase, as would occur during the condensation process, vibration frequency decreases [28]. Eaves et al. [50] suggest that a reasonable estimate for the average added vibrational frequency for all condensation processes is 0.5 cm−1 for coflow diffusion flames. Since particle sizes in BSS flames (1–60 nm) are smaller than particles form in coflow diffusion flames (1 μm) [28], the



cm3 mol.s



No.

Reaction

A

S1 S2 S3 S4 S5 S6

Csoot –H+H ↔ Csoot◦ + H2 Csoot –H+OH ↔ Csoot◦ + H2 O Csoot◦ +H → Csoot –H Csoot◦ +C2 H2 → Csoot –H + H Csoot◦ +O2 → 2CO + product Csoot –H+OH → CO + product

4.2 × 1013 1.0 × 1010 2.0 × 1013 8.0 × 107 2.2 × 1012

 kcal 

b

Ea

0.0 0.73 0.0 1.56 0.0

13.0 1.43 0.0 3.8 7.0

γOH = 0.13

mol

size of the PAH stacks are smaller; thus, Veshkini et al. [28] selected a larger average vibrational frequency, 7.5 cm−1 . In this work, since the addition of CO2 decreases both the size and number of particles, the average condensation frequency was selected to be 16 cm−1 . As long as condensation frequency does not exceed the nucleation frequency, the model remains physically reasonable [50]. The comparison between vibrational frequencies of 7.5 and 16 cm−1 will be presented in the result and discussion section. The model is also capable of switching between constant and reversible condensation. In addition to PAH condensation, there are other soot surface interactions with the gas phase which are responsible for additional growth or oxidation. The soot surface heterogeneous reactions with the gas-phase species used in this work are expressed in Table 2. The well known HACA process is responsible for the mass growth and oxidation by oxygen [20,51]. The kinetics of the surface reactions in HACA scheme is a function of surface sites [3]. These carbon atoms could be saturated (Csoot − H) or dehydrogenated (Csoot◦ ) on the particle surface. According to Table 2, the availability of unsaturated sites on the armchair is important for either acetylene addition or oxidation by O2 . The reaction S6 in Table 2 is another source for soot oxidation whose rate can be estimated based on kinetic theory with a probability γ OH . For more details on the implementation of these reactions the reader is directed to [43]. Coagulation is the sticking of the two soot particles when they collide. The role of this process is to increase the soot aggregate size. Coagulation adds to the number density of aggregates in higher mass sections, while decreases soot aggregate concentration in lower mass sections. Thus, coagulation keeps the number of primary particles constant, while reduces the number of aggregates. The coagulation rate is calculated using binary collision rate of soot particles estimated in the entire Knudsen number regime [36,52] using a sticking probability [26]. Zhang et al. [26] improved the coagulation model by introducing a constant aggregate coagulation efficiency. Veshkini et al. [28] has performed a sensitivity analysis on the effect of coagulation efficiency on the PSD profiles; they concluded that a 100% sticking probability provides the best results for the premixed BSS flames; the same value was chosen for this work. The pattern of the particle fragmentation was adopted from Zhang et al. [26,38]. This model allows aggregates to break into two daughter aggregates with equal mass during O2 oxidation. Although the fragmentation model is included, since the oxidation is negligible, fragmentation does not affect the PSD functions. The details of the soot model can be found in [26,28,39,43,53]. Using the modelling tool, the thermal influence of CO2 can be isolated by defining a fictitious specie, named FCO2 . FCO2 is a chemically inert specie that does not react, but possesses the identical thermal properties, transport characteristics, and the third body collision efficiencies as CO2 . This method has been successfully used in [7,8] to study the CO2 addition effects on the soot precursors and volume fraction. In order to calculate the soot volume fraction and other soot properties, the particle density of 1.9 g/cm3 was assumed. Up to here, the details of the soot model and all the publications that have been used to build the model

Soot Volume Fraction, FV

A. Naseri et al. / Combustion and Flame 183 (2017) 75–87

1-8 1-9 1-10 1-11 1-12 1-13

0.00 % Exp.

0.00 % Num.

12.0 % Exp.

12.0 % Num.

18.0 % Exp.

18.0 % Num.

1-14 0.4

0.6

0.8

1

Burner-to-stagnation Surface Separation, HP (cm) Fig. 3. Comparison of the computed (circles) and measured (triangles) soot volume fraction for the addition of 0.0%, 12%, and 18% of CO2 , respectively (see Table 1).

were discussed. The premixed flame soot model package presented here will be released in the near future as a separate publication. We are currently working to make the code more user friendly and easier to use. Our coflow diffusion flame soot model package [54] has been recently published. 3. Results and discussion The results on the contribution of CO2 addition to the soot formation in BSS premixed flames are presented in the following order. The first section will discuss the model validation in which computed soot volume fraction (fv ) and particle size distributions (PSDs) are compared with measurements from [8]. Then the detailed information derived from simulations will be used to answer the three mentioned questions regarding the thermal and chemical roles of CO2 addition, contribution of HACA, nucleation, and condensation in the process. The comparison between computed (circle-solid line) and measured (triangle-dashed line) soot volume fraction, fv , is shown in Fig. 3. For the flame A1 (0.0% CO2 ) the agreement between the modelling and experimental result is good. Addition of CO2 reduces the soot volume fraction. The results for flames A2 and A3 in Fig. 3 show the model can capture the reduction of soot volume fraction, but it does not predict the correct value for all the burnerto-stagnation surface separations, HP . The simulation results are in qualitative agreement with the experimental data in capturing the CO2 suppression effect which is adequate for the purpose of this study. The measured PSDs over 6 HP reported by Tang et al. [8] are depicted in Fig. 4; this figure shows the addition of CO2 reduces both the particle sizes and numbers. The experimental data are compared with model predictions in Fig. 4. According to Tang et al. [8], the reduction of the numbers could be attributable to the lower nucleation rates due to the CO2 inhibition effects; the smaller diameter size might stem from the lower agglomeration rates due to less availability of particles or less active PAH condensation. According to Abid et al. [16], the bimodality of PSDs is an indication of PAH condensation. Following the trends observed in the experimental measurements (symbols) for the HP of 0.7, 0.8, and 1.0 cm of Fig. 12, addition of CO2 cancels the bimodality of the PSDs which indicates CO2 addition weakens PAH condensation. However, the calculated PSDs (solid lines) still show a strong bimodality, which means the model does not capture the exact effect of CO2 addition on the condensation. For the burner-to-stagnation surface separation of 0.55 cm the measurements only include the PSD for 0% CO2 [8].

79

The soot model was used to reproduce the PSDs. For each of the 6 HP of interest, three flames had to be simulated which resulted in 18 simulations. The computed and measured PSDs are compared in Fig. 4, and they show a qualitative agreement. The numerical results also support that having CO2 as a diluent in the fuel and oxidizer mixture reduces the soot formation and growth. For the flame A1 (0.0% CO2 ), the agreement of the particle sizes seems fairly good; however, for the flames A2 and A3 as the HP increases the simulation is not able to capture the spread between the points which representing the increasing amounts of CO2 . In the methodology section it was discussed that the vibrational frequency of the condensation model has changed to 16 cm−1 . The effect of frequency is mostly seen in lower HP like 0.5 cm. In the first try the suggested value by Veshkini et al. [28], 7.5 cm−1 , was used; however, the results, Fig. 5a, show a bimodal behavior which seems strange at this burner-to-stagnation surface separation. Smaller vibrational frequency means lower reversibility in condensation. We increased the frequency until we get a reasonable agreement (Fig. 5b). We had the concern that this frequency may prevent a strong condensation in lager HP ; however, after performing the simulations, we found that the frequency of 16 cm−1 works good for all the cases. Increasing the frequency of the condensation model makes it more reversible. In order to examine the under prediction of the effect of CO2 on the particle size distribution at large separation distances, two tests have been performed for the HP 0.8 cm. In the first step the effect of chemical kinetic file is studied. The comparison of KAUST and CRECK chemical kinetic files is presented in Fig. 6. A soot model with similar parameters were used to generate the PSDs of Fig. 6; the differences between frames (a) and (b) are due to the chemical mechanism and thermodynamics data. Figure 6 shows both mechanisms underpredict the spread between the lines caused by CO2 addition, but the KAUST mechanism produces slightly better results than CRECK kinetic file. This result suggests that chemical kinetic mechanism is not the cause of the misprediction. It is worthwhile to mention that the CRECK mechanism was only used to create Fig. 6, and the rest of the results are based on the KAUST mechanism. For the addition of 12% and 18% CO2 , CRECK mechanism captures 25% and 39% reduction in the average diameter size, respectively; similarly, KAUST mechanism shows 33% and 50% reduction, respectively. In the second step, the effect of stagnation surface temperature is studied. As discussed in the methodology section, the temperature measurement uncertainty is ± 30 K [8]. Since the soot formation is a strong function of temperature, and most of the soot forms close to the stagnation wall, it is interesting to study the effect of wall temperature. The wall temperature of 486 K is reported by Tang et al. [8] and is the temperature boundary condition used for the simulation of HP =0.8 cm. Considering the temperature uncertainty, the upper limit of the temperature boundary condition is 516 K. Abid et al. [16] and Camacho et al. [17] have measured the temperature and PSD for a very similar flame to 0% CO2. Camacho et al. [17] reported 523 K for the wall temperature at HP = 0.8 cm. Thus, in order to test the sensitivity of the code to the wall temperature we decided to use 523 K. The PSD comparison of the two wall temperatures, 486 K and 523 K, is depicted in Fig. 7. The experimental data and soot model settings are exactly the same for both cases while the boundary temperature has changed. Figure 7 shows an increase of 37 K in wall temperature would cause a significant change in the predicted CO2 addition effects on the size reduction. This observation is in accordance with the recent findings about the effect of temperature on the soot particles in premixed flames [55]. The PSDs were both measured and computed at the stagnation surface. According to the physics of the problem, as the temperature decreases the chance of PAHs to desorb from the soot surface reduces

A. Naseri et al. / Combustion and Flame 183 (2017) 75–87

Particle Size Distribution, dNP/dlog DP (cm-3)

80

1012

112 HP = 0.50 cm

1012 HP = 0.55 cm

1011

111

1011

110

1010

19

109

108

18

108

107 1012

17

112 111 110 19 18 17

1010 109

1

10

HP = 0.60 cm

100

1

10

107 100 1 HP = 0.80 cm

10

100 HP = 1.00 cm

100 1

10

1001

10

100

HP = 0.70 cm 1011 1010

109 108 107 1

10

Particle Diameter, DP (nm) 0.00 % Exp. 12.0 % Exp. 18.0 % Exp.

0.00 % Num. 12.0 % Num. 18.0 % Num.

Fig. 4. Comparison of the computed (solid lines) and measured (symbols) PSDs. The experimental data is adopted from [8].

PSD, dN/dLog DP (cm-3)

112

112

112

111

111

HP = 0.50 cm

110

(a)

18

(b)

111

Vibrational Frequency = 7.5 cm-1

19 110

HP = 0.50 cm Vibrational Frequency = 16.0 cm-1

110

17 1

100

19

19

18

18

17 100 1

17

1

10

10

100

Particle Diameter, DP (nm) 00.0 % Exp. 12.0 % Exp. 18.0 % Exp.

00.0 % Num. 12.0 % Num. 18.0 % Num.

Fig. 5. Comparison of the effect of vibrational frequency on the PSD functions; (a) 7.5 cm−1 and (b) 16 cm−1 .

which results in a stronger condensation. At 486 K, we get a bimodal behavior which is an indication of condensation, whereas 523 K shows a unimodal condition. Concentration calculation for the 12% CO2 addition without considering soot shows that the anthanthrene mass fraction for 486 K and 523 K is 6.68E × 10−5 and 6.73 × 10−5 , respectively, which shows the weak temperature effect on the concentration. Looking at the mass fraction values with considering soot shows 3.0565 × 10−5 for 486 K, and 4.4458 × 10−5 for 523 K. At 486 K 54% of anthanthrene is used, while at

523 K 34% of anthanthrene is used to form soot. Thus, comparison of the species concentration for 486 K and 523 K shows that the wall temperature effect on the concentration is not significant, and the effect of temperature on soot model, i.e. condensation model, accounts for this observation. Since the purpose of this work is to investigate the effect of CO2 addition on the soot formation process, hereafter we will focus on the HP 0.6 cm to find the answer to the introduced questions. The HP 0.6 cm is chosen because among the range studied, the model

A. Naseri et al. / Combustion and Flame 183 (2017) 75–87

1012

KAUST, HP = 0.80 cm

1010

(b)

100

109

108

108

107 1

10

107 100 1

10

100

Particle Diameter, DP (nm) 00.0 % Exp. 12.0 % Exp. 18.0 % Exp.

1011

110

PSD, dN/dLog DP (cm-3)

112

112

111

HP = 0.80 cm Twall = 486 K

18

1400.0

1100.0 A1 0% CO2 A2 12% CO2 A3 18% CO2 Exp.

800.0 500.0 0.2

c)

0.4

Flame front

0.0006

1

0.225

0.0004

A1 0% CO2 A2 12% CO2 A3 18% CO2 0

0.0002

0.2

0.0000 0.6 0.55

0.4

0.6

X(cm)

100

19

108

Fig. 8. CO2 addition effect on the temperature profiles (a), zoomed-in temperature profile (b), soot volume fractions (c), and zoomed-in soot volume fraction (d) for the HP 0.6 cm.

18

107 1

0.125

d)

0.0006

0.0004

17

109

800.0 0.025

0.0010 0.6

0.0000

(b)

110

1100.0

0.0008

0.0002

(a)

19

1010

1400.0

HP = 0.80 cm Twall = 523 K

111

b)

1700.0

0.0008

Fig. 6. Particle size distribution function for (a) KAUST mechanism and (b) CRECK mechanism.

1012

HP = 0.6 cm

1700.0

200.0 0.0010 0

00.0 % Num. 12.0 % Num. 18.0 % Num.

2000.0

a)

2000.0

1010 1

109

2300.0

CRECK, HP = 0.80 cm

(a) 1011

112 111 110 19 18 17

Temperature (K)

1011

fV (ppm)

PSD, dN/dLog DP (cm-3)

1012

81

17 100 1

10

10

8.0

100

Particle Diameter, DP (nm) 00.0 % Num. 12.0 % Num. 18.0 % Num.

Fig. 7. PSD comparison for the original temperature boundary condition (486K), frame (a); and another reported temperature (523K) [55], frame (b).

6.0 CP/Ru

00.0 % Exp. 12.0 % Exp. 18.0 % Exp.

4.0 2.0 CO2

Ar

0.0 500 captures the reduction in diameter fairly good and both nucleation and condensation are present at this HP . The temperature boundary condition, i.e. 471 K, has been adopted from measurements by Tang et al. [8]. The temperature profile and soot volume fraction for 0, 12, and 18 percent CO2 addition are depicted for the HP 0.6 cm along the flame axis in Fig. 8. The temperature used for this validation is for 0% CO2 . Tang et al. [8] only provided the temperature profile for 0% CO2 flames. Addition of CO2 does not change the peak temperature value significantly, and the computed temperature profiles are in a good agreement with the experimental measurements (frames a and b of Fig. 8). Frames c and d of Fig. 8 show that the addition of CO2 reduces the soot volume fraction noticeably; in addition, this figure shows most of the mass growth takes place very close to the wall. Based on the soot volume fraction slope, the mass growth starts at around 880 K, and as the particles get closer to the wall, the growth becomes stronger. According to the model used in this work, there are two reasons to explain the observed phenomenon: first, as particles approach the wall, the residence time increases which increases their chance to interact with more gasphase species and smaller particles; second, the temperature close to the wall falls to around 500 K; the condensation process is a strong function of temperature. As the temperature decreases both PAH adsorption and desorption reduce; however, the rate of PAH desorption relative to adsorption decreases more which results in effective PAH condensation. Finally, since the thermophoresis effect is also included in the model, the large temperature gradient close to the cold wall pushes all the particles towards the stagnation wall.

1000 T(K) 1500

2000

Fig. 9. Comparison of the specific heat capacity of CO2 and Ar. The value CP /Ru is dimensionless.

3.1. Thermal effects This section seeks the answer to the first question of interest regarding the thermal or chemical influence of CO2 addition. The answer to this question cannot be found from experimental investigation since it is not possible to conduct experiments in which the thermal and dilution effects of CO2 could be separated from its chemical effects. The thermal influence can be isolated by defining a fictitious CO2 specie. Comparison of the computed temperature profiles of the flames diluted with CO2 and FCO2 reveals the temperature profile is only a function of thermal properties of the species rather their chemical reactions. According to the modelling results, the reactions that include CO2 as their reactants do not affect temperature. The computed temperature profiles for both CO2 and FCO2 are exactly the same (see the supplementary material section, Fig. 19). The chemical reactions of CO2 do not change the temperature profile. The temperature profile is a function of thermal characteristics of CO2 . The slight difference in the peak value of the temperature is attributable to the different values of argon and carbon dioxide heat capacities as well as the CO2 content. Figure 9 compares the nondimensionalized specific heat capacity of argon and carbon dioxide versus temperature. Carbon dioxide has a greater heat capacity than argon which causes the flame temperature to reduce.

82

A. Naseri et al. / Combustion and Flame 183 (2017) 75–87

PSD, dN/dLog DP (cm-3)

1012

112

112

HP = 0.60 cm CO2

111

(a)

110

1011

HP = 0.60 cm FCO2 (b)

111

19

18

1010

110

17 1

100

109

19

108

18

107

17

1

10

100 1

10

Particle Diameter, DP (nm) 00.0 % Exp. 12.0 % Exp. 18.0 % Exp.

100

00.0 % Num. 12.0 % Num. 18.0 % Num.

Fig. 10. Comparison of the effect of CO2 (frame (a)) and chemically inert specie FCO2 (frame (b)) on the PSDs.

For the addition of 0.0% CO2 or FCO2 , the model generates the same PSDs, solid navy blue lines in Fig. 10. Replacing 12.0% and 18.0% of argon with CO2 shows a significant diameter reduction in frame (a) of Fig. 10; frame (b), which only reflects the thermal effect, shows a slight size reduction compared to frame (a). Since the axes are in log-scale, the figure may not reflect the extent of reduction. The size reduction observed in frame (b) is attributable to higher specific heat of CO2 compared to argon. In order to elaborate the thermal effects of CO2 , similar comparisons for a few prominent species have been performed. Acetylene is one of the most important species in terms of PAH formation and growth. Any changes in acetylene concentration affects the PAH formation and result in noticeable variations in soot. The frame (b) of Fig. 11 shows that addition of FCO2 does not change the concentration of acetylene in the post flame region. The flame front is located at 0.1 cm, and the slight difference in acetylene concentration seen in preflame zone is due to the temperature differences; however, frame (a) clearly illustrates the chemical role of CO2 in acetylene suppression. A similar trend can be observed in benzene and pyrene (A4) mole fractions which are depicted in Figs. 12 and 13, respectively. All the concentration plots have been computed with the inclusion of the soot model. Similar results without considering soot formation are available in the supplementary material section. In sum, CO2 dilution suppresses the soot formation both chemically and thermally, but the chemical effect is more significant. The modelling capability can be used to investigate the chemistry in more depth to track the reactions that are behind this phenomenon. 3.2. Chemical effect Based on the discussion in Section 3.1, the nucleating species are of great importance since they are the bridge between the gasphase chemistry and condensed-phase particles. As a result, the current section tries to investigate the chemical reactions in order to find the exact role of CO2 in the observed phenomenon. The computed mole fractions of H radical, hydroxyl, acetylene and benzene, four major species, have been depicted for the HP 0.6 cm in Fig. 14. These concentrations have been computed with the soot model activated. Similar results without considering soot formation are available in the supplementary material section. Acetylene is the precursor to formation of the first aromatic ring, benzene, and the building block of the growth of larger PAHs. Benzene is the base molecule that larger PAHs form from this species through different pathways; thus, differences in the mole fraction of benzene due to addition of CO2 will result in a difference in nucleating species, PAHs with 6 or 7 rings, and soot

yield. Figure 14 shows that replacing 12% and 18% of Ar with CO2 causes the concentration of acetylene to reduce 15.6% and 21.5%, respectively; the reduction for benzene is 20.1% and 29.2%, respectively. In this study, dimers are assumed to form via physical coalescence of anthanthrene, benzo[ghi]perylene, and benzo(ghi)fluoranthene. These species, known as nucleating species, are key inputs to the soot model; moreover, the same species are allowed to condense on the surface of soot particles in the condensation growth model. Figure 15 compares the nucleating species concentrations for different CO2 contents for the HP 0.6 cm. The summation of the 3 PAHs at the stagnation surface location have been normalized for the flames A1, A2, and A3 over a range of HP in frame (b) of Fig. 15. This figure shows, addition of 12% and 18% CO2 results in 38.2% and 51.2 % PAH suppression for the flames A2 and A3, respectively. This reduction in the concentration of PAHs will result in a reduction in both soot nucleation and condensation as shown in Fig. 4. In order to understand the role of CO2 in this process and answer the second question, a thorough species sensitivity analysis is required. The reaction pathway analysis tool of the CHEMKIN pro software (CHEMKIN-PRO 15131, Reaction Design: San Diego, 2013) has been used to perform a sensitivity analysis on the species of interest including anthanthrene, acetylene, benzene, carbon monoxide, carbon dioxide, hydroxyl radical, and hydrogen radical without considering soot. The mentioned tool is capable of drawing reaction pathways and calculating the reaction sensitivity to track the most influential reactions for a specific specie. .For the location of the analysis, the inflection point of each specie’s concentration profile was selected. For instance, x = 0.05 cm and x=0.2 cm were selected for acetylene and anthanthrene, respectively. Since nucleating species are the bridge between gas-phase chemistry and solid particles, the sensitivity analysis was performed on them. Figure 16 shows the sensitivity analysis of anthanthrene. Contrarily to what is claimed about the reaction CO2 +H + CO+OH, the sensitivity analysis results shown in Fig. 16 do not include this reaction among the 30 reactions which means it is not playing a major role. Instead, the following reaction appears after addition of CO2 which does not exist in the base flame, A1.

CH∗2 + CO2 ↔ CH2 O + CO

(2)

CH∗2

where is activated methylene and CH2 O is formaldehyde. Interestingly, reaction 2 contains CO2 and CO. A direct relation between the participant species of the mentioned reaction and anthanthrene to account for the suppression effect was not found which means the role of reaction (2) should be investigated in the precursors species. CO2 affects the formation of acetylene and benzene which are the building blocks of PAHs. The differences observed in the concentration of acetylene and benzene in Fig. 14 stem from the reaction CH∗2 + CO2 ↔ CH2 O + CO. Addition of CO2 depletes the CH∗2 radical which is a precursor to the formation of H2 CC via the following reaction:

CH∗2 + C2 H4 ↔ H2 CC + CH4 .

(3)

H2 CC forms acetylene through the reaction:

H2 CC(+M ) ↔ C2 H2 (+M );

(4)

consequently, addition of CO2 reduces the acetylene formation. There are other acetylene formation pathways in KAUST mechanism [23], but reactions (2)–(4) are the ones which represent the effect of CO2 addition. In terms of benzene formation, CH∗2 is a precursor to propargyl, C3 H3 :

C2 H2 + CH∗2 ↔ C3 H3 + H;

(5)

and the recombination of propargyl C3 H3 (+M ) ↔ C6 H6 (+M ) has been recognized as one of the major pathways to form benzene

A. Naseri et al. / Combustion and Flame 183 (2017) 75–87

C2H2 Mole Fraction

2.5E-2

2.5E-2

(a)

CO2

2.0E-2

2.0E-2

1.5E-2

1.5E-2

1.0E-2

1.0E-2

5.0E-3

5.0E-3

0.0E+0 0.0

0.2

0.4

83

(b)

FCO2

00.0 % 12.0 % 18.0 %

0.0E+0 0.6 0.0

0.2

0.4

0.6

X(cm) Fig. 11. Comparison of the effect of CO2 (frame (a)) and chemically inert specie FCO2 (frame (b)) on the concentration of C2 H2 .

A1 Mole Fraction

4.0E-4

(a)

(b)

CO2

FCO2

3.0E-4

2.0E-4

1.0E-4

00.0 % 12.0 % 18.0 %

0.0E+0 0.0

0.2

0.4

0.6 0.0

0.2

0.4

0.6

X(cm) Fig. 12. Comparison of the effect of CO2 (frame (a)) and chemically inert specie FCO2 (frame (b)) on the concentration of benzene (C6 H6 ).

A4 Mole Fraction

4.0E-6

(a)

CO2

(b)

FCO2

3.2E-6 2.4E-6 1.6E-6

00.0 % 12.0 % 18.0 %

8.0E-7 0.0E+0 0.0

0.2

0.4

0.0E+0 0.6 0.0

0.2

0.4

0.6

X(cm) Fig. 13. Comparison of the effect of CO2 (frame (a)) and chemically inert specie FCO2 (frame (b)) on the concentration of pyrene (C16 H10 ).

[23]. Addition of CO2 reduces the concentration of both C2 H2 and CH∗2 ; these two species are the feed stock to the reaction 5. Thus, the effect of CO2 addition on the benzene formation would be more intense compared to acetylene. Additionally, the reaction 5 can account for the H radical reduction observed in Fig. 14. In conclusion, CO2 reduces the concentration of acetylene and benzene by depleting the CH∗2 radicals which consequently results in less formation of PAHs. The addition of CO2 changes the concentration of H and OH. Concentration of H radical affect the formation and growth of soot via HACA; however, HACA effect is not noticeable in BSS flames. Soot forms very close to the stagnation wall (Fig. 8) where the concentration of H is almost zero. The effect of CO2 addition on the soot is through PAH suppression. The

sensitivity analysis on the PAHs does not show the reaction CO2 +H + CO+OH. Instead, reactions including CH∗2 were found to account for the observed effect. 3.3. HACA, nucleation, and condensation effects This section seeks the answer to the third question; between the nucleation and condensation, which step is affected more. In order to compare the effect of CO2 on nucleation and condensation, the HP 0.6 cm has been selected because the model captures the reduction in diameter fairly well and both nucleation and condensation are present at this HP . In addition to condensation, there is another surface growth mechanism included in the model which

84

A. Naseri et al. / Combustion and Flame 183 (2017) 75–87

1.0E-4

6.0E-4

OH

H 7.5E-5 4.0E-4 5.0E-5

2.0E-4

Mole Fraction

2.5E-5

0.0E+0 2.5E-2 0.0

0.2

0.0E+0 0.6 4.0E-4 0.0

0.4

0.2

0.4

C2H2

C6H6

2.0E-2

3.0E-4

1.5E-2 2.0E-4 1.0E-2 1.0E-4

5.0E-3

0.0E+0

0.0E+0 0.0

0.2

0.4

0.6

A1 0.0 % CO2

0.0

0.2

X(cm)

A2 12.0% CO2

0.4

0.6

A3 18.0% CO2

Fig. 14. Effect of CO2 addition on the major species including hydrogen radical, hydroxyl, acetylene, and benzene for the HP 0.6 cm.

1.6E-6

Normalized PAH Level

Benzo[ghi]perylene

0.5

0.4

1.2E-6

0.3 8.0E-7

0.2 0.1

Mole Fraction

4.0E-7

0.0 0.5

0.0E+0 5.6E-6 0.0

0.6 HP (cm)

(a) 0.2

0.4

0.6 5.6E-6

A4R5 benzo(ghi)fluoranthene

4.8E-6

4.8E-6

4.0E-6

4.0E-6

3.2E-6

3.2E-6

2.4E-6

2.4E-6

1.6E-6

1.6E-6

8.0E-7

8.0E-7

0.0E+0

0.0E+0 0.6 0.0

0.2

0.4

(b)

Anthanthrene

(c)

0.0

0.8

(d) 0.2

0.4

0.6

X(cm) 00.0 %

12.0 %

18.0 %

Fig. 15. Effect of CO2 addition on the nucleating species concentrations for the HP 0.6 cm. Frame (b) represents the normalized PAH summation at the stagnation plane over a range of HP .

A. Naseri et al. / Combustion and Flame 183 (2017) 75–87

85

Fig. 16. Normalized sensitivity analysis of anthanthrene, compared at x = 0.2 cm for flames A1 (0.0% CO2 ), A2 (12.0% CO2 ), and A3 (18.0% CO2 ) on the HP 0.6 cm. Reactions include CO2 and CH∗2 are marked by green.

1.E-05

Soot Mass Comparison for HP = 0.6 cm

Mass of Soot 1.E-04

00.0% CO2 12.0% CO2 18.0% CO2

g soot / cm3 gas

g soot / cm3 gas

1.E-03

1.E-07 1.E-09 1.E-11

-80% -52%

-95%

1.E-05

-84%

-55% -37% 1.E-06

1.E-13 HACA

Nucleation Condensation

Total

1.E-07

Nucleation

Fig. 17. Comparison of soot mass generated by HACA, nucleation, and condensation for the HP 0.6 cm.

is called HACA surface growth which has been explained. Before comparing the influence of CO2 on the condensation and nucleation, it is worthwhile to discuss the effect of CO2 on HACA process. According to Tang et al. [8], CO2 reduces soot growth through HACA. In order to investigate this hypothesis, Fig. 17 compares the mass of soot generated by HACA, Nucleation, and Condensation separately for each percentage of CO2 . The vertical axis of the figure is in log scale. This figure shows that the addition of 12% and 18% CO2 reduces the HACA surface growth by 80% and 52% respectively. However, HACA mass growth is insignificant compared to nucleation and condensation growth. Figure 18 compares the mass of soot derived from nucleation and condensation for each percentage of CO2 separately. The vertical axis is in log scale. If we calculate the reduction of nucleation mass for 12% and 18% CO2 we get 55% and 37% reduction, respectively. Similar calculations for condensation show 95% and 84% reduction, respectively. The greater reductions observed in the condensation mass compared to the nucleation indicates addition

Condenstaion

Total

Fig. 18. Comparison of the nucleation mass, condensation mass, and total mass of soot for the HP 0.6 cm.

of CO2 reduces soot formation mostly via suppressing condensation step. It is worth mentioning that there is also a feedback loop among the nucleation and condensation processes; less nucleation causes less condensation because nucleation provides more surface area. 4. Conclusions In general, the model qualitatively captures the effect of CO2 addition on the PSDs and fv . The prediction of size and fv for 0% CO2 addition is in reasonable agreement with measurements, while predictions for 12% CO2 is acceptable; for 18% CO2 addition, neither the size nor fv agrees well with the experimental results. Predictions in size for lower burner-to-stagnation surface separations, e.g., 0.5 cm, is fine; however, as the distance between burner

86

A. Naseri et al. / Combustion and Flame 183 (2017) 75–87

and stagnation surface increases the model fails to capture the effect of CO2 on the spread between the diameters compared to the experiment. By increasing of the burner-to-stagnation surface separation, the condensation grows stronger. The condensation efficiency equation used in the reversible model is a strong function of temperature and PAH concentration; thus, the misprediction of particle sizes could be attributable to either temperature boundary condition or chemical kinetic mechanism. The role of both elements has been examined; it was shown that the effect of temperature is stronger. CO2 addition influences the soot formation via thermal and chemical factors. In order to separate the chemical and thermal effects a fictitious species, FCO2 , which only has the thermal properties of CO2 has been introduced into the chemical mechanism. Running simulations with FCO2 rather than CO2 reduces the average diameter of the particles from A1 to A2 and A3 by 18% and 27%, respectively; however, similar simulation with CO2 reveals that average particle sizes from A1 to A2 and A3 would reduce by 60% and 71%, respectively. This shows that chemical effect of CO2 addition is stronger than the thermal effect. In order to find out the chemical reaction that is behind this suppression capability of CO2 , an intensive sensitivity analysis has been performed. Sensitivity analysis revealed CO2 addition does not have a direct effect on large PAHs; it affects the formation of acetylene and benzene which are the building blocks of PAHs. The differences observed in the concentration of acetylene and benzene stem from the reaction CH∗2 + CO2 ↔ CH2 O + CO. A thorough mass analysis has been performed to separate the effect of CO2 addition on the nucleation and condensation. The simulation results show that most of the mass formed during the soot formation process comes from PAH condensation, and the CO2 effects on PAH condensation is more important than on nucleation. CO2 addition reduces the concentration of large PAHs; as a result, the condensation also grows weaker. The experimental results show that addition of CO2 reduces the bimodality of the PSDs which indicates lack of strong condensation. Supplementary material Supplementary material associated with this article can be found, in the online version, at 10.1016/j.combustflame.2017.04.028. References [1] K. Orhan, R. Mayerle, W.W. Pandoe, Assesment of energy production potential from tidal stream currents in indonesia, Energy Procedia 76 (2015) 7–16, doi:10.1016/j.egypro.2015.07.834. [2] J. Conti, P. Holtberg, S. Napolitano, A.M. Schaall, World Energy Outlook 2014, IEA, Washington DC (2014). [3] H. Wang, Formation of nascent soot and other condensed-phase materials in flames, Proc. Combust. Inst. 33 (1) (2011) 41–67, doi:10.1016/j.proci.2010.09. 009. [4] Y. Li, D.K. Henze, D. Jack, B.H. Henderson, P.L. Kinney, Assessing public health burden associated with exposure to ambient black carbon in the United States., Sci. Total Environ. 539 (2016) 515–525, doi:10.1016/j.scitotenv.2015.08.129. [5] M.T. Lund, T.K. Berntsen, C. Heyes, Z. Klimont, B.H. Samset, Global and regional climate impacts of black carbon and co-emitted species from the onroad diesel sector, Atmos. Environ. 98 (2014) 50–58, doi:10.1016/j.atmosenv. 2014.08.033. [6] V. Ramanathan, G. Carmichael, Global and regional climate changes due to black carbon, Nat. Geosci. 1 (4) (2008) 221–227, doi:10.1038/ngeo156. [7] F. Liu, A.E. Karata, Ö.L. Gülder, M. Gu, Numerical and experimental study of the influence of CO2 and N2 dilution on soot formation in laminar coflow C2H4/air diffusion flames at pressures between 5 and 20atm, Combust. Flame 162 (5) (2015) 2231–2247, doi:10.1016/j.combustflame.2015.01.020. [8] Q. Tang, J. Mei, X. You, Effects of CO2 addition on the evolution of particle size distribution functions in premixed ethylene flame, Combust. Flame 165 (2016) 424–432. [9] K.P. Schug, Y. Manheimer-Timnat, P. Yaccarino, I. Glassman, Sooting behavior of gaseous hydrocarbon diffusion flames and the influence of additives, Combust. Sci. Technol. 22 (5–6) (2007) 235–250, doi:10.1080/00102208008952387. [10] R.K.A. Kailasanathan, T.L. Yelverton, T. Fang, W.L. Roberts, Effect of diluents on soot precursor formation and temperature in ethylene laminar diffusion flames, Combust. Flame 160 (3) (2013) 656–670.

[11] D. Du, The influence of carbon dioxide and oxygen as additives on soot formation in diffusion flames, Symp. (Int.) Combust. 23 (1990) 1501. [12] C. Zhang, A. Atreya, K. Lee, Sooting structure of methane counterflow diffusion flames with preheated reactants and dilution by products of combustion, Symp. (Int.) Combust. 24 (1) (1992) 1049–1057, doi:10.1016/S0082-0784(06) 80124-4. [13] F. Liu, H. Guo, G.J. Smallwood, Ö.L. Gülder, The chemical effects of carbon dioxide as an additive in an ethylene diffusion flame: implications for soot and NOx formation, Combust. Flame 125 (1–2) (2001) 778–787, doi:10.1016/ S0 010-2180(0 0)0 0241-8. [14] H. Guo, G.J. Smallwood, A numerical study on the influence of CO2 addition on soot formation in an ethylene/air diffusion flame, Combusti. Sci. Technol. 180 (10–11) (2008) 1695–1708, doi:10.1080/0 010220 0802258072. [15] Y. Zhang, C. Lou, D. Liu, Y. Li, L. Ruan, Chemical Effects of CO2 Concentration on Soot Formation in Jet-stirred/Plug-flow Reactor, Chin. J. Chem. Eng. 21 (11) (2013) 1269–1283, doi:10.1016/S1004- 9541(13)60624- 2. [16] A.D. Abid, J. Camacho, D.A. Sheen, H. Wang, Quantitative measurement of soot particle size distribution in premixed flames â;; The burner-stabilized stagnation flame approach, Combust. Flame 156 (10) (2009) 1862–1870, doi:10.1016/ j.combustflame.2009.05.010. [17] J. Camacho, C. Liu, C. Gu, H. Lin, Z. Huang, Q. Tang, X. You, C. Saggese, Y. Li, H. Jung, L. Deng, I. Wlokas, H. Wang, Mobility size and mass of nascent soot particles in a benchmark premixed ethylene flame, Combust. Flame 162 (10) (2015) 3810–3822. http://dx.doi.org/10.1016/j.combustflame.2015.07.018. [18] M. Abián, A. Millera, R. Bilbao, M. Alzueta, Experimental study on the effect of different CO2 concentrations on soot and gas products from ethylene thermal decomposition, Fuel 91 (1) (2012) 307–312, doi:10.1016/j.fuel.2011.06.064. [19] P.D. Teini, D.M. Karwat, A. Atreya, The effect of CO2/H2O on the formation of soot particles in the homogeneous environment of a rapid compression facility, Combust. Flame 159 (3) (2012) 1090–1099, doi:10.1016/j.combustflame.2011.10. 002. [20] J. Appel, H. Bockhorn, M. Frenklach, Kinetic modeling of soot formation with detailed chemistry and physics: laminar premixed flames of C2 hydrocarbons, Combust. Flame 121 (1) (20 0 0) 122–136, doi:10.1016/S0010-2180(99)00135-2. [21] N.A. Slavinskaya, U. Riedel, S.B. Dworkin, M.J. Thomson, Detailed numerical modeling of PAH formation and growth in non-premixed ethylene and ethane flames, Combust. Flame 159 (3) (2012) 979–995, doi:10.1016/j.combustflame. 2011.10.005. [22] E. Ranzi, A. Frassoldati, R. Grana, A. Cuoci, T. Faravelli, A. Kelley, C. Law, Hierarchical and comparative kinetic modeling of laminar flame speeds of hydrocarbon and oxygenated fuels, Progr. Energy Combust. Sci. 38 (4) (2012) 468–501, doi:10.1016/j.pecs.2012.03.004. [23] Y. Wang, A. Raj, S.H. Chung, A PAH growth mechanism and synergistic effect on PAH formation in counterflow diffusion flames, Combust. Flame 160 (9) (2013) 1667–1676, doi:10.1016/j.combustflame.2013.03.013. [24] M. Smooke, M. Long, B. Connelly, M. Colket, R. Hall, Soot formation in laminar diffusion flames, Combust. Flame 143 (4) (2005) 613–628, doi:10.1016/j. combustflame.2005.08.028. [25] M. Sirignano, J. Kent, A. DAnna, Modeling formation and oxidation of soot in nonpremixed flames, Energy Fuels 27 (4) (2013) 2303–2315. [26] Q. Zhang, M. Thomson, H. Guo, F. Liu, G. Smallwood, A numerical study of soot aggregate formation in a laminar coflow diffusion flame, Combust. Flame 156 (3) (2009) 697–705, doi:10.1016/j.combustflame.2008.10.022. [27] C. Saggese, S. Ferrario, J. Camacho, A. Cuoci, A. Frassoldati, E. Ranzi, H. Wang, T. Faravelli, Kinetic modeling of particle size distribution of soot in a premixed burner-stabilized stagnation ethylene flame, Combust. Flame 162 (9) (2015) 3356–3369. [28] A. Veshkini, N.A. Eaves, S.B. Dworkin, M.J. Thomson, Application of PAHcondensation reversibility in modeling soot growth in laminar premixed and nonpremixed flames, Combust. Flame 167 (2016) 335–352. http://dx.doi.org/ 10.1016/j.combustflame.2016.02.024. [29] R. Lindstedt, B. Waldheim, Modeling of soot particle size distributions in premixed stagnation flow flames, Proc. Combust. Inst. 34 (1) (2013) 1861–1868, doi:10.1016/j.proci.2012.05.047. [30] J. Appel, H. Bockhorn, M. Wulkow, A detailed numerical study of the evolution of soot particle size distributions in laminar premixed flames, Chemosphere 42 (5) (2001) 635–645, doi:10.1016/S0 045-6535(0 0)0 0237-X. [31] P. Vlasov, J. Warnatz, Detailed kinetic modeling of soot formation in hydrocarbon pyrolysis behind shock waves, Proc. Combust. Inst. 29 (2) (2002) 2335– 2341, doi:10.1016/S1540- 7489(02)80284- X. [32] J. Singh, M. Balthasar, M. Kraft, W. Wagner, Stochastic modeling of soot particle size and age distributions in laminar premixed flames, Proc. Combust. Inst. 30 (1) (2005) 1457–1465, doi:10.1016/j.proci.2004.08.120. [33] E.K. Yapp, D. Chen, J. Akroyd, S. Mosbach, M. Kraft, J. Camacho, H. Wang, Numerical simulation and parametric sensitivity study of particle size distributions in a burner-stabilised stagnation flame, Combust. Flame 162 (6) (2015) 2569–2581, doi:10.1016/j.combustflame.2015.03.006. [34] G. Blanquart, H. Pitsch, Analyzing the effects of temperature on soot formation with a joint volume-surface-hydrogen model, Combust. Flame 156 (8) (2009) 1614–1626, doi:10.1016/j.combustflame.2009.04.010. [35] M. Frenklach, H. Wang, Detailed mechanism and modeling of soot particle formation, Soot Formation in Combustion, Springer (1994), pp. 165–192. [36] S.H. Park, S.N. Rogak, W.K. Bushe, J.Z. Wen, M.J. Thomson, An aerosol model to predict size and structure of soot particles, Combust. Theory Model. 9 (3) (2005) 499–513, doi:10.1080/1364783050 01950 05.

A. Naseri et al. / Combustion and Flame 183 (2017) 75–87 [37] J. Wen, M. Thomson, M. Lightstone, S. Rogak, Detailed kinetic modeling of carbonaceous nanoparticle inception and surface growth during the pyrolysis of c6h6 behind shock waves, Energy fuels 20 (2) (2006) 547–559. [38] Q. Zhang, M.J. Thomson, H. Guo, F. Liu, G.J. Smallwood, Modeling of oxidationdriven soot aggregate fragmentation in a laminar coflow diffusion flame, Combust. Sci. Technol. 182 (4–6) (2010) 491–504, doi:10.1080/0 010220 0903463050. [39] D. SB., Serial and distributed-memory parallel computation of sooting, steady and time-dependent, laminar flames using a modified vorticity-velocity formulation, Yale University, 2009 Ph.D. thesis. [40] N.A. Eaves, S.B. Dworkin, M.J. Thomson, Assessing relative contributions of PAHs to soot mass by reversible heterogeneous nucleation and condensation, Proc. Combust. Inst. 36 (1) (2017) 935–945. http://dx.doi.org/10.1016/j.proci. 2016.06.051. [41] R.J. Kee, J.A. Miller, G.H. Evans, G. Dixon-Lewis, A computational model of the structure and extinction of strained, opposed flow, premixed methane-air flames, Symposium (International) on Combustion 22 (1) (1989) 1479–1494, doi:10.1016/S0082-0784(89)80158-4. [42] A. Lutz, R. Kee, J. Grcar, F. Rupley, OPPDIF: A Fortran program for computing opposed-flow diffusion flames, Technical Report, Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States), 1997, doi:10.2172/ 568983. [43] A. Veshkini, Understanding soot particle growth chemistry and particle sizing using a novel soot growth and formation model, University of Toronto, 2015 Ph.D. thesis. [44] Y. Ju, H. Guo, K. Maruta, F. Liu, On the extinction limit and flammability limit of non-adiabatic stretched methane air premixed flames, J. Fluid Mech. 342 (1997) 315–334. [45] G.L. Hubbard, C.L. Tien, Infrared Mean Absorption Coefficients of Luminous Flames and Smoke, J. Heat Transf. 100 (2) (1978) 235, doi:10.1115/1.3450788. [46] R. Kee, J. Grcar, M. Smooke, J. Miller, E. Meeks, A fortran program for modelling steady laminar one-dimensional premixed flames, Reaction Design (20 0 0). [47] J. Grcar, The twopnt program for boundary value problems: Version 3.10 of March 1992 (1992).

87

[48] A. McIlroy, G. McRae, V. Sick, D.L. Siebers, C.K. Westbrook, P.J. Smith, C. Taatjes, A. Trouve, A.F. Wagner, E. Rohlfing, D. Manley, F. Tully, R. Hilderbrandt, W. Green, D. Marceau, J. O’Neal, M. Lyday, F. Cebulski, T.R. Garcia, D. Strong, >Basic research needs for clean and efficient combustion of 21st Century transportation fuels, Technical Report, USDOE Office of Science (SC) (United States), 2006, doi:10.2172/935428. [49] H. Sabbah, L. Biennier, S.J. Klippenstein, I.R. Sims, B.R. Rowe, Exploring the Role of PAHs in the Formation of Soot: Pyrene Dimerization, J. Phys. Chem. 1 (2010) 2962–2967, doi:10.1021/jz101033t. [50] N. Eaves, S. Dworkin, M. Thomson, The importance of reversibility in modeling soot nucleation and condensation processes, Proc. Combust. Inst. 35 (2) (2015) 1787–1794, doi:10.1016/j.proci.2014.05.036. [51] M. Frenklach, H. Wang, Detailed modeling of soot particle nucleation and growth, Symp. (Int.) Combust. 23 (1) (1991) 1559–1566, doi:10.1016/ S0082- 0784(06)80426- 1. [52] J. Wen, M. Thomson, M. Lightstone, S. Rogak, Detailed kinetic modeling of carbonaceous nanoparticle inception and surface growth during the pyrolysis of c6h6 behind shock waves, Energy fuels 20 (2) (2006) 547–559. [53] Q. Zhang, H. Guo, F. Liu, G.J. Smallwood, M.J. Thomson, Implementation of an advanced fixed sectional aerosol dynamics model with soot aggregate formation in a laminar methane/air coflow diffusion flame, Combust. Theory Model. 12 (4) (2008) 621–641, doi:10.1080/13647830801966153. [54] N.A. Eaves, Q. Zhang, F. Liu, H. Guo, S.B. Dworkin, M.J. Thomson, Coflame: A refined and validated numerical algorithm for modeling sooting laminar coflow diffusion flames, Comput. Phys. Commun. 207 (2016) 464–477. http: //dx.doi.org/10.1016/j.cpc.2016.06.016. [55] J. Camacho, A.V. Singh, W. Wang, R. Shan, E.K. Yapp, D. Chen, M. Kraft, H. Wang, Soot particle size distributions in premixed stretch-stabilized flat ethyleneoxygenargon flames, Proc. Combust. Inst. 36 (1) (2017) 1001–1009. http://dx.doi.org/10.1016/j.proci.2016.06.170.