Soot Predictions Research Articles

Two approaches for constructing multivariate injection models for prefilming airblast atomizers

More realistic droplet starting conditions for Euler–Lagrangian simulations enable e.g. more precise soot prediction in jet engines. Up to now, mainly the droplet size distribution of sprays is considered, but not the multivariate dependence structure of droplet size, starting position and initial velocity. A novel concept for extracting multivariate spray data efficiently from detailed simulations of the atomizing process into high fidelity Euler–Lagrangian simulations of spray combustion is presented in this paper. Therefore, simulations of a prefilming airblast atomizer using the Smoothed Particle Hydrodynamics method are considered. The multivariate dependence structure in the spray is identified using rank transformation. Two models of different nature are proposed which are able to reproduce the multivariate dependence structure. The first model follows a data-driven approach using vine copulas and marginal distributions. In contrast, the second model is based on human knowledge and assumptions enabling deeper insights into the atomization process. Both models demonstrated to reproduce the multivariate character of the spray data effectively. An assessment of their capabilities reveals that the first model might be more suitable for spray data of annular injectors.

Prediction of Soot in an RQL Burner Using a Semi-Detailed Jeta-1 Chemistry

Abstract The present work proposes a methodology to include accurate kinetics for soot modeling taking into account real fuel complexity in Large Eddy Simulation of aeronautical engines at a reasonable computational cost. The methodology is based on the construction of an analytically reduced kinetic mechanism describing both combustion and gaseous soot precursors growth with sufficient accuracy on selected target properties. This is achieved in several steps, starting from the selection of the detailed kinetic model for combustion and soot precursors growth, followed by the determination of a fuel surrogate model describing the complex real fuel blend. Finally the selected kinetic model is analytically reduced with the code ARCANE while controlling the error on flame properties and soot prediction for the considered fuel surrogate. To perform all evaluation and reduction tests on canonical sooting flames, a Discrete Sectional Model for soot has been implemented in Cantera. The resulting code (Cantera-soot) is now available for the fast calculation of soot production in laminar flames for any fuel. The obtained reduced kinetic scheme is finally validated in a Rich-Quench-Lean burner of the literature in terms of soot prediction capabilities by comparison of LES coupled to the Lagrangian Soot Tracking model with measurements. Results show a significant improvement of the soot level prediction when using the reduced more realistic kinetics, which also allows a more detailed analysis of the soot emission mechanisms. This demonstrates the gain in accuracy obtained with improved reduced kinetics, and validates the methodology to build such schemes.

A Combustion Kinetic Model Based on the Fast Chemistry Assumption: Application to Non-Sooty and Sooty Laminar Diffusion Flames

ABSTRACT In this article, a combustion kinetic model based on the fast chemistry (FC) assumption is proposed to compute the finite reaction rate of gaseous species in laminar diffusion flames. The algorithm developed for this purpose is numerically robust and relies on the adiabatic flame temperature to limit the fuel consumption rate. While there is no FC model specifically developed for laminar flames, the Eddy Dissipation Model (EDM) correction scheme for laminar regimes is taken as the reference model. The proposed model addresses the shortcomings of the reference model, i.e. the sensitivity to the transport time scale and the cell size and the need to calibrate a numerical constant repeatedly. The new model formulation does not rely on any numerical constant and completely removes the dependency on the cell size and the species transport time scale, as shown for multiple counterflow and axisymmetric laminar diffusion flames. Furthermore, the proposed model provides consistent temperature and species mass fraction predictions across the mentioned flames. Finally, a sooty axisymmetric flame is simulated using the mentioned models to investigate the numerical interaction between combustion and soot chemistry. The results show that the reference model requires recalibration to capture the temperature and soot volume fraction distributions while the proposed model yields reasonably accurate temperature and soot predictions.

Effect of simultaneous H2 and NH3 addition on soot formation in co-flow diffusion CH4 flame

Simultaneous blending of hydrogen (H2) and ammonia (NH3) to hydrocarbon fuels can tackle the safety issues of H2 and improve burning efficiency of NH3. While this strategy brings challenges for soot prediction due to the promotion effect of H2 and the suppression effect of NH3, and the interactions between H2 and NH3. In this study, the simultaneous addition of NH3 and H2 on soot formation was experimentally and numerically investigated in a co-flow diffusion CH4 flame. The interactions between NH3 and H2, and how they impacted different soot formation processes were comprehensively revealed using a detailed soot sectional model. The decrease of peak SVF in CH4 flame caused by NH3 was 0.013 ppm, about 31.6 % smaller than that in CH4/H2 flame (0.019 ppm), indicating that the inhibitive effect of NH3 on soot formation was promoted by H2. The existence of H2 promoted the suppression effect of NH3 on soot nucleation, condensation and HACA processes in the CH4 flame. Compared with CH4/NH3 flame, the pyrolysis rates of NH3, NH2 and NH in the CH4/NH3/H2 flame were higher since more H and OH radicals were generated via H2 decomposition. This led to a larger consumption rate of H and OH radicals, which decreased the reaction rates of CH4+OH=CH3+H2O and C2H4+OH=C2H3+H2O, and promoted the combination of NO and C2H. Both factors accounted for a stronger suppression effect of NH3 on the formation of A1 in CH4/H2 flame than that in CH4 flame, and thus a stronger inhibitive effect on soot inception and condensation. Compared with the CH4 flame, NH3 resulted in a larger decline of H and OH radicals mole fractions in the CH4/H2 flame, which explained the stronger suppression effect of NH3 on the HACA surface growth process in the CH4/H2 flame.

Assessing PAHs-based soot inception models in various laminar non-premixed flame configurations

Accurate soot modeling is challenging due to the complex underlying physical and chemical processes. To improve the fidelity of the soot prediction in inverse coflow flame configurations, an empirical reactive soot inception model with radical involvement was developed in our previous work [Guo et al. Combust Flame 254 (2023) 112,853]. In this study, the model is further assessed by comparing the predictions of polycyclic aromatic hydrocarbons (PAHs) and soot parameters, including volume fraction, number density, and particle size, with measurements in various laminar diffusion flame configurations. These configurations encompass counterflow diffusion flames (CDF), normal coflow diffusion flames (NDF), and inverse coflow diffusion flames (IDF). The empirical reactive inception model is also compared to the reported irreversible and reversible inception models. The results show that the empirical reactive inception model with radical involvement most accurately capture axial soot behavior in IDF configuration, while irreversible and reversible models predict a sustained increase in soot mass, inconsistent with measurements. In CDF configurations, the empirical reactive model and the reported reversible model have better performance than the irreversible inception model, regarding the predictions of spatial distributions of soot and PAHs. Moreover, in NDF configuration, the soot peaks predicted by the reactive and reversible models are in reasonable agreement with measurements within a factor of 2–3. For the soot spatial distribution in NDF, the difference in the predictions is mainly reflected in the flame wings, which is caused by the temperature dependences of the inception rate provided by the different models.

Soot Modeling in Large Eddy Simulation of Turbulent Buoyant Flames Using the Laminar Smoke Point and the Eddy Dissipation Concept

ABSTRACT The paper presents a comprehensive set of Large Eddy Simulations (LES) of turbulent diffusion buoyant flames with a focus on soot modeling. Combustion modeling is based on the Eddy Dissipation Model (EDM) with an infinitely fast chemistry approach. Soot formation and oxidation are modeled with finite-rate chemistry based on the Laminar Smoke Point (LSP) concept. A turbulence-soot interaction model is proposed based on the Eddy Dissipation Concept (EDC) to close the chemical sink/source term for soot. A novel aspect of the modeling is the hybrid EDC formulation, where the mean chemical source term is a weighted average of a laminar term and a turbulent term, based on the local turbulent Reynolds number. The correct limiting behavior in the laminar regime is demonstrated for a laminar ethylene flame. Improvements in soot predictions near the fuel injection are obtained for a 13.7 cm-diameter turbulent ethylene flame and a 30 cm-diameter heptane flame. Additionally, the influence of the combustion model on soot predictions is carefully examined, especially with respect to the mixing time scale formulation and the related influence of the cell size (used as filter width in LES). A proposed dynamic model allows obtaining improved results even in under-resolved simulations and mitigating mesh sensitivity for soot predictions.

LES investigation of soot formation in a turbulent non-premixed jet flame with sectional method and FGM chemistry

This study proposes a detailed soot modeling framework for large-eddy simulation (LES) to accurately predict soot formation and particle size distributions (PSD) in turbulent reacting flows. The framework incorporates Flamelet Generated Manifold (FGM) chemistry and a soot model based on the discrete sectional method (DSM) to predict both qualitative and quantitative sooting behavior while keeping the computational cost affordable. Two elementary modeling strategies are considered in the LES formalism for describing soot formation rates. These strategies rely on an a-priori tabulation of soot formation rates and their run-time computation. The LES formalism is applied to the simulations of a well-characterized, non-premixed, turbulent jet flame. A comparative analysis of strategies employed for filtered soot source term treatment is conducted to investigate their impact on the prediction of soot quantities and the evolution of soot PSDs. The LES results for the gas phase and soot phase are compared against the available experimental data. A good prediction of soot evolution is achieved with the two methodologies. The tabulation of soot formation rates leads to a significant reduction in computational cost compared to the model based on their explicit runtime computation. The LES results reveal that the modeling of filtered soot source terms has a significant impact on the quantitative prediction of soot formation. The possible reasons for the observed differences in the soot prediction are discussed. The run-time computation-based model provides a more consistent treatment of the non-linear interactions between the gas and soot phases in soot source terms compared to the tabulated soot chemistry approach. On the other hand, the tabulated soot chemistry model is an interesting and efficient modeling approach for predicting soot formation in turbulent conditions. Overall, both approaches have their strengths and limitations, and the choice of approach may depend on the specific needs of the application.

Simulation of a rapid compression machine for evaluation of ignition chemistry and soot formation using gasoline/ethanol blends

Due to the projected decline of demand for gasoline in light duty engines and the advent of ethanol as a green fuel, the use of gasoline/ethanol blend fuels in heavy duty applications are being investigated as they are projected to have lower cost and lower lifecycle green house gas (GHG) emissions. In heavy duty engines, the primary mode of combustion is mixing controlled combustion where wide range of mixture conditions (equivalence ratio) exist. Soot emissions of these fuels in richer conditions are not well understood. The goal of this research is to evaluate some commercially available soot modeling codes for the particulate matter emissions from gasoline/ethanol fuel blends, especially at fuel rich conditions. A Rapid Compression Machine (RCM) is modeled in a three-dimensional numerical simulation using CONVERGE computational software using a reduced chemical kinetic mechanism with SAGE chemistry solver and a RANS k-ϵ turbulence model with a sector model including the creviced piston. The creviced piston is used in the experimental setup to reduce boundary layer effects and to maintain a homogeneous core in the reaction cylinder. Computational fluid dynamics simulations are conducted for different gasoline-ethanol fuel blends from E10 (10% ethanol v/v) to E100. The fuel blend is modeled as a surrogate mixture of toluene, iso-octane, n-heptane for gasoline content, and ethanol. The computational results were validated against experimental results using pressure measurements and laser extinction diagnostics. Different soot models are investigated to evaluate their capability of predicting the sooting tendencies of fuel blends, especially in richer conditions experienced during mixing-controlled combustion. The experimental combustion characteristics such as the ignition delay of different blends of fuel are reasonably well predicted. The Particulate Size Mimic (PSM) model accurately predicts the soot generation characteristics of the different fuels, but the Hiroyasu-NSC model falls short in this regard. For accurate prediction of soot with the PSM model, the thermodynamic conditions during combustion must be accurately modeled. While the current computational modeling tools can produce accurate results for the prediction of particulate matter emissions, there is much work to be done in improving our understanding of the underlying fundamental processes.

A systematic analysis of chemical mechanisms for ethylene oxidation and PAH formation

Accurate prediction of soot formation and evolution remains a formidable challenge due to the complex interaction between gas-phase composition and solid-phase particles. Recent studies have shown that the choice of gas-phase mechanisms is of primary importance in affecting predictability. In this work, a systematic analysis of ethylene (C2H4) combustion mechanisms denoted as KAUST, Stanford, Aachen, Polimi, ABF, DLR/UT, Naples, Caltech, and SJTU, which have been widely used in the soot community, is performed to investigate their differences in fundamental chemistry, polycyclic aromatic hydrocarbon (PAH) chemistry, and soot prediction. It is found that the nine mechanisms exhibit large differences even in predicting canonical combustion properties (e.g., ignition delay time, laminar flame speed, and extinction strain rate), indicating significant variations in the fundamental chemistry. This is due to the fact that although most mechanisms share very similar dominant fuel-oxidation reactions, there are notable differences in the rate coefficients of sensitive reactions used in these mechanisms. Owing to the uncertainties in the fundamental chemistry, the predictions of C2H2 from the nine mechanisms show significant differences, which contributes to the differences in soot precursor prediction, in conjunction with the difference in benzene (A1) formation pathways demonstrated by the element flux analysis of the C atom. Furthermore, it is found that PAHs containing two rings play a dominant role in soot formation for most mechanisms. Moreover, it is observed that while Caltech and SJTU significantly under estimate soot formation, they reasonably reproduce its sensitivity to strain rate. These results indicate that despite substantial advances in the development of C2H4 oxidation and PAH formation chemistry, the various existing mechanisms lead to significant differences in predicting soot concentrations which can be traced back to considerable differences in both fundamental chemistry and PAH chemistry. This suggests that the fundamental chemistry should be calibrated or improved before further development of PAH chemistry and soot models.

Influence of operating conditions on flow field dynamics and soot formation in an aero-engine model combustor

Large-eddy simulations of a sooting aero-engine model combustor are performed for three operating points. They are analyzed to investigate the influence of secondary air injection and primary equivalence ratio on soot formation dynamics. It will be shown how the causes of a strongly intermittent sooting behavior can be investigated quantitatively by mode analysis and correlated statistics. Simulations rely on Arrhenius reaction rate-based finite-rate chemistry, where 79 species transport equations are solved simultaneously to accurately predict the combustion of ethylene: 43 species represent the gas phase, 36 the soot and soot precursors. Soot evolution is described by a well validated sectional soot model including a sectional model for polycyclic aromatic hydrocarbons and their radicals. This approach captures the influence of changing operating conditions on soot formation well. Hence, soot prediction is found to be accurate enough to investigate the formation processes. The presence of coherent and incoherent flow field dynamics in the combustor necessitates the use of multiresolution proper orthogonal decomposition and correlated statistics for the analysis. While the precessing vortex core is found to support soot production continuously, low frequency dynamics are identified to cause the intermittence at all operating conditions. Secondary air injection significantly increases the intensity of the low frequency dynamics. Yet, the soot volume in the combustor varies by the same order of magnitude without secondary air injection. The soot formation intermittence is closely linked to an axially symmetric mixture fraction mode near the primary injector at all operating conditions. Low mode energy required an additional mode analysis of joint statistics which confirmed this conclusion. Increasing the equivalence ratio at the primary injector decreases the influence of the low frequency dynamics on soot formation.

Diesel particulate filter regeneration mechanism of modern automobile engines and methods of reducing PM emissions: a review.

Diesel particulate filter (DPF) is considered as an effective method to control particulate matter (PM) emissions from diesel engines, which is included in the mandatory installation list by more and more national/regional laws and regulations, such as CHINA VI, Euro VI, and EPA Tier3. Due to the limited capacity of DPF to contain PM, the manufacturer introduced a method of treating deposited PM by oxidation, which is called regeneration. This paper comprehensively summarizes the most advanced regeneration technology, including filter structure, new catalyst formula, accurate soot prediction, safe and reliable regeneration strategy, uncontrolled regeneration and its control methods. In addition, due to the change of working conditions in the regeneration process, the additional emissions during regeneration are discussed in this paper. The DPF is not only the aftertreatment device but also can be combined with diesel oxidation catalyst (DOC), selective catalytic reduction (SCR) and exhaust recirculation (EGR). In addition, the impact of DPF modification on the original system of some old models has been reasonably discussed in order to achieve emission targets.

Large Eddy Simulation of turbulent nonpremixed sooting flames: Presumed subfilter PDF model for finite-rate oxidation of soot

Modeling soot evolution in turbulent reacting flows using Large Eddy Simulation (LES) is challenging due to the complex subfilter soot-turbulence-chemistry interactions. Soot particles form at fuel-rich mixtures and are subsequently oxidized as they are transported toward fuel-lean mixtures. In previous work, this phenomenology was explicitly encoded into a presumed subfilter PDF model for soot by confining soot strictly to mixtures where growth rates exceed oxidation rates. However, this model implicitly assumed that oxidation is infinitely fast. In this work, a new presumed subfilter PDF model for soot is proposed to account for finite-rate soot oxidation. The distribution of soot with respect to the flame structure (mixture fraction) is modeled by comparing the local relative motion of diffusionless soot particles with respect to mixture fraction iso-contours with the local oxidation rate. When the oxidation rate is suppressed or the transport rate is very fast, soot is allowed to penetrate further into fuel-lean mixtures. This model can allow for soot leakage across the flame, a critical phenomenon in smoking flames. The new model is validated a priori against Direct Numerical Simulation (DNS) databases of turbulent nonpremixed jet flames and then a posteriori against experimental measurements in a laboratory-scale turbulent jet flame. A priori results show remarkably good agreement with filtered DNS data and are shown to correctly allow for soot leakage at low Damköhler number. Finally, LES results of the turbulent sooting jet flame show an improvement of soot prediction using the new soot subfilter model compared to previous works. In this flame, as in experiments, evidence of soot leakage is found near the beginning of the sooting region, and this soot leakage cannot be predicted with previous subfilter models that presume infinitely fast oxidation.

Task Transfer Learning for Prediction of Transient Nitrogen Oxides, Soot, and Total Hydrocarbon Emissions of a Diesel Engine

Task Transfer Learning for Prediction of Transient Nitrogen Oxides, Soot, and Total Hydrocarbon Emissions of a Diesel Engine

The importance of accurately modelling soot and radiation coupling in laminar and laboratory-scale turbulent diffusion flames

Ultrafine and highly light absorbing soot particles have been known to be harmful to human health and a major climate forcer. Understanding the mechanisms of soot formation and developing reliable and robust soot models are essential to design efficient and clean combustion systems. Thermal radiation is often the dominant heat transfer mode in both turbulent and laminar flames and affects soot formation through its influence on temperature. Significant progress has been made in modelling soot formation, thermal radiation transfer, and turbulence radiation interaction in hydrocarbon flames over the last three decades. However, the optically thin approximation has still been used to simplify thermal radiation transfer modelling and turbulence radiation interaction has often been neglected in soot formation modelling without proper justification. In this paper, soot models developed for laminar and laboratory scale turbulent jet diffusion flames, non-gray radiative heat transfer models, and turbulence radiation interaction models are reviewed. The aim of this review is to highlight the importance of thermal radiation heat transfer in both laminar and turbulent diffusion flames and turbulence radiation interaction in turbulent jet diffusion flames. Use of the optically thin approximation is often not justified even in laminar diffusion flames. This is particularly true in diffusion flames at elevated pressures or microgravity. Although the prediction of soot in both laminar and turbulent flames is primarily influenced by soot kinetic model, it is also significantly affected by aerosol dynamic model, thermal radiation heat transfer model, and turbulence radiation interaction model. It is important to accurately model soot aerosol dynamics and non-gray thermal radiation transfer in laminar diffusion flames and additionally to adequately model turbulence radiation interaction in turbulent flames for the purpose of validating reaction mechanisms and soot kinetic models.

Predicting 3D distribution of soot particle from luminosity of turbulent flame based on conditional-generative adversarial networks

Optical detection has been fully applied and developed in combustion research, but its results are severely limited by detection methods and detection equipment with no expanded space. In this paper, a new method was developed to research soot formation and distribution in turbulent jet flame with Reynolds number range of 2000–8000, SSIM (structure similarity index) was selected as the metric. The main work of this paper included three parts: MART (Multiplicative Algebraic Reconstruction Technique) was used to implement the three-dimensional reconstruction of the turbulent jet flame by luminosity signal, the iteration of reconstruction algorithm would be stopped when the accuracy is greater than 0.9. C-GAN (Conditional-Generative Adversarial Network) was used to implement the prediction of two-dimensional soot signal, the results of soot prediction model showed that the prediction accuracies in different conditions are in the range of 0.83–0.91. Combining the previous two methods, the three-dimensional distribution of soot particle can be reconstructed by inputting the luminosity projections of jet flame, which provided new insight into the soot formation and distribution. The accuracy of this method can be verified by a unique validation experiment through oblique-sections of the flame, the validation accuracy is up to 0.92 in all conditions. The method can be applied to predict 3D soot field by using a well-trained deep learning model and several luminosity images to decrease the cost and limitation of experiment. It has the potential to be used for predicting other optical signals such as laser induced fluorescence and chemiluminescence if these datasets can be observed and trained. Therefore, this method is expected to be a solution for the visualization research of actual combustion devices.

Assessment of physical soot inception model in normal and inverse laminar diffusion flames

Despite the extensive studies, accurate and reliable modeling of the soot inception process, especially at high pressure conditions, amenable to multi-dimensional flame simulations remains a challenge. In this study, the physical inception model was comprehensively evaluated in the fully-resolved simulations of laminar normal diffusion flame (NDF) and inverse diffusion flame (IDF) at elevated pressures. The effects of inception models on polycyclic aromatic hydrocarbons (PAHs) and soot predictions were quantitatively analyzed, including the selection of soot precursors and collision efficiency models. The results show that the quantitative PAH predicted by different collision efficiency models can differ by an order of magnitude. Compared to the constant efficiency, the temperature-dependent collision efficiency was found to improve the quantitative PAH predictions and the prediction of the spatial soot distribution in NDF, with an increased level of soot on the flame centerline. The inclusion of small-sized PAH species (such as A2, A2R5, and A3) as soot precursors was also found to improve the quantitative prediction of soot volume fraction. The physical inception model performs well in NDF using the optimal parameters. Moreover, simultaneous measurements of PAH and soot were performed in IDF configuration for the evaluation of the physical inception model. Contrary to NDF, PAHs and soot are formed on the outer side of the flame and cannot be oxidized in IDF. The experiment observed that the PAHs concentration increased in the post-flame region, while the soot concentration remained unchanged. However, the opposite trend was obtained in simulations, that is, the PAHs concentration decreased while the soot concentration increased, because the physical inception model predicts the inception behavior in the post-flame area, resulting in persistent transformation of PAHs into soot particles. To improve the predictions in IDF, the radical effects in the inception process need to be considered in the model.

Exploring soot inception rate with stochastic modelling and machine learning

A diverse range of polycyclic aromatic compounds (PACs) is thought to exist in flame environments before and during soot inception. This work seeks to develop a machine learning (ML)-based soot inception model that considers detailed and diverse PAC properties such as oxygenation, aliphatic content, radical character, size, and shape. To this end, temporal rates of change of PAC properties were computed by the stochastic modelling code SNapS2 and used as input to an ML model that predicts soot inception rate. The model is trained using experimentally-derived soot inception rates for three atmospheric pressure laminar premixed ethylene/air flames. An ML model (kernel ridge regression with a linear kernel) was developed to predict the soot inception rate in the three premixed flames. The soot inception rate predictions from this SNapS2-informed ML model outperformed the predictions from both the advanced soot modelling CFD code CoFlame and an ML model which used CFD-determined inputs (temperature and species concentrations). The final model had an R2 value of approximately 0.71 and a mean absolute error approximately 25% of the target values. The performance of the SNapS2-informed model suggests that detailed PAC properties are important to consider in inception modelling. While expanding this approach to other types of flames and fuels is crucial for future improvement to the model’s accuracy and generality, this methodology provides a successful framework for the current system. The success of this method demonstrates that ML can offer improvements in accuracy compared to current CFD inception models and the highlights the potential for ML in soot predictions.

Investigation of conditional source-term estimation coupled with a semi-empirical model for soot predictions in two turbulent flames

The modelling of soot formation is investigated for two turbulent flames, at atmospheric and 3 atm pressure conditions. For the first time, a semi-empirical soot formulation that accounts for soot inception, coagulation, surface growth, and oxidation processes is coupled with the turbulent combustion model, Conditional Source-term Estimation (CSE) using Reynolds-Averaged Navier–Stokes equations. Detailed chemistry is included and an optically thin radiation model is considered. Non-adiabatic chemistry tabulations are created. Good agreement with the experiments is found for turbulent mixing and temperature fields in both flames, with some discrepancies believed to be due to the turbulence modelling approach. At 1 atm, the soot volume fractions are in reasonable agreement with the experiments, but typically smaller than the measurements with the centerline peak locating closer to the fuel exit. At 3 atm, good agreement between the numerical predictions and experimental data is achieved for the soot volume fraction within the experimental error. The centerline peak location is observed slightly farther downstream. Possible sources of discrepancies are examined and comparison with previously published numerical results is also undertaken. Differential diffusion and modified soot chemistry constants may bring further improvement. Without any particular tuning of soot chemistry, soot modelling within CSE is shown to be a promising approach.

A coupled MMC-LES and sectional kinetic scheme for soot formation in a turbulent flame

A soot kinetic sectional scheme is coupled with the sparse multiple mapping conditioning / large eddy simulation (MMC-LES) method to model soot formation in a turbulent, non-premixed, piloted methane flame (Delft Flame III). The kinetic sectional scheme used here discretises the population balance equation according to soot particle size. The sections (or bins) are treated as lumped species, and the physico-chemical soot formation is modelled through elementary-like reaction pathways with Arrhenius rate expressions. This allows the soot kinetics to be naturally coupled with the gas phase molecular kinetics. Interactions of soot formation and molecular chemistry with turbulence is achieved through the MMC-LES model. This is a Monte Carlo filtered density function approach in which the micromixing is local in an independent reference space permitting use of a computationally efficient sparse distribution of stochastic notional particles. The model results are compared to experimental data for the gas-phase velocity and molecular species in the upstream low-soot region and with soot statistics in the downstream region. The gaseous quantities have very good agreement with the experimental data. Overall the soot predictions are in good qualitative agreement with the data, particularly with respect to the peak value of the mean soot volume fraction, the shape and range of the joint probability density function of soot volume fraction and spatial location, and the rate of soot oxidation and burnout. However, in common with other published attempts at the Delft Flame III using a range of different models, the predicted soot formation begins and peaks well upstream of the experimental measurements. The minimum soot intermittency is underpredicted relative to the data but shows a significant improvement on previous attempts to model it. The analysis also explores the evolution of the mean and instantaneous particle size distributions and some aspects of soot-chemistry-turbulence interactions.

A virtual chemistry model for soot prediction in flames including radiative heat transfer

Modeling soot formation is a very difficult task which has been assessed by the numerical combustion community. Numerical simulations of turbulent sooting flames remain very challenging due to the complexity of soot formation phenomena, which implies development of elaborate soot models often too computationally demanding, or simplified soot models that are limited to a small range of operating conditions of interest. Here an innovative optimized global approach called virtual chemistry is proposed. It consists of a mathematical formalism including virtual species and virtual reactions, whose thermochemical parameters are optimized in order to describe a combustion system. Thermochemical properties are trained through a genetic algorithm, employing a learning database made of reference flame elements. This makes it possible to reproduce multiple combustion regimes and operating conditions. The virtual chemistry approach reproduces then the structure of hydrocarbon-air flames, as well as predicting specific user-defined pollutant species. The objective of this article is to extend the virtual chemistry methodology for soot formation prediction. As the radiative heat losses are important in sooting flames, a new radiative virtual model is also developed to account for them. The final reduced chemistry consists of 12 virtual species and 6 virtual reactions considerably decreasing the computational time compared to detailed chemistry models. Simulations of 1-D and 2-D laminar ethylene-air sooting flames are performed to evaluate the virtual soot and radiative models. Temperature, soot volume fraction and radiative heat losses are well predicted and are in good agreement with the reference data.