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Item type:Publication, Robust automatic retrieval of soot volume fraction, temperature and radiation for axisymmetric flames(Elsevier Ltd, 2024-01-01)This work presents a robust methodology to retrieve local soot properties from line-of-sight integrated measurements without the need to invert a poorly-conditioned matrix arising from the flame geometry and discretization procedureFirst, a forward fit method is presented. Another method, utilizing an Artificial Neural Network informed by the Abel equation (ANNAbel), is then introduced to circumvent the drawbacks of the forward fit method. Both methods are capable to retrieve soot volume fraction, temperature and radiation satisfactorily from experimental data of an ethylene coflow non-premixed flame, without the need for a tuning a regularization parameter. The ANNAbel approach exhibited greater smoothness for retrieved properties, with lower errors when comparing the reconstructed data against the original experimental data. This was also evident when comparing local soot properties in a numerical framework. The ANNAbel approach also showed high resilience to increased levels of noise, contrary to the fitting approach and classical deconvolution methods. Finally, the ANNAbel method was capable to obtain the local properties even with simulated corrupted data, with a level of precision slightly lower than treating the original experimental data. On the contrary, the rest of the methods failed to perform this task. The ANNAbel method is then a promising approach for the robust and accurate determination of local flame properties, which is especially important for obtaining complex soot properties such as size and composition, where involved data treatment is required, and the results are sensitive to noise. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Soot evolution in turbulent non-premixed bluff body flames: Assessment of detailed soot formation models using large eddy simulation(Centre National de la Recherche Scientifique, 2024-01-26)Exploring the complexities of soot formation in combustion systems implies recognizing the intricate interplay among turbulence, chemical kinetics, radiation, and soot particle dynamics. Achieving accurate predictions of soot levels in turbulent flames entails a meticulous representation of all stages of soot formation and oxidation, which prompts the development and use of detailed soot formation models. This work delves into three detailed soot models-Method of Moments with Interpolative Closure (MOMIC), Hybrid Method of Moments (HMOM), and Discrete Sectional Method (DSM)-integrated into the open-source computational tool OpenFOAM. Both the combustion process and the formation of soot precursors in the gas phase are characterized using the Flamelet Progress Variable combustion model along with the detailed ABF chemical kinetic mechanism. Turbulence is addressed through a Large Eddy Simulation based approach, and the computational results are compared with experimental data from the Adelaide ENB1 Bluff Body Flame. Specifically, the analysis extends to detailing flow velocities and their fluctuations, along with fields and profiles depicting soot volume fraction. In order to obtain quantitatively correct soot volume fractions, the nucleation sticking factor was adjusted. The comparative assessments of the soot formation models carried out provide a unique perspective on soot formation, highlighting different effects of each soot source terms and specific model limitations. HMOM described the measured soot volume fraction with greatest accuracy followed by DSM and MOMIC. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Soot formation models assessment in turbulent diffusion jet flames: A RANS-based comparison(Centre National de la Recherche Scientifique, 2024-10-02)A RANS-based Comparison Due to the intricate interaction between turbulence, chemical kinetics, radiation, and soot particle dynamics, modelling soot formation processes in flames is a challenging task. To predict the level of soot formed, it is essential to accurately capture all stages of soot formation and oxidation. Using a RANS approach, this study focuses on the implementation, within the computational open-source tool OpenFOAM, and comparison of three detailed soot formation models, (i) the Interpolative Closure Method of Moments (MOMIC), (ii) the Hybrid Method of Moments (HMOM), and (iii) the Discrete Sectional Method (DSM), as well as a semi-empirical two-equation model. Both the combustion process and the formation of soot precursors in the gas phase are described using the Steady Laminar Flamelet model and a detailed chemical kinetic mechanism. Radiation effects are modelled using the optically thin method. The computational results obtained here are compared with the experimental data characterizing the Adelaide ENH1 jet flame and other past numerical results. The results reveal significant differences in soot formation source terms among the models and for each of the soot formation stages. DSM best matches the experimental peak soot position, which is attributed to its modelling of condensation and surface growth occurring downstream compared to MOMIC and HMOM. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Computational assessment of detailed soot formation models in ethylene/air laminar diffusion flames(Centre National de la Recherche Scientifique, 2024-01-26)To improve the accuracy of soot formation and evolution predictions, several physical and chemical models have been developed over the last decades.These models include (i) detailed chemical kinetic mechanisms describing both gas-phase chemistry related to combustion processes and reaction pathways leading to large-sized aromatic molecules, which are needed for modeling soot formation, and (ii) soot models providing a comprehensive description of soot particle dynamics and interactions with gas-phase chemical species.Accordingly, in this work, two detailed soot models, the method of moments (MOM) and the discrete sectional method (DSM), are evaluated in ethylene/air laminar diffusion flames, and their corresponding results are compared with experimental measurements.Furthermore, the NBP and KM2 chemical kinetic mechanisms are assessed and compared with each other by examining key chemical species related to soot formation and evolution.To compute gas mixture's radiative properties, the weighted sum of gray gases model considering a gray medium is also utilized.Finally, the contributions of the soot precursors known as PAH (polycyclic aromatic hydrocarbon) to soot formation are also analyzed.The main results show that the discrepancies in PAH concentrations obtained with different chemical kinetic mechanisms can be significant.In addition, compared to MOM ones, DSM results obtained here show a better agreement with experimental data.Finally, the analysis of PAH shows that those with two (A2) to four (A4) aromatic rings impact the most on soot modeling.Specifically, contributions of A4 were found to be more significant at lower heights above the burner, whereas A2 was found to be more impactful downstream as the flame develops.Maximum contributions of A2 and A4 to the soot inception rate were 66% and 85%, respectively, whereas the maximum summed contribution of PAH with five (A4R5) to seven (A7) aromatic rings accounted for only 13% of the inception rate. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Sooting behavior of laminar flames produced with clear and black cast PMMA(Elsevier BV, 2026-09-01)The sooting behavior of poly(methyl methacrylate) (PMMA) remains poorly quantified despite its extensive use as a benchmark fuel in fire science. This study presents a systematic characterization of soot production in laminar diffusion flames fueled by clear and black PMMA. Reduced-size samples were burned in an axisymmetric configuration with controlled coflow conditions (20–60 L/min). Temporally and spatially-resolved soot volume fractions were retrieved using line-of-sight attenuation combined with neural network-based Abel inversion. Radiative emission and flame geometry were quantified concurrently. Results showed that pigmentation had a marked influence on soot behavior: black PMMA consistently produced higher soot volume fractions (up to ∼3 ppm) and sustained radiative output longer than clear PMMA. Power-law correlations between integrated soot observables and flame-scale parameters revealed distinct scaling laws. These correlations provide quantitative targets for calibration of soot yields in fire modeling. The dataset provides a comparative benchmark on the pigmentation effects in PMMA flames, supporting an improved understanding of soot formation pathways in polymer fires.