Abstract

Soot particles produced during the combustion of hydrocarbon fuels pose significant health and environmental risks while also holding potential for various industrial applications. Although extensive research has enhanced our understanding of soot characteristics, gaps persist in comprehending the role of polycyclic aromatic hydrocarbons (PAHs) in the soot formation process. Modeling soot involves complex multi -variable processes, requiring a detailed approach for an accurate representation of the primary particle diameter or the number of particles per aggregate. This study investigates a simplified reactive model for PAH dimerization and adsorption. The objective is to achieve results comparable to advanced thermodynamic -equilibrium -based models but with computational efficiency similar to simpler models. Four models are examined: an efficiency model with empirical parameters, a reversible model based on thermodynamic equilibrium, a simplified reactive model incorporating chemical reaction rates, and a radical reactive model solved in the gas phase. Validation against experimental measurements in laminar inverse diffusion flames (IDF), an ideal environment to study PAH dimerization and adsorption, reveals the superior capability of the reversible model to predict the primary particle diameter and number density data compared to the efficiency model. The simplified reactive model demonstrates comparable results to the reversible model, capturing primary particle size and number density magnitudes and positions accurately. The study also explores the impact of a variable coagulation model based on interaction potentials, improving the predictions along the flame axis. Furthermore, the coagulation model is reevaluated by contrasting empirical efficiencies with a model based on the potential well depth of colliding pairs. Notably, the selection of the coalescence model exhibits a significant influence on particle size. This research sheds light on the efficacy of simplified reactive models in capturing key soot formation characteristics, presenting a promising avenue for balancing computational efficiency and accuracy in predictive modeling for combustion -related processes. Novelty and Significance statement This research sheds light on soot formation modeling by addressing the unclear role of polycyclic aromatic hydrocarbons (PAHs). The study shows how models that varied in terms of complexity, either in physical or chemical components, have significant differences in terms of predicting capabilities. The focus was on the fitting adequacy of these models with respect to soot morphology parameters in an ideal setup for such research: Inverse Non -Premixed Flames. The research highlights the importance of including smaller PAHs as precursors and avoiding highly empirical models, leading to a more accurate representation of the soot primary particle density, which is essential for precisely modeling subsequent soot growth and agglomeration. The study's significance lies in providing a comprehensive approach to understanding soot formation, contributing valuable insights for predictive modeling in combustion -related processes, and a guide toward a cost-effective yet robust methodology for studying and predicting soot formation.