Improved soot prediction models for turbulent non-premixed ethylene/air flames

Abstract

Existing soot models for non-premixed ethylene/air flames often do not satisfactorily predict soot volume fraction. Due to intense energy exchange by radiative heat transfer, this leads to a situation where even basic scalar fields like temperature are inaccurately described. The objective of the present study is to develop an enhanced soot prediction model for such flames, valid for a wide range of flow and operating conditions. The emphasis in this work is specifically on the particulate phase and not on kinetics. Therefore, gas-phase chemistry is simply modeled with a mixture fraction approach combined with equilibrium chemistry, while turbulence–chemistry interactions are taken into account by a presumed β-PDF. The evolution of soot particles is described by physical models accounting for nucleation, surface growth, aggregation and oxidation. The Direct Quadrature Method of Moments (DQMOM) is employed to solve the Population Balance Equations (PBE) in a computationally efficient manner, assuming a mono-variate PBE with particle diameter as internal coordinate. The original soot model is optimized numerically by using Genetic Algorithms coupled with Computational Fluid Dynamics (CFD). The values of three model parameters, associated with nucleation, oxidation and soot particle radiation are optimized through comparison with recent experimental results. Two objective functions are formulated based on the difference between experimental and simulation results for temperature and soot volume fraction. After obtaining an optimal parameter set, the resulting model is further tested against three experimental configurations, leading to a good agreement and thus demonstrating a high level of generality.