A novel soot concentration field estimator applied to sooting ethylene/air laminar flames

Abstract

Soot formation modeling, when incorporated into computational fluid dynamics of industrial devices, can be numerically prohibitive. Nonetheless, there remains a significant push to predict soot formation, so as to aid in environmentally sustainable design. The present work features a redesign of an inexpensive soot estimator, that has been developed and applied to laminar flames with great success. It is much more accurate and efficient than previous versions. The soot estimator consists of a library generated from validated sooting flame models, in which the Lagrangian histories of soot-containing fluid parcels are stored. The library is used in post-processing to estimate soot concentrations. For the first time, the estimator framework is used to predict the entire soot field. Also, important parameters to the estimator technique are analyzed. This work is conducted for nine different sooting ethylene/air coflow diffusion flames. The framework successfully predicts the entire soot field. When the data from many flames were combined into one library based on mixture fraction, temperature, and H 2 histories, it could predict all flames with high accuracy. Finally, two scenarios were considered to assess the framework with an independent set of data, and the predictor presented very good accuracy in capturing soot formation.

An Artificial Neural Network for the Low-Cost Prediction of Soot Emissions

“…Conversely, as mentioned above, the low-cost and precise estimation of soot properties, such as concentration, has become highly desired for industry. Therefore, to tackle this controversy, a novel soot estimator concept has been recently developed and validated [6,[17][18][19].…”

“…As a proof of concept, a post-processing technique for laminar flames was proposed by Alexander et al [6] in 2018. The aforementioned work has been further amended where accuracy was improved and turbulent flame data was used, albeit with limited success [18,19]. The prediction method described in these papers is a rudimentary library consisting of a collection of data arrays from which desired values can be interpolated.…”

“…The prediction method described in these papers is a rudimentary library consisting of a collection of data arrays from which desired values can be interpolated. The most recent work [18] demonstrates exceptional success when estimating the soot volume fraction of nine different steady laminar flames when the library comprises data of the same nine flames being tested.…”

“…The idea of a soot estimator was based on this assumption that to calculate f v , libraries (like the thermodynamic tables) can be created in which the values of different variables such as temperature and mass fractions are stored. However, due to the longer timescales of soot formation as compared to those of flow and gas-phase chemistry, and the inherent non-linearity of the problem, soot characteristics cannot be considered to be related only to especially local properties [18]. Veshkini et al [20] and Kholghy et al [21] showed that soot evolution is a stronger function of the temporal history of soot particles than just local flame characteristics.…”

“…Veshkini et al [20] and Kholghy et al [21] showed that soot evolution is a stronger function of the temporal history of soot particles than just local flame characteristics. Therefore, within the soot estimator approach [6,17,18], it was assumed that the local instantaneous soot volume fraction f v is a function of the temporal history of key variables in a combustion process. The fundamental formula from which the soot estimator concept was developed is as follows:…”