Investigating the Effects of Chemical Mechanism on Soot Formation Under High-Pressure Fuel Pyrolysis

Nick J Killingsworth,Goutham Kukkadapu,Carter Brown,Julien Manin,Tuan M Nguyen

doi:10.3389/fmech.2021.765478

Abstract

We performed Computational Fluid Dynamics (CFD) simulations using a Reynolds-Averaged Navier-Stokes (RANS) turbulence model of high-pressure spray pyrolysis with a detailed chemical kinetic mechanism encompassing pyrolysis of n-dodecane and formation of polycyclic aromatic hydrocarbons. We compare the results using the detailed mechanism and those found using several different reduced chemical mechanisms to experiments carried out in an optically accessible, high-pressure, constant-volume combustion chamber. Three different soot models implemented in the CONVERGE CFD software are used: an empirical soot model, a method of moments, and a discrete sectional method. There is a large variation in the prediction of the soot between different combinations of chemical mechanisms and soot model. Furthermore, the amount of soot produced from all models is substantially less than experimental measurements. All of this indicates that there is still substantial work that needs to be done to arrive at simulations that can be relied on to accurately predict soot formation.

Highlights

The emissions from power generation and transportation are major contributors to climate change and the production of particulate matter has a detrimental effect on human health
These images are obtained by integrating the raw 3-D Eulerian soot mass field in the transverse direction. They qualitatively capture the soot behavior observed from the experimental diffuse back-illumination extinction imaging (DBI-EI) results (Figure 3 of Skeen and Yasutomi (2018))
The results show that the overall location of the soot field is similar amongst the different simulations, with the Wang et al mechanism consistently predicting the highest soot mass for all time instances

Summary

Introduction

The emissions from power generation and transportation are major contributors to climate change and the production of particulate matter has a detrimental effect on human health. While there is a strong drive to reduce the number of combustion engines in use, they will remain the principal mode of transportation and power generation for many decades (Newell et al, 2019), and understanding the processes that govern the formation of particulate matter is likely to lead to many benefits. There have been several studies through the Engine Combustion Network that have leveraged the extensive experimental database and explored the use of a number of different turbulence models (Skeen et al, 2016; Chishty et al, 2018) and soot models (Duvvuri et al, 2021; Ong et al, 2021)

Objectives

Methods

Results