Skip to content
naulon
Not checkedNot checked for notices yet.

Modeling of NOx and Soot Formation in Diesel Combustion Modélisation de la formation des NOx et de la suie en combustion diesel

Dederichs A. S., Balthasar M., Mauss F.

Oil & Gas Science and Technology · 2006

doi:10.2516/ogst:1999021 (opens in a new tab)

We email you when a new notice or a question about this paper appears.

Abstract

An approach to model the formation and oxidation or reduction of soot and NO in turbulent diffusion flames is presented. The model is based on the flamelet library approach and extended to account for radiative heat losses in the flame. Due to the rather slow processes leading to soot and NO a modified flamelet library approach is used. Instead of taking the mass fractions directly from flamelet libraries the different source terms for soot and NO formation are calculated and a transport equation for the mean mass fractions is solved in the CFD calculation. The source terms are obtained from laminar counterflow-flame calculations using a detailed chemistry model for the gas phase species and the formation and oxidation of soot. Transport equations for the mean mixture fraction and the mixture fraction variance are solved and the chemical source term is closed by presuming a beta-function like distribution of mixture fraction and a log-normal distribution of the scalar dissipation rate. The model was first tested in laminar and turbulent jet flames. By applying a reduction strategy for the flamelet libraries of the source terms it was made applicable to the simulation of soot formation in a Diesel spray taking different oxidizer temperatures and pressures into account. Additionally, different formulations of the flamelet equations have been tested and their accuracy has been evaluated by comparing them to turbulent flame experiments.

Abstract licence: CC BY

References2

    2 more references, whose wording the paper’s licence keeps private.

    Reads

    Read by people on this page

    None yet.

    Checked or read by AI through naulon

    None yet.

    A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

    Are you an author?

    Is this your paper?

    Claim it to add your note and be paid when agents cite it. Free.