Tabulation of combustion chemistry via Artificial Neural Networks (ANNs): methodology and application to LES-PDF simulation of Sydney flame L
File(s)paper_Franke_Chatzopoulos_Rigopoulos_accepted.pdf (1.25 MB)
Accepted version
Author(s)
Franke, LLC
Chatzopoulos, AK
Rigopoulos, S
Type
Journal Article
Abstract
In this work, a methodology for the tabulation of combustion mechanisms via Artificial Neural Networks (ANNs) is presented. The objective of the methodology is to train the ANN using samples generated via an abstract problem, such that they span the composition space of a family of combustion problems. The abstract problem in this case is an ensemble of laminar flamelets with an artificial pilot in mixture fraction space to emulate ignition, of varying strain rate up to well into the extinction range. The composition space thus covered anticipates the regions visited in a typical simulation of a non-premixed flame. The ANN training consists of two-stage process: clustering of the composition space into subdomains using the Self-Organising Map (SOM) and regression within each subdomain via the multilayer Perceptron (MLP). The approach is then employed to tabulate a mechanism of CH4-air combustion, based on GRI 1.2 and reduced via Rate-Controlled Constrained Equilibrium (RCCE) and Computational Singular Perturbation (CSP). The mechanism is then applied to simulate the Sydney Flame L, a turbulent non-premixed flame that features significant levels of local extinction and re-ignition. The flow field is resolved through Large Eddy Simulation (LES), while the transported Probability Density Function (PDF) approach is employed for modelling the turbulence-chemistry interaction and solved numerically via the Stochastic Fields method. Results demonstrate reasonable agreement with experiments, indicating that the SOM-MLP approach provides a good representation of the composition space, while the great savings in CPU time allow for a simulation to be performed with a comprehensive combustion model, such as the LES-PDF, with modest CPU resources such as a workstation.
Date Issued
2017-11-01
Online Publication Date
2018-08-01T06:00:25Z
Date Acceptance
2017-07-12
ISSN
0010-2180
Publisher
Elsevier
Start Page
245
End Page
260
Journal / Book Title
Combustion and Flame
Volume
185
Issue
1
Copyright Statement
© 2017 The Combustion Institute. Published by Elsevier Inc. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Source Database
manual-entry
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Identifier
https://www.sciencedirect.com/science/article/pii/S0010218017302596
Grant Number
EP/K026801/1
Subjects
Science & Technology
Physical Sciences
Technology
Thermodynamics
Energy & Fuels
Engineering, Multidisciplinary
Engineering, Chemical
Engineering, Mechanical
Engineering
Mechanism tabulation
Artificial Neural Network (ANN)
RCCE
PDF methods
TURBULENT NONPREMIXED FLAMES
PROBABILITY DENSITY-FUNCTION
CONTROLLED CONSTRAINED EQUILIBRIUM
REDUCED CHEMISTRY
CHEMICAL-KINETICS
DIFFUSION FLAME
REDUCTION
REPRESENTATION
AUTOIGNITION
FORMULATION
Energy
0902 Automotive Engineering
0904 Chemical Engineering
0913 Mechanical Engineering
Publication Status
Published
Date Publish Online
2017-08-01