Imperial College London
Alternate

ProfessorWilliamJones

Faculty of Engineering, Department of Mechanical Engineering

Professor
 
 
 
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Contact

 

+44 (0)20 7594 7037w.jones

 
 
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Assistant

 

Ms Fabienne Laperche +44 (0)20 7594 7033

 
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Location

 

607City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Readshaw:2021:10.1063/5.0041122,
author = {Readshaw, T and Ding, T and Rigopoulos, S and Jones, WP},
doi = {10.1063/5.0041122},
journal = {Physics of Fluids},
pages = {1--17},
title = {Modeling of turbulent flames with the large eddy simulation–probability density function (LES–PDF) approach, stochastic fields, and artificial neural networks},
url = {http://dx.doi.org/10.1063/5.0041122},
volume = {33},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - This work proposes a chemical mechanism tabulation method using artificial neural networks (ANNs) for turbulent combustion simulations. The method is employed here in the context of the Large-Eddy Simulation (LES)–Probability Density Function (PDF) approach and the method of stochastic fields for numerical solution, but can also be employed in other methods featuring real-time integration of chemical kinetics. The focus of the paper is on exploring an ANN architecture aiming at improved generalization, which uses a single multilayer perceptron (MLP) for each species over the entire training dataset. This method is shown to outperform previous approaches which take advantage of specialization by clustering the composition space using the Self-Organizing Map (SOM). The ANN training data are generated using the canonical combustion problem of igniting/extinguishing one-dimensional laminar flamelets with a detailed methane combustion mechanism, before being augmented with randomly generated data to produce a hybrid random/flamelet dataset with improved composition space coverage. The ANNs generated in this study are applied to the LES of a turbulent non-premixed CH4/air flame, Sydney flame L. The transported PDF approach is used for reaction source term closure, while numerical solution is obtained using the method of stochastic fields. Very good agreement is observed between direct integration (DI) and the ANNs, meaning that the ANNs can successfully replace the integration of chemical kinetics. The time taken for the reaction source computation is reduced 18-fold, which means that LES–PDF simulations with comprehensive mechanisms can be performed on modest computing resources.
AU  - Readshaw,T
AU  - Ding,T
AU  - Rigopoulos,S
AU  - Jones,WP
DO  - 10.1063/5.0041122
EP  - 17
PY  - 2021///
SN  - 1070-6631
SP  - 1
TI  - Modeling of turbulent flames with the large eddy simulation–probability density function (LES–PDF) approach, stochastic fields, and artificial neural networks
T2  - Physics of Fluids
UR  - http://dx.doi.org/10.1063/5.0041122
UR  - https://aip.scitation.org/doi/10.1063/5.0041122
UR  - http://hdl.handle.net/10044/1/88030
VL  - 33
ER  -
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