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{Ding:2022:10.1016/j.jaecs.2022.100086,
author = {Ding, T and Rigopoulos, S and Jones, WP},
doi = {10.1016/j.jaecs.2022.100086},
journal = {Applications in Energy and Combustion Science},
pages = {1--17},
title = {Machine learning tabulation of thermochemistry of fuel blends},
url = {http://dx.doi.org/10.1016/j.jaecs.2022.100086},
volume = {12},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The objective of the present work is to develop a machine learning tabulation methodology for thermochemistry that accounts for fuel blends. The approach is based on the hybrid flamelet/random data and multiple multilayer perceptrons (HFRD-MMLP) methodology (Ding et al., 2021), the essence of which is to train a set of artificial neural networks (ANNs) using random data so as to anticipate the composition space encountered in turbulent flame simulations. As such, it is applicable to any combustion modelling approach that involves direct coupling of chemistry and flow, such as transported probability density function (PDF) methods, direct numerical simulation (DNS), conditional moment closure (CMC), unsteady flamelet, multiple mapping closure (MMC), thickened flame model, linear eddy model (LEM), partially stirred reactor (PaSR) as in OpenFOAM and laminar flame computation. In this paper, the HFRD approach is further developed to generate data of varying fuel ratios. Furthermore, radiative heat losses are included and it is shown that the ANN-based simulations are able to account for it. The ANNs generated are first tested on 1-D laminar flame simulations and then applied to two turbulent flames with different fuel compositions: a pure methane flame, Sandia flame D, and Sydney flame HM1, which is a methane/hydrogen flame. The results of species mass fraction and temperature are compared between ANN and direct integration, and excellent agreement are achieved. These results indicate that the methodology has great capacity for generalisation and is applicable to a range of blended fuels. Furthermore, a speed-up ratio of 14 to 17 is attained for the reaction step compared with direct integration, which greatly reduces the computational cost of turbulent combustion simulations.
AU  - Ding,T
AU  - Rigopoulos,S
AU  - Jones,WP
DO  - 10.1016/j.jaecs.2022.100086
EP  - 17
PY  - 2022///
SN  - 2666-352X
SP  - 1
TI  - Machine learning tabulation of thermochemistry of fuel blends
T2  - Applications in Energy and Combustion Science
UR  - http://dx.doi.org/10.1016/j.jaecs.2022.100086
UR  - https://www.sciencedirect.com/science/article/pii/S2666352X22000292?via%3Dihub
UR  - http://hdl.handle.net/10044/1/101319
VL  - 12
ER  -
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