Machine learning tabulation of thermochemistry for turbulent dimethyl ether (DME) flames
File(s)
Accepted version
Author(s)
Liu, Anxiong
Ding, Tianjie
Liu, Runzhi
Rigopoulos, Stelios
Luo, Kun
Type
Journal Article
Abstract
The present work applies a machine learning tabulation methodology for the thermochemistry of dimethyl ether (DME), a typical biodiesel fuel, to accelerate the real-time computation of a large-size DME mechanism in turbulent non-premixed combustion. This approach is known as the hybrid flamelet/random data and multiple multilayer perceptrons (HFRD-MMLP) method (Ding et al., 2021). The essence of the HFRD-MMLP method lies in the generation of training data using the HFRD approach, which enhances the capacity of generalisation for the reactive composition space encountered in practical turbulent combustion by using the random process to expand the training dataset from laminar flamelets. The MMLP artificial neural networks (ANNs) are then trained to predict different composition states, aiming to improve the accuracy of ANN predictions. To validate the effectiveness of the ANNs, they are initially tested on 1-D laminar flame simulations with varying strain rates. Subsequently, a test is conducted on Sandia non-premixed turbulent DME series flames D and F, with an increasing jet Reynolds Number. The results regarding species mole fractions and temperature show overall excellent agreement with the direct integration method. The HFRD-MMLP method achieves speed-up factors of over 16 and 10 for the reaction step, and total computational costs, respectively, in the LES-PDF simulation with the DME mechanism in this work, surpassing the speed-up factors of approximately 12–14, and below 5 for the reaction step, and total time costs, respectively, in previous works on GRI-1.2 mechanism for CH
(Ding et al., 2021) and CH
/H
(Ding et al., 2022) combustion. This indicates that the HRFD-MMLP method is highly effective for mechanisms of large size in reducing the computational cost by avoiding a significant number of highly stiff ordinary differential equations (ODEs) integration. This approach can be applied in real-time calculations of reaction source terms in methods including Direct Numerical Simulation (DNS), Probability Density Function (PDF) methods, unsteady flamelet, Conditional Moment Closure (CMC), Multiple Mapping Closure (MMC), Linear Eddy Model (LEM), Thickened Flame Model, the Partially Stirred Reactor (PaSR) method (as in OpenFOAM) and laminar flame computation.
(Ding et al., 2021) and CH
/H
(Ding et al., 2022) combustion. This indicates that the HRFD-MMLP method is highly effective for mechanisms of large size in reducing the computational cost by avoiding a significant number of highly stiff ordinary differential equations (ODEs) integration. This approach can be applied in real-time calculations of reaction source terms in methods including Direct Numerical Simulation (DNS), Probability Density Function (PDF) methods, unsteady flamelet, Conditional Moment Closure (CMC), Multiple Mapping Closure (MMC), Linear Eddy Model (LEM), Thickened Flame Model, the Partially Stirred Reactor (PaSR) method (as in OpenFOAM) and laminar flame computation.
Date Issued
2024-03-15
Date Acceptance
2023-11-06
Citation
Fuel: the science and technology of fuel and energy, 2024, 360
ISSN
0016-2361
Publisher
Elsevier
Journal / Book Title
Fuel: the science and technology of fuel and energy
Volume
360
Copyright Statement
Copyright © Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Identifier
https://www.sciencedirect.com/science/article/pii/S0016236123029526
Subjects
Artificial neural networks (ANNs)
ARTIFICIAL NEURAL-NETWORKS
Biodiesel fuels
CHEMICAL-SYSTEM
CHEMISTRY REPRESENTATION
Chemistry tabulation
COMBUSTION CHEMISTRY
Energy & Fuels
Engineering
Engineering, Chemical
IGNITION DELAY
LARGE-EDDY SIMULATION
Machine learning
MECHANISM
METHANE
PDF SIMULATION
REDUCTION
Science & Technology
Technology
Turbulent non-premixed flames
Publication Status
Published
Rights Embargo Date
2025-12-04
Article Number
130338
Date Publish Online
2023-12-06