Wu, S;
Liang, W;
Luo, KH;
(2025)
Deep learning based combustion chemistry acceleration method for widely applicable NH₃/H₂ turbulent combustion simulations.
Combustion and Flame
, 278
, Article 114218. 10.1016/j.combustflame.2025.114218.
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Abstract
Simulating reacting flows with detailed chemistry is often prohibitively expensive due to the complexity of reaction mechanisms and the numerical stiffness arising from disparate chemical time scales. While recent advancements in neural networks offer potential for efficiently capturing the dynamics of stiff chemistry, its application to dual-fuels with drastic differences in reactivity such as ammonia (NH3 ) and hydrogen (H2 ) remains challenging. In this study, we present a neural network model with variable time steps aimed at enhancing the efficiency of combustion chemistry simulations focusing on the complex dual-fuel NH3∕H2 under premixed combustion. We improved the "sampling-training" workflow based on previous HFRD method to overcome the challenge of generalizing neural network models to fuel blends under premixed combustion. This workflow involves three improvements: defining the base manifold using unity Lewis number laminar flames, introducing continuously controllable randomization, and employing a training process with mass conservation and heat release rate similarity constraints. Our approach is validated against simulations of planar turbulent premixed flames and temporally-evolving jet flames across various conditions. The model demonstrates high accuracy and consistency, achieving a chemical calculation acceleration of 7 times and an overall simulation acceleration of 5 times using a model with 4 hidden layers and 800 neurons on the same CPU device. When a GPU is adopted, the chemical calculation acceleration increases to 30 times, and the overall simulation acceleration reaches 10 times.
| Type: | Article |
|---|---|
| Title: | Deep learning based combustion chemistry acceleration method for widely applicable NH₃/H₂ turbulent combustion simulations |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.combustflame.2025.114218 |
| Publisher version: | https://doi.org/10.1016/j.combustflame.2025.114218 |
| Language: | English |
| Additional information: | Copyright © 2025 The Authors. Published by Elsevier Inc. on behalf of The Combustion Institute. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Turbulent premixed combustion, Fuel blends, Detailed chemical mechanism, Combustion chemistry acceleration, Artificial neural network, Manifold sampling |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10210155 |
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