Development of a Global Mechanism for Ammonia Combustion Assisted by Machine Learning

Jieun Kang; Seong-kyun Im

doi:10.15231/jksc.2024.29.2.037

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2024 Vol.29, Issue 2 Preview Page Next Page

Research Article

Development of a Global Mechanism for Ammonia Combustion Assisted by Machine Learning 기계학습을 활용한 암모니아 연소의 총괄 반응식 개발

30 June 2024. pp. 37-43

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Abstract

To achieve carbon neutrality, sustainable energy solutions like ammonia are being explored. Ammonia, with its high energy density and existing infrastructure, is promising as a hydrogen carrier. This study aims to reduce the computational cost of ammonia combustion simulations by developing a global mechanism using a chemical reaction neural network (CRNN). The CRNN was trained using data from the detailed mechanism by Okafor, resulting in a global mechanism with 4 reactions and 7 species. Performance evaluations showed that the developed global mechanism accurately predicts temperature, species concentrations and ignition delay times, achieving high prediction accuracy for steady-state conditions. The results indicate that the CRNN-based global mechanism significantly enhances computational efficiency and accessibility for ammonia combustion research.

Keywords

Ammonia combustion

Global reaction mechanism

Machine learning

Chemical reaction neural network

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Information

Publisher :The Korean Society of Combustion
Publisher(Ko) :한국연소학회
Journal Title :Journal of the Korean Society of Combustion
Journal Title(Ko) :한국연소학회지
Volume : 29
No :2
Pages :37-43
Received Date : 2024-05-31
Revised Date : 2024-06-17
Accepted Date : 2024-06-17
DOI :https://doi.org/10.15231/jksc.2024.29.2.037

Journal of the Korean Society of Combustion ISSN:1226-0959(Print) 2466-2089(Online) 한국연소학회지

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