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2022 Vol.27, Issue 1 Preview Page

Research Article

Artificial Neural Network for the Representation of Chemical Kinetics in Reactive Flows

인공신경망을 이용한 반응유동 수치해석의 반응기구 대체 연구

Jeongyong Lee1
,
Bok Jik Lee2*
이 정용1
,
이 복직2*
1Interdisciplinary Program in Space Systems, Seoul National University
2Institute of Advanced Aerospace Technology, Seoul National University
1서울대학교 협동과정 우주시스템전공
2서울대학교 항공우주신기술연구소
*Corresponding Author
31 March 2022. pp. 17-26
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Abstract

Reducing the computational cost of chemical kinetics is essential to implement detailed reaction mechanisms into realistic numerical simulations. The present study introduces an artificial neural network (ANN) that can predict the chemical source terms of each species for the given species mass fractions and temperature, replacing the conventional chemical terms based on Arrhenius rate equations. The ANN was trained using numerical solutions of opposed-flow flames that can cover a wide range of combustion problems. The OPPDIF code and a detailed reaction mechanism for hydrogen and air with 9 species and 19 reactions were used to generate a training dataset comprised of species mass fractions, temperature, chemical source terms. A physics-guided loss function that considers mass conservation of elemental species was employed. Using the trained ANN, a modified OPPDIF, named OPPDIF-ANN, was prepared by replacing the CKWYP with CKWYP-ANN evaluating the chemical sources via the trained ANN. For multiple global strain rate conditions, the solutions using ANN-based source terms were proven to be identical to those using Arrhenius source terms.

Keywords
Machine learning
Artificial neural network
Chemical kinetics
Opposed-flow diffusion flames
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Information
  • Publisher :The Korean Society Combustion
  • Publisher(Ko) :한국연소학회
  • Journal Title :Journal of The Korean Society Combustion
  • Journal Title(Ko) :한국연소학회지
  • Volume : 27
  • No :1
  • Pages :17-26
  • Received Date : 2022-01-10
  • Revised Date : 2022-01-20
  • Accepted Date : 2022-02-24
  • DOI :https://doi.org/10.15231/jksc.2022.27.1.017
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