Utilizing neural networks to supplant chemical kinetics tabulation through mass conservation and weighting of species depletion

Franz M. Rohrhofer*, Stefan Posch, Clemens Gößnitzer, José M. García-Oliver, Bernhard Geiger

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Artificial Neural Networks (ANNs) have emerged as a powerful tool in combustion simulations to replace memory-intensive tabulation of integrated chemical kinetics. Complex reaction mechanisms, however, present a challenge for standard ANN approaches as modeling multiple species typically suffers from inaccurate predictions on minor species. This paper presents a novel ANN approach which can be applied on complex reaction mechanisms in tabular data form, and only involves training a single ANN for a complete reaction mechanism. The approach incorporates a network architecture that automatically conserves mass and employs a particular loss weighting based on species depletion. Both modifications are used to improve the overall ANN performance and individual prediction accuracies, especially for minor species mass fractions. To validate its effectiveness, the approach is compared to standard ANNs in terms of performance and ANN complexity. Four distinct reaction mechanisms (H2, C7H16, C12H26, OME34) are used as a test cases, and results demonstrate that considerable improvements can be achieved by applying both modifications.

Original languageEnglish
Article number100341
Number of pages10
JournalEnergy and AI
Volume16
Early online date30 Jan 2024
DOIs
Publication statusPublished - May 2024

Keywords

  • Chemical kinetics
  • Flamelet tabulation
  • Mass conservation
  • Neural network approach
  • Species loss weighting

ASJC Scopus subject areas

  • Engineering (miscellaneous)
  • General Energy
  • Artificial Intelligence

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