Deep Learning-Based Model for Progress Variable Dissipation Rate in Turbulent Premixed Flames

Research output: Contribution to journalArticlepeer-review

19 Scopus Citations


A deep neural network (DNN) based large eddy simulation (LES) model for progress variable dissipation rate in turbulent premixed flames is presented. The DNN model is trained using filtered data from direct numerical simulations (DNS) of statistically planar turbulent premixed flames with n-heptane as fuel. Training data was comprised of flames with varying turbulence levels leading to a range of Karlovitz numbers. Through a-priori tests the DNN model is shown to predict the subfilter contribution to progress variable dissipation rate accurately over a range of filter widths and for all Karlovitz numbers examined in this study. Superior performance of the DNN model relative to an established physics-based model is also demonstrated. Additionally, transferability of the DNN model is highlighted by a-priori evaluation of the model using filtered DNS data from multiple cases with different Karlovitz numbers and fuel species than those that were used for training the model.

Original languageAmerican English
Pages (from-to)2929-2938
Number of pages10
JournalProceedings of the Combustion Institute
Issue number2
StatePublished - Jan 2021

Bibliographical note

Publisher Copyright:
© 2020 The Combustion Institute

NREL Publication Number

  • NREL/JA-2C00-75428


  • Deep Neural Network (DNN)
  • Large Eddy Simulation (LES)
  • Premixed flames
  • Progress variable dissipation rate


Dive into the research topics of 'Deep Learning-Based Model for Progress Variable Dissipation Rate in Turbulent Premixed Flames'. Together they form a unique fingerprint.

Cite this