Supervised learning mixing characteristics of film cooling in a rocket combustor using convolutional neural networks

Hao Ma, Yu xuan Zhang, Oskar J. Haidn, Nils Thuerey, Xiang yu Hu

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

Machine learning approach has been applied previously to physical problem such as complex fluid flows. This paper presents a method of using convolutional neural networks to directly predict the mixing characteristics between coolant film and combusted gas in a rocket combustion chamber. Based on a reference experiment, numerical solutions are obtained from Reynolds-Averaged Navier–Stokes simulation campaign and then interpolated into the rectangular target grids. A U-net architecture is modified to encode and decode features of the mixing flow field. The influence of training data size and learning time with both normal and re-convolutional loss function is illustrated. By conducting numerical experiments about test cases, the modified architecture and related learning settings are demonstrated with global errors less than 0.55%.

Original languageEnglish
Pages (from-to)11-18
Number of pages8
JournalActa Astronautica
Volume175
DOIs
StatePublished - Oct 2020

Keywords

  • Combustion chamber
  • Convolutional neural network
  • Deep learning
  • Film cooling
  • Flow-field prediction

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