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 language | English |
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Pages (from-to) | 11-18 |
Number of pages | 8 |
Journal | Acta Astronautica |
Volume | 175 |
DOIs | |
State | Published - Oct 2020 |
Keywords
- Combustion chamber
- Convolutional neural network
- Deep learning
- Film cooling
- Flow-field prediction