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%.

    Highlights Application of the deep learning approach on film cooling in a rocket combustor. The U-net architecture of convolutional neural networks capturing the features of mixing characteristics. Training performance improvement by adding a re-convolutional term of loss function.


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    Title :

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


    Contributors:
    Ma, Hao (author) / Zhang, Yu-xuan (author) / Haidn, Oskar J. (author) / Thuerey, Nils (author) / Hu, Xiang-yu (author)

    Published in:

    Acta Astronautica ; 175 ; 11-18


    Publication date :

    2020-05-08


    Size :

    8 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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