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首页> 外文期刊>Acta astronautica >Supervised learning mixing characteristics of film cooling in a rocket combustor using convolutional neural networks
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Supervised learning mixing characteristics of film cooling in a rocket combustor using convolutional neural networks

机译：利用卷积神经网络监督火箭燃烧器中薄膜冷却的学习搅拌特性

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摘要

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%.
机译：在诸如复杂的流体流动之类的物理问题之前应用了机器学习方法。本文介绍了一种使用卷积神经网络直接预测冷却剂膜与火箭燃烧室中燃烧气体之间的混合特性的方法。基于参考实验，从雷诺平均Navier-Stokes仿真广告系列获得数值解决方案，然后在矩形目标网格中插入。修改U-NET架构以对混合流场的编码和解码的特征进行编码和解码。说明了训练数据大小和学习时间与正常和重新卷积损失函数的影响。通过对测试用例进行数值实验，通过小于0.55％的全局误差对修改的架构和相关的学习设置进行了说明。

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