  Deep learning has been used in many areas, such as feature detections in
images and the game of go. This paper presents a study that attempts to use the
deep learning method to predict turbomachinery performance. Three different
deep neural networks are built and trained to predict the pressure
distributions of turbine airfoils. The performance of a library of turbine
airfoils were firstly predicted using methods based on Euler equations, which
were then used to train and validate the deep learning neural networks. The
results show that network with four layers of convolutional neural network and
two layers of fully connected neural network provides the best predictions. For
the best neural network architecture, the pressure prediction on more than 99%
locations are better than 3% and 90% locations are better than 1%.
