C(NN)FD - A Deep Learning Framework for Turbomachinery CFD Analysis

Deep learning methods have seen a wide range of successful applications across different industries. Up until now, applications to physical simulations such as computational fluid dynamics (CFD) have been limited to simple test cases of minor industrial relevance. This article demonstrates the development of a novel deep learning framework for real-time predictions of the impact of manufacturing and build variations on the flow field and overall performance of axial compressors in gas turbines, with a focus on tip clearance variations. The scatter in compressor efficiency associated with manufacturing and build variations can significantly increase the
$CO_{2}$
emissions, thus being of great industrial and environmental relevance. The proposed
C(NN)FD
architecture achieves real-time accuracy comparable to the CFD benchmark. Predicting the flow field and using it to calculate the corresponding overall performance renders the methodology generalizable, while filtering only relevant parts of the CFD solution makes the methodology scalable to industrial applications.