  Deep Learning methods have seen a wide range of successful applications
across different industries. Up until now, applications to physical simulations
such as CFD (Computational Fluid Dynamics), have been limited to simple
test-cases of minor industrial relevance. This paper demonstrates the
development of a novel deep learning framework for real-time predictions of the
impact of manufacturing and build variations on the overall performance of
axial compressors in gas turbines, with a focus on tip clearance variations.
The associated scatter in efficiency can significantly increase the CO2
emissions, thus being of great industrial and environmental relevance. The
proposed C(NN)FD architecture achieves in real-time accuracy comparable to the
CFD benchmark. Predicting the flow field and using it to calculate the
corresponding overall performance renders the methodology generalisable, while
filtering only relevant parts of the CFD solution makes the methodology
scalable to industrial applications.
