C(NN)FD -- a deep learning framework for turbomachinery CFD analysis
This addresses the industrial and environmental need to reduce CO2 emissions by efficiently analyzing turbomachinery, though it is incremental in applying deep learning to a specific domain.
The paper tackles the problem of predicting the impact of manufacturing variations on axial compressor performance in gas turbines, achieving real-time accuracy comparable to CFD benchmarks.
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.