Application of deep learning methods to physical simulations such as CFD
(Computational Fluid Dynamics), have been so far of limited industrial
relevance. This paper demonstrates the development and application of a deep
learning framework for real-time predictions of the impact of tip clearance
variations on the aerodynamic performance of multi-stage axial compressors in
gas turbines. The proposed C(NN)FD architecture is proven to be scalable to
industrial applications, and achieves in real-time accuracy comparable to the
CFD benchmark. The deployed model, is readily integrated within the
manufacturing and build process of gas turbines, thus providing the opportunity
to analytically assess the impact on performance and potentially reduce
requirements for expensive physical tests.

应用深度学习方法进行物理模拟（如计算流体动力学）在工业上的实际应用受到限制。本文证明了一种基于深度学习框架的实时预测多级轴流压缩机在燃气涡轮中轴向间隙变化对空气动力性能的影响的方法。所提出的 C（NN）FD 架构可扩展到工业应用，并实时达到与 CFD 基准相当的准确性。部署的模型容易集成到燃气涡轮的制造和构建过程中，从而提供了评估性能影响并可能减少对昂贵物理测试的需求的机会。

多级轴流压缩机气动性能顶部间隙变化的深度学习预测

C(NN)FD -- deep learning predictions of tip clearance variations on  multi-stage axial compressors aerodynamic performance

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 $CO_2$
emissions, thus being of great industrial and environmental relevance. The
proposed \textit{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.

本文介绍了一种基于深度学习的新型框架（C（NN）FD），实时预测燃气轮机中轴向压缩机制造和构建变化对整体性能的影响。通过预测流场并计算相应的整体性能，该方法具有普适性，而仅过滤 CFD 解决方案的相关部分使得该方法可扩展到工业应用。