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。