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.

翻译：将深度学习方法应用于CFD（计算流体动力学）等物理模拟，至今在工业领域仍具有有限的相关性。本文展示了一种深度学习框架的开发与应用，用于实时预测燃气轮机多级轴流压气机中叶顶间隙变化对气动性能的影响。所提出的C(NN)FD架构被证明可扩展至工业应用，并在实时性下达到了与CFD基准相当的精度。该部署模型已顺利集成至燃气轮机的制造与装配流程中，从而提供了分析评估性能影响的机会，并可能减少对昂贵物理试验的需求。