Application of deep learning methods to physical simulations such as CFD (Computational Fluid Dynamics) for turbomachinery applications, 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 flow field and aerodynamic performance of multi-stage axial compressors in gas turbines. The proposed 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：深度学习方法在物理模拟中的应用，例如针对涡轮机械领域的计算流体动力学（CFD），迄今为止在工业相关性方面仍较为有限。本文展示了一种深度学习框架的开发与应用，用于实时预测叶尖间隙变化对燃气轮机多级轴流压气机流场和气动性能的影响。所提出的架构被证明可扩展至工业应用，并能在实时精度上达到与CFD基准相当的水平。部署后的模型可无缝集成至燃气轮机的制造与装配流程中，从而提供分析评估性能影响的机会，并有可能减少对昂贵物理试验的需求。