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 manufacturing and build variations, such as tip clearance and surface roughness, on the flow field and aerodynamic performance of multi-stage axial compressors in gas turbines. The associated scatter in compressor efficiency is known to have a significant impact on the corresponding overall performance and emissions of the gas turbine, therefore posing a challenge of great industrial and environmental relevance. The proposed architecture is proven to achieve an accuracy comparable to that of the CFD benchmark, in real-time, for an industrially relevant application. 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：深度学习在涡轮机械计算流体动力学（CFD）物理仿真等领域的应用，迄今工业相关性有限。本文展示了一个深度学习框架的开发与应用，该框架可实时预测制造与装配变异（如叶尖间隙和表面粗糙度）对燃气轮机多级轴流压气机流场及气动性能的影响。已知压气机效率的相应离散会对燃气轮机的整体性能与排放产生显著影响，从而构成一项具有重大工业与环境意义的挑战。该架构被证明在工业相关应用中能够达到与CFD基准相当的实时精度。部署后的模型可直接集成至燃气轮机的制造与装配流程，从而为分析性能影响提供机会，并可能减少对昂贵物理测试的需求。