In recent years, artificial intelligence (AI) technologies have been widely applied in many different fields including in the design, maintenance, and control of aero-engines. The air-cooled turbine vane is one of the most complex components in aero-engine design. Therefore, it is interesting to adopt the existing AI technologies in the design of the cooling passages. Given that the application of AI relies on a large amount of data, the primary task of this paper is to realize massive simulation automation in order to generate data for machine learning. It includes the parameterized three-dimensional (3-D) geometrical modeling, automatic meshing and computational fluid dynamics (CFD) batch automatic simulation of different film cooling structures through customized developments of UG, ICEM and Fluent. It is demonstrated that the trained artificial neural network (ANN) can predict the cooling effectiveness on the external surface of the turbine vane. The results also show that the design of the ANN architecture and the hyper-parameters have an impact on the prediction precision of the trained model. Using this established method, a multi-output model is constructed based on which a simple tool can be developed. It is able to make an instantaneous prediction of the temperature distribution along the vane surface once the arrangements of the film holes are adjusted.

近年来，人工智能（AI）技术已广泛应用于许多不同领域，包括航空发动机的设计，维护和控制。风冷涡轮叶片是航空发动机设计中最复杂的组件之一。因此，在冷却通道的设计中采用现有的AI技术很有趣。鉴于AI的应用依赖大量数据，因此本文的主要任务是实现大规模仿真自动化，以生成用于机器学习的数据。它包括通过UG，ICEM和Fluent的定制开发对不同的薄膜冷却结构进行参数化的三维（3-D）几何建模，自动网格划分和计算流体力学（CFD）批处理自动仿真。结果表明，训练有素的人工神经网络（ANN）可以预测涡轮叶片外表面的冷却效果。结果还表明，人工神经网络架构和超参数的设计对训练模型的预测精度有影响。使用这种已建立的方法，可以构建一个多输出模型，并以此为基础开发一个简单的工具。一旦调整了薄膜孔的排列，就可以即时预测沿叶片表面的温度分布。构建多输出模型，基于该模型可以开发简单的工具。一旦调整了薄膜孔的排列，就可以即时预测沿叶片表面的温度分布。构建多输出模型，基于该模型可以开发简单的工具。一旦调整了薄膜孔的排列，就可以即时预测沿叶片表面的温度分布。