Accurately and rapidly predicting the multi-conditions characteristics of turbines is fundamental for realizing efficient energy conversion and optimal layout schemes. Based on supervised graph learning approaches, this work is dedicated to establishing a fast multidisciplinary prediction model for gas turbines. The research target is a gas turbine blade with complex cooling channels. An Aerodynamic Strength Prediction Graph neural network (ASP-GNN) is proposed to predict the aerodynamic-strength characteristics and temperature field under different boundary conditions. The superiority of our approach is demonstrated by prediction precision and time cost. The generalizability of the network is also investigated by adopting different training set sizes, and the ASP-GNN can achieve satisfactory accuracy with a limited number of training samples. Based on the established model, the effects of various boundary conditions on aerodynamic and strength performance are quantified. The unsteady characteristic of performance and temperature field are also obtained conveniently. The proposed model could serve as a fast analysis approach to aid the design and analysis of turbomachinery. It may relieve the workload of numerical simulations for complex engineering analysis.

准确快速地预测涡轮机的多工况特性是实现高效能量转换和优化布局方案的基础。基于有监督的图学习方法，这项工作致力于建立燃气轮机的快速多学科预测模型. 研究对象是具有复杂冷却通道的燃气轮机叶片。提出了一种气动强度预测图神经网络 (ASP-GNN) 来预测不同边界条件下的气动强度特性和温度场。预测精度和时间成本证明了我们方法的优越性。还通过采用不同的训练集大小来研究网络的泛化性，并且 ASP-GNN 可以在有限数量的训练样本下达到令人满意的精度。基于所建立的模型，量化了各种边界条件对气动和强度性能的影响。性能和温度场的非稳态特性也很容易获得。所提出的模型可以作为一种快速分析方法来帮助涡轮机械的设计和分析。它可以减轻复杂工程分析的数值模拟的工作量。