增强端壁气膜冷却预测的泛化能力：将叠加原理融入基于Transformer的神经算子
Enhancing generalization in endwall film cooling prediction: Incorporating the superposition principle into transformer-based neural operators
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中文总结 AI 辅助
本研究提出基于叠加原理的深度神经算子SDNO，分两阶段预测涡轮端壁气膜冷却温度场，实现对未见冷却布局的准确泛化预测。
中文摘要 AI 辅助
本研究提出了一种物理增强的神经算子框架，以增强对具有可变数量气膜孔的涡轮端壁冷却布局的泛化预测能力。具体而言，受气膜冷却叠加原理的启发，我们提出了一种气膜冷却预测模型，即基于叠加的深度神经算子（SDNO），该模型将端壁温度场预测分为两个阶段。在第一阶段，将涡轮端壁的冷却布局划分为若干子部分，每个子部分随机分配气膜孔，并设计了一种基于Transformer的神经算子网络（即Calculate Net）来预测每个子部分的温度场。然后，在第二阶段，训练另一个神经算子网络（即Super Net），以组合Calculate Net预测的各子部分温度场，并获得完整冷却布局的叠加温度场。此外，不直接将气膜冷却轮廓作为像素图，而是设计了一种对冷却孔可变位置敏感的有符号距离函数（SDF）来编码冷却孔的位置信息。进一步地，所提出的端壁气膜冷却预测模型使用改变气膜孔数量（从1到5个且位置可变）的样本进行训练。随后，训练后的预测显示出优异的泛化预测能力，能够准确预测训练样本中未见过的具有10至20个气膜冷却孔的冷却布局的冷却效率。所提出的SDNO相对于全监督基线也提高了预测精度。综上所述，我们提出的预测模型的有效性得到了充分验证。
英文摘要
In this study, a physics-enhanced neural operator framework is proposed to enhance the generalization prediction ability of the cooling layout of a turbine endwall with variable number of film holes. Specifically, inspired by the film cooling superposition principle, we propose a film cooling prediction model, namely superposition-based deep neural operator (SDNO), that divides the endwall temperature field prediction into two stages. In the first stage, the cooling layout of a turbine endwall is divided into several sub-parts with randomly assigned film holes, and a Transformer-based neural operator network, namely Calculate Net, is designed to predict the temperature field of each sub-part. Then, in the second stage, another neural operator network, i.e., Super Net, is trained to combine the temperature fields predicted by Calculate Net for each sub-part and obtain the superposed temperature field of the full cooling layout. Additionally, instead of directly taking the film cooling contours as pixel plots, a signed distance function (SDF) which is sensitive to the variable locations of cooling holes, is designed to encode the location information of cooling holes. Furthermore, the proposed endwall film cooling prediction model is trained with the samples that changing the number of film holes from 1-5 with variable locations. Then, the trained prediction shows excellent generalization prediction ability, which can accurately predict the film effectiveness of the cooling layout with 10-20 film cooling holes that are unseen in the training samples. The proposed SDNO also improves prediction accuracy relative to the fully supervised baseline. With the above, the effectiveness of our proposed prediction model has been well demonstrated.
