基于Transformer增强神经算子的叶轮机械叶栅全景气动性能预测方法
A panoramic aerodynamic performance prediction method for turbomachinery cascades using transformer-enhanced neural operator
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中文总结 AI 辅助
本文提出一种基于Transformer增强神经算子（TNO）的全景性能预测框架，先预测NS方程基本参数再推得叶轮机械性能，精度优于FNO和DeepONet，计算成本降低四个数量级。
中文摘要 AI 辅助
为了实现叶轮机械设计中灵活且快速的气动性能评估，本文提出了一种全景性能预测框架。与以往大多数直接预测目标函数的预测模型不同，我们的方法首先预测纳维-斯托克斯方程的基本参数，如温度、压力和密度。利用这些基本物理量，随后预测涡轮级子午面的关键性能参数。通过采用这种方法，我们提出的全景性能预测框架功能类似于CFD模拟器，能够预测设计者感兴趣的各种目标。为了提高预测精度，该框架中引入了Transformer增强神经算子（TNO）。以Rotor 37叶片为参考，所提出的TNO被训练用于预测跨声速压气机叶片在子午面上的性能。TNO能够准确预测等熵效率、质量流量等总量以及总压比分布。值得注意的是，观察到TNO的预测误差小于最先进的深度学习算子如FNO和DeepONet。此外，TNO被应用于下游任务，包括灵敏度分析和各种目标函数的优化。结果证实，TNO几乎可以像CFD模拟器一样运行，同时将下游任务的计算成本降低四个数量级。所提出的TNO在解决不同类型下游任务方面的有效性和可靠性已得到充分证明。
英文摘要
To enable flexible and rapid aerodynamic performance evaluation in turbomachinery design, this paper proposes a panoramic performance prediction framework. Unlike most previous prediction models that directly predict the objective functions of interest, our approach first predicts the basic parameters of the Navier-Stokes equations, such as temperature, pressure, and density. Utilizing these basic physical quantities, it subsequently predicts key performance parameters of the turbine stage meridian plane. By adopting this methodology, our proposed panoramic performance prediction framework functions similarly to a CFD simulator, capable of predicting various objective of interest to the designers. To enhance prediction accuracy, a transformer-enhanced neural operator (TNO) is introduced within this framework. Using the Rotor 37 blades as a reference, the proposed TNO is trained to predict the performance of a transonic compressor blade in the meridian plane. The TNO can accurately predict total quantities such as isentropic efficiency, mass flow, and distributions of total pressure ratio. Remarkably, the prediction error of TNO is observed to be smaller than that of state-of-the-art deep learning operators such as the FNO and DeepONet. Furthermore, the TNO is applied to downstream tasks, including sensitivity analysis and optimization of various objective functions. The results confirm that the TNO can operate almost like a CFD simulator, while reducing the computational cost of downstream tasks by four orders of magnitude. The effectiveness and reliability of the proposed TNO for solving different kinds of downstream tasks have been well demonstrated.
发表机构
- ENN Science and Technology Development China Co., Ltd.（新奥科技发展有限公司）
- Hebei Key Laboratory of Compact Fusion（河北省紧凑型聚变重点实验室）
机构由 AI 辅助整理，请以论文原文为准。
