A panoramic aerodynamic performance prediction method for turbomachinery cascades using transformer-enhanced neural operator

📅 2026-09-13
📈 Citations: 0
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🤖 AI Summary
本文提出了一种基于变压器增强神经算子的全景气动性能预测方法,用于涡轮机械级联设计中的快速评估,通过预测基本物理量来提高精度。
📝 Abstract
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.
Problem

Research questions and friction points this paper is trying to address.

aerodynamic performance
turbomachinery design
Navier-Stokes equations
CFD simulator
transformer-enhanced neural operator
Innovation

Methods, ideas, or system contributions that make the work stand out.

transformer-enhanced neural operator
panoramic performance prediction
turbomachinery cascades
CFD simulator
downstream tasks
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Qineng Wang
Qineng Wang
Northwestern University
Foundation ModelsEmbodied AgentsSpatial IntelligenceReasoning
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Zhendong Guo
Institute of Turbomachinery, Xi’an Jiaotong University, Xi’an, 710049, China
L
Liming Song
Institute of Turbomachinery, Xi’an Jiaotong University, Xi’an, 710049, China
Tianyuan Liu
Tianyuan Liu
Donghua University
Welding AutomationComputer VisionDeep Learningand Intelligent Manufacturing Systems