神经算子增强的双级低压涡轮子午面流场全景式预测模型

蒋首民; 成辉; 宋立明; 朱若愚; 郭振东

doi:10.7652/xjtuxb202506011

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神经算子增强的双级低压涡轮子午面流场全景式预测模型

专题　智能预测 | 更新时间：2025-08-26

- 神经算子增强的双级低压涡轮子午面流场全景式预测模型
- A Neural Operator Enhanced Panoramic Prediction Model for Meridional Flow Field of Two-Stage Low Pressure Turbine
- 西安交通大学学报 2025年59卷第6期页码：103-111
- 作者机构：
  
  1.西安交通大学能源与动力工程学院， 710049，西安
  2.中国航发沈阳发动机研究所， 110015，沈阳
- 作者简介：
  
  蒋首民（1989—），男，博士生；
  郭振东，男，副教授。
- 基金信息：
  
  国家科技重大专项资助项目(2019-Ⅱ-0008-0028)
- DOI：10.7652/xjtuxb202506011
  中图分类号： TK14
- 收稿：2024-11-04，
  
  网络首发：2025-02-17，
  
  纸质出版：2025-06-10
- 稿件说明：
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蒋首民, 成辉, 宋立明, 等. 神经算子增强的双级低压涡轮子午面流场全景式预测模型[J]. 西安交通大学学报, 2025,59(6):103-111.

JIANG Shoumin, CHENG Hui, SONG Liming, et al. A Neural Operator Enhanced Panoramic Prediction Model for Meridional Flow Field of Two-Stage Low Pressure Turbine[J]. Journal of Xi’an Jiaotong University, 2025, 59(6): 103-111.
蒋首民, 成辉, 宋立明, 等. 神经算子增强的双级低压涡轮子午面流场全景式预测模型[J]. 西安交通大学学报, 2025,59(6):103-111. DOI： 10.7652/xjtuxb202506011.

JIANG Shoumin, CHENG Hui, SONG Liming, et al. A Neural Operator Enhanced Panoramic Prediction Model for Meridional Flow Field of Two-Stage Low Pressure Turbine[J]. Journal of Xi’an Jiaotong University, 2025, 59(6): 103-111. DOI： 10.7652/xjtuxb202506011.

摘要

针对传统流场预测模型需对涡轮各性能参数分别构建导致其建模效率低、工程适用性差的问题，提出了一种可对涡轮级子午面任意关键性能参数进行高精度评估的高效全景式预测框架，并以双级低压涡轮为例建立了高精度涡轮子午面全景式预测模型。所提出的预测框架首先对涡轮级子午面温度、压力、密度及速度等6大基础物理量进行预测，进而对涡轮级总体性能参数、关键截面性能参数沿叶高分布和关键参数子午面进行预测。与此同时，为提升对子午面性能参数的预测精度，在全景式预测框架下将Transformer融入到神经算子网络，建立了Transformer神经算子（TNO）增强的子午面全景式预测模型。对基于TNO所构建的双级子午面预测模型进行测试表明，TNO模型可对双级低压涡轮质量流量、功率、膨胀比等总体性能参数和出口气流角、级反动度等沿叶高的分布，以及子午面熵值分布等进行高精度预测，且相对预测误差小于1%，TNO预测精度显著高于基于经典UNet网络的全景式预测模型。研究结果验证了所提的全景式预测框架与模型的有效性。

Abstract

To address the inefficiency and poor engineering applicability of traditional flow field prediction models due to separate construction required for each performance parameter of the turbine

an efficient panoramic prediction framework is proposed to predict accurately any key performance parameter of the turbine stage meridional plane. With a two-stage low-pressure turbine taken as an example

a high-precision panoramic prediction model for the turbine meridional plane was established. Specifically

the proposed prediction framework first predicted the six basic physical parameters of the turbine stage meridian plane

such as temperature

pressure

density and velocity

and then predicted the overall performance parameters of the turbine stage and the distribution of key cross-section performance parameters along the blade height as well as the meridian plane contour of the key parameters. Meanwhile

in order to improve the prediction accuracy of the performance parameters of meridional plane

Transformer was integrated into a Neural Operator network under the framework of panoramic prediction

and a Transformer enhanced Neural Operator (TNO) prediction model was established. The test results of the two-stage meridian prediction model based on TNO showed that

the TNO prediction model could accurately predict the overall performance parameters such as mass flow rate/turbine power/expansion ratio of the turbine stage

the distribution of outlet flow angle/stage reaction along the span

and the meridian contour of entropy

etc. The relative prediction error was less than 1%

and the prediction accuracy of TNO was significantly higher than that of the panoramic prediction model based on the classical UNet network. Thus

the effectiveness of the proposed panoramic prediction framework and model has been well demonstrated.

关键词

Keywords

references

陈海昕 , 邓凯文 , 李润泽 . 机器学习技术在气动优化中的应用 [J ] . 航空学报 , 2019 , 40 ( 1 ): 47 - 63 .

CHEN Haixin , DENG Kaiwen , LI Runze . Utilization of machine learning technology in aerodynamic optimization [J ] . Acta Aeronautica et Astronautica Sinica , 2019 , 40 ( 1 ): 47 - 63 .

刘浩 , 李国庆 , 张深 , 等 . 机器学习在涡轮机械中的应用进展 [J ] . 工程热物理学报 , 2023 , 44 ( 4 ): 938 - 951 .

LIU Hao , LI Guoqing , ZHANG Shen , et al . Application progress of machine learning in turbomachinery [J ] . Journal of Engineering Thermophysics , 2023 , 44 ( 4 ): 938 - 951 .

ZHANG Weihao , LI Lele , LI Ya , et al . A parameterized-loading driven inverse design and multi-objective coupling optimization method for turbine blade based on deep learning [J ] . Energy , 2023 , 281 : 128209 .

VIANA F A C , SIMPSON T W , BALABANOV V , et al . Special section on multidisciplinary design optimization: metamodeling in multidisciplinary design optimization: how far have we really come? [J ] . AIAA Journal , 2014 , 52 ( 4 ): 670 - 690 .

FUJIYOSHI H , HIRAKAWA T , YAMASHITA T . Deep learning-based image recognition for autonomous driving [J ] . IATSS Research , 2019 , 43 ( 4 ): 244 - 252 .

CHOWDHARY K R . Natural language processing [M ] // CHOWDHARY K R . Fundamentals of Artificial Intelligence . New Delhi : Springer India , 2020 : 603 - 649 .

LECUN Y , BENGIO Y , HINTON G . Deep learning [J ] . Nature , 2015 , 521 ( 7553 ): 436 - 444 .

汪奇 , 杨力 , 饶宇 . 深度学习方法在涡轮冷却中的应用综述 [J ] . 工程热物理学报 , 2022 , 43 ( 3 ): 656 - 662 .

WANG Qi , YANG Li , RAO Yu . A review of deep learning methods in turbine cooling [J ] . Journal of Engineering Thermophysics , 2022 , 43 ( 3 ): 656 - 662 .

张伟伟 , 寇家庆 , 刘溢浪 . 智能赋能流体力学展望 [J ] . 航空学报 , 2021 , 42 ( 4 ): 20 - 65 .

ZHANG Weiwei , KOU Jiaqing , LIU Yilang . Prospect of artificial intelligence empowered fluid mechanics [J ] . Acta Aeronautica et Astronautica Sinica , 2021 , 42 ( 4 ): 20 - 65 .

BRUNTON S L , NOACK B R , KOUMOUTSAKOS P . Machine learning for fluid mechanics [J ] . Annual Review of Fluid Mechanics , 2020 , 52 : 477 - 508 .

SEKAR V , JIANG Qinghua , SHU Chang , et al . Fast flow field prediction over airfoils using deep learning approach [J ] . Physics of Fluids , 2019 , 31 ( 5 ): 057103 .

CHEN Junfeng , LIU Changxing , XUAN Liming , et al . Knowledge-based turbomachinery design system via a deep neural network and multi-output Gaussian process [J ] . Knowledge-Based Systems , 2022 , 252 : 109352 .

LI Jinxing , LIU Tianyuan , WANG Yuqi , et al . Integrated graph deep learning framework for flow field reconstruction and performance prediction of turbomachinery [J ] . Energy , 2022 , 254 , Part C : 124440 .

BRUNI G , KRISHNABABU S , JACKSON S . Application of machine learning to forced response predictions of an industrial axial compressor rotor blade [J ] . Journal of Engineering for Gas Turbines and Power , 2023 , 145 ( 1 ): 011019 .

BRUNI G , MALEKI S , KRISHNABABU S K . C(NN)FD: a deep learning framework for turbomachinery CFD analysis [J ] . IEEE Transactions on Industrial Informatics , 2024 , 20 ( 8 ): 10230 - 10237 .

郭振东 , 成辉 , 陈云 , 等 . 融合相似性原理的涡轮叶型流场预测方法研究 [J ] . 力学学报 , 2023 , 55 ( 11 ): 2647 - 2660 .

GUO Zhendong , CHENG Hui , CHEN Yun , et al . Study on flow field prediction of turbine blades by coupling similarity principle [J ] . Chinese Journal of Theoretical and Applied Mechanics , 2023 , 55 ( 11 ): 2647 - 2660 .

WANG Qi , YANG Li , RAO Yu . Establishment of a generalizable model on a small-scale dataset to predict the surface pressure distribution of gas turbine blades [J ] . Energy , 2021 , 214 : 118878 .

赵宇轩 , 陈子峰 , 黄丞明 , 等 . 压气机叶片叶根轮槽物理场预测及快速优化设计 [J ] . 西安交通大学学报 , 2024 , 58 ( 4 ): 96 - 106 .

ZHAO Yuxuan , CHEN Zifeng , HUANG Chengming , et al . Physical field prediction and fast optimization design of compressor blade roots and grooves [J ] . Journal of Xi'an Jiaotong University , 2024 , 58 ( 4 ): 96 - 106 .

孙逸 . 基于S1/S2流面耦合理论的航空发动机流道设计技术与研究 [D ] . 南京 : 南京航空航天大学 , 2018 .

蒲永彬 . 一体化二元变几何进气道设计及气动性能研究 [D ] . 南京 : 南京航空航天大学 , 2018 .

CHEN Tianping , CHEN Hong . Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems [J ] . IEEE Transactions on Neural Networks , 1995 , 6 ( 4 ): 911 - 917 .

PARMAR N , VASWANI A , USZKOREIT J , et al . Image transformer [C ] // Proceedings of the 35th International Conference on Machine Learning . Chia Laguna Resort, Sardinia, Italy : PMLR , 2018 : 4055 - 4064 .

VASWANI A , SHAZEER N , PARMAR N , et al . Attention is all you need [C ] // Proceedings of the 31st International Conference on Neural Information Processing Systems . Red Hook, NY, USA : Curran Associates Inc. , 2017 : 6000 - 6010 .

PRITCHARD L J . An eleven parameter axial turbine airfoil geometry model [C ] // ASME 1985 International Gas Turbine Conference and Exhibit . New York, USA : ASME , 1985 : V001T03A058 .

HAMAKHAN I A , KORAKIANITIS T . Aerodynamic performance effects of leading-edge geometry in gas-turbine blades [J ] . Applied Energy , 2010 , 87 ( 5 ): 1591 - 1601 .

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