A Neural Operator Enhanced Panoramic Prediction Model for Meridional Flow Field of Two-Stage Low Pressure Turbine

JIANG Shoumin; CHENG Hui; SONG Liming; ZHU Ruoyu; GUO Zhendong

doi:10.7652/xjtuxb202506011

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A Neural Operator Enhanced Panoramic Prediction Model for Meridional Flow Field of Two-Stage Low Pressure Turbine

Special Topic　Intelligent Prediction | 更新时间：2025-08-26

- A Neural Operator Enhanced Panoramic Prediction Model for Meridional Flow Field of Two-Stage Low Pressure Turbine
- Journal of Xi’an Jiaotong University Vol. 59, Issue 6, Pages: 103-111(2025)
- 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：
- 基金信息：
- DOI：10.7652/xjtuxb202506011
  CLC： TK14
- Received：04 November 2024，
  
  Online First：17 February 2025，
  
  Published：10 June 2025
- 稿件说明：
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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：

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.

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references

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