南京师范大学学报（工程技术版）2026，Vol.26Issue(2)：9-20,12.DOI:10.3969/j.issn.1672-1292.2026.02.002
基于机器学习的篦齿封严流动传热性能预测
Prediction of Flow and Heat Transfer Performance of Labyrinth Seals Based on Machine Learning
高峰 1张敏慧 2王春华 2张居兵1
作者信息
- 1. 南京师范大学能源与机械工程学院,江苏 南京 210023
- 2. 南京航空航天大学能源与动力学院,江苏 南京 210016
- 折叠
摘要
Abstract
The study focuses on the fine-grained and rapid prediction of the flow and heat transfer performance of labyrinth seals with smooth bushings using machine learning methods.Input parameters are selected for the machine learning model include the labyrinth seal clearance,tooth spacing,forward angle of the labyrinth tooth,inlet total pressure,and rotational speed.Learning samples required for the machine learning model are established based on Latin hypercube sampling and computation fluid dynamic(CFD)simulation methods.Radial basis function neural networks are independently constructed at each node of the machine learning grid(with a number of nodes significantly fewer than the CFD grid)to directly predict the total pressure,total temperature,and velocity at the corresponding locations.Comparison between the radial basis function neural network and CFD calculation results reveals that the mean absolute percentage errors for total pressure,total temperature,and velocity are 0.07%,0.17%,and 0.14%,with root mean square errors of 1.18 kPa,1.8 K,and 6.45 m/s,respectively.A detailed analysis of the numerical relationships between the input and output parameters of the machine learning model is conducted.The research findings provide a new technical approach for efficient and low-cost modeling of the heat transfer performance of labyrinth seals.关键词
航空发动机/篦齿封严/机器学习/径向基函数神经网络/泄漏系数Key words
aircraft engine/labyrinth seals/machine learning/radial basis function neural network/leakage coefficient分类
航空航天引用本文复制引用
高峰,张敏慧,王春华,张居兵..基于机器学习的篦齿封严流动传热性能预测[J].南京师范大学学报（工程技术版）,2026,26(2):9-20,12.
