基于神经网络的涡轮分子泵性能预测  

Prediction of Turbomolecular Pump Performance Using Neural Networks

作　　者：匡永麟 王晓冬[1] 宁久鑫 孙坤 蔡永航 杜志华 KUANG Yonglin;WANG Xiaodong;NING Jiuxin;SUN Kun;CAI Yonghang;DU Zhihua(School of Mechanical Engineering and Automation,Northeastern University,Shenyang 110819,China;Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences,Changchun 130033,China;School of Mechanical Engineering,Anhui University of Science and Technology,Huainan 232001,China)

机构地区：[1]东北大学机械工程与自动化学院,沈阳110819 [2]中国科学院长春光学精密机械与物理研究所,长春130033 [3]安徽理工大学机械工程学院,淮南232001

出　　处：《真空科学与技术学报》2024年第9期811-818,共8页Chinese Journal of Vacuum Science and Technology

基　　金：国家重大科学仪器设备开发专项项目(2013YQ130429);国家自然科学基金项目(52305240)。

摘　　要：涡轮分子泵作为维持洁净高真空环境的关键设备,广泛应用于半导体制造、空间模拟等诸多领域。然而,准确高效预测涡轮分子泵性能一直是亟待解决的难题,现有方法往往因模型缺乏通用性和计算效率不足而难以令人满意。文章聚焦于开发一种基于神经网络模型的涡轮分子泵抽气性能预测方法,旨在攻克上述挑战。提出了一种神经网络模型,该模型能够有效学习和模拟涡轮分子泵的复杂动态行为。模型训练数据涵盖数值模拟结果和实验测试数据,使其具备良好的泛化能力和预测精度。通过数值模拟和实验测试的双重验证,证明了神经网络模型能够准确预测涡轮分子泵抽气性能,并且根据实验测试结果综合分析了影响其抽气能力的诸多因素。结果表明,该模型可作为在分子流态下涡轮分子泵结构设计和性能评估的工具,能助力优化涡轮分子泵设计,进而提升其抽气效率和适用性。Turbomolecular pumps,as critical equipment for maintaining a clean high-vacuum environment,are widely utilized in various fields such as semiconductor manufacturing and space simulation.However,accurately and efficiently predicting the performance of turbomolecular pumps has been a challenging issue.Existing methods often suffer from a lack of generality and computational efficiency,making them less than satisfactory.This study focuses on developing a performance prediction method for turbomolecular pumps based on a neural network model to address these challenges.A neural network model is proposed,which can effectively learn and simulate the complex dynamic behavior of turbomolecular pumps.The model is trained using a combination of numerical simulation results and experimental test data,endowing it with excellent generalization and predictive accuracy.Through dual verification via numerical simulation and experimental testing,it is demonstrated that the neural network model can accurately predict the pumping performance of turbomolecular pumps.Additionally,a comprehensive analysis of the factors influencing pumping capacity is performed based on experimental test results.The results indicate that this model can serve as a tool for evaluating the structural design and performance assessment of turbomolecular pumps,aiding in the optimization of their design to enhance pumping efficiency and applicability.

关 键 词：涡轮分子泵 神经网络 数值计算 性能预测 

分 类 号：TB752.27[一般工业技术—真空技术]