随机工况主动式动压型干气密封调控力智能预测及其算法性能对比研究  

Intelligent Prediction of Control Force of Active Hydrodynamic Pressure Dry Gas Seal under Random Conditions and Comparative Study of Its Algorithm Performance

作　　者：卓志愿 陈源 李运堂 王冰清 金杰 彭旭东[2] ZHUO Zhiyuan;CHEN Yuan;LI Yuntang;WANG Bingqing;JIN Jie;PENG Xudong(College of Mechanical and Electrical Engineering,China JiLiang University,Zhejiang Hangzhou 310018,China;College of Mechanical Engineering,Zhejiang University of Technology,Zhejiang Hangzhou 310032,China)

机构地区：[1]中国计量大学机电工程学院,浙江杭州310018 [2]浙江工业大学机械工程学院,浙江杭州310032

出　　处：《摩擦学学报》2023年第11期1370-1380,共11页Tribology

基　　金：浙江省自然科学基金项目(LZ23E050002);国家自然科学基金项目(51905513,52005470);浙江省高校基本科研业务费专项(2021YW07)资助。

摘　　要：干气密封在运行过程中常因转速或介质压力等工况扰变影响使用性能和寿命.为提高干气密封在变工况下的适应性,本文中以具有可控闭合力的主动式动压型干气密封为研究对象,基于气体润滑理论建立螺旋槽干气密封膜压控制方程,研究工况变化对密封性能参数的影响,基于拉丁超立方抽样的方式采集变工况下维持膜厚稳定所需调控力的样本数据,并对比研究4种典型智能预测算法的性能.结果表明:当工况变化时,密封端面膜厚发生显著变化,通过调节闭合力以平衡开启力变化是维持膜厚稳定的有效途径;随训练样本数增加,算法模型训练程度及预测精度均得到提升,通过对比4种模型的预测结果,优化后的BP神经网络模型预测能力较强且综合性能优异,满足干气密封性能调控精度要求.This study delved into optimizing the performance and longevity of dry gas seal,a crucial component frequently impacted by operational disturbances,such as inconsistencies in rotational speed and alterations in sealed medium pressure.The research innovatively employed active hydrodynamic pressure and a nuanced controllable closing force,aspiring to bolster the adaptability of the dry gas seal amidst these challenges.Utilizing foundational principles from gas lubrication theory,a strategic equation had been developed,instrumental in proficiently navigating the film pressure within the spiral groove.This initiative facilitated a more profound and precise understanding of the ramifications of various disturbance conditions on sealing performance parameters.Strategic data collection methods,specifically Latin hypercube sampling were harnessed to glean valuable insights into the requisite control force essential for sustaining a stable film thickness in the face of prevailing disturbances.A meticulous comparative assessment was conducted,encompassing four pivotal intelligent prediction algorithms:BP neural network,RBF neural network,multiple linear regression,and locally weighted linear regression.This comparative approach aimed to discern the predictive capabilities and effectiveness of these algorithms in navigating the complexities of seal performance under variable conditions.Consequential findings from the study unveiled that the end face film thickness was intrinsically susceptible to significant variations induced by operational disturbances.By optimizing the adjustment of the closing force,a practical methodology had been illuminated,contributing significantly to the stabilization of the film thickness amidst the spectrum of encountered disturbances.The study also revealed the pivotal influence of augmenting the volume of training samples,demonstrating a marked improvement in the refinement,accuracy,and overall predictive aptitude of the analyzed models.Comparing the prediction results of the four models,the p

关 键 词：干气密封 主动式 调控力 智能预测 性能对比 

分 类 号：TB42[一般工业技术]