李超,刘忠良,金银霞,侯军军,魏宁,尹贺龙
LI Chao,LIU Zhongliang,JIN Yinxia,HOU Junjun,WEI Ning,YIN Helong

• 1:兰州理工大学石油化工学院

摘要(Abstract)：

为了准确预测涡旋压缩机热力性能，提出一种基于粒子滤波算法的性能预测方法，首先建立了压缩腔内气体压力、温度以及质量随主轴转角的变化的数学模型，基于数学模型建立其压力与温度变化的状态方程和预测方程；然后搭建标准的粒子滤波算法，将温度初始值18.7 ℃与压力初始值102 kPa代入预测模型，对气体状态参数进行预测。结果表明：粒子滤波算法对压缩腔内气体的温度和压力变化预测结果较好，温度预测过程最大偏差为1.2 ℃，压力预测过程最大偏差为2.4 kPa，真实值与预测值间的相对误差在10%左右，预测精度较高，并且随着粒子数增加，算法稳定性提升，预测精度提高。
In order to accurately predict the thermal performance of scroll compressor，a performance prediction method based on particle filter algorithm was proposed.First，the mathematical model of the gas pressure，temperature and mass in the compression chamber with the rotation angle of the main shaft was established，and the state equation and prediction equation of the pressure and temperature change were established based on the mathematical model.Then the standard particle filter algorithm was built，and the initial temperature 18.7 ℃ and pressure 102 kPa were substituted into the prediction model to predict the gas state parameters.The results show that the particle filter algorithm has better prediction results for the temperature and pressure change of gas in the compression chamber，the max.deviation of temperature prediction process is 1.2 ℃，the max.deviation of pressure prediction process is 2.4 kPa，the relative error between the true value and the predicted value is around 10%，so the prediction accuracy is higher，and with the increase of particle population，the algorithm is improved stably，and the prediction accuracy is improved.

关键词(KeyWords)： 涡旋压缩机;热力性能;粒子滤波;温度;压力
scroll compressor;thermal performance;particle filter;temperature;pressure

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(51265026)

作者(Author): 李超,刘忠良,金银霞,侯军军,魏宁,尹贺龙
LI Chao,LIU Zhongliang,JIN Yinxia,HOU Junjun,WEI Ning,YIN Helong

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