气液两相流流型特征无监督提取的卷积自编码器:机理及应用
Unsupervised-feature extraction of gas-liquid two-phase flow pattern based on convolutional autoencoder:principle and application
陈思睿 1毕景良 2王雷 1李元媛 3陆规3
作者信息
- 1. 华北电力大学数理学院,北京 102206
- 2. 中国核动力研究设计院,四川 成都 610213
- 3. 华北电力大学能源动力与机械工程学院,北京 102206
- 折叠
摘要
在气液两相流的测量中,流型的准确识别是压降、换热等热工参数测量的基础.传统的两相流方法由于试验条件和数据的局限性使得在不同工况下的适用性有限.人工智能算法可以同时兼顾效率和精度,但特征提取方法仍是识别的难点.流型的准确识别对于解释数据,改进模型,以及提高应用效果方面具有重要意义.因此提出了一种基于卷积自编码器的无监督学习的特征提取方法,将特征提取后分别输入到随机森林,支持向量机以及前馈神经网络分类器中进行分类.实验结果表明,对四种流型的识别精度都达到了99%以上,说明卷积自编码器特征提取方法能显著地提高分类算法的准确率,对不同的分类器具有很好的兼容性,也为今后流型识别的特征提取方法提供了帮助.
Abstract
In two-phase flow measurements,the accurate identification of the flow pattern is the basis for the measurement of pressure drop,heat transfer and other thermal parameters.Traditional two-phase flow methods have limited applicability under different operating conditions due to the limitations of test conditions and data.Artificial intelligence algorithms can take into account both efficiency and accuracy,but the feature extraction method is still the difficulty in its identification.The accurate identification of flow patterns is of great significance for interpreting data,improving models,and improving application effects.Therefore,this paper proposes a feature extraction method based on unsupervised learning of convolutional autoencoder,which inputs the extracted features into random forest,support vector machine,and back propagation neural network classifiers for classification,respectively.The experimental results show that the recognition accuracy for all four stream types reaches more than 99%,which indicates that the convolutional autoencoder feature extraction method can significantly improve the accuracy of the classification algorithm,has good compatibility with different classifiers,and also provides help for the feature extraction method for subsequent popular recognition.
关键词
气液两相流/气泡/神经网络/算法Key words
gas-liquid flow/bubble/neural networks/algorithm引用本文复制引用
基金项目
国家自然科学基金面上项目(12005217)
国家自然科学基金面上项目(52076074)
国家自然科学基金面上项目(52376008)
中央高校基本业务费专项(2022JG006)
出版年
2024
国家科技期刊平台
NSTL
万方数据