基于深度森林及电阻层析成像的气液两相流流型辨识

doi:10.3969/j.issn.1000-1158.2023.06.09

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Abstract A flow pattern identification method of gas-liquid two-phase flow based on deep forest algorithm and electrical resistance tomography (ERT) was proposed. Firstly, ERT experimental device was used to collect data of four typical flow patterns, and the collected data was preprocessed by averaging multi-frame data. Then, some proper basic classifiers were selected to construct a deep forest model, and the maximum number of layers of the model was adjusted to ensure the accuracy of classification. Finally, the validity of multi-frame data averaging and the flow pattern identification ability of deep forest model were verified, and compared with two traditional deep learning algorithms, deep neural network and convolutional neural network. The results show that the accuracy of flow pattern identification of deep forest is better than that of other two algorithms, and the average identification accuracy can reach 98.75%. The preprocessing method of multi-frame data averaging is more conducive to flow pattern identification.

Key words： metrology electrical resistance tomography flow pattern identification deep forest algorithm gas-liquid two-phase flow

Received: 14 February 2022 Published: 25 June 2023

PACS:

TB937

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	Articles by authors
	ZHANG Li-feng
	TONG Tong
	XIAO Kai

Cite this article:

ZHANG Li-feng,TONG Tong,XIAO Kai. Flow Pattern Identification of Gas-Liquid Two-phase Flow Based on Deep Forest and Electrical Resistance Tomography[J]. Acta Metrologica Sinica, 2023, 44(6): 893-898.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2023.06.09 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2023/V44/I6/893

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