NDLI: ANN Based Data Integration for Multi-Path Ultrasonic Flowmeter

Content Provider	IEEE Xplore Digital Library
Author	Huichao Zhao Lihui Peng Takahashi, T. Hayashi, T. Shimizu, K. Yamamoto, T.
Copyright Year	2001
Abstract	This paper presents an artificial neural network (ANN) based data integration method for a four-path ultrasonic flowmeter to improve its performance under complex flow profiles. Computational fluid dynamics (CFD) has been used to obtain the flow profiles inside a single elbow and an out-plane double-elbow pipeline, respectively, to extract the flow velocities on different individual sound paths and the corresponding mean flow velocities on the cross-sections located at 5 and 10 times pipe diameter downstream the elbow. The results from the CFD simulation for Reynolds number in the range from 3.25×10³ to 3.25×10⁵ were used to construct the data set. A three-layer ANN was designed, in which the flow velocities on individual sound paths and the cross-sectional mean flow velocity are taken as the input and output, respectively. Part of the data set is used to train the ANN. The other part of the data set is used to test the feasibility of the ANN. It was found that the error of the estimated cross-sectional mean flow velocity based on the ANN is within ±0.3% without the requirement of any flow conditioner for Reynolds number in the range from 3.25×10³ to 3.25×10⁵, which is significantly better than the results from the traditional weighted integration method. The proposed ANN based data integration method is of extending the limitation of straight pipe length for the installation of multi-path ultrasonic flowmeter and promoting its practical applications under complex flow profiles.
Sponsorship	IEEE Sensors Council
Starting Page	362
Ending Page	370
Page Count	9
File Size	1851554
File Format	PDF
ISSN	1530437X
Volume Number	14
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Acoustics Artificial neural networks Computational fluid dynamics Neurons Elbow Pipelines Data integration flow profile Multipath ultrasonic flowmeter ANN CFD
Content Type	Text
Resource Type	Article
Subject	Instrumentation Electrical and Electronic Engineering

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