A benchmark data set for two-phase Coriolis metering

Olga L. Ibryaeva, Vyacheslav V. Barabanov, Manus P. Henry, Michael Tombs, Feibiao Zhou

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
2 Downloads (Pure)

Abstract

For more than a decade there has been growing interest in the use of Coriolis mass flow metering applied to two-phase (gas/liquid) and multiphase (oil/water/gas) conditions. It is well-established that the mass flow and density measurements generated from multiphase flows are subject to large errors, and a variety of physical models and correction techniques have been proposed to explain and/or to compensate for these errors. One difficulty is the absence of a common basis for comparing correction techniques, because different flowtube designs and configurations, as well as liquid and gas properties, may result in quite different error curves. Furthermore, some researchers with interests in the modelling aspects of the field may not have suitable multiphase laboratory facilities to generate their own data sets. This paper offers a small data set that may be used by researchers as a benchmark i.e. a common data set for comparing correction techniques. The data set was collected at the UK National Flow Laboratory TUV-NEL, using air and a viscous oil, and provides experimental points over a wide flow range (8:1 turndown) and with Gas Volume Fraction (GVF) values up to 60%. As a first investigation using the benchmark data set, we consider how data sparsity (i.e. the flow rate and GVF spacing in the experimental grid) affects the accuracy of a correction model. A range of neural network models are evaluated, based on different subsets of the benchmark data set. The data set and some exemplary code are provided with the paper. Additional data sets are available on a web site created to support this initiative.

Original languageEnglish
Article number101721
JournalFlow Measurement and Instrumentation
Volume72
Early online date9 Mar 2020
DOIs
Publication statusPublished - 1 Apr 2020
Externally publishedYes

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Flow Measurement and Instrumentation. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Flow Measurement and Instrumentation, 72, (2020) DOI: 10.1016/j.flowmeasinst.2020.101721

© 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

  • Benchmark data set
  • Coriolis mass flow metering
  • Data driven model
  • Multiphase flow
  • Neural networks
  • Two-phase flow

ASJC Scopus subject areas

  • Modelling and Simulation
  • Instrumentation
  • Computer Science Applications
  • Electrical and Electronic Engineering

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