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Title: Calibrationless rotating Lorentz-force flowmeters for low flow rate applications

Journal Article · · Measurement Science and Technology

A 'weighted magnetic bearing' has been developed to improve the performance of rotating Lorentz-force flowmeters (RLFFs). Experiments have shown that the new bearing reduces frictional losses within a double-sided, disc-style RLFF to negligible levels. Operating such an RLFF under 'frictionless' conditions provides two major benefits. First, the steady-state velocity of the RLFF magnets matches the average velocity of the flowing liquid at low flow rates. This enables an RLFF to make accurate volumetric flow measurements without any calibration or prior knowledge of the fluid properties. Second, due to minimized frictional losses, an RLFF is able to measure low flow rates that cannot be detected when conventional, high-friction bearings are used. As a result, this paper provides a brief background on RLFFs, gives a detailed description of weighted magnetic bearings, and compares experimental RLFF data to measurements taken with a commercially available flowmeter.

Research Organization:
Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC02-09CH11466
OSTI ID:
1459562
Journal Information:
Measurement Science and Technology, Vol. 29, Issue 7; ISSN 0957-0233
Publisher:
IOP PublishingCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 5 works
Citation information provided by
Web of Science

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Cited By (5)

Liquid Metal Diagnostics journal November 2019
Numerical calibration of a Lorentz force particle analyzer journal November 2018
Calibrationless rotating Lorentz-force flowmeters for low flow rate applications dataset January 2018
Numerical Solutions of Heat Transfer for Magnetohydrodynamic Jeffery-Hamel Flow Using Spectral Homotopy Analysis Method journal September 2019
Calibrationless rotating Lorentz-force flowmeters for low flow rate applications dataset January 2018

Figures / Tables (5)