Optimum estimation of the mean flow velocity for the multi-electrode inductance flowmeter

Abstract

A multi-electrode inductance flowmeter is a combination of the traditional inductance flowmeter with the electromagnetic tomographic technique. In order to eliminate the random error of data acquisition of the multi-electrode inductance flowmeter, a chord measurement method and a data fusion algorithm are presented. Using the chord measurement method, not only could the number of measurement data be decreased to half that demanded by Engl's equation but also the signal to noise ratio (SNR) of measurement was improved. The algorithm presented was used to fuse abundant data measured at multiple angles and to yield an optimum estimate of the mean flow rate in terms of a minimum mean square error. Simulation results showed that this algorithm was robust and effective.