Acoustic Limescale Layer and Temperature Measurement in Ultrasonic Flow Meters

Abstract

Guided acoustic waves are commonly used in domestic water meters to measure the flow rate. The accuracy of this measurement method is affected by factors such as variations in temperature and limescale deposition inside of the pipe. In this work, a new approach using signals from different sound propagation paths is used to determine these quantities and allow for subsequent compensation. This method evaluates the different propagation times of guided Lamb waves in flow measurement applications. A finite element method-based model is used to identify the calibration curves for the device under test. The simulated dependencies on temperature and layer thickness are validated by experimental data. Finally, a test on simulated data with varying temperatures and limescale depositions proves that this method can be used to separate both effects. Based on these values, a flow measurement correction scheme can be derived that provides an improved resolution of guided acoustic wave-based flow meters.

Keywords: FEM simulation; Lamb waves; flow metering; guided acoustic waves; limescale layers; predictive maintenance; product lifetime extension; temperature compensation; ultrasound.

Figure 1

(a) Schematics of the setup including an ultrasound transmitter (T), receiver (R), the direct (D) and indirect (V) propagation paths in the Lamb wave flow meter as investigated in this article. (b) The studied ultrasonic flow meters in the climate chamber.

Figure 2

Measured voltage signal (U), marked main peak and zero crossing of the D-wave (29–45 µs) and the V-wave (48–65 µs) contributions, respectively.

Figure 3

Simulated voltage signal (U) at the receiver at 20 °C and without a limescale layer with D-wave (29–39 µs) and V-wave (47–57 µs) contributions.

Figure 4

Experimentally measured propagation times $t_D$ and $t_V$ depending on the temperature in a range from 5 °C to 50 °C. The vertical offset in the curves is related to variations in the transducer attachment on the different sensors or variations in limescale layer thickness.

Figure 5

Experimentally measured propagation times $t_D$ and $t_V$ as a function of the limescale layer thickness. A total of six layers are incrementally removed with acid and measurements are made at intermediate steps. Attempts are made to compensate for temperature fluctuations according to the previous results. In the last measurement (red), the temperature was kept as constant as possible, leading to an improved result.

Figure 6

Evaluated propagation times $t_D$ and $t_V$ (blue crosses) related to changes in layer thickness d and temperature T without mutual interdependencies. In addition to the simulation results, fitted calibration curves (red lines) including their mathematical representations are shown.

Figure 7

Evaluated propagation times $t_D$ and $t_V$ related to changes in layer thickness (d) and temperature (T). The trends show independent sensitivities to the individual parameters. These data points are used for testing the evaluation algorithm in Section 3.3.

Figure 8

(a) Test grid with the evaluated values for the layer thickness d and temperature T based on the simulated propagation times $t_D$ and $t_V$ displayed in Figure 7. (b) Absolute deviation of the evaluated data from the test grid.