Calibration of an ultrasonic flow meter for hot water

Tawackolian, Karsten; Büker, Oliver; Hogendoorn, Jankees; Lederer, Thomas

doi:10.1016/j.flowmeasinst.2012.05.003

Cited by 28 publications

(24 citation statements)

References 4 publications

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“…A transient particular solution of (11) can be written as [24] X (16) with N MA = 100. Black line shows the prediction by the analytical solution (15). A comparison between the experimentally received signal and the prediction by the proposed model indicates a high degree of similarity between the two curves.…”

Section: A Determination Of the Reference Signalmentioning

confidence: 93%

“…In addition, a precise determination of the transducers exact location is a challenging task. These setbacks can be absorbed by introducing a calibration factor K, which require appropriate adjustment to allow for the combined effects of the transducer placement and the average flow rate in the pipe cross section for laminar, transitional, or turbulent profiles that can be either steady or unsteady [13][14][15]. With this, the actual flow rate is measured as…”

Section: Introductionmentioning

confidence: 99%

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High-Precision Time-of-Flight Determination Algorithm for Ultrasonic Flow Measurement

Suñol

Ochoa

García

2019

IEEE Trans. Instrum. Meas.

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Commercial time-of-flight (TOF) ultrasonic flowmeters are rapidly expanding in the general industry. Among the different techniques that can be applied to determine the TOF of ultrasonic waves, the cross-correlation method presents numerous advantages, such as robustness for weak signals and noise suppression. However, the selection of an appropriate reference wave is presumably a key element in the precise measurement of TOF. In the present paper, an algorithm to compute an accurate TOF is proposed. The form of the electric signal received by the transducer is obtained from an acoustically-forced underdamped oscillator model, and the analytical solution of the model is proposed as a reference wave. In order to validate the effectiveness of this procedure, an ultrasonic flowmeter system is designed and tested in a flowmeter calibration test rig. It is demonstrated that the use of the presented scheme overcome the average method limitations, and turns out to be a convenient solution in a wide range of conditions. Robust measurements of near-zero flow values are acquired, which allow the achievement of a high dynamic range. The error curve of the proposed system have been obtained, revealing that the absolute value of the relative errors are lower than 2% within all the spectrum of flow rates considered (from 0.2 to 150 m 3 /h). Results demonstrate that the algorithm provides high-precision measurements within a wide dynamic range. The algorithm is portable and versatile: it can be adapted to different types of transducers without the need of additional measurements, allowing to adjust parameters on-the-fly for an optimal performance of the ultrasonic flowmeter system.

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Section: A Determination Of the Reference Signalmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

High-Precision Time-of-Flight Determination Algorithm for Ultrasonic Flow Measurement

Suñol

Ochoa

García

2019

IEEE Trans. Instrum. Meas.

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show abstract

“…The analysed meters estimate heat consumption both in multi-family houses as well as in individual apartments or offices. To calculate the amount of received heat, the meter must measure the volume of the flowing medium 1 and -with two sensors -its temperature in the supply and return lines. The microcontroller, on grounds of these three values, determines the amount of heat in kilowatt-hours [kWh].…”

Section: Introductionmentioning

confidence: 99%

“…The former calculates the volume based on the classical volumetric flow meter. The ultrasonic one ( Figure 1) does not directly measure the volume, but measures the flow rate and knowing the cross-sectional area of the line is able to determine it [1].…”

Section: Introductionmentioning

confidence: 99%

Markov Reliability Model for Heat Meters

Pałasz

Żurek²,

Przysowa³

2018

Journal of KONBiN

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The reliability of thermal energy meters is analysed using the Markov model which describes the operation of these meters in a large number of apartments and offices by a media accounting company. The data has been extracted from a relational database storing information on the operation, installation and exchange of these measures from the last 10 years. The built Markov model turned out to be ergodic, which allowed determining its limiting distribution. In addition, the probability distributions for the cumulated consumption were determined in the work - separately for all meters and meters’ failures.

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“…The meter with an ultrasonic module (Fig. 2) does not measure the volume itself, but calculates it by measuring the velocity of the flowing medium and the pipe's cross-section area [1]. A digital system calculates the amount of heat in kilowatt-hours [kWh] according to the following formulas:…”

Section: Introductionmentioning

confidence: 99%

Predicting Heat Meters’ Failures With Selected Machine Learning Models

Pałasz¹,

Przysowa²

2018

Preprint

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Heat metres are used to calculate the consumed energy in central heating systems. The subject of this article is to prepare a method of predicting a failure of a heat meter in the next settlement period. Predicting failures is essential to coordinate the process of exchanging the heat metres and to avoid inaccurate readings, incorrect billing and additional costs. The reliability analysis of heat metres was based on historical data collected over many years. Three independent models of machine learning were proposed, and they were applied to predict failures of metres. The efficiency of the models was confirmed and compared using the selected metrics. The optimisation of hyperparameters characteristics for each of models was successfully applied. The article shows that the diagnostics of devices does not have to rely only on newly collected information, but it is also possible to use the existing big data sets.

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Calibration of an ultrasonic flow meter for hot water

Cited by 28 publications

References 4 publications

High-Precision Time-of-Flight Determination Algorithm for Ultrasonic Flow Measurement

High-Precision Time-of-Flight Determination Algorithm for Ultrasonic Flow Measurement

Markov Reliability Model for Heat Meters

Predicting Heat Meters’ Failures With Selected Machine Learning Models

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Calibration of an ultrasonic flow meter for hot water

Cited by 28 publications

References 4 publications

High-Precision Time-of-Flight Determination Algorithm for Ultrasonic Flow Measurement

High-Precision Time-of-Flight Determination Algorithm for Ultrasonic Flow Measurement

Markov Reliability Model for Heat Meters

Predicting Heat Meters&rsquo; Failures With Selected Machine Learning Models

Contact Info

Product

Resources

About

Predicting Heat Meters’ Failures With Selected Machine Learning Models