@article{article_1136816, title={Evaluation and Compensation of Temperature Effects on Ultrasonic Flow Measurement}, journal={Avrupa Bilim ve Teknoloji Dergisi}, pages={113–118}, year={2022}, DOI={10.31590/ejosat.1136816}, author={Gökçen, Alkım and Yeşil, Bahadır}, keywords={Ultrasonik Transdüser, Akış Ölçümü, Kompenzasyon, Uçuş Süresi Ölçümü, Gömülü Sistemler}, abstract={This paper presents an evaluation of temperature effects on ultrasonic piezoelectric transducers for electronic flow measurement devices. Transducers generates ultrasonic wave against electrical signals and electrical signals against ultrasonic waves due to their bidirectional characteristics. Temperature dynamics of the physical environment is one of the most crucial parameters which affects the electrical dynamics of the ultrasonic transducers. Due to the temperature related false sensor readings, flow measurement process for different temperature causes calibration errors. In order to identify the temperature effects on transducers characteristics and constitute a generalized solution, a test procedure and data collection process are developed. Initially, two identical transducers are located reciprocally on a flow meter body. Secondly, bodies are located on a test bench to get signal measurements for different flows. A wireless communication data acquisition card is employed to collect ultrasonic signal measurements. Test procedure is repeated for 5 different temperatures and 13 flow rates. The created dataset is evaluated and visualized in MATLAB environment. A temperature effect compensation process, which is based on machine learning algorithms, is proposed. This method considers time domain information of transducer elements. Experiment temperature value and average values of Time of Flight (TOF) signals for each
transducers are considered to predict actual flow velocity. In this manner, machine learning algorithms linear regression, support vector regression (SVR), Gaussian process regression (GPR) and artificial neural networks (ANN) are employed to construct the relation between temperature variation and flow measurement. Compensation performance is investigated by considering the 𝑅2, root mean square error ( 𝑅𝑀𝑆𝐸), mean absolute error 𝑀𝐴𝐸) and mean square error ( 𝑀𝑆𝐸) model evaluation metrics. According to the results, neural network based compensation algorithm gives the best result with 𝑅2=0.95}, number={37}, publisher={Osman SAĞDIÇ}, organization={BAYLAN Su Sayaçları}