Optimizing Calibration for a Capacitance-Based Void Fraction Sensor with Asymmetric Electrodes under Horizontal Flow in a Smoothed Circular Macro-Tube

• Main Authors: Jeong, J. (Author), Kim, M. (Author), Komeda, K. (Author), Oinuma, M. (Author), Saito, K. (Author), Sato, T. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Accurate measurement; Asymmetric electrodes; Calibration; calibration method; Calibration method; Capacitance; Capacitance measurement; capacitance sensor; Capacitance sensors; Electrodes; Flow patterns; Horizontal flows; Macro-tubes; quick-closing valve; Quick-closing valve; R32 refrigerant; Real time measurements; real-time measurement; Refrigerants; void fraction; Void fraction; Void fraction sensor
• Online Access: View Fulltext in Publisher

 In this study, a technique that uses a capacitance sensor with an asymmetric electrode to measure the void fraction of a refrigerant was developed. It is known that the void fraction and flow pattern affect the measured capacitance. Therefore, the relationship between the void fraction and capacitance is not linear; hence, a calibration method for obtaining accurate measurements is necessary. A calibration method was designed in this study based on repeated capacitance measurements and the bimodal temporal distribution to calibrate the atypical and repetitive flow patterns of slug flow and its transition to the intermittent flow regime. The calibration method also considers the weighted-average relation for the gradual transition of the intermittent to annular flow pattern according to the change from low to high quality. The proposed method was experimentally analyzed under the conditions of R32 refrigerant, a tube inner diameter of 7.1 mm, saturation temperature of 25◦C, mass flux of 100–400 kg m−2 s−1, and vapor quality of 0.025–0.900, and it was validated using a quick-closing valve (QCV) system under identical conditions. A relative error of 2.99% was obtained for the entire system, indicating good agreement between the proposed and QCV-based methods. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.