Time-dependent analysis of flow pattern developments in two-phase flow using capacitance sensors: Fast fourier transform and total power spectrum exploration

• Published in: Flow measurement and instrumentation Vol. 102; p. 102818
• Main Authors: Al-Alweet, Fayez M., Almutairi, Zeyad, Alothman, Othman Y., Peng, Zhengbiao, Alshammari, Basheer A., Almakhlafi, Ahmad
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2025
• Subjects: Capacitance sensors; Experimental analysis; Experimental facility setup; Fast Fourier transform and total power spectrum; Gas; Multiphase flow; Oil; Pipelines; Fast Fourier transform and total power spectrum; Oil; Multiphase flow; Pipelines; Experimental analysis; Gas; Experimental facility setup; Capacitance sensors
• ISSN: 0955-5986
• DOI: 10.1016/j.flowmeasinst.2025.102818

In the intricate field of multiphase flow systems, accurately characterizing flow patterns and their development within pipelines is crucial for optimizing fluid dynamics and enhancing overall system performance. This study undertakes a comprehensive investigation employing five strategically positioned capacitance sensors along a designated test section, complemented by high-speed imaging techniques to capture real-time changes in evolving flow patterns. The analysis employs the Fast Fourier Transform (FFT) to explore the correlations between the visual evolution of flow patterns, as observed through imaging, and the variations in sensor signals. This approach encompasses the calculation of the total power within the signal spectrum alongside the comprehensive analysis of the Power Spectral Density (PSD) graph, yielding invaluable insights into the influence of flow dynamics on sensor responses. Key findings reveal significant relationships between the outputs of all sensors and the variations resulting from the evolution of two-phase flow patterns within the test section. Moreover, as these patterns progress or transition to different configurations, distinct changes are evident in the signals from each sensor. Notably, these alterations encompass variations in properties, shapes, and densities of spikes, alongside significant changes in the magnitudes of spike amplitudes and frequency range components in the graphical representation of PSD, along with a change in total power level. This rigorous analysis of visual and sensor data significantly enhances our understanding of the complex interplay between flow dynamics and sensor performance, establishing a strong foundation for advancing monitoring and automation strategies within pipeline systems. Ultimately, this work aims to foster improved efficiency, reliability, and safety in practical applications involving two-phase flows. The primary objective of this study is to elucidate the development of distinct flow patterns within a two-phase flow system in a pipeline, utilizing a straightforward setup of five geometrically identical capacitance sensors. The research centers on evaluating the effects of these complex flow dynamics on the output signals produced by each sensor. To achieve this aim, the study follows a set of specific objectives, outlined as follows.•Design, construct, and commission an experimental flow system to facilitate the investigation of two-phase flow patterns through a range of measurement techniques.•Employ five viewing boxes and a high-speed camera to identify generated flow patterns and record their development along the test section in the experimental setup.•Fabricate five simple capacitance sensors capable of detecting the time-dependent characteristics of different flow patterns.•Install five capacitance sensors equidistantly across the test section and incorporate associated capacitance measurement devices and a data acquisition system into the flow system.•Collect time-dependent data from the five-capacitance sensors.•Subject the time-dependent capacitance sensors' signals to frequency analysis methods, specifically utilizing Fast Fourier Transform (FFT) and Total Power Spectrum (TPS), to obtain insights into flow pattern development.