| Abstrak/Abstract |
This study investigates the flow dynamics of a non-Newtonian two-phase flow within a horizontal square
microchannel. The experimental apparatus employed an acrylic channel with a side length of 8 × 10− 4 m. Nitrogen served as the test gas, and the test liquids consisted of water and CMC (Carboxymethylcellulose) aqueous
solutions at concentrations of 0.2 and 0.4 %wt. The superficial velocity of working gas and liquids spanned
ranges of 0.26–20 m/s and 0.05–1.0 m/s, respectively. A differential pressure transducer was used to measure
pressure gradients, while a high-speed video camera captured the flow behavior for analysis with a custom image
processing technique. Nonlinear regression statistical analysis was applied to establish the criteria for flow
pattern transition. Furthermore, signal processing techniques such as PSD (Power Spectral Density), DWT
(Discrete Wavelet Transform), ANN (Artificial Neural Network), and Kolmogorov entropy were employed to
analyze the flow behavior based on the pressure gradient signals from the differential pressure transducers. Our
findings reveal the occurrence of bubbly, slug, slug-annular, and churn flow patterns. The nonlinear regression
statistical analysis offered ambiguous results for determining flow pattern transitions. However, an empirical
coefficient was obtained for the nondimensional liquid height. Moreover, the empirical nondimensional liquid
height aligned well with the experimental values. The PSD and DWT analysis corroborated each other in clearly
characterizing the flow pattern based on pressure gradient fluctuation and wavelet energy. The Kolmogorov
entropy effectively captured the chaotic flow pattern. Furthermore, the ANN analysis demonstrated a solid
agreement between the predicted and actual flow patterns. |
