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 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.

中文翻译：

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检查水平方形微通道中非牛顿气液两相流的界面行为

本研究研究了水平方形微通道内非牛顿两相流的流动动力学。实验装置采用边长为8×10 m的亚克力通道。测试气体为氮气，测试液体由水和浓度为0.2%wt和0.4%wt的CMC（羧甲基纤维素）水溶​​液组成。工作气体和液体的空塔速度范围分别为0.26-20 m/s和0.05-1.0 m/s。压差传感器用于测量压力梯度，而高速摄像机捕获流动行为，以便使用定制图像处理技术进行分析。应用非线性回归统计分析来建立流型转变的标准。此外，还采用 PSD（功率谱密度）、DWT（离散小波变换）、ANN（人工神经网络）和 Kolmogorov 熵等信号处理技术，根据来自差压传感器的压力梯度信号来分析流动行为。我们的研究结果揭示了气泡流、段塞流、段塞环流和搅动流模式的发生。非线性回归统计分析为确定流型转变提供了不明确的结果。然而，获得了无量纲液体高度的经验系数。此外，经验无量纲液体高度与实验值非常吻合。PSD 和 DWT 分析相互印证，清楚地表征了基于压力梯度波动和小波能量的流型。柯尔莫哥洛夫熵有效地捕捉了混沌流模式。此外，人工神经网络分析表明预测的流动模式与实际的流动模式之间完全一致。