A Gaussian mixture model (GMM) was implemented to investigate the relationship between the liquid holdup (in various parts of the flow) and the pressure for different experimental realizations of high-viscosity gas–liquid flows. We considered a Newtonian fluid with a constant viscosity of 6 Pa s (600 cP) under a laboratory-controlled temperature. Because the pressure and the holdup do not exhibit a clear-cut relationship in the time domain, a supervised classification algorithm and a “deep” neural network (DNN) were first applied to classify the data points and predict average holdup values. Then, the GMM was applied to determine the holdup in various liquid aggregation structures of the flow as a function of the pressure. The growth rates of the cumulative lengths of the liquid structures (i.e., slug body, mixing front, and liquid film) and the gas bubbles were obtained. The GMM predicted holdup values were in close agreement with the experimental data.

中文翻译：

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高粘度间歇流持液率分析的概率学习方法

采用高斯混合模型 (GMM) 来研究高粘度气液流不同实验实现的持液率（在流动的各个部分）和压力之间的关系。我们考虑了在实验室控制温度下粘度恒定为 6 Pa s (600 cP) 的牛顿流体。由于压力和持率在时域中没有表现出明确的关系，因此首先应用监督分类算法和“深度”神经网络（DNN）对数据点进行分类并预测平均持率值。然后，应用 GMM 来确定流量的各种液体聚集结构中的持留量作为压力的函数。获得了液体结构（即段塞体、混合前沿和液膜）和气泡的累积长度的增长率。 GMM 预测的滞留值与实验数据非常一致。