This study introduces a machine learning (ML)-assisted image segmentation method for automatic bubble identification in gas–solid quasi-2D fluidized beds, offering enhanced accuracy in bubble recognition. Binary images are segmented by the ML method, and an in-house Lagrangian tracking technique is developed to track bubble evolution. The ML-assisted segmentation method requires few training data, achieves an accuracy of 98.75%, and allows for filtering out common sources of uncertainty in hydrodynamics, such as varying illumination conditions and out-of-focus regions, thus providing an efficient tool to study bubbling in a standard, consistent, and repeatable manner. In this work, the ML-assisted methodology is tested in a particularly challenging case: structured oscillating fluidized beds, where the spatial and time evolution of the bubble position, velocity, and shape are characteristics of the nucleation-propagation-rupture cycle. The new method is validated across various operational conditions and particle sizes, demonstrating versatility and effectiveness. It shows the ability to capture challenging bubbling dynamics and subtle changes in velocity and size distributions observed in beds of varying particle size. New characteristic features of oscillating beds are identified, including the effect of frequency and particle size on the bubble morphology, aspect, and shape factors and their relationship with the stability of the flow, quantified through the rate of coalescence and splitting events. This type of combination of classic analysis with the application of the ML assisted techniques provides a powerful tool to improve standardization and address the reproducibility of hydrodynamic studies, with the potential to be extended from gas–solid fluidization to other multiphase flow systems.

中文翻译：

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机器学习辅助气固流化床中气泡动力学的实验表征

本研究介绍了一种机器学习（ML）辅助图像分割方法，用于气固准二维流化床中的气泡自动识别，提高了气泡识别的准确性。通过机器学习方法对二值图像进行分割，并开发了内部拉格朗日跟踪技术来跟踪气泡的演变。机器学习辅助分割方法需要很少的训练数据，准确率达到 98.75%，并且可以过滤掉流体动力学中常见的不确定性来源，例如变化的照明条件和失焦区域，从而提供有效的研究工具以标准、一致且可重复的方式冒泡。在这项工作中，机器学习辅助方法在一个特别具有挑战性的情况下进行了测试：结构化振荡流化床，其中气泡位置、速度和形状的空间和时间演变是成核-传播-破裂循环的特征。新方法在各种操作条件和颗粒尺寸下进行了验证，展示了多功能性和有效性。它显示出捕获具有挑战性的冒泡动力学以及在不同颗粒尺寸的床中观察到的速度和尺寸分布的微妙变化的能力。确定了振荡床的新特征，包括频率和颗粒尺寸对气泡形态、方面和形状因素的影响及其与流动稳定性的关系，通过聚结和分裂事件的速率进行量化。这种经典分析与机器学习辅助技术应用的结合提供了一个强大的工具，可以提高标准化并解决流体动力学研究的可重复性，并有可能从气固流态化扩展到其他多相流系统。