Microfluidics has wide applications in different technologies such as biomedical engineering, chemistry engineering, and medicine. Generating droplets with desired size for special applications needs costly and time-consuming iterations due to the nonlinear behavior of multiphase flow in a microfluidic device and the effect of several parameters on it. Hence, designing a flexible way to predict the droplet size is necessary. In this paper, we use the Adaptive Neural Fuzzy Inference System (ANFIS), by mixing the artificial neural network (ANN) and fuzzy inference system (FIS), to study the parameters which have effects on droplet size. The four main dimensionless parameters, i.e. the Capillary number, the Reynolds number, the flow ratio and the viscosity ratio are regarded as the inputs and the droplet diameter as the output of the ANFIS. Using dimensionless groups cause to extract more comprehensive results and avoiding more experimental tests. With the ANFIS, droplet sizes could be predicted with the coefficient of determination of 0.92.

中文翻译：

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使用基于自适应网络的模糊推理系统在微流体流动聚焦装置中预测液滴尺寸。

微流体在生物医学工程、化学工程和医学等不同技术中有着广泛的应用。由于微流体装置中多相流的非线性行为以及几个参数对其的影响，为特殊应用生成所需尺寸的液滴需要昂贵且耗时的迭代。因此，设计一种灵活的方法来预测液滴尺寸是必要的。在本文中，我们使用自适应神经模糊推理系统（ANFIS），通过混合人工神经网络（ANN）和模糊推理系统（FIS）来研究影响液滴尺寸的参数。四个主要的无量纲参数，即毛细管数、雷诺数、流量比和粘度比被视为输入，液滴直径作为 ANFIS 的输出。使用无量纲组会导致提取更全面的结果并避免更多的实验测试。使用 ANFIS，可以使用 0.92 的确定系数预测液滴尺寸。