In the complex domain of microfluidics systems, analysing fluid flow patterns through random-shaped circular microchannels is significantly challenging task. Conventional approach of solving such problems using computational fluid dynamics often incapable due to their intensive computational requirements and high simulation times. In this study, addressing these limitations, we introduce $μ$-FlowNet, a deep learning framework based on the adaptable U-Net autoencoders. This model provides a data-driven approach that enhances the prediction and mapping of random-shaped circular microchannels and their corresponding fluid flow patterns. The datasets required for the training of the model is generated by performing extensive simulations using conventional approach of computational fluid dynamics methods. The datasets are then pre-processed and accessed the required spatial and temporal features that are essential for the training. We have trained three different models based on U-Net framework namely, standard U-Net, T-Net, and U-Net with attention mechanism to compare the prediction accuracy and loss. The accuracy of the $μ$-FlowNet is compared using metrics of dice score and intersection over union and it shows that U-Net with attention mechanism shows the highest dice score and IoU of 0.9317 and 0.8731, respectively and shows the highest structural similarity as compared to standard U-Net and T-Net. This show that U-Net with attention mechanism serves best model to map the fluid flow pattern with random datasets on testing.

翻译：在微流控系统的复杂领域中，分析随机形状圆形微通道内的流体流动模式是一项极具挑战性的任务。传统上采用计算流体力学方法求解此类问题，常因其巨大的计算需求和较长的模拟时间而难以胜任。为应对这些局限性，本研究提出了一种基于可自适应U-Net自编码器的深度学习框架$μ$-FlowNet。该模型提供了一种数据驱动方法，能够增强对随机形状圆形微通道及其对应流体流动模式的预测与映射能力。模型训练所需的数据集通过采用传统计算流体力学方法进行大量模拟生成。随后对数据集进行预处理，并提取训练所必需的空间和时间特征。我们基于U-Net框架训练了三种不同模型，即标准U-Net、T-Net以及带有注意力机制的U-Net，以比较其预测精度与损失。采用Dice系数和交并比指标对$μ$-FlowNet的精度进行比较，结果表明，带有注意力机制的U-Net表现最优，其Dice系数和IoU分别达到0.9317和0.8731，且与标准U-Net和T-Net相比，其结构相似性最高。这表明带有注意力机制的U-Net是映射随机数据集上流体流动模式的最佳模型。