Numerical simulation of fluids plays an essential role in modeling many physical phenomena, which enables technological advancements, contributes to sustainable practices, and expands our understanding of various natural and engineered systems. The calculation of heat transfer in fluid flow in simple flat channels is a relatively easy task for various simulation methods. However, once the channel geometry becomes more complex, numerical simulations become a bottleneck in optimizing wall geometries. We present a combination of accurate numerical simulations of arbitrary, flat, and non-flat channels and machine learning models predicting drag coefficient and Stanton number. We show that convolutional neural networks (CNN) can accurately predict the target properties at a fraction of the time of numerical simulations. We use the CNN models in a virtual high-throughput screening approach to explore a large number of possible, randomly generated wall architectures. Data Augmentation was applied to existing geometries data to add generated new training data which have the same number of parameters of heat transfer to improve the model's generalization. The general approach is not only applicable to simple flow setups as presented here but can be extended to more complex tasks, such as multiphase or even reactive unit operations in chemical engineering.

翻译：流体数值模拟在众多物理现象建模中起着关键作用，它推动技术进步，助力可持续发展，并加深我们对各类自然与工程系统的理解。对于简单平直通道中的流体换热计算，各种模拟方法相对容易实现。然而，一旦通道几何结构变得复杂，数值模拟便成为优化壁面几何的瓶颈。我们提出了一种结合精确数值模拟（适用于任意平直与非平直通道）与机器学习模型（预测阻力系数和斯坦顿数）的方法。研究表明，卷积神经网络（CNN）能以数值模拟极小部分的时间准确预测目标属性。我们采用CNN模型进行虚拟高通量筛选，以探索大量随机生成的壁面架构。对现有几何数据应用数据增强技术，生成了具有相同换热参数数量的新训练数据，从而提升模型的泛化能力。该方法不仅适用于本文所述的简单流动设置，还可扩展至更复杂的任务，例如化学工程中的多相流甚至反应单元操作。