Deep Learning of Forced Convection Heat Transfer
Deep Learning of Forced Convection Heat Transfer
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DOI：
10.1115/1.4052893
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发表时间：
2022-02-01
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通讯作者：
Kwon, Beomjin
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文献类型：
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作者：
Kang, Munku;Kwon, Beomjin
我们提出了内部强制对流传热问题的深度学习模型。条件生成对抗网络（cGAN）经过训练，根据描述流体通道几何形状和初始流动条件的图形输入来预测解决方案。在没有交互式求解物理控制方程的情况下，经过训练的cGAN模型在雷诺数范围从100到27，750的范围内快速近似加热通道中的流动的流动温度、努塞尔数（Nu）和摩擦因子（f）。为了进行有效的训练，我们优化了数据集大小，训练时间和超参数lambda。cGAN模型在预测Nu和f的局部分布时表现出高达97.6%的准确性。我们还表明，如果训练数据集得到适当的增强，经过训练的cGAN模型可以预测看不见的流体通道几何形状，例如变窄、变宽和旋转的通道。一个简单的数据增强技术提高了模型的准确性高达70%。这项工作展示了深度学习方法在实现热流体过程的成本效益预测方面的潜力。
We present the deep learning model for internal forced convection heat transfer problems. Conditional generative adversarial networks (cGAN) are trained to predict the solution based on a graphical input describing fluid channel geometries and initial flow conditions. Without interactively solving the physical governing equations, a trained cGAN model rapidly approximates the flow temperature, Nusselt number (Nu), and friction factor (f) of a flow in a heated channel over Reynolds number ranging from 100 to 27,750. For an effective training, we optimize the dataset size, training epoch, and a hyperparameter lambda. The cGAN model exhibited an accuracy up to 97.6% when predicting the local distributions of Nu and f. We also show that the trained cGAN model can predict for unseen fluid channel geometries such as narrowed, widened, and rotated channels if the training dataset is properly augmented. A simple data augmentation technique improved the model accuracy up to 70%. This work demonstrates the potential of deep learning approach to enable cost-effective predictions for thermofluidic processes.