Conjugate heat transfer (CHT) models are vital for the design of many engineering systems. However, high-fidelity CHT models are computationally intensive, which limits their use in applications such as design optimization, where hundreds to thousands of model evaluations are required. In this work, we develop a modular deep convolutional encoder-decoder hierarchical (DeepEDH) neural network, a novel deep-learning-based surrogate modeling methodology for computationally intensive CHT models. Leveraging convective temperature dependencies, we propose a two-stage temperature prediction architecture that couples velocity and temperature models. The proposed DeepEDH methodology is demonstrated by modeling the pressure, velocity, and temperature fields for a liquid-cooled cold-plate-based battery thermal management system with variable channel geometry. A computational model of the cold plate is developed and solved using the finite element method (FEM), generating a dataset of 1,500 simulations. The FEM results are transformed and scaled from unstructured to structured, image-like meshes to create training and test datasets. The DeepEDH methodology's performance is examined in relation to data scaling, training dataset size, and network depth. Our performance analysis covers the impact of the novel architecture, separate field models, output geometry masks, multi-stage temperature models, and optimizations of the hyperparameters and architecture. Furthermore, we quantify the influence of the CHT thermal boundary condition on surrogate model performance, highlighting improved temperature model performance with higher heat fluxes. Compared to other deep learning neural network surrogate models, such as U-Net and DenseED, the proposed DeepEDH methodology for CHT models exhibits up to a 65% enhancement in the coefficient of determination ($R^{2}$).

翻译：共轭传热（CHT）模型对许多工程系统的设计至关重要。然而，高保真度的CHT模型计算密集，限制了其在设计优化等需要数百至数千次模型评估的应用中的使用。本文开发了一种模块化的深度卷积编码器-解码器分层（DeepEDH）神经网络，这是一种基于深度学习的新型代理建模方法，用于计算密集型的CHT模型。利用对流温度依赖性，我们提出了一种两阶段温度预测架构，将速度场与温度场模型耦合。通过模拟可变通道几何结构的液冷冷板式电池热管理系统的压力场、速度场和温度场，展示了所提出的DeepEDH方法。建立冷板的计算模型并使用有限元方法（FEM）求解，生成了包含1,500次模拟的数据集。将FEM结果从非结构化网格转换并缩放到结构化、类图像网格，以创建训练和测试数据集。考察了DeepEDH方法在数据缩放、训练数据集大小和网络深度方面的性能。性能分析涵盖了新架构、独立场模型、输出几何掩码、多阶段温度模型以及超参数和架构优化的影响。此外，我们量化了CHT热边界条件对代理模型性能的影响，强调了更高热流密度下温度模型性能的提升。与其他深度学习神经网络代理模型（如U-Net和DenseED）相比，所提出的用于CHT模型的DeepEDH方法将决定系数（$R^{2}$）提升了高达65%。