Conjugate heat transfer (CHT) analyses are vital for the design of many energy systems. However, high-fidelity CHT numerical simulations are computationally intensive, which limits their applications such as design optimization, where hundreds to thousands of evaluations are required. In this work, we develop a modular deep encoder-decoder hierarchical (DeepEDH) convolutional neural network, a novel deep-learning-based surrogate modeling methodology for computationally intensive CHT analyses. Leveraging convective temperature dependencies, we propose a two-stage temperature prediction architecture that couples velocity and temperature fields. 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 mesh and CHT formulation of the cold plate is created and solved using the finite element method (FEM), generating a dataset of 1,500 simulations. Our performance analysis covers the impact of the novel architecture, separate DeepEDH models for each field, output geometry masks, multi-stage temperature field predictions, and optimizations of the hyperparameters and architecture. Furthermore, we quantify the influence of the CHT analysis' thermal boundary conditions 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 architecture for CHT analyses exhibits up to a 65% enhancement in the coefficient of determination $R^{2}$. (*Due to the notification of arXiv "The Abstract field cannot be longer than 1,920 characters", the appeared Abstract is shortened. For the full Abstract, please download the Article.)

翻译：共轭传热分析对于许多能源系统的设计至关重要。然而，高保真度的共轭传热数值模拟计算成本高昂，这限制了其在需要成百上千次评估的设计优化等领域的应用。在本研究中，我们开发了一种模块化的深度编码器-解码器分层卷积神经网络，这是一种新颖的、基于深度学习的代理建模方法，用于计算密集型的共轭传热分析。利用对流温度依赖性，我们提出了一种耦合速度场与温度场的两阶段温度预测架构。所提出的DeepEDH方法通过模拟一个具有可变通道几何形状的液冷式冷板电池热管理系统的压力场、速度场和温度场得到验证。我们创建了冷板的计算网格和共轭传热公式，并使用有限元方法进行求解，生成了包含1,500个模拟的数据集。我们的性能分析涵盖了新颖架构的影响、针对每个物理场的独立DeepEDH模型、输出几何掩码、多阶段温度场预测，以及超参数和架构的优化。此外，我们量化了共轭传热分析中热边界条件对代理模型性能的影响，结果表明在更高热通量下温度模型的性能有所提升。与其他深度学习神经网络代理模型（如U-Net和DenseED）相比，所提出的用于共轭传热分析的DeepEDH架构在决定系数$R^{2}$上表现出高达65%的提升。（*由于arXiv通知“摘要字段不能超过1,920个字符”，此处出现的摘要为缩短版。完整摘要请下载文章。）