  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 ($R^{2}$).
