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  1. An accurate estimation of three-dimensional (3D) temperature fields in channel flows is challenging but critical for many important applications such as heat exchangers, radiation energy collectors, and enhanced geothermal systems. In this paper, we demonstrate the possibility of inferring temperature fields from concentration fields for laminar convection flows in a 3D channel using a machine learning (ML) approach. The study involves generation of data using 3D numerical simulations, application of deep learning methodology using conditional generative adversarial networks (cGANs), and analysis of how dataset selection affects model performance. The model is also tested for applicability in different convection scenarios. Results show that cGANs can successfully infer temperature fields from concentration fields, and the reconstruction accuracy is sensitive to the training dataset selected. In this study, we demonstrate how ML can be used to overcome the limitations of traditional heat and mass analogy functions widely used in heat transfer research.

     
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    Free, publicly-accessible full text available June 7, 2025
  2. Free, publicly-accessible full text available March 1, 2025
  3. Abstract

    Large discrepancies between well-mixed reaction rates and effective reactions rates estimated under fluid flow conditions have been a major issue for predicting reactive transport in porous media systems. In this study, we introduce a framework that accurately predicts effective reaction rates directly from pore structural features by combining 3D pore-scale numerical simulations with machine learning (ML). We first perform pore-scale reactive transport simulations with fluid–solid reactions in hundreds of porous media and calculate effective reaction rates from pore-scale concentration fields. We then train a Random Forests model with 11 pore structural features and effective reaction rates to quantify the importance of structural features in determining effective reaction rates. Based on the importance information, we train artificial neural networks with varying number of features and demonstrate that effective reaction rates can be accurately predicted with only three pore structural features, which are specific surface, pore sphericity, and coordination number. Finally, global sensitivity analyses using the ML model elucidates how the three structural features affect effective reaction rates. The proposed framework enables accurate predictions of effective reaction rates directly from a few measurable pore structural features, and the framework is readily applicable to a wide range of applications involving porous media flows.

     
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  4. Abstract 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 λ. 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. 
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