INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, cilt.194, sa.September, ss.1-18, 2022 (SCI-Expanded)
The present study investigates the flow pattern and thermal behavior of constructal theory-based pore-scale porous media (CTPSPM) using computational fluid dynamics (CFD) and machine learning. Due to the increasing complexity of the geometry, as the number of pairs (Np) and clusters (Nc) increase, the CFD approach is unable to present the precise results. For the first time, we present a novel hybrid computational method to predict the flow pattern and thermal behavior of pore-scale porous media (PSPM) based on the direct training data set from high-fidelity numerical simulation. Tensors of component velocity, temperature for fluid and solid, and pressure drop will be used to update the training dataset in accordance with the geometry matrix. A tensor-based combination of CFD and block-based machine learning (BBML) is developed for this target due to the complex interface between flow and solid. An elliptical tube is filled with two distinct types of PSPM: CTPSPM and constant-porosity PSPM (CPPSPM) with different porosity ratios (β=0.6, 0.8, 1.2, 1.4). A laminar flow (Re=300, 500, and 800) of Al2O3 nanofluids through an elliptical tube in constant volume fractions (