Optimization of convective heat transfer in packed beds: A pore‐scale approach

Optimizing convective heat transfer in packed beds is critical for energy‐efficient process design, yet the interplay between particle configuration and pore‐scale transport remains poorly understood. We propose an experimentally validated dual network model (DNM) to explore a wide range of packing configurations and flow conditions to guide packing optimization. Using stratified beds for demonstration, we show that placing a small‐particle layer adjacent to the heating surface enhances heat transfer, with an optimal layer thickness (~7.1% of bed width representing a single‐layer), beyond which a rising pressure drop reduces overall efficiency. Cross‐flow analysis attributes these improvements to increased conduction, suppressed channeling, and intensified local mixing. Increasing particle size differences further boosts heat transfer but increases hydraulic resistance, shifting the optimal thickness toward smaller values. This experimental–modeling integrated framework provides pore‐scale insights into the trade‐off between heat transfer and pressure drop, offering practical guidelines for scalable thermal management and process intensification.