Heat transfer in granular systems plays a vital role in a wide range of engineering applications. The Discrete Element Method coupled with Computational Fluid Dynamics (DEM-CFD) is a powerful approach for simulating such systems. Previous studies have developed various heat transfer simulation methods within the DEM-CFD framework and applied them to diverse scenarios. However, these methods often suffer from high computational costs, limiting their applicability to large-scale problems. To address this, reducing computational expense is essential for enabling efficient heat transfer simulations. In this context, reduced-order models (ROMs) have garnered considerable attention. We have developed a ROM for predicting the temperature field in packed beds and demonstrate its effectiveness in significantly improving computational efficiency.
This research was supported by MEXT Quantum Leap Flagship Program (MEXT Q-LEAP) with Grant No. JPMXS0118067246.
