Stratification and Charging Efficiency in Compact Thermal Storage Under Variable Flow Conditions: An AI-Assisted Simulation Study

Simanjuntak, Janter P.
Daryanto, Eka
Tambunan, Bisrul Hapis
Silaban, Robert
Sinaga, Denny Haryanto
Zainon, Mohd Zamri
Muhammad Ibrahim

Advance Sustainable Science, Engineering and Technology (ASSET)
Vol. 8 No. 1 (2026): November - January

Thermal Energy Storage (TES) systems are essential for managing low-grade heat in renewable energy applications. This study evaluates the impact of flow rate and heating power on thermal stratification and efficiency within a 30-liter TES unit. Using an AI-assisted simulation framework, the system's performance was analyzed across varying flow rates (0.3–0.9 LPM) and heater capacities (1.5–2.0 kW). Results indicate that lower flow rates (0.3–1.2 LPM) effectively preserve stratification, whereas higher rates induce thermal mixing. While charging efficiency generally decreases as target temperatures rise, it improves significantly with higher heater power. Notably, the configuration using a 0.7 LPM flow rate and 2.0 kW heater achieved a peak efficiency of 78% while maintaining stable thermal layering. This research demonstrates how AI-driven modeling can optimize charging behavior, providing critical insights for the design and thermal management of compact TES systems in low-grade heat applications.