Real-time AI-optimized elastocaloric cooling: Enhancing efficiency and durability in compression-mode Ni-Ti systems
Ismailov B. Shambilova A. Yilmaz A.C.
December 2025Elsevier Ltd
International Journal of Refrigeration
2025#180154 - 163 pp.
Elastocaloric cooling based on stress-induced phase transformations in shape memory alloys (SMAs) offers a promising solid-state alternative to vapor-compression refrigeration. In this study, a laboratory-scale compression-mode elastocaloric cooling system utilizing Ni-Ti SMA tubes was developed and dynamically optimized through real-time artificial intelligence (AI) control. Baseline testing under fixed operational parameters (4.5 % compressive strain, 0.2 Hz cycle frequency, 0.6 L/min HTF flowrate) demonstrated a net cooling capacity of 520–550 W and a coefficient of performance (COP) of 2.8–3.1, with cold-side outlet temperatures dropping by 7–9 °C. A genetic algorithm (GA) search identified optimal operational regions, improving steady-state COP to 3.6–3.7 and cooling capacities to approximately 600–625 W. Building upon these findings, a reinforcement learning (RL) agent was deployed for real-time cycle-by-cycle optimization, dynamically adjusting strain amplitudes, cycle timing, and HTF flowrates. Under AI supervision, the system achieved a stabilized COP of 3.8–3.9 and cooling capacities of 640–660 W, while demonstrating robust adaptability to step changes in external thermal loads with minimal transient performance penalties. Long-term durability tests over 104 cycles uncovered only a ∼5 % decline in cooling performance, linked to adaptive strain management strategies that mitigated SMA fatigue progression. Compared to conventional fixed-parameter operation, the AI-enhanced system showed a 20–30 % improvement in efficiency and extended functional lifetime. These results demonstrate that integrating real-time AI control into elastocaloric systems can noteworthily enhance both cooling performance and material durability, providing a critical step toward scalable, sustainable solid-state cooling technologies.
Artificial intelligence optimization , Compression-mode refrigeration , Elastocaloric cooling , Real-time adaptive control , Shape memory alloys (SMA)
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Department of Information Systems and Modeling, M. Auezov South Kazakhstan University, Shymkent, Kazakhstan
Department of Technological Machines and Equipment, M.Auezov South Kazakhstan University, Shymkent, Kazakhstan
Department of Motor Vehicles and Transportation Technologies, Cukurova University, Adana, Turkey
Department of Information Systems and Modeling
Department of Technological Machines and Equipment
Department of Motor Vehicles and Transportation Technologies
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