Akademik Veri Yönetim Sistemi
INTERNATIONAL JOURNAL OF REFRIGERATION, cilt.180, ss.223-232, 2025 (SCI-Expanded)
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.