Multi-objective optimization of a spiral elastocaloric regenerator for high-performance heat pump applications based on high-fidelity numerical modelling

Cirillo, Luca (avtor), Orabona, Vincenzo (avtor), Gargiulo, Sabrina (avtor), Verneau, Lucrezia (avtor), Masselli, Claudia (avtor), Welsch, Felix (avtor), Motzki, Paul (avtor), Ahčin, Žiga (avtor), Tušek, Jaka (avtor), Greco, Adriana (avtor)

This study presents a comprehensive numerical and surrogate-assisted framework for the design optimization of a spiral cross-section elastocaloric regenerator developed within the SMACOOL project, aimed at the realization of a rotary elastocaloric air-conditioning system. The work addresses the limitations of conventional one-dimensional approaches by employing high-fidelity CFD simulations, providing a more realistic description of heat transfer within the regenerator under Active elastocaloric Regeneration (AeR) conditions. The analysis focuses on identifying the optimal combination of geometric and operating parameters to enhance heat transfer and overall energy performance of the cycle. The regenerator performance is first evaluated under representative operating conditions at a cycle frequency of 1 Hz using CFD simulations. The results reveal an inherent trade-off between cold-side temperature variation, cooling power and COP (at regenerator level), as operating conditions and regenerator geometries that favor large temperature variations do not coincide with those yielding the highest cooling power or energy efficiency. Cold-side temperature variations up to 7.08 K, cooling powers up to 90 W, and COP values up to 3.14 are achieved for different combinations of geometric and operating parameters from CFD simulations. An artificial intelligence–based framework combining a Gaussian Process Regression (GPR) surrogate model with a Non-dominated Sorting Genetic Algorithm (NSGA-II) is adopted to explore the combined geometric and operating design space without the need to evaluate all possible parameter combinations through high-fidelity simulations. This approach enables the identification of optimal trade-offs between the competing objectives ▫$\Delta$▫T▫$_{cold}$▫, ▫$\dot{Q}_{cold}$▫ and COP resulting in dense Pareto front that extends beyond the solutions obtained from CFD alone. The optimization identifies a maximum cold-side temperature variation of 8.1 K, a maximum cooling power of 132 W and a maximum COP of 4.7. A best-compromise solution, determined using the utopia-point criterion, provides a balanced performance with▫$\Delta$▫T▫$_{cold}$▫= 5.2 K, ▫$\dot{Q}_{cold}$▫=100 W and COP = 3.4. The proposed methodology provides quantitative guidelines for the design and experimental realization of high-performance elastocaloric regenerators and supports the future development of scalable, solid-state refrigeration systems for sustainable HVAC applications.

• Jezik: Angleški jezik
• Ključne besede: elastocaloric effect, heat pumps, numerical modelling, shape memory alloys, multi-objective optimization, artificial intelligence, genetic algorithms, surrogate model
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2026
• Št. strani: Str. 1-18
• Številčenje: Vol. 31, art.101989
• PID: 20.500.12556/RUL-184530
• UDK: 621.577:004.942:519.876.5
• ISSN pri članku: 2590-1745
• DOI: 10.1016/j.ecmx.2026.101989
• COBISS.SI-ID: 284153603
• Datum objave v RUL: 09.07.2026

Gradivo je del revije

• Naslov: Energy conversion and management : X.
• Založnik: Elsevier Ltd.
• ISSN: 2590-1745
• COBISS.SI-ID: 529866265

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: elastokalorični učinek, toplotne črpalke, numerično modeliranje, zlitine z oblikovnim spominom, večkriterijska optimizacija, umetna inteligenca, genetski algoritmi, nadomestni model

Projekti

• Financer: EC - European Commission
• Številka projekta: 101162223
• Naslov: Shape Memory Alloy based elastocaloric Cooling system
• Akronim: SMACool