Multi-objective optimization of a spiral elastocaloric regenerator for high-performance heat pump applications based on high-fidelity numerical modellingCirillo, 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) elastocaloric effectheat pumpsnumerical modellingshape memory alloysmulti-objective optimizationartificial intelligencegenetic algorithmssurrogate modelThis 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.20262026-07-09 10:51:15Članek v reviji184530sl