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<p>Traditional Stirling cryocooler optimization methods often oversimplify non-ideal effects such as heat transfer inefficiencies, pressure drops, and mechanical friction, leading to suboptimal designs with significant discrepancies between predicted and actual performance. This study presents a novel thermodynamic optimization framework integrating machine learning-driven metaheuristic strategies to address these limitations. The framework employs a gradient boosting regressor coupled with particle swarm optimization (PSO) to intelligently tune critical parameters including geometric characteristics and operational pressures. A comprehensive thermodynamic model accounting for non-idealities was developed and validated against experimental data from the Philips PPG-102 Stirling cryocooler, showing significant improvements over traditional models with only 0.00749% error in coefficient of performance (COP) prediction compared to 0.2645% for Schmidt model and 9.7% error in input power prediction compared to 62.3% for Schmidt model. The optimization achieved a 9.4% reduction in cooler wall temperature (from 80&#xa0;K to 72.5&#xa0;K) and an 11.9% increase in COP (from 0.1101 to 0.1233). Sensitivity analysis revealed that operational frequency and average pressure have the most significant impact on performance. Study limitations include the focus on steady-state conditions and specific working fluid (helium). Future work will extend to transient conditions and alternative working fluids. This framework offers practical applications in aerospace thermal management, electronics cooling, and cryogenic systems where precise temperature control and energy efficiency are critical.</p>

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Machine learning-driven metaheuristic optimization framework for enhanced stirling cryocooler performance: analysis of non-ideal heat transfer, pressure drop, and mechanical friction losses

  • 期刊论文

  • 发表日期：

    2025年10月7日

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  • Mohammad Abbas Fakharmanesh,
  • Mojtaba Babaelahi

摘要

Traditional Stirling cryocooler optimization methods often oversimplify non-ideal effects such as heat transfer inefficiencies, pressure drops, and mechanical friction, leading to suboptimal designs with significant discrepancies between predicted and actual performance. This study presents a novel thermodynamic optimization framework integrating machine learning-driven metaheuristic strategies to address these limitations. The framework employs a gradient boosting regressor coupled with particle swarm optimization (PSO) to intelligently tune critical parameters including geometric characteristics and operational pressures. A comprehensive thermodynamic model accounting for non-idealities was developed and validated against experimental data from the Philips PPG-102 Stirling cryocooler, showing significant improvements over traditional models with only 0.00749% error in coefficient of performance (COP) prediction compared to 0.2645% for Schmidt model and 9.7% error in input power prediction compared to 62.3% for Schmidt model. The optimization achieved a 9.4% reduction in cooler wall temperature (from 80 K to 72.5 K) and an 11.9% increase in COP (from 0.1101 to 0.1233). Sensitivity analysis revealed that operational frequency and average pressure have the most significant impact on performance. Study limitations include the focus on steady-state conditions and specific working fluid (helium). Future work will extend to transient conditions and alternative working fluids. This framework offers practical applications in aerospace thermal management, electronics cooling, and cryogenic systems where precise temperature control and energy efficiency are critical.