Guided Pairwise Variable Optimization Method Applied to an Alpha-Type Stirling Engine

This study presents a guided pairwise variable optimization methodology applied to an Alpha-type Stirling engine to enhance its efficiency by optimizing its design parameters. The study was conducted using a second-order numerical model implemented in the MATLAB platform (R2019a). The proposed methodology, referred to as Guided Pairwise Variable Optimization (G.I.P.O.), is based on the identification, categorization, and prioritization of interactions between pairs of variables, establishing guidelines for conducting parametric explorations that allow the proper selection of the design dimensions of the Stirling engine variables. The piston stroke, cylinder diameter, piston crown length, phase angle between cylinders, regenerator length and diameter, and the length and diameter of the heater and cooler tubes were analyzed. This methodology resulted in a 4.55% increase in efficiency compared to univariate optimization techniques, demonstrating its effectiveness in reducing computational complexity while improving system performance.