Seasonally Adaptive and Geometry-Dependent PCM Distribution Strategies for Reducing AC Demand

Abstract

Conventional applications of Phase Change Materials (PCMs) in building envelopes, typically as uniform layers, suffer from limited adaptability, often hindering thermal performance in off-target seasons and leading to inefficient material use. This rigidity contributes to buildings’ heavy reliance on air conditioning (AC) systems to maintain comfort, especially in climates with wide seasonal temperature variations. As energy prices rise and emissions targets tighten, there is a growing need for passive thermal strategies that are both adaptive and material-efficient. This study explores whether strategic spatial distribution of encapsulated PCMs can enhance year-round thermal performance while reducing AC demand and material use. Using a transient finite element model in COMSOL Multiphysics, multiple PCM capsule configurations were evaluated across summer, winter, and dual-season scenarios (cascaded configurations). A composite performance metric incorporating PCM usability, AC energy demand, and indoor thermal comfort was used to identify enhanced geometric arrangements. Results show that the spatial distribution and geometry of PCM capsules play a dominant role in determining system performance. Capsules with higher vertical aspect ratios and reduced vertical spacing enhance coupling with the wall’s dominant conductive heat flux, accelerating phase change and improving latent heat utilization. Horizontal spacing has minimal effect, given the weak lateral thermal gradients. In cascaded configurations, inactive capsules must be positioned to avoid obstructing active thermal pathways, ensuring continuous heat transfer. These findings confirm that intelligent capsule placement, not just material quantity, drives energy savings and comfort, enabling more efficient PCM use and reduced AC demand. This work redefines PCM integration as a design variable rather than a static material layer, unlocking year-round adaptability and energy savings without increasing material volume. The findings have broader implications for low-carbon building design, offering a pathway to reduce HVAC reliance, peak loads, and lifecycle emissions. Moreover, the modeling framework provides a foundation for future AI-driven envelope optimization, enabling intelligent, climate-responsive building systems.