A simplified methodology to determine the latent heat of macroencapsulated phase change materials for building envelope applications

Abstract

Incorporating macroencapsulated phase change materials (PCMs) in building envelopes is a strategy to reduce energy consumption. Current characterization methods focus on small quantities of PCM alone, even if it was demonstrated that they are not representative of the thermal behavior of macroencapsulated PCMs, and rely on highly specific equipment. This work proposes a novel methodology to determine the latent heat of whole macroencapsulated PCMs for building envelope applications using only temperature measurements. This method takes into account the greater quantity of PCM and the capsule material, unlike previous works, while it simplifies the necessary equipment. To develop the predictive model, a wide set of PCM panels was subjected to controlled temperature variations of 0.5 °C/h and 1 °C/h employing an insulated hotbox connected to a climatic module. Transient surface temperature and heat flux were measured. Experimental results demonstrated that not only heat flux measurements, but also temperature can be used to identify the beginning and end of the phase change process. The experimental results were then divided into a training and validation dataset. The training dataset was used to build the predictive model that correlates the area under the heat flux curve to that enclosed by the temperature curve during phase transitions. The model was validated with the remaining experimental data and optimized to assess the influence of temperature variation rate and phase change processes (melting and solidification). We found that the quotient of the heat flux area over the temperature area is 20.5 W/m2·K. Based on this, we identified that the latent heat can be accurately estimated using only temperature data, with an average error of less than 6 %. This methodology is simple but efficient requiring only thermocouples, and no other sophisticated instrumentation. The proposed model predicts the thermal response of macroencapsulated PCM with robustness, versatility, and accuracy (R2 value above 0.95). The trained empirical model applies to other macroencapsulated PCMs, contributing to the effective deployment of PCM-based thermal storage in buildings. Numerical models of envelopes with macroencapsulated PCMs will benefit from the results presented in this work, since the presented model determines the latent heat of whole PCM macrocapsules, unlike previous studies where only small quantities of PCM alone were analyzed.