A data-driven model on the thermal transfer mechanism of composite phase change materials

Phase change materials (PCMs) that are incorporated with highly conductive nanomaterials to fabricate composite phase change materials (CPCMs), received much focus as a promising energy strategy for latent heat storage and conversion systems, due to their excellent thermophysical properties such as oxidation resistance and large enthalpies of fusion. However, the correct prediction of the thermal conductivity of these CPCMs remains deficient, mainly due to the lack of knowledge on the microscopic heat transfer mechanisms between the nanofiller and matrix interphase. Herein, a data-driven, modified Maxwell model is proposed to determine the thermal conductivity of these CPCMs, using milled carbon fiber (MCF)-reinforced PCMs as validation. This new model incorporates the aspect ratio and morphology smoothness of MCFs and introduces compatibility factors for different types of PCM matrices, which are paraffin and polyethylene glycol (PEG) respectively. At filler loadings above 15 wt%, the theoretical model gave poorer forecasts (with an average prediction error of 0.075) due to the random agglomeration of MCF nanoparticles, which can obstruct the phonon pathway. Regardless, this model accurately estimated the thermal conductivities of MCF/PCMs up to 9 wt% and 11 wt% MCF loading, with percentage fit values being 0.983 and 0.996 for PEG and paraffin systems, respectively. This model also eliminates the limitations of existing models, that were only suitable for composites with low filler loadings (