Incremental analysis of thermal conductivity optimization model for polymer carbon nanotube composite materials considering branch heat conduction

Vertically aligned carbon nanotube composites have emerged as premium thermal interface materials due to their exceptional axial thermal conductivity and structural adaptability. However, inherent growth angle variations during manufacturing introduce significant uncertainties in thermal performance prediction, severely compromising the design optimization of oriented CNT composites. This study develops a statistical model incorporating probabilistic growth angle distributions and preparation-induced defects to quantify thermal conductivity deviations. The validated framework systematically evaluates parametric influences, including CNT aspect ratios, volume fractions, interfacial thermal resistance, and matrix spacing coefficients. Computational analyses reveal a maximum thermal conductivity enhancement of 54.70 W·m -1 ·K -1 at 20% volume fraction when optimizing spacing coefficients and aspect ratios from 1000 to 10,000. These findings establish critical structure-property relationships governing filler-matrix interactions under constrained volume fractions, demonstrating that aspect ratio optimization surpasses interfacial tuning in maximizing thermal transport. The model further elucidates non-linear correlations between growth angle dispersion and conductivity degradation, providing quantitative guidelines for manufacturing tolerance control. This methodology advances precision engineering of CNT composites by resolving the historical disconnect between theoretical predictions and experimental realizations, offering direct implications for industrial production of high-performance thermal management systems.