Computational Predictions of Thermo-Electro Properties of Novel 3D Interpenetrating Phase Composites

Abstract

Composites are materials that are made by coalescing two or more material constituents to produce a material having different properties from the constituents. Composites are widespread in nature and engineered materials, and they play an imperative role in various engineering application since they are tailored to produce materials with desired properties including the mechanical, chemical, thermal, and electrical properties. In this research project, new interpenetrating phase composites (IPCs) based on the mathematically-known triply periodic minimal surfaces (TPMS) are proposed. In these IPCs, different TPMS architectures are used as reinforcing solid sheets to enhance the effective multifunctional properties of composites. Several three dimensional representative volume elements (RVEs) are generated and studied using the finite element method (FEM) in order to predict the effective properties for different TPMS-IPCs. Specifically, three effective properties of the proposed TPMS-IPCs are thoroughly investigated within this research project; namely, thermal conductivity, electrical conductivity, and coefficient of thermal expansion (CTE). The effective properties are predicted using analytical models and compared with some conventional composites such (fiber-reinforced and particulate-reinforced composites). It is shown that the proposed TPMS-IPCs have superiority and promising results compared with the conventional composites. Limited experimental validation of the computational predictions of effective conductivity is presented where the TPMS is made of conductive carbon nanostructures (CNSs) based polymer composite and the matrix is fabricated using 3D printing technology (Connex 260). Scanning electron microscope (SEM) was utilized to characterize the dispersion of the CNSs and the effect of printing direction. The numerical results are in reasonable agreement with the experimental ones.

Date of Award	May 2015
Original language	American English
Supervisor	Rashid Abu Al Rub (Supervisor)