A novel approach for prediction of transverse thermal conductivity of unidirectional random fiber composites

Abstract

In fiber-reinforced composites, fibers are randomly positioned during the manufacturing process. Most of the theoretical studies assume regular arrangement of fibers in matrix that permits the use of representative volume element (RVE) for prediction of mechanical properties. Theoretical methods established in the literature for random fiber composites are complex and approximate. A simple approach is established in this work to accurately predict the transverse thermal conductivity of unidirectional random fiber-reinforced composites using the principle of electrical analogy (EA). In this study, circular fiber is transformed into an equivalent square fiber that facilitates the application of EA, and predicted the transverse thermal conductivity of random fiber composites. The conductivity of equivalent square fiber is evaluated by back-track approach using an equivalent thermal system. Python code is developed to generate the random location of fibers in the composite. The results obtained in this way are verified and found to be in good agreement with benchmark results over a wide range of fiber composites with varying conductivity ratios, fiber volume fraction, and randomness.