Thermal conductivity of a thick 3D textile composite using an RVE model with specialized thermal periodic boundary conditions

Abstract

Finite element analysis is performed to virtually measure homogenized thermal conductivity of a thick 3D woven textile composite (T3DWC). Temperature-dependent thermal and mechanical properties of constituents are considered for the measurements over a wide range of temperature. A two-step homogenization approach is adopted here to simplify the analysis at the microscopic level without losing heterogeneity of the material at the macroscopic scale. First-step homogenization is carried out at a tow level using an analytical homogenization scheme. Fiber tows are homogenized and assigned with effective elastic and thermal properties. The solid tows are then implemented into a representative volume element considering the unique in-plane periodic fiber architecture of the thick composite material. Due to the unique in-plane periodicity, conventional periodic boundary conditions for thermal and mechanical loading conditions are reformulated. Anisotropic thermal conductivity of T3DWC is obtained from the second-step homogenization based on virtual thermal tests performed at ambient to elevated temperatures.

Microscale Thermal Modelling of Multifunctional Composite Materials Made from Polymer Electrolyte Coated Carbon Fibres Including Homogenization and Model Reduction Strategies

“…Therefore a multiphysical coupling to the electrical field is included in our study. In order to simplify the results interpretation, two different sets of boundary conditions are distinguished hereinafter: Case 1: Virtual thermal conductivity measurement For the virtual measurement of the effective heat conductivity in the transversal plane of a RVE, periodic boundary conditions are set to the RVE boundaries [24], see Figure 2):…”

“…With the resulting temperature difference θ + y 1 − θ − y 1 Equation ( 10) can be solved for λ and an effective, virtually measured, heat conductivity can be computed. These boundary conditions can also be altered to study heat conductivities in other directions [24].…”

“…Therefore, a suitable method needs to be indicated to homogenize the thermal coefficients (heat conductivity, specific heat and density) of the composite on the microscale. Several approaches, such as the series and parallel method from the rule of mixture (ROM), the Lewis-Nielsen model as well as spatial averaging are known to compute effective properties for two phase composites with cylindrical inclusions (fibres) [22,24,25]. In this work, these models are adopted to the three phase PeCCF composite, with existing modifications of the Lewis-Nielsen model from Kochetov et al [26] but also with a newly proposed Two-Level Lewis-Nielsen homogenization method.…”

A Data-Driven Framework for Designing Microstructure of Multifunctional Composites with Deep-Learned Diffusion-Based Generative Models

“…In particular, computational materials engineering using numerical techniques such as the finite element method (FEM) along with microstructure characterization and reconstruction (MCR) algorithms [10][11][12][13][14][15] is becoming a crucial tool for understanding material behavior and performance in various applications [16,17]. For instance, determining the representative volume element (RVE), which yields a response representative of the whole, is often required for understanding the material behavior [18][19][20][21][22]. If the RVE is defined, we can predict how the material will behave under different loads and environmental conditions with multiscale analysis.…”

A novel numerical method to evaluate the thermal conductivity of the unidirectional fiber‐reinforced composites

“…According to a selected representative volume element (RVE), microscale modeling methods, including finite element method (FEM), [21] finite volume method (FVM), [22] discrete element method (DEM), [23] shown a good robustness [24,25] in predicting effective thermal property of the composites with full consideration of their microscopic geometric characteristics. Liu et al [26] introduced the random sequential addition algorithm into the finite element method, the fillers are placed randomly and sequentially into the cubic matrix.…”