Zia Javanbakht

Automatized Estimation of the Effective Thermal Conductivity of Carbon Fiber Reinforced Composite Materials

In the current study, the representative volume element (RVE) is used to model randomly generated nanocomposite structures consisting of carbon nanotubes (CNTs) embedded in an epoxy resin matrix. The finite element Method is utilized for numerical simulations and investigation of the influential parameters on the generated RVEs. In order to automatize the whole procedure - fromgenerating the finite element models to conducting the analyses - a subroutine-based programming approach is adopted using the MSC Marc finite element package and Fortran programming language. The simulations can successfully predict the increase in thermal conductivity of CNT-reinforced nanocomposites by increasing the fiber volume fraction.

Finite Element Evaluation of Effective Thermal Conductivity of Short Carbon Nano Tubes: A Comparative Study

In the current study, two extreme cases are considered for the dispersion of carbon nanotubes(CNTs) in a polymeric matrix: randomly-oriented and randomly-aligned. The representative volume element (RVE) is used to represent the composite material consisting of epoxy resin matrix and CNT-reinforcement. The finite element method acts as the computational tool to conduct the simulations and investigate the effective parameters, i.e., the influence of the aspect ratio and the orientation, on the thermal conductivity of the matrix. A Fortran subroutine was used for both generation and analysis of the models by means of the MSC Marc finite element package and a Python script was used for the sensitivity analysis. The results indicate that optimum performance of the CNTs in terms of thermal conductivity can be reached by orienting them along the temperature gradient whereas a random distribution improves the conductivity by a smaller magnitude.

The Effect of Partitioning on the Clustering Index of Randomly-Oriented Fiber Composites: A Parametric Study

In the current study, five cases of fiber distributions are considered in a fiber-reinforced composite: one random, three partitioned (one uniform and two biased cases), and one aligned case for benchmarking. The finite element method and the principal component analysis were used to interpret the results of orientation tensors and detect any possible clusterings of a representative volume element (RVE). The obtained effective conductivity values were extensively controlled by the fiber volume fraction. At the same time, the uniformity of the random distributions could be recognized. Cross-partition resistance was also detected for the partitioned cases which contributed to a reduced heat transfer capability. Finally, the clustering indexes did not show a direct correlation with the conductivity results, and thus a case-by-case investigation is recommended to consider the anisotropic aspects of a microstructure.

Effective Thermal Conductivity of Fiber Reinforced Composites Under Orientation Clustering

A parametric finite element analysis was carried out to investigate the sensitivity of the effective thermal conductivity of fibers to orientation clustering. Randomly-positioned fibers with von Mises orientation distributions were used in different considerations and volume fractions to generate the dispersion in a partitioned representative volume element. It was found that increasing the fiber volume fraction increases the thermal conductivity; this improvement is significant specially when a preferred orientation is detected with a cluster-free state. Further reinforcement of the composite is made possible by increasing the maximum principal value of the orientation tensor provided that the principal direction is set accordingly. Furthermore, clustering index does not seems to be affected by volume fraction when an equal distribution is present in partitions.

Review of Engineering Mathematics

This chapter briefly reviews two important mathematical topics in the scope of the finite element context. To solve a finite element problem means finally to solve a system of equations. This is, in the simplest case, a linear system of equations and this chapter introduces two simple solution strategies, i.e. the Gaussian elimination and the inversion of the coefficient matrix. The second topic covers the analytical and numerical integration which is needed, for example, to evaluate the elemental stiffness matrix and the column matrix of equivalent nodal loads.

Computational Evaluation of Transverse Thermal Conductivity of Natural Fiber Composites

The finite element analysis is used to investigate the sensitivity of the effective transverse thermal conductivity of polymeric composites reinforced with Manila hemp fibers in terms of their degree of saturation. It is predicted that the hierarchical structure of the fiber bundle will highly magnify the rate of water absorption and in consequence, the effective transverse thermal conductivity of the composite is altered. This influence is quantized in terms of the volume fraction of the fiber bundle and the lumen to produce a homogenized representative continuum. It was found that increasing the fiber volume fraction in a dry medium results in a decrease in the thermal conductivity whereas an increase of conductivity will be evident in a wet condition. Furthermore, the increase in the volume fraction of the lumen enhances the thermal conductivity by retaining more water during saturation which supports the developed hypothesis.

Listing of the Customized Modules

There is no unique way of handling a complicated modeling task. However, some typical procedures are in common between various methods.

Fortran – Advanced Features

Considering the fact that Marc is based on the Fortran programming language, not only is the basic knowledge of the language is indispensable, but becoming familiar with advanced features will definitely improve the structure of the code. In this chapter, a comprehensive review of the advanced capabilities of the Fortran language will be presented.