S.M. Mirkhalaf

Bio: S.M. Mirkhalaf is an academic researcher from University of Gothenburg. The author has contributed to research in topics: Materials science & Composite material. The author has an hindex of 6, co-authored 9 publications receiving 96 citations. Previous affiliations of S.M. Mirkhalaf include University of Porto & Chalmers University of Technology.

A finite element based orientation averaging method for predicting elastic properties of short fiber reinforced composites

Abstract: Short fiber reinforced composites have a variety of micro-structural parameters that affect their macro-mechanical performance. A modeling methodology, capable of accommodating a broad range of these parameters, is desirable. This paper describes a micro-mechanical model which is developed using Finite Element Analysis and Orientation Averaging. The model is applicable to short fiber reinforced composites with a wide variety of micro-structural parameters such as arbitrary fiber volume fractions, fiber aspect ratios and fiber orientation distributions. In addition to the Voigt and Reuss assumptions, an interaction model is developed based on the self-consistent assumption. Comparisons with experimental results, and direct numerical simulations of Representative Volume Elements show the capability of the model for fair predictions.

The mechanical behavior of polylactic acid (PLA) films: fabrication, experiments and modelling

Abstract: Polylactic acid (PLA) is one of the highly applicable bio-polymers in a wide variety of applications including medical fields and packaging. In order to quantitatively model the mechanical behavior of PLA and PLA based bio-composite materials, and also tailor new bio-composites, it is required to characterize the mechanical behavior of PLA. In this study, thin films of PLA are fabricated via hot-pressing, and tensile experiments are performed under different strain rates. To model the mechanical behavior, an elasto-viscoplastic constitutive model, developed in a finite strain setting, is adopted and calibrated. Using the physically-based constitutive model, all regimes of deformation under uniaxial stress state, including post-yield softening, were adequately captured in the simulations. Also, the rate dependency of the stress–strain behavior was properly modelled.

Micromechanical modeling of the mechanical behavior of unidirectional composites – A comparative study:

Abstract: Laminated fiber reinforced composites are increasingly being used in various load-bearing applications including for instance aerospace and wind energy power industries. Understanding the mechanica...

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

A review of artificial neural networks in the constitutive modeling of composite materials

Abstract: Machine learning models are increasingly used in many engineering fields thanks to the widespread digital data, growing computing power, and advanced algorithms. The most popular machine learning model in recent years is artificial neural networks (ANN). Although many ANN models are used in the constitutive modeling of composite materials, there are still some unsolved issues that hinder the acceptance of ANN models in the practical design and analysis of composite materials and structures. Moreover, the emerging machine learning techniques are posing new opportunities and challenges in the data-based design paradigm. This paper aims to give a state-of-the-art literature review of ANN models in the constitutive modeling of composite materials, focusing on discovering unknown constitutive laws and accelerating multiscale modeling. This review focuses on the general frameworks, benefits, and challenges and opportunities of ANN models to the constitutive modeling of composite materials. Moreover, potential applications of ANN-based constitutive models in composite materials and structures are also discussed. This review is intended to initiate discussion of future research scope and new directions to enable efficient, robust, and accurate data-driven design and analysis of composite materials and structures.

Comparative analysis of micromechanical models for the elastic composite laminae

Abstract: This paper deals with the first step required for the analysis and design of composite materials and structures: estimation of the effective macromechanical properties according to the structure of composite, properties of constituent materials and their volume fractions. There exist many micromechanical models proposed in the literature to estimate these effective elastic properties. Each one of these models is based on hypotheses that are valid for certain types of composite structures. The present paper aims to highlight the main assumptions of these models and compare their predictions with a set of 188 experimental data, compiled from 25 references, assuming that just the constituents’ properties are available as input. The following nine major micromechanical models are evaluated: asymptotic homogenization with hexagonal unit cell; asymptotic homogenization with square unit cell; Bridging; Chamis; generalized self-consistent; Halpin-Tsai; modified Halpin-Tsai; Mori-Tanaka; and rule of mixture (ROM). Besides, a novel modified version of the rule of mixture allowing better agreement with the experimental data is also proposed. It is shown, in particular, that the newly proposed modified rule of mixture model provides the best correlation with the experimental data among the ROM-based models, while the asymptotic homogenization presents the best predictions among the elasticity-based models.