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Advanced Experimental Techniques for Multiscale Modeling of Materials

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Virtual Testing and Predictive Modeling

Abstract

From a scientific viewpoint, direct comparison between mechanical tests and computational simulations on a one-to-one basis has the potential to lead to substantial development in the concept of virtual testing of materials. Successful application of virtual testing methodology in our daily life basis requires the use of high-fidelity computational models that are being validated through accurate characterization techniques. The content of this chapter is prepared to cover some of the most recent developments in the area of materials characterizations with great potential for virtual testing and modeling applications. During the last decade, atomic force microscopy (AFM) has evolved into an essential tool for direct measurements of intermolecular forces that can be employed for verification of first-principle and molecular dynamic models. Novel techniques in the area of in situ electron microscopy have been developed in the last decade for investigating the structure–mechanical property relationship of advanced materials. X-ray ultra-microscopy (XuM) and microelectromechanical systems (MEMS) are among the two newest in situ microscopy developments. These techniques provide an excellent platform for direct correlation between structure and properties of nanoscale materials. These systems contain a limited number of atoms and possible equilibrium configurations, which can be identified in real time by means of in situ electron microscopy techniques. In addition, because of the limited number of atoms, these systems can be atomistically modeled within the reach of currently available computational power. This chapter provides a comprehensive review on the above-mentioned characterization techniques that can be used to validate computational models at nanometer length scales.

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Bahram Farahmand

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Yassar, R.S., Ghassemi, H.M. (2009). Advanced Experimental Techniques for Multiscale Modeling of Materials. In: Farahmand, B. (eds) Virtual Testing and Predictive Modeling. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-95924-5_13

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  • DOI: https://doi.org/10.1007/978-0-387-95924-5_13

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