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Title: Multiscale Computational Materials Methods.


Abstract not provided.

Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Proposed for presentation at the Tri-Lab 2007 Engineering Conference held May 7-10, 2007 in Albuquerque, NM.
Country of Publication:
United States

Citation Formats

Thompson, Aidan Patrick. Multiscale Computational Materials Methods.. United States: N. p., 2007. Web.
Thompson, Aidan Patrick. Multiscale Computational Materials Methods.. United States.
Thompson, Aidan Patrick. Tue . "Multiscale Computational Materials Methods.". United States. doi:.
title = {Multiscale Computational Materials Methods.},
author = {Thompson, Aidan Patrick},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue May 01 00:00:00 EDT 2007},
month = {Tue May 01 00:00:00 EDT 2007}

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  • In this work, a multiscale modeling framework for CFRP is introduced to study hierarchical structure of CFRP. Four distinct scales are defined: nanoscale, microscale, mesoscale, and macroscale. Information at lower scales can be passed to higher scale, which is beneficial for studying effect of constituents on macroscale part’s mechanical property. This bottom-up modeling approach enables better understanding of CFRP from finest details. Current study focuses on microscale and mesoscale. Representative volume element is used at microscale and mesoscale to model material’s properties. At microscale, unidirection CFRP (UD) RVE is used to study properties of UD. The UD RVE can bemore » modeled with different volumetric fraction to encounter non-uniform fiber distribution in CFRP part. Such consideration is important in modeling uncertainties at microscale level. Currently, we identified volumetric fraction as the only uncertainty parameters in UD RVE. To measure effective material properties of UD RVE, periodic boundary conditions (PBC) are applied to UD RVE to ensure convergence of obtained properties. Properties of UD is directly used at mesoscale woven RVE modeling, where each yarn is assumed to have same properties as UD. Within woven RVE, there can be many potential uncertainties parameters to consider for a physical modeling of CFRP. Currently, we will consider fiber misalignment within yarn and angle between wrap and weft yarns. PBC is applied to woven RVE to calculate its effective material properties. The effect of uncertainties are investigated quantitatively by Gaussian process. Preliminary results of UD and Woven study are analyzed for efficacy of the RVE modeling. This work is considered as the foundation for future multiscale modeling framework development for ICME project.« less
  • The Symposium on which this volume is based was conceived as a timely expression of some of the fast-paced developments occurring throughout materials science and engineering. It focuses particularly on those involving modern computational methods applied to model and predict the response of materials under a diverse range of physico-chemical conditions. The current easy access of many materials scientists in industry, government laboratories, and academe to high-performance computers has opened many new vistas for predicting the behavior of complex materials under realistic conditions. Some have even argued that modern computational methods in materials science and engineering are literally redefining themore » bounds of our knowledge from which we predict structure-property relationships, perhaps forever changing the historically descriptive character of the science and much of the engineering.« less
  • Abstract not provided.
  • Abstract not provided.
  • Integrated digital human modeling has seen increased interest over the last decade. We describe two efforts to develop computational frameworks for digital human modeling and describe the progress toward understanding the requirements for implementation. Both projects addressed data repository, computational environment, and visualization of results. But neither environment was a true problem-solving environment in that integration of computations with visualization capabilities was limited or absent. We detail the development of the computational environments for each effort and then provide proposals for improving the integration of the various components of a future "Digital Human" computational environment.