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Multi-scale Microstructure and Property-Based Statistically Equivalent RVEs for Modeling Nickel-Based Superalloys

  • Somnath GhoshEmail author
  • George Weber
  • Maxwell Pinz
  • Akbar Bagri
  • Tresa M. Pollock
  • Will Lenthe
  • Jean-Charles Stinville
  • Michael D. Uchic
  • Christopher Woodward
Chapter
  • 61 Downloads

Abstract

This chapter discusses fundamental aspects of the development of statistically equivalent virtual microstructures (SEVMs) and microstructure and property-based statistically equivalent representative volume elements (M-SERVE and P-SERVE) of the Ni-based superalloy at multiple scales. The two specific scales considered for this development are the subgrain scale of intragranular γ − γ′ microstructures and the polycrystalline scale of grain ensembles with annealing twins. A comprehensive suite of computational methods that can translate microstructural data in experimental methods to optimally defined representative volumes for effective micromechanical modeling is the objective of this study. The framework involves a sequence of tasks, viz., serial sectioning, image processing, feature extraction, and statistical characterization, followed by micromechanical analysis and convergence tests for statistical functions. A principal motivation behind this paper is to translate high-fidelity microstructural image data into statistics of parametric descriptors in constitutive laws governing material performance.

Keywords

Ni-based superalloys Gamma-gamma’ distribution M-SERVE P-SERVE SEVM Two-point correlation function Annealing twins EBSD 

Notes

Acknowledgements

This work has been supported through a grant No. FA9550-12-1-0445 to the Center of Excellence on Integrated Materials Modeling (CEIMM) at Johns Hopkins University awarded by the AFOSR/RSL Computational Mathematics Program (Manager Dr. A. Sayir) and AFRL/RX (Monitors Drs. C. Woodward and C. Przybyla). These sponsorships are gratefully acknowledged. Computing support by the Homewood High Performance Compute Cluster (HHPC) and Maryland Advanced Research Computing Center (MARCC) is gratefully acknowledged.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Somnath Ghosh
    • 1
    Email author
  • George Weber
    • 2
  • Maxwell Pinz
    • 2
  • Akbar Bagri
    • 2
  • Tresa M. Pollock
    • 3
  • Will Lenthe
    • 3
  • Jean-Charles Stinville
    • 3
  • Michael D. Uchic
    • 4
  • Christopher Woodward
    • 5
  1. 1.Departments of Civil, Mechanical Engineering and Materials Science & EngineeringJohns Hopkins UniversityBaltimoreUSA
  2. 2.Departments of Mechanical and Civil EngineeringJohns Hopkins UniversityBaltimoreUSA
  3. 3.The Materials DepartmentUniversity of California Santa BarbaraSanta BarbaraUSA
  4. 4.Materials and Manufacturing DirectorateAir Force Research Laboratory, Wright-Patterson AFBDaytonUSA
  5. 5.Air Force Research Laboratory/RXWright-Patterson Air Force BaseDaytonUSA

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