Advertisement

JOM

, Volume 67, Issue 1, pp 148–153 | Cite as

Hierarchical Bridging Between Ab Initio and Atomistic Level Computations: Sensitivity and Uncertainty Analysis for the Modified Embedded-Atom Method (MEAM) Potential (Part B)

  • J. M. Hughes
  • M. F. HorstemeyerEmail author
  • R. Carino
  • N. Sukhija
  • W. B. LawrimoreII
  • S. Kim
  • M. I. Baskes
Article

Abstract

In this paper, a sensitivity and general uncertainty analysis is performed related to the modified embedded-atom method (MEAM) potential calibration of pure aluminum for data garnered from lower length scale (ab initio) simulations. Input uncertainties were quantified from 95% normal distribution confidence intervals of the various calibrated MEAM potential parameters from Part A of this study. A perturbation method was used to quantify the MEAM sensitivities to input parameters. The input uncertainties and sensitivities were then combined in a general uncertainty propagation analysis method. The results of the sensitivity analysis show that all the MEAM parameters interdependently influence all MEAM model outputs to varying degrees, allowing for the definition of an ordered calibration procedure to target specific MEAM outputs. In relation to the generalized stacking fault energy (GSFE) curve, the coefficient of the embedding function related to the background electron density, asub, was the most influential parameter related to the first peak. The first peak of the GSFE curve is related to unstable dislocations, in effect dislocation nucleation, and the first trough is related to stable dislocations. This connection of tying asub to the dislocation nucleation and motion was not obvious before this study indicating the power of the sensitivity and uncertainty method that was employed.

Keywords

Dislocation Nucleation Integrate Computational Material Engineering Input Uncertainty Vacancy Formation Energy Density Functional Theory Result 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors would like to acknowledge the Center for Advanced Vehicular Systems (CAVS) at Mississippi State University for support of this work.

References

  1. 1.
    M.I. Baskes, Phys Rev B 59, 2666 (1987).Google Scholar
  2. 2.
    M.I. Baskes, Phys Rev B 46, 2727 (1992).CrossRefGoogle Scholar
  3. 3.
    B.J. Lee, Acta Mater. 85, 701 (2012).Google Scholar
  4. 4.
    B. Jelenik, S. Groh, M.F. Horstemeyer, J. Houze, S.G. Kim, G.J. Wagner, A. Moitra, and M.I. Baskes, Phys Rev B 85, 245102 (2012).CrossRefGoogle Scholar
  5. 5.
    C. Cruz, P. Chantrenne, R.G.A. Veiga, M. Perez, and X. Leber, J. Appl. Phys. 113, 023710 (2013).CrossRefGoogle Scholar
  6. 6.
    M.F. Horstemeyer, Integrated Computational Materials Engineering (ICME) for Metals: Reinvigorating Engineering Design with Science (Hoboken: Wiley Press, 2012).CrossRefGoogle Scholar
  7. 7.
    M.F. Horstemeyer, J.M Hughes, N. Sukhija, W.B. Lawrimore II, S. Kim, R.L. Carino, and M.I. Baskes, JOM, this issue (2014). doi: 10.1007/s11837-014-1244-0.
  8. 8.
    H.W. Coleman and W.G. Steele, Experimentation and Uncertainty Analysis for Engineers (New York: Wiley, 1999).Google Scholar
  9. 9.
    M. Pusa, Sci Tech Nucl Install 2012, 157029 (2012).CrossRefGoogle Scholar

Copyright information

© The Minerals, Metals & Materials Society 2014

Authors and Affiliations

  • J. M. Hughes
    • 1
  • M. F. Horstemeyer
    • 1
    • 2
    • 4
    Email author
  • R. Carino
    • 1
  • N. Sukhija
    • 1
  • W. B. LawrimoreII
    • 1
  • S. Kim
    • 1
  • M. I. Baskes
    • 3
  1. 1.Center for Advanced Vehicular SystemMississippi State UniversityStarkvilleUSA
  2. 2.Department of Mechanical EngineeringMississippi State UniversityStarkvilleUSA
  3. 3.Department of Aerospace EngineeringMississippi State UniversityStarkvilleUSA
  4. 4.Predictive Design TechnologiesStarkvilleUSA

Personalised recommendations