Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-25T15:21:39.497Z Has data issue: false hasContentIssue false
  • English
  • Français

Metropolis Monte Carlo simulations of ordering and clustering in FeCr alloys

Published online by Cambridge University Press:  15 March 2011

Evgeny E. Zhurkin ,
Romain Pereira ,
Nicolas Castin ,
Lorenzo Malerba  and
Marc Hou
Evgeny E. Zhurkin
Affiliation:
Experimental Nuclear Physics Department K-89, Faculty of Physics and Mechanics, Saint-Petersburg State Polytechnical University, 29 Polytekhnicheskaya str., 195251 St.Petersurg, Russian Federation Physique des Solides Irradiés et des Nanostrucutres CP234, Faculté des Sciences, Université Libre de Bruxelles, Bd du Triomphe, B-1050 Bruxelles, Belgium
Romain Pereira
Affiliation:
INSTN, CEA Saclay, Gif sur Yvette, France
Nicolas Castin
Affiliation:
Structural Material Group, Institute of Nuclear Materials Science, SCK-CEN, Mol, Belgium
Lorenzo Malerba
Affiliation:
Structural Material Group, Institute of Nuclear Materials Science, SCK-CEN, Mol, Belgium
Marc Hou
Affiliation:
Physique des Solides Irradiés et des Nanostrucutres CP234, Faculté des Sciences, Université Libre de Bruxelles, Bd du Triomphe, B-1050 Bruxelles, Belgium
Get access

Abstract

The Metropolis Monte Carlo (MMC) algorithm is a computational method to study equilibrium thermodynamic properties of a system at the atomic level. The algorithm accounts for all terms that contribute to defining the free energy difference between states: not only chemical, configurational and interfacial, but also due to strain fields and thermal vibrations. In this work, the MMC method with a two bands empirical many-body potential is used to predict the ordering properties of Fe1-xCrx alloys at various compositions and temperatures in the absence of defects. The particular goal of the work was to reveal the effect of atomic relaxations and vibrations on the phase diagram. It is found that vibrations and local relaxation effects contribute to lowering the order-disorder transition temperature by about 25 percent as compared to MMC predictions with a rigid lattice.

Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 1125: Symposium R – Materials for Future Fusion and Fission Technologies , 2008 , 1125-R07-37
Copyright
Copyright © Materials Research Society 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Massalsky, T.B., Okamoto, H., Subramanian, P.R., Kacprzac, L., “Binary Alloy Phase Diagrams” (ASM International, Materials Park, OH, 1990) 1273.Google Scholar
2. Andersson, J.-O., Sundman, B., CALPHAD 11, 83 (1987).Google Scholar
3. Bonny, G., Terentyev, D. and Malerba, L., Scripta Materialia 59, 1193 (2008).Google Scholar
4. Caro, A., Caro, M., Lopasso, E.M., Crowson, D.A.; Appl. Phys. Lett. 89, 121902 (2006)]Google Scholar
5. Allen, M. P. and Tildesley, D., “Computer Simulation of Liquids” (Clarendon Press, Oxford, 1987).Google Scholar
6. Bonny, G., Pasianot, R.C., Malerba, L., Caro, A., Olsson, P., Lavrentiev, M.Yu., J. Nucl. Mater. 385, 268 (2009)Google Scholar
7. Malerba, L., Caro, A., Wallenius, J., J. Nucl. Mater. 382, 112 (2008)Google Scholar
8. Olsson, P., Wallenius, J., Domain, C., Nordlund, K., Malerba, L., Phys. Rev. B 72, 214119 (2005)Google Scholar
9. Ackland, G.J., Mendelev, M.I., Srolovitz, D.J., Han, S., Barashev, A.I., J.Phys.: Condens. Matter 16, 1 (2004)Google Scholar
10. Cowley, J.M., Phys. Rev. 77(3), 669 (1950)Google Scholar