Numerical Analyses of Effectiveness of Magnetic Field on Variant Selection in FePd by Phase Field Modeling
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概要
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The effect of magnetic field on variant selection in FePd has been investigated by phase field modeling. In this study, multi phase field modeling [Physica D, 94 (1996), 135] was used for calculations. Chemical free energy, interface energy and magnetic energy were incorporated in our calculation. As for magnetic energy, magnetic crystalline anisotropy energy was taken into account. Disordered FePd at 50 K below the transition temperature is selected as the initial state. Then, calculations have been performed with the presence or absence of the external magnetic field as a variable. First of all, calculations under no magnetic field showed that the volume fractions of three variants were almost equal to each other. Secondly, calculations under magnetic field of 5 T showed that the volume fraction of magnetically favorable variant was much more than other variants. Finally, the timing to apply magnetic field was intentionally changed with fixing the length of time for application, in computational experiments to examine at what stage the magnetic field is most effective. It is found that applying magnetic field from the beginning of order–disorder transition results in the slightly more volume fraction of the favorable variant, and, in turn, the less interface energy per unit volume, than others, which leads to the dominance of the favorable variant at the end after variant coarsening driven by interface energy, resulting in successful grain alignment, although applying the magnetic field at later stage shows little effect.
- 2010-12-15
著者
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YASUDA Hideyuki
Department of Adaptive Machine Systems, Osaka University
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Yasuda Hideyuki
Department Of Adaptive Machine System Osaka University
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Yoshiya Masato
Department Of Adaptive Machine Systems Osaka University
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UESHIMA Nobufumi
Department of Adaptive Machine Systems, Osaka University
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Ueshima Nobufumi
Department Of Adaptive Machine Systems Osaka University
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Yoshiya Masato
Department Of Adaptive Machine Systems Graduate School Of Engineering Osaka University
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