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The Finite Phase-Field Method - A Numerical Diffuse Interface Approach for Microstructure Simulation with Minimized Discretization Error

Published online by Cambridge University Press:  19 March 2012

Janin Eiken*
Affiliation:
Access e. V., Intzestraße 5, 52072 Aachen, Germany
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Abstract

The Phase-field method is recognized as the method of choice for space-resolved microstructure simulation. In theoretic phase-field approaches, the underlying diffuse interface representation is discussed in the sharp interface limit. Applied phase-field models, however, have to cope with interfaces of finite size. Numerical solution based on finite differences naturally implies a discretization error. This error may result in significant deviations from the analytical sharp-interface solution, especially in cases of interface-controlled growth. Benchmark simula-tions revealed a direct correlation between the accuracy of the finite-difference solution and the number of numerical cells used to resolve the finite-sized interface width. This poses a problem, because high numbers of interface cells are unfavorable for numerical performance. To enable efficient high-accuracy computations, a new Finite Phase-Field approach is proposed, which closely links phase-field modeling and numerical discretization. The approach is based on a parabolic potential function, corresponding to phase-field solutions with a sinusoidal interface pro-file. Consideration of this profile during numerical differentiation allows an exact quantification of the bias evoked by grid spacing and interface width, which then a priori can be compensated.

Type
Articles
Copyright
Copyright © Materials Research Society 2012

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References

REFERENCES

1. Boettinger, W. J., Warren, J. A., Beckermann, C., and Karma, A.: Annu. Rev. Mater. Sci. 32 (2002) 163.Google Scholar
2. Steinbach, I., Modelling Simul. Mater. Sci. Eng. 17 (2009) 073001, DOI: 10.1088/0965-0393/17/7/073001.Google Scholar
3. MICRESS®, the microstructure evolution simulation software, http://www.micress.de.Google Scholar
4. Schmitz, G.J., Böttger, B., Eiken, J., Apel, M., Viardin, A., Carré, A., and Laschet, G., Int. J. Adv. Eng. Sci. Appl. Math. (2011) DOI: 10.1007/s12572-011-0026-y, in press.Google Scholar
5. Mecozzi, M.G., Eiken, J., Apel, M., and Sietsma, J., Comp. Mat. Sci. 50(6), 1846 (2011).Google Scholar
6. Eiken, J., Böttger, B., and Steinbach, I., Phys. Rev. E 73(6), 066122 (2006) .Google Scholar
7. Eiken, J., PhD Thesis, RWTH Aachen, 2010, ISBN 978-3-8322-9010-8.Google Scholar