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A POD-EIM reduced two-scale model for crystal growth

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Abstract

Complex physical models depending on microstructures developing over time often result in simulation schemes that are very demanding concerning computational time. The two-scale model considered in the current presentation describes a phase transition of a binary mixture with the evolution of equiaxed dendritic microstructures. It consists of a macroscopic heat equation and a family of microscopic cell problems modeling the phase transition. Those phase transitions need to be resolved by very fine computational meshes leading to the demanding numerical complexity. The current study presents a reduced version of this two-scale model. The reduction aims at accelerating the microscopic model, which is parametrized by the macroscopic temperature, while maintaining the accuracy of the detailed system. Parameter dependency, non-linearity, time-dependency, coupled field-variables and high solution complexity are challenging difficulties. They are addressed by a combination of several approaches: Proper Orthogonal Decomposition (POD), Empirical Interpolation Method (EIM) and a partitioning approach generating sub-models for different solution regimes. A new partitioning criterion based on feature extraction is applied. The applicability of the reduction scheme is demonstrated experimentally: while the accuracy is largely maintained, the dimensionality of the detailed model and the computation time are reduced significantly.

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References

  1. Albrecht, F., Haasdonk, B., Kaulmann, S., Ohlberger, M.: The localized reduced basis multiscale method. In: Proceedings of Algorithmy 2012, pp. 393–403 (2012)

  2. Barrault, M., Maday, Y., Nguyen, N.C., Patera, A.T.: An ‘empirical interpolation’ method: Application to efficient reduced-basis discretization of partial differential equations. C. R. Acad. Sci. Paris Series I 339, 667–672 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  3. Boyaval, S.: Reduced-basis approach for homogenization beyond the periodic setting. Multiscale Model Simul. 7(1), 466–494 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  4. Caginalp, G.: An analysis of a phase field model of a free boundary. Arch. Ration. Mech. Anal. 92, 205–245 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  5. Chaturantabut, S., Sorensen, D.C.: Discrete empirical interpolation for nonlinear model reduction. SIAM J. Sci. Comput. 32(5), 2737–2764 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  6. Cioranescu, D., Donato, P.: An Introduction to Homogenization. Oxford University Press, New York (1999)

    MATH  Google Scholar 

  7. Davis, S.: Theory of Solidification. Cambridge University Press, Cambridge (2001)

    Book  MATH  Google Scholar 

  8. Dihlmann, M., Drohmann, M., Haasdonk, B.: Model reduction of parametrized evolution problems using the reduced basis method with adaptive time-partitioning. In: Proceedings of ADMOS 2011 (2011)

  9. Drohmann, M., Haasdonk, B., Ohlberger, M.: Reduced basis approximation for nonlinear parametrized evolution equations based on empirical operator interpolation. SIAM J. Sci. Comput. 34(2)(2), A937—A969 (2012)

    MathSciNet  Google Scholar 

  10. Eck, C.: Analysis of a two-scale phase field model for liquid-solid phase transitions with equiaxed dendritic microstructure. Multiscale Model Simul. 3, 28–49 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  11. Eck, C.: Homogenization of a phase field model for binary mixtures. Multiscale Model Simul. 3, 1–27 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  12. Eck, C.: A two-scale phase field model for liquid-solid phase transitions of binary mixtures with dendritic microstructure. Professorial dissertation, Universität Erlangen-Nürnberg (2004)

    Google Scholar 

  13. Eck, C.: Error estimates for a finite element discretization of a two-scale phase field model. Multiscale Model Simul. 6, 1–26 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  14. Eftang, J.L., Knezevic, D., Patera, A.: An hp certified reduced basis method for parametrized parabolic partial differential equations. Math. Comp. Model Dyn. 17(4), 395–422 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  15. Eftang, J.L., Patera, A.T., Rønquist, E.M.: An hp certified reduced basis method for parametrized elliptic partial differential equations. SIAM J. Sci. Comput. 32(6), 3170–3200 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  16. Eftang, J.L., Stamm, B.: Parameter multi-domain “hp” empirical interpolation. Internat. J. Numer. Methods Engrg. 90(4), 412–428 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  17. Haasdonk, B.: Convergence rates of the POD-Greedy method. M2AN Math. Model. Numer. Anal. 47, 859–873 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  18. Haasdonk, B., Dihlmann, M., Ohlberger, M.: A training set and multiple basis generation approach for parametrized model reduction based on adaptive grids in parameter space. Math. Comp. Model Dyn. 17, 423–442 (2012)

    Article  MathSciNet  Google Scholar 

  19. Haasdonk, B., Ohlberger, M.: Reduced basis method for finite volume approximations of parametrized linear evolution equations. Math. Model Numer. Anal. 42(2), 277–302 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  20. Hornung, U. (ed.): Homogenization and Porous Media. Springer, New York (1997)

  21. Hou, T.Y., Wu, X.-H.: A multiscale finite element method for elliptic problems in composite materials and porous media. J. Comput. Phys. 134, 169–189 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  22. Jikov, V.V., Kozlov, S.M., Oleinik, O.A.: Homogenization of Differential Operators and Integral Functionals. Springer, Berlin (1994)

    Book  Google Scholar 

  23. Jolliffe, I.T.: Principal Component Analysis. Springer-Verlag (2002)

  24. Kaulmann, S., Ohlberger, M., Haasdonk, B.: A new local reduced basis discontinuous Galerkin approach for heterogeneous multiscale problems. Comput. Ren. Math. 349(23–24), 1233–1238 (2011)

    MATH  MathSciNet  Google Scholar 

  25. Knezevic, D.J., Patera, A.T.: A certified reduced basis method for the Fokker–Planck equation of dilute polymeric fluids, Fene dumbbells in extensional flow. SIAM J. Sci. Comput. 32(2), 793–817 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  26. Kobayashi, R.: Modeling and numerical simulations of dendritic crystal growth. Phys. D 63, 410–423 (1993)

    Article  MATH  Google Scholar 

  27. Kurz, W., Fisher, D.J.: Fundamentals of Solidification. TransTech Publications, Switzerland (1998)

    Google Scholar 

  28. Maday, Y., Nguyen, N. C., Patera, A. T., Pau, G. S. H.: A general multipurpose interpolation procedure: The magic points. CPAA 8(1), 383–404 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  29. Nguyen, N.C.: A multiscale reduced-basis method for parametrized elliptic partial differential equations with multiple scales. J. Comput. Phys. 227(23), 9807–9822 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  30. Peherstorfer, B., Butnaru, D., Willcox, K., Bungartz, H.: Localized Discrete Empirical Interpolation Method. Technical Report TR-13-1, Aerospace Computational Design Lab, Dept. of Aeronautics & Astronautics. MIT (2013)

  31. Redeker, M., Eck, C.: A fast and accurate adaptive solution strategy for two-scale models with continuous inter-scale dependencies. J. Comput. Phys. 240, 268–283 (2013)

    Article  MathSciNet  Google Scholar 

  32. Tan, L., Zabaras, N.: Multiscale modeling of alloy solidification using a database approach. J. Comp. Phys. 227, 728–754 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  33. Viet, H.H., Schwab, C.: High-dimensional finite elements for elliptic problems with multiple scales. Multiscale Model Simul. 3, 168–194 (2005)

    Article  MATH  Google Scholar 

  34. Visintin, A.: Models of Phase Transitions. Birkhäuser, Boston (1996)

    Book  MATH  Google Scholar 

  35. Volkwein, S.: Lecture Notes: Proper Orthogonal Decomposition: Theory and Reduced-Order Modelling. University of Constance (2013)

  36. E W, Engquist, B.: The heterogeneous multiscale method. Commun. Math. Sci. 1, 87–123 (2003)

    Article  MathSciNet  Google Scholar 

  37. Wieland, B.: Reduced Basis Methods for Partial Differential Equations with Stochastic Influences. PhD Thesis, Universität Ulm (2013)

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Correspondence to Magnus Redeker.

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Communicated by: Karsten Urban

This presentation is dedicated to Prof. Christof Eck.

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Redeker, M., Haasdonk, B. A POD-EIM reduced two-scale model for crystal growth. Adv Comput Math 41, 987–1013 (2015). https://doi.org/10.1007/s10444-014-9367-y

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  • DOI: https://doi.org/10.1007/s10444-014-9367-y

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