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A Symbolic Approach to Compute a Null-Space Basis in the Projection Method

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Computer Mathematics

Abstract

We present a hybrid symbolic-numeric approach for the so-called projection method for solving the parameterized differential-algebraic constraint equations associated with multibody mechanical systems. A primary problem in this approach is computing a null-space basis of a matrix of multivariate rational functions, the Jacobian of the symbolic constraint matrix. A purely symbolic approach is untenable in terms of the sheer size of the output, whereas a purely numerical approach does not offer the flexibility of leaving some or all parameters unspecified. Instead we propose a hybrid approach, which does a symbolic preconditioning, followed by representing the null-space basis by straight-line C code, i.e., a black-box null-space basis. We do this in a numerically sensitive way, and show that our black box is numerically robust at almost all parameter settings. This is verified by experimental results on inputs from typical multibody models.

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References

  1. Aghili, F., Piedbœuf, J.-C.: Simulation of motion of constrained multibody systems based on projection operator. Multibody Sys. Dyn. 9(3), 283–309 (2003)

    Article  Google Scholar 

  2. Arczewskia, K., Blajer, W.: A unified approach to the modelling of holonomic and nonholonomic mechanical systems. Math. Model. Syst. 2(3), 157–174 (1996)

    Article  Google Scholar 

  3. Blajer, W.: A projection method approach to constrained dynamic analysis. J. Appl. Mech. 59(3), 643 (1992)

    Article  MATH  Google Scholar 

  4. Geddes, K.O., Czapor, S.R., Labahn, G.: Algorithms For Computer Algebra. Kluwer Academic Publishers, Alphen aan den Rijn (1992)

    Book  MATH  Google Scholar 

  5. Giesbrecht, M., Labahn, G., Lee, W-s: Symbolic-numeric sparse interpolation of multivariate polynomials. J. Symbolic Comput. 44, 943–959 (2009). doi:10.1016/j.jsc.2008.11.003

    Article  MathSciNet  MATH  Google Scholar 

  6. Golub, G., Van Loan, C.: Matrix Computations. Johns Hopkins University Press, Baltimore (1996)

    MATH  Google Scholar 

  7. Haug, E.J.: Computer Aided Kinematics and Dynamics of Mechanical Systems. volume 1 of Allyn and Bacon series in engineering. Prentice Hall College Div, Upper Saddle River (1989)

    Google Scholar 

  8. Jalón, J.G., Bayo, E.: Kinematic and Dynamic Simulation of Multibody Systems: The Real-Time challenge. Springer, New York (1994)

    Book  Google Scholar 

  9. Kaltofen, E., Yang, Z., Zhi, L.: On probabilistic analysis of randomization in hybrid symbolic-numeric algorithms. In: Proceedings of SNC’07, pp. 11–17 (2007)

    Google Scholar 

  10. Li, X.S., Demmel, J.W.: Making sparse gaussian elimination scalable by static pivoting. In: Proceedings of Supercomputing ’98, pp. 1–17 (1998)

    Google Scholar 

  11. McPhee, J., Schmitke, C., Redmond, S.: Dynamic modelling of mechatronic multibody systems with symbolic computing and linear graph theory. Math. Comput. Model. Dyn. Syst. 101(1), 1–23 (2004)

    Article  Google Scholar 

  12. Meyer, C.D.: Matrix Analysis and Applied Linear Algebra. SIAM (2000)

    Google Scholar 

  13. Moore, B., Piedbœuf, J.-C., Bernardin, L.: Maple as an automatic code generator? Maple Summer Workshop, (2002)

    Google Scholar 

  14. Schwartz, J.T.: Fast probabilistic algorithms for verification of polynomial identities. J. Assoc. Comput. Mach. 27, 701–717 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  15. Waterloo Maple Inc., MapleSim User’s Guide. (2011). http://www.maplesoft.com/view.aspx?SF=122742/387839/MapleSimUserGuid.pdf

  16. Zhou, W., Jeffrey, D.J., Reid, G.J., Schmitke, C., McPhee, J.: Implicit reduced involutive forms and their application to engineering multibody systems. In IWMM/GIAE, pp. 31–43 (2004)

    Google Scholar 

  17. Zhou, W., Carette, J., Jeffrey, D.J., Monagan, M.B.: Hierarchical representations with signatures for large expression management. Proceedings of Artificial Intelligence and Symbolic Computation, Lecture Notes in Computer Science 4120, (2006)

    Google Scholar 

  18. Zippel, R.: Probabilistic algorithms for sparse polynomials. In: Proceedings of EUROSAM 79, pp. 216–226, Marseille (1979)

    Google Scholar 

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Acknowledgments

The authors thank Dr. Jürgen Gerhard, Maplesoft Inc., for the motivating problem and his many helpful remarks. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and MITACS Canada.

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Correspondence to Mark Giesbrecht or Nam Pham .

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Giesbrecht, M., Pham, N. (2014). A Symbolic Approach to Compute a Null-Space Basis in the Projection Method. In: Feng, R., Lee, Ws., Sato, Y. (eds) Computer Mathematics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43799-5_19

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