Skip to main content
Log in

Six-dimensional basins of attraction computation on small clusters with semi-parallelized SCM method

  • Published:
International Journal of Dynamics and Control Aims and scope Submit manuscript

Abstract

In modern engineering applications it is needed to determine the robustness of stable steady states, which depends on the shape and size of the associated basins of attraction. Once the basins are known, the quantitative measures can be computed by means of dynamical integrity tools and arguments. Those numerical techniques applied to strongly nonlinear systems, with six of more phase-space variables, demand considerable amounts of computing power, available only on High Performance Computing platforms. With the aim to minimize utilization of computer resources, we developed a software to adapt basin computations to small, affordable, clusters. It is based on Simple Cell Mapping method, modified to reduce the memory load, adjust integration time and overcome discretization discontinuities. Resource intensive part of computations is parallelized with Message Passing Interface and less demanding operations are kept serial, due to their inherit sequential nature. As intended, the program computes full six-dimensional basins of attraction at the adequate accuracy to distinguish compact parts of basins, but not to fully disclose fractalities or chaos. Disadvantages of the SCM method are addressed and the effectiveness of proposed solutions are demonstrated and discussed by the paradigmatic example composed of three coupled Duffing oscillators.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

2D:

Two-dimensional

3D:

Three-dimensional

6D:

Six-dimensional

BoA:

Basins of attraction

CM:

Cell mapping

CPA:

Connecting post-processing algorithm

CPU:

Central processing unit

CSCM:

Clustered Simple Cell Mapping

DOF:

Degree of Freedom

FCVDP:

Forced coupled Van der Pol oscillators

FDO:

Forced Duffing oscillators

GB:

Giga-byte

GoS:

Grid of Starts

GPU:

Graphical processing unit

HPC:

High Performance Computing

MPI:

Message Passing Interface standard

NSSCM:

Not So Simple Cell Mapping

OpenMP:

Open multi-processing application programming interface

PSCM:

Parallel Simple Cell Mapping

RAM:

Random access memory

SCM:

Simple cell mapping

References

  1. Brzeski P, Belardinelli P, Lenci S, Perlikowski P (2018) Revealing compactness of basins of attraction of multi-DoF dynamical systems. Mech Syst Signal Process 111:348–361. https://doi.org/10.1016/j.ymssp.2018.04.005

    Article  Google Scholar 

  2. Lenci S, Rega G (2011) Load carrying capacity of systems within a global safety perspective. Part II. Attractor/basin integrity under dynamic excitations. Int J Non Linear Mech 46(9):1240–1251. https://doi.org/10.1016/j.ijnonlinmec.2011.05.021

    Article  Google Scholar 

  3. Lenci S, Rega G, Ruzziconi L (2013) The dynamical integrity concept for interpreting/predicting experimental behaviour: from macro- to nano-mechanics. Philos Trans R Soc Lond A Math Phys Eng Sci 371 (1993). https://doi.org/10.1098/rsta.2012.0423

  4. Rega G, Lenci S (2005) Identifying, evaluating, and controlling dynamical integrity measures in non-linear mechanical oscillators. Nonlinear Anal Theory Methods Appl 63(5):902–914. https://doi.org/10.1016/j.na.2005.01.084

    Article  MathSciNet  MATH  Google Scholar 

  5. Soliman M, Thompson J (1989) Integrity measures quantifying the erosion of smooth and fractal basins of attraction. J Sound Vib 135(3):453–475

    Article  MathSciNet  Google Scholar 

  6. Hilborn R (2000) Chaos and nonlinear dynamics: an introduction for scientists and engineers, 2nd edn. Oxford University Press, Oxford

    Book  Google Scholar 

  7. Strogatz S (1994) Nonlinear dynamics and Chaos: with applications in physics, biology, chemistry, and engineering. Addison-Wesley Pub, Boston

    Google Scholar 

  8. Wiggins S (2003) Introduction to applied nonlinear dynamical systems and chaos. Texts in applied mathematics. Springer, New York

    MATH  Google Scholar 

  9. Nusse H, Hunt B, Kostelich E, Yorke J (1998) Dynamics: numerical explorations. Applied mathematical sciences, 2nd edn. Springer, New York

    Book  Google Scholar 

  10. Belardinelli P, Lenci S (2016b) A first parallel programming approach in basins of attraction computation. Int J Non Linear Mech 80:76–81. https://doi.org/10.1016/j.ijnonlinmec.2015.10.016

    Article  Google Scholar 

  11. Hsu C (1987) Cell-to-cell mapping: a method of global analysis for nonlinear systems. Springer, New York

    Book  Google Scholar 

  12. Sun J, Xiong F, Schütze O, Hernández C (2018) Cell mapping methods: algorithmic approaches and applications. Springer, Singapore

    MATH  Google Scholar 

  13. Aguirre J, Viana R, Sanjuán M (2009) Fractal structures in nonlinear dynamics. Rev Mod Phys 81:333–386. https://doi.org/10.1103/RevModPhys.81.333

    Article  Google Scholar 

  14. Iman RL, Helton JC, Campbell JE (1981) An approach to sensitivity analysis of computer models. Part 1. Introduction, input variable selection and preliminary variable assessment. J Qual Technol 13(3):174–183

    Article  Google Scholar 

  15. Kendall W, Liang F, Wang JS (2005) Markov Chain Monte Carlo: innovations and applications. Lecture notes series, Institute for Mathematical Sciences, National University of Singapore 7, World Scientific, Singapore

  16. Szemplinska-Stupnicka W, Troger H (2014) Engineering applications of dynamics of chaos. CISM international centre for mechanical sciences. Springer, Vienna

    Google Scholar 

  17. Tongue B, Gu K (1988b) Interpolated cell mapping of dynamical systems. J Appl Mech 55(2):461–466

    Article  MathSciNet  Google Scholar 

  18. van der Spek, J, van Campen, D, de Kraker, A (1994) Cell mapping for multi degrees of freedom systems. ASME, AMD, pp 151–159

  19. Ge Z, Lee S (1997) A modified interpolated cell mapping method. J Sound Vib 199(2):189–206

    Article  MathSciNet  Google Scholar 

  20. Tongue B, Gu K (1988a) A higher order method of interpolated cell mapping. J Sound Vib 125(1):169–179

    Article  MathSciNet  Google Scholar 

  21. van der Spek J (1994) Cell mapping methods: modifications and extensions. Ph.D. thesis, Department of Mechanical Engineering. https://doi.org/10.6100/IR411481

  22. Rauber T, Rünger G (2013) Parallel programming for multicore and cluster systems, 2nd edn. Springer, Berlin

    MATH  Google Scholar 

  23. Eason RP, Dick AJ (2014) A parallelized multi-degrees-of-freedom cell mapping method. Nonlinear Dyn 77(3):467–479. https://doi.org/10.1007/s11071-014-1310-8

    Article  Google Scholar 

  24. Gyebrószki G, Csernák G (2017) Clustered simple cell mapping: an extension to the simple cell mapping method. Commun Nonlinear Sci Numer Simul 42:607–622

    Article  MathSciNet  Google Scholar 

  25. Belardinelli P, Lenci S (2016a) An efficient parallel implementation of cell mapping methods for MDOF systems. Nonlinear Dyn 86(4):2279–2290. https://doi.org/10.1007/s11071-016-2849-3

    Article  MathSciNet  Google Scholar 

  26. Belardinelli P, Lenci S (2017) Improving the global analysis of mechanical systems via parallel computation of basins of attraction. Proc IUTAM 22:192–199. https://doi.org/10.1016/j.piutam.2017.08.028

    Article  Google Scholar 

  27. Belardinelli P, Lenci S, Rega G (2018) Seamless variation of isometric and anisometric dynamical integrity measures in basins s erosion. Commun Nonlinear Sci Numer Simul 56:499–507. https://doi.org/10.1016/j.cnsns.2017.08.030

    Article  MathSciNet  Google Scholar 

  28. Message Passing Interface (MPI) Forum. https://www.mpi-forum.org. Accessed 18 Jan 2019

  29. Reif J (1985) Depth-first search is inherently sequential. Inf Process Lett 20(5):229–234

    Article  MathSciNet  Google Scholar 

  30. Fernández J, Schütze O, Hernández C, Sun J, Xiong F (2016) Parallel simple cell mapping for multi-objective optimization. Eng Optim 48(11):1845–1868

    Article  MathSciNet  Google Scholar 

  31. Aruga Y, Endo T, Hasegawa A (2002) Bifurcation of modes in three-coupled oscillators with the increase of nonlinearity. In: 2002 IEEE international symposium on circuits and systems. Proceedings (Cat. No.02CH37353), vol 5, pp V–V. https://doi.org/10.1109/ISCAS.2002.1010702

  32. ParaView—scientific data analysis and visualization. https://www.paraview.org. Accessed 15 Feb 2019

Download references

Acknowledgements

Authors would like to thank to Franco Moglie, Polytechnic University of Marche, Ancona, Italy, Radu Serban and Dan Negrut, University of Wisconsin-Madison, USA, for help with HPC.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nemanja Andonovski.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Andonovski, N., Lenci, S. Six-dimensional basins of attraction computation on small clusters with semi-parallelized SCM method. Int. J. Dynam. Control 8, 436–447 (2020). https://doi.org/10.1007/s40435-019-00557-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40435-019-00557-2

Keywords

Navigation