Parallel Evolutionary Optimization of Natural Convection Problem

Chapter
First Online:
Part of the Emergence, Complexity and Computation book series (ECC, volume 24)

Abstract

Computer simulations of complex natural phenomena become an approach of choice if experimental work is impractical or dangerous. Often, optimization approaches are used in a closed cycle with the simulation to obtain the desired performances. To test and validate such cases an optimization of a coupled thermo-fluid transport in a two dimensional cavity is elaborated. We seek for optimal positions and dimensions of obstacles in the cavity to minimize the heat flux through the domain. One can apply such an approach to maximize the insulation by using minimal amount of insulation material. The governing equations are solved with a meshless numerical method while the optimization is performed with differential evolution. The solution and optimization procedures are designed for execution on parallel computers. Incentive scalability and speed-up are demonstrated on the presented test case.

Keywords

Execution Time Differential Evolution Computing Node Master Process Natural Convection Flow 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgments

We acknowledge the financial support from the Slovenian Research Agency under the programme group P2-0095.

References

  1. 1.
    Abraham, A., Jain, L., Goldberg, R. (eds.): Evolutionary Multiobjective Optimization. Springer, London (2005)MATHGoogle Scholar
  2. 2.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press (1999)Google Scholar
  3. 3.
    Burke, E.K., Kendall, G. (eds.): Introduction to Stochastic Search and Optimization. Wiley, Hoboken (2003)Google Scholar
  4. 4.
    Chandra, R., Dagum, L., Kohr, D., Maydan, D., McDonald, J., Menon, R.: Parallel Programming in OpenMP. Academic Press, San Diego (2001)Google Scholar
  5. 5.
    Coello, C.A.C., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation). Springer New York Inc, Secaucus, NJ, USA (2006)MATHGoogle Scholar
  6. 6.
    Depolli, M., Trobec, R., Filipi, B.: Asynchronous master-slave parallelization of differential evolution for multiobjective optimization. Evolutionary Computation 21(2), 261–291 (2013)CrossRefGoogle Scholar
  7. 7.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Berlin (2003)CrossRefMATHGoogle Scholar
  8. 8.
    Kosec, G., Depolli, M., Rashkovska, A., Trobec, R.: Super linear speedup in a local parallel meshless solution of thermo-fluid problems. Comput. Struct. 133, 30–38 (2014)CrossRefGoogle Scholar
  9. 9.
    Kosec, G., Trobec, R.: Simulation of semiconductor devices with a local numerical approach. Eng. Anal. Boundary Elements 50, 69–75 (2015)Google Scholar
  10. 10.
    Kosec, G., Šarler, B.: Solution of thermo-fluid problems by collocation with local pressure correction. Int. J. Numer. Methods Heat Fluid Flow 18, 868–882 (2008)Google Scholar
  11. 11.
    Kosec, G., Zinterhof, P.: Local strong form meshless method on multiple graphics processing units. CMES. Comput. Model. Eng. Sci. 91(5), 377–396 (2013)Google Scholar
  12. 12.
    Malan, A.G., Lewis, R.W.: An artificial compressibility cbs method for modelling heat transfer and fluid flow in heterogeneous porous materials. Int. J. Numer. Methods Eng. 87, 412–423 (2011)Google Scholar
  13. 13.
    Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Natural Computing Series. Springer, Berlin (2005)MATHGoogle Scholar
  14. 14.
    Snir, M., Otto, S., Huss-Lederman, S., Walker, D., Dongarra, J.: MPI—The Complete Reference. The MIT Press, Cambridge (1996)Google Scholar
  15. 15.
    Šterk, M., Trobec, R.: Meshless solution of a diffusion equation with parameter optimization and error analysis. Eng. Anal. Boundary Elements 32(7), 567–577 (2008)CrossRefMATHGoogle Scholar
  16. 16.
    Trobec, R., Kosec, G.: Parallel Scientific Computing: Theory, Algorithms, and Applications of Mesh Based and Meshless Methods. Springer (2015)Google Scholar
  17. 17.
    de Vahl Davis, : G.: Natural Convection of Air in a Square Cavity: A Bench Mark Numerical Solution. Int. J. Numer. Methods Fluids 3, 249–264 (1983)Google Scholar
  18. 18.
    Wang, C.A., Sadat, H., Prax, C.: A new meshless approach for three dimensional fluid flow and related heat transfer problems. Comput. Fluids 69, 136–146 (2012)Google Scholar
  19. 19.
    Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms—a comparative case study. In: Proceedings of the Fifth Conference on Parallel Problem Solving from Nature—PPSN V, pp. 292–301. Springer, Heidelberg (1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Jožef Stefan InstituteLjubljanaSlovenia

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