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Evaluation of a Computer Model for Wavy Falling Films Using EFCOSS

  • Christian H. Bischof
  • H. Martin Bücker
  • Arno Rasch
  • Emil Slusanschi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2668)

Abstract

Computer simulations are an essential part in computational science and engineering disciplines and they provide a valuable tool toward designing new and accurate models describing underlying physical phenomena observed in actual experiments. The adjustment of model parameters, also known as parameter identification, requires the use of numerical optimization algorithms if it is to provide credible and useful results. We report on the use of a modular framework, named EFCOSS (Environment For Combining Optimization and Simulation Software), to solve a particular parameter identification problem arising from the modeling of falling films. The underlying computer model is formulated using the multi-purpose computational fluid dynamics package FLUENT. The derivatives required in the parameter identification are obtained by applying the automatic differentiation tool ADIFOR to FLUENT. By using EFCOSS we point out, in a systematic way, areas of validity and needed improvements of a proposed model of a wavy falling film.

Keywords

Optimization Software Parameter Identification Problem Combine Optimization Modular Framework Numerical Optimization Algorithm 
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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References

  1. 1.
    Adomeit, P., Leefken, A., Renz, U.: Experimental and numerical investigations on wavy films. In Hahne, E.W.P., Heidemann, W., Spindler, K., eds.: Proceedings of the 3rd European Thermal Science Conference, Heidelberg, Germany, September 10–13, 2000. Volume 2., Edizioni ETS, Pisa (2000) 1003–1009Google Scholar
  2. 2.
    Leefken, A., Renz, U.: Numerical analysis on hydrodynamics and heat transfer in laminar-wavy films. In: Proceedings of the XIII School-Seminar of Young Scientists and Specialists, St. Petersburg, Russia, May 20-25, 2001. Volume 1., MPEI Publishers, Moscow (2001) 193–196Google Scholar
  3. 3.
    Bischof, C.H., Bücker, H.M., Lang, B., Rasch, A., Risch, J.W.: A CORBA-Based Environment for Coupling Large-Scale Simulation and Optimization Software. In Arabnia, H.R., ed.: Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, PDPTA 2001, Las Vegas, USA, June 25-28, 2001. Volume 1., CSREA Press (2001) 8–72Google Scholar
  4. 4.
    Bischof, C.H., Bücker, H.M., Lang, B., Rasch, A.: An interactive environment for supporting the transition from simulation to optimization. Scientific Programming (2003) To appear.Google Scholar
  5. 5.
    Fluent Inc. Lebanon, NH: FLUENT Tutorial Guide. (1995)Google Scholar
  6. 6.
    Gay, D.M.: Usage summary for selected optimization routines. Computing Science Technical Report 153, AT&T Bell Laboratories, Murray Hill (1990)Google Scholar
  7. 7.
    Bischof, C.H., Bücker, H.M., Lang, B., Rasch, A.: Recent Progress in Automatic Differentiation: Advanced Tools and Large-Scale Applications. Preprint of the Institute for Scientific Computing RWTH-CS-SC-01-18, Aachen University of Technology (2001) Submitted for publication.Google Scholar
  8. 8.
    Griewank, A.: Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation. SIAM, Philadelphia (2000)zbMATHGoogle Scholar
  9. 9.
    Rall, L.B.: Automatic Differentiation: Techniques and Applications. Volume 120 of Lecture Notes in Computer Science. Springer-Verlag, Berlin (1981)zbMATHGoogle Scholar
  10. 10.
    Griewank, A., Corliss, G.: Automatic Differentiation of Algorithms. SIAM, Philadelphia (1991)zbMATHGoogle Scholar
  11. 11.
    Berz, M., Bischof, C., Corliss, G., Griewank, A.: Computational Differentiation: Techniques, Applications, and Tools. SIAM, Philadelphia (1996)zbMATHGoogle Scholar
  12. 12.
    Corliss, G., Faure, C., Griewank, A., Hascoët, L., Naumann, U., eds.: Automatic Differentiation of Algorithms: From Simulation to Optimization. Springer, New York (2002)zbMATHGoogle Scholar
  13. 13.
    Bischof, C., Carle, A., Khademi, P., Mauer, A.: ADIFOR 2.0: Automatic differentiation of Fortran 77 programs. IEEE Computational Science & Engineering 3 (1996) 18–32CrossRefGoogle Scholar
  14. 14.
    Bischof, C.H., Bücker, H.M., Lang, B., Rasch, A., Slusanschi, E.: Automatic Differentiation of Large-Scale Simulation Codes is no Illusion. Preprint of the Institute for Scientific Computing RWTH-CS-SC-02-12, Aachen University, Aachen (2002) Submitted for publication.Google Scholar
  15. 15.
    Launder, B., Spalding, D.B.: The numerical computation of turbulent flows. Computer Methods in Applied Mechanics and Engineering 3 (1974) 269–289CrossRefzbMATHGoogle Scholar
  16. 16.
    Dennis, J.E., Gay, D.M., Welsch, R.E.: An adaptive nonlinear least squares algorithm. ACM Transactions on Mathematical Software 7 (1981) 348–368zbMATHCrossRefGoogle Scholar
  17. 17.
    Gay, D.M.: A trust region approach to linearly constrained optimization. In Griffith, D.F., ed.: Numerical Analysis, Proceedings, Dundee, 1983. Lecture Notes in Mathematics, Berlin, Springer (1984)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Christian H. Bischof
    • 1
  • H. Martin Bücker
    • 1
  • Arno Rasch
    • 1
  • Emil Slusanschi
    • 1
  1. 1.Institute for Scientific ComputingAachen UniversityGermany

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