Skip to main content
Book cover

Parallel Computational Fluid Dynamics 2007 pp 49–58Cite as

Aerodynamic Shape Optimization Methods on Multiprocessor Platforms

Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE,volume 67)

Abstract

Abstract: An overview of modern optimization methods, including Evolutionary Al-gorithms (EAs) and gradient{based optimization methods adapted for Cluster and Grid Computing is presented. The basic tool is a Hierarchical Distributed Metamodel{Assisted EA supporting Multilevel Evaluation, Multilevel Search and Multilevel Parameterization. In this framework, the adjoint method computes the first and second derivatives of the objective function with respect to the design variables, for use in aerodynamic shape optimization. Such a multi{component, hierarchical and distributed scheme requires particular attention when Cluster or Grid Computing is used and a much more delicate parallelization compared to that of conventional EAs.

Keywords

  • Grid Resource
  • Adjoint Method
  • Total Pressure Loss
  • Wall Clock Time
  • Parallel Genetic 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.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-540-92744-0_6
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   179.00
Price excludes VAT (USA)
  • ISBN: 978-3-540-92744-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   229.00
Price excludes VAT (USA)
Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. D. Lim, Y-S. Ong, Y. Jin, B. Sendhoff, B-S. Lee, Efficient Hierarchical Parallel Genetic Algorithms using Grid Computing, J. Fut. Gener. Comput. Syst., 23(4):658–670, 2007.

    Google Scholar 

  2. N. Lambropoulos, D. Koubogiannis, K. Giannakoglou, Acceleration of a Navier-Stokes Equation Solver for Unstructured Grids using Agglomeration Multigrid and Parallel Processing, Comp. Meth. Appl. Mech. Eng., 193:781–803, 2004.

    Google Scholar 

  3. A. Giotis, K. Giannakoglou, An Unstructured Grid Partitioning Method Based on Genetic Algorithms, Advances in Engineering Software, 29(2):129–138, 1998.

    CrossRef  Google Scholar 

  4. G. Karypis, V. Kumar, Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs, SIAM J. Scientific Computing, 20(1):359–392, 1998.

    MathSciNet  Google Scholar 

  5. K. Giannakoglou, Design of Optimal Aerodynamic Shapes using Stochastic Optimization Methods and Computational Intelligence,Int. Review J. Progress in Aerospace Sciences, 38:43–76, 2002.

    Google Scholar 

  6. M. Karakasis, D. Koubogiannis, K. Giannakoglou, Hierarchical Distributed Evolutionary Algorithms in Shape Optimization, Int. J. Num. Meth. Fluids, 53:455–469, 2007.

    CrossRef  Google Scholar 

  7. D. Papadimitriou, K. Giannakoglou, A Continuous Adjoint Method with Objective Function Derivatives Based on Boundary Integrals for Inviscid and Viscous Flows, Computers & Fluids, 36:325–341, 2007.

    Google Scholar 

  8. D. Papadimitriou, K. Giannakoglou, Direct, Adjoint and Mixed Approaches for the Computation of Hessian in Airfoil Design Problems, Int. J. Num. Meth. Fluids, to appear.

    Google Scholar 

  9. E. Cantu-Paz, A Survey of Parallel Genetic Algorithms”, Calculateurs Paralleles, Reseaux et Systemes Repartis, 10(2):141–171, 1998.

    Google Scholar 

  10. M. Nowostawski, R. Poli, Parallel Genetic Algorithm Taxonomy, Proc. 3rd Int. Conf. on Knowledge-based Intelligent Information Engineering Systems KES’99:88–92, IEEE, 1999.

    Google Scholar 

  11. E. Alba, M. Tomassini, Parallelism and Evolutionary Algorithms, IEEE Trans. Evol. Comp., 6(5), Oct. 2002.

    Google Scholar 

  12. A. Keane, P. Nair, Computational Approaches for Aerospace Design. The Pursuit of Excellence, John Wiley & Sons, Ltd, 2005.

    Google Scholar 

  13. K. Giannakoglou, A. Giotis, M. Karakasis, Low–Cost Genetic Optimization based on Inexact Pre-evaluations and the Sensitivity Analysis of Design Parameters, Inverse Problems in Engineering, 9:389–412, 2001.

    Google Scholar 

  14. M. Karakasis, K. Giannakoglou, Inexact Information aided, Low-Cost, Distributed Genetic Algorithms for Aerodynamic Shape Optimization, Int. J. Num. Meth. Fluids, 43(10–11):1149–1166, 2003.

    Google Scholar 

  15. N. Srinivas, K. Deb, Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms, Evolutionary Computation, 2(3):221–248, 1995.

    Google Scholar 

  16. E. Zitzler, M. Laumans, L. Thiele, SPEA2: Improving the Strength Pareto Evolutionary Algorithm, Report, Swiss Federal Institute of Technology (ETH), Computer Engineering and Communication Networks Lab., May 2001.

    Google Scholar 

  17. D. Papadimitriou, K. Giannakoglou, Total Pressure Losses Minimization in Turbomachinery Cascades, Using a New Continuous Adjoint Formulation, Journal of Power and Energy (Special Issue on Turbomachinery), to appear, 2007.

    Google Scholar 

  18. K. Giannakoglou, D. Papadimitriou, Adjoint Methods for gradient- and Hessian-based Aerodynamic Shape Optimization, EUROGEN 2007, Jyvaskyla, June 11–13, 2007.

    Google Scholar 

  19. D. Thain and T. Tannenbaum, M. Livny, Distributed computing in practice: the Condor experience, Concurrency - Practice and Experience, 17(2–4):323–356, 2005.

    Google Scholar 

  20. I. Foster, Globus Toolkit Version 4: Software for Service-Oriented Systems, International Conference on Network and Parallel Computing, LNCS: 2–1317(2–4), Springer-Verlag, 2006.

    Google Scholar 

  21. E. Huedo, R. Montero, I. Llorente, A Framework for Adaptive Execution on Grids, Journal of Software - Practice and Experience, 34:631–651, 2004.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Technical University of Athens (NTUA), School of Mechanical Engineering, Lab. of Thermal Turbomachines,Parallel CFD & Optimization Unit, Athens 157 10, P.O. Box 64069, Greece

    K.C. Giannakoglou

Authors
  1. K.C. Giannakoglou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. I.C. Kampolis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P.I.K. Liakopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M.K. Karakasis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. D.I. Papadimitriou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. T. Zervogiannis
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. V.G. Asouti
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and Permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Giannakoglou, K. et al. (2009). Aerodynamic Shape Optimization Methods on Multiprocessor Platforms. In: Parallel Computational Fluid Dynamics 2007. Lecture Notes in Computational Science and Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92744-0_6

Download citation