DFG
FAU Erlangen-Nuremberg

Multilevel Parameterizations and Fast Multigrid Methods for Aerodynamic Shape Optimization

From DFG-SPP 1253

Jump to: navigation, search

Project leaders

Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)
in der Helmholtz-Gemeinschaft

Tel: 0531 295-3339
Fax: 0531 295-2914

Email: nicolas.gauger@dlr.de

Homepage:


FB IV, Mathematik
Uni Trier

Tel: 0651 201-3484
Tel: 0651 201-3485 (Sekr.)
Fax: 0651 201-3966

Email: volker.schulz@uni-trier.de

Homepage: http://www.mathematik.uni-trier.de/~schulz/

DFG funded assistants

Stephan Schmidt (Uni Trier)
stephan.schmidt@uni-trier.de

Caslav Ilic (DLR Braunschweig)
caslav.ilic@dlr.de


Description

Numerical flow simulation is an integral part of the construction process of commercial aircrafts. It is nowadays conceivable to design parts or even the whole aircraft solely on the computer. Therefore, numerical shape optimization will play a strategic role for future aircraft design and the respective industries. Because of the considerable complexity of numerical flow models, highly efficient optimization algorithms are of utmost importance. Moreover, the optimal representation of shapes on the computer is principally open, resulting in questions of parameterizations and local design refinements.

Image:RAE1a.jpg RAE2822 airfoil before optimization

Image:RAE2a.jpg RAE2822 airfoil after optimization

Numerical flow simulation is an integral part of the construction process of commercial aircrafts. It is nowadays conceivable to design parts or even the whole aircraft solely on the computer. Therefore, numerical shape optimization will play a strategic role for future aircraft design and the respective industries. Because of the considerable complexity of numerical flow models, highly efficient optimization algorithms are of utmost importance. Moreover, the optimal representation of shapes on the computer is principally open, resulting in questions of parameterizations and local design refinements.


The Goals of the Project are:

  • Improving the performance of optimization solvers in a multilevel/multigrid strategy
  • Giving an algorithmic optimization performance independent of the grid resolutions
  • Being able to refine locally, where it is required by optimization
  • Complying with requirements from various physical aspects (multidisciplinary design objective)

    Mathematical Issues:

  • Definition of an appropriate hierarchical shape family
  • Incorporation of this shape family within an overall multigrid method for the complete constrained optimization problem
  • Definition of a local adaptation criterion for the refinement of the shape parameterization
  • Personal tools
    organisation