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Optimization of Airfoil Design Using Low-dimensional POD Method

Marvi, Morteza | 2012

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 47006 (45)
  4. University: Sharif University of Technology
  5. Department: Aerospace Engineering
  6. Advisor(s): Tayyebi Rahni, Mohammad
  7. Abstract:
  8. The aim of this research was investigation, development and application of lowdimensional proper orthogonal decomposition method for simulation of flow field and airfoil design. Sine analytical methods have low accuracy and limitations and also experimental methods have other problems, researchers usually use computational approach, which mostly do not have acceptable efficiency and speeds of computational. The idea of application of reduced order modeling which originally comes from control theories and structural analysis, have attracted many researcher in recent years. In this research, we also used POD and flow data to obtain a fast module of aerodynamic airfoil design. The goals of these reduced order models is to obtain solutions to the navier-stokes equations with sufficient accuracy for design optimization at a computational cost with is far lower than from traditional computational fluid dynamic (CFD) methods. Proper orthogonal decomposition is then used to produce the optimal linear representation of snapshots using a finite series of basis function or modes. These basis modes are then used to construct arbitrary solutions to the navier-stokes equations about modified airfoil geometries with very small computational expense. For design purpose, a gradient-based optimization procedure is used with the information by the reduced order model. The results for direct airfoil analysis and for an inverse design optimization problem are presented. Finally, the results obtained using POD reduced order models (ROM) were compared with those of direct numerical simulation (DNS) which showed adequate agreements with much lower cpu requirements
  9. Keywords:
  10. Proper Orthogonal Decomposition ; Optimization ; Reduced Order Model ; Airfoil Optimization ; Inverse Design

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