Aerodynamic Data Reconstruction and Inverse Design Using Proper Orthogonal Decomposition

The application of proper orthogonal decomposition for incomplete (gappy) data for compressible external aerodynamic problems has been demonstrated successfully in this paper for the first time. Using this approach, it is possible to construct entire aerodynamic flowfields from the knowledge of computed aerodynamic flow data or measured flow data specified on the aerodynamic surface, thereby demonstrating a means to effectively combine experimental and computational data. The sensitivity of flow reconstruction results to available measurements and to experimental error is analyzed. Another new extension of this approach allows one to cast the problem of inverse airfoil design as a gappy data problem. The gappy methodology demonstrates a great simplification for the inverse airfoil design problem and is found to work well on a range of examples, including both subsonic and transonic cases.

[1]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[2]  Raymond M. Hicks,et al.  Wing design by numerical optimization , 1977 .

[3]  A. Jameson,et al.  Numerical solution of the Euler equations by finite volume methods using Runge Kutta time stepping schemes , 1981 .

[4]  L Sirovich,et al.  Low-dimensional procedure for the characterization of human faces. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[5]  L. Sirovich Turbulence and the dynamics of coherent structures. I. Coherent structures , 1987 .

[6]  Antony Jameson,et al.  Aerodynamic design via control theory , 1988, J. Sci. Comput..

[7]  Lawrence Sirovich,et al.  Karhunen–Loève procedure for gappy data , 1995 .

[8]  L Sirovich,et al.  The Karhunen-lo Eve Procedure for Gappy Data , 1995 .

[9]  Michael C. Romanowski Reduced order unsteady aerodynamic and aeroelastic models using Karhunen-Loeve eigenmodes , 1996 .

[10]  P. Holmes,et al.  Turbulence, Coherent Structures, Dynamical Systems and Symmetry , 1996 .

[11]  Earl H. Dowell,et al.  Reduced order models in unsteady aerodynamics , 1999 .

[12]  Earl H. Dowell,et al.  Reduced-order modelling of unsteady small-disturbance flows using a frequency-domain proper orthogonal decomposition technique , 1999 .

[13]  Philip S. Beran,et al.  Reduced-order modeling - New approaches for computational physics , 2001 .

[14]  Juan J. Alonso,et al.  Investigation of non-linear projection for POD based reduced order models for Aerodynamics , 2001 .

[15]  Earl H. Dowell,et al.  Mach Number Influence on Reduced-Order Models of Inviscid Potential Flows in Turbomachinery , 2002 .

[16]  Kelly Cohen,et al.  Sensor Placement Based on Proper Orthogonal Decomposition Modeling of a Cylinder Wake , 2003 .

[17]  P. Beran,et al.  Reduced-order modeling: new approaches for computational physics , 2004 .

[18]  P. Astrid,et al.  On the Construction of POD Models from Partial Observations , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[19]  K. Willcox Unsteady Flow Sensing and Estimation via the Gappy Proper Orthogonal Decomposition , 2004 .

[20]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .