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Improved extrapolation of steady turbulent aerodynamics using a non-linear POD-based reduced order model

Published online by Cambridge University Press:  27 January 2016

R. Zimmermann*
Affiliation:
Institute Computational Mathematics, TU Braunschweig, Germany
S. Görtz
Affiliation:
Institute of Aerodynamics and Flow Technology, German Aerospace Center (DLR), Germany

Abstract

A reduced-order modelling (ROM) approach for predicting steady, turbulent aerodynamic flows based on computational fluid dynamics (CFD) and proper orthogonal decomposition (POD) is presented. Model-order reduction is achieved by parameter space sampling, solution space representation via POD and restriction of a CFD solver to the POD subspace. Solving the governing equations of fluid dynamics is replaced by solving a non-linear least-squares optimisation problem. The method will be referred to as LSQ-ROM method. Two approaches of extracting POD basis information from CFD snapshot data are discussed: POD of the full state vector (global POD) and POD of each of the partial states separately (variable-by-variable POD). The method at hand is demonstrated for a 2D aerofoil (NACA 64A010) as well as for a complete industrial aircraft configuration (NASA Common Research Model) in the transonic flow regime by computing ROMs of the compressible Reynolds-averaged Navier-Stokes equations, pursuing both the global and the variable-by-variable POD approach. The LSQ-ROM approach is tried for extrapolatory flow conditions. Results are juxtaposed with those obtained by POD-based extrapolation using Kriging and the radial basis functions spline method. As a reference, the full-order CFD solutions are considered. For the industrial aircraft configuration, the cost of computing the reduced-order solution is shown to be two orders of magnitude lower than that of computing the reference CFD solution.

Type
Research Article
Copyright
Copyright © Royal Aeronautical Society 2012 

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References

1. Klenner, J., Becker, K., Cross, M. and Kroll, N. Future simulation concept, 2007, First CEAS Conference, 2007-09-10, Egmond and Zee, Berlin, Germany.Google Scholar
2. Tinoco, E.N., Bogue, D.R., Kao, T.J., Yu, N.J., Li, P. and Ball, D.N. Progress toward CFD for full flight envelope, Aeronaut J, October 2005, 109, (1100), pp 451460.Google Scholar
3. Salas, M.D. Digital flight: The last CFD grand challenge, J Scientific Computing, 2006, 28, (213), pp 479505.Google Scholar
4. Zimmermann, R. and Görtz, S. Non-linear reduced order models for steady aerodynamics, Procedia Computer Science, 2010, 1, (1), pp 165174.Google Scholar
5. Zimmermann, R. and Görtz, S. Non-linear POD-based reduced order models for steady turbulent aerodynamics, 2010, RAeS 2010 Conference Applied Aerodynamics: Capabilities and Future Requirements, Bristol, UK.Google Scholar
6. Legresley, P.A. and Alonso, J.J. Investigation of non-linear projection for POD-based reduced order models for aerodynamics, 2001, AIAA Paper, number 2001-0926.Google Scholar
7. Gerhold, T., Friedrich, O., Evans, J. and Galle, M. Calculation of complex three-dimensional configurations employing the dlr-tau-code, 1997, AIAA-Paper number 97-0167.Google Scholar
8. Galle, M., Gerhold, T. and Evans, J. Parallel computation of turbulent flows around complex geometries on hybrid grids with the dlr-tau code, 1999, 11th Parallel CFD Conference, 23-26 May 1999, Ecer, A. and R., , E.D., , (Eds).Google Scholar
9. Schwamborn, D., Gerhold, T. and Heinrich, R. The dlr tau-code: recent applications in research and industry, 2006, Technical Report, European Conference on Computational Fluid Dynamics, ECCOMAS CFD 2006, Egmond and Zee, The Netherlands.Google Scholar
10. Forrester, A.I.J., Sobester, A. and Keane, A.J. Engineering Design via Surrogate Modelling: A Practical Guide, 2008, John Wiley & Sons, UK.Google Scholar
11. Chaturantabut, S. and Sorensen, D. Discrete empirical interpolation for nonlinear model reduction, 2009, Technical Report TR09-05, Rice University, Houston, TX, USA.Google Scholar
12. Astrid, P., Weiland, S., Wilcox, K. and Backx, T. Missing points estimation in models described by proper orthogonal decomposition, IEEE Transactions on Automatic Control, 2008, 53, (10), pp 22372251.Google Scholar
13. Bui-Thanh, T., Damadoran, M. and Willcox, K. Proper orthogonal decomposition extensions for parametric applications in transonic aerodynamics, 2003, 2003-4213 AIAA, 21th AIAA Applied Aerodynamics Conference, Orlando, FL, USA.Google Scholar
14. Sacks, J., Welch, W.J., Mitchell, T.J. and Wynn, H.P. Design and analysis of computer experiments, Statistical Science, 1989, 4.Google Scholar
15. Han, Z.H., Görtz, S. and Zimmermann, R. On improving efficiency and accuracy of variable-fidelity surrogate modeling in aero-data for loads context, 2009, CEAS 2009 European Air and Space conference, Manchester, UK.Google Scholar
16. Vassberg, J.C., Dehaan, M.A., Rivers, S.M., and A., , W.R., Development of a common research model for applied CFD validation studies, 2008, AIAA 2008-6919, 26th AIAA Applied Aerodynamics Conference, Hawaii, HI, USA.Google Scholar
17. Blazek, J. Computational Fluid Dynamics: Principles and Applications, 2001, First edition, Elsevier, Amsterdam – London – New York – Oxford – Paris – Shannon – Tokyo.Google Scholar
18. Pinnau, R. Model reduction via proper orthogonal decomposition, 2008, Model Order Reduction: Theory, Research Aspects and Applications, Schilders, W.H.A., Van der Vorst, H.A. and Rommes, J. (Eds), volume 13 of Springer Series Mathematics in Industry, 95–109. Springer.Google Scholar
19. Zimmermann, R. Towards best-practice guidelines for POD-based reduced order modeling of transonic flows, 2011, EUROGEN 2011, Poloni, C., Quagliarella, D., Périaux, J., Gauger, N. and Giannakoglou, K. (Eds), CIRA — Italian Aerospace Research Centre, Capua, Italy.Google Scholar
20. Holmes, P., Lumley, J., and Berkooz, G. Turbulence, Coherent Structures, Dynamical Systems and Symmetry, 1996, Cambridge University Press, Cambridge, UK.Google Scholar
21. Santner, T.J., Williams, B.J. and Notz, W.I. The Design and Analysis of Computer Experiments, 2003, Springer, New York, Berlin, Heidelberg.Google Scholar
22. Madsen, K., Nielsen, H.B. and Tingleff, O. Methods for Non-linear Least Squares Problems, IMM Informatics and Mathematical Modelling, April 2004, Second edition, Technical University of Denmark.Google Scholar
23. Mifsud, M., Zimmermann, R., Sippli, J. and Görtz, S. A POD-based reduced order modeling approach for the efficient computation of high-lift aerodynamics, 2011, EUROGEN 2011, Poloni, C., Quagliarella, D., Périaux, J., Gauger, N. and Giannakoglou, K. (Eds), (CIRA —Italian Aerospace Research Centre, Capua, Italy.Google Scholar
24. Crippa, S. Improvement of unstructured computational fluid dynamics simulations through novel mesh generation methodologies, J Aircr, 2011, 48, (3), pp 10361044.Google Scholar