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Model order reduction for steady aerodynamics of high-lift configurations

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Abstract

In aerodynamic applications, many model reduction methods use proper orthogonal decomposition (POD). In this work, a POD-based method, called missing point estimation (MPE), is modified and applied to steady-state flows with variation of the angle of attack. The main idea of MPE is to select a subset of the computational grid points (control volumes) and to limit the governing equations to this subset. Subsequently, the limited equations are projected onto the POD subspace. This approach has the advantage that the nonlinear right-hand side of the governing equations has to be evaluated only for a small number of points (control volumes) in contrast to POD, for which the full right-hand side has to be evaluated. An error estimation for MPE in the continuous ODE setting is tackled. Numerical results are presented for the Navier–Stokes equations for two different industrially relevant, two-element high-lift airfoils, one which is normally adopted during landing and the other during take-off.

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Notes

  1. Note that the scaling with the volume matrix \(\tilde{V}\) is not included in the matrix, since in [5, 6] the Euclidean scalar product instead of the \(L_2\) scalar product is used.

  2. Note that the chord of the main element with stowed flap was 0.6 grid units.

References

  1. Rowley, C., Colonius, T., Murray, R.: Model reduction for compressible flows using POD and Galerkin projection. Phys. D Nonlinear Phenom. 189(1–2), 115–129 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  2. Lucia, D.J., King, P.I., Beran, P.S.: Reduced order modeling of a two-dimensional flow with moving shocks. Comput. Fluids 32(7), 917–938 (2003)

    Article  MATH  Google Scholar 

  3. Lucia, D., Beran, P., Silva, W.: Reduced-order modeling: new approaches for computational physics. Prog. Aerosp. Sci. 40(1–2), 51–117 (2004)

    Article  Google Scholar 

  4. Astrid, P., Reduction of process simulation models: a proper orthogonal decomposition approach, Ph.D. thesis, Technische Universiteit Eindhoven (2004)

  5. Astrid, P., Weiland, S., Willcox, K., Backx, T.: Missing point estimation in models described by proper orthogonal decomposition. IEEE Trans. Automatic Control 53(10), 2237–2251 (2008)

    Article  MathSciNet  Google Scholar 

  6. Astrid, P., Verhoeven, A.: Application of least squares MPE technique in the reduced order modeling of electrical circuits, In: Proceedings of the 17th Int. Symp. MTNS, pp. 1980–1986 (2006)

  7. Cardoso, M.A., Durlofsky, L.J., Sarma, P.: Development and application of reduced-order modeling procedures for subsurface flow simulation. Int. J. Numer. Methods Eng. 77(9), 1322–1350 (2009)

    Article  MATH  Google Scholar 

  8. Vendl, A., Faßbender, H.: Efficient POD-based model order reduction for steady aerodynamic applications. In: Poloni, C., Quagliare, D., Périaux, J., Gauger, N., Giannakoglou, K. (eds.) Evolutionary and deterministic methods for design, optimization and control, EUROGEN 2011, pp. 296–309. CIRA, Capua, Italy (2011)

    Google Scholar 

  9. Vendl, A., Faßbender, H.: Missing point estimation for steady aerodynamic applications. PAMM 11(1), 839–840 (2011)

    Article  Google Scholar 

  10. Vendl, A., Faßbender, H.: Projection-based model order reduction for steady aerodynamics. In: Kroll, N., Radespiel, N. R., Burg, J.W., Sorensen, K. (eds.). Computational flight testing—results of the closing Symposium of the German Research Initiative ComFliTe, Braunschweig, Germany, June 11th–12th, 2012, Springer Series Notes on Numerical Fluid Mechanics and Multidisciplinary Design, vol. 123, pp. 151–166 ( 2013)

  11. Vendl, A.: Projection-based model order reduction for aerodynamic applications, Ph.D. thesis, TU Braunschweig (2013), see http://www.digibib.tu-bs.de/?docid=00051712

  12. Hoffmann, K.A., Chiang, S.T.: Computational Fluid Dynamics, vol. I, 4th edn. Engineering Education System, Wichita (2000)

  13. Grepl, M. A., Maday, Y., Nguyen, N. C., Patera, A. T.: Efficient reduced-basis treatment of nonaffine and nonlinear partial differential equations. ESAIM Math. Model. Numer. Anal. (M2AN) 41(3):575–605 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  14. Nguyen, N.C., Peraire, J.: An efficient reduced-order modeling approach for non-linear parametrized partial differential equations. Int. J. Numer. Methods Eng. 76, 27–55 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  15. Chaturantabut, S., Sorensen, D.C., Steven, J.C.: Nonlinear model reduction via discrete empirical interpolation. SIAM J. Sci. Comput. 32(5), 2737–2764 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  16. Galbally, D., Fidkowski, K., Willcox, K., Ghattas, O.: Non-linear model reduction for uncertainty quantification in large-scale inverse problems. Int. J. Numer. Methods Eng. 81(12), 1581–1608 (2010)

    MathSciNet  MATH  Google Scholar 

  17. Carlberg, K., Farhat, C., Bou-Mosleh, C.: Efficient non-linear model reduction via a least-squares Petrov-Galerkin projection and compressive tensor approximations. Int. J. Numer. Methods Eng. 86(2), 155–181 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  18. Ryckelynck, D.: A priori hyperreduction method: an adaptive approach. J. Comput. Phys. 202(1), 346–366 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  19. Sorensen, D.C., Chaturantabut, S.: A state space error estimate for POD-DEIM nonlinear model reduction. SIAM J. Numer. Anal. 50(1), 46–63 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  20. Blazek, J., Computational fluid dynamics: principles and applications, 1st Edition, Elsevier, (2001)

  21. Holmes, P., Lumley, J.L., Berkooz, G.: Turbulence, coherent structures, dynamical systems and symmetry. Cambridge, New York (1996)

    Book  MATH  Google Scholar 

  22. Sirovich, L.: Turbulence and the dynamics of coherent structures. Part I: Coherent structures. Quarterly Appl. Math. 45, 561–571 (1987)

    MathSciNet  MATH  Google Scholar 

  23. Saad, Y.: Iterative methods for sparse linear systems, 2nd Edition, Society for Industrial and Applied Mathematics, (2003)

  24. Antoulas, A.C.: Approximation of large-scale dynamical systems, advances in design and control. SIAM, Philadelphia (2005)

    Book  Google Scholar 

  25. Powell, M.: A hybrid method for nonlinear equations. Numer. Methods Nonlinear Algebr. Equ. 7, 87–114 (1970)

    Google Scholar 

  26. Madsen, K., Nielsen, H.B., Tingleff, O.: Methods for non-linear least squares problems 2nd edn, pp. 60. Informatics and Mathematical Modelling, Technical University of Denmark, DTU, Richard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby (2004)

  27. Jones, E., Oliphant, T., Peterson, P., et al.: SciPy: Open source scientific tools for Python. http://www.scipy.org/(2001). Accessed 14 July 2014

  28. Moré, J.J., Garbow, B.S., Hillstrom, K.E.: User Guide for MINPACK-1, Tech. Rep. ANL-80-74, Argonne National Laboratory, Argonne, IL, USA (Aug. 1980)

  29. Ferziger, J., Perić, M.: Computational methods for fluid dynamics, 3rd edn. Springer, Berlin (2002)

    Book  MATH  Google Scholar 

  30. Söderlind, G.: The logarithmic norm. history and modern theory. BIT Numer. Math. 46(3), 631–652 (2006)

    Article  MATH  Google Scholar 

  31. Wild, J.: Experimental investigation of Mach- and Reynolds-number dependencies of the stall behavior of 2-element and 3-element high-lift wing sections, In: AIAA Paper 2012–0108, 50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, Nashville, TN, (2012)

  32. Gerhold, T., Friedrich, O., Evans, J., Galle, M.: Calculation of complex three-dimensional configurations employing the DLR-TAU-code, AIAA paper 167

  33. Galle, M., Gerhold, T., Evans, J.: Parallel computation of turbulent flows around complex geometries on hybrid grids with the dlr-tau code. In: Ecer, A., Emerson, D. (eds.) In: Proceedings of the 11th Parallel CFD Conference. North Holland, Williamsburg, VA (1999)

  34. Mifsud, M., Zimmermann, R., Sippli, J., Görtz, S.: A POD-based reduced order modeling approach for the efficient computation of high-lift aerodynamics. In: Poloni, C., Quagliare, D., Périaux, J., Gauger, N., Giannakoglou, K. (eds.) Evolutionary and deterministic methods for design, optimization and control, EUROGEN 2011. CIRA, Capua (2011)

    Google Scholar 

  35. Pinnau, R.: Model reduction via proper orthogonal decomposition. In: Wilhelmus, H.A., Schilders, Henk A., van der Vorst, Joost Rommes, (eds.) Model order reduction: theory, research aspects and applications, pp. xii+471. European Consortium for Mathematics in Industry (Berlin). Springer-Verlag, Berlin (2008). ISBN 978-3-540-78840-9

  36. Zimmermann, R.: Towards best-practice guidelines for POD-based reduced order modeling of transonic flows. In: Poloni, C., Quagliare, D., Périaux, J., Gauger, N., Giannakoglou, K. (eds.) Evolutionary and deterministic methods for design, optimization and control, EUROGEN 2011, pp. 326–341. CIRA, Capua (2011)

    Google Scholar 

  37. Bui-Thanh, T., Damodaran, M., Willcox, K.: Proper orthogonal decomposition extensions for parametric applications in transonic aerodynamics. AIAA J. 42(8), 1505–1516 (2004)

    Article  Google Scholar 

  38. Forrester, A., Sobester, A., Keane, A.: Engineering design via surrogate modelling: a practical guide, Wiley, Chichester, United Kingdom (2008)

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Acknowledgments

The research of the first author was supported by the German Federal Ministry of Economics and Technology (BMWi), Grant No. 20A0801D. The work of the German Aerospace Center (DLR) was supported in part by the European Regional Development Fund, Economic Development Fund of the Federal German State of Lower Saxony, Contract/Grant Number: W3-80026826, and also by the German Federal Ministry of Economics and Technology (BMWi), Grant No. 20A0801A.

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Correspondence to Alexander Vendl.

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Present address for the author Ralf Zimmermann is Technische Universität Braunschweig, Institut Computational Mathematics, AG Numerik, Fallersleber-Tor-Wall 23, 38100 Braunschweig, Germany.

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Vendl, A., Faßbender, H., Görtz, S. et al. Model order reduction for steady aerodynamics of high-lift configurations. CEAS Aeronaut J 5, 487–500 (2014). https://doi.org/10.1007/s13272-014-0116-1

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