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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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)
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)
Lucia, D., Beran, P., Silva, W.: Reduced-order modeling: new approaches for computational physics. Prog. Aerosp. Sci. 40(1–2), 51–117 (2004)
Astrid, P., Reduction of process simulation models: a proper orthogonal decomposition approach, Ph.D. thesis, Technische Universiteit Eindhoven (2004)
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)
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)
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)
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)
Vendl, A., Faßbender, H.: Missing point estimation for steady aerodynamic applications. PAMM 11(1), 839–840 (2011)
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)
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
Hoffmann, K.A., Chiang, S.T.: Computational Fluid Dynamics, vol. I, 4th edn. Engineering Education System, Wichita (2000)
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)
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)
Chaturantabut, S., Sorensen, D.C., Steven, J.C.: Nonlinear model reduction via discrete empirical interpolation. SIAM J. Sci. Comput. 32(5), 2737–2764 (2010)
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)
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)
Ryckelynck, D.: A priori hyperreduction method: an adaptive approach. J. Comput. Phys. 202(1), 346–366 (2005)
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)
Blazek, J., Computational fluid dynamics: principles and applications, 1st Edition, Elsevier, (2001)
Holmes, P., Lumley, J.L., Berkooz, G.: Turbulence, coherent structures, dynamical systems and symmetry. Cambridge, New York (1996)
Sirovich, L.: Turbulence and the dynamics of coherent structures. Part I: Coherent structures. Quarterly Appl. Math. 45, 561–571 (1987)
Saad, Y.: Iterative methods for sparse linear systems, 2nd Edition, Society for Industrial and Applied Mathematics, (2003)
Antoulas, A.C.: Approximation of large-scale dynamical systems, advances in design and control. SIAM, Philadelphia (2005)
Powell, M.: A hybrid method for nonlinear equations. Numer. Methods Nonlinear Algebr. Equ. 7, 87–114 (1970)
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)
Jones, E., Oliphant, T., Peterson, P., et al.: SciPy: Open source scientific tools for Python. http://www.scipy.org/(2001). Accessed 14 July 2014
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)
Ferziger, J., Perić, M.: Computational methods for fluid dynamics, 3rd edn. Springer, Berlin (2002)
Söderlind, G.: The logarithmic norm. history and modern theory. BIT Numer. Math. 46(3), 631–652 (2006)
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)
Gerhold, T., Friedrich, O., Evans, J., Galle, M.: Calculation of complex three-dimensional configurations employing the DLR-TAU-code, AIAA paper 167
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)
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)
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
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)
Bui-Thanh, T., Damodaran, M., Willcox, K.: Proper orthogonal decomposition extensions for parametric applications in transonic aerodynamics. AIAA J. 42(8), 1505–1516 (2004)
Forrester, A., Sobester, A., Keane, A.: Engineering design via surrogate modelling: a practical guide, Wiley, Chichester, United Kingdom (2008)
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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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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DOI: https://doi.org/10.1007/s13272-014-0116-1