Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-10T15:52:03.071Z Has data issue: false hasContentIssue false
  • English
  • Français

Reduced-order modelling of non-linear, transient aerodynamics of the HIRENASD wing

Published online by Cambridge University Press:  20 April 2016

K. Lindhorst ,
M.C. Haupt  and
P. Horst
K. Lindhorst*
Affiliation:
Institute of Aircraft Design and Lightweight Structures (IFL), Technische Universität Braunschweig, Hermann-Blenk-Straße, Braunschweig, Germany
M.C. Haupt
Affiliation:
Institute of Aircraft Design and Lightweight Structures (IFL), Technische Universität Braunschweig, Hermann-Blenk-Straße, Braunschweig, Germany
P. Horst
Affiliation:
Institute of Aircraft Design and Lightweight Structures (IFL), Technische Universität Braunschweig, Hermann-Blenk-Straße, Braunschweig, Germany
*
k.lindhorst@tu-bs.de
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper, a surrogate model approach for non-linear aerodynamics is presented in order to reduce the computational effort of coupled aeroelastic analyses. The usability of the approach is demonstrated in static as well as transient aeroelastic analyses of the HIRENASD wing-fuselage configuration. Furthermore, it is shown that the surrogate model approach is able to cover variations of flow conditions at a fixed Mach and Reynolds number.

Keywords

aeroelasticityreduced order modelingsurrogate modelingneural networksproper orthogonal decompositionHIRENASD
Type
Research Article
Information
The Aeronautical Journal , Volume 120 , Issue 1226 , April 2016 , pp. 601 - 626
Copyright
Copyright © Royal Aeronautical Society 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. de C. Henshaw, M.J., Badcock, K.J., Vio, G.A., Allen, C.B., Chamberlain, J., Kaynes, I., Dimitriadis, G., Cooper, J.E., Woodgate, M.A., Rampurawala, A.M., Jones, D., Fenwick, C., Gaitonde, A.L., Taylor, N.V., Amor, D.S., Eccles, T.A. and Denley, C.J. Non-linear aeroelastic prediction for aircraft applications, Progress in Aerospace Sciences, 2007, 43, (4-6), pp 65–137.CrossRefGoogle Scholar
2. Farhat, C. Real-time CFD-based flutter analysis of complex aircraft configurations on a mobile device, International Forum on Aeroelasticity and Structural Dynamics (IFASD), Keynote Lecture, 2011, Paris, France.Google Scholar
3. Willcox, K. and Peraire, J. Balanced model reduction via the proper orthogonal decomposition, AIAA J, 2002, 40, (11), pp 2323–2330.CrossRefGoogle Scholar
4. Amsallem, D. and Farhat, C. An interpolation method for adapting reduced-order models and application to aeroelasticity, AIAA J, 2008, 46, pp 1803–1813.CrossRefGoogle Scholar
5. Amsallem, D., Cortial, J., Carlberg, K. and Farhat, C. A method for interpolating on manifolds structural dynamics reduced-order models, Int J for Numerical Methods in Engineering, 2009, 80, (9), pp 1241–1258.CrossRefGoogle Scholar
6. Thomas, E.H., Dowell, J.P. andHall, K.C. Modeling viscous transonic limit-cycle oscillation behaviour using a harmonic balance approach, J Airc, 2004, 41, (6), pp 1266–1274.CrossRefGoogle Scholar
7. Chen, G., Li, Y. and Yan, G. A nonlinear POD reduced order model for limit cycle oscillation prediction, Science in China G: Physics and Astronomy, July 2010, 53, (7), pp 1325–1332.CrossRefGoogle Scholar
8. Ahmed, M.Y.M. and Qin, N. Surrogate-based aerodynamic design optimization: Use of surrogates in aerodynamic design optimization, 13th International Conference on Aerospace Sciences & Aviation Technology, 26-28 May 2009, Cairo, Egypt, number ASAT-13-AE-14.CrossRefGoogle Scholar
9. Voitcu, O. andWong, Y.S. A neural network approach for nonlinear aeroelastic analysis, 43th AIAA Structures, Structural Dynamics and Materials Conference, 22-25 April 2002, Denver, Colorado, US, number AIAA 2002-1286.CrossRefGoogle Scholar
10. Voitcu, O. and Wong, Y.S. An improved neural network model for nonlinear aeroelastic analysis, 44th AIAA Structures, Structural Dynamics and Materials Conference, 7-10 April 2003, Norfolk, Virginia, US, number AIAA 2003-1493.CrossRefGoogle Scholar
11. Lucia, D.J., Beran, P.S. and Silva, W. Aeroelastic system development using proper orthogonal decomposition and Volterra theory, 44th AIAA Structures, Structural Dynamics and Materials Conference, 7-10 April 2003, Norfolk, Virginia, US, number AIAA 2003-1922.CrossRefGoogle Scholar
12. Silva, W. Identification of nonlinear aeroelastic systems based on the Volterra theory: Progress and opportunities, Nonlinear Dynamics, 2005, 39, (1-2), pp 25–62.CrossRefGoogle Scholar
13. Han, Z.-H., Zimmermann, R. and Görtz, S. A new cokriging method for variable-fidelity surrogate modeling of aerodynamic data, 48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, 4-7 January 2010, Orlando, Florida, US, number AIAA 2010-12250.CrossRefGoogle Scholar
14. Chen, G., Zuo, Y., Sun, J. and Li, Y. Support-vector-machine-based reduced-order model for limit cycle oscillation prediction of nonlinear aeroelastic system, Mathematical Problems in Engineering, 2012, (2012), Article ID 152123. http://dx.doi.org/10.1155/2012/152123.CrossRefGoogle Scholar
15. Fagley, C., Seidel, J., Siegel, S. and McLaughlin, T. Reduced order modeling using proper orthogonal decomposition (POD) and wavenet system identification of a free shear layer. Rudibert King, editor, Active Flow Control II, volume 108 of Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 2010, Springer, Berlin, Heidelberg, pp 325–339.CrossRefGoogle Scholar
16. Zhang, W., Wang, B., Ye, Z. and Quan, J. Efficient method for limit cycle flutter analysis based on nonlinear aerodynamic reduced-order models, AIAA J, May 2012, 50, (5), pp 1019–1028.CrossRefGoogle Scholar
17. Lindhorst, K., Haupt, M.C. andHorst, P. Efficient surrogate modelling of nonlinear aerodynamics in aerostructural coupling schemes, AIAA J, June 2014, 52, (9), pp 1952–1966.CrossRefGoogle Scholar
18. Meyer, M. and Matthies, H.G. Efficient model reduction in non-linear dynamics using the Karhunen-Love expansion and dual-weighted-residual methods, Computational Mechanics, 2003, 31, (1-2), pp 179–191.CrossRefGoogle Scholar
19. Meyn, S.P. and Tweedie, R.L. Markov Chains and Stochastic Stability, Communications and Control Engineering Series, 1993, Springer-Verlag, London, UK.CrossRefGoogle Scholar
20. Won, K., Tsai, H.M., Sadeghi, M. and Liu, F. Non-linear impulse methods for aeroelastic simulations, AIAA-2005-4845, presented at the 23rd AIAA Applied Aerodynamics Conference, 6-9 June 2005, Toronto, Ontario, Canada.CrossRefGoogle Scholar
21. Broomhead, D.S. and Lowe, D. Multivariable functional interpolation and adaptive networks, Complex Systems, 1998, 2, (3), pp 321–355.Google Scholar
22. Kecman, V. Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models, 2001, MIT Press, Cambridge, Massachusetts, US.Google Scholar
23. Orr, M.J.L. Introduction to radial basis function networks. Technical report, 1996, Center for Cognitive Science, University of Edinburgh, UK.Google Scholar
24. de Boer, A., van der Schoot, M.S. and Bijl, H. Mesh deformation based on radial basis function interpolation, Computers & Structures, 2007, 85, (11-14), pp 784–795.CrossRefGoogle Scholar
25. Lindhorst, K., Haupt, M.C. and Horst, P. Reduced order modeling of nonlinear, transient aerodynamics of the HIRENASD wing, 16th International Forum on Aeroelasticity and Structural Dynamics, 24-26 June 2013, Bristol, UK, number IFASD-2014-6A.Google Scholar
26. Heeg, J., Ballmann, J., Bhatia, K., Blades, E., Boucke, A., Chwalowski, P., Dietz, G., Dowell, E., Florance, J., Hansen, T., Mani, M., Mavriplis, D., Perry, B., Ritter, M., Schuster, D., Smith, M., Taylor, P., Whiting, B. and Wieseman, C. Plans for an aeroelastic prediction workshop, International Forum on Aeroelasticity and Structure Dynamics, 2011, Paris, France, number IFASD-2011-110.Google Scholar
27. Chwalowski, P., Florance, J.P., Heeg, J., Wieseman, C. and Perry, B. Preliminary computational analysis of the HIRENASD configuration in preparation for the aeroelastic prediction workshop, International Forum on Aeroelasticity and Structure Dynamics, June 2011, Paris, France, number IFASD-2011-108.Google Scholar
28. Lindhorst, K., Haupt, M.C. and Horst, P. Aeroelastic analyses of the high-Reynolds-number-aerostructural-dynamics configuration using a nonlinear surrogate model approach, AIAA J, 2015, 53, (9), pp 2784–2796. doi: 10.2514/1.J053743 CrossRefGoogle Scholar
29. Wieseman, C. and Boucke, A. Filename: $ms103\_modi\_o1\_nsm1\_modrho.bdf$ . https://c3.nasa.gov/dashlink/resources/425/. Accessed 28 November 2011.Google Scholar
30. Hughes, T.J.R. and McCulley, J. The Finite Element Method, 1987, Prentice-Hall International, Englewood Cliffs, New Jersey, US.Google Scholar
31. Braun, C. Ein modulares Verfahren für die numerische aeroelastische Analyse von Luftfahrzeugen, PhD Thesis, 2007, Rheinisch Westfälische Technische Hochschule (RWTH) Aachen.Google Scholar
32. Reimer, L., Braun, C., Wellmer, G., Behr, M. and Ballmann, J. Development of a modular method for computational aero-structural analysis of aircraft, Schröder, W., editor, Summary of Flow Modulation and Fluid-Structure Interaction Findings, 2010, Springer-Verlag, Berlin, Heidelberg, pp 205–238.CrossRefGoogle Scholar
33. Wieseman, C. and Boucke, A. Filename: StructuralAnalysisStatus.pdf. https://c3.nasa.gov/dashlink/resources/425/. Accessed 23 April 2013.Google Scholar
34. Ritter, M. Static and forced motion aeroelastic simulations of the HIRENASD wind tunnel model, 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 23-26 April 2012, Honolulu, Hawaii, US, number AIAA 2012-1633.CrossRefGoogle Scholar
35. Ritter, M. Filename: $aepw\_solar\_hirenasd\_coarse\_netcdf.nc$ . https://c3.nasa.gov/dashlink/resources/627/. Accessed 20 November 2012.Google Scholar
36. Ritter, M. Filename: $aepw\_solar\_hirenasd\_medium\_netcdf.nc$ . https://c3.nasa.gov/dashlink/resources/627/. Accessed 20 November 2012.Google Scholar
37. Ritter, M. Filename: $aepw\_solar\_hirenasd\_fine\_netcdf.nc$ . https://c3.nasa.gov/dashlink/resources/627/. Accessed 20 November 2012.Google Scholar
38. Haupt, M., Niesner, R., Unger, R. and Horst, P. Computational aero-structural coupling for hypersonic applications, 9th AIAA/ASME Joint Thermophysics and Heat Transfer Conference, 2006, San Francisco, California, US, number AIAA 2006-3252.CrossRefGoogle Scholar
39. Gerhold, T., Hannemann, V. and Schwamborn, D. On the validation of the DLR-TAU code, New Results in Numerical and Experimental Fluid Mechanics, Notes on Numerical Fluid Mechanics, 1999, Vieweg, Berlin, pp 426-433.CrossRefGoogle Scholar
40. Unger, R., Haupt, M.C. and Horst, P. Coupling techniques for computational non-linear transient aeroelasticity, J Aerospace Engineering, 2008, 222, pp 435–447.Google Scholar