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Constrained proper orthogonal decomposition based on QR-factorization for aerodynamical shape optimization. (English) Zbl 1329.76147

Summary: While performing numerical simulations for design optimization, one of the major issues is reaching a good compromise between accuracy and computational effort. Simulation methods such as finite elements, finite volumes, etc. provide ‘high fidelity’ numerical solutions at a cost which may be prohibitive in optimization problems requiring frequent calls to the discretized equations’ solver. The Proper Orthogonal Decomposition (POD) constitutes an economical and efficient option to decrease the cost of the solution; however, the truncation of the POD basis implies an error in the calculation of the global quantities used as objectives and optimization constraints, which in turn might bias the optimization results. Our idea is thus to improve the snapshot POD by means of a reduced basis constructed to provide an exact interpolation of the quantities of interest obtained by integration of the ‘physical’ fields. To this end, we reformulate the POD as a minimization problem where the desired properties are expressed as a set of constraints impacting the calculation of both the modes and the coefficients. The main contribution of this paper is to provide a detailed mathematical justification for our constrained variant of the POD, including a graphical interpretation of the proposed approach. The constrained POD method is then applied to the problem of representing the pressure field around a 2D wing, and is compared with the traditional POD.

MSC:

76G25 General aerodynamics and subsonic flows
76M25 Other numerical methods (fluid mechanics) (MSC2010)

Software:

LHS
Full Text: DOI

References:

[1] Forrester, A. I.J.; Keane, A. J., Recent advances in surrogate-based optimization, Prog. Aerosp. Sci., 45, 50-79 (2009)
[2] Swiler, L. P.; Wyss, G. D., A User’s Guide to Sandia’s Latin Hypercube Sampling Software: LHS Unix Library Standalone Version (2004), Sandia National Laboratories: Sandia National Laboratories Albuquerque, New Mexico
[4] Jones, D. R., A taxonomy of global optimization methods based on response surfaces, J. Global Optim., 21, 345-383 (2001) · Zbl 1172.90492
[5] Field, R. V.; Grigoriu, M., On the accuracy of the polynomial chaos approximation, Probab. Eng. Mech., 19, 65-80 (2004)
[6] Nayroles, B.; Touzot, G.; Villon, P., Generalizing the finite element method. In: Diffuse approximation and diffuse elements, Comput. Mech., 307-318 (1992) · Zbl 0764.65068
[7] Lancaster, P.; Salkauska, K., Surfaces generated by moving least squares methods, J. Math. Comput., 37, 141-155 (1981) · Zbl 0469.41005
[8] Breitkopf, P.; Naceur, H.; Rassineux, A moving least squares response surface approximation: formulation and metal forming applications, Comput. Struct., 83, 1411-1428 (2005)
[9] Maiorov, V., Approximation by neural networks and learning theory, J. Complexity, 22, 102-117 (2006) · Zbl 1156.68541
[10] Gutmann, H. M., A radial basis function method for global optimization, J. Global Optim., 19, 3, 201-227 (2001) · Zbl 0972.90055
[11] Samuelides, M., Surfaces de réponse et réduction de modèles, Optimisation multidisciplinaire en mécanique 2 (2009), Hermes Science Publications
[12] Berkooz, G.; Holmes, P.; Lumley, J. L., The proper orthogonal decomposition in the analysis of turbulent flows, Annu. Rev. Fluid Mech., 25, 539-575 (1993)
[13] Willcox, K.; Peraire, J., Balanced model reduction via the proper orthogonal decomposition, AIAA Pap. (2001)
[14] Hall, K. C.; Thomas, J. P.; Dowell, E. H., Proper orthogonal decomposition technique for transonic unsteady aerodynamic flows, AIAA J., 38, 2, 1853-1862 (2000)
[15] Kim, T.; Bussoletti, J. E., An optimal reduced-order aeroelastic modeling based on a response-based modal analysis of unsteady cfd models, AIAA Pap. (2001)
[16] Thomas, J. P.; Dowell, E. H.; Hall, K. C., Three-dimensional transonic aeroelasticity using proper orthogonal decomposition-based reduced order models, J. Aircr., 40, 3, 544-551 (2003)
[17] Lieu, T.; Lesoinne, M., Parameter adaptation of reduced order models for three-dimensional flutter analysis, AIAA Pap. (2004)
[18] Lieu, T.; Farhat, C.; Lesoinne, M., Reduced-order fluid/structure modeling of a complete aircraft configuration, Comput. Methods Appl. Mech. Eng., 195, 5730-5742 (2006) · Zbl 1124.76042
[19] Breitkopf, P.; Filomeno Coelho, R., Multidisciplinary Design Optimization in Computational Mechanics (2010), ISTE/John Wiley & Sons: ISTE/John Wiley & Sons Chippenham, UK
[20] Filomeno Coelho, R.; Breitkopf, P.; Knopf-Lenoir, C.; Villon, P., Bi-level model reduction for coupled problems - application to a 3D wing, Struct. Multidiscip. Optim. (2008)
[21] Filomeno Coelho, R.; Breitkopf, P.; Knopf-Lenoir, C., Model reduction for multidisciplinary optimization - application to a 2D wing, Struct. Multidiscip. Optim. (2007)
[22] Xiao, M.; Breitkopf, P.; Filomeno Coelho, R.; Knopf-Lenoir, C.; Sidorkiewicz, M.; Villon, P., Model reduction by CPOD and Kriging - application to the shape optimization of an intake port, Struct. Multidiscip. Optim., 41, 4, 555-574 (2010) · Zbl 1274.90365
[24] Xiao, M.; Breitkopf, P.; Filomeno Coelho, R.; Knopf-Lenoir, C.; Villon, P., Enhanced POD projection basis with application to shape optimization of car engine intake port, Struct. Multidiscip. Optim. (2012), 1
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