[1] F. Afonso, J. Vale, F. Lau, and A. Suleman. Performance based multidisciplinary design optimiza- tion of morphing aircraft. Aerospace Science and Technology, 67:1–12, 2017.
[2] C. Hirsch. Numerical Computation of internal and External Flows. Elsevier, second edition, 2007.
ISBN 978-0-7506-6594-0.
[3] AeroGust-Team. About aerogust (grant agreement number 636053).
[4] M. Ohlberger and S. Rave. Reduced basis methods: success, limitations and future challenges.
InALGORITMY 2016, 2016.
[5] F. Chinesta, P. Ladeveze, and E. Cueto. A short review on model order reduction based on proper generalized decomposition. Archives of Computational Methods in Engineering, 18:395–
404, 2011.
[6] A. L. Francisco Chinesta, Roland Keunings.The Proper Generalized Decomposition for Advanced Numerical Simulations. Springer, 2014.
[7] B. Bognet, F. Chinesta, A. Leygue, and A. Poitou. Proper generalized decomposition et s ´eparation de variables spatiales pour la r ´esolution en thermo ´elasticit ´e lin ´eaire appliqu ´e `a des plaques com- posites. In10e colloque national en calcul des structures, 2011.
[8] Reduced-order models for efficient computational analysis of complex systems. Technical report, NASA, 2014. Patent No: 8,060,350, Report LAR-TOPS-65.
[9] J. Qian, Y. Wang, H. Song, K. Pant, H. Peabody, H. Ku, and C. Butler. Projection-based reduced order modeling for spacecraft thermal analysis. techreport, NASA, 2015.
[10] Reduced Order Models for Aerodynamic Applications, Loads and MDO, 2017. DLR.
[11] M. Verveld, T. Kier, N. Karcher, T. Franz, M. Abu-Zurayk, M. Ripepi, and S. G ¨ortz. Reduced order models for aerodynamic applications, loads and mdo. 2016.
[12] Cea hpc summer school 2016, 2016. http://www-hpc.cea.fr/SummerSchools2016.htm, Accessed Nov. 2017.
[13] N. Martins. Apontamentos das aulas te ´oricas de ´Algebra linear. PDF, Feb. 2014. URL https:
//www.math.tecnico.ulisboa.pt/~nmartins/ALT1314.pdf. Accessed Nov. 2017.
[14] D. A. Charbel Farhat. Cme 345 model reduction, projection based model order reduc- tion. Presentation, 2017. URL https://web.stanford.edu/group/frg/course_work/CME345/
CA-CME345-Ch3.pdf. Accessed Nov. 2017.
[15] J. Oliveira. Estabilidade din ˆamica : Modos laterais. Presentation, Dec. 2010. URL https://
fenix.tecnico.ulisboa.pt/downloadFile/3779576359546/EstDinam2.pdf. IST flight stability course, Accessed Nov. 2017.
[16] M. B. J. Weller, E. Lombardi. Numerical methods for low-order modeling of fluid flows based on pod. International Journal for Numerical Methods in Fluids, 63(2):249–268, 2010.
[17] A. I. Marcelo Buffoni, Haysam Telib. Iterative methods for model reduction by domain decomposi- tion. Computers and Fluids, 38(6):1160–1167, 2009.
[18] T. Bui-Thanh, K. Willcox, O. Ghatas, and B. Waanders. Goal-oriented, model-constrained opti- mization for reduction of large scale systems.Journal of Computational Physics, 224(2):880–896, 2007.
[19] R. Abgrall, D. Amsallem, and R. Crisovan. Robust model reduction of hyperbolic problems by l1-norm minimization and dictionary approximation.HAL, 2016.
[20] D. A. Charbel Farhat. Cme 345: Model reduction - methods for nonlinear systems. Presentation, 2017. URLhttps://web.stanford.edu/group/frg/course_work/CME345/CA-CME345-Ch9.pdf.
Accessed Nov. 2017.
[21] M. Chevreuil and A. Nouy. Model order reduction based on proper generalized decomposition for the propagation of uncertainties in structural dynamics. International Journal for Numerical Methods in Engineering, 89(2):241–268, 2012.
[22] A. A. Antoine Dumon, Cyrille Allery. Proper generalized decomposition method for incompressible navier-stokes equations with a spectral discretization. Applied Mathematics and Computation, 219(15):8145–8162, 2013.
[23] A. Nouy. Proper generalized decomposition: une maniere de vaincre la malediction de la dimen- sionalite dans les methodes spectrales stochastiques. Presentation, March 2010. In french.
[24] D. Amsallem. Parameterized partial differential equations and the proper orthogonal decompo- sition. Slides online, 04 2014. URL http://matperso.mines-paristech.fr/Donnees/data12/
1282-POD_doc_lecture.pdf. Accessed Nov. 2017.
[25] S. Volkwein. Proper orthogonal decomposition : Theory and reduced-order modeling, 2013.
[26] L. Sirovich. Turbulence and the dyanmics of coherent structures part i, coherent structures.Quar- terly of applied mathematics, 45(3):561–571, 1987.
[27] Y. Liang. Proper orthogonal decomposition and its applications part i theory. Journal of Sound and Vibration, 252(3):527–544, 2002.
[28] T. Bui-Thanh, M. Damodaran, and K. Willcox. Proper orthogonal decomposition extensions for parametric applications in compressible aerodynamics. In21st AIAA Applied Aerodynamics Con- ference, 2003.
[29] C. W. Rowley, T. Colonius, and R. M. Murray. Model reduction for compressible flows using pod and galerkin projection. Pysica D, 189(1-2):115–129, 2004.
[30] K. Carlberg and C. Farhat. A low-cost, goal-oriented ’compact proper orthogonal decomposi- tion’ basis for model reduction of static systems. International Journal for Numerical Methods in Engineering, 86(3):381–402, 2010.
[31] L. Willcox, O. Ghattas, B. Waanders, and B. Bader. An optimization framework for goal-oriented, model-based reduction of large-scale systems. InProceedings of the 44th IEEE Conference on Decision and Control. IEEE, 2005.
[32] S. Ullmann, M. Rotkvic, and J. Lang. Pod-galerkin reduced-order modeling with adaptive finite- element snapshots. Journal of Computational Physics, 325:244–258, 2016.
[33] E. Liberge and A. Hamdouni. Reduced order modelling method via proper orthogonal decom- position for flow around an oscillating cylinder. Journal of fluids and structures, 26(2):292–311, 2010.
[34] K. Carlberg and C. Farhat. A compact proper orthogonal decomposition basis for optimization- oriented reduced-order models. In12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2008.
[35] L. P. Muruhan Rathinam. A new look at proper orthogonal decomposition. SIAM Journal on Numerical Analysis, 41(5):1893–1925, 2003.
[36] D. A. Charbel Farhat. Cme 345: Model reduction, proper orthogonal decomposition (pod). Pre- sentation, 2017. Spring course.
[37] M. O. Bernard Haasdonk, Markus Dihlmann. A training set and multiple bases generation ap- proach for parameterized model reduction based on adaptive grids in parameter space. Mathe- matical and computer modelling of dynamical systems, 17(4):423–442, 2011.
[38] Y. Choi, D. Amsallem, and C. Farhat. Gradient-based constrained optimization using a database of linear reduced-order models. 2015.
[39] M. Fahl and E. W. Sachs. Reduced order modelling approaches to pde-constrained optimiza- tion based on proper orthogonal decomposition. InLarge-Scale PDE-Constrained Optimization, volume 30 ofLNCSE, pages 268–280, 2003.
[40] S. V. Karl Kunisch. Proper orthogonal decomposition for optimality systems.ESAIM: Mathematical Modelling and Numerical Analysis, 42(1):1–23, 2008.
[41] M. A. Dihlmann and B. Haasdonk. Certified pde-constrained parameter optimization using re- duced basis surrogate models for evolution problems. Computational Optimization an Applica- tions, 60(3):753–787, 2014.
[42] M. Zahr and C. Farhat. Progressive construction of a parametric reduced-order model for pde- constrained optimization. International Journal for Numerical Methods in Engineering, 102(5):
1111–1135, 2015.
[43] T. Braconnier, M. Ferrier, J.-C. Jouhaud, M. Montagnac, and P. Sagaut. Towards an adaptive pod svd surrogate model for aeronautic design. Computers and Fluids, 40(1):195–209, 2011.
[44] M. Xiao, P. Breitkopf, R. F. Coelho, C. Knopf-Lenoir, M. Sidorkiewicz, and P. Villon. Model reduction by cpod and kriging.Structural and Multidisciplinary Optimization, 41(4):555–574, 2010.
[45] M. Brand. Fast low rank modifications of the thin singular value decomposition. Linear Algebra and its Applications, 415(1):20–30, 2006.
[46] A. Paul-Dubois-Taine and D. Amsallem. An adaptive and efficient greedy procedure for the opti- mal training of parametric reduced-order models. International Journal for Numerical Methods in Engineering, 102(5):1–32, 2015.
[47] F. Behzad. Proper Orthogonal Decomposition Based Reduced Order Modeling for Fluid Flow.
PhD thesis, Clarkson University, 2014.
[48] A. I. Michel Bergmann, Charles-Henri Bruneau. Enabler for robust pod models. Journal of Com- putational Physics, 228(2):516–538, 2009.
[49] T. H. Michel Mallet. A new finite element fomulation for computational fluid dynamics : Ii the gen- eralized streamline operator for multidimensional advective diffusive systems. Computer methods in applied Mechanics and engineering, 58(3):305–328, 1986.
[50] L. M. Thomas Hughes, Gonzalo Feijoo. The variational multiscale method, a paradigm for com- putational mechanics.Computer Methods in Applied Mechanics and Engineering, 166(1-2):3–24, 1998.
[51] M. Bergmann, A. Ferrero, A. lollo, A. Scardigli, and H. Telib. An approach to perform shape opti- misation by means of hybrid rom-cfd simulations. InRecent developments in numerical methods for model reduction., pages 747–752, 2016.
[52] M. Bergmann, C.-H. Bruneau, and A. Iollo. Improvement of reduced order modeling based on proper orthogonal decomposition. resreport 6561, INRIA, June 2008.
[53] F. N. Victorita Dolean, Pierre Jolivet. An introduction to Domain Decomposition Methods : al- gorithms, theory and parallel implementation, chapter Iterative Schwarz methods : RAS, ASM, page 15. HAL, SIAM, 2016.
[54] H. Telib. Optimad eighteen month review meeting. Technical report, Aerogust, 2016.
[55] A. I. M. Bergmann, A. Ferrero. A hybrid pod-cfd approach for gust computation. In7th International Conference on Computational Methods, 2016.
[56] C. C. A. Scardigli, H. Telib. Efficient sampling strategies in hybrid full/reduced - order cfd models.
In2017 International Conference on Adaptive Modeling and Simulation, 2017.
[57] A. Radermacher and S. Reese. Model reduction in elastoplasticity : Proper orthogonal decompo- sition combines with adaptie sub-structuring. Computational Mechanics, 54(3):677–687, 2014.
[58] A. M. H. H. Annika Radermacher, Stefanie Reese. Selective proper orthogonal decomposition model reduction for forming simulations. PAMM, 13(1):115–116, 2013.
[59] R. C. Mervyn Bampton. Coupling of substructures for dynamic analyses. AIAA Journal, 6(7):
1313–1319, 1968.
[60] IMTEK. Oberwolfach model reduction benchmark collection. Online Page. URLhttps://portal.
uni-freiburg.de/imteksimulation/downloads/benchmark. Accessed Nov. 2017.
[61] J. K. Christian Moosmann, Evgenii Rudnyi. Model order reduction for linear convective thermal flow. In L. TIMA, editor,10th international workshop on THERmal INvestigations of ICs and Sys- tems, April 2004.
[62] A. G. Christian Moosmann. Convective thermal flow problems. URL https://portal.
uni-freiburg.de/imteksimulation/downloads/benchmark/Convection%20%2838867%29. Ac- cessed Nov. 2017.
[63] J. Mahaffy. Numerical analysis and computing lecture notes 07. Slides, 2010.
[64] J. Mahaffy. Numerical analysis and computing lecture notes 08. Slides, 2010.
[65] D. Billger. The butterfly gyro. Online page, 2017. URL https://portal.uni-freiburg.de/
imteksimulation/downloads/benchmark/The%20Butterfly%20Gyro%20%2835889%29. Accessed Nov. 2017.
[66] L. Feng. Gyroscope. Online, May 2013. URL https://morwiki.mpi-magdeburg.mpg.de/
morwiki/index.php/Gyroscope. Accessed Nov. 2017.
[67] C. Moosmann.ParaMOR, Model Order Reduction for parameterized MEMS applications. phdthe- sis, Universitat Freiburg, 2007.
[68] SimuTech. Detecting units with an apdl commands object in ansys mechanical (workbench).
Online, 2017. URL https://www.simutechgroup.com/tips-and-tricks/fea-articles/
186-fea-tips-tricks-ansys-detecting-units. Accessed Nov. 2017.
[69] A. Dazio. Fundamentals of structural dynamics. PDF, April 2013. URL http://www.sasparm.
ps/en/Uploads/file/SD_An_Najah_2013_DS-alesandro.pdf. Taken from the UME School, Ac- cessed Nov. 2017.
[70] S. Nielsen. Structural dynamics lecture 5. PDF, 2009. URLhttp://www.wind.civil.aau.dk/
lecture/7sem/notes/Lecture5.pdf. from AALBORG Universitet, Accessed Nov. 2017.
[71] S. Nielsen. Structural dynamics lecture 4. PDF, 2009. URLhttp://www.wind.civil.aau.dk/
lecture/7sem/notes/Lecture4.pdf. Accessed Nov. 2017.
[72] C. M. e. a. U. Baur, P. Benner. Parameter preserving model order reduction for mems applications.
InMathematical and Computer Modelling of Dynamical Systems: Methods, Tools and Applications in Engineering and Related Sciences, volume 17, pages 297–317, 2011.
[73] U. Baur. Anemometer. Online Page, 2014. URL https://morwiki.mpi-magdeburg.mpg.de/
morwiki/index.php/Anemometer. Taken from the MORWiki, Accessed Nov. 2017.
[74] J. G. K. J. Lienemann, A. Yousefi. Nonlinear heat transfer modelling. In Dimension Re- duction of Large-Scale Systems, pages 327–331. Springer, Jan. 2005. URL https:
//portal.uni-freiburg.de/imteksimulation/downloads/benchmark/Nonlinear%20heat%
20transfer%20%2838883%29/files/fileinnercontentproxy.2010-02-05.0369225308. Ac- cessed Nov. 2017.
[75] J. G. K. Jan. Lienemann, A. Yousefi. Nonlinear heat transfer modelling. Online Page, 2017.
URLhttps://portal.uni-freiburg.de/imteksimulation/downloads/benchmark/Nonlinear%
20heat%20transfer%20%2838883%29. Accessed Nov. 2017.
[76] F. Palacios, M. R. Colonno, A. C. Aranake, A. Campos, S. R. Copeland, T. Economon, A. Lonkar, T. Lukaczyk, T. Taylor, and J. Alonso. Stanford university unstructured : An open-source integrated computational environment for multi-physics simulation and design. In51st AIAA Aerospace Sci- ences Meeting, 2013.
[77] Su2 developers. Online. URLhttp://su2.stanford.edu/develop.html. Accessed Nov. 2017.
[78] V. Schmitt and F. Charpin. Pressure distributions on the onera-m6-wing at transonic mach num- bers. techreport 138, AGARD, 1979. AGARD AR 138.
[79] J. W. Slater. Onera m6 wing, 2015. URLhttps://www.grc.nasa.gov/www/wind/valid/m6wing/
m6wing.html. Accessed Nov. 2017.
[80] P. Cook, M. McDonald, and M. Firmin. Aerofoil rae 2822 - pressure distributions, and boundary layer and wake measurements. techreport 138, AGARD, 1979.
[81] J. W. Slater. Rae2822 transonic airfoil. Online Page, 2008. URLhttps://www.grc.nasa.gov/
www/wind/valid/raetaf/raetaf.html. Accessed Nov. 2017.
[82] F. Palacios, T. D. Economon, A. C. Aranake, S. R. Copeland, A. K. Lonkar, T. Lukaczyk, D. E.
Manosalvas, K. R. Naik, A. S. Padron, B. Tracey, A. Variyar, and J. J. Alonso. Stanford university unstructured (su 2 ): Open-source analysis and design technology for turbulent flows. In52nd Aerospace Sciences Meeting. AIAA, 2014.
[83] T. D. Economon. An unsteady continuous adjoint approach for aerodynamic design on dynamic meshes. AIAA Journal, 53(9):2437–2453, 2015.
[84] A. Variyar, T. D. Economon, and J. J. Alonso. Multifidelity conceptual design and optimization of strut-braced wing aircraft using physics-based methods. In 54th AIAA Aerospace Sciences Meeting. AIAA, AIAA, 2016.
[85] A. A. Kanoria and D. . D. J. Chandar. American institute of aeronautics and astronautics 1 inte- grating the stanford university unstructured (su 2 ) code with overset grids. In22nd AIAA Compu- tational Fluid Dynamics Conference, 2015.
[86] Su2 doxygen documentation. URLhttp://su2.stanford.edu/doxygen/. Accessed Nov. 2017.
[87] Openfoam description, 2017. URLhttp://www.openfoam.com/products/openfoam-plus.php.
Accessed Nov. 2017.
[88] D. A. Lysenko, I. S. Ertesvag, and K. E. Rian. Modeling of turbulent separated flows using open- foam. Computers & Fluids, pages 408,422, 2012.
[89] U. Millewa, P. Senathilaka, W. Dayarathna, S. Samarasingha, and S. Rangajeeva. Validation of openfoam as computational fluid dynamics (cfd) software. In Proceedings of 8th International Research Conference, 2015.
[90] U. A. Rahman and F. Mustapha. Validations of openfoam steady state compressible solver rhosim- plefoam. InICMAIE 2015, 2015.
[91] S. Rumeau and M. Henneton. Validation du code openfoam sur des ´ecoulements de type rotos d’helicopteres. In22ieme Congres Franc¸ais de Mecanique, 2015.
[92] H. Nilsson, M. Page, M. Beaudoin, B. Gschaider, and H. Jasak. The openfoam turbomachinery working group and conclusions from the turbomachinery session of the third openfoam workshop.
In24th Symposium on Hydraulic Machinery and Systems, 2008.
[93] Sig numerical optimization, 2014. URL http://www.openfoamwiki.net/index.php/Sig_
Numerical_Optimization. Accessed Nov. 2017.
[94] U. Nilsson. Description of adjointShapeOptimizationFoam and how to implement new objective functions, 2014.
[95] I. Spisso. Parametric and optimization study: Openfoam and dakota. InHPC enabling of Open- FOAM for CFD applications, 2012.
[96] Running applications in parallel. OpenFOAM Foundation, 2017.
[97] C. Fernandes, L. Ferras, and J. M. Nobrega. Solver development in openfoam. InOpenFOAM Course 2nd Edition, 2015.
[98] ODESolver Class Reference. The OpenFOAM Foundation. URLhttps://cpp.openfoam.org/
v5/ODESolver_8C_source.html#l00142. Accessed Nov. 2017.
[99] F. Hecht. New development in freefem++. Journal of Numerical Mathematics, 20:251,265, 2012.
ISSN 1570-2820.
[100] F. Hecht. Freefem++ Manual, 3rd edition.
[101] F. D. Vuyst. Numerical modeling of transport problems using freefem++ software with examples in biology, cfd, traffic flow and energy transfer. 09 2013.
[102] A. P. Pazos. Aspectos matematicos y numericos de algunas leyes de conservacion escalares:
aplicacion al control. mathesis, Universidad del Pais Vasco.
[103] G. Sadaka. Solving shallow water flows in 2d with freefem++ on structured mesh. 2012.
[104] R. G. Hill. Benchmark testing the alpha models of turbulence. mathesis, Clemson University, 2010.
[105] S. Auliac.Developpement doutils doptimisation pour FreeFem++. PhD thesis, Universite Pierre et Marie Curie Paris IV, 2014.
[106] G. Allaire, B. Boutin, C. Dousset, and O. Pantz. A freefem++ toolbox, 2008.
[107] C. Dapogny, P. Frey, F. Omnes, and Y. Privat. Geometrical shape optimization in fluid mechanics using freefem++. 2017.
[108] P. Jolivet. Recent advances in hpc with freefem++. InFourth workshop on FreeFem++, 2012.
[109] G. Pitton. Pod for navier-stokes, 2014. URLhttp://www.um.es/freefem/ff++/pmwiki.php?n=
Main.POD. Accessed Nov. 2017.
[110] L. DEDE. Adaptive and Reduced Basis Methods for Optimal Control Problems in Environmental Applications. PhD thesis, Politecnico Milano, 2008.
[111] G. Pitton. Numerical investigation of buoyant flows in tigh lattice bundles. mathesis, Politecnico di Milano, 2012.
[112] E. Erturk, T. Corke, and C. Gokc¸ol. Numerical solutions of 2-d steady incompressible driven cavity flow at high reynolds numbers. International Journal for Numerical Methods in Fluids, 48 (7):747–774, 2005.
[113] U. Ghia, K. Ghia, and C. Shin. High-re solutions for incompressible flow using the navier-stokes equations and a multigrid method. Journal of Computational Physics, 48(3):387–411, 1982.
[114] R. LeVeque. Numerical Methods for Conservation Laws, chapter Conservative Methods for Nonlinear Problems, pages 122–133. Birk ¨auser, second edition, 1999. URL https://pdfs.
semanticscholar.org/1470/c6f43c769572c4cfc94ffc9c5710484ff1e5.pdf. Accessed Nov.
2017.
[115] J.-F. R. Christophe Geuzaine. Gmsh 3.0, Sept. . URLhttp://gmsh.info/doc/texinfo/gmsh.
html. Accessed Nov. 2017.