14 Best Practices for Surrogate Based Uncertainty Quantification in. . .
453
16. D. Maruyama, S. Goertz, D. Liu, General introduction to surrogate model-based approaches
to UQ, in Uncertainty Management for Robust Industrial Design in Aeronautics (Springer,
Berlin, 2018), pp. 203–211
17. V. Schulz, C. Schillings, Optimal aerodynamic design under uncertainty, in Notes on Numerical
Fluid Mechanics and Multidisciplinary Design (Springer, Berlin, 2013), pp. 297–338
18. D.R. Jones, M. Schonlau, W.J. Welch, Efficient global optimization of expensive black-box
functions. J. Global Optim. 13(4), 455–492 (1998)
19. A.I.J. Forrester, A.J. Keane, Recent advances in surrogate-based optimization. Prog. Aerosp.
Sci. 45(1–3), 50–79 (2009)
20. D. Maruyama, D. Liu, S. Goertz, Comparing surrogates for estimating aerodynamic uncertainties of airfoils, in Uncertainty Management for Robust Industrial Design in Aeronautics
(Springer, Berlin, 2018), pp. 213–228
21. K. Shimoyama, S. Kawai, J.J. Alonso, Dynamic adaptive sampling based on kriging surrogate
models for efficient uncertainty quantification, in 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference (American Institute of Aeronautics and
Astronautics, Reston, 2013)
22. Z.-H. Han, M. Abu-Zurayk, S. Goertz, C. Ilic, Surrogate-based aerodynamic shape optimization of a wing-body transport aircraft configuration, in Notes on Numerical Fluid Mechanics
and Multidisciplinary Design (Springer, Berlin, 2018), pp. 257–282
23. A. Merle, A. Stueck, A. Rempke, An adjoint-based aerodynamic shape optimization strategy
for trimmed aircraft with active engines, in 35th AIAA Applied Aerodynamics Conference
(American Institute of Aeronautics and Astronautics, Reston, 2017)
24. T. Gerhold, Overview of the hybrid RANS code TAU, in MEGAFLOW—Numerical Flow
Simulation for Aircraft Design (Springer, Berlin, 2015), pp. 81–92
25. T. Gerhold, J. Neumann, The parallel mesh deformation of the DLR TAU-code, in Notes on
Numerical Fluid Mechanics and Multidisciplinary Design (NNFM) (Springer, Berlin, 2006),
pp. 162–169
26. M. Meinel, G. Einarsson, The FlowSimulator framework for massively parallel CFD applications, in PARA 2010—State of the Art in Scientific and Parallel Computing—Extended Abstract
no. 44 (University of Iceland, Reykjavik, 2010)
27. R. M. Hicks, P.A. Henne, Wing design by numerical optimization. J. Aircr. 15(7), 407–412
(1978)
28. T.H. Rowan, Functional Stability Analysis of Numerical Algorithms. Ph.D. thesis, University
of Texas Austin, Austin, 1990. UMI Order No. GAX90-31702
29. J.A. Nelder, R. Mead, A simplex method for function minimization. Comput. J. 7(4), 308–313
(1965)
30. Z.-H. Han, S. Goertz, R. Zimmermann, Improving variable-fidelity surrogate modeling via
gradient-enhanced kriging and a generalized hybrid bridge function. Aerosp. Sci. Technol.
25(1), 177–189 (2013)
453
16. D. Maruyama, S. Goertz, D. Liu, General introduction to surrogate model-based approaches
to UQ, in Uncertainty Management for Robust Industrial Design in Aeronautics (Springer,
Berlin, 2018), pp. 203–211
17. V. Schulz, C. Schillings, Optimal aerodynamic design under uncertainty, in Notes on Numerical
Fluid Mechanics and Multidisciplinary Design (Springer, Berlin, 2013), pp. 297–338
18. D.R. Jones, M. Schonlau, W.J. Welch, Efficient global optimization of expensive black-box
functions. J. Global Optim. 13(4), 455–492 (1998)
19. A.I.J. Forrester, A.J. Keane, Recent advances in surrogate-based optimization. Prog. Aerosp.
Sci. 45(1–3), 50–79 (2009)
20. D. Maruyama, D. Liu, S. Goertz, Comparing surrogates for estimating aerodynamic uncertainties of airfoils, in Uncertainty Management for Robust Industrial Design in Aeronautics
(Springer, Berlin, 2018), pp. 213–228
21. K. Shimoyama, S. Kawai, J.J. Alonso, Dynamic adaptive sampling based on kriging surrogate
models for efficient uncertainty quantification, in 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference (American Institute of Aeronautics and
Astronautics, Reston, 2013)
22. Z.-H. Han, M. Abu-Zurayk, S. Goertz, C. Ilic, Surrogate-based aerodynamic shape optimization of a wing-body transport aircraft configuration, in Notes on Numerical Fluid Mechanics
and Multidisciplinary Design (Springer, Berlin, 2018), pp. 257–282
23. A. Merle, A. Stueck, A. Rempke, An adjoint-based aerodynamic shape optimization strategy
for trimmed aircraft with active engines, in 35th AIAA Applied Aerodynamics Conference
(American Institute of Aeronautics and Astronautics, Reston, 2017)
24. T. Gerhold, Overview of the hybrid RANS code TAU, in MEGAFLOW—Numerical Flow
Simulation for Aircraft Design (Springer, Berlin, 2015), pp. 81–92
25. T. Gerhold, J. Neumann, The parallel mesh deformation of the DLR TAU-code, in Notes on
Numerical Fluid Mechanics and Multidisciplinary Design (NNFM) (Springer, Berlin, 2006),
pp. 162–169
26. M. Meinel, G. Einarsson, The FlowSimulator framework for massively parallel CFD applications, in PARA 2010—State of the Art in Scientific and Parallel Computing—Extended Abstract
no. 44 (University of Iceland, Reykjavik, 2010)
27. R. M. Hicks, P.A. Henne, Wing design by numerical optimization. J. Aircr. 15(7), 407–412
(1978)
28. T.H. Rowan, Functional Stability Analysis of Numerical Algorithms. Ph.D. thesis, University
of Texas Austin, Austin, 1990. UMI Order No. GAX90-31702
29. J.A. Nelder, R. Mead, A simplex method for function minimization. Comput. J. 7(4), 308–313
(1965)
30. Z.-H. Han, S. Goertz, R. Zimmermann, Improving variable-fidelity surrogate modeling via
gradient-enhanced kriging and a generalized hybrid bridge function. Aerosp. Sci. Technol.
25(1), 177–189 (2013)