Publications on Automatic Differentiation in Aerodynamics

Ralf Giering, Thomas Kaminski, Bernhard Eisfeld, Nicolas Gauger, Jochen Raddatz, and Lars Reimer. Automatic Differentiation of FLOWer and MUGRIDO. In Kroll, N and Schwamborn, D and Becker, K and Rieger, H and Thiele, F, editor, MEGADESIGN AND MEGAOPT - GERMAN INITIATIVES FOR AERODYNAMIC SIMULATION AND OPTIMIZATION IN AIRCRAFT DESIGN, volume 107 of Notes on Numerical Fluid Mechanics and Multidisciplinary Design, pages 221-235, 2009. [ .pdf ]

M. Voßbeck, R. Giering, and T. Kaminski. Development and First Applications of TAC++. In C. Bischof, H. M. Bücker, P. D. Hovland, U. Naumann, and J. Utke, editors, Advances in Automatic Differentiation, Lecture Notes in Computational Science and Engineering, pages 187-197, Berlin, 2008. Springer. [ .pdf ]

T. Kaminski, R. Giering, and C. Othmer. Topological design based on highly efficient adjoints generated by automatic differentiation. In G. Winter, J. Périaux, W. Haase, B Galván, B. González, D. Greiner, and I Fránquiz, editors, ERCOFTAC 2006, Design an Optimisation: Methods and Applications, pages 223-226, Spain, 2006. University of Las Palmas de Gran Canaria. [ .pdf ]

TAF application to 3d CFD simulation in the automotive design process.

C. Othmer, T. Kaminski, and R. Giering. Computation of topological sensitivities in fluid dynamics: Cost function versatility. In P. Wesseling, E. O nate, and J. Périaux, editors, ECCOMAS CFD 2006. TU Delft, 2006. [ .pdf ]

TAF application to 3d CFD simulation in the automotive design process. The paper demonstrates the dependence of the optimal topology on the formulation of the cost function.

J. P. Thomas, K. C. Hall, and E. H. Dowell. A discrete adjoint approach for modeling unsteady aerodynamic design sensitivities. AIAA Journal., 43(9):1931-1936, 2005.

A TAF generated adjoint is used to study steady and unsteady aerodynamic design sensitivities for compressible viscous flows around airfoil configurations.

R. Giering, T. Kaminski, and T. Slawig. Generating Efficient Derivative Code with TAF: Adjoint and Tangent Linear Euler Flow Around an Airfoil. Future Generation Computer Systems, 21(8):1345-1355, 2005. [ DOI | http | .pdf ]

We give a TAF overview and describe in some detail, how iterative solvers can be handled efficiently. As an application we demonstrate the generation of adjoint, tangent linear, and Hessian code for a CFD solver in an airfoil configuration. We also list a few other large-scale applications and their performance.

J.D. Müller and P. Cusdin. On The Performance Of Discrete Adjoint Cfd Codes Using Automatic Differentiation. International Journal For Numerical Methods In Fluids, 47(8-9):939-945, 2005. [ http ]

M. Voßbeck, R. Giering, and T. Kaminski. Towards a tool for forward and reverse mode source to source transformation in C++. In M. Bücker, G. Corliss, P. Hovland, U. Naumann, and B. Norris, editors, NOT accepted for AD 2004, Lecture Notes in Computational Science and Engineering, Berlin, 2004. Springer. [ .pdf ]

A feasibility study for our C++ AD-tool. A short 129 C-line Roe solver is differentiated in reverse mode. The automatically generated adjoint code runs about three times slower than the solver itself.

M. Hinze and T. Slawig. Adjoint gradients compared to gradients from algorithmic differentiation in instataneous control of the Navier-Stokes equations. Optimization Methods & Software, 18(3):299-315, 2003.

J. P. Thomas, K. C. Hall, and E. H. Dowell. A discrete adjoint approach for modeling unsteady aerodynamic design sensitivities. 41st AIAA Aerospace Sciences Meeting, Reno, Nevada, 2003.

A TAF generated adjoint is used to study steady and unsteady aerodynamic design sensitivities for compressible viscous flows around airfoil configurations.

P. Cusdin and J.-D. Müller. Improving the performance of code generated by automatic differentiation. Technical Report QUB-SAE-03-04, QUB School of Aeronautical Engineering, 2003.

P. Cusdin and J.-D. Müller. Automatic differentiation and sensitivity analysis methods for cfd. Technical Report QUB-SAE-03-01, QUB School of Aeronautical Engineering, 2003.

P. Cusdin and J.-D. Müller. Deriving linear and adjoint codes for cfd using automatic differentiation. Technical Report QUB-SAE-03-06, QUB School of Aeronautical Engineering, 2003.

P. Cusdin. Timelog: Timing fortran code. Technical Memorandum QUB-SAE-03-03, QUB School of Aeronautical Engineering, 2003. [ http ]

M. Hinze and T. Slawig. Adjoint gradients compared to gradients from algorithmic differentiation in instataneous control of the Navier-Stokes equations. Preprint 735-2002, Institute of Mathematics, Technische Universität Berlin, 2002. [ .html ]

The authors first used TAMC and then TAF. The model uses an iterative solver, for which, after inserting 5 TAF flow directives, TAF can generate a very efficient adjoint. The TAF generated adjoint is slightly faster than its hand coded counterpart.

K. C. Hall and J. P. Thomas. Sensitivity analysis of coupled aerodynamic/structural dynamic behavior of blade rows. Extended Abstract for the 7th National Turbine Engine High Cycle Fatigue (HCF) Conference, Palm Beach Gardens, Florida, 14-17 May 2002, 2002.

A sensitivity analysis using the TAMC generated adjoints of inviscid flow solvers.

S. S. Collis, K. Ghayour, M. Heinkenschloss, M. Ulbrich, and S. Ulbrich. Optimal control of unsteady compressible viscous flows. Int. J. Numer. Meth. Fluids, 40(11):1401-1429, 2002. [ .html ]

M. Ulbrich. Nonsmooth Newton-like Methods for Variational Inequalities and Constrained Optimization Problems in Function Spaces, Habilitationsschrift. Fakultät für Mathematik, Technische Universität München, Germany, 2002. [ http | .pdf ]

S. Ulbrich. Optimal Control of Nonlinear Hyperbolic Conservation Laws with Source Terms , Habilitationsschrift. Fakultät für Mathematik, Technische Universität München, Germany, 2002. [ .ps.gz ]

M. Tadjouddine, S. A. Forth, and J. D. Pryce. AD tools and prospects for optimal AD in CFD flux Jacobian calculations. In George Corliss, Christèle Faure, Andreas Griewank, Laurent Hascoët, and Uwe Naumann, editors, Automatic Differentiation of Algorithms: From Simulation to Optimization, Computer and Information Science, chapter 30, pages 255-261. Springer, New York, NY, 2002. [ .html | .ps.gz | .pdf ]

S. S. Collis, K. Ghayour, M. Heinkenschloss, M. Ulbrich, and S. Ulbrich. Towards Adjoint-Based Methods for Aeroacoustic Control. IAAA Paper 2001-0821, AIAA, Reston Va, USA, 2001. [ .pdf ]

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