Automatic Differentiation of an Entire Design Chain for Aerodynamic Shape Optimization

Gauger, Nicolas R.; Walther, Andrea; Moldenhauer, Carsten; Widhalm, Markus

doi:10.1007/978-3-540-74460-3_56

Cited by 34 publications

(23 citation statements)

References 7 publications

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“…This extremely powerful approach is extensively used in sensitivity analysis of complex models, such as those used in meteorology (Marotzke and Giering, 1999) or in the field of aerodynamics (Gauger et al, 2008). Sambridge et al (2007) was the very first application of AD in geophysics.…”

Section: Introductionmentioning

confidence: 99%

A Study of the Sensitivity of Response Spectral Amplitudes on Seismological Parameters Using Algorithmic Differentiation

Molkenthin

Scherbaum

Griewank

et al. 2014

Bulletin of the Seismological Society of America

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Response spectra are of fundamental importance in earthquake engineering and represent a standard measure in seismic design for the assessment of structural performance. However, unlike Fourier spectral amplitudes, the relationship of response spectral amplitudes to seismological source, path, and site characteristics is not immediately obvious and might even be considered counterintuitive for high oscillator frequencies. The understanding of this relationship is nevertheless important for seismic-hazard analysis. The purpose of the present study is to comprehensively characterize the variation of response spectral amplitudes due to perturbations of the causative seismological parameters. This is done by calculating the absolute parameter sensitivities (sensitivity coefficients) defined as the partial derivatives of the model output with respect to its input parameters. To derive sensitivities, we apply algorithmic differentiation (AD). This powerful approach is extensively used for sensitivity analysis of complex models in meteorology or aerodynamics. To the best of our knowledge, AD has not been explored yet in the seismic-hazard context. Within the present study, AD was successfully implemented for a proven and extensively applied simulation program for response spectra (Stochastic Method SIMulation [SMSIM]) using the TAPENADE AD tool. We assess the effects and importance of input parameter perturbations on the shape of response spectra for different regional stochastic models in a quantitative way. Additionally, we perform sensitivity analysis regarding adjustment issues of groundmotion prediction equations.

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Section: Introductionmentioning

confidence: 99%

A Study of the Sensitivity of Response Spectral Amplitudes on Seismological Parameters Using Algorithmic Differentiation

Molkenthin

Scherbaum

Griewank

et al. 2014

Bulletin of the Seismological Society of America

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“…The gradient is then used for optimizing the wing shape, which involves iterated simulation and gradient evaluation. See [5,7,12] for more details and background information.…”

Section: Application Examplementioning

confidence: 99%

Memory Allocation Tracing with VampirTrace

Jurenz

Brendel

Knüpfer

et al. 2007

Computational Science – ICCS 2007

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Abstract. The paper presents methods for instrumentation and measurement of applications' memory allocation behavior over time. It provides some background about possible performance problems related to memory allocation as well as to memory allocator libraries. Then, different methods for data acquisition and representation are discussed. Finally, memory allocation tracing integrated in VampirTrace is demonstrated with a real-world HPC example application from aerodynamical simulation and optimization.

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“…Because it is often not immediately clear how to compute these "mesh sensitivities", that is the variation of the PDE with respect to a change in the geometry, there is often a strong desire for a very smooth parametrization of the domain with as few design parameters as possible. Although there have been successful attempts to incorporate problem structure exploitations in order to efficiently compute these partial derivatives for very large problems, such as differentiating the entire design chain at once or by considering the adjoint process of the mesh deformation [5,17], very often one is still forced into finite differencing, which means the PDE residual at steady state has to be evaluated on meshes that have been perturbed by a variation in each design parameter of the shape, a process that makes large scale optimization usually prohibitive. This negates some of the advantages of the adjoint approach, such as the independence of the number of design parameters.…”

Section: Introductionmentioning

confidence: 99%

Shape derivatives for the compressible Navier–Stokes equations in variational form

Sonntag

Schmidt

Gauger

2016

Journal of Computational and Applied Mathematics

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Shape optimization based on surface gradients and the Hadarmard-form is considered for a compressible viscous fluid. Special attention is given to the difference between the "function composition" approach involving local shape derivatives and an alternate methodology based on the weak form of the state equation. The resulting gradient expressions are found to be equal only if the existence of a strong form solution is assumed. Surface shape derivatives based on both formulations are implemented within a Discontinuous Galerkin flow solver of variable order. The gradient expression stemming from the variational approach is found to give superior accuracy when compared to finite differences.2010 Mathematics Subject Classification. 49Q10, 49Q12, 65K10, 76Nxx, 76M30.

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Automatic Differentiation of an Entire Design Chain for Aerodynamic Shape Optimization

Cited by 34 publications

References 7 publications

A Study of the Sensitivity of Response Spectral Amplitudes on Seismological Parameters Using Algorithmic Differentiation

A Study of the Sensitivity of Response Spectral Amplitudes on Seismological Parameters Using Algorithmic Differentiation

Memory Allocation Tracing with VampirTrace

Shape derivatives for the compressible Navier–Stokes equations in variational form

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