Publication: Robust Aircraft Conceptual Design Using Automatic Differentiation in Matlab
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Robust Aircraft Conceptual Design Using Automatic Differentiation in Matlab

- incollection -
 

Area
Computational Fluid Dynamics

Author(s)
Mattia Padulo , Shaun A. Forth , Marin D. Guenov

Published in
Advances in Automatic Differentiation

Editor(s)
Christian H. Bischof, H. Martin Bücker, Paul D. Hovland, Uwe Naumann, J. Utke

Year
2008

Publisher
Springer

Abstract
The need for robust optimisation in aircraft conceptual design, for which the design parameters are assumed stochastic, is introduced. We highlight two approaches, first-order method of moments and Sigma-Point reduced quadrature, to estimate the mean and variance of the design's outputs. The method of moments requires the design model's differentiation and here, since the model is implemented in Matlab, is performed using the automatic differentiation (ad) tool MAD. Gradient-based constrained optimisation of the stochastic model is shown to be more efficient using ad-obtained gradients than finite-differencing. A post-optimality analysis, performed using ad-enabled third-order method of moments and Monte-Carlo analysis, confirms the attractiveness of the Sigma-Point technique for uncertainty propagation.

Cross-References
Bischof2008AiA

AD Tools
TOMLAB /MAD

BibTeX
@INCOLLECTION{
         Padulo2008RAC,
       author = "Mattia Padulo and Shaun A. Forth and Marin D. Guenov",
       title = "Robust Aircraft Conceptual Design Using Automatic Differentiation in {M}atlab",
       doi = "10.1007/978-3-540-68942-3_24",
       pages = "271--280",
       abstract = "The need for robust optimisation in aircraft conceptual design, for which the
         design parameters are assumed stochastic, is introduced. We highlight two approaches, first-order
         method of moments and Sigma-Point reduced quadrature, to estimate the mean and variance of the
         design's outputs. The method of moments requires the design model's differentiation and
         here, since the model is implemented in Matlab, is performed using the automatic differentiation
         (AD) tool MAD. Gradient-based constrained optimisation of the stochastic model is shown to be more
         efficient using AD-obtained gradients than finite-differencing. A post-optimality analysis,
         performed using AD-enabled third-order method of moments and Monte-Carlo analysis, confirms the
         attractiveness of the Sigma-Point technique for uncertainty propagation.",
       crossref = "Bischof2008AiA",
       booktitle = "Advances in Automatic Differentiation",
       publisher = "Springer",
       editor = "Christian H. Bischof and H. Martin B{\"u}cker and Paul D. Hovland and Uwe
         Naumann and J. Utke",
       isbn = "978-3-540-68935-5",
       issn = "1439-7358",
       year = "2008",
       ad_area = "Computational Fluid Dynamics",
       ad_tools = "TOMLAB /MAD"
}


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