Robust Aircraft Conceptual Design using Automatic Differentiation in Matlab
Mattia Padulo, Shaun A. Forth & Marin D. Guenov
Advances in Automatic
Differentiation, eds. Christian H. Bishof, H. Martin Bücker, Paul Hovland, Uwe
Naumann and Jean Utke, Lecture Notes in Computational Science and Engineering,
Volume 64, p271-280, Springer, 2008. ISBN 978-3-540-68935-5
Presented at
AD2008: The 5th International Conference on Automatic Differentiation,
August 11th-15rd B-IT Center, Bonn, Germany.
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 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 ADenabled third-order method of moments and
Monte-Carlo analysis, confirms the attractiveness of the Sigma-Point technique
for uncertainty propagation.
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Authors PDF: mp_ad08.pdf
Conference slides PDF: mp_ad08slides.pdf
DOI = 10.1007/978-3-540-68942-3_24