Robust Aircraft Conceptual Design using Automatic Differentiation in Matlab
Mattia Padulo, Shaun A. Forth & Marin D. Guenov
Submitted to
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