Applications and enhancements of aircraft design optimization techniques
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @PhdThesis{Powell:thesis,
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author = "Stephen R. Powell",
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title = "Applications and enhancements of aircraft design
optimization techniques",
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year = "2012",
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school = "Faculty of Engineering and the Environment, University
of Southampton",
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address = "UK",
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month = jun,
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keywords = "genetic algorithms, genetic programming, TL Motor
vehicles. Aeronautics. Astronautics",
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URL = "http://eprints.soton.ac.uk/348869/",
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URL = "http://eprints.soton.ac.uk/348869/1/finalThesis2.pdf",
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bibsource = "OAI-PMH server at eprints.soton.ac.uk",
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oai = "oai:eprints.soton.ac.uk:348869",
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size = "167 pages",
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abstract = "The aircraft industry has been at the forefront in
developing design optimisation strategies ever since
the advent of high performance computing. Thanks to the
large computational resources now available, many new
as well as more mature optimisation methods have become
well established. However, the same cannot be said for
other stages along the optimisation process - chiefly,
and this is where the present thesis seeks to make its
first main contribution, at the geometry
parametrisation stage. The first major part of the
thesis is dedicated to the goal of reducing the size of
the search space by reducing the dimensionality of
existing parametrisation schemes, thus improving the
effectiveness of search strategies based upon them.
Specifically, a refinement to the Kulfan
parametrisation method is presented, based on using
Genetic Programming and a local search within a
Baldwinian learning strategy to evolve a set of
analytical expressions to replace the standard class
function at the basis of the Kulfan method. The method
is shown to significantly reduce the number of
parameters and improves optimisation performance - this
is demonstrated using a simple aerodynamic design case
study. The second part describes an industrial level
case study, combining sophisticated, high fidelity, as
well as fast, low fidelity numerical analysis with a
complex physical experiment. The objective is the
analysis of a topical design question relating to
reducing the environmental impact of aviation: what is
the optimum layout of an over-the-wing turbofan engine
installation designed to enable the airframe to shield
near-airport communities on the ground from fan noise.
An experiment in an anechoic chamber reveals that a
simple half-barrier noise model can be used as a first
order approximation to the change of inlet broadband
noise shielding by the airframe with engine position,
which can be used within design activities. Moreover,
the experimental results are condensed into an acoustic
shielding performance metric to be used in a
Multidisciplinary Design Optimisation study, together
with drag and engine performance values acquired
through CFD. By using surrogate models of these three
performance metrics we are able to find a set of
non-dominated engine positions comprising a Pareto
Front of these objectives. This may give designers of
future aircraft an insight into an appropriate engine
position above a wing, as well as a template for
blending multiple levels of computational analysis with
physical experiments into a multidisciplinary design
optimisation framework.",
- }
Genetic Programming entries for
Stephen R Powell
Citations