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MDOlab Newsletter—Fall
2014
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Dear
Friend,
Welcome to the
MDOlab newsletter, an update on
research and open source software
that we send a few times a year. You
are receiving this because I think
you are interested in numerical
optimization, MDO, engineering
design, or aircraft design. If this
is not the case, feel free to
unsubscribe. If you know
someone who might like to subscribe,
please forward them this
newsletter. Best
regards,
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Wing aerodynamic
shape optimization
benchmark
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The AIAA Aerodynamic Design Optimization
Discussion Group developed a
series of benchmark cases. In this
paper, we solve the RANS-based wing
optimization problem, try to find
multiple local minima, and solve a
number of related wing design
optimization problems. The initial and
optimized geometries and meshes are
provided
here.
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Aerodynamic design
optimization of a blended-wing body
aircraft
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Satellite
multidisciplinary design optimization
benchmark
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In
collaboration with NASA and the
Michigan
Exploration Lab, we developed a new
large-scale benchmark MDO problem, and
solved a problem with 25,000 design
variables and 2.2 million state
variables by optimizing the data
downloaded from a CubeSat subject to
operational and physical constraints.
This problem is now a plugin in the
OpenMDAO
open source project.
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Parallel
structural finite-element analysis for
design optimization
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The Toolkit for
Analysis of Composite Structures (TACS)
was developed by Prof.
Graeme Kennedy, and is the
structural solver used in the MDOlab.
Its main advantages are the parallel
scalability and efficient gradient
computation via a parallel adjoint
method.
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pyOpt: A common
Python interface for nonlinear
optimizers
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GeoMACH: A geometry
engine for aircraft
configurations
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This is a geometry
engine that enables the rapid
generation of parametric aircraft outer
mold lines and internal structures,
which facilitates high-fidelity MDO.
GeoMACH computes all the geometry
derivatives, so it is well suited for
use with gradient-based
optimizers.
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OpenMDAO: A
framework for multidisciplinary
optimization
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This framework is
being developed by NASA to facilitate the
implementation of MDO problems. It also
provides a number of built-in algorithms
for the solution of such problems.
The latest version provides
an efficient method for computing
coupled derivatives for gradient-based
optimization.
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Other recent publications
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A survey of MDO
architectures
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There are a number
of architectures available to solve
multidisciplinary design optimization
problems, but so far, they had not been
explained in a consistent notation.
This survey paper describes all known
MDO architectures and provides flow
diagrams for each architecture.
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High-fidelity
aerostructural optimization of aircraft
configurations
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We have enabled
design optimization of lifting surfaces
with respect to hundreds of aerodynamic
and structural design variables. The
numerical methods used to achieve this
are described in the first paper, and
the second one applies these methods to
the multipoint optimization of a
transport aircraft.
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A review of methods
for computing derivatives
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This
paper reviews all available methods for
computing derivatives numerically
(finite-differences, complex-step, adjoint method,
and algorithmic differentiation), and
unifies these methods using a single
equation.
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