Campuses:

Team 4: High dimensional, nonlinear, non-convex optimization problems in the area of aircraft and vehicle design

Wednesday, August 8, 2007 - 11:00am - 11:20am
EE/CS 3-180
John Hoffman (Lockheed Martin)
Presently, when a physics motivated vehicle designer explores
vehicle
designs for a new concept, he is often faced with an enormous
range of
choices and constraints. For an example, an aircraft designer
has
Aircraft shape, fuel type, and engine as his main free
variables. While
his main constraints are dictated by the laws of physics
(weight, size,
power, lift, and stall). Additionally, he has his objective
which is
typically some combination/subset of acceleration,
maneuverability,
range, endurance, payload capacity (size, weight and power),
max and min
speeds, manufacturing cost, maintainability, reliability,
development
cost, takeoff length, landing length, noise footprint and other
items.


I am interested in examining the following problem: Given a
set of
performance objectives, how does one determine the space of
designs
available to the designer and find the optimal designs? How
does the
designer best visualize this space of options? Because he
doesn't want
just the answer, he wants to understand many aspects of the
answer.
While I'm interested in the general vehicle design problem, we
will
focus on aircraft design using a baseline tool that is to be
determined
as a concrete example with which we can test our ideas.