DSA Volume 1 Issue 4 December 2010 - Defence Science and ...

DEFENCE SCIENCE AUSTRALIA
Block diagram depictions for research
Applying the method involves representing an
LTV system in block diagram form depicting
all the effects in sequence with any feedback
paths that impact on system outcomes.
The elements of the block diagram are then
manipulated by a set of mathematical rules
to arrive at a second block diagram, this
one now being in adjoint system format.
If the LTV system is simple, the conversion
process can be readily undertaken manually
by the analyst in ‘pen and paper’ manner.
However, for very complex systems with
many feedback paths, application of the
adjoint rules can be extremely tedious,
time consuming and error prone.
Automation
Dr Bucco therefore saw the desirability
of automating the adjoint system
construction process. The approach he
devised was predicated on the use of two
commercially available software packages.
One of these is known as MATLAB,
a computing environment and
programming language widely
used by industry and academia in
engineering, science and economics.
The other is called Simulink, which
provides a means to graphically design,
simulate, implement and test timevarying
systems, such as communications,
missile guidance, signal processing,
and video and image processing.
These were harnessed for adjoint system
operations by Dr Bucco with a suite of software
tools called COVAD that he developed.
Easy-to-use system
Via graphical user interface, the COVAD tools
facilitate the creation of a simulation block
diagram, which can then be automatically
converted into adjoint block diagram form at
the touch of a button.
Following this, a simulation of the system’s
performance is run, and the results can be
displayed in graph form. In guided missile
studies, for example, these plots may be
rendered as miss distance versus flight time.
Superior to Monte Carlo
The upshot of Dr Bucco’s work is
that his approach produces results
from a single simulation run that
are of comparable accuracy to
those obtainable with the Monte
Carlo method requiring several
hundred runs, thus delivering
findings much more quickly.
The adjoint simulation method also
provides data from its single run
about error effects for each input in
the system. A plot of these, known
as an ‘error budget’, can then be used to
show which input sources pose the greatest
problem for successful system performance.
Further developments of the work will
focus on enhancing the COVAD analysis
capability to more realistic missile
guidance systems such as those that
contain on-board digital processors.
Dr Bucco’s work was assisted with funding
from the DSTO Fellowship Program, which
was set up in 2006 to encourage scientific
innovation and creativity within the
organisation. Six researchers have now
benefited from this form of assistance.
Top left: Screen capture of adjoint method modelling software developed by Dr Bucco.
Above: Dr Bucco with graph comparing the predictive performance
of adjoint method modelling with that of the Monte Carlo approach.
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