Edited by Leslie Gordon
We use the software to design
and analyze missioncritical
components such
as adapters that connect
rocket launchers to their
payloads. Comprehensively
measuring complexity provides
a quantity that can
then be used as a design attribute
(just like mass, frequency,
or stress) to develop products
that are easier to build, assemble,
service, and repair.
Engineers start by setting up
and running a Monte Carlo simulation
(for probability) in programs
such as iSight, LMS-Optimus,
Ansys-PDS, PAM-Opt, or
VeroSolve. (OntoSpace can also
process data from tests, sensors,
and historical records.) Statistics
of input variables, such as loads,
dimensions, and material properties,
determine factors such
as a component’s probability of
failure. Results are in the form of
rectangular tables of data, which
are imported into OntoSpace.
Basically, the software postprocessors
the results and extracts
knowledge from them.
Some of this is shown in what is
called the Process or Knowledge
Map. It provides a new way to
see information and understand
how dynamic systems function.
It illustrates which design variables
affect performance. It also
highlights weak points as well as
“hubs,” i.e., variables crucial to
system functioning, and lets users
verify if designs are redundant.
The software calculates the
total amount of entropy (a measure
of disorder or randomness
in a system) and uncertainty (the
estimated amount by which a calculated
value might differ from
its true value) and uses this information
to quantify the system’s
complexity and also provide a
measure of its robustness — the
capability to maintain functionality
in the presence of internal or
external disturbances.
Another tool, The Navigation
Table, lets engineers establish constraints
and objectives, and verify
if a system can perform required
functions and at what level of risk.
The tool is helpful in understanding
combinations of inputs that will
cause certain effects in outputs.
An important feature detects what
are called outliers. These represent
pathological circumstances — unlikely events — that often
lead to failure. The feature is key to
assessing risk in mechanical components.
No other CAE software
performs this kind of analysis. Because
OntoSpace is not based on
statistics, it can handle data of
any kind, even if ill-conditioned,
that is, in principle, suitable for
the intended purpose but in some
way distorted or incomplete.
In the past, using complexity as
a design goal or attribute was not
possible. OntoSpace technology
makes it possible to imagine complexity-
based CAD. The concept:
Given two or more equivalent designs
in terms of cost, function,
and performance, it’s always better
to select the one with the lowest
complexity. This is something
the developer should look into.
As components become increasingly
complex, it is important to
include complexity in the design
loop from the start.
A downside is the software
currently reads only comma separated
values or plain text files.
It would be great if the software
could directly read Excel data.
The software is easy to use.
Execution times for typical problems
are on the order of minutes,
sometimes seconds. However,
running Monte Carlo simulations
might be a problem for many engineers.
The developer provides
a tutorial on this, and documentation
is generally complete. It
also provides a two-day training
course on complexity-management
technology. E-mail support
is efficient.
Users can upload a rectangular
table of up to 100 variables
and 1,000 samples to the developer’s
online service and have it
analyzed in a matter of minutes.
Cases of up to 10 variables are analyzed
for free. OntoSpace comes
from Ontonix Srl, Via Lega Insurrezionale
7, 22100 Como, Italy,
+39-031-3100059, ontonix.com
—Jorge Vilanova