NAFEMS_Complexity_CAE

July 2008 I 13
Complexity Management:
New Perspectives and Challenges
for CAE in the 21-st Century
Jacek Marczyk, of Ontonix S.r.l., Italy, looks at the future of CAE, and the problem of managing complexity in the CAE process.
T
he trend to articulate product offering is putting
pressure on manufacturing companies like never
before. Therefore, the complexity of modern
products and of the associated manufacturing
processes is rapidly increasing. High complexity,
as we know, is a prelude to vulnerability. It is a fact that in
all spheres of social life excessive complexity leads to
inherently fragile situations. Humans perceive this
intuitively and try to stay away from highly complex
situations. But can complexity be taken into account in the
design and manufacturing of products? The answer is
affirmative. Recently developed technology, which allows
engineers to actually measure the complexity of a given
design or product, makes it possible to use complexity as a
design attribute. Therefore, a product may today be
conceived and designed with complexity in mind from day
one. Not only stresses, frequencies or fatigue life but also
complexity can become a design target for engineers.
Evidently, if CAE is to cope with the inevitable increase of
product complexity, complexity must somehow enter the
design-loop. As mentioned, today this is possible. Before
going into details of how this may be done, let us first take
a look at the underlying philosophy behind a “Complexity-
Based CAE” paradigm. Strangely enough, the principles of
this innovative approach to CAE have been established in
the 14-th century by Francis William of Ockham when he
announced his law of parsimony - "Entia non sunt
multiplicanda praeter necessitatem" - which boils down to
the more familiar "All other things being equal, the simplest
solution is the best." The key, of course, is measuring
simplicity (or complexity). Today, we may phrase this
fundamental principle in slightly different terms:
Complexity X Uncertainty = Fragility
This is a more elaborate version of Ockham’s principle
(known as Ockham’s razor) which may be read as follows:
The level of fragility of a given system is the product of the
complexity of that system and of the uncertainty of the
environment in which it operates. In other words, in an
environment with a given level of uncertainty or “turbulence”
(sea, atmosphere, stock market, etc.) a more complex
system/product will be more fragile and therefore more
vulnerable. Evidently, in the case of a system having a given
level of complexity, if the uncertainty of its environment is
increased, this too leads to an increase of fragility. We could
articulate this simple concept further by stating that:
C_design X (U_manuf + U_env) = F
In the above equation we explicitly indicate that the
imperfections inherent to the manufacturing and assembly
process introduce uncertainty which may be added to that of
the environment. What this means is simple: more audacious
(highly complex) products require more stringent
manufacturing tolerances in order to survive in an uncertain
environment. Conversely, if one is willing to decrease the
complexity of a product, then a less sophisticated and less
expensive manufacturing process may be used if the same
level of fragility is sought.
It goes without saying that concepts such as fragility and
vulnerability are intimately related to robustness. High
fragility = low robustness. In other words, for a given level of
uncertainty in a certain operational environment, the
robustness of a given system or product is proportional to its
complexity. As mentioned, excessive complexity is a source of
risk, not only in business or in politics, but in engineering too.
In other words, do it simply. Be pragmatic.
Complexity – A New Frontier
Now that we understand why measuring complexity may
open new and exciting possibilities in CAE and CAD let us
take a closer look at what complexity is and how it can be
incorporated in the engineering process by becoming a
fundamental design attribute. In order to expose the nature
of complexity, an important semantic clarification is due at
this point: the difference between complex and complicated.
A complicated system, such as a mechanical wrist watch, is
indeed formed of numerous components – in some cases as
many as one thousand - which are linked to each other but,
at the same time, the system is also deterministic in nature.
It cannot behave in an uncertain manner. It is therefore easy
to manage. It is very complicated but with extremely low
“ ”
...if CAE is to cope with the inevitable increase of product
complexity, complexity must somehow enter the design-loop.

I July 200814
complexity. Complexity, on the other hand, implies the capacity to
deliver surprises. This is why humans intuitively don’t like to find
themselves in highly complex situations. In fact, highly complex
systems can behave in a myriad of ways (called modes) and have
the nasty habit of spontaneously switching mode, for example
from nominal to failure. If the complexity in question is high, not
only the number of failure modes increases, the effort necessary
to cause catastrophic failure decreases in proportion.
Highly complicated products do not necessarily have to be highly
complex. It is also true that high complexity does not necessarily
imply very many interconnected components. In fact, a system
with very few components can be extremely difficult to understand
and control.
And this brings us to our definition of complexity. Complexity is a
function of two fundamental components:
• Structure. This is reflected via the topology of the
information flow between the components in a system.
Typically, this is represented via a Process Map or a graph
in which the components are the nodes (vertices) of the
graph, connected via links.
• Entropy. This is a fundamental quantity which measures
the amount of uncertainty of the interactions between the
components of the system.
An example of a Process Map is shown in Figure 1.
Figure 1: Process Map of a CFD model of a power plant.
Nodes are aligned along the diagonal of the map and significant
relationships between them are indicated via blue connectors.
Obtaining a process map is simple. Two alternatives exist.
• Run a Monte Carlo Simulation with a numerical (e.g.
FEM) model, producing a rectangular array in which the
columns represent the variables (nodes of the map) and
the rows correspond to different stochastic realizations of
these variables.
• Collect sensor readings from a physical time-dependent
system, building a similar rectangular array, in which the
realizations of the variables are obtained by sampling the
sensor channels at a specific frequency.
Once such arrays are available, they may be processed by
OntoSpace™ which directly produces the maps. A Process
Map, together with its topology, reflects the functionality of a
given system. Functionality, in fact, is determined by the way
the system transmits information from inputs to outputs and
also between the various outputs. In a properly functioning
system at steady-state, the corresponding Process Map is
stable and does not change with time. Evidently, if the system
in question is deliberately driven into other modes of
functioning – for example from nominal to maintenance –
the map will change accordingly.
A key concept is that of a hub. Hubs are nodes in the map
which possess the highest degree (number of connections to
other nodes). Hubs may be regarded as critical variables in
a given system since their loss causes massive topological
damage to a Process Map and therefore loss of functionality.
Loss of a hub means one is on the path to failure. In
ecosystems, hubs of the food-chain are known as keystone
species. Often, keystone species are innocent insects or even
single-cell animals. Wipe it out and the whole ecosystem
may collapse. Clearly, single-hub ecosystems are more
vulnerable than multi-hub ones. However, no matter how
many hubs a system has, it is fundamental to know them.
The same concept applies to engineering of course. In a
highly sophisticated system, very often even the experienced
engineer who has designed it does not know all the hubs.
One reason why this is the case is because CAE still lacks the
so-called systems-thinking and models are built and
analyzed in “stagnant compartments” in a single-discipline
setting. It is only when a holistic approach is adopted,
sacrificing details for breadth, that one can establish the
hubs of a given system in a significant manner. In effect, the
closer you look the less you see!
Using Complexity to Measure
Robustness
How can complexity be used to define and measure
robustness? There exist many “definitions” of robustness.
None of them is universally accepted. Most of these
definitions talk of insensitivity to external disturbances. It is
often claimed that low scatter in performance reflects high
robustness and vice-versa. But scatter really reflects quality,
not robustness. Besides, such “definitions” do not allow
engineers to actually measure the overall robustness of a
given design. Complexity, on the other hand, not only allows
us to establish a new and holistic definition of robustness,
but it also makes it possible to actually measure it, providing
a single number which reflects “the global state of health” of
the system in question. We define robustness as the ability of
a system to maintain functionality. How do you measure
this? In order to explain this new concept it is necessary to
introduce the concept of critical complexity. Critical
complexity is the maximum amount of complexity that any
system is able to sustain before it starts to break down. Every
system possesses such a limit. At critical complexity, systems
become fragile and their corresponding Process Maps start
“
”
Highly complicated
products do not
necessarily have to
be highly complex.

July 2008 I 15
The importance of knowing how much one can trust a digital
model is of paradigm importance:
• Models are supposed to be cheaper than the real thing -
physical tests are expensive.
• Some things just cannot be tested (e.g. spacecraft in orbit).
• If a model is supposed to replace a physical test but one
cannot quantify how credible the model is (80%, 90% or
maybe 50%) how can any claims or decisions based on that
model be taken seriously?
• You have a model with one million elements are you
seriously considering mesh refinement in order to get “more
precise answers” but you cannot quantify the level of trust
of your model? How significant is the result of the mesh
refinement?
• You use a computer model to deliver an optimal design but
you don't know the level of trust of the model. It could very
well be 70% or 60%. Or less. You then build the real thing.
Are you sure it is really optimal?
But is it possible to actually measure the level of credibility of a
computer model? The answer is affirmative. Based on complexity
technology, a single physical test and a single simulation are
sufficient to quantify the level of trust of a given computer model,
providing the phenomenon in question is time-dependent. The
process of measuring the quality of the model is simple:
• Run a test and collect results (outputs) in a set of points
(sensors). Arrange them in a matrix.
• Run the computer simulation, extracting results at the same
points and with the same frequency. Arrange them in a
matrix.
• Measure the complexity of both data sets. You will obtain a
Process Map and the associated complexity for each case,
C_t and C_m (test and model, respectively).
The following scenarios are possible:
• The values of complexity for the two data sets are similar:
your model is good and credible.
• The test results prove to be more complex than simulation
results: your model misses physics or is based on wrong
assumptions.
• The simulation results prove to be more complex than the
physical test results: your model probably generates noise.
But clearly there is more. Complexity is equivalent to structured
information. It is not just a number. If the complexities of the test
and simulation results are equal (or very similar) one has satisfied
only the necessary condition of model validity. A stronger sufficient
condition requires in addition the following to hold:
• The topologies of the two Process Maps are identical.
• The hubs of the maps are the same.
• The densities of the maps (i.e. ratio of links to nodes) are
the same.
• The entropy content in both cases is the same.
The measure of model credibility, or level of trust, may now be
quantified as:
MC = abs[ (C_t - C_m)/C_t) ]
“ ”
In effect, the closer you
look the less you see...to break-up. The critical complexity threshold is determined
together with the current value of complexity. The global
robustness of a system may therefore be expressed as the
distance that separates its current complexity from the
corresponding critical complexity. In other words, R= (C_cr –
C)/C_cr, where C is the system complexity while C_cr the
critical complexity. With this definition in mind it now
becomes clear why Ockham’s rule so strongly favours
simpler solutions! A simpler solution is farther from its
corresponding criticality threshold than a more complex one
– it is intrinsically more robust.
The new complexity-based definition of robustness may also
be called topological robustness as it quantifies the
“resilience” of the system’s Process Map in the face of
external and internal perturbations (noise). However, the
Process map itself carries additional fundamental
information that establishes additional mechanisms to
assess robustness in a more profound way. It is obvious that
a multi-hub system is more robust – the topology of its
Process Map is more resilient, its functionality is more
protected - than a system depending on a small number of
hubs. A simple way to quantify this concept is to establish the
degree of each node in the Process Map – this is done by
simply counting the connections stemming from each node
– and to plot them according to increasing order. This is
known as the connectivity histogram. A spiky plot, known
also as a Zipfian distribution, points to fragile systems, while
a flatter one reflect a less vulnerable Process Map topology.
The density of a Process Map is also a significant parameter.
Maps with very low density (below 5-10%) point to systems
with very little redundancy, i.e. with very little fail-safe
capability. Highly dense maps, on the other hand, reflect
situations in which it will be very difficult to make
modifications to the system’s performance, precisely
because of the high connectivity. In such cases, introducing
a change at one node will immediately impact other nodes.
Such systems are “stiff” in that reaching acceptable
compromises is generally very difficult and often the only
alternative is re-design.
Complexity-based Model Validation.
Models are only models. Remember how many assumptions
one must make to write a partial differential equation (PDE)
describing the vibrations of a beam? The beam is long and
slender, the constraints are perfect, the displacements are
small, shear effects are neglected, rotational inertia is
neglected, the material is homogenous, the material is
elastic, sections remain plane, loads are applied far from
constraints, etc., etc. How much physics has been lost in the
process? 5%? 10%? But that’s not all. The PDE must be
discretized using finite difference or finite element schemes.
Again, the process implies an inevitable loss of physical
content. If that were not enough, very often, because of high
CPU-consumption, models are projected onto the so-called
response surfaces. Needless to say, this too removes physics.
At the end of the day we are left with a numerical artefact
which, if one is lucky (and has plenty of grey hair) the model
captures correctly 80-90% of the real thing. Many questions
may arise at this point. For instance, one could ask how
relevant is an optimization exercise which, exposing such
numerical constructs to a plethora of algorithms, delivers an
improvement of performance of, say, 5%. This and other
similar questions bring us to a fundamental and probably
most neglected aspect of digital simulation – that of model
credibility and model validation.

I July 200816
Figure 2 illustrates the Process Maps obtained from a crash test
(left) and simulation (right). The simulation model has a
complexity of 6.53, while the physical test 8.55. This leads to
a difference of approximately 23%. In other words, we may
conclude that according to the weak condition, the model
captures approximately 77% of what the test has to offer.
Moreover, the Process Maps are far from being similar.
Evidently, the model still requires a substantial amount of work.
But clearly there is more, the same index may be used to
"measure the difference" between two models in which:
• The FE meshes have different bandwidth (a fine and a
coarse mesh are built for a given problem).
• One model is linear, the other is non-linear (one is not
sure if a linear model is suitable for a given problem).
• One model is run on 1 CPU and then on 4 CPUs (it is
known that with explicit models this often leads to
different results).
Complexity-based CAD
It is evident to every engineer that a simpler solution to a given
problem is almost always:
• Easier to design
• Easier to assemble/manufacture
• Easier to service/repair
• Intrinsically more robust
The idea behind complexity-based CAD is simple: design a
system that is as simple as possible but which fulfils functional
requirements and constraints. Now that complexity may be
measured in a rational manner, it can become a specific
design objective and target and we may put the “Complexity X
Uncertainty = Fragility” philosophy into practice. One way to
proceed is as follows:
• Establish a nominal parametric model of a system (see
example in Figure 3, illustrating a pedestrian bridge)
• Generate a family of topologically feasible solutions
using Monte Carlo Simulation (MCS) to randomly
perturb all the dimensions and features of the model.
• Generate a mesh for each Monte Carlo realization.
• Run an FE solver to obtain stresses and natural
frequencies.
• Process the MCS with OntoSpace™.
• Define constraints (e.g. dimensions) and performance
objectives (e.g. frequencies, mass).
• Obtain a set of solutions which satisfy both the
constraints as well as the performance objectives.
• Obtain the complexity for each solution
• Select the solution with the lowest complexity.
The above process may be automated using a commercial
CAD system with meshing capability, a multi-run environment
which supports Monte Carlo simulation and an FE solver. In the
case of our bridge example, Figure 4 illustrates two solutions,
possessing very similar mass, natural frequencies, stresses and
robustness but dramatically different values of complexity. The
solution on the right has complexity of 8.5 while the one on the
left 5.4.
Conclusions
Given that the complexity of man-made products, and the
related manufacturing processes, is quickly growing, these
products are becoming increasingly exposed to risk, given that
high complexity inevitably leads to fragility. At the same time,
the issues of risk and liability management are becoming
crucial in today’s turbulent economy. But highly complex and
sophisticated products are characterized by a huge number of
possible failure modes and it is a practical impossibility to
analyze them all. Therefore, the alternative is to design
systems that are intrinsically robust, i.e. that possess built-in
capacity to absorb both expected and unexpected random
variations of operational conditions, without failing or
compromising their function. Robustness is reflected in the fact
Figure 2: Process Maps obtained for a physical car crash-test (left) and for a simulation (right). Figure 3: Parametric quarter-
“...the issues of risk and liability management are

July 2008 I 17
that the system is no longer optimal, a property that is linked
to a single and precisely defined operational condition, but
the results are acceptable (fit for the function) in a wide
range of conditions. In fact, contrary to popular belief,
robustness and optimality are mutually exclusive.
Complexity-based design, i.e. a design process in which
complexity becomes a design objective, opens new avenues
for the engineering. While optimal design leads to
specialization and, consequently, fragility outside of the
portion of the design space in which the system is indeed
optimal, complexity-based design yields intrinsically robust
systems. The two paradigms may therefore be compared as
follows:
• Old Paradigm: Maximize performance, while, for
example, minimizing mass.
• New Paradigm: Reduce complexity accepting
compromises in terms of performance.
A fundamental philosophical principle that sustains the new
paradigm is L. Zadeh’s Principle of Incompatibility: High
complexity is incompatible with high precision. The more
something is complex, the less precise we can be about it. A
few examples: the global economy, our society, climate,
traffic in a large city, the human body, etc., etc. What this
means is that you cannot build a precise (FE) model of a
highly sophisticated system. And it makes little sense to insist
– millions of finite elements will not squeeze precision from
where there isn’t any. Nature places physiological limits to
the amount of precision in all things. The implications are
clear. Highly sophisticated and complex products and
systems cannot be designed via optimization, precisely
because they cannot be described with high precision. In
fact, performance maximization (optimization) is an exercise
of precision and this, as we have seen, is intrinsically limited
by Nature. For this very reason, models must be realistic, not
precise.
Credits:
The pedestrian bridge example has been performed by Alex.
Veltdman from ESTEQ Engineering, Pretoria, South Africa.
Contact
Jacek Marczyk
Ontonix S.r.l.
jacek@ontonix.com
Figure 4: Two solutions to the pedestrian bridge. Note the critical variables (hub) indicated in red (inputs) and blue (outputs).model of a pedestrian bridge.
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