Thermal evolution
Generative design and machine learning point to novel ways to cool
systems. By Chris Edwards
Two years ago, a team of HP
was faced with the challenge
of getting more air ow out of a
duct used to cool the print-heads of an
upcoming inkjet printer. Without ef cient
cooling to keep the ink owing at the
right temperature the printer cannot
operate at full speed. Simulations
showed the traditional duct design was
causing too much recirculation and
turbulence to work effectively.
To get around the issues, HP
worked with engineers at Siemens
Digital Industries who coupled uid-
ow simulations with a technique
mechanical CAD companies now term
generative design to come up with a
radically different structure.
“As the design was shaping up
it kind of de ed whatever everyone
included me expected,“ said Siemens
simulation engineer Julian Gaenz after
the launch of the printer that would
use the duct, pointing to a tongue-like
protrusion in the nal design that its
predecessor lacked.
There are two main tactics used
in this kind of generative design. One
is to randomly generate variants after
each simulation run in the expectation
that one or more will work better.
Another is to home in on problem
areas, such as the recirculation in the
HP printer’s duct, and come up with
shapes that seem likely to reduce the
problem before simulating a complete
part to see how well the changes
worked. HP is far from alone in
exploiting simulation-driven design.
Tom Gregory, product manager
of thermal-analysis tools supplier
Future Facilities says his company’s
6SigmaET tool has been used to help
determine the optimum shape for pins
used in that style of heatsink.
“The optimisation was performed by
such as CNC machining, extrusion,
die-casting and sheet metal forming,”
Vervecken says. Each has its use in
different markets. For example, sheet
metal forming has proved useful for
making the large battery cold plates
needed by electric vehicles.
Diabatix has built a generative
environment around a selection of opensource
design and simulation tools
that iteratively re nes the structure of a
heatsink or cooling component based
on the toolsuite’s analysis of thermal
ows and mechanical structure.
The company is launching a cloudhosted
version of the tools in April.
Although generative design can
use randomisation to create novel
designs, this was not the approach
taken by Diabatix. The problem with
randomisation is that the simulation
overhead needed to assess each
variant makes the technique too
unwieldy. Instead, Vervecken says the
approach used by the start-up uses
machine learning to build algorithms
that can come up with effective
but often unexpected shapes and
structures rather than the parallel ns
found in most conventional designs,
with manufacturing constraints used
to stop them from becoming too
impractical to manufacture.
“This is why we can generate up
to 30 per cent additional cooling
performance,” Vervecken claims.
Thermal engineering
Another potential target for machine
learning in thermal engineering is to
reduce the bottleneck introduced by
using genetic algorithms in combination
with CFD. The aim of the optimisation
was to reduce the pressure drop across
the heat sink while maintaining thermal
performance.”
Though it is entirely possible to
create the variants or iterations by
hand, machine learning looks to be
a useful tool for generative design
because, in principle, it can create
variations quickly and come up
with counterintuitive but effective
unexpected results. Gregory points to
several research papers have come
up with novel heat-sink designs.
”The designs that are generated are
impressive and visually stunning.
However, they cannot be manufactured
by traditional methods.”
3D printing not essential
In terms of research heatsinks often
need 3D printing, as did the HP nozzle.
However as that was replacing six
injection-moulded parts, the newer part
worked out more cost effective once
the 3D printing process was optimised.
Lieven Vervecken, CEO and
co-founder of Belgium-based generativedesign
startup Diabatix says 3D
printing is not essential.
“We have a few manufacturing
techniques that we support by default,
Above and below:
Work by the
Fraunhofer IISB
to determine the
optimum shape for
pins used in heatsinks
22 9 March 2021 www.newelectronics.co.uk
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