Heat Exchangers & HVAC

Fast design modifications and design improvement beyond current boundaries.

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NC SHAPE APPLICATION IN HEAT EXCHANGERS & HVAC

Optimization tools are often employed but hardly deliver at scale because of limitations related to design parametrization and to the cost of siloed, independent simulation campaigns.

Optimization tools are often employed but rarely deliver at scale. This is due to limitations related to design parametrization and to the cost of siloed, independent simulation campaigns.

If you think that you have reached the performance limits of your heat exchangers or your fluid mixers, please take a look at Shape!

Find the best design out of numerous concepts

FIND THE BEST,
MOST INNOVATIVE DESIGN

Extract the best out of many design concepts.

Shape will help you find the best design out of numerous concepts. It will allow you to move away from your existing parametrizations, to converge to truly innovative designs that outperform classical ones.

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SAve time and resources

FLEXIBLE
OPTIMIZATION WORKFLOW

A more flexible approach to design optimization.

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Because it is predicting simulation-like results from simulation‑like inputs, Shape makes your optimization workflow more flexible. You can change your design space or design objectives at any time, without any additional cost or effort.

FASTER, CUSTOMIZED
DESIGNS

Customize designs in no time by never starting an optimization from zero again.

Adapting to the ever-changing performance requirements is key. Hence, you need to iterate between multiple new and existing design concepts.
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With Shape, you can capitalize on everything you do. You will continuously feed an ever smarter deep-learning model, saving you precious time and resources.

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Credit: EPFL Laboratory of Renewable Energy Science and Engineering

DETAILED ANALYSIS OF DESIGN PERFORMANCES

Handle complex design and structures with many sub‑parts and intricate physics.

With Shape, you can analyse the design performances through temperature and velocities on surfaces, slices, or directly in 3D. The Geometric Neural Networks implemented in Shape, are able to to extract local and global features in large and complex design assemblies.

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