External Aerodynamics

With Neural Concept, you will enhance the possibilities of your external aero simulation chain, and get the most out of your flow data.


Since many operating conditions need to be run to get a full understanding of the aerodynamic behavior of a vehicle, companies in the automotive and aerospace sectors generate innumerable hours of CFD calculations.

Most of these results are only looked at once and never used again. Since there is no reason to make the same mistake twice, NC will learn from your heterogenous Computational Fluid Dynamics (CFD) data overnight.

Predict hundreds of results in a few seconds.

While most engineers usually analyze only a few operating conditions, NC makes it possible to predict hundreds of results in a few seconds, spanning a range of flow velocities or angles. This can be predicted for a new design, provided actual simulation data on only a few operating conditions, or with no new simulation at all.

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Prediction (R2 = 95%)

With NC, you can predict not only integrated scalar quantities such as lift and drag, but also pressures and velocities on surfaces, slices, or directly in 3D.

The Geometric Neural Networks used in NC are particularly good at reproducing strong non-linearities, such as those induced by shock-waves or turbulent flows.

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With NC, you can gather data from different design exploration campaigns and naturally explore and compare concepts.

Your ideas and your history are not restricted by parameters

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NC allows you to exploit your wind tunnel data in the best possible way.

Using transfer learning techniques, you can complement your CFD with physical measurement.

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NC is easily integrated with StarCCM+, Fluent, Openfoam, OMNIS and Paraview.
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