Machine Learning for Aerodynamics - Deep Learning & Neural Networks applied to CFD simulations

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In this video, we look at how machine learning / deep learning / neural networks can be applied to aerodynamic CFD simulations.

Neural Concept
We interviewed Pierre Baqué, CEO of Neural Concept, a Swiss startup developing & offering Deep Learning software. They have developed algorithms to connect 3D shape morphing, deep learning and aerodynamics.

senseFly
senseFly is a Swiss drone company that wanted to improve the flight time of their fixed-wing drones. senseFly, Neural Concept, EPFL (the technical University of Lausanne - École polytechnique fédérale de Lausanne) and AirShaper teamed up to apply Deep Learning to drone design to improve the aerodynamics, as improvements to the lift/drag ratio directly extend the range / increase flight time.

Deep Learning setup
The Neural Concept software can create & explore new 3D shapes to train its network, but it needs an aerodynamics component to give feedback on the lift/drag performance (and other aerodynamic parameters) of each design. For that, the Neural Concept software connected to the AirShaper cloud via an API interface.

Network Training
The training of the network was done in multiple phases with increasing accuracy. The initial warm-up of the network was done using older, in-house simulations from other projects. In the second phase, medium accuracy AirShaper simulations were applied. And in the final phase, high-accuracy AirShaper simulations were used for final tweaking of the network.

Output
Without any design input, the network came up with special drone shapes that partially matched what engineers had been applying for years in practice (anhedral/dihedral setup, ...). The lift/drag ratio was improved by more than 4%. Because the Reynolds number is quite different compared to large aircraft, so were the suggested design solutions.

AIRBUS
Neural Concept worked on the prediction of shock waves (transonic simulations). These results were presented at NEURIPS.

Future of Deep Learning for Aerodynamics
- Today
For industry specific, repetitive tasks, it pays off to train a network so that new designs can be analysed using the predictive model
- Short term
In the short term, machine learning can be used to make existing CFD codes faster and more accurate
- Long term
It's uncertain if it will ever work, but it might be possible to create generic Neural Networks that cover various industry segments, without needing to train the network.

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