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

For more information on adjoint shape optimization: https://youtu.be/cZAhPQFINZ8 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. ---------------------------------------------------------------------------------------- Full Research Paper: https://www.airshaper.com/research ----------------------------------------------------------------------------------------------------------- The AirShaper videos cover the basics of aerodynamics (aerodynamic drag, drag & lift coefficients, boundary layer theory, flow separation, reynolds number...), simulation aspects (computational fluid dynamics, CFD meshing, ...) and aerodynamic testing (wind tunnel testing, flow visualization, ...). We then use those basics to explain the aerodynamics of (race) cars (aerodynamic efficiency of electric vehicles, aerodynamic drag, downforce, aero maps, formula one aerodynamics, ...), drones and airplanes (propellers, airfoils, electric aviation, eVTOLS, ...), motorcycles (wind buffeting, motogp aerodynamics, ...) and more! For more information, visit www.airshaper.com