![]() This allows for much faster iterations between designs, and a better understanding of the complex phenomena occurring above a given speed. The model then predicts the corresponding pressure profile at a higher Mach number which can be specified by the user. In this setting, the user is able to upload an airfoil geometry, with the corresponding simulation at a low Mach number. It uses as input a computationally non-expensive single incompressible flow simulation. ![]() It makes it possible to predict the whole Mach envelope, i.e., pressure fields at the various angle of attack and higher Mach numbers in a few seconds. The proposed methodology is based on Neural Concept’s Deep Learning models, which are trained to “transpose” simulations from low-mach to high-mach.
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