Inverse aerodynamic design of turbo components for Carnot batteries by means of physics informed networks enhanced by generative learning

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We develop new simulation methods based on deep neural networks that shorten computation times with respect to traditional CFD considerably. In particular, we apply physics informed neural networks for a rough representation of fluid flows in turbo-machinery components operating in Carnot batteries. Such rough solutions are refined by conditional generative adversarial networks (GAN) in order to create realistic fine structure of turbulent flows. in particular, we study the physical and the generalization properties of such deep learning based solutions to fluid flow with respect to changing boundary conditions and geometry. This enables us to rapidly evaluate designs under strongly changing boundary conditions, as they are typical for the discharging cycle of a Carnot battery. In this way, we provide an invaluable tool for the inverse design of turbocomponents for Carnot batteries.