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On the way to lower energy consumption in the transport sector with the help of machine learning

The research in WP3 and the collaboration with WP2 is strongly related to the growing environmental awareness. To be more precise, it is a must in the transport sector to lower energy consumption which is strongly determined by friction drag. Therefore, in task 3.1 fundamental and applied numerical research in the field of active flow control for external flows via spanwise traveling transversal surface waves are performed to lower the friction drag by, say, 30 percent and obtain net power savings in the range of 10 percent. Together with artificial intelligence experts from WP2, the numerical investigations will be substantiated by machine learning approaches and extended to engineering configurations such as lightweight wings for transonic flow. This kind of research will ensure mobility and save the environment.

Use Case:

https://www.coe-raise.eu/boundary-layers