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NEXTFLOW aims to revolutionize flow diagnostics for active flow control by integrating advanced data-driven methods. The project focuses on achieving comprehensive flow characterization and developing simplified measurement techniques to enhance the applicability of flow control in real-world scenarios.
Fast-paced advancements of hardware and machine-learning algorithms have triggered successful applications of active flow control, even though mainly limited to laboratory-scale applications.
One of the main limits resides in the lesson we are able to learn today from experiments.
We can successfully train actuators with probes in a controlled environment to reach a certain goal, e.g. aerodynamic drag minimization or noise reduction; on the other hand, an experimental technique that provides a f…
UNIVERSIDAD CARLOS III DE MADRID
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