Next-Generation flow diagnostics for control
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 full description of the flow is not available, thus generalization of the actuation effects to real applications is often prohibitive.
The objective of NEXTFLOW is to conceive the next-generation flow-diagnostics aimed to flow control by leveraging the principles of completeness and compactness of the measurements. Completeness implies aiming to pursue a complete flow description, i.e. a time-resolved 3D characterization of velocity and thermodynamic variables. This will be achieved through a technique-integration approach based on data-driven methods. This grounds its basis on the principle that the superposed application of techniques is superior to their separate use. Compactness is pursued by exploring solutions with minimum technological complexity, and on developing new data output formats that are directly aimed at flow control applications. Key enablers for this task are (i) the novel concepts I recently proposed on data-driven techniques integration, (ii) the deep embedding of compressed-sensing methods in the data processing and (iii) the data-driven discovery of simplified governing equations of the dynamics. 
The next-generation flow diagnostics concept will deeply change experimental fluid mechanics and flow control, allowing bridging the gap between the laboratory experiment to the real application, with tremendous potential impact on numerous industrial applications.