The vast majority of flows in nature and man-made applications is turbulent, i.e. with chaotic and apparently irregular behaviour. The impact of turbulent flows in our life is overwhelming. For instance, the drag of all transport vehicles, and consequently fuel consumption and emissions, depend strongly on the behaviour of turbulent flows. Understanding and learning how to control them has a potentially tremendous impact on the sustainability of human development. Measuring and controlling turbulent flows represents, however, a formidable challenge. Their chaotic three-dimensional multi-scale character requires measurement techniques capable of extracting complete high-fidelity detailed views of their behaviour. In most cases, however, current measurement techniques are able to provide only partial views, for instance only of velocity or thermodynamic quantities, and often only in point or planar domains, or with loss in resolution for volumetric measurements.
The project NEXTFLOW aims at developing the next-generation flow-measurement techniques for turbulent flows by augmenting existing methods with data-driven tools. The central hypothesis is that combining multiple measurement techniques, each one providing a different incomplete view, and physical principles, makes it possible to disclose a complete description of the fluid flow. We build upon three-dimensional particle image velocimetry and extend its capabilities beyond its limits in temporal and spatial resolution by blending in the information of fast probes and including constraints based on flow physics. The final objective is to exploit the complete flow description to distil compact models, which can be used for flow control applications.
