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Next-generation flow diagnostics for control

Periodic Reporting for period 1 - NEXTFLOW (Next-generation flow diagnostics for control)

Reporting period: 2021-01-01 to 2022-06-30

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.
During the first period of the project, the NEXTFLOW team worked mainly in two directions.
On one hand, we focused on temporal resolution enhancement of the standard Particle Image Velocimetry (PIV). The main strategy has been based on using simultaneously fast probes and non-time-resolved PIV to obtain time-resolved velocity fields. The estimation is performed with linear (Extended Proper Orthogonal Decomposition) or non-linear (e.g. neural networks) estimators. The Navier-Stokes equations are then enforced to extract pressure fields using the estimated time-resolved velocity fields. We explored this process in advection-dominated flows, and we are currently working on the case of wall-bounded flows. The method has been tested and validated on synthetic and experimental datasets. In this same line of research, we have introduced novel concepts based on data-driven system identification and on enforcing physical constraints in the time-resolution enhancement to regularize the estimated fields.
On the other hand, novel concepts to dramatically increase the spatial resolution of PIV measurements have been developed and assessed. While most of the existing methods exploit time consistency in time-resolved sequences to improve the spatial resolution, we have demonstrated that, even if time resolution is not at hand, statistical information can be embedded in the process to increase the amount of data in a single instantaneous realization. Two novel concepts, one based on deep-learning architectures, and the other focused on leveraging local similarity between snapshots, have now been consolidated as tools which cover a wide range of flow conditions.
NEXTFLOW opened the way to time-resolved pressure measurements based on velocity fields estimated using the simultaneous acquisition of non-time-resolved velocity fields and fast probes. Unlike existing methods, it does not require modelling assumptions, nor necessarily 3D data to build upon. Furthermore, the project has delivered techniques to increase the spatial resolution of non-time-resolved particle image velocimetry to an unprecedented level. Considering the cost and technical limitations of time-resolved equipment, both advances in time and space resolution are building the ground for the use of the consolidated (and widely available in laboratories) low-repetition-rate PIV to perform accurate time-resolved velocity and pressure measurements.
Resolution enhancement with super-resolution GANs of PIV measurements of a TBL