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A Full Meshless Algorithm For Super-Resolution In Image Velocimetry Based On KNN-PTV And Constrained RBFs

Iacopo Tirelli (1), Miguel Alfonso Mendez (2), Andrea Ianiro (3), Stefano Discetti (4)

1. Unniversidad Carlos III de Madrid, Leganes, Spain
2. von Karman Institute for Fluid Dynamics, Madrid, Belgium
3. Unniversidad Carlos III de Madrid, Sint-Genesius-Rode, Spain
4. Unniversidad Carlos III de Madrid, bruxelles, Spain


A novel full meshless super-resolution method for image velocimetry is proposed. This method builds upon the combination, presented in Tirelli et al. (2023a), of K-nearest neighbour Particle Tracking Velocimetry (Tirelli et al., 2023b, KNN-PTV) and constrained Radial Basis Functions regression (Sperotto et al., 2022, c-RBFs). The main novelty is that the algorithm is implemented here in a fully meshless version, i.e. it does not require the definition of a common Eulerian grid at any step of the process. KNN-PTV enhances the spatial resolution of vector fields by exploiting data coherence in space and time, even for non-time-resolved measurements. On the other and, c-RBFs provide an analytical representation of the field from scattered data while allowing the introduction of physical constraints. In this new version, the dictionary to blend data from different snapshots is computed with a meshless POD approach developed by the authors and presented in another contribution to this conference. This approach removes the last constraint of Eulerian grids and paves the way for a fully meshless algorithm. The algorithm is validated on a challenging 2D synthetic case, such as turbulent channel flow, and an experimental case involving the separation bubble around the frontal portion of a Ground Transportation System (GTS).

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