Spatial and snapshot clustering approaches are presented and discussed for particle image velocimetry (PIV) data of high-Reynolds number uniform and buoyant jets and4- and 7-bladed propeller wakes respectively. Data clustering is based on the k-means algorithm, along with the identification of the optimal number of clusters based on three metrics, namely the within-cluster sum of squares, average silhouette, and number of proper orthogonal decomposition (POD) modes required to resolve a desiredvariance. Spatial clustering for jets flow is based on three sets of clustering variables, namely cross-section velocity profiles, point-wise energy spectra, and pointwiseReynolds stress tensor components. Snapshot clustering of phase-locked propellers wake data is based on the vorticity with focus on tip vortices regions. PODand t-distributed stochastic neighbor embedding along with kernel density estimation are used to provide a twodimensional visualization of data clusters for assessmentand discussion. The objective of this work is to lay the ground for a systematic data-clustering analysis of PIV data. The examples discussed show how clustering methodscan help in achieving physical insights of complex fluid dynamics problems.
Observing PIV Measurements Through the Lens of Data Clustering
Andrea Serani
;Mario Felli;Matteo Diez
2020
Abstract
Spatial and snapshot clustering approaches are presented and discussed for particle image velocimetry (PIV) data of high-Reynolds number uniform and buoyant jets and4- and 7-bladed propeller wakes respectively. Data clustering is based on the k-means algorithm, along with the identification of the optimal number of clusters based on three metrics, namely the within-cluster sum of squares, average silhouette, and number of proper orthogonal decomposition (POD) modes required to resolve a desiredvariance. Spatial clustering for jets flow is based on three sets of clustering variables, namely cross-section velocity profiles, point-wise energy spectra, and pointwiseReynolds stress tensor components. Snapshot clustering of phase-locked propellers wake data is based on the vorticity with focus on tip vortices regions. PODand t-distributed stochastic neighbor embedding along with kernel density estimation are used to provide a twodimensional visualization of data clusters for assessmentand discussion. The objective of this work is to lay the ground for a systematic data-clustering analysis of PIV data. The examples discussed show how clustering methodscan help in achieving physical insights of complex fluid dynamics problems.File | Dimensione | Formato | |
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