Three spatial clustering approaches of a high-Reynolds number transient buoyant jet in a linearly stratified environment are applied along with proper orthogonal decomposition to identify similar/consistent regions in the domain of interest. The velocity fields analyzed are obtained from an experimental test with large scale, time-resolved, particle image velocimetry (PIV) measurements. Clustering is performed by the k-means method considering: (a) cross-section velocity profiles, (b) point-wise energy spectra, and (c) point-wise Reynolds stress tensor components. Three metrics are used for the assessment of clustering approaches, namely: (a) within-cluster sum of squares, (b) average silhouette, and (c) within-cluster number of POD modes required to resolve prescribed levels of total variance/energy. Results are promising and lay the foundation for an in depth analysis of local features of complex flows as well as the formulation of efficient reduced order models.
PIV data clustering of a buoyant jet in a stratified environment
Serani Andrea;Durante Danilo;Diez Matteo;
2019
Abstract
Three spatial clustering approaches of a high-Reynolds number transient buoyant jet in a linearly stratified environment are applied along with proper orthogonal decomposition to identify similar/consistent regions in the domain of interest. The velocity fields analyzed are obtained from an experimental test with large scale, time-resolved, particle image velocimetry (PIV) measurements. Clustering is performed by the k-means method considering: (a) cross-section velocity profiles, (b) point-wise energy spectra, and (c) point-wise Reynolds stress tensor components. Three metrics are used for the assessment of clustering approaches, namely: (a) within-cluster sum of squares, (b) average silhouette, and (c) within-cluster number of POD modes required to resolve prescribed levels of total variance/energy. Results are promising and lay the foundation for an in depth analysis of local features of complex flows as well as the formulation of efficient reduced order models.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.