In many real world applications the physical knowledge of a phenomenon and data science can be combined together in order to get mutual benefits. As a result, it is possible to formulate a so-called hybrid model from the combination of the two approaches. In this work, we propose an hybrid approach for the prediction of the ship propeller cavitating vortex noise, adopting real data collected during extensive model scale tests in a cavitation tunnel. Results will show the eectiveness of the proposal.

Cavitation Noise Spectra Prediction with Hybrid Models

Cipollini F.;Oneto L.;Tani G.;Viviani M.;Anguita D.
2019-01-01

Abstract

In many real world applications the physical knowledge of a phenomenon and data science can be combined together in order to get mutual benefits. As a result, it is possible to formulate a so-called hybrid model from the combination of the two approaches. In this work, we propose an hybrid approach for the prediction of the ship propeller cavitating vortex noise, adopting real data collected during extensive model scale tests in a cavitation tunnel. Results will show the eectiveness of the proposal.
2019
978-3-030-16840-7
978-3-030-16841-4
File in questo prodotto:
File Dimensione Formato  
C066.pdf

accesso chiuso

Tipologia: Documento in versione editoriale
Dimensione 499.93 kB
Formato Adobe PDF
499.93 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/945445
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact