Colanera, Antonio (2023) On the data-driven reduced order modelling in fluid dynamics. [Tesi di dottorato]
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| Tipologia del documento: | Tesi di dottorato |
|---|---|
| Lingua: | English |
| Titolo: | On the data-driven reduced order modelling in fluid dynamics |
| Autori: | Autore Email Colanera, Antonio antonio.colanera@unina.it |
| Data: | 31 Ottobre 2023 |
| Numero di pagine: | 268 |
| Istituzione: | Università degli Studi di Napoli Federico II |
| Dipartimento: | Ingegneria Industriale |
| Dottorato: | Ingegneria aerospaziale, navale e della qualità |
| Ciclo di dottorato: | 36 |
| Coordinatore del Corso di dottorato: | nome email Grassi, Michele michele.grassi@unina.it |
| Tutor: | nome email de Luca, Luigi [non definito] Chiatto, Matteo [non definito] |
| Data: | 31 Ottobre 2023 |
| Numero di pagine: | 268 |
| Parole chiave: | Data-Driven analysis, Reduced order modelling, Stability, Modal decomposition. |
| Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-IND/06 - Fluidodinamica |
| Depositato il: | 29 Dic 2023 15:29 |
| Ultima modifica: | 09 Mar 2026 12:10 |
| URI: | http://www.fedoa.unina.it/id/eprint/15708 |
Abstract
Fluid dynamics is characterized by several physical phenomena, leading to complex spatial and temporal structures characterized by different spatial and temporal scales. This thesis is a comprehensive exploration of data-driven approaches for modal analysis, stability analysis and reduced-order modeling in fluid dynamics, aiming to enhance the understanding of complex flow phenomena. As regarding the modal analysis, this work delves into the extraction of coherent flow structures using conventional techniques like Spectral Proper Orthogonal Decomposition (SPOD) and introduces innovative approaches like Robust SPOD to deal with noisy or corrupt data and Gappy POD for handling two-phase PIV measurements. These methods are applied to various flow configurations, including the vertical liquid jet, the turbulent jet, the open cavity flow and the two-phase mixing layer. A data-driven approach to estimate the global spectrum of gravitational liquid jet is presented. The underlying linear operator has been extracted with the Dynamic Mode Decomposition (DMD), considering random perturbations of the base flow. This analysis has shed light on sinuous and varicose modes, their interaction, and the influence of the main governing parameters. Conventional and novel Reduced-order models (ROM) are presented, including Extended Cluster-based Network Modeling (eCNM) and functional based CNM. These methods offer efficient ways to capture flow dynamics, forecast fluid behaviours and handle undersampled data. This thesis advances data-driven approaches in fluid dynamics, providing valuable tools for the comprehension of complex flow phenomena.
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