This thesis presents the development of a multi-objective optimization framework for the design of the appendages of an A-Class catamaran. The starting point of the study consists of the mathematical reconstruction of the actual geometries, performed from 3D scans using reverse engineering techniques. Subsequently, thanks to a close collaboration with the boat's sailor, it was possible to analyze the actual operating conditions during regattas. These data allowed the implementation of a Velocity Prediction Program (VPP) capable of simulating the hull's kinematics and identifying the optimal configuration to maximize the Velocity Made Good (VMG). From this analysis, the operational fluid dynamic conditions were derived, defined in terms of lift coefficient and Reynolds number, which constitute the input for the optimization core: a code developed in Python and coupled with the viscous solver XFOIL. The algorithm pursues two conflicting objectives: the minimization of hydrodynamic drag, weighted on the actual operating conditions, and the maximization of structural bending strength. The process, which integrates rigorous geometric and fluid dynamic constraints, leads to the generation of a Pareto Front of optimal solutions. Among these, some individuals were selected and subsequently validated: the reliability of the fluid dynamic results initially obtained with XFOIL was indeed verified through high-fidelity CFD (Computational Fluid Dynamics) simulations within the OpenFOAM environment.
Il presente lavoro di tesi illustra lo sviluppo di un framework di ottimizzazione multi-obiettivo per la progettazione delle appendici di un catamarano Classe A. Il punto di partenza dello studio è costituito dal processo di ricostruzione matematica delle geometrie reali, effettuato a partire da scansioni 3D tramite tecniche di reverse engineering. Successivamente, grazie alla stretta collaborazione con il timoniere dell'imbarcazione, è stato possibile analizzare le effettive modalità di utilizzo delle appendici in regata. Tali dati hanno permesso di implementare un Velocity Prediction Program (VPP) capace di simulare la cinematica dello scafo e identificare l’assetto ottimale per la massimizzazione della Velocity Made Good (VMG). Da questa analisi sono state ricavate le condizioni fluidodinamiche operative, definite in termini di coefficiente di portanza e numero di Reynolds, che costituiscono l'input per il nucleo dell'ottimizzazione: un codice sviluppato in Python e accoppiato al solutore viscoso XFOIL. L’algoritmo persegue due obiettivi contrastanti: la minimizzazione della resistenza idrodinamica, pesata sulle reali condizioni operative, e la massimizzazione della resistenza strutturale a flessione. Il processo, che integra rigorosi vincoli geometrici e fluidodinamici, conduce alla generazione di un Fronte di Pareto delle soluzioni ottime. Tra queste, alcuni individui sono stati selezionati e successivamente validati: l’affidabilità dei risultati fluidodinamici ottenuti in prima istanza con XFOIL è stata infatti verificata attraverso simulazioni CFD (Computational Fluid Dynamics) ad alta fedeltà in ambiente OpenFOAM.
OTTIMIZZAZIONE IDRODINAMICA DELLE APPENDICI DI UN CATAMARANO CLASSE A
CARINGELLA, FRANCESCO
2024/2025
Abstract
This thesis presents the development of a multi-objective optimization framework for the design of the appendages of an A-Class catamaran. The starting point of the study consists of the mathematical reconstruction of the actual geometries, performed from 3D scans using reverse engineering techniques. Subsequently, thanks to a close collaboration with the boat's sailor, it was possible to analyze the actual operating conditions during regattas. These data allowed the implementation of a Velocity Prediction Program (VPP) capable of simulating the hull's kinematics and identifying the optimal configuration to maximize the Velocity Made Good (VMG). From this analysis, the operational fluid dynamic conditions were derived, defined in terms of lift coefficient and Reynolds number, which constitute the input for the optimization core: a code developed in Python and coupled with the viscous solver XFOIL. The algorithm pursues two conflicting objectives: the minimization of hydrodynamic drag, weighted on the actual operating conditions, and the maximization of structural bending strength. The process, which integrates rigorous geometric and fluid dynamic constraints, leads to the generation of a Pareto Front of optimal solutions. Among these, some individuals were selected and subsequently validated: the reliability of the fluid dynamic results initially obtained with XFOIL was indeed verified through high-fidelity CFD (Computational Fluid Dynamics) simulations within the OpenFOAM environment.| File | Dimensione | Formato | |
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Tesi_definitiva_Caringella_Francesco.pdf
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https://hdl.handle.net/20.500.14251/5301