A shape optimization pipeline for marine propellers by means of reduced order modeling techniques

DOI10.1002/NME.7426WikidataQ130081961 ScholiaQ130081961MaRDI QIDQ6499905

Nicola Demo, Anna Ivagnes, Gianluigi Rozza

Publication date: 10 May 2024

Published in: International Journal for Numerical Methods in Engineering (Search for Journal in Brave)

zbMATH Keywords

genetic algorithmdata-driven reduced order modelingmesh parametrizationblade propeller designindustrial shape optimization

Mathematics Subject Classification ID

Numerical approximation and computational geometry (primarily algorithms) (65Dxx) Mathematical programming (90Cxx) Numerical methods for mathematical programming, optimization and variational techniques (65Kxx)

Cites Work

• Unnamed Item
• Tessellating trimmed NURBS surfaces
• An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques
• An integrated data-driven computational pipeline with model order reduction for industrial and applied mathematics
• The finite volume method in computational fluid dynamics. An advanced introduction with OpenFOAM and Matlab
• Modeling of vortex dynamics in the wake of a marine propeller
• A Limited Memory Algorithm for Bound Constrained Optimization
• A Supervised Learning Approach Involving Active Subspaces for an Efficient Genetic Algorithm in High-Dimensional Optimization Problems
• Free-form deformation, mesh morphing and reduced-order methods: enablers for efficient aerodynamic shape optimisation
• The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations
• Genetic Algorithms and the Optimal Allocation of Trials
• Methods of conjugate gradients for solving linear systems

This page was built for publication: A shape optimization pipeline for marine propellers by means of reduced order modeling techniques