A5058266948- Model Reduction and Neural Networks
- Advanced Numerical Analysis Techniques
- Probabilistic and Robust Engineering Design
- Fluid Dynamics and Turbulent Flows
- Computational Fluid Dynamics and Aerodynamics
- Numerical methods in engineering
- Advanced Numerical Methods in Computational Mathematics
- Fluid Dynamics and Vibration Analysis
- Ship Hydrodynamics and Maneuverability
Scuola Internazionale Superiore di Studi Avanzati
2014-2018
We present the results of first application in naval architecture field a methodology based on active subspaces properties for parameters space reduction. The physical problem considered is one simulation hydrodynamic flow past hull ship advancing calm water. Such extremely relevant at preliminary stages design, when several simulations are typically carried out by engineers to assess dependence total resistance geometrical hull, and others related with flows properties. Given high number...
In this work, we provide an integrated pipeline for the model-order reduction of turbulent flows around parametrised geometries in aerodynamics. particular, free-form deformation is applied geometry parametrisation, whereas two different reduced-order models based on proper orthogonal decomposition (POD) are employed order to speed-up full-order simulations: first method exploits POD with interpolation, while second one domain decomposition. For sampling parameter space, adopt a Greedy...
Several problems in applied sciences and engineering require reduction techniques order to allow computational tools be employed the daily practice, especially iterative procedures such as optimization or sensitivity analysis.Reduced methods need face increasingly complex mechanics, into a multiphysics setting.Several issues should faced: stability of approximation, efficient treatment nonlinearities, uniqueness possible bifurcations state solutions, proper coupling between fields, well...
In this work we provide a combination of isogeometric analysis with reduced order modelling techniques, based on proper orthogonal decomposition, to guarantee computational reduction for the numerical model, and free-form deformation, versatile geometrical parametrization. We apply it fluid dynamics problems considering Stokes flow model. The proposed model combines efficient shape deformation accurate stable velocity pressure approximation incompressible viscous flows, computed method....
The work provides an integrated pipeline for the model order reduction of turbulent flows around parametrised geometries in aerodynamics. In particular, Free-Form Deformation is applied geometry parametrisation, whereas two different reduced-order models based on Proper Orthogonal Decomposition (POD) are employed to speed-up full-order simulations: first method exploits POD with interpolation, while second one domain decomposition. For sampling parameter space, we adopt a Greedy strategy...
We provide a new concept "tool" from CAD-like geometry to final simulation with the aim of dealing parametrized shapes managed by efficient free-form deformation techniques into an isogeometric analysis setting. This tool is totally integrated model order reduction techniques, based on POD, developed for stable incompressible viscous flows (velocity and pressure) in shapes. computational environment has been created framework project UBE Underwater Blue Efficiency optimization immersed parts...