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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2020
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2004.11201 |
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| _version_ | 1866917570230091776 |
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| author | Demo, Nicola Ortali, Giulio Gustin, Gianluca Rozza, Gianluigi Lavini, Gianpiero |
| author_facet | Demo, Nicola Ortali, Giulio Gustin, Gianluca Rozza, Gianluigi Lavini, Gianpiero |
| contents | This contribution describes the implementation of a data--driven shape optimization pipeline in a naval architecture application. We adopt reduced order models (ROMs) in order to improve the efficiency of the overall optimization, keeping a modular and equation-free nature to target the industrial demand. We applied the above mentioned pipeline to a realistic cruise ship in order to reduce the total drag. We begin by defining the design space, generated by deforming an initial shape in a parametric way using free form deformation (FFD). The evaluation of the performance of each new hull is determined by simulating the flux via finite volume discretization of a two-phase (water and air) fluid. Since the fluid dynamics model can result very expensive -- especially dealing with complex industrial geometries -- we propose also a dynamic mode decomposition (DMD) enhancement to reduce the computational cost of a single numerical simulation. The real--time computation is finally achieved by means of proper orthogonal decomposition with Gaussian process regression (POD-GPR) technique. Thanks to the quick approximation, a genetic optimization algorithm becomes feasible to converge towards the optimal shape. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2004_11201 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques Demo, Nicola Ortali, Giulio Gustin, Gianluca Rozza, Gianluigi Lavini, Gianpiero Numerical Analysis This contribution describes the implementation of a data--driven shape optimization pipeline in a naval architecture application. We adopt reduced order models (ROMs) in order to improve the efficiency of the overall optimization, keeping a modular and equation-free nature to target the industrial demand. We applied the above mentioned pipeline to a realistic cruise ship in order to reduce the total drag. We begin by defining the design space, generated by deforming an initial shape in a parametric way using free form deformation (FFD). The evaluation of the performance of each new hull is determined by simulating the flux via finite volume discretization of a two-phase (water and air) fluid. Since the fluid dynamics model can result very expensive -- especially dealing with complex industrial geometries -- we propose also a dynamic mode decomposition (DMD) enhancement to reduce the computational cost of a single numerical simulation. The real--time computation is finally achieved by means of proper orthogonal decomposition with Gaussian process regression (POD-GPR) technique. Thanks to the quick approximation, a genetic optimization algorithm becomes feasible to converge towards the optimal shape. |
| title | An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2004.11201 |