期刊
BOLLETTINO DELLA UNIONE MATEMATICA ITALIANA
卷 14, 期 1, 页码 211-230出版社
SPRINGER INT PUBL AG
DOI: 10.1007/s40574-020-00263-4
关键词
Finite volume method; Computational fluid dynamics; Data-driven reduced order modeling; Free form deformation; Shape optimization
类别
资金
- Scuola Internazionale Superiore di Studi Avanzati -SISSA within the CRUI-CARE Agreement
This contribution describes the implementation of a data-driven shape optimization pipeline in a naval architecture application. Reduced order models are adopted to improve efficiency and a dynamic mode decomposition enhancement is used to reduce computational costs. Real-time computation is achieved through proper orthogonal decomposition with Gaussian process regression technique, enabling convergence towards the optimal shape using a genetic optimization algorithm.
This contribution describes the implementation of a data-driven shape optimization pipeline in a naval architecture application. We adopt reduced order models 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. 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 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 technique. Thanks to the quick approximation, a genetic optimization algorithm becomes feasible to converge towards the optimal shape.
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