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Article

Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing

Mathematics Area, mathLab, SISSA, Via Bonomea 265, I-34136 Trieste, Italy
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Academic Editors: Stefano Brizzolara and Giuliano Vernengo
J. Mar. Sci. Eng. 2021, 9(2), 185; https://doi.org/10.3390/jmse9020185
Received: 4 January 2021 / Revised: 3 February 2021 / Accepted: 7 February 2021 / Published: 11 February 2021
In the field of parametric partial differential equations, shape optimization represents a challenging problem due to the required computational resources. In this contribution, a data-driven framework involving multiple reduction techniques is proposed to reduce such computational burden. Proper orthogonal decomposition (POD) and active subspace genetic algorithm (ASGA) are applied for a dimensional reduction of the original (high fidelity) model and for an efficient genetic optimization based on active subspace property. The parameterization of the shape is applied directly to the computational mesh, propagating the generic deformation map applied to the surface (of the object to optimize) to the mesh nodes using a radial basis function (RBF) interpolation. Thus, topology and quality of the original mesh are preserved, enabling application of POD-based reduced order modeling techniques, and avoiding the necessity of additional meshing steps. Model order reduction is performed coupling POD and Gaussian process regression (GPR) in a data-driven fashion. The framework is validated on a benchmark ship. View Full-Text
Keywords: shape optimization; reduced order modeling; high-dimensional optimization; parameter space reduction; computational fluid dynamics shape optimization; reduced order modeling; high-dimensional optimization; parameter space reduction; computational fluid dynamics
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MDPI and ACS Style

Demo, N.; Tezzele, M.; Mola, A.; Rozza, G. Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing. J. Mar. Sci. Eng. 2021, 9, 185. https://doi.org/10.3390/jmse9020185

AMA Style

Demo N, Tezzele M, Mola A, Rozza G. Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing. Journal of Marine Science and Engineering. 2021; 9(2):185. https://doi.org/10.3390/jmse9020185

Chicago/Turabian Style

Demo, Nicola; Tezzele, Marco; Mola, Andrea; Rozza, Gianluigi. 2021. "Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing" J. Mar. Sci. Eng. 9, no. 2: 185. https://doi.org/10.3390/jmse9020185

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