Hull shape design optimization with parameter space and model reductions, and self-learning mesh morphing
Nicola Demo,
Marco Tezzele,
Andrea Mola
et al.
Abstract: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 subsp… Show more
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