An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques

Abstract

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.

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

“…We set the full order model in scale 1:59.407, keeping it unaltered from the original work mainly for validation purpose. The computational domain, that is a parallelepiped of dimension [−26, 16] 16,4] along x, y and z directions is discretized in 8.5 × 10 5 cells, with anisotropic vertical refinements located particular in the free-surface region, in order to avoid a too diffusive treatment of the VOF variable. Boundaries of such domain are imposed as follows:…”

“…Indeed, the datadriven POD based ROM employed in the present optimization framework can be coupled with any PDE solver, as the data integration is enforced through the output of interest of the full order problem. Similar reduced methods have been proposed in [3,4] for the shape optimization of a benchmark hull, while additional improvements have been made coupling the ROM with active subspace analysis and different shape parameterization algorithms in [5][6][7][8]. We refer the readers interested in parametric hull shape variations using ROMs to [9], while we mention [10,11] for design-space dimensionality reduction in shape optimization with POD.…”

Gaussian process approach within a data-driven POD framework for fluid dynamics engineering problems

“…We now proceed to discuss the main ingredients of the optimization pipeline in order to understand the role of the POD-GPR reduction. We avoid going into many technical details and refer the reader to [18,5] for a more extensive presentation of all the optimization pipeline.…”

A Cost-Efficient Approach towards Computational Fluid Dynamics Simulations on Quantum Devices

“…By reducing the state-space dimension of the model (or degrees of freedom), an approximation to the original model is computed, which is commonly referred to as a reduced-order model (ROM) [15][16][17][18]. ROMs are small in complexity and cheap in terms of computational time; thus, they can be effectively applied in the early stages of product development, as in conceptual design, virtual prototyping and optimization (such as in naval shape design [19] and wind-driven ocean flows [20]), where highly accurate results…”