Numerical simulation of a research vessel's aftpart hull form

This paper presents results of numerical simulation of the aftpart hull form of a research vessel. The wake rolls onto the transom platform, therefore creating additional resistance to the movement of the vessel. To solve the problem and to propose the optimal transom form, the study was carried out using the method of successive approximations and computational fluid dynamics (CFD) algorithms. The flow lines around the hull of the vessel and the wave pattern behind the hull were analysed, and the optimal transom form was chosen. Water resistance analysis was carried out to prove the efficiency of the chosen solution

Numerical simulation of a research vessel's aftpart hull form

This paper presents results of numerical simulation of the aftpart hull form of a research vessel. The wake rolls onto the transom platform, therefore creating additional resistance to the movement of the vessel. To solve the problem and to propose the optimal transom form, the study was carried out using the method of successive approximations and computational fluid dynamics (CFD) algorithms. The flow lines around the hull of the vessel and the wave pattern behind the hull were analysed, and the optimal transom form was chosen. Water resistance analysis was carried out to prove the efficiency of the chosen solution

ShipHullGAN : a generic parametric modeller for ship hull design using deep convolutional generative model

In this work, we introduce ShipHullGAN, a generic parametric modeller built using deep convolutional generative adversarial networks (GANs) for the versatile representation and generation of ship hulls. At a high level, the new model intends to address the current conservatism in the parametric ship design paradigm, where parametric modellers can only handle a particular ship type. We trained ShipHullGAN on a large dataset of 52,591 physically validated designs from a wide range of existing ship types, including container ships, tankers, bulk carriers, tugboats, and crew supply vessels. We developed a new shape extraction and representation strategy to convert all training designs into a common geometric representation of the same resolution, as typically GANs can only accept vectors of fixed dimension as input. A space-filling layer is placed right after the generator component to ensure that the trained generator can cover all design classes. During training, designs are provided in the form of a shape-signature tensor (SST) which harnesses the compact geometric representation using geometric moments that further enable the inexpensive incorporation of physics-informed elements in ship design. We have shown through extensive comparative studies and optimisation cases that ShipHullGAN can generate designs with augmented features resulting in versatile design spaces that produce traditional and novel designs with geometrically valid and practically feasible shapes