Development of damage control training scenarios of naval ships based on simplified vulnerability analysis results

The survivability of a naval ship refers to its ability to withstand the damage caused by enemy attacks and to maintain their missions and functions. During naval warfare, if the armed space of a naval ship is exposed to the enemy, even a single shot can damage the combat system. Therefore, it is important to minimize damage to the armed space of naval ships and improve survivability. In this study, a system was implemented to automate the ship compartment layout design process considering survivability and to derive the optimal compartment layout. The survivability of a compartment can be calculated using the susceptibility distribution obtained using the axis-aligned bounding box (AABB) model and damage ellipsoid volume. To derive the survivability suitable for each naval ship, the susceptibility distribution of the naval ship was derived by radar cross section analysis. In addition, the line distribution space division method was developed to extract the AABB model from the analysis model. A genetic algorithm was used to derive an optimal compartment layout considering survivability. To compare the differences in survivability according to the compartment layout, optimal and worst compartment layout analysis was performed.

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Automated topology design to improve the susceptibility of naval ships using geometric deep learning

Hwang

Hong

Song

2023

Journal of Computational Design and Engineering

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The survivability of a naval ship is defined as its ability to evade or withstand a hostile environment while performing a given mission. Stealth technology, which reduces the probability of detection by enemy detection equipment using a highly advanced detection system, is one of the most important technologies to improve the survivability of naval ships. Moreover, radar cross section (RCS) reduction is a very important factor in stealth technology because a small RCS, which is the main parameter determining susceptibility, improves the ability of ships to evade enemy detection equipment. In this study, an automated topology design for reducing susceptibility was developed by combining geometric deep learning and topology optimization. A convolutional neural network model was used as the geometric deep-learning model, and the triangular meshes of the naval ship models and equipment models were used as datasets. To compensate for the lack of training data, randomly generated meshes were additionally used as datasets. To express the feature data of the mesh as a matrix, points at equal intervals were projected orthogonally and the distance between the plane and point was set as a matrix value. The label data were defined as the highest RCS values excluding the cardinal points. After realizing the topology design for reducing susceptibility using the developed system, verification was performed through RCS analysis of the original model and the topology-designed model.

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Development of damage control training scenarios of naval ships based on simplified vulnerability analysis results

Cited by 6 publications

References 1 publication

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AI-enhanced fire detection and suppression system for autonomous ships

Weapon compartment layout design method for improving the survivability of a naval ship by considering susceptibility distribution based on radar cross section analysis

Automated topology design to improve the susceptibility of naval ships using geometric deep learning

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