Machine Learning Combined with Numerical Simulations: An Effective Way to Reconstruct the Detonation Point of Contact Underwater Explosions with Seabed Reflection

Abstract

In marine engineering, the study of underwater explosion effects on naval and offshore structures has gained significant attention due to its critical impact on structural integrity and safety. In practical applications, a crucial aspect is determining the precise point at which an underwater explosive charge has detonated. This information is vital for assessing damage, implementing defensive and security strategies, and ensuring the structural integrity of marine structures. This paper presents a novel approach that combines coupled numerical simulations performed using the MSC Dytran suite with machine learning techniques to reconstruct the trigger point of underwater explosions based on onboard sensor data and leverage seabed wave reflection information. A Multi-Layer Neural Network (MLNN) was devised to identify the position of the denotation point of the charge using a classification task based on a user-defined two-dimensional grid of potential triggering locations. The MLNN underwent training, validation, and testing phases using simulation data from different underwater blast-loading scenarios for metallic target plates. Different positions of the charge, seabed typologies, and distances between the structure and the seabed are considered. The ability to accurately identify a detonation point using measurable data from onboard systems enhances the knowledge of ship and offshore structures’ response strategies and the overall safety of naval operations.

Keywords

• underwater explosion
• fluid-structure interaction
• machine learning
• multi-layer neural network
• seabed reflection
• onboard sensors