Enhancing Submersible Safety: Predictive Modeling and Search Model for Lost Submersibles

The Titan submersible accident has raised global concerns regarding submersible safety. To mitigate such risks and enhance survival probabilities, this study develops a safety support system aimed at predicting submersible trajectories and optimizing search operations. A Submersible Position Prediction Model is established, incorporating seawater salinity, temperature, current, and seabed elevation data from the Ionian Sea. Force analysis based on computer simulations is used to generate minute-by-minute positional data, resulting in a probability distribution of its location. Model validation is conducted using Caribbean Sea data. Furthermore, a Two-Dimensional Kernel Density Estimation Search Model is proposed to minimize search time for a lost submersible. The method partitions the probability distribution into four regions, each searched by a robot moving outward from the center, significantly improving search efficiency. A function relating search success probability to time is derived based on robot speed and detection range. The results show that the lost submarine can be found within 1.3 hours. Finally, a multi-submersible position prediction model is introduced, which updates the dive path of one submersible based on real-time positions of others, enhancing coordination and emergency response capabilities.