Deep Dive Safeguard: Advanced Searching for MCMS

Authors

  • Tianyi Zhou
  • Lianghuan Wu
  • Keyu Lin

DOI:

https://doi.org/10.61173/91adp918

Keywords:

Differential Equation, the Runge-Kutta, the Analytic Hierarchy Process

Abstract

Maritime Cruises Mini-Submarines (MCMS) plans to offer a unique underwater experience in the Ionian Sea, contingent on regulatory approval and establishing safety procedures.  We developed a Location Predictive Model using Runge-Kutta methods to predict submersible positions considering various uncertainties.  Monte Carlo simulation handled random components.  Recommended Search and Rescue (SAR) equipment, evaluated by Analytic Hierarchy Process, includes sonar systems and autonomous underwater vehicles, with the Remote Operated Vehicle being the most suitable. The Fast-Search Model optimizes SAR equipment deployment, reducing search time. The Destination Extension Model is adaptable to other tourist destinations. In conclusion, this article highlights critical location prediction and innovative SAR models supporting MCMS’s application and approval, promising Greece leadership in maritime tourism with high safety standards.

References

plexity of marine environments. The models necessitate Echinodermata. CRC Press, 2020: 185-193. continual refinement and validation against real-world [3]Castellanos M B, Serpa A L, Biazussi J L, et al. Fault SAR scenarios to ensure their robustness and reliability. identification using a chain of decision trees in an electrical submersible pump operating in a liquid-gas flow[J]. Journal of 7.2 Future Improvements Petroleum Science and Engineering, 2020, 184: 106490. To advance submersible SAR operations, future improve-

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Published

2024-06-06

Issue

Vol. 2 No. 3 (2024): Volume 2, Issue 3

Section

Articles

License

Copyright (c) 2024 by the authors.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.