The You Only Look Once (YOLO) series of object detection models is widely recognized for its efficiency and real-time performance, particularly under the challenging conditions of underwater environments, characterized by insufficient lighting and visual disturbances. By modifying the YOLOv9s model, this study aims to improve the accuracy and real-time capabilities of underwater object detection, resulting in the introduction of the YOLOv9s-UI detection model. The proposed model incorporates the Dual Dynamic Token Mixer (D-Mixer) module from TransXNet to improve feature extraction capabilities. Additionally, it integrates a feature fusion network design from the LocalMamba network, employing channel and spatial attention mechanisms. These attention modules effectively guide the feature fusion process, significantly enhancing detection accuracy while maintaining the model’s compact size of only 9.3 M. Experimental evaluation on the UCPR2019 underwater object dataset shows that the YOLOv9s-UI model has higher accuracy and recall than the existing YOLOv9s model, as well as excellent real-time performance. This model significantly improves the ability of underwater target detection by introducing advanced feature extraction and attention mechanisms. The model meets portability requirements and provides a more efficient solution for underwater detection.

In this study, a multi-objective shape optimization framework was established for photovoltaic-powered underwater vehicles (PUVs) to systematically investigate multidisciplinary coupled design methodologies. Specifically, a global sensitivity analysis was conducted to identify four critical design parameters with 24 h energy consumption and cabin volume serving as dual optimization objectives. An integrated automated optimization workflow was constructed by incorporating parametric modeling, computational fluid dynamics (CFD) simulations, and dynamic surrogate models. Additionally, a new phased hybrid adaptive lower confidence bound (PHA-LCB) infill criterion was designed under the consideration of error-driven mechanisms, improvement feedback loops, and iterative attenuation factors to develop high-precision dynamic surrogate models. Coupled with the NSGA-II multi-objective genetic algorithm, this framework generated Pareto-optimal front solutions possessing significant engineering value. Furthermore, an optimal design configuration was ultimately determined through multi-criteria decision analysis. Compared to the initial form, it generates an additional 1148.12 Wh of electrical energy within 24 h, with an 22.36% increase in sailing range and a 2.77% improvement in cabin volume capacity. The proposed closed-loop “modeling–simulation–optimization” framework realized multi-objective optimization of PUV shape parameters, providing methodological paradigms and technical foundations for the engineering design of next-generation autonomous underwater vehicles.

Emergency surfacing acts as the final line of defense in preserving the operational viability of submarines, playing a crucial role in their safety. To investigate the dynamic characteristics of submarine emergency surfacing, utilizing whole moving mesh technology, a method for coupled simulation of high-pressure air blowing out water tanks and emergency surfacing motion of submarines is proposed, enhancing the simulation’s fidelity to real-world dynamics. Based on meeting the requirements for simulation accuracy, utilizing the coupled simulation model, this study explored the effects of varying expulsion pressures on submarine motion parameters including depth, roll, pitch, and yaw angles. The findings indicate that the hull emerges slightly earlier and reaches a marginally higher point when coupling effects are accounted for compared to scenarios where these effects are neglected. At consistent expulsion pressures, as the pitch and roll angles increase and the back pressure decreases, the expulsion rate from the ballast tank accelerates. Higher expulsion pressures result in quicker surfacing of the hull, smaller amplitude of pitch angles, and larger amplitudes of roll angles, while the changes in yaw angle displayed no clear pattern. The methodologies and conclusions of this study offer valuable insights for the design and operational strategies of actual submarines.