A bolt-connected deep-sea manned cabin made of maraging steel 18Ni(250) with an inner diameter of 2.1 m is under development, which will be applied to full ocean depth. As a promising guideline, the design formula by CCS is considered to be applied. However, the formula is originally derived based on titanium alloys with a strength level of 800 MPa. Whether it can be used for manned cabin of maraging steel in case of no change in the design coefficients should be checked. A model sphere will be designed and tested to collapse under uniform external pressure inside a high-pressure chamber to check the effectiveness and practicability of the design formula and the manufacture technology. Results of ultimate strength predicted or calculated by the design formula and direct finite element methods are compared with the experimentally obtained value. The influence of simplification on material properties and initial geometry imperfection is considered and discussed.

This study introduces FlowRes, an adapted ResNet-50 architecture, to predict flow fields around underwater vehicles, aiming to improve the efficiency of Computational Fluid Dynamics (CFD) through deep learning. The background highlights the necessity for rapid and accurate flow field predictions to enhance the hydrodynamic shape design of an Autonomous Remote-Controlled Vehicle (ARV) for inspection of offshore energy underwater infrastructure. Employing a decoder-only CNN-based model, the methodology involves modifying ResNet-50 for image-to-image generation, focusing on flow field visualization of underwater vehicles using a compact dataset. Results from training the model with 240 paired entries of flow fields and design parameters show significant computational speed improvements, with generated images deviating less than 1% from traditional CFD-generated images. The conclusions underline the potential of integrating advanced deep learning techniques with CFD, demonstrating FlowRes’s capability in significantly accelerating the design process of underwater vehicles by offering a faster, more resource-efficient alternative to traditional methods.

To better study the biology and ecology of hadal trenches for marine scientists, the Hadal Science and Technology Research Center (HAST) of Shanghai Ocean University proposed to construct a movable laboratory that includes a mothership, several full-ocean-depth (FOD) submersibles, and FOD landers to obtain samples in the hadal trenches. Among these vehicles, the project of an FOD autonomous and remotely-operated vehicle (ARV) named “Dream Chaser” was started in July 2018. The ARV could work in both remotely-operated and autonomous-operated modes, and serves large-range underwater observation, on-site sampling, surveying, mapping, etc. This paper proposed a novel three-body design of the FOD ARV. A detailed illustration of the whole system design method is provided. Numerical simulations and experimental tests for various sub-systems and disciplines have been conducted, such as resistance analysis using the computational fluid mechanics method and structural strength analysis for FOD hydrostatic pressure using the finite element method and pressure chamber tests. In addition, components tests and the entire system tests have been performed on land, underwater, and in the pressure chamber in the laboratory of HAST, and the results are discussed. Extensive experiments of two critical components, i.e., the thrusters and ballast-abandoning system, have been conducted and further analyzed in this paper. Finally, the procedures and results of lake trials, South China Sea trials and the first phase of Mariana Trench sea trials of the ARV in 2020 are also introduced. This paper provides a design method for the novel three-body FOD ARV. More importantly, the lessons learned from the FOD pressure test, lake tests, and sea trials, no matter the success or failure, will guide future endeavors and the application of ARV Dream Chaser and underwater vehicles of this kind.

To address the phenomenon of color shift and low contrast in underwater images caused by wavelength- and distance-related attenuation and scattering when light propagates in water, we propose a method based on an attention mechanism and adversarial autoencoder for enhancing underwater images. Firstly, the pixel and channel attention mechanisms are utilized to extract rich discriminative image information from multiple color spaces. Secondly, the above image information and the original image reverse medium transmittance map are feature-fused by a feature fusion module to enhance the network response to the image quality degradation region. Finally, the encoder learning is guided by the adversarial mechanism of the adversarial autoencoder, and the hidden space of the autoencoder is continuously approached to the hidden space of the pre-trained model. The results of the experimental images acquired from the Beihai Bay area of China on the HYSY-163 platform show that the average value of the Natural Image Quality Evaluator is reduced by 27.8%, the average value of the Underwater Color Image Quality Evaluation is improved by 28.8%, and the average values of the Structural Similarity and Peak Signal-to-Noise Ratio are improved by 35.7% and 42.8%, respectively, compared with the unprocessed real underwater images, and the enhanced underwater images have improved clarity and more realistic colors. In summary, our network can effectively improve the visibility of underwater target objects, especially the quality of images of submarine pipelines and marine organisms, and is expected to be applied in the future with underwater robots for pile legs of offshore wellhead platforms and large ship bottom sea life cleaning.