Due to the need for accurate exploration of deep-sea scientific research, drilling techniques by combining the operational advantages of the Jiaolong manned submersible is considered one of the most feasible methods for deep-sea bedrock drilling. Based on deep sea bedrock cutting model and discrete element simulation, as well as efficient drilling as the design criterion, the development of a deep sea 7000 m electromechanical coring apparatus was carried out. The outstanding feature of this technology is that the bit load produced by the drill pressure is usually within the range 100–400 N while the recommended load for diamond drilling is 1–3 KN or even more. Therefore, searching for the drilling bits that can drill in extremely hard formations with minimal load and acceptable rates of penetration and rotary speed is the necessary step to prove the feasibility of electromechanical deep-sea drilling technology. A test has been designed and constructed to examine three types of drill bits. The results of experiments show that the new low-load polycrystalline diamond compact (PDC) bit has the highest penetration length of 138 mm/15 min under a 300 N load and 250 rpm rotary speed. Finally, field tests with the Jiaolong submersible were used to conduct deep sea experiments and verify the load model, which provides theoretical and technical data on the use of a low-load core sampling drill developed specifically for a deep sea submersible.

Acoustic bottom scattering is the main source of reverberation background in target detection and recognition near the seafloor. Accurate estimation of bottom scattering strength is necessary for prediction of sonar performance in the presence of reverberation in shallow-water environments. A measurement of bottom backscattering strength at midfrequency in a shallow-water environment of the South Yellow Sea of China was carried out in April 2016, where the water depth was 39 m. The surface of the seafloor was relatively flat and covered by some shell fragments observed through a digital camera system. Core data showed that the main component of sediment at this experiment site was fine sand. The backscattering strength as a function of grazing angle (20°–70°) in the frequency range of 6–12 kHz was extracted from monostatic backscattering data, employing an omni-directional source and an omni-directional hydrophone. The measured backscattering strength increases with the increase of the grazing angle, and changes more rapidly at large grazing angles (60°–70°). Comparing the data at different frequencies, it is found that the measured backscattering strength slightly increases with the increase of the acoustic frequency. A fit of Lambert's law to the measured data shows that the backscattering strength deviates from the Lambert's law at large grazing angles. Finally, the fitted result of a scattering model to the measured data suggests that seafloor roughness scattering is larger than that from sediment volume heterogeneity in the measurement frequency band.

A drogue is used to stabilise and straighten seismic arrays so that seismic waves can be well-received. To embed the effect of a cone-shaped drogue into the numerical modelling of the deep-towed seismic survey system, one surrogate model that maps the relationship between the hydrodynamic characteristics of the drogue and towing conditions was obtained based on data-driven simulations. The sample data were obtained by co-simulation of the commercial software RecurDyn and Particleworks, and the modelling parameters were verified by physical experiments. According to the Morison formula, the rotational angle, angular velocity, angular acceleration, towing speed, and towing acceleration of the drogue were selected as the design variables and drag forces and aligning torque were selected as the research objectives. The sample data of more than 8500 sets were obtained from virtual manoeuvres. Subsequently, both polynomial and neural network regression algorithms were used to study these data. Finally, analysis results show that the surrogate model obtained by machine learning has good performance in predicting research objectives. The results also reveal that the neural network regression algorithm is superior to the polynomial regression algorithm, its largest error of mean square is less than 0.8 (N²/N² mm²), and its R-squared is close to 1.

In order to make the watertight connector localized, this paper mainly studies the outer insulating material neoprene 2442 (CR2442) of the watertight connector from the material, and improves the physical and mechanical properties of the CR2442 and the adhesion of the CR2442 to the brass by designing the formula. The performance and finalization of the watertight connector molding and vulcanization molds are designed to meet the molding needs of watertight connectors. Through research, it is found that the CR2442 optimized by this paper has good physical and mechanical properties and adhesive properties.