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To achieve a stable addition of foaming agent under high flow rates, an independently designed venturi structure with self‐suction of liquid to add foaming agent was numerically simulated. We simulated the effects of different self‐suction liquid structures' diameters (diameter of the tube in which the liquid is sucked in), contraction angles, throat length, and position on the suction liquid ratio (ratio of venturi self‐suction liquid flow rate to total flow rate), compared and analyzed the data, and determined the optimal self‐suction liquid structures parameters. The suction liquid ratio shows a tendency to first stabilize and then decrease as the outlet pressure increases. When the outlet pressure in range of 0.30–0.45 MPa, the suction liquid ratio remains stable at 1.14%. However, as the outlet pressure increases, the ability of the venturi structure to add foaming agent through the self‐suction liquid decreases under high flow rates, causing the suction liquid ratio to rapidly decrease to a minimum value of 0.60 MPa. We have conducted a detailed analysis of the process and flow field of suction liquid. As the outlet pressure increases, the high‐speed area of the throat gradually shrinks, and the flow field in the diffusion section fluctuates. When the outlet pressure is 0.6 MPa and the velocity field shifts toward the upper part of the diffusion section, the cavitation area will gradually disappear and lose its liquid suction ability. [ABSTRACT FROM AUTHOR]

Based on the data of the acoustic and physical properties of seafloor sediments obtained on the continental shelf area of the East China Sea (ECS), prediction equations of the sediment sound speed based on single and dual parameters were established, and the correlation of the sound speed with physical parameters was discussed. The results show that the sediment sound speed (c) is strongly correlated with water content (w), density (ρ), void ratio (e), mean grain size (Mz) and median grain size (Md), and the coefficient of determination R2 of the empirical regression equation is generally greater than 0.80, while the empirical regression equation coefficient of determination R2 of the compression coefficient (a) and compression modulus (E) are slightly lower, with 0.79 and 0.73, respectively. The coefficients of determination of the dual parameter regression equations between sediment sound speed with physical property parameters are generally higher than those of the single parameter equations, which are all higher than 0.90 indicating better prediction performance. The sensitivity of the physical parameters to the sound speed was analyzed, and the result shows that the sequence of sensitivity from high to low influence on sediment sound speed is void ratio, density, compressibility coefficient, median grain size and mean grain size. The prediction equations established in this study are a good extension and supplement to marine geoacoustic models and is of great significance for obtaining the acoustic properties of the seafloor sediments on the shelf of the East China Sea. [ABSTRACT FROM AUTHOR]

Based on the data of the sound speed and the physical–mechanical properties of 300 seafloor sediment samples collected in the middle area of the Southern Yellow Sea, a backpropagation (BP) neural network algorithm is used herein to study the neural network topology and the corresponding neuron characteristic factors. A neural network model for predicting the sound speed of the seafloor sediment based on density, water content, porosity, void ratio, liquid limit, plastic limit, and clay content in the middle area of the Southern Yellow Sea is established. The results show that the sound speed prediction accuracy is the highest when the network model contains seven input layer neurons, ten hidden layer neurons, and one output node. According to the prediction results, the MAE(Mean Absolute Error) and the MAPE(Mean Absolute Percentage Error) are 13.19 m s−1 and 0.85%, respectively. The prediction results illustrate that the established BP neural network prediction method for the seafloor sediment sound speed is better than the traditional single, two and multi-parameter prediction equations. [ABSTRACT FROM AUTHOR]

AbstractBased on the sound speed and physical property measurements of seafloor sediments obtained in the central part of the South Yellow Sea, a multi-parameter prediction model for the sound speed of seafloor sediments based on seven parameters, including density, porosity, water content, liquid limit, plasticity index, compression coefficient, and median grain size, was developed by using the BP-AdaBoost fusion algorithm. The results show that the model, configured with 7 input layer neurons, 10 hidden layer neurons, 1 output node, a learning rate of 0.1, and 300 training iterations, achieved a correlation coefficient of 0.962. The mean absolute error (MAE) of the predicted sound speed was 10.19 m/s, and the mean relative error (MAPE) was 0.66%. These results are better than those of single-parameter and dual-parameter prediction equations and other machine learning models. The BP-AdaBoost model prediction method of sound speed of seafloor sediments established in this paper is better than the single-parameter and dual-parameter prediction equations and other prediction models, and it can provide a new way for the prediction of sound speed of seafloor sediments in the study area. [ABSTRACT FROM AUTHOR]