Slope stability analyses are often performed using Limit Equilibrium Methods (LEMs) and Finite Element Method (FEM). However, these methods can only model the slope condition up to the point of failure. Meshfree methods, which do not require a mesh or a grid in the simulation process, have the potential to model the post-failure slope behavior as mesh tangling would not occur to cause numerical instability and non-convergence. Hence, while retaining the benefits of conventional numerical schemes, meshfree method can be more advantageous when problems with large deformation are encountered. In this paper, Semi-Lagrangian Reproducing Kernel Particle Method (SLRKPM), which is a type of meshfree method, is extended to analyze geomechanics problems such as the stability of a slope and post failure slope behavior. The results from SLRKPM agree well with those from convention methods (LEMs and FEM) in terms of factor-of-safety and failure surface. In addition, SLRKPM is able to simulate the slope failure process and successfully capture the development of shear band. This proves that SLRKPM has a significant advantage over FEM when dealing with problems involving large deformation and failure of geomaterials.

Navigating with low noise is the key capability in the submarine design considerations, and noise reduction is also one of the most critical issues in the related fields. Therefore, it is necessary to identify the source of noise during design stage to improve the survivability of the submarines. The main objective of this research is using the supervised neural network to construct the system of noise localization to identify noise source in the large acoustic tunnel. Firstly, we started our proposed method by improving the Yangzhou’s method and Shunsuke’s method. In the test results, we find that the errors of the both can be reduced by using the min-max normalization to highlight the data characteristics of the low amplitude in some frequency. And Yangzhou’s method has higher accuracy than Shunsuke’s method. Then, we reset the diagonal numbers of the cross spectral matrix in Yangzhou’s method to zero and replace mean absolute error to be the loss function for improving the stability of training, and get the most suitable neural network construction for our research. After our optimization, the error decreases from 0.315 m to 0.008 m in cuboid model test. Finally, we apply our method to the cavitation tunnel model. A total of 100 data sets were used for training, 10 sets for verification, and 5 for testing. The average error of the test result is 0.13 m. For the model test in cavitation tunnel in National Taiwan Ocean University, the length of ship model is around 7 m. And the average error is sufficient to determine the noise source position.

Cooling-water intake basins in coastal power plants may experience particle retention and sediment deposition when complex inlet geometry produces large-scale recirculation and low-velocity zones near intake structures. This study examines the use of submerged barriers to mitigate particle retention in a de-identified coastal power-plant intake basin using hydraulic model experiments and hydraulic-model-supported three-dimensional computational fluid dynamics (CFD). A geometrically consistent model-scale configuration, including the inlet channel, main basin, three intake openings, and two outlet passages, was used to preserve site confidentiality while retaining the essential hydraulic mechanisms. Surface-flow patterns, water-depth variations, and sediment-deposition behavior were measured in the physical model and used to assess the numerical model. The numerical results, supported by the available hydraulic-model observations, reproduced the dominant counterclockwise recirculation and a broadly similar retention-prone region. The simulated water depths agreed closely with the measurements, with relative errors below 0.50% for the finest mesh. A water-depth-based grid-sensitivity assessment using 1,221,165; 2,414,216; and 3,378,840 computational cells further showed that the predicted mean water depths remained within 1.15% of the experimental measurements. The assessed numerical model was then applied to examine submerged-barrier configurations installed near the inlet-to-basin transition, with the barrier-performance interpretation limited to the tested mesh, the assumed representative particle condition, and the available qualitative flow/deposition evidence. Under this assumed particle-tracking condition, the original configuration retained 6989 particles at t = 200 s, whereas the 6 cm submerged barrier reduced the retained-particle count to 4956, corresponding to a reduction of approximately 29.1%. In contrast, the 15 cm emergent barrier increased the retained-particle count to 7573 because it blocked overtopping flow and induced new separated low-velocity regions. These results indicate that, among the tested configurations and under the assumed representative particle condition, the 6 cm submerged barrier yielded the lowest retained-particle count, whereas an excessively high barrier may deteriorate the internal flow structure and increase particle accumulation.