The accurate prediction of vertical deformation (VD) in tidal flat terrains is crucial for their rational development and disaster warning. However, the high-precision and strong robustness prediction method of VD data in tidal flat terrain is still a great challenge due to the complex environmental conditions. In this study, VD data was collected over 7 consecutive days using Micro-Electro-Mechanical system (MEMS) sensor arrays deployed in a tidal flat. Particle swarm optimization (PSO)-optimized variational mode decomposition (VMD) is applied to decompose the acquired VD data for noise removal. The Fast Fourier Transform (FFT) and Spearman correlation coefficient analysis were employed to identify the inducing factors by comparing them with the decomposed VD. Four machine learning algorithms were evaluated to predict the trend and periodic components of VD, and the results showed that long short-term memory neural network (LSTM) outperformed the other three, achieving a root mean square error (RMSE) less than 0.15 and an R-squared ( R ² ) greater than 0.90. The proposed combined PSO-VMD-LSTM model effectively addresses the challenges of feature recognition, noise removal and deformation prediction of vertical deformation data in tidal flat terrains.

The artificially submerged cavitation water jet is effectively utilized by ejecting a high-pressure water stream into a low-pressure water stream through concentric nozzles and utilizing the cavitation phenomenon generated by the shear layer formed between the two streams. In this study, we investigated the cavitation characteristics of artificially submerged cavitation water jets by combining numerical simulations and erosion experiments. The results indicate that an appropriate standoff distance can generate more cavitation clouds on the workpiece surface, and the erosion characteristics of the artificially submerged cavitation water jet are most pronounced at a dimensionless standoff distance of S_D = 30. The shear effect formed between the two jets plays a crucial role in generating initial cavitation bubbles within the flow field of the artificially submerged cavitation water jet. Moreover, increasing the convergent angle between the two jets can significantly enhance the cavitation effect between them and lead to a more substantial cavitation effect. Simultaneously, increasing the pressure of the high-pressure inner nozzle also contributes to enhancing the cavitation effect of the artificially submerged cavitation water jet.

Dir Journal Diagnostic and Interventional Radiology