In the construction of grouting holes in high-mud-content layers, high-pressure jet cleaning technology effectively cuts and removes soil and sediments from the strata. This research designs the structure of a high-pressure jet cleaning device and establishes a numerical simulation model for the high-pressure jet cleaning nozzle, conducting orthogonal simulation tests. Based on the data from these tests, a Backpropagation (BP) Neural Network-based numerical prediction model for the high-pressure jet cleaning flow field is developed, enabling the prediction of cleaning flow rates and pressures for different nozzle channel structure parameters. Targeting jet fluid velocity and cleaning pressure, parametric shape optimization is performed on the nozzle channel structure: key parameters are identified via Analysis of Variance (ANOVA) and sensitivity analysis; an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to establish a multi-objective optimization model, which exhibits superior convergence speed and solution diversity compared to the traditional algorithm. The optimal jet fluid velocity, cleaning pressure, and fluid structure parameter solution space for the high-pressure jet cleaning nozzle are obtained. Through simulation and experimental verification, it is found that with the same number of nozzles, the optimized design significantly enhances both the average cleaning flow rate and the cleaning pressure. Finally, a high-pressure jet cleaning nozzle and device are prototyped based on the simulation and optimization results and tested in the grouting test area A2W-2-III-6 of the South-to-North Water Diversion Project Xiong’an Storage Reservoir Project. This study provides a scientific basis and technical support for the application of high-pressure jet cleaning technology in complex geological formations.
Huang et al. (Wed,) studied this question.
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