Research On The Nozzle Structure Design And Structural Optimization Of Underwater Marine Cleaning Robots

Underwater marine cleaning robots often use cavitation water jet nozzles for operations.Cavitation water jet is a technology that enhances the erosion effect of the jet by utilizing the cavitation effect of high-pressure water jet.Due to its efficient and environmentally friendly characteristics,it is widely used in underwater cleaning.Improving the cavitation performance of the nozzle can effectively improve cleaning efficiency,which is one of the current research hotspots.This article presents the structural design of the nozzle of an underwater marine cleaning robot,aiming to improve the cavitation performance of the nozzle through multi-objective optimization.(1)The basic structure of the nozzle has been designed.Based on the Fluent numerical simulation platform,a numerical model for the underwater operation of the nozzle of an underwater marine cleaning robot was established,and a suitable model was selected for numerical simulation.Co MParing the numerical simulation results with the experimental results,the numerical simulation results can reliably simulate the real underwater flow field and cavitation of the nozzle.(2)Based on the established numerical model,analyze the factors that affect the underwater cavitation performance of the nozzle one by one from environmental factors to nozzle structural parameters.Research has found that an increase in water depth can seriously suppress the cavitation performance of the nozzle.Increasing the working pressure of the nozzle is an effective and intuitive method to improve cavitation performance,but it has its drawbacks;Among the structural parameters of the nozzle,the diffusion angle and the radius of the cylindrical section have a significant i MPact on the nozzle cavitation performance,while the influence of other structural parameters is not strong.The Multiple R value of the multiple regression analysis of nozzle structural parameters and nozzle cavitation performance is 0.904,indicating a high correlation between the overall structural parameters and cavitation performance.To improve the cavitation performance of the nozzle,multi-objective optimization of the overall structural parameters is necessary.(3)Due to the lack of a clear linear relationship between the structural parameters of the nozzle and its cavitation performance,it is difficult to use intelligent algorithms for multi-objective optimization.To solve this problem,an artificial neural network model is established to predict this relationship.Two models,BP neural network and RBF neural network,were established and trained.After co MParing the prediction effects of the two models,the RBF neural network with higher prediction accuracy was selected as the prediction model.(4)On the basis of the traditional genetic algorithm,the selection method of the initial population is improved,the adaptive change function of mutation probability and cross probability is introduced,and the prediction model is used to replace the fitness calculation formula to calculate the individual fitness.The improved algorithm iteratively obtains the optimal nozzle structure parameters faster than traditional genetic algorithms.After co MParing the cavitation performance of the optimized and pre optimized nozzles,it was found that the optimized nozzle significantly improved its cavitation performance and could better resist the suppression of depth on cavitation performance.