Design and optimization of wall-climbing robot impeller by genetic algorithm based on computational fluid dynamics and kriging model

Abstract

Abstract In recent years, wall-climbing robots have begun to replace manual work at heights to reduce economic losses and casualties caused by working at heights. This paper designs a negative pressure adsorption type wall-climbing robot and analyzes the internal fluid movement state of its negative pressure device and the force analysis of the robot when it is adsorbed and balanced. Furthermore, through the experimental prototype, the influence of wall material, robot pose, negative pressure cavity shape and sealing method on the adsorption performance of the wall-climbing robot is explored. The computational fluid dynamics simulation (CFD) simulation method and experimental results are used to verify each other, which proves the correctness of the simulation results. Based on the Kriging surrogate model, the functional relationship between the impeller blade outlet angle, the impeller inlet diameter, the number of blades as the design variables, the negative pressure as the dependent variable was established, and the genetic algorithm (GA) was used to optimize it. Compared with the original design, the optimized design results of impeller parameters have increased the negative pressure value from 3534.75 to 4491.19 Pa, an increase of 27.06%.