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Design and optimization of wall-climbing robot impeller by genetic algorithm based on computational fluid dynamics
Yi Fang1, Shuai Wang2, Da Cui3
1School of Mechanical and Aerospace Engineering, Jilin University, Changchun, 130025, China.
Scientific Reports
|June 10, 2022
Summary
This study introduces a negative pressure adsorption wall-climbing robot, optimizing its design for enhanced negative pressure and adsorption performance. The optimized robot design achieved a 27.06% increase in negative pressure.
Area of Science:
- Robotics
- Mechanical Engineering
- Fluid Dynamics
Background:
- Manual work at heights poses risks and economic challenges.
- Wall-climbing robots offer a safer alternative for high-altitude operations.
- Negative pressure adsorption is a key technology for secure wall adhesion.
Purpose of the Study:
- To design and analyze a negative pressure adsorption wall-climbing robot.
- To investigate factors affecting adsorption performance.
- To optimize the robot's negative pressure generation through parameter tuning.
Main Methods:
- Design of a negative pressure adsorption wall-climbing robot.
- Analysis of internal fluid dynamics using computational fluid dynamics (CFD).
- Experimental validation of adsorption performance considering various parameters.
- Optimization using Kriging surrogate model and genetic algorithm (GA).
Main Results:
- CFD simulations and experimental results were mutually verified.
- Adsorption performance was analyzed concerning wall material, robot pose, cavity shape, and sealing.
- Optimized impeller parameters significantly increased negative pressure by 27.06% (from 3534.75 to 4491.19 Pa).
Conclusions:
- The developed negative pressure adsorption wall-climbing robot demonstrates improved performance.
- CFD and experimental methods are effective for analyzing and validating robot performance.
- The optimization strategy successfully enhanced the robot's negative pressure generation capability.
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