Performance Optimization of Omnidirectional Cleaning System Nozzle Based on Response Surface and MOGA

To address the current issues of manual cargo space cleaning costs and low cleaning efficiency, the research team developed an omnidirectional cleaning system. To further improve the spraying performance of the cleaning system, the key hydraulic component, the nozzle, was selected as the research subject for multi-objective optimization of its structure. Using the nozzle’s inlet radius (R1), outlet radius (R2), and length (L1), as variables, with a constraint of outlet average velocity ≥ 24.5 m/s, and aiming to minimize pressure difference and turbulent kinetic energy, the central composite design (CCD) method was employed for experimental design, and a response surface model was established in conjunction with numerical simulation and it was found that the outlet radius (R2) has the greatest impact on the nozzle’s performance, followed by the inlet radius (R1), with the length (L1) having the least impact. The Multi-Objective Genetic Algorithm (MOGA) was used to optimize the nozzle structure and obtain the Pareto optimality. The optimization results indicated that the optimized nozzle structure resulted in a 14.75% reduction in pressure difference, a 2.08% increase in outlet average velocity, and a length reduction of nearly 75 mm. This not only optimized the spraying performance but also reduced material consumption and costs.