Optimization of vacuum cleaner fan components is a low-cost and time-saving solution to satisfy the increasing requirement for compact energy-efficient cleaners. In this study, surrogate-based optimization technique is used and for the first time it is focused on maximization of Airwatt parameter, which describes the fan suction power, as an objective function (Case II). Besides, the shaft power is minimized (Case I) as another optimization target in order to reduce the power consumption of the vacuum cleaner. 11 geometrical variables of 3 fan components including impeller, diffuser and return channel are selected as the optimization design variables. 80 training points are distributed in the sample space using Advanced Latin Hypercube Sampling (ALHS) technique and the outputs of sample points are calculated by means of CFD simulations. Kriging and RSA surrogate models have been fitted to the outputs of the sample space. Through coupling of constructed Kriging models and Multi-Island Genetic Algorithm (MIGA), the optimal design for each of the optimization cases is presented and evaluated using numerical simulations. A 20.22% reduction in shaft power in Case I and an improvement of 27.73% in Airwatt in Case II have been achieved as the overall results of this study. Despite achieving goals in both optimization cases, a slight decrease in Airwatt in Case I (−6.20%) and a slight increase in shaft power in Case II (+4.82%) are observed relative to primary fan. Furthermore, the Analysis of Variance (ANOVA) determines the importance level of design variables and their 2-way interactions on the objective functions. It was concluded that geometrical parameters related to all of the fan components must be considered simultaneously to conduct a comprehensive optimization. The reasons of enhancement in optimal cases compared with the reference design have been further investigated by analysis of the fan internal flow field. Post-processing of the CFD results demonstrates that the applied geometrical modifications cause a more uniform flow through the flow passages of the optimal fan components.

真空吸尘器风扇组件的优化是一种低成本、省时的解决方案，可满足对紧凑型节能吸尘器日益增长的需求。在这项研究中，使用了基于代理的优化技术，并且首次将重点放在 Airwatt 参数的最大化上，该参数描述了风扇吸力，作为目标函数（案例 II）。此外，将轴功率最小化（案例一）作为另一个优化目标，以降低真空吸尘器的功耗。选取叶轮、扩压器、回风道等3个风机部件的11个几何变量作为优化设计变量。使用高级拉丁超立方采样（ALHS）技术将80个训练点分布在样本空间中，并通过CFD模拟计算样本点的输出。克里金法和 RSA 代理模型已拟合到样本空间的输出。通过构建的克里金模型和多岛遗传算法 (MIGA) 的耦合，每个优化案例的优化设计被提出并使用数值模拟进行评估。作为本研究的总体结果，案例 I 中轴功率降低了 20.22%，案例 II 中 Airwatt 提高了 27.73%。尽管在两种优化情况下都实现了目标，但观察到相对于主风扇而言，案例 I 中的 Airwatt 略有下降 (-6.20%)，而案例 II 中的轴功率略有增加 (+4.82%)。此外，方差分析 (ANOVA) 确定了设计变量的重要性级别及其对目标函数的 2 向交互。得出的结论是，必须同时考虑与所有风扇组件相关的几何参数才能进行全面优化。通过对风扇内部流场的分析，进一步研究了与参考设计相比优化情况下增强的原因。CFD 结果的后处理表明，应用的几何修改会导致通过最佳风扇部件的流道的流动更加均匀。