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.
SonI-HNohYChoiE-H, et al. Optimization of the flow path efficiency in a vacuum cleaner fan. Stroj Vestnik/J Mech Eng2018; 64: 1–11.
2.
ParkCJunSParkK, et al. Methodology for system-level analysis of a fan–motor design for a vacuum cleaner. Proc IMechE, Part C: J Mechanical Engineering Science2017; 231: 3840–3854.
3.
CaiJ-CZhangJ-QYangC.Numerical study of the unsteady flow inside a centrifugal fan and its downstream pipe using detached eddy simulation. ASME Int Mech Eng Congr Expo2020; 84584: V010T10A032.
4.
Wang J and Kruyt N. Validation of CFD Simulations of Aerodynamic Performance of Low Speed Axial Fans with Low Hub-to-Tip Ratio. In: AIAA Scitech 2020 Forum, Jan. 2020, pp. 1–12, DOI: doi:10.2514/6.2020-0345.
5.
RajuGPKumarTJ.Analytical investigation of centrifugal blower guide vane using CFD technique. J Eng Sci2020; 11: 53–63.
6.
ZhouBHeXYangH, et al. Unsteady flow characteristics of rotating stall and surging in a backward centrifugal fan at low Flow-Rate conditions. Processes2020; 8: 872. doi: 10.3390/pr8070872.
7.
ZhangXZhangYLuC.Flow and noise characteristics of centrifugal fan in low pressure environment. Processes2020; 8: 985. doi: 10.3390/pr8080
8.
LiZYeXWeiY.Investigation on vortex characteristics of a Multi-Blade centrifugal fan near volute outlet region. Processes2020; 8: 1240. doi: 10.3390/pr810
9.
ZhaiSXiaoWH.Study on model of fans with uneven blade spacing. IOP Conf Ser: Earth Environ Sci2020; 508: 012168. DOI:doi: 10.1088/1755-1315/508/1/012168.
10.
ShenYLiYWangH, et al. Numerical simulation and performance optimization of the centrifugal fan in a vacuum cleaner. Mod Phys Lett B2019; 33: 1950440.,
11.
WangKJuYZhangC.Experimental and numerical investigations on effect of blade trimming on aerodynamic performance of squirrel cage fan. Int J Mech Sci2020; 177: 105579. doi: https://doi.org/10.1016/j.ijmecsci.2020.105579.
12.
YanyanDWangJWangW, et al. Multi-condition optimization of a cross-flow fan based on the maximum entropy method. Proc IMechE, Part A: J Power and Energy2021; 0: 1–10.
13.
LaiXWangHLiuH.Research on duct flow field optimisation of a robot vacuum cleaner. Int J Adv Robot Syst2011; 8: 65.
14.
ChooYHwangHChoJ, et al. Investigation of systematic efficiency in a high-speed single-phase brushless DC motor using multi-physics analysis for a vacuum cleaner. IEEE Trans Magn2019; 55: 1–6.
15.
HwangHChoJHwangS-H, et al. Design of a single-phase BLDC motor for a cordless vacuum cleaner considering the efficiency of airflow. Energies2019; 12: 465.
16.
ParkJWHwangDYParkHK.Performance improvement of a vacuum cleaner by analysis of the flow around motor. Fluids Eng Division Summer Meeting2008; 48401: 1167–1171.
17.
JungU-HKimJ-HKimJ-H, et al. Optimum design of diffuser in a small high-speed centrifugal fan using CFD & DOE. J Mech Sci Technol2016; 30: 1171–1184.
18.
KhelladiSKouidriSBakirF, et al. Flow study in the impeller–diffuser interface of a vaned centrifugal fan. J Fluids Eng2005; 127: 495–502.
19.
KhelladiSKouidriSBakirF, et al. Predicting tonal noise from a high rotational speed centrifugal fan. J Sound Vib2008; 313: 113–133.
20.
ParkI-SSohnCHLeeS, et al. Flow-induced noise in a suction nozzle with a centrifugal fan of a vacuum cleaner and its reduction. Appl Acoust2010; 71: 460–469.
21.
Sato K and Honda T. Analysis method for CFD results of centrifugal blower in vacuum cleaner using proper orthogonal decomposition. In: ASME International Mechanical Engineering Congress and Exposition, 2015, vol. 7B: Fluids, pp. 1–6, doi: 10.1115/IMECE2015-51178.
22.
PrezeljJNovakovićT.Centrifugal fan with inclined blades for vacuum cleaner motor. Appl Acoust2018; 140: 13–23.
23.
SamadAKimK-YGoelT, et al. Multiple surrogate modeling for axial compressor blade shape optimization. J Propuls Power2008; 24: 301–310.
24.
De DonnoRFracassiANoventaG, et al. Surrogate-based shape optimization of centrifugal pumps for automotive engine cooling systems BT. In: Gaspar-CunhaAPeriauxJGiannakoglouKC (eds) Advances in evolutionary and deterministic methods for design, optimization and control in engineering and sciences. Cham: Springer International Publishing, 2021, pp. 277–290.
25.
Waesker M, Buelten B, Kienzle N and Doetsch C. Optimization of Supersonic Axial Turbine Blades Based on Surrogate Models. In: Proceedings of the ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition, Sep. 2020, vol. 2B: Turbomachinery, doi: 10.1115/GT2020-14465.
26.
BambergerKCarolusTBelzJ, et al. Development, validation, and application of an optimization scheme for impellers of centrifugal fans using computational fluid dynamics-trained metamodels. J Turbomach2020; 142: DOI:doi: 10.1115/1.4048022.
27.
MarcoADde PaolaECamussiR, et al. Noise emission prediction of small scale twin pushing propellers through surrogate models. INTER-NOISE and NOISE-CON Congr Conf Proc2020; 261: 2265–2274.
28.
FracassiADe DonnoRGhidoniA, et al. Shape optimization and uncertainty assessment of a centrifugal pump. Eng Optim2020; 0: 1–18. Dec. doi: 10.1080/0305215X.2020.1858075.
29.
MaralHŞenelCBDeveciK, et al. A genetic algorithm based multi-objective optimization of squealer tip geometry in axial flow turbines: a constant tip gap approach. J Fluids Eng2019; 142: Oct DOI:doi: 10.1115/1.4044721.
30.
ZhengZ-YLiuQ-ZDengY-K, et al. Multi-objective optimization design for the blade angles of hydraulic torque converter with adjustable pump. Proc IMechE, Part C: J Mechanical Engineering Science2020; 234: 2523–2536.
31.
ZhangHGaoXSunB, et al. Parameter analysis and performance optimization for the vertical pipe intake-outlet of a pumped hydro energy storage station. Renew Energy2020; 162: 1499–1518.
32.
WangWLiYOsmanMK, et al. Multi-condition optimization of cavitation performance on a double-suction centrifugal pump based on ANN and NSGA-II. Processes2020; 8: 1124.
33.
Orso R, Benini E, Minozzo M, Bergamin R and Magrini A. Two-Objective Optimization of a Kaplan Turbine Draft Tube Using a Response Surface Methodology. Energies, vol. 13, no. 18, 2020, doi: 10.3390/en13184899.
34.
UmamaheswararaoLChandrasimhaV.Performance optimization of centrifugal fan with ducts through CFD simulation of impeller design. Int J Mech Eng2020; 5.
35.
ZhouSZhouHYangK, et al. Research on blade design method of multi-blade centrifugal fan for building efficient ventilation based on Hicks-Henne function. Sustain Energy Technol Assessments2021; 43: 100971.
36.
HeoM-WKimJ-HSeoT-W, et al. Aerodynamic and aeroacoustic optimization for design of a forward-curved blades centrifugal fan. Proc IMechE, Part A: J Power and Energy2016; 230: 154–174.
37.
Jaron R, et al. Multidisciplinary Design Optimization of a Low-Noise and Efficient Next-Generation Aero-Engine Fan. In: Proceedings of the ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition, Sep. 2020, vol. 2D: Turbomachinery, doi: 10.1115/GT2020-14580.
38.
EisenmengerCFrankSThamsenPU. Optimization of the aerodynamic and aeroacoustic characteristics of a small centrifugal fan by means of inverse design and evolutionary algorithms. In: Proceedings ASME 2020 Fluids Engineering Division Summer Meeting collocated with the ASME 2020 Heat Transfer Summer Conference and the ASME 2020 18th International Conference on Nanochannels, Microchannels, and Minichannels, vol. 1: Fluid A, Jul. 2020. doi: 10.1115/FEDSM2020-20219.
39.
QianBWuPHuangB, et al. Optimization of a centrifugal impeller on blade thickness distribution to reduce hydro-induced vibration. J Fluids Eng2019; 142: doi: 10.1115/1.4044965.
40.
BuYSongWHanZ, et al. Aerodynamic/aeroacoustic variable-fidelity optimization of helicopter rotor based on hierarchical Kriging model. Chinese J Aeronaut2020; 33: 476–492.
41.
QiuS.Design of a low noise turbofan duct via an acoustic gradient-enhanced Kriging method. Proc IMechE, Part C: J Mechanical Engineering Science2020; 0: 1–15.
42.
WangH, et al. Numerical optimization of horizontal-axis wind turbine blades with surrogate model. Proc IMechE, Part A: J Power and Energy2020; 235(5): 1173–1186.
43.
LiuLZhuBBaiL, et al. Parametric design of an ultrahigh-head pump-turbine runner based on multiobjective optimization. Energies2017; 10: 1169.
44.
WangWYuanSPeiJ, et al. Optimization of the diffuser in a centrifugal pump by combining response surface method with multi-island genetic algorithm. Proc IMechE, Part E: J Process Mechanical Engineering2017; 231: 191–201.
45.
ZhangYHuSWuJ, et al. Multi-objective optimization of double suction centrifugal pump using Kriging metamodels. Adv Eng Softw2014; 74: 16–26.
46.
CarrascoADVValles-RosalesDJMendezLC, et al. A site-specific design of a fixed-pitch fixed-speed wind turbine blade for energy optimization using surrogate models. Renew Energy2016; 88: 112–119.
47.
DuanBLuoMYuanC, et al. Multi-objective hydraulic optimization and analysis in a minipump. Sci Bull2015; 60: 1517–1526.
48.
KimJHKimKY.Hydrodynamic performance enhancement of a mixed-flow pump. IOP Conf Ser: Earth Environ Sci2012; 15: 012006.
49.
ZhaoXHuangBChenT, et al. Numerical simulations and surrogate-based optimization of cavitation performance for an aviation fuel pump. J Mech Sci Technol2017; 31: 705–716.
50.
Ma S.-B, Roh M.-S and Kim K.-Y. Optimization of Discrete Cavities with Guide Vanes in A Centrifugal Compressor based on A Comparative Analysis of Optimization Techniques. Int. J. Aeronaut. Sp. Sci., 2021, doi: 10.1007/s42405-020-00341-z.
51.
BadhurshahRSamadA.Multiple surrogate based optimization of a bidirectional impulse turbine for wave energy conversion. Renew Energy2015; 74: 749–760.
52.
KimK-YSeoS-J.Shape optimization of forward-curved-blade centrifugal fan with Navier-Stokes analysis. J Fluids Eng2004; 126: 735–742.
53.
GhoraniMMSotoude HaghighiMHRiasiA.Entropy generation minimization of a pump running in reverse mode based on surrogate models and NSGA-II. Int Commun Heat Mass Transf2020; 118: 104898.
54.
MaS-BKimSKimJ-H.Optimization design of a two-vane pump for wastewater treatment using machine-learning-based surrogate modeling. Processes2020; 8: 1170.
GhoraniMMSotoude HaghighiMHMalekiA, et al. A numerical study on mechanisms of energy dissipation in a pump as turbine (PAT) using entropy generation theory. Renew Energy2020; 162: 1036–1053.
57.
Sotoude HaghighiMHMirghavamiSMGhoraniMM, et al. A numerical study on the performance of a superhydrophobic coated very low head (VLH) axial hydraulic turbine using entropy generation method. Renew Energy2020; 147, doi: 10.1016/j.renene.2019.09.003.
58.
MojaddamMPullenKR.Optimization of a centrifugal compressor using the design of experiment technique. Appl Sci2019; 9: 291.
59.
HuangPGBardinaJCoakleyT.Turbulence modeling validation, testing, and development. NASA Tech Memo1997; 110446: 147.
60.
Sotoude HaghighiMHMirghavamiSMChiniSF, et al. Developing a method to design and simulation of a very low head axial turbine with adjustable rotor blades. Renew Energy2019; 135: 266–276.
61.
RoachePJGhiaKNWhiteFM.Editorial policy statement on the control of numerical accuracy. American Society of Mechanical Engineers, 1986.
62.
Giunta A, Wojtkiewicz S and Eldred M. Overview of Modern Design of Experiments Methods for Computational Simulations (Invited). In: 41st Aerospace Sciences Meeting and Exhibit, American Institute of Aeronautics and Astronautics, 2003, pp. 1–17.
63.
MartinJDSimpsonTW.Use of Kriging models to approximate deterministic computer models. AIAA J2005; 43: 853–863.
64.
DebKPratapAAgarwalS, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Computat2002; 6: 182–197.
65.
LenthRV.Quick and easy analysis of unreplicated factorials. Technometrics1989; 31: 469–473.
