Multi-objective Optimization of Single-stage High-flow Centrifugal Blower Based on Sparrow Search Algorithm-back Propagation Neural Network and Non-dominated Sorting Genetic Algorithm -Ⅱ

Lei, C.; Zhang, J.; Li, F.; Wang, Z.; Yang, H.; Zhang, W.; Wei, Y.

doi:10.47176/jafm.18.11.3362

Multi-objective Optimization of Single-stage High-flow Centrifugal Blower Based on Sparrow Search Algorithm-back Propagation Neural Network and Non-dominated Sorting Genetic Algorithm -Ⅱ

Document Type : Regular Article

Authors

¹ Zhejiang Key Laboratory of Multiflow and Fluid Machinery,Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China

² Denair Energy Equipment Co., Ltd., Jiaxing, Zhejiang 314211, China

³ General Machinery and Key Basic Component Innovation Center (Anhui) Co., Ltd, State Key Laboratory of High-end Compressor and System Technology, Hefei General Machinery Research Institute Co., Ltd., Hefei, Anhui 230031, China

10.47176/jafm.18.11.3362

Abstract

In this paper, the aerodynamic performance of the single-stage high-flow centrifugal blower is enhanced and optimized through multi-objective optimization by modifying the geometry parameters of the impeller. Seven design variables, which define the angle distribution of the impeller, are employed to parameterize its geometry. The polytropic efficiency and total pressure ratio of the centrifugal blower are selected as the two primary objective functions in the optimization process. The geometric parameters of the centrifugal impeller are sampled using the Latin Hypercube Sampling (LHS) method. Based on Computational Fluid Dynamics (CFD), the sample library comprising 60 sets of new geometric parameters for centrifugal impellers. The Sparrow Search Algorithm-Back Propagation Neural Network (SSA-BPNN) is utilized to train the sample set. Subsequently, the second-generation Non-dominated Sorting Genetic Algorithm (NSGA-II) is employed for the optimization of the centrifugal blower. Compared with the reference centrifugal impeller, the optimized impeller demonstrates a higher average outlet relative Mach number and a lower absolute Mach number at the outlet, leading to improved flow uniformity at the impeller exit. The flow separation on the diffuser blades is diminished, and the vortex structure near the impeller shroud is reduced. The polytropic efficiency and total pressure ratio of the centrifugal blower increase by up to 2.49% and 3.18%, respectively. The operational range with high polytropic efficiency is effectively expanded for the centrifugal blowers. The aforementioned findings underscore the effectiveness of the deployed multi-objective optimization techniques in refining the performance of the centrifugal blower.

Keywords

Main Subjects

Turbomachinary

References

Afandi, A., Lusi, N., Catrawedarma, I. G. N. B., Subono, Rudiyanto, B. (2022). Prediction of temperature in 2 meters temperature probe survey in Blawan geothermal field using artificial neural network (ANN) method. Case Studies in Thermal Engineering, 38, 102309. https://doi.org/https://doi.org/10.1016/j.csite.2022.102309

Arias-Montaño, A., Coello Coello, C. A., & Mezura-Montes, E. (2011). Evolutionary ALGORITHMS applied to multi-objective aerodynamic shape optimization. In S. Koziel & X. S. Yang (Eds.), Computational Optimization, Methods and Algorithms (pp. 211-240). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-20859-1_10

Bourgeois, J. A., Martinuzzi, R. J., Savory, E., Zhang, C, & Roberts, D. A. (2010). Assessment of Turbulence Model Predictions for an Aero-Engine Centrifugal Compressor. Journal of Turbomachinery, 133(1). https://doi.org/10.1115/1.4001136

Catrawedarma, I. G. N. B., Resnaraditya, F. A., & Deendarlianto, Indarto (2021). Statistical characterization of the flow structure of air-water-solid particles three-phase flow in the airlift pump-bubble generator system. Flow Measurement and Instrumentation, 82, 102062. https://doi.org/https://doi.org/10.1016/j.flowmeasinst.2021.102062

Chen, Z. K., Huang, H. Y., Chen, Q. L., Peng, X. Y., & Feng, J. M. (2023). Novel multidisciplinary design and multi-objective optimization of centrifugal compressor used for hydrogen fuel cells. International Journal of Hydrogen Energ, 48(33), 12444-12460. https://doi.org/10.1016/j.ijhydene.2022.11.312

Colorni, A., Dorigo, M., Maniezzo, V., Varela, F. J., & Bourgine, P. E. (1992). Distributed optimization by ant colonies.

Ekradi, K., & Madadi, A. (2020). Performance improvement of a transonic centrifugal compressor impeller with splitter blade by three-dimensional optimization. Energy, 201, 117582. https://doi.org/https://doi.org/10.1016/j.energy.2020.117582

Fu, J. Q., Wang, H. L., Sun, X. L., Bao, H H, Wang, X, Liu, J. P. (2024). Multi-objective optimization for impeller structure parameters of fuel cell air compressor using linear-based boosting model and reference vector guided evolutionary algorithm. Applied Energy, 363. https://doi.org/10.1016/j.apener gy.2024.123057

He, X., & Zheng, X. (2017). Performance improvement of transonic centrifugal compressors by optimization of complex three-dimensional features. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 231(14), 2723-2738. https://doi.org/10.1177/0954410016673395

Holland, J. H. (1975). Adaptation In Natural And Artificial Systems.

Ju, Y., Liu, Y., Jiang, W., & Zhang, C. (2021). Aerodynamic analysis and design optimization of a centrifugal compressor impeller considering realistic manufacturing uncertainties. Aerospace Science and Technology, 115, 106787. https://doi.org/https://doi.org/10.1016/j.ast.2021.106787

Ju, Y., Qin, R., Kipouros, T., Parks, G. T., & Zhang, C. (2016). A high-dimensional design optimisation method for centrifugal impellers. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 230, 272 - 288. https://doi/org/10.1177/0957650915626274

Kennedy, J., & Eberhart, R. (1995). Particle Swarm optimization. Icnn95-international Conference on Neural Networks,

Kirkpatrick, S., Gelatt, C. D., & Vecchi, M, P, (1983). Optimization by simulated annealing. Science, 220(4598), 671-680. https://doi.org/doi:10.1126/science.220.4598.671

Li, J., Cheng, J. H., Shi, J. Y., & Huang, F. (2012). Brief introduction of back propagation (BP) neural network algorithm and its improvement. Springer Berlin Heidelberg,

Li, X., Zhao, Y., & Liu, Z. (2019). A novel global optimization algorithm and data-mining methods for turbomachinery design. Structural and Multidisciplinary Optimization, 60(2), 581-612. https://doi.org/10.1007/s00158-019-02227-5

Li, Y., Zhang, J., Liu, Z., Shu, Y., Wang, Z., Yang, H., Zhang, W., & Wei, Y. (2024). Spatiotemporal characteristics of rotating stall in a centrifugal compressor with a volute at low-flow rate conditions. Physics of Fluids, 36(3), 037152. https://doi.org/10.1063/5.0197097

Mengistu, T., & Ghaly, W. (2008). Aerodynamic optimization of turbomachinery blades using evolutionary methods and ANN-based surrogate models. Optimization and Engineering, 9(3), 239-255. https://doi.org/10.1007/s11081-007-9031-1

Menter, F. R. (1994). Two-equation eddy-viscosity turbulence models for engineering applications. AIAA Journal, 32, 1598-1605. https://doi.org/10.2514/3.12149

Nejat, A., Mirzabeygi, P., & Panahi, M. S. (2014). Airfoil shape optimization using improved Multiobjective Territorial Particle Swarm algorithm with the objective of improving stall characteristics. Structural and Multidisciplinary Optimization, 49(6), 953-967. https://doi.org/10.1007/s00158-013-1025-3

Ou, M., Yan, L., Huang, W., & Zhang, T. T. (2019). Design exploration of combinational spike and opposing jet concept in hypersonic flows based on CFD calculation and surrogate model. Acta Astronautica, 155, 287-301. https://doi.org/https://doi.org/10.1016/j.actaastro.2018.12.012

Pinapatruni, G., Duddupudi, M., Dash, S., & Routray, A. (2024). On the investigation of the aerodynamics performance and associated flow physics of the optimized tubercle airfoi. Physics of Fluids, 36, 051907. https://doi.org/10.1063/5.0203519

Roy, D., Samanta, S., & Ghosh, S. (2020). Performance optimization through response surface methodology of an integrated biomass gasification based combined heat and power plant employing solid oxide fuel cell and externally fired gas turbine. Energy Conversion and Management, 222, 113182. https://doi.org/10.1016/j.enconman.2020.113182

Sun, L., Yang, J., Liu, X., Sun, D., & Dong, X. (2024a). Diagnosis of unsteady disturbance characteristics induced by the tip leakage vortex in a compressor based on data-driven modal decomposition methods. Physics of Fluids, 36(5), 055151. https://doi.org/10.1063/5.0205339

Sun, X. L., Wang, H. L., Fu, J. Q., Xia, Y., & Liu, J. P. (2024b) Many-objective optimization for structural parameters of the fuel cell air compressor based on the Stacking model under multiple operating conditions. Applied Thermal Engineering, 245. https://doi.org/10.1016/j.applthermaleng.2024.122786

Tang, X., Gu, N., Wang, W., Wang, Z., & Peng, R. (2021). Aerodynamic robustness optimization and design exploration of centrifugal compressor impeller under uncertainties. International Journal of Heat and Mass Transfer, 180, 121799. https://doi.org/https://doi.org/10.1016/j.ijheatmasstransfer.2021.121799

Vavruska, P., Kratena, T,. Cech, D., Macalka, A., & Peterka, T. (2023). Determination of impeller blade fillet radius for productive finish milling. The International Journal of Advanced Manufacturing Technology, 126(11), 5541-5554. https://doi.org/10.1007/s00170-023-11483-1

Verstraete, T., Alsalihi, Z., & Van den Braembussche, R. A. (2010). Multidisciplinary Optimization of a Radial Compressor for Microgas Turbine Applications. Journal of Turbomachinery, 132(3). https://doi.org/10.1115/1.3144162

Wang, Y., Liu, T., Meng, Y., Zhang, D., & Xie, Y. (2022). Integrated optimization for design and operation of turbomachinery in a solar-based Brayton cycle based on deep learning techniques. Energy, 252, 123980. https://doi.org/https://doi.org/10.1016/j.energy.2022.123980

Wang, Z., Wei, Y., & Qian, Y. (2020). A bounce back-immersed boundary-lattice Boltzmann model for curved boundary. Applied Mathematical Modelling, 81, 428-440. https://doi.org/https://doi.org/10.1016/j.apm.2020.01.012

Wang, Z., Zhao, R. C., Zhu, Z. Y., Zhuge, W. L., Zhang, Y. J. (2024). A novel multi-objective optimization scheme of electric turbo compressor system in hydrogen fuel cell for reducing energy consumption and axial thrust. Energy, 309. https://doi.org/10.1016/j.energy.2024.133096

Wei, Y., Zhu, L., Zhang, W., & Wang, Z. (2020). Numerical and experimental investigations on the flow and noise characteristics in a centrifugal fan with step tongue volutes. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 234(15), 2979-2993. https://doi.org/10.1177/0954406219890920

Xue, J., & Shen, B. (2020). A novel swarm intelligence optimization approach: sparrow search algorithm. Systems Science & Control Engineering, 8(1), 22-34. https://doi.org/10.1080/21642583.2019.1708830

Yang, H., Zhang, W., & Zhu, Z. (2019). Unsteady mixed convection in a square enclosure with an inner cylinder rotating in a bi-directional and time-periodic mode. International Journal of Heat and Mass Transfer, 136, 563-580. https://doi.org/https://doi.org/10.1016/j.ijheatmasstransfer.2019.03.041

Zhang, K., Li, J., Zeng, F., Wang, Q., & Yan, C. (2022). Uncertainty Analysis of parameters in sst turbulence model for shock wave-boundary layer interaction. Aerospace, 9, 55. https://doi.org/10.3390/aerospace9020055

Zhang, T. T., Wang, Z. G., Huang, W., & Yan, L. (2016). Parameterization and optimization of hypersonic-gliding vehicle configurations during conceptual design. Aerospace Science and Technology, 58, 225-234. https://doi.org/https://doi.org/10.1016/j.ast.2016.08.020