Parameterization and multi-objective optimization of centrifugal compressor volute based on genetic algorithm

doi:10.19799/j.cnki.2095-4239.2020.0413

Abstract

Abstract:

An outlet volute has a direct and non-negligible influence on the overall performance and working range of a centrifugal compressor. The complete three-dimensional turbulent internal flow of the outlet volute can cause a circumferential pressure distortion at the inlet of the volute, which directly affects the flow stability of the upstream components. In this paper, the multi-objective optimization design of the centrifugal compressor volute of a compressed-air energy storage system was performed, and a parameterized design method of variable cross-sectional shape was proposed. With the total pressure loss coefficient and static pressure recovery coefficient as optimization target variables, using multiple control surfaces and control points to control the cross-sectional shape of the centrifugal compressor volute and combining the optimal Latin hypercube experimental design method and the full three-dimensional computational fluid dynamics numerical method to generate the sample space, the second-generation non-dominated sorting genetic algorithm was used for multi-objective optimization of the Kriging approximation model, and an optimization platform and an optimization method were established. The results show that the optimized cross-section shape can reduce the shear stress in the vortex center and cause the uniform distribution of the meridional velocity in the outlet volute. In the design point, the overall isentropic efficiency and pressure ratio of the optimal design increased by 0.45% and 0.36%, respectively. Compared with the initial model, the volute with an optimized cross-sectional shape can improve the overall performance of the centrifugal compressor effectively. This research promotes the application of numerical optimization design method in the outlet volute of a centrifugal compressor and provides a reference for the optimal design of centrifugal compressors with high performance and low total pressure loss.

Key words: centrifugal compressor, outlet volute, numerical calculation, multi-objective optimization

CLC Number:

TH 452

Wei LI, Zhitao ZUO, Hucan HOU, Qi LIANG, Zhihua LIN, Haisheng CHEN. Parameterization and multi-objective optimization of centrifugal compressor volute based on genetic algorithm[J]. Energy Storage Science and Technology, 2021, 10(3): 1071-1079.

Figures/Tables 15

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Table 1

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Table 2

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Fig. 13

References 17

1	陈海生, 刘金超, 郭欢, 等. 压缩空气储能技术原理[J]. 储能科学与技术, 2013, 2(2): 146-151.CHEN H S, LIU J C, GUO H, et al. Technical principle of compressed air energy storage system[J]. Energy Storage Science and Technology, 2013, 2(2): 146-151.
2	张新敬, 陈海生, 刘金超, 等. 压缩空气储能技术研究进展[J]. 储能科学与技术, 2012, 1(1): 26-40.ZHANG X J, CHEN H S, LIU J C, et al. Research progress in compressed air energy storage system: A review[J]. Energy Storage Science and Technology, 2012, 1(1): 26-40.
3	AYDER E, BRAEMBUSSCHE R V D. Numerical analysis of the three-dimensional swirling flow in centrifugal compressor volutes[J]. Journal of Turbomachinery, 1994, 116(3): 462-468.
4	HAGELSTEIN D, HILLEWAERT K, VAN DEN BRAEMBUSSCHE R A, et al. Experimental and numerical investigation of the flow in a centrifugal compressor volute[J]. Journal of Turbomachinery, 2000, 122(1): 22-31.
5	TANGANELLI A, BALDUZZI F, BIANCHINI A, et al. An investigation on the loss generation mechanisms inside different centrifugal compressor volutes for turbochargers[J]. Journal of Engineering for Gas Turbines and Power, 2019, 141(2):doi:10.1115/1.4040864
6	MOJADDAM M, BENISI A H, MOVAHHEDI M R. Investigation on effect of centrifugal compressor volute cross-section shape on performance and flow field[C]//Proceedings of ASME Turbo Expo 2012: Turbine Technical Conference and Exposition, Copenhagen, Denmark, 2013: 871-880.
7	ZHENG X Q, LIN Y, SUN Z Z. Effects of volute's asymmetry on the performance of a turbocharger centrifugal compressor[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2018, 232(7): 1235-1246.
8	ABDELMADJID C, MOHAMED S A, BOUSSAD B. CFD analysis of the volute geometry effect on the turbulent air flow through the turbocharger compressor[J]. Energy Procedia, 2013, 36: 746-755.
9	吴亚东, 曹安国, 刘鹏寅, 等. Pareto多目标算法在离心压缩机蜗壳优化中的应用[J]. 航空动力学报, 2016, 31(1): 92-99.WU Y D, CAO A G, LIU P Y, et al. Application of Pareto multi-objective algorithm in centrifugal compressor volute optimization[J]. Journal of Aerospace Power, 2016, 31(1): 92-99.
10	叶道星, 李浩, 马秋妍, 等. 采用近似模型和NSGA-Ⅱ遗传算法的旋流泵性能优化研究[J]. 灌溉排水学报, 2019, 38(7): 76-83.YE D X, LI H, MA Q Y, et al. Optimal design of vortex pump using approximate model and the non-dominated sorting genetic algorithm[J]. Journal of Irrigation and Drainage, 2019, 38(7): 76-83.
11	徐忠. 离心式压缩机原理[M]. 2版(修订本). 北京: 机械工业出版社, 1990: 100-109.XU Z. The principle of Centrifugal compressor[M]. 2 Edition (Revised). Beijing: China Machine Press, 1990: 100-109
12	CARL D B. A Practical Guide to Splines[M]. New York: Springer, 1978.
13	ECKARDT D. Detailed flow investigations within a high-speed centrifugal compressor impeller[J]. Journal of Fluids Engineering, 1976, 98(3): 390-399.
14	ECKARDT D. Instantaneous measurements in the jet-wake discharge flow of a centrifugal compressor impeller[J]. Journal of Engineering for Power, 1975, 97(3): 337-345.
15	席光, 王志恒, 王尚锦. 叶轮机械气动优化设计中的近似模型方法及其应用[J]. 西安交通大学学报, 2007, 41(2): 125-135, 184.XI G, WANG Z H, WANG S G. Aerodynamic optimization design of turbomachinery with approximation model method[J]. Journal of Xi'an Jiaotong University, 2007, 41(2): 125-135, 184.
16	JIN R C, CHEN W, SUDJIANTO A. An efficient algorithm for constructing optimal design of computer experiments[J]. Journal of Statistical Planning and Inference, 2005, 134(1): 268-287.
17	MISHINA H, GYOBU I. Performance investigations of large capacity centrifugal compressors[C]//Proceedings of ASME 1978 International Gas Turbine Conference and Products Show, 1978, London, England, 1978.

参数	设计值
设计流量/kg·s^-1	5.31
压比	2.1
叶尖速度/m·s^-1	293
叶片数	20
额定转速/r·min^-1	14000
叶轮出口直径/mm	400
叶轮出口叶片角/(°)	90
叶顶间隙/mm	0.525
进口叶尖相对马赫数	0.65
扩压器出口位置/mm	680

项目	静压恢复系数C_p	总压损失系数K_p	等熵效率η/%	压比π
初始模型	0.6343	0.2847	87.73	2.471
优化最佳解	0.6683	0.2532	—	—
CFD数值验证	0.6693	0.2527	88.13	2.480

[1]	Shuai HAN, Leping SUN, Jianbin LU, Xiaoxuan GUO. Multi-objective optimal dispatch strategy of gas-electric interconnected virtual power plant interval with electric vehicles [J]. Energy Storage Science and Technology, 2022, 11(5): 1428-1436.
[2]	Xiaobin XU, Yefei XU, Hengyun ZHANG, Shunliang ZHU, Haifeng WANG. Multiobjective optimization of thermal performance and grouping efficiency for air cooling battery module [J]. Energy Storage Science and Technology, 2022, 11(2): 553-562.
[3]	Bowen YANG, Jun YAN, Changying ZHAO. Investigating the performance of a fluidized bed reactor for a magnesium hydroxide thermochemical energy storage system [J]. Energy Storage Science and Technology, 2021, 10(5): 1735-1744.
[4]	Lei HOU, Zichi WANG, Yingchao LI, Saihao WANG, Yajie ZHANG, Yusen ZHANG. Analysis and multi-objective optimization of CAES system [J]. Energy Storage Science and Technology, 2021, 10(1): 379-384.
[5]	Kun XIE, Chenglong JIANG, Liangxuan WU, Hong CHANG, Bin FAN, Liduo CHEN, Haoran WEN. Optimal capacity-allocation of generation and battery storage system based on cascade utilization of outdated battery [J]. Energy Storage Science and Technology, 2020, 9(S1): 23-30.
[6]	Changming DING, Hua WEN. Multi-objective thermal optimization of ternary lithium-ion battery [J]. Energy Storage Science and Technology, 2020, 9(6): 1961-1968.
[7]	ZHANG Yang1, ZUO Zhitao2, LIANG Qi1, ZHOU Xin2, CHEN Haisheng2. Optimal design and adjustment analysis of an adjustable vane diffuser in a centrifugal compressor [J]. Energy Storage Science and Technology, 2017, 6(6): 1231-.
[8]	YAN Xue1,2, ZUO Zhitao1, LIANG Qi1,2, TANG Hongtao1, CHEN Haisheng1. Analysis on joint adjusting of a two-stage centrifugal compressor with inter-stage cooling and variable guide vanes [J]. Energy Storage Science and Technology, 2017, 6(1): 108-115.
[9]	ZHAO Qianqian, ZHANG Shaohua. Estimation of zinc-bromine battery flow channel based on numerical simulation [J]. Energy Storage Science and Technology, 2016, 5(2): 228-234.
[10]	LIU Qiannan, FU Zhongguang, BIAN Jichao. The effect of an energy storage unit on the performance micro CCHP systems [J]. Energy Storage Science and Technology, 2015, 4(6): 622-626.