|
|
|
| Surrogate model-based optimization design of hydraulic performance of mixed-flow pump at multi-operational point |
| XU Shengli1*, ZHONG Shaowei1, ZHAO Haixin2, XU Gang1 |
| 1. Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian, Liaoning 116000, China; 2. Department of Engineering Mechanics, Dalian University of Technology, Dalian, Liaoning 116000, China |
|
|
|
|
Abstract A method for optimizing the hydraulic performance of a mixed-flow pump at multi-operational point was proposed based on surrogate model in this paper to improve the performance under off-design conditions and broaden the flow rate range with a higher efficiency. The initial design of the pump was the hydraulic design made under the design condition, and the initial computational models of the impeller and vaned diffuser were established by parameterrizing their meridional shape and blade angle profile. The poorest efficiency under the part-load, design and over-load conditions was used as the objective function to be maximized and the head at the design point is employed as a constraint. Surrogate models between performance and geometrical variables were generated by using response surface method, and the best combination of these variables was obtained by using CORS-RBF optimization algorithm. According to characteristics of flow in the pump, a two-step optimizationstrategy was proposed, in which the meridional shape and blade angle profile are optimized sequentially to overcome the problem in the whole pump design arose from a large number of design variables and extensive computational cost required.The optimized results show that the pump hydraulic efficiencies were increased by 2.2%, 0.8% and 0.7% under 0.8Qd,1.0Qd and 1.2Qd working conditions, respectively. Additionally, the head satisfies the constraint imposed but also slightly improved. The flow rate range with better pump efficiency is significantly wider in the optimal pump than in the initial design. The relative velocity distribution in the optimal pump is more uniform, showing an obviously optimized effect.
|
|
Received: 27 March 2018
|
|
|
|
[1]何中伟,郑源,阚阚,等.大型斜流泵模型泵装置水力性能的研究[J].流体机械,2016,44(10):1-4.
HE Zhongwei, ZHENG Yuan, KAN Kan, et al. Study on hydraulic performance of model pump for large-scale oblique flow pump [J]. Fluid machinery, 2016,44(10):1-4.(in Chinese)
[2]李星峰,尹湘云,殷国富.基于CFD和多目标算法的离心叶轮参数优化[J].流体机械,2019,47(3):31-36.
LI Xingfeng, YIN Xiangyun, YIN Guofu. Centrifugal impeller parameter optimization based on CFD and multi-objective algorithm [J]. Fluid machinery, 2019, 47(3): 31-36.(in Chinese)
[3]PEI Ji, WANG Wenjie, YUAN Shouqi. Multi-point optimization on meridional shape of a centrifugal pump impeller for performance improvement [J]. Journal of mechanical science & technology, 2016, 30(11): 4949-4960.
[4]王文杰, 袁寿其, 裴吉, 等. 基于Kriging模型和遗传算法的泵叶轮两工况水力优化设计[J]. 机械工程学报, 2015, 51(15): 33-38.
WANG Wenjie, YUAN Shouqi, PEI Ji, et al. Two-point hydraulic optimization of pump impeller based on Kriging model and neighborhood cultivation genetic algorithm[J]. Journal of mechanical engineering, 2015, 51(15):33-38.(in Chinese)
[5]徐胜利, 刘海涛, 王晓放,等. 基于序列采样算法的轮盘减质优化[J]. 航空动力学报, 2014, 29(9): 2098-2102.
XU Shengli, LIU Haitao, WANG Xiaofang, et al. Mass optimization of turbine disk based on sequential sampling algorithm[J]. Journal of aerospace power, 2014, 29(9):2098-2102.(in Chinese)
[6]HUANG Renfang, LUO Xianwu, JI Bin, et al. Multi-objective optimization of a mixed-flow pump impeller using modified NSGA-II algorithm [J]. Science China technological sciences, 2015, 58(12): 2122-2130.
[7]冀春俊, 徐浩, 孙琦,等. 基于Kriging模型的快速拆装叶轮轮盘多目标优化设计[J]. 风机技术, 2017, 59(3):44-48.
JI Chunjun, XU Hao, SUN Qi, et al. Multi-objective structure optimization of a fast disassembling connection impeller disk based on Kriging model[J]. Chinese journal of turbomachinery, 2017, 59(3): 44-48.(in Chinese)
[8]袁寿其, 王文杰, 裴吉, 等. 低比转速离心泵的多目标优化设计[J]. 农业工程学报, 2015, 31(5): 46-51.
YUAN Shouqi, WANG Wenjie, PEI Ji, et al. Multi-objective optimization of low-specific-speed centrifugal pump[J]. Transactions of the CSAE, 2015, 31(5): 46-51.(in Chinese)
[10]HEO M W, MA S B, SHIM H S, et al. High-efficiency design optimization of a centrifugal pump [J]. Journal of mechanical science & technology, 2016, 30(9): 3917-3927.
[11]石丽建, 汤方平, 刘超, 等. 轴流泵多工况优化设计及效果分析[J]. 农业工程学报,2016, 32(8): 63-68.
SHI Lijian, TANG Fangping, LIU Chao, et al. Optimization design and effect analysis of multi-operation conditions of axial-flow pump device[J]. Transactions of the CSAE, 2016, 32(8):63-68.(in Chinese)
[12]KIM S, LEE K Y, KIM J H, et al. High performance hydraulic design techniques of mixed-flow pump impeller and diffuser [J]. Journal of mechanical science and technology, 2015, 29(1): 227-240.
[13]LAI Xide, CHEN Xiaoming, ZHANG Xiang, et al. An approach to automatically optimize the hydraulic performance of blade system for hydraulic machines using multi-objective genetic algorithm [C]// Proceedings of the IOP Conference Series: Earth and Environmental Science, 2016: 062027.
[14]CASEY M V. A computational geometry for the blades and internal flow channels of centrifugal compressors [J]. Journal of engineering for gas turbines & power, 1983, 105(2): 288.
[15]BRAEMBUSSCHE. Challenges and progress in turbomachinery design systems [C]// Proceedings of the 6th International Conference on Pumps and Fans with Compressors and Wind Turbines, 2013: 012001.
[16]REGIS R G, SHOEMAKER C A. Constrained global optimization of expensive black box functions using radial basis functions [J]. Journal of global optimization, 2005, 31(1): 153-171.
[17]XU Shengli, LIU Haitao, WANG Xiaofang,et al. A robust error-pursuing sequential sampling approach for global metamodeling based on Voronoi diagram and cross validation [J]. Journal of mechanical design, 2014, 136(7): 071009.
[18]LIU Haitao, XU Shengli, WANG Xiaofang. Sequential sampling designs based on space reduction [J]. Engineering optimization, 2015, 47(7): 867-884.
[19]ARLOT S, CELISSE A. A survey of cross-validation procedures for model selection[J]. Statistics surveys, 2010(4):40-79. |
|
|
|
