Ship & Boat ›› 2023, Vol. 34 ›› Issue (06): 65-72.DOI: 10.19423/j.cnki.31-1561/u.2023.06.065

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On Matching Optimization Method of Impeller and Guide Vane of Waterjet Pump Based on Artificial Neural Network and Genetic Algorithm

WANG Jun1,2, FAN Sheming1,2, CAI Youlin1,2, YIN Xiaohui1,2, FENG Chao1   

  1. 1. Marine Design & Research Institute of China, Shanghai 200011, China;
    2. Science and Technology of Water Jet Propulsion Laboratory, Shanghai 200011, China
  • Received:2023-10-10 Revised:2023-11-14 Online:2023-12-25 Published:2023-12-28

基于人工神经网络和遗传算法的喷水推进泵叶轮和导叶匹配优化方法研究

王俊1,2, 范佘明1,2, 蔡佑林1,2, 尹晓辉1,2, 冯超1   

  1. 1.中国船舶及海洋工程设计研究院 上海 200011;
    2.喷水推进技术重点实验室 上海 200011
  • 作者简介:王 俊(1988-),男,博士研究生,高级工程师。研究方向:喷水推进技术研究。范佘明(1963-),男,博士,研究员。研究方向:船舶操纵性及耐波性研究。蔡佑林(1976-),男,博士,研究员。研究方向:喷水推进泵技术研究。尹晓辉(1982-),女,硕士,研究员。研究方向:喷水推进技术研究。冯 超(1982-),男,硕士,高级工程师。研究方向:喷水推进技术研究。
  • 基金资助:
    基础加强计划重点基础研究项目(MKS20210003)

Abstract: The matching optimization design of the blade geometry of the existing hydraulic model of the impeller and guide vane is carried out in order to improve the hydraulic performance of the waterjet pump. The Latin Hypercube sampling method is used for the space sampling of parameters that affect the performance of the waterjet pumps, such as the impeller blade load, the guide vane blade load and the stacking position. The performance parameters of the pump is obtained through steady numerical simulation of each sample by using the RANS equations. The mapping response model is built between the pump design parameters and the calculated performance parameters based on the artificial neural network. The genetic algorithm is adopted to optimize the waterjet pump hydraulic model with the aim of the maximum hydraulic efficiency. The iterative cycling of the optimization process is automatically completed, which can shorten the design and development period of the hydraulic model. The results show that after optimization, the internal flow field is significantly improved with the same head of the pump, and the efficiency of the calculation design point reaches 91.8%, while the efficiency validated by model test reaches 88.9%, as well as the range of the flow rate in high efficiency region is widened by 15%.

Key words: waterjet pump, load, optimization, neural network, genetic algorithm

摘要: 为提升喷水推进泵的水力性能,对现有水力模型动叶轮和导叶体的叶片几何形状进行匹配优化设计。该文针对影响喷水推进泵性能的叶轮叶片载荷、导叶叶片载荷、积迭位置等参数,采用拉丁超立方抽样方法进行样本空间采样,应用RANS方程对各样本进行定常数值求解,得到泵的性能参数,基于人工神经网络,建立泵设计参数和性能参数之间的映射响应模型,并以效率最高为目标,采用遗传算法进行优化,获得性能优异的喷水推进泵水力模型。该过程迭代循环自动完成,可缩短水力模型的设计开发周期。结果表明:优化后,在保持泵扬程不变的情况下,内流场显著改善,计算设计点效率达到91.8%,经过试验验证后效率达到88.9%,高效区流量范围拓宽15%。

关键词: 喷水推进泵, 载荷, 优化, 神经网络, 遗传算法

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