A Neural Network Model for Spool Throttling Groove Topology Combination of Multi-Way Valves

doi:10.7652/xjtuxb201606006

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A Neural Network Model for Spool Throttling Groove Topology Combination of Multi-Way Valves

更新时间：2025-07-09

- A Neural Network Model for Spool Throttling Groove Topology Combination of Multi-Way Valves
- Vol. 50, Issue 6, Pages: 36-41(2016)
- 作者机构：
  
  同济大学机械与能源工程学院,上海,201804
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201606006
  CLC： TH137.52
- Online First：10 June 2016，
  
  Published：2016
- 稿件说明：
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A Neural Network Model for Spool Throttling Groove Topology Combination of Multi-Way Valves[J]. 2016, 50(6): 36-41.
DOI：

A Neural Network Model for Spool Throttling Groove Topology Combination of Multi-Way Valves[J]. 2016, 50(6): 36-41. DOI： 10.7652/xjtuxb201606006.

摘要

针对多路阀设计中阀芯节流槽结构的拓扑形态表达和其流固耦合响应的大组合、大自由度解析问题

提出多路阀阀芯节流槽拓扑结构组合的神经网络模型。在利用多路阀动态特性台架实验验证其三维流体解析结果的基础上

将节流槽拓扑结构分类为由半圆槽、U型槽、圆孔等结构的参数化组合构成

通过正交实验法得到各参数组合条件下的多路阀三维流体解析响应

作为反向传播神经网络的训练样本

实现其节流槽拓扑结构组合的神经网络表达; 采用进化神经网络优化训练过程的初始权重和阈值

优化后的神经网络能够对非训练样本集合的多路阀三维流体解析响应实现准确预测。研究结果表明

此模型为阀芯节流槽结构设计中拓扑形态表达提供了一种新的思路

对多路阀数字化设计具有实际意义。

Abstract

A neural network model for the spool throttling groove topology combination of multi-way valves is proposed to deal with the problem that there exist various combinations of topological form expression of throttling groove structure on spool and huge degrees of freedom of analysis of fluid-structure coupling response in the design of multi-way valves. The throttling groove topological structures are classified into semicircle groove

U-shaped groove

round hole groove and so on

and form different parameterized combinations of these structures based on validation of analytical results in using the multi-way valve dynamic characteristic test. The orthogonal experiment method is used to obtain three-dimensional fluid analytical responses of the multi-way valve for each parameter combination

then these responses are used as training samples of a back propagation neural network to achieve the neural network expression of the throttling groove topology combination. The initial weights and threshold of training process are optimized by an evolutionary neural network to improve prediction accuracy of the model

so that accurate predictions for non-training samples are achieved. The results show that the proposed model provides a new idea for topological form expression in the design of spool throttling groove structures

and has practical significance for digital design of multi-way valves.

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Keywords

references

AMIRANTE R, DEL VESCOVO G, LIPPOLOS A. Flow forces analysis of an open center hydraulic directional control valve sliding spool [J]. Energy Conversion and Management, 2006, 47(1): 114-131.

DASGUPTA K, WATTON J. Dynamic analysis of proportional solenoid controlled piloted relief valve by bondgraph [J]. Simulation Modelling Practice and Theory, 2005, 13(13): 21-38.

GUILLERMO P S. Three-dimensional modeling and geometrical influence on the hydraulic performance of a control valve [J]. Journal of Fluids Engineering, 2008, 130(1): 151-163.

方文敏, 成琳琳, 傅新, 等. 带U形节流槽的滑阀稳态液动力研究 [J]. 浙江大学学报: 工学版, 2010, 44(3): 574-580.

FANG Wenmin, CHENG Linlin, FU Xin, et al. Investigation on steady-state flow force of spool valve with U-grooves [J]. Journal of Zhejiang University: Engineering Science, 2010, 44(3): 574-580.

冀宏, 王东升, 刘小平, 等. 滑阀节流槽阀口的流量控制特性 [J]. 农业机械学报, 2009, 40(1): 198-202.

JI Hong, WANG Dongsheng, LIU Xiaoping, et al. Flow control characteristic of the orifice in spool valve with notches [J]. Transactions of the Chinese Society for Agricultural Machinery, 2009, 40(1): 198-202.

王安麟, 吴小锋, 周成林, 等. 基于CFD的液压滑阀多学科优化设计 [J]. 上海交通大学学报, 2010, 44(12): 1767-1772.

WANG Anlin, WU Xiaofeng, ZHOU Chenglin, et al. Multidisciplinary optimization of a hydraulic slide valve based on CFD [J]. Journal of Shanghai Jiaotong University, 2010, 44(12): 1767-1772.

孙泽刚, 肖世德, 王德华, 等. 多路阀双U型节流槽结构对气穴的影响及优化 [J]. 华中科技大学学报: 自然科学版, 2015, 43(4): 38-43.

SUN Zegang, XIAO Shide, WANG Dehua, et al. Impaction and optimization of double U-throttle groove structure of multi-way valve on cavitation [J]. Journal of Huazhong University of Science and Technology: Natural Science Edition, 2015, 43(4): 38-43.

刘锋. 中型液压挖掘机回转系统工作性能的研究 [D]. 长沙: 中南大学, 2011: 3-10.

高隽. 人工神经网络原理及仿真实例 [M]. 北京: 机械工业出版社, 2007: 1-10.

王吉权, 王福林, 邱立春. 基于BP神经网络的农机总动力预测 [J]. 农业机械学报, 2011, 42(12): 121-126.

WANG Jiquan, WANG Fulin, QIU Lichun. Prediction of total power in agriculture machinery based on BP neural network [J]. Transactions of the Chinese Society for Agricultural Machinery, 2011, 42(12): 121-126.

贾振元, 马建伟, 刘巍, 等. 多几何要素影响下液压阀件特性的混合神经网络预测模型 [J]. 机械工程学报, 2010, 46(2): 126-131.

JIA Zhenyuan, MA Jianwei, LIU Wei, et al. Hybrid neural network prediction model of hydraulic valve characteristics under the affection of multiple geometric factors effected [J]. Journey of Mechanical Engineering, 2010, 46(2): 126-131.

MOMENI E, NAZIR R, ARMAGHANI D J, et al. Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN [J]. Measurement, 2014, 57(11): 122-131.

MELIH I. ANN and ANFIS performance prediction models for hydraulic impact hammers [J]. Tunnelling and Underground Space Technology, 2012, 27(1): 23-29.

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