多路阀阀芯节流槽拓扑结构组合的神经网络模型

姜涛; 黄伟; 王安麟

doi:10.7652/xjtuxb201606006

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多路阀阀芯节流槽拓扑结构组合的神经网络模型

更新时间：2025-07-09

- 多路阀阀芯节流槽拓扑结构组合的神经网络模型
- A Neural Network Model for Spool Throttling Groove Topology Combination of Multi-Way Valves
- 西安交通大学学报 2016年50卷第6期页码：36-41
- 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：
  
  : 2015-09-01。作者简介: 姜涛(1969—),男,副教授,硕士生导师;黄伟(通信作者),男,硕士生。基金项目: 工业和信息化部2011年科技成果转化资助项目(财建〔2011〕30号)
- DOI：10.7652/xjtuxb201606006
  中图分类号： TH137.52
- 网络首发：2016-06-10，
  
  纸质出版：2016
- 稿件说明：
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姜涛, 黄伟, 王安麟. 多路阀阀芯节流槽拓扑结构组合的神经网络模型[J]. 西安交通大学学报, 2016,50(6):36-41.

A Neural Network Model for Spool Throttling Groove Topology Combination of Multi-Way Valves[J]. 2016, 50(6): 36-41.
姜涛, 黄伟, 王安麟. 多路阀阀芯节流槽拓扑结构组合的神经网络模型[J]. 西安交通大学学报, 2016,50(6):36-41. DOI： 10.7652/xjtuxb201606006.

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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references

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