Optimal Design of Multi-section Proportional Directional Valve Throttle Grooves with Artificial Neural Networks
This paper presents a method for design multi-section proportional directional valve Throttle grooves with ANN method, which aims at getting a better flow stability. There exists a coupling matter during the opening and closing process between the throttling notches, so that it’s difficult to parame...
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2018-01-01
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Online Access: | https://doi.org/10.1051/matecconf/201823703003 |
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doaj-7373735547394569a90fcdfc6872c6d72021-02-02T04:01:23ZengEDP SciencesMATEC Web of Conferences2261-236X2018-01-012370300310.1051/matecconf/201823703003matecconf_d2me2018_03003Optimal Design of Multi-section Proportional Directional Valve Throttle Grooves with Artificial Neural NetworksZhang Xiaolu0Wang Anlin1Tang Jiangwei2Research Institute of Mechanical and Electronic Engineering, School of mechanical and energy engineering, Tongji UniversityResearch Institute of Mechanical and Electronic Engineering, School of mechanical and energy engineering, Tongji UniversityResearch Institute of Mechanical and Electronic Engineering, School of mechanical and energy engineering, Tongji UniversityThis paper presents a method for design multi-section proportional directional valve Throttle grooves with ANN method, which aims at getting a better flow stability. There exists a coupling matter during the opening and closing process between the throttling notches, so that it’s difficult to parameterize the complex flow field characteristics Cd and the structure boundary of the spool grooves. However, in this paper, an ANN was built with data from CFD results, while the typical structural parameters (U type, the O-type and C-type), operating parameters was input vectors, the discharge coefficient as output vectors. Meanwhile, all of the needed data is taken from the three-dimensional CFD analysis, which are organized properly and verified by a bench scale test on a rig. Then, with throttling stiffness as optimization objective to evaluate flow stability, an optimal design process is carried out to optimize to optimize the structure of coupling grooves with ANN models and genetic algorithm. Ultimately, the optimized structure is verified better by the physical test on test rig, therefore, the significance of design method is proved.https://doi.org/10.1051/matecconf/201823703003 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhang Xiaolu Wang Anlin Tang Jiangwei |
spellingShingle |
Zhang Xiaolu Wang Anlin Tang Jiangwei Optimal Design of Multi-section Proportional Directional Valve Throttle Grooves with Artificial Neural Networks MATEC Web of Conferences |
author_facet |
Zhang Xiaolu Wang Anlin Tang Jiangwei |
author_sort |
Zhang Xiaolu |
title |
Optimal Design of Multi-section Proportional Directional Valve Throttle Grooves with Artificial Neural Networks |
title_short |
Optimal Design of Multi-section Proportional Directional Valve Throttle Grooves with Artificial Neural Networks |
title_full |
Optimal Design of Multi-section Proportional Directional Valve Throttle Grooves with Artificial Neural Networks |
title_fullStr |
Optimal Design of Multi-section Proportional Directional Valve Throttle Grooves with Artificial Neural Networks |
title_full_unstemmed |
Optimal Design of Multi-section Proportional Directional Valve Throttle Grooves with Artificial Neural Networks |
title_sort |
optimal design of multi-section proportional directional valve throttle grooves with artificial neural networks |
publisher |
EDP Sciences |
series |
MATEC Web of Conferences |
issn |
2261-236X |
publishDate |
2018-01-01 |
description |
This paper presents a method for design multi-section proportional directional valve Throttle grooves with ANN method, which aims at getting a better flow stability. There exists a coupling matter during the opening and closing process between the throttling notches, so that it’s difficult to parameterize the complex flow field characteristics Cd and the structure boundary of the spool grooves. However, in this paper, an ANN was built with data from CFD results, while the typical structural parameters (U type, the O-type and C-type), operating parameters was input vectors, the discharge coefficient as output vectors. Meanwhile, all of the needed data is taken from the three-dimensional CFD analysis, which are organized properly and verified by a bench scale test on a rig. Then, with throttling stiffness as optimization objective to evaluate flow stability, an optimal design process is carried out to optimize to optimize the structure of coupling grooves with ANN models and genetic algorithm. Ultimately, the optimized structure is verified better by the physical test on test rig, therefore, the significance of design method is proved. |
url |
https://doi.org/10.1051/matecconf/201823703003 |
work_keys_str_mv |
AT zhangxiaolu optimaldesignofmultisectionproportionaldirectionalvalvethrottlegrooveswithartificialneuralnetworks AT wanganlin optimaldesignofmultisectionproportionaldirectionalvalvethrottlegrooveswithartificialneuralnetworks AT tangjiangwei optimaldesignofmultisectionproportionaldirectionalvalvethrottlegrooveswithartificialneuralnetworks |
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1724306686913544192 |