CFD-Based Physical Failure Modeling of Direct-Drive Electro-Hydraulic Servo Valve Spool and Sleeve

Abstract

Direct-drive electro-hydraulic servo valves are used extensively in aerospace, military and control applications, but little research has been conducted on their service life and physical failure wear. Based on computational fluid dynamics, the main failure forms of direct-drive electro-hydraulic servo valves are explored using their continuous phase flow and discrete phase motion characteristics, and then combined with the theory of erosion for calculation. A mathematical model of the direct-drive electro-hydraulic servo valve is established by using Solidworks software, and then imported into Fluent simulation software to establish its physical failure model and carry out simulation. Finally, the physical failure form of the direct drive electro-hydraulic servo valve is verified by the simulation results, and the performance degradation law is summarized. The results show that temperature, differential pressure, solid particle diameter and concentration, and opening degree all have an impact on the erosion and wear of direct-drive electro-hydraulic servo valves, in which differential pressure and solid particle diameter have a relatively large impact, and the servo valve must avoid working in the range of high differential pressure and solid particle diameter of 20–40 um as far as possible. This also provides further theoretical support and experimental guidance for the industrial application and life prediction of electro-hydraulic servo valves.

Throttling characteristics prediction methodology for combined grooves in directional spool valves under variable flow rates

“…The standard one was 10 % and 11 % faster than the realizable and RNG ones while the differences in results were negligible. Huang et al [ 25 ] and Xie et al [ 26 ] also applied the standard k - ε turbulence model. Therefore, the standard k - ε turbulence model was applied in this study.…”

Direct-Drive Electro-Hydraulic Servo Valve Performance Characteristics Prediction Based on Big Data and Neural Networks

“…In previous studies [ 18 , 19 ], it was found that direct-drive electro-hydraulic servo valves are mainly affected by erosive wear and the degree of wear is different because of different openings, differential pressures, and oil contamination. In the first stage of the experiment, pressure gain and leakage were selected as the performance parameters of the servo valve and thus the exponential smoothing and ARIMA model algorithms were chosen for prediction and analysis.…”

“…We used the same setup as a previous study [ 19 ], and the oil contamination, opening degree, and differential pressure were still used as the orthogonal setup parameters for the experimental conditions. In the previous study [ 18 ], the erosive wear was explored as the main wear of the servo valve spool and sleeve, with opening degree, differential pressure, and contamination degree being the main influencing factors. Therefore, the erosion wear of the servo valve was simplified in this experiment, and the diameter of the maximum number of solid particles in oil was used to reflect the different oil pollution degree.…”

“…Experts [ 21 , 22 ] have found that in high temperature environments of 718 °C, high temperatures lead to an increase in the oxidation cycle of this material layer, affecting the components’ lifespan. However, considering that it is difficult to control the temperature in the experiment, and according to the literature [ 18 ], it can also be concluded that temperature has less influence on the erosion and wear of the servo valve compared with other factors, in this study, temperature was not taken into account as one of the experimental working conditions. Instead, the initial temperature of the oil fluid was controlled at around 35 °C, which is a commonly used value in the field of aviation and aerospace.…”

“…Secondly, the use of big data for neural network predictive analysis, because it is based on data obtained from the actual application of servo valves and its predictive effect is closer to the real value and the actual situation than other purely theoretical analyses, can be considered to be directly referenced by the aerospace field. This paper combines the author’s previous research [ 18 , 19 ] to design and improve the performance degradation experiment of servo valves using pressure difference, opening, and oil contamination as the pre-influence conditions. Experimental data are obtained through long-term experiments, and a life prediction model is constructed based on the performance parameter data obtained from the investigation combined with neural networks.…”