Performance characterization of a solenoid-type gas valve for the H− magnetron source at FNAL

Sosa, A.; Bollinger, D. S.; Karns, P. R.

doi:10.1063/1.4995720

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…D.C. and high voltage pulses deform the piezoelectric crystal structure and create a leak for the gas passing through the valve. As the relationship between the gas flow rate and the applied voltage is nonlinear [22,23], the amount of gas flow is measured using a flow controller mounted in between the reservoir and the piezo valve. The total counts of the flow controller indicate approximate amount of the gas flow during the plasma discharges.…”

Section: Gas Puffing Subsystemmentioning

confidence: 99%

Real-time density feedback control in ADITYA-U Tokamak

et al. 2022

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A real-time density feedback control system with a 100 GHz heterodyne interferometer has been designed, developed, and commissioned in ADITYA-U tokamak. It consists of three subsystems i.e. density measurement, feedback control, and gas fuelling. A proportional feedback controller is configured in voltage amplitude mode to operate the piezo valve for gas injection. Experiments are performed during plasma discharges to achieve a predefined density evolution as well as constant density. Stickiness of the piezo valve restricts the temporal response of the valve. It is required to minimize during the plasma discharge for a fast density response which is achieved by a short preceding voltage pulse known as a stick pulse. This paper describes the implementation of a real-time density feedback control system which is successfully validated using another existing 140 GHz interferometer system in ADITYA-U. Moreover, the developed system consumes low power, is cost-effective and re-programmable, can be easily upgraded, and provides interlock with plasma parameters.

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Section: Gas Puffing Subsystemmentioning

confidence: 99%

Real-time density feedback control in ADITYA-U Tokamak

et al. 2022

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“…In electromechanical solenoids (EMS), also called electromagnetic devices (EMD), driven by a control solenoid, the armature is positioned by balancing the electromagnetic force against that of a return spring. They are relatively an inexpensive construction [1], have a simple design and control circuit, require little energy for control, are highly reliable [2], these electromagnetic actuators are used in wide range of modern industrial equipment such as digital actuator arrays [3], vehicle vibration control systems [4], gas valve [5], robotic manipulators [6], positioners [7], anti-braking systems [8], and polarization controllers where solenoids are used as mechanical actuator on the fiber to adjust the output light power [9]. In hydraulic systems, these electromagnetic actuators are commonly used in three basic categories to actuate hydraulic control valves directional-Journal homepage: http://ijece.iaescore.com Ì ISSN: 2088-8708 control, flow-control, and pressure-control.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of solenoid parameters based on neural networks and optical fiber squeezer for solenoid valves diagnosis

Zahidi

Amrane

Azami

et al. 2021

IJECE

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As crucial parts of various engineering systems, solenoid valves (SVs) operated by electromagnetic solenoid (EMS) are of great importance and their failure may lead to cause unexpected casualties. This failure, characterized by a degradation of the performances of the SVs, could be due to a fluctuations in the EMS parameters. These fluctuations are essentially attributed to the changes in the spring constant, coefficient of friction, inductance, and the resistance of the coil. Preventive maintenance by controlling and monitoring these parameters is necessary to avoid eventual failure of these actuators. The authors propose a new methodology for the functional diagnosis of electromagnetic solenoids (EMS) used in hydraulic systems. The proposed method monitors online the electrical and mechanical parameters varying over time by using artificial neural networks algorithm coupled with an optical fiber polarization squeezer based on EMS for polarization scrambling. First, the MATLAB/Simulink model is proposed to analyze the effect of the parameters on the dynamic EMS model. The result of this simulation is used for training the neural network, then a simulation is proposed using the neural net fitting toolbox to determine the solenoid parameters (Resistance of the coil R, stiffness K and coefficient of friction B of the spring) from the coefficients of the transfer function, established from the model step response. Future work will include not only diagnosing failure modes, but also predicting the remaining life based on the results of monitoring.

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“…They are very reliable and require low power for control [2]. They are used in a wide range of modern industrial equipment such as digital actuator arrays [3], gas valve [4], robotic manipulators [5], positioners [6], anti-braking systems [7], vehicle vibration control systems [8], and polarization controllers where they are used as a mechanical actuator to adjust the light power at the output of an optical fiber [9]. To improve the security measures and performance of the systems based on the EMS, a strategy of regular maintenance of these EMS certainly reduces the failure rate [10].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Monitoring of the Solenoid Valves Coil Resistance Based on Optical Fiber Squeezer

Amrane¹,

Zahidi²,

Abouricha³

et al. 2021

JESA

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Solenoid valves represent indispensable elements in various engineering systems. Their failure can lead to unexpected problems. This failure may be caused by fluctuations in the coil resistance of the electromagnetic solenoid (EMS) which actuates these solenoid valves. Hence the need to monitor this parameter for a preventive maintenance of these actuators. The proposed method consists to use supervised machine learning to monitor coil resistance of the EMS valve. The EMS valve is coupled to an optical fiber squeezer which, acts as a force sensor. The solenoid armature applies a mechanical force to the optical fiber and changes the polarization state of the light that travels through the optical fiber and then this force infects the power of the light. A Simulink model is used to determine the open loop system step response. The identification of the system allows obtaining its transfer function, which depends on the parameters of the EMS and in particular on its coil resistance. By varying the coil resistance while fixing the other physical parameters of the EMS, we generate a database whose elements are the coefficients of the transfer function of the solenoid open loop and the electrical resistance of its coil. The generated database is used for training several supervised machine learning models whose predictors are the elements of the transfer function; the response is the coil resistance. The Gaussian process for regression allows to predict the variations of the coil resistance with the smallest relative error although it takes a relatively long time for the training compared to the other models used.

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Performance characterization of a solenoid-type gas valve for the H− magnetron source at FNAL

Cited by 6 publications

References 2 publications

Real-time density feedback control in ADITYA-U Tokamak

Real-time density feedback control in ADITYA-U Tokamak

Monitoring of solenoid parameters based on neural networks and optical fiber squeezer for solenoid valves diagnosis

Machine Learning for Monitoring of the Solenoid Valves Coil Resistance Based on Optical Fiber Squeezer

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