A Simulation Model of a Hydraulic Buck Converter Based on a Mixed Time Frequency Domain Iteration

2013 ◽  
Author(s):  
Helmut Kogler ◽  
Rudolf Scheidl ◽  
Michael Ehrentraut
Keyword(s):  
Wave Propagation ◽  
Frequency Domain ◽  
Buck Converter ◽  
Switching Converters ◽  
Hydraulic Systems ◽  
Pipe System ◽  
Time Frequency ◽  
Crucial Component ◽  
Propagation Effects ◽  
Pressure Changes

Digital hydraulics is an opportunity to realize simple, robust, cheap and energy efficient hydraulic drives. In such systems digital on/off valves are used instead of proportional valves. Moreover, in hydraulic switching converters the valves are actuated within a few milliseconds, which create sharp pressure changes and, in turn, significant wave propagation effects in the pipe system. For a proper design of digital hydraulic systems a sound understanding of these effects is required to achieve the desired behavior of the switching drive system. In such converters, like the buck-, boost or boost-buck-converter, the inductance is one crucial component. It is realized by a simple pipe mainly for cost reasons. Furthermore, switching converters contain some components with nonlinear characteristics, like valves or accumulators, which prevent a comprehensive analysis in frequency domain. For a convenient analysis a qualified model of a hydraulic buck converter based on a mixed time frequency domain iteration is presented. Main parameters of this model are identified and wave propagation effects in the inductance pipe of the converter are investigated by simulation.

Mixed time–frequency domain simulation of a hydraulic inductance pipe with a check valve

2011 ◽  
Vol 225 (10) ◽  
pp. 2413-2421 ◽  
Author(s):  
R Scheidl ◽  
B Manhartsgruber ◽  
H Kogler
Keyword(s):  
Frequency Domain ◽  
Check Valve ◽  
Buck Converter ◽  
Frequency Domain Method ◽  
Parameter Study ◽  
Algebraic Equations ◽  
Efficient Computation ◽  
Time Frequency ◽  
Linear Algebraic Equations ◽  
Domain Modelling

This paper deals with the efficient computation of hydraulic switching systems with a check valve by a mixed time–frequency domain method; frequency-domain modelling is performed on the wave propagation in a pipe and time-domain modelling is applied to the switching valve and the check valve. The dual property of the check valve makes the complete problem have variational inequality properties. A solution method is presented which replaces the pressure and the flowrate of the check valve as a function of one new variable. The resulting system of non-linear algebraic equations is solved using a Newton–Raphson method in combination with a smoothing of the non-smooth properties of the check valve. The method is applied to a parameter study of a hydraulic buck converter.

Analysis of Wave Propagation Effects in Transmission Lines due to Digital Valve Switching

2015 ◽  
Author(s):  
Helmut Kogler ◽  
Rudolf Scheidl ◽  
Bernd Hans Schmidt
Keyword(s):  
Wave Propagation ◽  
Transmission Lines ◽  
Linear Wave ◽  
Hydraulic Systems ◽  
Pipe System ◽  
Linear Wave Propagation ◽  
Airborne Noise ◽  
Valve Opening ◽  
Switching Characteristics ◽  
Human Ear

In digital hydraulic systems, switching valves have opening and closing times in the range of a few milliseconds. Due to this fast switching, high bandwidth pressure pulsation is excited, which is the stimulus for airborne noise up to some kilohertz. Since the human ear is very sensitive to audible noise in this frequency range, an analysis of the influence of the valve’s opening curve on the pressure surge in the pipe system is intended. The study is based on simulations employing dynamic pipe models for linear wave propagation and laminar fluid flow. In particular, a simple pipe system with a valve at one end and a pressure boundary at the other end of the pipe is investigated. It is shown, how the valve opening characteristics of spool and seat type switching valves influences the pipe responses. Also the role of parasitic inductances due to the valve block bores is discussed and it is shown how the switching characteristics influences the dynamical effects on the pressure pulsations in the pipe system.

Acoustic-based Upper Facial Action Recognition for Smart Eyewear

2021 ◽  
Vol 5 (2) ◽  
pp. 1-28
Author(s):  
Wentao Xie ◽  
Qian Zhang ◽  
Jin Zhang
Keyword(s):  
Frequency Domain ◽  
Orthogonal Frequency Division Multiplexing ◽  
Interaction Mechanism ◽  
Phase Changes ◽  
Experimental Result ◽  
Selective Fading ◽  
Time Frequency ◽  
Facial Action ◽  
Frequency Selective Fading ◽  
Frequency Selective

Smart eyewear (e.g., AR glasses) is considered to be the next big breakthrough for wearable devices. The interaction of state-of-the-art smart eyewear mostly relies on the touchpad which is obtrusive and not user-friendly. In this work, we propose a novel acoustic-based upper facial action (UFA) recognition system that serves as a hands-free interaction mechanism for smart eyewear. The proposed system is a glass-mounted acoustic sensing system with several pairs of commercial speakers and microphones to sense UFAs. There are two main challenges in designing the system. The first challenge is that the system is in a severe multipath environment and the received signal could have large attenuation due to the frequency-selective fading which will degrade the system's performance. To overcome this challenge, we design an Orthogonal Frequency Division Multiplexing (OFDM)-based channel state information (CSI) estimation scheme that is able to measure the phase changes caused by a facial action while mitigating the frequency-selective fading. The second challenge is that because the skin deformation caused by a facial action is tiny, the received signal has very small variations. Thus, it is hard to derive useful information directly from the received signal. To resolve this challenge, we apply a time-frequency analysis to derive the time-frequency domain signal from the CSI. We show that the derived time-frequency domain signal contains distinct patterns for different UFAs. Furthermore, we design a Convolutional Neural Network (CNN) to extract high-level features from the time-frequency patterns and classify the features into six UFAs, namely, cheek-raiser, brow-raiser, brow-lower, wink, blink and neutral. We evaluate the performance of our system through experiments on data collected from 26 subjects. The experimental result shows that our system can recognize the six UFAs with an average F1-score of 0.92.

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