THE DYNAMIC CALIBRATION UNCERTAINTY OF FAST RESPONSE PRESSURE PROBES

2021 ◽  
pp. 1-12
Author(s):  
Elissavet Boufidi ◽  
Fabrizio Fontaneto
Keyword(s):  
Transfer Function ◽  
Linear Time ◽  
Fast Response ◽  
Dynamic Calibration ◽  
Shock Tubes ◽  
Linear Time Invariant ◽  
Uncertainty Sources ◽  
Pressure Step ◽  
Post Shock ◽  
Response Pressure

Abstract In this paper, error sources affecting the dynamic calibration of fast response pressure probes in shock tubes are examined. In particular, the sensors uncertainty, the uncertainty in the rising point of the pressure step and the nonideality of the step are treated. The latter refers to the presence of pressure oscillations past the shock front, which are particularly important in the case of low-pressure shock tubes, typically used for the calibration of pressure probes for turbomachinery applications. The nonideality effect is investigated using a Linear Time Invariant (LTI) second order model for the transfer function of the probe's line-cavity system and an existing analytical model for the post-shock oscillations. The effect of these uncertainty sources to the experimentally determined transfer function of a fast response probe calibrated in the von Karman Institute (VKI) shock tube are finally presented.

The Dynamic Calibration Uncertainty of Fast Response Pressure Probes

2020 ◽  
Author(s):  
Elissavet Boufidi ◽  
Fabrizio Fontaneto
Keyword(s):  
Transfer Function ◽  
Linear Time ◽  
Fast Response ◽  
Dynamic Calibration ◽  
Shock Tubes ◽  
Linear Time Invariant ◽  
Uncertainty Sources ◽  
Pressure Step ◽  
Post Shock ◽  
Response Pressure

Abstract In this paper, error sources affecting the dynamic calibration of fast response pressure probes in shock tubes are examined. In particular, the sensors uncertainty, the uncertainty in the rising point of the pressure step and the nonideality of the step are treated. The latter refers to the presence of pressure oscillations past the shock front, which are particularly important in the case of low-pressure shock tubes, typically used for the calibration of pressure probes for turbomachinery applications. The nonideality effect is investigated using a Linear Time Invariant (LTI) second order model for the transfer function of the probe’s line-cavity system and an existing analytical model for the post-shock oscillations. The effect of these uncertainty sources to the experimentally determined transfer function of a fast response probe calibrated in the von Karman Institute (VKI) shock tube are finally presented.

scholarly journals Laboratory Calibration of D-dot Sensor Based on System Identification Method

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3255 ◽  
Author(s):  
Ke Wang ◽  
Yantao Duan ◽  
Lihua Shi ◽  
Shi Qiu
Keyword(s):  
System Identification ◽  
Transfer Function ◽  
Electric Fields ◽  
Linear Time ◽  
Low Frequency ◽  
Calibration Method ◽  
Sensitivity Coefficient ◽  
Corner Frequency ◽  
Linear Time Invariant ◽  
The Time Domain

D-dot sensors can realize the non-contact measurement of transient electric fields, which is widely applied to electromagnetic pulse (EMP) measurements with characteristics of the wide frequency band, high linearity, and good stability. In order to achieve accurate calibration of D-dot sensors in the laboratory environment, this paper proposed a new calibration method based on system identification. Firstly, the D-dot sensor can be considered as a linear time-invariant (LTI) system under corner frequency, thus its frequency response can be characterized by the transfer function of a discrete output error (OE) model. Secondly, based on the partial linear regression of the transfer function curve, the sensitivity coefficient of the D-dot sensor is obtained. By increasing the influence weight of low-frequency components, this proposed method has better calibration performance when the waveform is distorted in the time domain, and can artificially adapt to the operating frequency range of the sensor at the same time.

Estimating the forcing function in a mechanical system by an inverse calibration method

2021 ◽  
pp. 107754632110310
Author(s):  
Chapel Rice ◽  
Jay I Frankel
Keyword(s):  
Experimental Data ◽  
Transfer Function ◽  
Linear Time ◽  
Regularization Parameter ◽  
Calibration Method ◽  
Phase Plane Analysis ◽  
Constant Property ◽  
Vehicle Suspensions ◽  
Linear Time Invariant ◽  
Forcing Function

This article proposes and demonstrates a calibration-based integral formulation for resolving the forcing function in a mass–spring–damper system, given either displacement or acceleration data. The proposed method is novel in the context of vibrations, being thoroughly studied in the field of heat transfer. The approach can be expanded and generalized further to multi-variable systems associated with machine parts, vehicle suspensions, translational and rotational systems, gear systems, etc. when mathematically described by a system of constant property, linear, time-invariant ordinary differential equations. The analytic approach and subsequent numerical reconstruction of the forcing function is based on resolving a parameter-free inverse formulation for the equation(s) of motion. The calibration approach is formulated in the frequency domain and takes advantage of several observations produced by the dimensionality reduction leading to an algebratized system involving an input–output relationship and a transfer function possessing all the system parameters. The transfer function is eliminated in lieu of experimental data, from a calibration effort, thus leading to a reduction of systematic errors. These parameter-free, reduced systematic error aspects are the distinct and novel advantages of the proposed method. A first-kind Volterra integral equation is formed containing only the unknown forcing function and experimental data. As with all ill-posed problems, regularization must be introduced for system stabilization. A future-time technique is instituted for forming a family of predictions based on the chosen regularization parameter. The optimal regularization parameter is estimated using a combination of phase–plane analysis and cross-correlation principles. Finally, a numerical simulation is performed verifying the proposed approach.

scholarly journals A Unified Frequency Domain Model to Study the Effect of Demyelination on Axonal Conduction

2016 ◽  
Vol 7 ◽  
pp. BECB.S38554 ◽  
Author(s):  
Saurabh Chaubey ◽  
Shikha J. Goodwin
Keyword(s):  
System Identification ◽  
Transfer Function ◽  
Frequency Domain ◽  
Linear Time ◽  
Signal Propagation ◽  
Nerve Fibers ◽  
Domain Model ◽  
Linear Time Invariant ◽  
Cable Properties ◽  
Identification Approach

Multiple sclerosis is a disease caused by demyelination of nerve fibers. In order to determine the loss of signal with the percentage of demyelination, we need to develop models that can simulate this effect. Existing time-based models does not provide a method to determine the influences of demyelination based on simulation results. Our goal is to develop a system identification approach to generate a transfer function in the frequency domain. The idea is to create a unified modeling approach for neural action potential propagation along the length of an axon containing number of Nodes of Ranvier (N). A system identification approach has been used to identify a transfer function of the classical Hodgkin-Huxley equations for membrane voltage potential. Using this approach, we model cable properties and signal propagation along the length of the axon with N node myelination. MATLAB/ Simulink platform is used to analyze an N node-myelinated neuronal axon. The ability to transfer function in the frequency domain will help reduce effort and will give a much more realistic feel when compared to the classical time-based approach. Once a transfer function is identified, the conduction as a cascade of each linear time invariant system-based transfer function can be modeled. Using this approach, future studies can model the loss of myelin in various parts of nervous system.

Instrument Response Removal and the 2020 MLg 3.1 Marlboro, New Jersey, Earthquake

2021 ◽  
Author(s):  
Alexander L. Burky ◽  
Jessica C. E. Irving ◽  
Frederik J. Simons
Keyword(s):  
New Jersey ◽  
Transfer Function ◽  
Seismic Analysis ◽  
Linear Time ◽  
Displacement Velocity ◽  
Linear Time Invariant ◽  
Princeton University ◽  
Instrument Response ◽  
Linear Time Invariant System ◽  
Physical Displacement

Abstract To better understand earthquakes as a hazard and to better understand the interior structure of the Earth, we often want to measure the physical displacement, velocity, or acceleration at locations on the Earth’s surface. To this end, a routine step in an observational seismology workflow is the removal of the instrument response, required to convert the digital counts recorded by a seismometer to physical displacement, velocity, or acceleration. The conceptual framework, which we briefly review for students and researchers of seismology, is that of the seismometer as a linear time-invariant system, which records a convolution of ground motion via a transfer function that gain scales and phase shifts the incoming signal. In practice, numerous software packages are widely used to undo this convolution via deconvolution of the instrument’s transfer function. Here, to allow the reader to understand this process, we start by taking a step back to fully explore the choices made during this routine step and the reasons for making them. In addition, we introduce open-source routines in Python and MATLAB as part of our rflexa package, which identically reproduce the results of the Seismic Analysis Code, a ubiquitous and trusted reference. The entire workflow is illustrated on data recorded by several instruments on Princeton University campus in Princeton, New Jersey, of the 9 September 2020 magnitude 3.1 earthquake in Marlboro, New Jersey.

Synthesis of Digital PID Controllers for Discrete-Time Systems With Guaranteed Non-Overshooting Transient Response

2011 ◽  
Author(s):  
Navid Mohsenizadeh ◽  
Swaroop Darbha ◽  
Shankar P. Bhattacharyya
Keyword(s):  
Transfer Function ◽  
Impulse Response ◽  
Transient Response ◽  
Discrete Time ◽  
Linear Time ◽  
Sufficient Conditions ◽  
Pid Controllers ◽  
Linear Time Invariant ◽  
Digital Pid ◽  
Error Transfer

In this paper, we present a new method of synthesizing digital PID controllers for discrete-time, Linear Time Invariant (LTI) Systems satisfying a class of transient response specifications. The problem of synthesizing a controller to achieve desirable transient specifications, such as requiring the transient response to be within an allowable range of overshoot, can be carried out as a problem of guaranteeing the impulse response of an appropriate closed loop error transfer function to be non-negative. An earlier result by the authors provides necessary and sufficient conditions for the impulse response of a discrete-time transfer function to be non-negative in terms of the requirement of a sequence of polynomials to be sign-invariant on the interval [1, ∞). An application of this result to the error transfer function yields a sequence of polynomials which are required to be sign-invariant on [1, ∞) but whose coefficients are polynomial functions of the controller gains k1, k2 and k3.

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