Calibration method of electromagnetic-flowmeter for LMFBR using root mean square frequency weighted by power spectral density of output voltage fluctuation

Measuring and analysing the roughness of agricultural field and road have great significance for studying the characteristics of tractor dynamic response. This study was designed to analyse and compare the roughness characteristics of agricultural field and asphalt road profiles. A profiling apparatus was developed to measure field and road surface profiles of parallel tracks. The profile measurements were conducted in a grass field, a corn stubble field, a harvested potato field and on an asphalt road. The root mean square value and two spectrum parameters of surface profiles were calculated and analysed to investigate the roughness characteristics of fields and asphalt road. The results of the study indicate that for the values of the agricultural field and asphalt road surface roughness, waviness and roughness index are both positive associated with the root mean square value. Most of the waviness values of all measured field profiles were less than 2 with the average of 1.8, while the waviness values of all measured asphalt road profiles were greater than 2 with the average of 2.08. The roughness of both field and asphalt road profiles can be distinguished by the power spectral density fitting method. However, it has better performance in characterizing asphalt road profiles than characterizing field profiles with the power spectral density fitting method.

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The Silencing of U.S. Campuses Following the COVID-19 Response: Evaluating Root Mean Square Seismic Amplitudes Using Power Spectral Density Data

Seismological Research Letters ◽

10.1785/0220200391 ◽

2021 ◽

Author(s):

David L. Guenaga ◽

Omar E. Marcillo ◽

Aaron A. Velasco ◽

Chengping Chai ◽

Monica Maceira

Keyword(s):

Spectral Density ◽

Root Mean Square ◽

Power Spectral Density ◽

Active Regions ◽

The United States ◽

School Closure ◽

Mean Square ◽

School Closures ◽

Power Spectral ◽

Seismic Amplitudes

Abstract In response to the COVID-19 global pandemic, many populated and active regions have become deserted and show significant reductions in their background seismicity, especially campuses across the United States (U.S.). Seismic sensors located in the vicinity of or within U.S. campuses show that anthropogenic seismic noise remains elevated during the ordinary, nonpandemic, academic year, only subduing during periods of recess (e.g., winter break). Here, we use power spectral density (PSD) data computed by the Incorporated Research Institutions for Seismology Data Management Center for quality assessment to calculate root mean square (rms) amplitude and analyze the effects of the COVID-19 school closures. We processed and analyzed PSD data for 46 seismic stations located within 50 m of a U.S. university or college. Results show that 42 campus stations show an overall rms drop following a statewide school closure.

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Comparing Random Vibration Inputs: Power Spectral Density (PSD) Versus Root Mean-Square Acceleration (Grms)

2003 International Electronic Packaging Technical Conference and Exhibition, Volume 2 ◽

10.1115/ipack2003-35056 ◽

2003 ◽

Author(s):

Ivan Straznicky

Keyword(s):

Spectral Density ◽

Root Mean Square ◽

Power Spectral Density ◽

Fatigue Damage ◽

Random Vibration ◽

Mean Square ◽

Vibration Effect ◽

Operating Environments ◽

Power Spectral ◽

Magnitude Difference

Many defense programs have vibration requirements for electronics which are often specified as random vibration input. Often, this input is based on measurements taken at the locations of interest for the spectrum of vehicle operating environments. The resulting specification is typically several power spectral density, or PSD, curves with associated durations. The root mean square acceleration, or Grms, can be readily calculated for each PSD curve. Grms values are sometimes used to compare different PSD curves for severity. However, this can be misleading. The impacts of two different random vibration inputs, with the same Grms value, can be very different. By calculating fatigue damage values for various components on a circuit card assembly subjected to these inputs, it can be shown that equal Grms values do not result in equal damage. In fact, there can be two orders of magnitude difference in component damage values. This means that Grms values are very poor indicators of random vibration effect, and should not be used for comparison purposes.

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Optimal Wiener Filter for a Body Mounted Inertial Attitude Sensor

Journal of Navigation ◽

10.1017/s0373463308004736 ◽

2008 ◽

Vol 61 (3) ◽

pp. 455-472 ◽

Cited By ~ 5

Author(s):

Peter Rizun

Keyword(s):

Spectral Density ◽

White Noise ◽

Power Spectral Density ◽

Human Body ◽

Linear Acceleration ◽

Mean Square ◽

White Noise Process ◽

Noise Process ◽

Power Spectral ◽

The Mean

An optimal attitude estimator is presented for a human body-mounted inertial measurement unit employing orthogonal triads of gyroscopes, accelerometers and magnetometers. The estimator continuously fuses gyroscope and accelerometer measurements together in a manner that minimizes the mean square error in the estimate of the gravity vector, based on known spectral characteristics for the gyroscope noise and the linear acceleration of points on the human body. The gyroscope noise is modelled as a white noise process of power spectral density δn2/2 while the linear acceleration is modelled as the derivative of a band-limited white noise process of power spectral density δv2/2. The estimator is robust to centripetal acceleration and guaranteed to have zero mean error regardless of the motion of the sensor. The mean square angular error in attitude is shown to be independent of the module's angular velocity and equal to 21/2g−1/2δn3/2δv1/2.

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Study on Slurry Noise of Electromagnetic Flowmeter Based on ARMA Power Spectrum Estimation

Mathematical Problems in Engineering ◽

10.1155/2015/976487 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Song Gao ◽

Bin Li

Keyword(s):

Power Spectrum ◽

Spectral Density ◽

Coordinate System ◽

Flow Velocity ◽

Power Spectral Density ◽

Electromagnetic Flowmeter ◽

Noise Power ◽

Noise Power Spectral Density ◽

Power Spectral ◽

Power Spectral Density Curve

The output signal of the electromagnetic flowmeter measuring the slurry flow will be disturbed by the slurry noise. It is necessary to study the characteristics of slurry noise in order to reduce the interference. The methodology for the estimation of the slurry noise power spectrum based on the ARMA model is presented in this paper, and the relation among the slurry noise, the velocity, and the concentration is obtained by means of the methodology above according to the1/fcharacteristics of the slurry noise. The results by computer simulation experiment show that if the concentration or flow velocity is increased, then slurry noise power spectral density curve in the logarithmic coordinate system will move to the upper right; if the concentration or flow velocity is reduced, then slurry noise power spectral density curve in the logarithmic coordinate system will move to the lower left.

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