Transient Energy Analysis in Water-Filled Viscoelastic Pipelines

2022 ◽  
Vol 148 (1) ◽  
Author(s):  
B. Pan ◽  
A. Keramat ◽  
C. Capponi ◽  
S. Meniconi ◽  
B. Brunone ◽  
...  
2021 ◽  
Vol 263 (6) ◽  
pp. 619-625
Author(s):  
Yosuke Tanabe ◽  
Takashi Yoshizawa ◽  
Shinji Sugimoto ◽  
Takafumi Hara

This paper presents a transient SEA (Statistical Energy Analysis) approach to predict the structure-borne interior noise in trains from an induction motor controlled by multi-mode PWM (Pulse Width Modulation). Most of the induction motors installed in trains are controlled by multi-mode PWM, which switches between asynchronous and synchronous modes according to the speed to reduce switching losses. This control causes the electromagnetic forces of PWM harmonics to change, resulting in a transient interior noise depending on the vehicle's speed. In this paper, we model the bogie using FEM to calculate the transmission of the electromagnetic forces to the vehicle body through traction bars and dampers. Next, we model the vehicle body using a transient SEA to calculate transient energy in a 1/3 octave band excited by the transmitted electromagnetic forces. Finally, we restore the waveform of interior noise by applying the appropriate phase to the transient energy to auralize the analysis result. We obtained reasonable agreement by comparing the analysis results of the interior noise with the actual measurements.


Author(s):  
J. R. Fields

The energy analysis of electrons scattered by a specimen in a scanning transmission electron microscope can improve contrast as well as aid in chemical identification. In so far as energy analysis is useful, one would like to be able to design a spectrometer which is tailored to his particular needs. In our own case, we require a spectrometer which will accept a parallel incident beam and which will focus the electrons in both the median and perpendicular planes. In addition, since we intend to follow the spectrometer by a detector array rather than a single energy selecting slit, we need as great a dispersion as possible. Therefore, we would like to follow our spectrometer by a magnifying lens. Consequently, the line along which electrons of varying energy are dispersed must be normal to the direction of the central ray at the spectrometer exit.


Author(s):  
V. Serin ◽  
K. Hssein ◽  
G. Zanchi ◽  
J. Sévely

The present developments of electron energy analysis in the microscopes by E.E.L.S. allow an accurate recording of the spectra and of their different complex structures associated with the inner shell electron excitation by the incident electrons (1). Among these structures, the Extended Energy Loss Fine Structures (EXELFS) are of particular interest. They are equivalent to the well known EXAFS oscillations in X-ray absorption spectroscopy. Due to the EELS characteristic, the Fourier analysis of EXELFS oscillations appears as a promising technique for the characterization of composite materials, the major constituents of which are low Z elements. Using EXELFS, we have developed a microstructural study of carbon fibers. This analysis concerns the carbon K edge, which appears in the spectra at 285 eV. The purpose of the paper is to compare the local short range order, determined by this way in the case of Courtauld HTS and P100 ex-polyacrylonitrile carbon fibers, which are high tensile strength (HTS) and high modulus (HM) fibers respectively.


Author(s):  
Weihai Sun ◽  
Lemei Han

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.


2017 ◽  
Vol 10 (6) ◽  
pp. 323
Author(s):  
Raffaella Di Sante ◽  
Marcello Vanali ◽  
Elisabetta Manconi ◽  
Alessandro Perazzolo

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