scholarly journals Fundamentals of turbulent flow spectrum imaging

2021 ◽  
Author(s):  
Hannes Dillinger ◽  
Charles McGrath ◽  
Christian Guenthner ◽  
Sebastian Kozerke
Keyword(s):  
Turbulent Flow ◽  
Spectrum Imaging

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Quantitative parallel EELS spectrum imaging developed as a new tool in AEM

1992 ◽  
Vol 50 (2) ◽  
pp. 1202-1203
Author(s):  
Gianluigi Botton ◽  
Gilles L'espérance
Keyword(s):  
Statistical Analysis ◽  
Spatial Resolution ◽  
Personal Computer ◽  
Systematic Study ◽  
Present Author ◽  
Chemical Elements ◽  
Detection Limits ◽  
Research Groups ◽  
Quantitative Performance ◽  
Spectrum Imaging

As interest for parallel EELS spectrum imaging grows in laboratories equipped with commercial spectrometers, different approaches were used in recent years by a few research groups in the development of the technique of spectrum imaging as reported in the literature. Either by controlling, with a personal computer both the microsope and the spectrometer or using more powerful workstations interfaced to conventional multichannel analysers with commercially available programs to control the microscope and the spectrometer, spectrum images can now be obtained. Work on the limits of the technique, in terms of the quantitative performance was reported, however, by the present author where a systematic study of artifacts detection limits, statistical errors as a function of desired spatial resolution and range of chemical elements to be studied in a map was carried out The aim of the present paper is to show an application of quantitative parallel EELS spectrum imaging where statistical analysis is performed at each pixel and interpretation is carried out using criteria established from the statistical analysis and variations in composition are analyzed with the help of information retreived from t/γ maps so that artifacts are avoided.

Energy-filtered imaging of polymer microstructure

1996 ◽  
Vol 54 ◽  
pp. 162-163
Author(s):  
K. Siangchaew ◽  
J. Bentley ◽  
M. Libera
Keyword(s):  
Energy Loss ◽  
Heavy Element ◽  
Spectroscopic Imaging ◽  
Data Set ◽  
Polymer Microstructure ◽  
Entire Energy ◽  
Staining Methods ◽  
Spectrum Imaging ◽  
Resolution Data

Energy-filtered electron-spectroscopic TEM imaging provides a new way to study the microstructure of polymers without heavy-element stains. Since spectroscopic imaging exploits the signal generated directly by the electron-specimen interaction, it can produce richer and higher resolution data than possible with most staining methods. There are basically two ways to collect filtered images (fig. 1). Spectrum imaging uses a focused probe that is digitally rastered across a specimen with an entire energy-loss spectrum collected at each x-y pixel to produce a 3-D data set. Alternatively, filtering schemes such as the Zeiss Omega filter and the Gatan Imaging Filter (GIF) acquire individual 2-D images with electrons of a defined range of energy loss (δE) that typically is 5-20 eV.

An Introduction to Turbulent Flow

2000 ◽  
Author(s):  
Jean Mathieu ◽  
Julian Scott
Keyword(s):  
Turbulent Flow

Modal Analysis of Turbulent Flow near an Inclined Bank–Longitudinal Structure Junction

2021 ◽  
Vol 147 (3) ◽  
pp. 04020100
Author(s):  
Nasser Heydari ◽  
Panayiotis Diplas ◽  
J. Nathan Kutz ◽  
Soheil Sadeghi Eshkevari
Keyword(s):  
Turbulent Flow ◽  
Modal Analysis ◽  
Longitudinal Structure

Numerical simulation of subsonic turbulent flow of oscillating NACA 0015 airfoi

2018 ◽  
Vol 2 ◽  
pp. 133-145
Author(s):  
D.O. Redchyts ◽  
◽  
S.V. Moiseenko ◽  
Keyword(s):  
Numerical Simulation ◽  
Turbulent Flow ◽  
Naca 0015

LIMITATIONS OF THE STOCHASTIC APPROACH IN TWO-PHASE TURBULENT FLOW CALCULATIONS

1996 ◽  
Vol 6 (2) ◽  
pp. 211-225 ◽  
Author(s):  
Keh-Chin Chang ◽  
Wen-Jing Wu ◽  
Muh-Rong Wang
Keyword(s):  
Turbulent Flow ◽  
Stochastic Approach ◽  
Two Phase

Analysis of Frost Growth through a Two-Dimensional Duct with Turbulent Flow

2008 ◽  
Vol 39 (4) ◽  
pp. 347-370
Author(s):  
M. Salmanpour ◽  
O. Nourani Zonouz ◽  
Mahmood Yaghoubi
Keyword(s):  
Turbulent Flow ◽  
Two Dimensional ◽  
Frost Growth
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