Three Dimensional Simulation of Galvanostatic Discharge of LiCoO2Cathode Based on X-ray Nano-CT Images

2012 ◽  
Vol 159 (10) ◽  
pp. A1604-A1614 ◽  
Author(s):  
Bo Yan ◽  
Cheolwoong Lim ◽  
Leilei Yin ◽  
Likun Zhu
Keyword(s):  
Three Dimensional ◽  
Ct Images ◽  
X Ray ◽  
Galvanostatic Discharge ◽  
Dimensional Simulation

Measurement of Three-Dimensional Time-Averaged Void Fraction Distribution in Rod Bundle in Air-Water System by X-Ray CT Technique

2013 ◽  
Author(s):  
Kenichi Katono ◽  
Jun Nukaga ◽  
Takuji Nagayoshi ◽  
Kenichi Yasuda
Keyword(s):  
Fuel Assembly ◽  
Void Fraction ◽  
Two Phase Flow ◽  
Three Dimensional ◽  
Ct Images ◽  
Phase Flow ◽  
Two Phase ◽  
X Ray ◽  
Void Fraction Distribution ◽  
Fraction Distribution

We have been developing a void fraction distribution measurement technique using the three-dimensional (3D) time-averaged X-ray CT (computed tomography) system to understand two-phase flow behavior inside a fuel assembly for BWR (boiling water reactor) thermal hydraulic conditions of 7.2 MPa and 288 °C. Unlike CT images of a normal standstill object, we can obtain 3D CT images that are reconstructed from time-averaged X-ray projection data of the intermittent two-phase flow. We measured the 3D void fraction distribution in a vertical square (5 × 5) rod array that simulated a BWR fuel assembly in the air-water test. From the 3D time-averaged CT images, we confirmed that the void fraction at the center part of the channel box was higher than that near the channel box wall, and the local void fraction at the central region of a subchannel was higher than that at the gap region of the subchannel. A comparison of the volume-averaged void fractions evaluated by the developed X-ray CT system with those evaluated by a differential pressure transducer in a void fraction range from 0.05 to 0.40 showed satisfactory agreement within a difference of 0.03.

Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images

2003 ◽  
Vol 22 (8) ◽  
pp. 940-950 ◽  
Author(s):  
D. Aykac ◽  
E.A. Hoffman ◽  
G. McLennan ◽  
J.M. Reinhardt
Keyword(s):  
Three Dimensional ◽  
Ct Images ◽  
X Ray ◽  
Human Airway ◽  
Airway Tree

Analysis of Pore Network in Three-dimensional (3D) Grain Bulks Using X-ray CT Images

2007 ◽  
Vol 73 (3) ◽  
pp. 319-332 ◽  
Author(s):  
Sureshraja Neethirajan ◽  
Digvir S. Jayas
Keyword(s):  
Three Dimensional ◽  
Pore Network ◽  
Ct Images ◽  
X Ray ◽  
Grain Bulks

scholarly journals RSAtrace3D: robust vectorization software for measuring monocot root system architecture

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shota Teramoto ◽  
Takanari Tanabata ◽  
Yusaku Uga
Keyword(s):  
Root System ◽  
System Architecture ◽  
Three Dimensional ◽  
Root System Architecture ◽  
Ct Images ◽  
X Ray ◽  
Single Root ◽  
Base Points ◽  
Using Data ◽  
Crown Roots

Abstract Background The root distribution in the soil is one of the elements that comprise the root system architecture (RSA). In monocots, RSA comprises radicle and crown roots, each of which can be basically represented by a single curve with lateral root branches or approximated using a polyline. Moreover, RSA vectorization (polyline conversion) is useful for RSA phenotyping. However, a robust software that can enable RSA vectorization while using noisy three-dimensional (3D) volumes is unavailable. Results We developed RSAtrace3D, which is a robust 3D RSA vectorization software for monocot RSA phenotyping. It manages the single root (radicle or crown root) as a polyline (a vector), and the set of the polylines represents the entire RSA. RSAtrace3D vectorizes root segments between the two ends of a single root. By utilizing several base points on the root, RSAtrace3D suits noisy images if it is difficult to vectorize it using only two end nodes of the root. Additionally, by employing a simple tracking algorithm that uses the center of gravity (COG) of the root voxels to determine the tracking direction, RSAtrace3D efficiently vectorizes the roots. Thus, RSAtrace3D represents the single root shape more precisely than straight lines or spline curves. As a case study, rice (Oryza sativa) RSA was vectorized from X-ray computed tomography (CT) images, and RSA traits were calculated. In addition, varietal differences in RSA traits were observed. The vector data were 32,000 times more compact than raw X-ray CT images. Therefore, this makes it easier to share data and perform re-analyses. For example, using data from previously conducted studies. For monocot plants, the vectorization and phenotyping algorithm are extendable and suitable for numerous applications. Conclusions RSAtrace3D is an RSA vectorization software for 3D RSA phenotyping for monocots. Owing to the high expandability of the RSA vectorization and phenotyping algorithm, RSAtrace3D can be applied not only to rice in X-ray CT images but also to other monocots in various 3D images. Since this software is written in Python language, it can be easily modified and will be extensively applied by researchers in this field.

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