X-ray CT Scans—Set 5
This report includes a collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
59 BASIC BIOLOGICAL SCIENCES↗
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X-ray CT Scans - Rodents - Set 3
A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
59 BASIC BIOLOGICAL SCIENCES↗
X-ray diffraction and scanning electron microscope characterizations of the salt waste processing facility tank 101 and tank 104 solid samples
The “as-received” Salt Waste Processing Facility (SWPF) Tank 101 and two Tank 104 samples (pre-wash and washed solids) have been characterized using x-ray diffraction (XRD) and scanning electron microscope/energy dispersive x-ray (SEM/EDX) techniques to address the concerns for the existence of solids, which may interfere with the operational performance of the SWPF crossflow filtration process.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Computed Tomography Scanning and Geophysical Measurements of Core from the Boggess 17H Well
The computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) at the National Energy Technology Laboratory (NETL) in Morgantown, West Virginia were used to characterize the Marcellus Shale and underlying formations. The core is from a vertical pilot well (Boggess 17H) drilled in western Monongalia County near Core, West Virginia by Northeast Natural Energy for the second Marcellus Shale Energy and Environmental Laboratory (MSEEL). MSEEL is a joint venture between NETL, Northeast Natural Energy, and West Virginia University. The primary impetus for this report is to characterize the core to better understand the structure and variation of the Marcellus Shale and surrounding formations. This report, and the associated scans, provide detailed datasets not typically available from unconventional shales for analysis.
58 GEOSCIENCES↗
X-ray CT Scans - Molluscs - Set 1
A collection of x-ray computed tomography scans of specimens from the Bailey-Matthews National Shell Museum.
59 BASIC BIOLOGICAL SCIENCES↗
X-ray CT Scans - Molluscs - Set 2
A collection of x-ray computed tomography scans of specimens from the Denver Museum of Nature & Science.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
X-ray CT Scans - Rodents - Set 4
A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
X-Ray CT Scans - Rodents Set 5
A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
59 BASIC BIOLOGICAL SCIENCES↗
Computed Tomography Scanning and Geophysical Measurements of the Smackover Formation from the Roberson 18-19 Core
The computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) at the U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) site in Morgantown, West Virginia, were used to characterize the Upper Jurassic (Oxfordian) Smackover limestone in the Roberson 18-19 1-15H core retrieved from the Atlanta Field, Columbia County, Arkansas. The 365.68-ft long Roberson core came from a vertical well at depths 8,802 ft to 9,169.60 ft, drilled by Southwestern Energy targeting the Smackover Formation as a potential unconventional oil reservoir. The primary impetus of this work was to non-destructively characterize core from the Smackover Formation that was acquired through a core exchange with Southwestern Energy. The Smackover Formation is an unconventional limestone play in southern Arkansas. This report and the associated scans provide detailed datasets not typically made publicly available from unconventional limestone for analysis. The resultant datasets are presented in this report and can be accessed from NETL’s Energy Data eXchange (EDX) online system using the following link: https://edx.netl.doe.gov/dataset/roberson-smackover.
58 GEOSCIENCES↗
X-ray CT Scans - Skeletons - Set 1
A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
59 BASIC BIOLOGICAL SCIENCES↗
X-ray CT Scans - Matryoshka Dolls
A collection of x-ray computed tomography scans of wooden Matryoshka dolls.
36 MATERIALS SCIENCE↗
X-ray CT Scans - Candy - Set 1
A collection of x-ray computed tomography scans of candy.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
X-ray CT Scans - Candy - Set 4
A collection of x-ray computed tomography scans of candy.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
X-ray CT Scans - Candy - Set 3
A collection of x-ray computed tomography scans of candy.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
X-ray CT Scans - Candy - Set 2
A collection of x-ray computed tomography scans of candy.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
X-ray CT Scans - Minerals - Set 1
A collection of x-ray computed tomography scans of mineral specimens from private collections.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Specification of EBR-II Gamma Scan Data from the Precision Gamma Scanner
The gamma scan data stored in the FIPD was originally measured using the Precision Gamma Scanner (PGS) at the Hot Fuel Examination Facility (HFEF) at Idaho National Laboratory (INL). This system was originally manufactured in 1971 (when the facilities, now at INL, were operated by Argonne National Laboratory) and was upgraded on several occasions. It consists of a germanium gamma ray detector (the exact nature of which changed over time as technology improved), a variable-slit collimator that blocks the view of gamma emissions from all but the selected area of the target, and a computer-controlled system for translating a sample past the slit and logging the data.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Uniform Beam Simulation Technique for Beam Scans and Machine Learning Studies at Fermilab
Fermilab's neutrino facilities, including NuMI and the upcoming LBNF, use proton beams to produce positively and negatively charged pions and kaons. Detailed simulations are necessary to study particle interactions and beam propagation. To efficiently analyze beam scan effects, we propose a technique to generate multiple simulation samples with high statistics. These samples can be used to develop beamline simulation based machine learning applications. In this technique, we generate a uniformly distributed single simulation data sample. We calculate Gaussian weights for each beam configurations and apply them to post-processing measurements. In this poster, we demonstrate the proposed simulation technique. This technique reduces simulation time and computing resources significantly.
Wickremasinghe, Athula↗