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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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This presentation will be presented at FY 2020 SDRD Annual (Webex) Meeting scheduled for September 23–24, 2020 High-Fidelity Dynamic Neutron Imaging for Subcritical Experiments and Other Applications LO-005-19, Year 2
A collaborative project between Idaho National Laboratory (INL) and Paul-Scherrer Institute (PSI) is investigating a new type of scintillation screen that uses ZnS scintillator material with a dysprosium neutron converter instead of traditional prompt converters such as 6Li. Such a screen exposed to a neutron beam creates a latent image by neutron activation of the dysprosium in the scintillator screen. The activated screen is transported into a camera box allowing the camera to read a digital image from the photons emitted by the activated scintillation screen. Such an imaging system combines modern camera-based system architecture with the approach of traditional indirect transfer method radiography. The results show for the first time that the combination of dysprosium with a scintillation material like ZnS can produce light which is measurable under common camera-based detection conditions and that neutron radiographic images of reasonable quality can be produced. The resolution was poorer than expected at ~ 300 µm, but is on the order of the desired resolution of 100 µm. Potential improvements and additional converter materials may be investigated in the future that could increase the light output and improve spatial resolution.
Geologic hydrogen production and underground storage are increasingly important for meeting rising energy demands while providing clean-combustion advantages. However, hydrogen’s high diffusivity and propensity for leakage through porous media necessitate direct evaluation of its transport behavior in subsurface materials. Whereas X-ray microcomputed tomography (μCT) studies often employ contrast agents or surrogate gases, this study leverages neutron transmission radiography/CT to observe hydrogen migration in situ. This work represents the first demonstration of real-time neutron radiography of hydrogen migration in reservoir and caprock lithologies. Cylindrical cores of Indiana limestone, Amherst Gray sandstone, and Tumey shale were subjected to constant-pressure hydrogen charging and scanned in real time using high-resolution neutron radiography. Results indicate immediate hydrogen infiltration in sandstone and limestone, with homogeneous distribution detected throughout their pore structure. In contrast, hydrogen remained largely absent from fine-grained shale under the same pressure, except in an apparently localized fracture zone, where neutron signatures confirmed the presence of hydrogen. Subsequent neutron CT of the sandstone sample, using image subtraction against an uncharged reference, corroborated hydrogen distribution patterns. Even under lowpressure, single-phase conditions, distinct neutron imaging signatures of hydrogen were achieved. These preliminary findings underscore the potential of neutron imaging for advancing subsurface hydrogen migration research.
With an increased interest in the use of molten salts for energy generation, obtaining thermophysical properties of salt mixtures becomes critical for the understanding of salt performance and behavior. Density is one of the significant thermophysical properties of salt systems. This work presents the density measurement of molten chloride salt mixtures using neutron imaging. This work was performed at the Oak Ridge National Laboratory High-Flux Isotope Reactor. Here, resulting densities as a function of temperature for different molten chloride salts from this work were compared with calculated values using Redlich–Kister modeling. Agreement between the calculated and measured values was within 1–10%, with the exception of the ternary UCl 3 –NaCl–KCl salt that showed a 32% discrepancy between several literature reports; however, the results did align well with another neutron radiography article. Analysis of the radiographs suggests that microbubbles in the ternary mixture might have biased the density measurements.
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Platinum group metal-free (PGM-free) catalysts are a low-cost alternative to expensive PGM catalysts for polymer electrolyte fuel cells. However, due to the low volumetric activity of PGM-free catalysts, the catalyst layer thickness of the PGM-free catalyst electrode is an order of magnitude higher than PGM based electrodes. The thick PGM-free electrodes suffer from increased transport resistance and poor water management, which ultimately limits the fuel cell performance. This manuscript presents the study of water management in the PGM-free electrodes to understand the transport limitations and improve fuel cell performance. Here, in-operando neutron imaging is performed to estimate the water content in different components across the fuel cell thickness. Water saturation in thick PGM electrodes, with similar catalyst layer thickness to PGM-free electrodes, is lower than in the PGM-free electrodes irrespective of the operating conditions, due to high water retention by PGM-free catalysts. Improvements in fuel cell performance are accomplished by enhancing water removal from the flooded PGM-free electrode in three ways: (i) enhanced water removal with a novel microporous layer with hydrophilic pathways incorporated through hydrophilic additives, (ii) water removal through anode via novel GDL in the anode, and (iii) lower water saturation in PGM-free electrode structures with increased catalyst porosity.
Time-of-flight neutron diffraction and energy-resolved imaging each provide unique perspectives into material properties. Neutron diffraction is useful for assessing microstructural parameters such as phase composition, texture, and dislocation densities, though it typically provides averaged data over the sampled volume. Energy-resolved imaging, on the other hand, offers both spatial and spectral information by detecting Bragg edges and neutron absorption resonances, which enables detailed mapping of microstructure and isotopic composition. When combined, these techniques have the potential to enrich our understanding of material behavior across different scales, enhancing our understanding of complex materials. Traditionally, these modalities are conducted on separate instruments, which is time-consuming and poses challenges for data integration. Here, we report the integration of the LumaCam, an event-mode energy-resolved neutron imaging camera with the HIPPO time-of-flight diffractometer at LANSCE. This integration enables simultaneous diffraction and imaging across the full spectrum, with analysis optimized for diffraction and Bragg-edge imaging in the thermal range (0.45–10 Å) and resonance imaging in the epithermal range (0.5–3000 eV), facilitating comprehensive multi-modal analysis. We demonstrate its capabilities through case studies, including spatial mapping of grain orientations in a steel sample and accurate thickness estimations for irregular samples including a depleted uranium cylinder and a natural silver-containing mineral specimen. The combined setup enhances real-time sample alignment and provides comprehensive data for crystal structure, texture, and isotopic composition analysis. This approach opens new possibilities for advanced applications in nuclear engineering, archaeology, and materials science.
The nuclear imaging system at the National Ignition Facility (NIF) is a crucial diagnostic for determining the geometry of inertial confinement fusion implosions. The geometry is reconstructed from a neutron aperture image via a set of reconstruction algorithms using an iterative Bayesian inference approach. An important step in these reconstruction algorithms is finding the fusion source location within the camera field-of-view. Currently, source localization is achieved via an iterative optimization algorithm. In this paper, we introduce a machine learning approach for source localization. Specifically, we train a convolutional neural network to predict source locations given a neutron aperture image. We show that this approach decreases computation time by several orders of magnitude compared to the current optimization-based source localization while achieving similar accuracy on both synthetic data and a collection of recent NIF deuterium–tritium shots.
The nuclear imaging system at the National Ignition Facility (NIF) is a crucial diagnostic for determining the geometry of inertial confinement fusion implosions. The geometry is reconstructed from a neutron aperture image via a set of reconstruction algorithms using an iterative Bayesian inference approach. An important step in these reconstruction algorithms is finding the fusion source location within the camera field-of-view. Currently, source localization is achieved via an iterative optimization algorithm. In this paper, we introduce a machine learning approach for source localization. Specifically, we train a convolutional neural network to predict source locations given a neutron aperture image. We show that this approach decreases computation time by several orders of magnitude compared to the current optimization-based source localization while achieving similar accuracy on both synthetic data and a collection of recent NIF deuterium–tritium shots.
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The presentation at the first Prompt Radiation Detection and Imaging Workshop covered fast neutron and tomography work at the National Ignition Facility (NIF).
A particle imaging method for distinguishing between types of incident particles, such as neutrons, photons, and alphas, and improving the position resolution of particle imaging devices with matrix readout. The method includes high frequency multisampling readout electronics that provides the sequences of multiple measurements for each detected event, resulting in recorded detailed waveform information describing the signals. Such detailed information is used to approximate each signal waveform with a parameterized function in which the extracted parameter sets determine the type of the incident particle in an optimized fashion. The detailed event-by-event multisampling information for each signal readout channel in the matrix readout of the radiation imaging devices improves and optimizes the position resolution for variable shapes of the signals. Such devices can be used in mixed radiation fields, creating a new class of multimodal photon and neutron imagers.
Contains the neutron radiography and tomography images of hydrogen uptake in different geological lithologies.
Recent research has explored the development of boron-based neutron scintillator screens, which potentially offer improved spatial resolution and neutron capture efficiency compared to traditional lithium-based screens. This work builds upon previous efforts to improve boron-based neutron scintillators by assessing a newer generation of boron-based scintillator screens fabricated using different compositions and fabrication approaches compared to previous generations of screens. Some of the test screens exhibit higher light output than previous efforts and higher neutron capture efficiency than lithium-based screens. This paper describes the current state of screen development, measurement results for the most recent generation of screens, and future activities.
The neutron radiography (nR) dataset provides information of the neutron events as measured for the Siemens star mask using the Timepix3 detector. The recordings consist of the position (x and y axes), the time-stamp, and time-over-threshold (TOT) values of each neutron event.
Presentation on Neutron Radiography of Transient Irradiated Nuclear Fuels
We present the development and application of a collision tracking feature within the OpenMC Monte Carlo particle transport code, designed for diverse applications such as neutron spectroscopy, scatter camera system, neutron noise, and multiplicity counting simulations. This feature enables the tracking of individual particle collisions, with potential applications in nuclear nonproliferation, reactor physics, and nuclear security. Additionally, the feature holds potential for the calibration of neutron detectors, specifically in converting light output into energy deposited within the detectors. The implementation consists of a set of filters—such as reaction type, energy, cell, and material—that constrain the set of collisions that are tracked, extensions to the Python API to enable simple input specification, and support for writing either OpenMC’s native HDF5-based format or the Monte Carlo particle list format. This feature was added to the official OpenMC release in version 0.15.3. In this work, the feature will be applied to showcase scenarios such as time-of-flight simulations, scatter-camera imaging for neutron source localization, neutron-noise analysis to extract integral kinetic parameters such as the prompt decay constant α, and multiplicity counting to estimate the mass of special nuclear materials. Ultimately, this feature aims to expand the application scope of open-source Monte Carlo particle transport codes such as OpenMC.