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Results for “Radiographic imaging”

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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Experimental studies on metallic fuel relocation in a pin bundle core structure of a sodium-cooled fast reactor

Understanding the relocation behavior of metallic fuel in severe accident scenarios is one of the most important factors in the safety assessment of sodium-cooled fast reactors (SFRs). In the present study, the relocation behavior of uranium metallic fuel was investigated by conducting metallic fuel relocation experiments using 19-pin bundle test sections in the Argonne's Metallic Uranium Safety Experiment (MUSE) facility. Post-test radiographic images were taken to characterize the uranium fuel relocation within the pin bundle test section. In addition, the pressure drop in the test sections was measured after the relocation experiments to evaluate the blockage of the sodium coolant channel. The pressure drop in the pin bundle test sections was also analytically calculated by varying the porosity and the pore size of the relocated fuel. The porosity and pore size of the relocated fuel in the pin bundle test sections were estimated by comparing those calculated pressure drop values with the measured pressure drop values from the pressure drop tests. In this work, the experimental results showed that the more formation of the eutectics could lead to more fuel dispersion and less blockage due to the lower freezing point of the eutectics.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Irradiation of an enriched uranium (NaCl-UCl 3 ) fuel salt capsule, summary of nondestructive post-irradiation examinations, and solidification modeling

Molten salt reactors (MSRs) are gaining attention due to their potential for safe, carbon-free nuclear energy with reduced waste. However, licensing these reactors is hindered by limited experimental data on fueled salts, both pre- and post-irradiation. Here, the novel Molten-salt Research Temperature-controlled Irradiation (MRTI) vehicle was designed to address knowledge gaps in irradiating enriched‑uranium-bearing salts. The MRTI experiment irradiated 13 cm 3 of UCl₃-NaCl (93 % U-235) salt in the Neutron Radiography (NRAD) Reactor, achieving a burnup of 0.196 GWd/MTU over 390 h. Despite a heater failure, the thermocouple data suggested fission heat kept the salt molten. The MRTI assembly was remotely disassembled for nondestructive post-irradiation examination (PIE), which included precision gamma-ray scanning (PGS) and neutron radiography. Radiograph images showed the location of the salt and the solidification pattern. PGS results provided an early indication that activated materials of construction did not increase in relative intensity in the region of the capsule where the salt was in contact with the material of construction. Additionally, PGS data showed the presence of several gamma emitting fission products, such as Nb-95, Zr-95, Ru-103, Ce-141, and La-140, where Ru-103 had the highest counts at the bottom of the capsule. Computational fluid dynamics modeling supported observations of salt solidification patterns and proved to be a valuable tool to inform PIE activities. The MRTI experiment has thus far provided critical data and lessons learned for fuel salt PIE activities, essential for advancing the technical readiness of MSRs.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Multi frame radiography of supersonic water jets interacting with a foil target

Pulsed-power-driven underwater electrical explosion of cylindrical or conical wire arrays produces supersonic water jets that emerge from a bath, propagating through the air above it. Interaction of these jets with solid targets may represent a new platform for attaining materials at high pressure (>1010 Pa) conditions in a university-scale laboratory. However, measurements of the internal structure of such jets and how they interact with targets are difficult optically due to large densities and density contrasts involved. We utilized multi-frame x-ray radiographic imaging capabilities of the ID19 beamline at the European Synchrotron Radiation Facility to explore the water jet and its interaction with a 50 μm thick copper foil placed a few mm from the surface of water. The jet was generated with a ∼130 kA-amplitude current pulse of ∼450 ns rise time applied to a conical wire array. X-ray imaging revealed a droplet-type structure of the jet with an average density of <400 kg/m3 propagating with a velocity of ∼1400 m/s. Measurements of deformation and subsequent perforation of the target by the jet suggested pressures at the jet–target interface of ∼5 × 109 Pa. The results were compared to hydrodynamic simulations for better understanding of the jet parameters and their interaction with the foil target. These results can be used in future research to optimize the platform, and extend it to larger jet velocities in the case of higher driving currents supplied to the wire array.

Physics↗
DOE OSTI2020

Uncertainties in Density and Simulation Parameters for Radiographic Reconstructions Using Machine Learning

We will develop new and improved methods that will enable a fuller and more extensive use of experimental data, such as from radiographic imaging, towards better characterizing and reducing uncertainties in predictive modeling of weapons performance. We will achieve this by leveraging recent developments in the areas of computational imaging, statistical and machine learning, and reduced order modeling. Key new aspects of our approach include (a) a coupling of deep learning based density reconstruction with fast hydrodynamics simulators that will permit the development of certain guarantees on extrapolatability and bounds on certain other hydrodynamics-related para- metric uncertainties and (b) a dual approach towards better characterizing uncertainties in density reconstruction using deep learning based surrogates to accelerate Bayesian reconstructions on the one hand and using variational inference and Bayesian machine learning on the other hand.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Developing inductively driven diagnostic X-ray sources to enable transformative radiography and diffraction capabilities on Z

Penetrating X-rays are one of the most effective tools for diagnosing high energy density experiments, whether through radiographic imaging or X-ray diffraction. To expand the X-ray diagnostic capabilities at the 26-MA Z Pulsed Power Facility, we have developed a new diagnostic X-ray source called the inductively driven X-pinch (IDXP). This X-ray source is powered by a miniature transmission line that is inductively coupled to fringe magnetic fields in the final power feed. The transmission line redirects a small amount of Zs magnetic energy into a secondary cavity where 150+ kA of current is delivered to a hybrid X-pinch. In this report, we describe the multi-stage development of the IDXP concept through experiments both on Z and in a surrogate setup on the 1 MA Mykonos facility. Initial short-circuit experiments to verify power ow on Z are followed by short-circuit and X-ray source development experiments on Mykonos. The creation of a radiography-quality X-pinch hot spot is verified through a combination of X-ray diode traces, laser shadowgraphy, and source radiography. The success of the IDXP experiments on Mykonos has resulted in the design and fabrication of an IDXP for an upcoming Z experiment that will be the first-ever X-pinch fielded on Z. We have also pursued the development of two additional technologies. First, the extended convolute post (XCP) has been developed as an alternate method for powering diagnostic X-pinches on Z. This concept, which directly couples the current owing in one of the twelve Z convolute posts to an X-pinch, greatly increases the amount of available current relative to an IDXP (900 kA versus 150 kA). Initial short-circuit XCP experiments have demonstrated the efficacy of power ow in this geometry. The second technology pursued here is the inductively driven transmission line (IDTL) current monitor. These low-current IDTLs seek to measure the current in the final power feed with high fidelity. After three generations of development, IDTL current monitors frequently return cleaner current measurements than the standard B-dot sensors that are fielded on Z. This is especially true on high-inductance experiments where the harshest conditions are created in the nal power feed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Microtron Data Log

The Microtron at Los Alamos National Laboratory (LANL) is a versatile electron accelerator originally designed for medical therapy. Since 2001, it has been used for non-destructive radiographic imaging and research and development applications. Operating at four different energy levels—6, 10, 15, and 20 MeV—the Microtron produces dose rates of approximately 780, 1800, 2700, and 2800 R/min at a distance of one meter from the source, respectively. This high-energy X-ray source enables detailed internal examination of dense and thick objects without causing damage, making it invaluable for various scientific and industrial applications. For instance, LANL’s Microtron has been utilized to study the performance of large-panel cerium-doped lutetium yttrium silicon oxide (LYSO) scintillators, which are essential components in advanced imaging systems.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

New Design Yields Robust Large-Area Framing Camera

Manuscript to be submitted to Review of Scientific Instruments to determine interest in possible publication. Website: https://aip.scitation.org/rsi/authors/manuscript High-speed imaging is a core diagnostic capability for the Nevada National Security Site (NNSS). Data from experiments that measure the behavior of materials under extreme conditions are essential for the stockpile mission. For example, during subcritical experiments, materials that are shocked and compressed under high-explosive drive change behavior rapidly over timescales measured in millionths or even billionths of a second. Characterizing and understanding this behavior requires radiographic and/or optical imaging to track motion on these timescales. Framing cameras provide a discrete series of images over a short period of time in a manner that mimics a high-speed movie camera, but with individual shutter times that must be extremely short in order to capture freeze-frame images of rapidly evolving phenomena such as high-explosive shock-driven ejecta, dynamic compression of metals, and high-velocity fluid flow.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Improving radiograph analysis throughput through transfer learning and object detection

SIGN Fracture Care International partners with surgeons in low-resource hospitals worldwide to provide access to effective orthopedic care by donating educational materials and innovatively designed surgical implants. Over two decades, SIGN’s Online Surgical Database (SOSD) has grown to contain over 500,000 medical images, with radiographs holding the majority share. One challenge in working with hospitals worldwide is that both the radiographs uploaded to the SOSD and the data entry accompanying the uploads vary in quality. To improve the accuracy of data in the SOSD, we trained a model to detect surgical implants in radiographs. We first developed a tool to automatically detect radiographs, then trained an object detection model to determine the number and placement of surgical implants visible in the radiograph. Active learning was used to generate a training set containing 2,510 radiographs with screws, nails, and plates labeled by bounding boxes. Training a model to simultaneously recognize all three classes of implants gave a low average precision (AP) for the plate class, likely due to the low number of plate instances in our training set and the large variety of surgical plates used by SIGN-partnered surgeons. Applying standard image augmentation techniques to increase the plate count in our training set did not appreciably increase the AP of plate detection. To improve plate detection, we redrew the bounding boxes to account for correlations between the screw and plate classes. Training one model to detect nails and screws and a separate model to detect plates increased the AP of plate detection by 78.8 percentage points. The AP of each class was 80.7% for screws, 93.6% for nails, and 92.6% for plates; meanwhile, the sensitivity was 92% for screws, 86% for nails, and 81% for plates. We show that object detection methods can be used to detect surgical implants in radiographs of varying quality; however, the detection ability is dependent on the type of implant, and some implants, in our case plates, must be treated differently than others. Such tools can improve the throughput of radiograph analysis, assisting physicians and surgeons with the treatment of bone fractures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Kraken Camera System

Specification sheet for Kraken camera system being developed by NNSS Transformational Diagnostics and Imaging in Los Alamos. Will be shared internally and externally.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Kraken Imager and Camera Systems

Presentation describes the Kraken imager and camera systems under development for use at the Nevada National Security Site for work conducted by the National Nuclear Security Administration.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

New Design Yields Robust Large-Area Framing Camera

Framing cameras provide a discrete series of images over a short period of time in a manner that mimics a high-speed movie camera, but with individual shutter times that must be extremely short in order to capture freeze-frame images of rapidly evolving phenomena such as high-explosive shock-driven ejecta, dynamic compression of metals, and high-velocity fluid flow. The NNSS has designed and fielded a new, large-area, gated framing camera called Kraken. The camera design emphasized manufacturability and flexibility by improving imager yield and creating a camera architecture to shorten design cycles. Design strategies and field data are presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Neural network methods for radiation detectors and imaging

Recent advances in image data proccesing through deep learning allow for new optimization and performance-enhancement schemes for radiation detectors and imaging hardware. This enables radiation experiments, which includes photon sciences in synchrotron and X-ray free electron lasers as a subclass, through data-endowed artificial intelligence. We give an overview of data generation at photon sources, deep learning-based methods for image processing tasks, and hardware solutions for deep learning acceleration. Most existing deep learning approaches are trained offline, typically using large amounts of computational resources. However, once trained, DNNs can achieve fast inference speeds and can be deployed to edge devices. A new trend is edge computing with less energy consumption (hundreds of watts or less) and real-time analysis potential. While popularly used for edge computing, electronic-based hardware accelerators ranging from general purpose processors such as central processing units (CPUs) to application-specific integrated circuits (ASICs) are constantly reaching performance limits in latency, energy consumption, and other physical constraints. These limits give rise to next-generation analog neuromorhpic hardware platforms, such as optical neural networks (ONNs), for high parallel, low latency, and low energy computing to boost deep learning acceleration (LA-UR-23-32395).

edge computing↗
DOE OSTI2021

First Experiments and Radiographs on the MegaJOuLe Neutron Imaging Radiography (MJOLNIR) Dense Plasma Focus

We report a dense plasma focus (DPF) is a relatively compact coaxial plasma gun, which completes its discharge as a Z-pinch. These devices are designed to operate at a variety of scales to produce short (<100 ns) pulses of ions, X-rays, and/or neutrons. LLNL recently constructed and brought into operation a new device, the MegaJOuLe Neutron Imaging Radiography (MJOLNIR) DPF, which is designed for radiography and high-yield operations. This device has been commissioned and has achieved neutron yields of up to 3.8E11 neutrons/pulse at 2.5-MA peak current while operating at up to 1 MJ of stored energy in its original pulsed power configuration. MJOLNIR is equipped with a wide range of diagnostics, including nuclear activation detectors, neutron time-of-flight (nToF) detectors, a fast-framing camera, optical light gates, and a time-gated neutron and X-ray imager. LLNL also runs unique particle-in-cell (PIC) simulations of DPF discharges in the Chicago code and has gained significant insight into the various physical factors that influence neutron yield. MJOLNIR is one of the first DPFs whose design and continual upgrades are heavily influenced by model predictions. In this article, we describe insights from modeling, device operation, and recent results. Comparisons between modeling predictions and measurements, as well as X-ray and neutron images are presented.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Multi-Probe Ejecta Diagnostic

Presentation to be given at a Radiography Workshop to be held April 6, 2021 at Los Alamos National Laboratory's LANSCE facility. Presentations are requested to be delivered in advance, by April 2.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Sparse-Data Deep Learning Strategies for Radiographic Non-Destructive Testing

Radiography is an imaging technique used in a variety of applications, such as medical diagnosis, airport security, and nondestructive testing. We present a deep learning system for extracting information from radiographic images. We perform various prediction tasks using our system, including material classification and regression on the dimensions of a given object that is being radiographed. Our system is designed to address the sparse-data issue for radiographic nondestructive testing applications. It uses a radiographic simulation tool for synthetic data augmentation, and it uses transfer learning with a pre-trained convolutional neural network model. Using this system, our preliminary results indicate that the object geometry regression task saw an improvement of 70% in the R-squared value when using a multi-regime model. In addition, we increase the performance of the object material classification tasks by utilizing data from different imaging systems. In particular, using neutron imaging improved the material classification accuracy by 20% when compared to x-ray imaging.

convolutional neural networks↗