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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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Explanation of ST40 Langmuir probe and infrared thermography diagnostics and their planned upgrades as part of the Lithium Evaporations to Advance PFCs in ST40 (LEAPS) program.
The Fast Ignition (FI) Concept for Inertial Confinement Fusion (ICF) has the potential to provide a significant advance in the technical attractiveness of Inertial Fusion Energy reactors. FI differs from conventional “central hot spot” (CHS) target ignition by decoupling compression from heating: using a laser (or heavy ion beam or Z pinch) drive pulse (10’s of nanoseconds) to create a dense fuel and a second, much shorter (~10 picoseconds) high intensity pulse to ignite a small volume within the dense fuel. The physics of fast ignition process was the focus of our Advanced Concept Exploration (ACE) program. Ignition depends critically on two major issues involving Relativistic High Energy Density (RHED) physics: The laser-induced creation of fast electrons and their propagation in high-density plasmas. Our program has developed new experimental platforms, diagnostic packages, computer modeling analyses, and taken advantage of the increasing energy available at laser facilities to advance understanding of the fundamental physics underlying these issues.
Temperature and density asymmetry diagnosis is critical to advance inertial confinement fusion (ICF) science. A multi-monochromatic x-ray imager (MMI) is an attractive diagnostic for this purpose. The MMI records the spectral signature from an ICF implosion core with time resolution, 2-D space resolution, and spectral resolution. While narrow-band images and 2-D space-resolved spectra from the MMI data constrain temperature and density spatial structure of the core, the accuracy of the images and spectra depends not only on the quality of the MMI data but also on the reliability of the post-processing tools. In this paper, we synthetically quantify the accuracy of images and spectra reconstructed from MMI data. Errors in the reconstructed images are less than a few percent when the space-resolution effect is applied to the modeled images. The errors in the reconstructed 2-D space-resolved spectra are also less than a few percent except those for the peripheral regions. Spectra reconstructed for the peripheral regions have slightly but systematically lower intensities by ~6% due to the instrumental spatial-resolution effects. However, this does not alter the relative line ratios and widths and thus does not affect the temperature and density diagnostics. We also investigate the impact of the pinhole size variation on the extracted images and spectra. A 10% pinhole size variation could introduce spatial bias to the images and spectra of ~10%. A correction algorithm is developed, and it successfully reduces the errors to a few percent. Finally, it is desirable to perform similar synthetic investigations to fully understand the reliability and limitations of each MMI application.
The highly transient nature of shock loading and pronounced microstructure effects on dynamic materials response call for in situ, temporally and spatially resolved, x-ray-based diagnostics. Third-generation synchrotron x-ray sources are advantageous for x-ray phase contrast imaging (PCI) and diffraction under dynamic loading, due to their high photon fluxes, high coherency, and high pulse repetition rates. The feasibility of bulk-scale gas gun shock experiments with dynamic x-ray PCI and diffraction measurements was investigated at the beamline 32ID-B of the Advanced Photon Source. The x-ray beam characteristics, experimental setup, x-ray diagnostics, and static and dynamic test results are described. We demonstrate ultrafast, multiframe, single-pulse PCI measurements with unprecedented temporal (<100 ps) and spatial ({approx}2 {mu}m) resolutions for bulk-scale shock experiments, as well as single-pulse dynamic Laue diffraction. The results not only substantiate the potential of synchrotron-based experiments for addressing a variety of shock physics problems, but also allow us to identify the technical challenges related to image detection, x-ray source, and dynamic loading.
Large-scale battery manufacturing requires understanding the fundamental principles of materials and interfaces and relies on advanced techniques for detailed interrogation. Despite advancements in the industrial scale production and their associated quality control tools, challenges such as electrode heterogeneity, internal defects, and largescale material waste (e.g., scrap) can hamper manufacturing. Synchrotron X-ray characterization techniques offer spatial, temporal, and chemical resolution that can provide diagnostic insights for metrology across various manufacturing steps. This review examines the use of synchrotron tools to advance understanding of key steps in the battery manufacturing process. Recent examples demonstrate how synchrotron methods resolve manufacturing challenges and uncover degradation pathways that are otherwise inaccessible. Future directions for advancing battery manufacturing emphasize collaboration between academia and industry through the use of synchrotron X-ray techniques.
This paper addresses the challenge of tritium inventory management in ITER and future fusion reactors, highlighting the importance of accurate tritium measurement and its spatial distribution within the vacuum vessel. Given ITER’s operational constraints, especially the limit on tritium retention, precise measurement is essential for both safety and regulatory compliance. To tackle these questions, the paper presents the T-monitor diagnostic system developed by Forschungszentrum Jülich, which uses Laser-Induced Desorption (LID) in combination with Diagnostic Residual Gas Analysis (DRGA) to measure hydrogen isotope concentrations on the surface of divertor tiles. The system integrates a high-power laser, advanced optical components, and a Fast Scanning Mirror Unit (FSMU) for accurate laser spot positioning with rapid response. Designed to measure in situ tritium retention, the diagnostic provides high-resolution spatial mapping, vital for evaluating detritiation strategies. The laser heating process increases the divertor surface temperature to 1600 K within the laser spot, promoting hydrogen isotope desorption. Accurate measurements require the precise control of laser parameters, including pulse duration and spot size, with a target relative accuracy of 20%. The optical design includes both in-vessel and ex-vessel components, such as durable high-reflectivity mirrors made of gold and copper, selected not only for their infrared performance but also for their transmission of visible wavelengths for observation purposes. To protect optical components from contamination, a pneumatic shutter is used.
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Diagnostic Ultrasound Corporation's Bladder Scan Monitor continuously records and monitors bladder fullness and alerts the wearer or caretaker when voiding is required. The sensor is held against the lower abdomen by a belt and connected to the monitor by a cable. The sensor obtains bladder volume data from sound waves reflecting off the bladder wall. The device was developed by Langley Research Center, the Ames Research Center and the NASA Technology Applications Team. It utilizes Langley's advanced ultrasound technology. It is licensed to the ARC for medical applications, and sublicensed to Diagnostics Ultrasound. Central monitoring systems are planned for the future.
Cardiac sarcoidosis (CS) remains an intriguing infiltrating disorder and one of the most important forms of inflammatory cardiomyopathy. Identification of patients with CS is of extreme importance because they are at higher risk of sudden death, and heart-failure progression. And while it remains a diagnostic conundrum, a great amount of experience has been accumulated over the last decade with the advent of fluorine-18 fluorodeoxyglucose positron emission tomography and cardiac magnetic resonance with late gadolinium enhancement imaging. They have both proven to be advanced imaging techniques that provide important, and often complementary, diagnostic and prognostic information for the management of CS. However, they have also shown to have limitations, and, thus, there is a continued need for developing more specific imaging probes for identifying cardiac inflammation. The aim of the present manuscript is to provide the reader with a better understanding of the histopathology of the disease, how this potentially relates to noninvasive imaging detection, and the best strategies available for the diagnosis and management of patients with CS.
A detailed description of a prototype setup for real-time (rt) Thomson scattering (TS) analysis is presented and implemented in the multi-point Thomson scattering (MPTS) diagnostic system at the National Spherical Torus Experiment Upgrade(NSTX-U). The data acquisition hardware was upgraded with rt capable electronics (rt-analog digital converters (ADCs) and a rt server) that allow for fast digitization of the laser pulse signal of eight radial MPTS channels. In addition, a new TS spectrum analysis software for a rapid calculation of electron temperature (Te) and electron density (ne) was developed. Testing of the rt hardware and data analysis soft-ware was successfully completed and benchmarked against the standard, post-shot evaluation. Timing tests were performed showing that the end-to-end processing time was reproducibly below 17 ms for the duration of at least 5 s, meeting a 60 Hz deadline by the laser pulse repetition rate over the length of a NSTX-U discharge. The presented rt framework is designed to be scalable in system size, i.e. incorporation of additional radial channels by solely adding additional rt capable hardware. Furthermore, it is scalable in its operation duration and was continuously run for up to 30 min, making it an attractive solution for machines with long discharge duration such as advanced, non-inductive tokamaks or stellarators.
The inherent complexity of boundary plasma, characterized by multi-scale and multi-physics challenges, has historically restricted high-fidelity simulations to scientific research due to their intensive computational demands. Consequently, routine applications such as discharge control and scenario development have relied on faster but less accurate empirical methods. This work introduces DivControlNN, a novel machine-learning-based surrogate model designed to address these limitations by enabling quasi-real-time predictions (i.e., ~ 0.2 ms) of boundary and divertor plasma behavior. Trained on over 70,000 2D UEDGE simulations from KSTAR tokamak equilibria, DivControlNN employs latent space mapping to efficiently represent complex divertor plasma states, achieving a computational speed-up of over 10 8 compared to traditional simulations while maintaining a relative error below 20% for key plasma property predictions. During the 2024 KSTAR experimental campaign, a prototype detachment control system powered by DivControlNN successfully demonstrated detachment control on its first attempt, even for a new tungsten divertor configuration and without any fine-tuning. These results highlight the transformative potential of DivControlNN in overcoming diagnostic challenges in future fusion reactors by providing fast, robust, and reliable predictions for advanced integrated control systems.
The mechanical characterization of materials relies more and more often on sophisticated experimental methods that permit to acquire a large amount of data and, contemporarily, to reduce the invasiveness of the tests. This evolution accompanies the growing demand of non-destructive diagnostic tools that assess the safety level of components in use in structures and infrastructures, for instance in the strategic energy sector. Advanced material systems and properties that are not amenable to traditional techniques, for instance thin layered structures and their adhesion on the relevant substrates, can be also characterized by means of combined experimental-numerical tools elaborating data acquired by full-field measurement techniques. In this context, parameter identification procedures involve the repeated simulation of the laboratory or in situ tests by sophisticated and usually expensive non-linear analyses while, in some situation, reliable and accurate results would be required in real time. The effectiveness and the filtering capabilities of reduced models based on decomposition and interpolation techniques can be profitably used to meet these conflicting requirements. This communication intends to summarize some results recently achieved in this field by the author and her co-workers. The aim is to foster further interaction between engineering and mathematical communities.
Los Alamos Neutron Science Center (LANSCE) and Dual-Axis Radiographic Hydrodynamic Test facility (DARHT) play a pivotal role in advancing scientific research and national security initiatives. As a premier facility for neutron science, LANSCE and DARHT provide invaluable insights into fundamental research, materials science, nuclear physics, high-energy physics, material science, and medical imaging. Existing (DC) power supply and pulse modulation technologies used in LANSCE and DARHT face limitations in terms of reliability, maintainability, and diagnostics which hinder the progress of these cutting-edge facilities. By proposing improvements to LANL accelerator facilities’ performance and addressing component failure, we aim to enhance scientific outcomes and minimize disruptions, allowing for more efficient and productive research activities while ensuring the continuity of valuable contributions to the user community. This proposal aims to revolutionize (DC) power supply and pulse power systems for future particle accelerators via the development of a resilient GaN-based power module with improved diagnostic and redundant submodules.
Knowledge and control of the axial outflow of plasma particles and energy along open-magnetic-field lines are of crucial importance to the stability and longevity of the advanced beam-driven field-reversed configuration plasma. An overview of the diagnostic methods used to perform measurements on the open field line plasma on C-2U is presented, including passive Doppler impurity spectroscopy, microwave interferometry, and triple Langmuir probe measurements. Results of these measurements provide the jet ion temperature and axial velocity, electron density, and high frequency density fluctuations.
Precision neutron-induced reaction data are important for modeling the network of isotope production and destruction within a given diagnostic chain. This network modeling has many applications such as the design of advanced fuel cycle for reactors and the interpretation of radiochemical data related to the stockpile stewardship and nuclear forensics projects. Our current funded effort is to improve the neutron-induced reaction data on the short-lived actinides and the specific goal is to improve the neutron capture data on {sup 238}Pu with a half-life of 87.7 years. In this report, the fabrication of a {sup 238}Pu target for the proposed measurement using the DANCE array at LANL is described. The {sup 238}Pu target was fabricated from a sample enriched to 99.35%, acquired from ORNL. A total of 395 {micro}g was electroplated onto both sides of a 3 {micro}m thick Ti foil using a custom-made plating cell, shown in Fig 1. The target-material loaded Ti foil is sandwiched between two double-side aluminized mylar foils with a thickness of 1.4 {micro}m. The mylar foil is glued to a polyimide ring. This arrangement is shown partially in Fig. 2. The assembled target is then inserted into an aluminum container with a wall thickness of 0.76 mm, shown in Fig. 3. A derlin ring is used to keep the target assembly in place. The ends of this cylindrical container are vacuum-sealed by two covers with thin Kapton foils as windows for the beam entrance and exit. Shown in Fig. 4 is details of the arrangement. This target is used for phase I of the proposed measurement on {sup 238}Pu scheduled for Nov 2010 together with the DANCE array to address the safety issues raised by LANL. Shown in Fig. 5 is the preliminary results on the yield spectrum as a function of neutron incident energy with a gate on the total {gamma}-ray energy of equivalent Q value. Since no fission PPAC is employed, the distinction between the capture and fission events cannot be made, which is important for the higher neutron incident energy. However, it indicates that a cross section of less than one barn can be measured. The second phase of this experiment will be carried out in 2011 by assembling a PPAC with the {sup 238}Pu target to extend the measurement to higher neutron incident energies by distinguishing the capture from fission events. The fission cross section becomes dominant for neutron incident energies above 30 keV. This PPAC was developed in FY2010 under the NA22 funding and performed very well for the {sup 239}Pu and {sup 241}Pu measurements. A new {sup 238}Pu target will be fabricated for the phase II measurement using the same electroplating technique.
The Department of Energy (DOE) Office of Nuclear Energy (NE) released a request for information (RFI) (DE-SOL-0008318) for “University, National Laboratory, Industry and International Input on Potential Office of Nuclear Energy Infrastructure Investments” on April 13, 2015. DOE-NE solicited information on five specific types of capabilities as well as any others suggested by the community. The RFI proposal period closed on June 19, 2015. From the 26 responses, 34 individual proposals were extracted. Eighteen were associated with a DOE national laboratory, including Argonne National Laboratory (ANL), Brookhaven National Laboratory (BNL), Idaho National Laboratory (INL), Los Alamos National Laboratory (LANL), Pacific Northwest National Laboratory (PNNL) and Sandia National Laboratory (SNL). Oak Ridge National Laboratory (ORNL) was referenced in a proposal as a proposed capability location, although the proposal did not originate with ORNL. Five US universities submitted proposals (Massachusetts Institute of Technology, Pennsylvania State University, Rensselaer Polytechnic Institute, University of Houston and the University of Michigan). Three industrial/commercial institutions submitted proposals (AREVA NP, Babcock and Wilcox (B&W) and the Electric Power Research Institute (EPRI)). Eight major themes emerged from the submissions as areas needing additional capability or support for existing capabilities. Two submissions supported multiple areas. The major themes are: Advanced Manufacturing (AM), High Performance Computing (HPC), Ion Irradiation with X-Ray Diagnostics (IIX), Ion Irradiation with TEM Visualization (IIT), Radiochemistry Laboratories (RCL), Test Reactors, Neutron Sources and Critical Facilities (RX) , Sample Preparation and Post-Irradiation Examination (PIE) and Thermal-Hydraulics Test Facilities (THF).
An earlier report described a procedure for optimal sensor set selection and its implementation on a computational cluster. This new and innovative capability was developed to facilitate a reduction in operations staffing levels to improve plant economics. By automating surveillance and maintenance tasks through early detection of degrading sensors and equipment, staff can be more efficiently deployed. The method uses automated reasoning and domain knowledge in the form of the conservation equations to infer from plant measurements the state of equipment health. Inclusion of domain knowledge addresses the problem that exists with pure data-driven methods that there are no rigorous guidelines for determining what constitutes an adequate sensor set. Formalizing the procedure for sensor set selection as we have done results in a more reliable and explainable diagnosis of plant equipment health. Importantly, from the standpoint of the plant owner, personnel are provided with an early and explicit diagnosis of an equipment problem. That in principle automates the process and eliminates having to send personnel into the plant to find the cause as typically occurs when a data-driven method detects an anomaly. In this report we describe first results obtained using a computational cluster to solve the sensor set selection problem as framed above. The case described addresses the problem of equipment health monitoring in the high-pressure (HP) feedwater system of a pressurized light water reactor as seen through the eyes of our collaborating utility partner. Maintenance of this system can amount to millions of dollars per year if equipment health issues go undiagnosed and lead to loss of function. On examining the potential that is inherent in the installed sensor set for diagnosing equipment health degradation, it was found that greater fault resolution capability can be achieved using a sensor set that is 20 percent fewer in number. The take-away is that compared to the installed sensor set there exists a more strategic assignment of sensors that will furnish better health monitoring capability and with fewer sensors. Where the problem defies solution by manual inspection, as is the case here, one can be found by an algorithm. The solution was obtained in four hours using 30 computational cores. The HP feedwater problem as posed above illustrates the added value of approaching the sensor selection problem as one amenable to algorithmic solution. This problem is of interest to advanced reactor designers and to utilities that are setting up remote monitoring and diagnostic centers.
Current state-of-the-art techniques for non-invasive imaging of cardiac electrical phenomena require voltage recordings from dozens of different torso locations and anatomical models built from expensive medical diagnostic imaging procedures. Here this study aimed to assess if recent machine learning advances could alternatively reconstruct electroanatomical maps at clinically relevant resolutions using only the standard 12-lead electrocardiogram (ECG) as input. To that end, a computational study was conducted to generate a dataset of over 16000 detailed cardiac simulations, which was then used to train neural network (NN) architectures designed to exploit both spatial and temporal correlations in the ECG signal. Analysis over a validation set showed average errors in activation map reconstruction below 1.7 msec over 75 intracardiac locations. Furthermore, phenotypical patterns of activation and the morphology of the activation potential were correctly reconstructed. The approach offers opportunities to stratify patients non-invasively, both retrospectively and prospectively, using metrics otherwise only available through invasive clinical procedures.