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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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205 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Experimental Safe Extremum Seeking for Accelerators

Here, we demonstrate the recent designs of safe extremum seeking (Safe ES) on the 1-km-long charged particle accelerator at the Los Alamos Neutron Science Center (LANSCE). Safe ES is a modification of extremum seeking (ES) which, in addition to minimizing an analytically unknown cost, also employs a safety filter based on an analytically unknown control barrier function (CBF) safety metric. Tuning is necessitated by accelerators being large complex systems, with many drifting parameters due to thermal effects and degradation. At the same time, safe operation (the maintenance of state constraints) is crucial, as damage brings astronomical costs, both financially and in operation downtime. Our measured (but analytically unknown) safety metric is the beam current. We perform multivariable Safe ES on three accelerator applications, in which we adapt 4, 6, and 3 magnet strength parameters, respectively. Two of the three applications are for validated simulation models of beamlines at LANSCE: the first for the proton radiography (pRad) beamline of 800-MeV protons for spot size tuning; the second on a high-performance code, HPSim, for tuning the low-energy beam transport (LEBT) region that contains a beam of 750-keV protons. The third is an experimental tuning of the steering magnets in the LEBT at LANSCE.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Optical versus radiographic imaging and tomography: introduction to the ROADS feature issue

Optical imaging is an ancient branch of imaging dating back to thousands of years. Radiographic imaging and tomography (RadIT), including the first use of X-rays by Wilhelm Röntgen, and then, $γ$ -rays, energetic charged particles, neutrons, etc. are about 130 years young. The synergies between optical and radiographic imaging can be cast in the framework of these building blocks: Physics, Sources, Detectors, Methods, and Data Science, as described in Appl. Opt. 61, RDS1 (2022). Optical imaging has expanded to include three-dimensional (3D) tomography (including holography), due in to part the invention of optical (including infrared) lasers. RadIT are intrinsically 3D because of the penetrating power of ionizing radiation. Both optical imaging and tomography (OIT) and RadIT are evolving into even higher dimensional regimes, such as time-resolved tomography (4D) and temporarily and spectroscopically resolved tomography (4D + ). Further advances in OIT and RadIT will continue to be driven by desires for higher information yield, higher resolutions, and higher probability models with reduced uncertainties. Synergies in quantum physics, laser-driven sources, low-cost detectors, data-driven methods, automated processing of data, and artificially intelligent data acquisition protocols will be beneficial to both branches of imaging in many applications. These topics, along with an overview of the Radiography, Applied Optics, and Data Science virtual feature issue, are discussed here.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Demonstration of neutron-yield enhancement by laser preheating and magnetization of laser-driven cylindrical implosions

Magnetized liner inertial fusion (MagLIF) is a fusion concept that uses magnetized, preheated fuel to reduce the implosion velocities and convergence ratios required for ignition. A scaled, laser-driven experimental platform to study MagLIF has been demonstrated on the OMEGA laser system, providing comprehensive experimental data on MagLIF scaling, utilizing the higher shot rate on OMEGA compared to the Z machine. Using this platform, a broader experimental space for MagLIF has been studied. Presented in this article are experimental results that demonstrate that the combination of preheat and magnetization enhances the neutron yield by 470% compared to a reference implosion, significantly more than the yield enhancement by the field or preheat alone. These results are achieved while maintaining a relatively low convergence ratio (<20). Here, the experiments were supported by one-, two-, and three-dimensional radiation-hydrodynamics simulations, all of which suggest that multiple sources of mix play different key roles depending on the scale of the MagLIF experiment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Achieving 280 Gbar hot spot pressure in DT-layered CH capsule implosions at the National Ignition Facility

We are reporting on a series of indirect-drive 0.9-scale CH capsule implosions (inner radius = 840 μm) fielded in low gas-fill (0.6 mg/cm 3 ) hohlraums of 6.72 mm diameter at the National Ignition Facility. Thanks to the 11%-reduction of the capsule size at a given hohlraum diameter compared to previously tested full-scale capsules, we achieved good hot spot symmetry control near 33% cone-fraction and without the need to invoke cross beam energy transfer. As a result, we achieved a hot spot pressure of 280 ± 40 Gbar, which is the highest pressure demonstrated in layered DT implosions with CH capsules to date. Pushing this design to higher velocity resulted in a reduction of neutron yield. Highly resolved capsule simulations suggest that higher Au M-shell preheat resulted in an increase in Atwood number at the ablator–ice interface, which leads to increased fuel-ablator instability and mixing. The results reported here provide important scaling information for next-generation CH designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Machine learning for detection of 3D features using sparse x-ray tomographic reconstruction

In many inertial confinement fusion (ICF) experiments, the neutron yield and other parameters cannot be completely accounted for with one and two dimensional models. This discrepancy suggests that there are three dimensional effects that may be significant. Sources of these effects include defects in the shells and defects in shell interfaces, the fill tube of the capsule, and the joint feature in double shell targets. Due to their ability to penetrate materials, x rays are used to capture the internal structure of objects. Methods such as computational tomography use x-ray radiographs from hundreds of projections, in order to reconstruct a three dimensional model of the object. In experimental environments, such as the National Ignition Facility and Omega-60, the availability of these views is scarce, and in many cases only consists of a single line of sight. Mathematical reconstruction of a 3D object from sparse views is an ill-posed inverse problem. These types of problems are typically solved by utilizing prior information. Neural networks have been used for the task of 3D reconstruction as they are capable of encoding and leveraging this prior information. We utilize half a dozen, different convolutional neural networks to produce different 3D representations of ICF implosions from the experimental data. Deep supervision is utilized to train a neural network to produce high-resolution reconstructions. These representations are used to track 3D features of the capsules, such as the ablator, inner shell, and the joint between shell hemispheres. Machine learning, supplemented by different priors, is a promising method for 3D reconstructions in ICF and x-ray radiography, in general.

Wolfe, Bradley T. (ORCID:0000000268301614)↗
DOE OSTI2021

2D perovskite-based high spatial resolution X-ray detectors

Abstract X-ray radiography is the most widely used imaging technique with applications encompassing medical and industrial imaging, homeland security, and materials research. Although a significant amount of research and development has gone into improving the spatial resolution of the current state-of-the-art indirect X-ray detectors, it is still limited by the detector thickness and microcolumnar structure quality. This paper demonstrates high spatial resolution X-ray imaging with solution-processable two-dimensional hybrid perovskite single-crystal scintillators grown inside microcapillary channels as small as 20 µm. These highly scalable non-hygroscopic detectors demonstrate excellent spatial resolution similar to the direct X-ray detectors. X-ray imaging results of a camera constructed using this scintillator show Modulation Transfer Function values significantly better than the current state-of-the-art X-ray detectors. These structured detectors open up a new era of low-cost large-area ultrahigh spatial resolution high frame rate X-ray imaging with numerous applications.

2D perovskite↗
DOE OSTI2023

Material Identification From Radiographs Without Energy Resolution

We propose a method for performing material identification from radiographs without energy-resolved measurements. Material identification has a wide variety of applications, including in biomedical imaging, nondestructive testing, and security. While existing techniques for radiographic material identification make use of dual energy sources, energy-resolving detectors, or additional (e.g., neutron) measurements, such setups are not always practical— requiring additional hardware and complicating imaging. We tackle material identification without energy resolution, allowing standard X-ray systems to provide material identification information without requiring additional hardware. Assuming a setting where the geometry of each object in the scene is known and the materials come from a known set of possible materials, we pose the problem as a combinatorial optimization with a loss function that accounts for the presence of scatter and an unknown gain and propose a branch and bound algorithm to efficiently solve it. We present experiments on both synthetic data and real, experimental data with relevance to security applications— thick, dense objects imaged with MeV X-rays. We show that material identification can be efficient and accurate, for example, in a scene with three shells (two copper, one aluminum), our algorithm ran in six minutes on a consumer-level laptop and identified the correct materials as being among the top 10 best matches out of 8,000 possibilities.

36 MATERIALS SCIENCE↗