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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

FOSSIL—Finding Our Cosmic Roots

FOSSIL (Fragments from the Origins of the Solar System and our Interstellar Locale) is a concept to explore the largest solar system object visible to the unaided eye—the zodiacal cloud. The cloud’s many dust particles are each a tiny time capsule from a comet or asteroid. The FOSSIL concept is for the in situ compositional analysis of interplanetary dust particles, and also of the particles passing through the solar system from interstellar space. By measuring the zodiacal and interstellar particles’ velocity vectors and compositions, the approach resolves fundamental questions about the solar system’s origins. The FOSSIL concept is to: (1) Discover whether today’s local interstellar dust matches the composition of the feedstock from which the solar system formed; (2) Determine whether comets’ fine-grained component preserves unprocessed pre-solar dust or shows signs of processing in the early solar system; and (3) Learn whether comets’ and asteroids’ organic material share a common source or formed from distinct reservoirs. The FOSSIL concept is based on the use of a Dust Telescope (DT) with the capability to measure the composition and the velocity vector of dust particles to unambiguously separate interstellar from interplanetary particles, and identify a subset of zodiacal particles that are exclusively cometary. In the DT, particles pass through the trajectory sensor unharmed and impact a target plate, where they are vaporized and partially ionized. The ions are electrostatically focused onto a detector where the time-of-flight mass spectrum is recorded, enabling measurement of the composition of the interstellar solids, detection of cometary minerals altered by high temperatures or exposure to liquid water in the early solar system, and the characterization of the organic materials from the comets and asteroids that are still being delivered to the Earth today. FOSSIL’s objectives crosscut several disciplines with planetary science. Astrophysics interests lie in understanding interstellar solar matter and the only debris disk accessible in situ. Heliophysics interest is in the verification of large-scale heliospheric magnetic field models by the measured effect on the motion of electrically charged interstellar dust. Reporting the makeup of interplanetary dust that ablates in our atmosphere is valuable to Earth sciences. FOSSIL’s DT is scalable and can be accommodated to a variety of mission opportunities, without restriction on launch dates, and for a large number of possible orbits.

Turner, Neal↗
NASA NTRSDate not supplied

ArcjetCV: a new machine learning application for extracting time-resolved recession measurements from arc jet test videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗
NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗
NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗
NASA NTRSDate not supplied

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗
NASA NTRSDate not supplied

arcjetCV: automating recession extraction from video

Arc jet Computer Vision (arcjetCV)[1][2] is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗
NASA NTRSDate not supplied

ArcjetCV: Automating Recession Tracking

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗
NASA NTRSDate not supplied

ArcjetCV: Automating Arc Jet Analysis

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗
DOE OSTI2025

Physics of laser direct-drive liquid deuterium–tritium wetted foam capsules for inertial confinement fusion

Laser direct-drive liquid deuterium–tritium (DT) wetted foam capsules hold substantial promise for future advancements in inertial confinement fusion (ICF). For this new class of ICF capsules, additive manufacturing (AM) techniques are used to create low aspect ratio spherical shells of a low-density, foam-like CH lattice, which is wetted with cryogenic liquid DT. In the present paper, we discuss key physics issues intrinsic to ignition and burn propagation in laser direct drive wetted foam capsules. These include requirements on laser energy and power, implosion velocity, fuel adiabat, and initial density and dimensions of the CH lattice shell.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Advanced Data Science Model for Detecting Intelligent Malware

This study focused on developing a robust artificial intelligence (AI) model capable of detecting and characterizing advanced malware in Internet of Things (IoT) devices using network data. By analyzing network traffic with various machine learning (ML) models, our AI model can identify and characterize malicious activities to significantly improve malware detection accuracy and reliability as compared to traditional methods. The developed AI/ML model was trained using network data from IoT devices, leveraging classifiers such as Random Forest, Gradient Boosting, AdaBoost, and others to optimize detection performance. This project demonstrates a scalable framework for real-time malware detection and characterization in IoT networks, capable of identifying infected devices and facilitating the necessary steps to remove or isolate them, thereby preventing further infections. Although digital twin (DT) integration is not yet implemented in the current model, it represents a promising future enhancement. By creating a virtual replica of physical IoT devices, DT technology would allow for real-time monitoring and analysis without directly accessing operational technology, thus reducing the risk of compromising or reducing the performance of actual devices. This integration would further enhance the security of IoT ecosystems, combining AI technology to better flag and detect indications of malware-infected devices within a nuclear system environment.

42 ENGINEERING↗
DOE OSTI2024

Autonomous anomaly detection of proliferation in the AGN-201 nuclear reactor digital twin

The expansion of global nuclear power necessitates advanced methods for analyzing proliferation indicators. This study introduces a novel application of the Isolation Forest Machine Learning (IFML) algorithm within a digital twin (DT) of the AGN-201 nuclear reactor to autonomously detect anomalies. Leveraging real-time operational data from the AGN-201 DT, the IFML algorithm identifies outliers without prior data labeling and operates as a lightweight, complementary approach to traditional physics-based anomaly detection methods for nuclear safeguards. In a simulated Red vs. Blue team exercise, the IFML algorithm successfully detected six significant unseen anomalies related to reactivity changes, achieving an accuracy of 99% for identifying operational deviationxs. These anomalies, caused by deliberate perturbations, were detected alongside known physics-based models, underscoring the potential of IFML to enhance real-time monitoring without displacing traditional methods. Further, this study highlights the applicability of IFML in nuclear environments by providing an additional, redundant layer of anomaly detection to improve safeguards and operational safety in complex systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Use of tritium-rich fuel to improve the yield of layered deuterium/tritium inertial fusion capsules

In deuterium–tritium (DT) ice layered implosions, nearly all hot spot mass at peak burn comes from the dense fuel. Accurate prediction of the fuel mass ablation, including the enthalpy associated with mass inflow into the hot spot from the dense fuel, is essential to understanding the energetics and ignition of the hot spot in layered implosions. A recently published boundary layer analysis (Daughton et al., 2023) indicates a faster mass ablation rate than in previous analyses of layered implosions. Inclusion of this effect provides a better match to simulations and leads to a new ignition threshold where the temperature of the dense fuel plays a critical role. This analysis motivates possible new directions for improved capsule performance. Here, the authors present evidence in support of one such approach: the use of tritium-rich ice to decrease 14 MeV neutron scattering and heating of the dense fuel, resulting in less mass ablation and more robust burn of the hot spot. It is found from numerical simulations that despite a less favorable D:T ratio in the ice, the use of a 40:60 D:T ratio leads to an increase in capsule yield of 17% percent compared with that of a 50:50 D:T ratio fuel for capsules resembling those of the recent N210808 ignition experiment on the NIF (Abu-Shawareb et al., 2022) and an increase of 74% compared with that of a 60:40 D:T ratio fuel capsule. Finally, these results are potentially important for modeling all layered implosions, since some degree of DT fractionization may arise naturally during the beta layering process. In addition, this physics is important for the feasibility of high-gain capsule designs that seek to minimize tritium usage, as in some inertial fusion energy concepts.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Design and analysis of dudded fuel experiments at the National Ignition Facility

Recent experiments conducted at the National Ignition Facility (NIF) within the past 2 years have achieved the burning plasma state and exceeded the Lawson criterion for the first time in the laboratory. Here, we report on a set of experiments where the deuterium and tritium (DT) ice layers were replaced with dudded tritium, hydrogen, and deuterium (THD) fuel mixtures to remove the influence of alpha-heating on hot spot dynamics. The hot spot compression and yield in the absence of alpha particle self-heating were measured to assess the proximity of NIF implosions toward the ignition cliff. We find that the “burn-off” Lawson parameters χnoα inferred from the THD experiments are in good agreement with the inferences from postshot simulations of the DT-layered implosions. The THD for burning plasma shot N210307 yielded χnoα≈0.88±0.03 while the THD for ignition shot N210808 yielded χnoα≈1.04±0.04. These results also provide important context for the observed variability in the repeat attempts of ignition shot N210808 since implosions on the ignition cliff are expected to exhibit very large variations in the fusion yield from small changes in the initial conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Characterization of a CMOS camera based film digitization platform for gated x-ray imaging diagnostics at the National Ignition Facility

Hardened gated x-ray detectors use photographic film as the data recording medium due to its low sensitivity to the high-yield neutron environments at the National Ignition Facility (NIF). The photographic film is digitized with a Photometric Data Systems (PDS) microdensitometer, which measures the film’s optical density. The PDS scanner is able to measure a dynamic range of 0–5 OD; however, raster scanning the film is time consuming and maintenance of the instrument is challenging due to legacy technology. Since film usage at NIF is expected to continue in the foreseeable future, a digitization platform that is faster and more maintainable would benefit the NIF’s current and future operations. Here, this work presents the characterization of the digital transitions (DT) atom, a CMOS camera-based digitization platform that records film data in a single image capture very quickly and has widely available user support. The preliminary results suggest that the DT atom is able to reconstruct exposures accurately enough to be a competitive alternative to the PDS Scanner.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Ultra-fast single-crystal CVD diamonds in the particle time-of-flight (PTOF) detector for low yield burn-history measurements on the NIF (invited)

The Particle Time of Flight (PTOF) diagnostic is a chemical vapor deposition diamond-based detector and is the only diagnostic for measuring nuclear bang times of low yield (<1013) shots on the National Ignition Facility. Recently, a comprehensive study of detector impulse responses revealed certain detectors with very fast and consistent impulse responses with a rise time of <50 ps, enabling low yield burn history measurements. At the current standoff of 50 cm, this measurement is possible with fast 14 MeV neutrons from deuterium–tritium (DT) fusion plasmas. PTOF-inferred DT burn width numbers compare well with widths inferred from the gamma reaction history diagnostic on mid-yield (1013–1015) shots, where both systems are capable of making this measurement. These new capabilities could be extended to 2.5 MeV deuterium–deuterium neutrons from D plasmas and to even lower yield by reducing the detector standoff distance to 10 cm; a design for this is also presented.

42 ENGINEERING↗
DOE OSTI2024

Three-dimensional reconstruction of laser-direct-drive inertial confinement fusion hot-spot plasma from x-ray diagnostics on the OMEGA laser facility (invited)

A deep-learning convolutional neural network (CNN) is used to infer, from x-ray images along multiple lines of sight, the low-mode shape of the hot-spot emission of deuterium–tritium (DT) laser-direct-drive cryogenic implosions on OMEGA. The motivation of this approach is to develop a physics-informed 3-D reconstruction technique that can be performed within minutes to facilitate the use of the results to inform changes to the initial target and laser conditions for the subsequent implosion. The CNN is trained on a 3D radiation-hydrodynamic simulation database to relate 2D x-ray images to 3D emissivity at stagnation. The CNN accounts for the lack of an absolute spatial reference and the different bands of photon energies in the x-ray images. While previous works studied the effect of mode-1 asymmetries on implosion performance using nuclear diagnostics, this work focuses on the effect of mode 2 inferred from x-ray diagnostics on implosion performance. A current analysis of 19 DT cryogenic implosions indicates there is an upper limit of ~20% reduction in the neutron yield caused by an ℓ = 2 amplitude for ℓ 2 /ℓ 0 ≤ 0.32. Here, these conclusions are supported by 2D simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Reaction-in-flight neutrons as a diagnostic for hydrodynamical mixing in double shell inertial confinement fusion capsules

We examine reaction-in-flight (RIF) neutrons as diagnostics for hydrodynamical mixing of high-Z shell material into the hotspot of double shell capsules that are designed for the National Ignition Facility. In particular, we consider the effects of different levels of mixing of tungsten shell material into the DT gas on RIF spectra. Using a set of 1D simulations from the radiation hydrodynamic code xRAGE to determine the temperature and density profiles of the mixed W-DT gas, we find that increasing the mass of mix systematically reduces the ratio of RIF neutrons to primary 14 MeV neutrons. The shape of the RIF spectrum also changes with mix, with the predicted spectrum softening in energy with increasing mix.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗