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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Validation of the imaging neutral particle analyzer in nearly MHD quiescent plasmas using injected beam ions on DIII-D

The imaging neutral particle analyzer (INPA) is a scintillator-based diagnostic that provides energy and radially resolved measurements of confined fast ions. To verify operation, understanding, and modeling of the diagnostic performance, distributions of deuterium beam ions in low density, nearly MHD-quiescent plasmas are measured on the DIII-D tokamak using the INPA. Here, the distribution is altered by changing the pitch-angle scattering rate with electron cyclotron heating (ECH). Experimental images are mostly reproduced with simulation of core fast ions charge exchanging with injected beam and edge cold neutrals in classical cases. Image comparisons between the experiment and simulation show agreement with less than 25% discrepancies near the beam injection energy of 50 keV. Other strong passive signals, observed in a localized region on the image, are suggested to come from charge exchange between cold neutrals and fast ions around the plasma boundary.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

The CeBrA demonstrator for particle-γ coincidence experiments at the FSU Super-Enge Split-Pole Spectrograph

We report on a highly selective experimental setup for particle-γ coincidence experiments at the Super-Enge Split-Pole Spectrograph (SE-SPS) of the John D. Fox Superconducting Linear Accelerator Laboratory at Florida State University (FSU) using fast CeBr scintillators for γ-ray detection. Specifically, we report on the results of characterization tests for the first five CeBr 3 scintillation detectors of the CeBr 3 Array (CeBrA) with respect to energy resolution and timing characteristics. We also present results from the first particle-γ coincidence experiments successfully performed with the CeBrA demonstrator and the FSU SE-SPS. We show that with the new setup, γ-decay branching ratios and particle-γ angular correlations can be measured very selectively using narrow excitation energy gates, which are possible thanks to the excellent particle energy resolution of the SE-SPS. In addition, we highlight that nuclear level lifetimes in the nanoseconds regime can be determined by measuring the time difference between particle detection with the SE-SPS focal-plane scintillator and γ-ray detection with the fast CeBrA detectors. As a result, selective excitation energy gates with the SE-SPS exclude any feeding contributions to these lifetimes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Modeling the Low-Pressure High-Voltage Branch of the Paschen Curve for Hydrogen and Deuterium

A physical and numerical model of the Townsend discharge in molecular hydrogen and deuterium has been developed to meet the needs of designing a plasma-based switching device for power grid applications. Here, the model allows to predict the low-pressure branch of the Paschen curve for applied voltage in the range of several hundred kilovolts. In the regime of interest, electrons are in a runaway state and ionization by ions and fast neutrals sustains the discharge. It was essential to correctly account for both gas-phase and surface interactions (electron emission and electron backscattering), especially in terms of their dependence on particle energy. The model yields results consistent with prior data obtained for lower voltage. The three-species (electrons, ions, and fast neutrals) model successfully captures the essential physics of the process.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data

High-energy large-scale particle colliders produce data at high speed in the order of 1 terabytes per second in nuclear physics and petabytes per second in high energy physics. Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention. Specifically, at the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC), a time projection chamber is used as the main tracking detector, which records particle trajectories in a volume of three-dimensional (3D) cylinder. The resulting data are usually very sparse with occupancy around 10.8%. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. The 3D convolutional neural network (CNN)-based approach, Bicephalous Convolutional Autoencoder (BCAE), outperforms traditional methods both in compression rate and reconstruction accuracy. BCAE can also utilize the computation power of graphical processing units suitable for deployment in a modern heterogeneous highperformance computing environment. This work introduces two BCAE variants: BCAE++ and BCAE-2D. BCAE++ achieves a 15% better compression ratio and a 77% better reconstruction accuracy measured in mean absolute error compared with BCAE. BCAE-2D treats the radial direction as the channel dimension of an image, resulting in a 3× speedup in compression throughput. In addition, we demonstrate an unbalanced autoencoder with a larger decoder can improve reconstruction accuracy without significantly sacrificing throughput. Lastly, we observe both the BCAE++ and BCAE-2D can benefit more from using half-precision mode in throughput (76 - 79% increase) without loss in reconstruction accuracy. The source code and links to data and pretrained models can be found at https://github.com/BNL-DAQ-LDRD/NeuralCompression_v2

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

The Development of a Generalized Riser Flow Regime Map Based Upon Higher Moment and Chaotic Statistics Using Electrical Capacitance Volume Tomography (ECVT)

Dynamic analyses have been applied to the temporal signals from an Electro Capacitance Volume Tomography instrument located near mid-height on the riser of an industrial-scale cold-flow circulating fluidized bed to characterize gas-solids flow behavior in the riser. Twelve capacitance electrodes surround the cylindrical riser over a height of 1.3 m. The instrument used a neural network deconvolution algorithm to determine the spatially resolved solids fraction recorded at 52 Hz. Experiments were carried out over a range of gas and solids flows in the transport regime using a Geldart Group B bed material, high density polyethylene with mean particle size of 880 μm. The radial solids distribution was found to vary from one-time step to the next between profiles typical of laminar and turbulent flow. The duration of time spent in each of these flow profiles depended upon the operating regime – dilute, core-annular, or fast fluidized bed. The chaotic structure of the temporal data was characterized using the three conventional approaches: the first 4 moments from the distribution of signal in time, system memory parameters from the autocorrelation function and the Hurst exponent, and analysis of the correlationentropy and correlation dimension of the attractor. These signal analysis techniques were used to clearly distinguish differences between different transport operating regimes. Specifically, it was experimentally observed that a riser transitions from core annular flow profile to dilute and dense regimes via increasing the frequency of short term transients to either dilute or dense flow profiles, respectively. A regime map was generated based upon these dynamics using solids flux and gas velocity axes. Fast fluidized, core annular, and dilute each exhibited different degree of dynamic characteristics typical of fluid dominated or particle compromising behavior. It should be noted that the magnitude for the different statistics was in the same range regardless of the regime, it was the radial profile for the statistic that changed and subsequently identified that there was a change in the regime. Finally, a reduced regime map was developed consisting of plotting the gas velocity normalized by the upper transport velocity versus the solids flux normalized by the saturation carrying capacity. The use of this reduced plot allowed the data from widely different conditions to be plotted and compared on the same<p>graph. Note that in many instances, some of the statistics identified the operating point as being in one regime while others indicated that it was in another indicating a transition region between dilute or core annular regimes and between the core annular and fast fluidization regimes. This now provides a tool that can be used to optimize process performance, identify changes in operating states, or replicate process dynamics during process scaling or changing operating parameters. </p>

Breault, Ronald↗
DOE OSTI2023

Imitating Architectural Mortise‐Tenon Structure for Stable Ni‐Rich Layered Cathodes

Ni-rich layered oxides are the most promising cathodes for Li-ion batteries, but chemo-mechanical failures during cycling and large first-cycle capacity loss hinder their applications in high-energy batteries. Herein, by introducing spinel-like mortise-tenon structures into the layered phase of LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NCM811), the adverse volume variations in cathode materials can be significantly suppressed. Meanwhile, these mortise-tenon structures play the role of the expressway for fast lithium-ion transport, which is substantiated by experiments and calculations. Moreover, the particles with mortise-tenon structures usually terminate with the most stable (003) facet. The new cathode exhibits a discharge capacity of 215 mAh g –1 at 0.1 C with an initial Coulombic efficiency of 97.5%, and capacity retention of 82.2% after 1200 cycles at 1 C. Importantly, this work offers a viable lattice engineering to address the stability and low initial Coulombic efficiency of the Ni-rich layered oxides, and facilitates the implementation of Li-ion batteries with high-energy density and long durability.

25 ENERGY STORAGE↗
DOE OSTI2025

NPFTURBULENCE: Fast Response Temperature by Airborne Measurements

The original data were collected during the field campaign of the “Turbulent layers promoting New Particle Formation” experiment (NPFTURBULENCE; https://www.arm.gov/research/campaigns/aaf2024npfturbulence; https://www.arm.gov/publications/programdocs/doe-sc-arm-25-004.pdf ) over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Atmospheric Observatory (https://www.arm.gov/capabilities/observatories/sgp ) in north-central Oklahoma. The ARM Aerial Facility ArcticShark uncrewed aerial system (UAS, https://www.arm.gov/capabilities/observatories/aaf/uas) was based at Blackwell–Tonkawa Municipal Airport (IATA: BWL, ICAO: KBKN, FAA LID: BKN, 36.74475° N, 97.34918° W, 313.9 m MSL), for the field campaign from May 5 through May 29, 2024. The ArcticShark UAS performed 11 flights, including 10 research flights over the Central Facility of the ARM SGP to measure atmospheric state, turbulence, surface IR temperature and imagery, aerosol number concentration and size distribution. The current dataset presents fast response temperature in the atmospheric boundary layer and lower free troposphere measured on the airborne platform throughout the field campaign. The primary instruments used to create the current dataset were the fine wire thermocouple probe, Pitot-static system (part of the UAS control), and infrared gas analyzer sensor for H2O and CO2 (LI-840A). All parameters used in the temperature calculations (static and dynamic pressure, absolute humidity in form of dew point temperature) were included in the data set. For user convenience, one additional parameter was also included: the type of flight flag (level, up, down, turn, and combination of thereof).

Air temperature↗
DOE OSTI2024

Investigation of cosmic ray impact on transmon qubits with different materials through fast decay detection at Fermilab’s SQMS laboratory

Noise sources affecting superconducting qubits, such as cosmic rays and ambient gamma radiation, have garnered attention for their potential impact. These radiations deposit energy in the qubit's substrate, generating high-energy phonons that break Cooper pairs, leading to the formation of quasi-particles. When these quasi-particles tunnel across Josephson junctions, they induce qubit de-excitation, causing relaxation errors. In this study, we employ a fast feedback mechanism for performing qubit reset to detect decay events with high time resolution. This capability along with frequency multiplexed readout allows us to explore error correlations across time and space. Transmon qubits, manufactured within the Superconducting Quantum Materials and Systems (SQMS) center [1], are investigated at the above-ground lab. We study the impact of cosmic rays on qubits with different encapsulations, geometries, and locations on the same or different chips. We present preliminary findings and discuss the implications for the performance of contemporary quantum hardware.

Roy, Tanay↗
DOE OSTI2023

Development of digital electronics for the search of SHE nuclei using GARIS-II/III at RIKEN

This work describes the implementation of a digital data acquisition system for the search and study of short-lived isotopes or superheavy elements at the GARIS-III separator at the RIKEN Nishina Center. Here a reduction to 26 ± 0.5 keV in the average value of the Full Width at Half Maximum of the alpha spectrum at 7.133 MeV was achieved, combined with a reduction of the dead time of the overall system to around a few tens of nanoseconds for the detection of very fast decay chains. Moreover, we measured a reduction of the background induced by particles crossing the implantation detector using pulse shape analysis techniques. The detail of the optimization and analysis will be presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Assessment of CASL VERA for BWR analysis and application to accident tolerant SiC/SiC channel box

Application of the Virtual Environment for Reactor Applications (VERA) to BWR analysis is assessed in this paper by comparing results to those calculated using other widely-used modeling tools, namely the U.S. Nuclear Regulatory Commission’s PARCS/PATHS and the Serpent Monte Carlo particle transport code. Additionally, VERA is used to calculate 3-D temperature and fast neutron flux distributions in silicon carbide (SiC) fiber-reinforced, SiC matrix composite (SiC/SiC) BWR channel boxes, which are being studied as an Accident Tolerant Fuel core structural material concept. We found that the code-to-code comparisons were favorable, and the SiC/SiC channel box evaluation demonstrates the many advanced modeling features VERA provides while also highlighting the non-uniformity in fast neutron flux distributions that can play a role in potential SiC/SiC channel box deformation. Traditional BWR analysis tools do not have the calculation fidelity necessary for coupled assessment of flux and temperature gradients in a SiC/SiC channel box. VERA is a state-of-the-art modeling environment that was developed to increase the safety and economic competitiveness of nuclear power through improved modeling accuracy. While VERA has already been deployed in the nuclear industry for PWR applications, the current study is a vital initial step in the extensive development, validation, and verification that VERA must go through to be useful for BWR applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Learning protocols for the fast and efficient control of active matter

Exact analytic calculation shows that optimal control protocols for passive molecular systems often involve rapid variations and discontinuities. However, similar analytic baselines are not generally available for active-matter systems, because it is more difficult to treat active systems exactly. Here we use machine learning to derive efficient control protocols for active-matter systems, and find that they are characterized by sharp features similar to those seen in passive systems. We show that it is possible to learn protocols that effect fast and efficient state-to-state transformations in simulation models of active particles by encoding the protocol in the form of a neural network. We use evolutionary methods to identify protocols that take active particles from one steady state to another, as quickly as possible or with as little energy expended as possible. Our results show that protocols identified by a flexible neural-network ansatz, which allows the optimization of multiple control parameters and the emergence of sharp features, are more efficient than protocols derived recently by constrained analytical methods. Our learning scheme is straightforward to use in experiment, suggesting a way of designing protocols for the efficient manipulation of active matter in the laboratory.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

DAMSA Experiment Conceptual Design White Paper

DAMSA (DArk Messenger Searches at an Accelerator) is a novel short-baseline accelerator experiment aimed at probing short-lived physics processes, including searches for evidence of a dark sector of particle physics and well-motivated Standard Model signals. Motivated by open questions in neutrino physics and the absence of conclusive evidence for conventional weakly interacting massive particles, DAMSA targets MeV-to-sub-GeV dark-sector messengers with feeble couplings that can be produced in abundance at the PIP-II LINAC. By employing an ultra-short baseline of order one meter, DAMSA is uniquely positioned to overcome the beam-dump "ceiling" that limits sensitivity to promptly decaying particles in longer-baseline experiments. The conceptual design emphasizes a beam-dump production scheme combined with a compact detector optimized for rare decays while mitigating intense neutron-induced backgrounds inherent to high-power proton beams. To validate the experimental strategy and detector technologies, the Little DAMSA Path-Finder (LDPF) proof-of-concept experiment is proposed, focusing on axion-like particles decaying to two photons and operating with 300 MeV electron beams at FAST. Successful realization of LDPF will establish the feasibility of the DAMSA approach, enabling a broad and powerful program to explore short-lived new physics and precision Standard Model processes in a previously inaccessible regime. This conceptual design document outlines the technical details of DAMSA's physics goals, the beam facility proposals, key experimental challenges and how to overcome them, and the proposed experimental staging campaigns.

Bhattarai, Prithak [Texas U., Arlington]↗
DOE OSTI2020

In Situ and Operando Morphology Study of Germanium-Selenium Alloy Anode for Li-ion Batteries

Selenium-doped germanium (GeSe) micrometersized particles have been reported with good cycling performance and rate capability due to a Li-Se-Ge network formed during the first lithiation that provides a Li-ion fast pathway. To understand the effect of the Li-Se-Ge network at a high cycling rate, we monitored the morphology change of both pure Ge and GeSe particles during cycling with an in situ/operando focused-ion beam-scanning electron microscope method. Our results showed that the proposed inactive Li-Se-Ge network can provide fast Li-ion transport and also buffer volume variation, resulting in homogeneous volume change and uniform microstructural evolution.

germanium↗
DOE OSTI2022

Neural conditional reweighting

There is a growing use of neural network classifiers as unbinned, high-dimensional (and variable-dimensional) reweighting functions. To date, the focus has been on marginal reweighting, where a subset of features are used for reweighting while all other features are integrated over. There are some situations, though, where it is preferable to condition on auxiliary features instead of marginalizing over them. Here, we introduce neural conditional reweighting, which extends neural marginal reweighting to the conditional case. This approach is particularly relevant in high-energy physics experiments for reweighting detector effects conditioned on particle-level truth information. Furthermore we leverage a custom loss function that not only allows us to achieve neural conditional reweighting through a single training procedure, but also yields sensible interpolation even in the presence of phase space holes. As a specific example, we apply neural conditional reweighting to the energy response of high-energy jets, which could be used to improve the modeling of physics objects in parametrized fast simulation packages.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Binder-Free Graphite Anodes for Next-Generation High-Performance Lithium-Ion Batteries

High-energy density anodes are crucial for next-generation lithium-ion batteries (LIBs) particularly for electric vehicle (EV) applications. Sluggish lithium-diffusion kinetics coupled with conventional anode fabrication processes containing polymeric binders hinder fast-charging capabilities and high-energy density of graphite. Herein, we introduce a binder-free graphite anode fabrication strategy using the electrospinning technique that contains ~2.41% carbon nanotubes (CNTs). Our strategy relies on the formation of an interconnecting conductive CNT network coupled with an ultrathin N-doped carbon coating on graphite particles from sacrificial binders. This combination enhances both structural integrity and electrical conductivity and, in turn, improves fast-charging capabilities and high energy density of LIBs. The binder-free graphite anode achieves ~335.0 mAh g–1 capacity at C/3 rate over 400 cycles with capacity retention of >95% and average Coulombic efficiencies >99.95%. These promising results suggest that the binder-free anode fabrication with a multifunctional design approach could elevate the energy-density limits of the graphite anodes, solving high-energy density requirements of EVs, and potentially provides a path forward for the development of economically feasible energy storage systems for various applications.

Ozcan, Muca [ORNL] (ORCID:0000000320020474)↗
DOE OSTI2020

Microparticle cloud imaging and tracking for data-driven plasma science

Oceans of image and particle track data encountered in plasma interactions with microparticle clouds motivate development and applications of machine-learning (ML) algorithms. A local-constant-velocity tracker, a Kohonen neural network or self-organizing map, the feature tracking kit, and U-Net are described and compared with each other for microparticle cloud datasets generated from exploding wires, dusty plasmas, and atmospheric plasmas. Particle density and the signal-to-noise ratio have been identified as two important factors that affect the tracking accuracy. Fast Fourier transform is used to reveal how U-Net, a deep convolutional neural network developed for non-plasma applications, achieves the improvements for noisy scenes. Viscous effects are revealed in the ballistic motions of the particles from the exploding wires and atmospheric plasmas. Subdiffusion of microparticles satisfying Δr 2 ∝t k (k=0.84±0.02) is obtained from the dusty plasma datasets. Microparticle cloud imaging and tracking, when enhanced with data and ML models, present new possibilities for plasma physics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗