Search NASA⌕ Search

SEARCH · Search NASA

Results for “73 NUCLEAR PHYSICS AND RADIATION PHYSICS”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

1,698 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Physical thickness characterization of the FRIB production targets

The FRIB heavy-ion accelerator, commissioned in 2022, is a leading facility for producing rare isotope beams (RIBs) and exploring nuclei beyond the limits of stability. These RIBs are produced via reactions between stable primary beams and a graphite target. Approximately 20–40% of the primary beam power is deposited in the target, requiring efficient thermal dissipation. Currently, FRIB operates with a primary beam power of up to 20 kW. To enhance thermal dissipation efficiency, a single-slice rotating graphite target with a diameter of approximately 30 cm is employed. The effective target region is a 1 cm-wide outer rim of the graphite disc. To achieve high RIB production rates, the areal thickness variation must be constrained within 2%. This paper presents physical thickness characterizations of FRIB production targets with various nominal thicknesses, measured using a custom-built non-contact thickness measurement apparatus.

Instrumentation for radioactive beams (fragmentati↗
DOE OSTI2026

Capturing Secondary Kinetic Instabilities in Three‐Dimensional Dayside Reconnection Using an Improved Gradient‐Based Closure

Magnetic reconnection is a highly dynamic process that excites a wide variety of kinetic waves and instabilities. Transverse current sheet instabilities such as the lower-hybrid drift and secondary drift-kink instabilities in particular have been shown by kinetic simulations to modify the reconnection and introduce significant turbulence and mixing to the reconnection layer. Past studies using the ten-moment fluid model to capture important kinetic physics such as the electron inertia and full representation of the pressure tensor proved advantageous to a two-fluid representation of reconnection, but the model struggled when using a local relaxation closure for the heat flux to replicate the current sheet instabilities and subsequent mixing seen in kinetic simulations. This work uses the Gkeyll software framework to perform simulations of asymmetric reconnection based on the 16 October 2015 MMS crossing of a diffusion region, the Burch event. An improved gradient-based heat flux closure is implemented, showing significant improvement in secondary kinetic instabilities that grow in the current sheet. These instabilities generate turbulence which leads to growth of secondary magnetic islands and flux ropes.

Bradshaw, K. [Princeton University, NJ (United Sta↗
DOE OSTI2024

Surface phase diagrams from nested sampling

From nested sampling, we compute the partition function and, from that, the phase diagram of gas adsorbates, including their anharmonic and configurational degrees of freedom, on flat and stepped surfaces of the Lennard-Jones solid.

Chemistry↗
DOE OSTI2026

Additively manufactured refractory high-entropy alloys with superior radiation resistance

Refractory high-entropy alloys (RHEAs) are promising candidates for next-generation nuclear and high-temperature applications. Among many approaches to manufacture RHEAs, additive manufacturing (AM) represents the most recent and advanced metal manufacturing method which allows near-net-shape manufacturing to reduce material waste and post-processing time. However, performance of AM RHEAs under complex irradiation conditions remains largely unexplored. Here, in this study, we demonstrate for the first time the response of directed energy deposition (DED) AM quaternary RHEAs (HfTaVW, CrTaVW) subjected to sequential dual-beam ion irradiation, consisting of helium pre-implantation followed by high-dose heavy ion bombardment. Compositions of DED AM RHEAs were selected using Monte Carlo (MC) simulations based on a cluster expansion (CE) Hamiltonian parameterized by density functional theory (DFT). Post-irradiation microstructural characterization revealed that the AM RHEA maintained remarkable stability, with suppressed helium bubble growth and reduced defect accumulation compared to conventional alloys. Even at high doses (∼100 dpa), the alloy exhibited no void swelling, a low density of dislocation loops, and no evidence of severe degradation. These results highlight the intrinsic ability of AM-derived microstructures and multicomponent chemistry to synergistically mitigate irradiation effects. Our findings establish AM RHEAs as a class of materials with superior resistance to radiation damage under conditions relevant to advanced fusion and fission environments and demonstrate the importance of sequential ion beam studies in evaluating their long-term performance.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Imaging a terahertz superfluid plasmon in a two-dimensional superconductor

The superconducting gap defines the fundamental energy scale for the emergence of dissipationless transport and collective phenomena in a superconductor. In layered high-temperature cuprate superconductors, in which the Cooper pairs are confined to weakly coupled two-dimensional (2D) copper–oxygen (CuO 2 ) planes, terahertz (THz) spectroscopy at subgap millielectronvolt (meV) energies has provided crucial insights into the collective superfluid response perpendicular to the superconducting layers. However, within the CuO 2 planes, the collective superfluid response manifests as plasmonic charge oscillations at energies far exceeding the superconducting gap, obscured by strong dissipation. Here, in this study, we present spectroscopic evidence of a below-gap, 2D superfluid plasmon in few-layer Bi 2 Sr 2 CaCu 2 O 8+x and spatially resolve its deeply subdiffractive THz electrodynamics. By placing the superconductor in the near field of a spintronic THz emitter, we reveal this distinct resonance—absent in bulk samples and observed only in the superconducting phase—and determine its plasmonic nature by mapping the geometric anisotropy and dispersion. Crucially, these measurements offer a direct view of the momentum-dependent and frequency-dependent superconducting transition in two dimensions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Toward an AI-Powered Software Pipeline for Real-Time Tracking and Analysis of Wildfire and Smoke

Real-time tracking of wildfires and smoke is crucial for effective response, minimizing damage, protecting lives, and efficiently managing resources during fire emergencies. We develop a web-based AI-powered pipeline that detects wildfires in aerial video and estimates deployment-relevant behavior metrics, including cumulative burned area, burned-area growth rate, fire spread direction, and smoke dispersion. The system combines a YOLO-based detector with YCbCr-based fire segmentation, HSV-based smoke segmentation, Farneback optical flow, and centroid-based spatiotemporal tracking. Using ground sampling distance (GSD), pixel-level fire masks are converted to physical burned-area measurements by correlating fire pixel counts with camera altitude and tilt angle. We benchmark YOLO variants and non-YOLO baselines (GoogLeNet, CNN, DBN, Autoencoder, U-Net, and AlexNet) on the IEEE FLAME dataset and a newly created aerial frame dataset, Wildfire-DB. Cross-dataset evaluation uses a strict threshold-transfer protocol: decision thresholds are selected on FLAME validation and transferred unchanged to Wildfire-DB to quantify generalization under domain shift. YOLOv6 achieves the strongest cross-dataset frame-level fire detection on Wildfire-DB (ROC-AUC 0.8200, PR-AUC 0.8044, and transferred-threshold F1 0.7596). For tracking-oriented deployment requiring oriented localization, YOLO11-OBB provides the most reliable cross-dataset behavior among OBB-capable models while remaining computationally feasible. To analyze the feasibility of UAV deployment, we further measure inference efficiency using synchronized GPU and CPU power logs on a fixed workload of 1569 frames. YOLO-family models process the video in 5.73–12.47 seconds with net energy of 1247.28–1775.39 J, substantially lower latency and energy than heavier classification and reconstruction baselines. Overall, model optimality depends on operational objectives: YOLOv6 is best for cross-dataset detection robustness, whereas YOL...

Color segmentation↗
DOE OSTI2026

Ion Transport and Crystal Rotation in Plastic Crystal Electrolytes Under Applied Electric Fields

Organic ionic plastic crystal electrolytes, containing a plastic crystal and lithium salt, offer a potential balance between mechanical and electrochemical properties for solid state lithium-ion battery electrolytes. These electrolytes contain multiple mobile ionic species (three or four), resulting in complex transport mechanisms which have not yet been established. Plastic crystals are defined by long-range positional order and short-range rotational disorder. It is therefore necessary to quantify changes in the local crystal structure of the electrolyte as current flows through it. Herein, we examine the electrochemical properties of pyrrolidinium-based plastic crystal electrolytes containing lithium salt and zwitterion additives, including measurements of current fraction and limiting current. We obtain species-specific insight into electrolyte transport using pulsed-field gradient nuclear magnetic resonance spectroscopy and find that, while the zwitterion additive increases ionic conductivity, it decreases lithium diffusivity with respect to other ionic components. With operando spatiotemporally resolved wide-angle X-ray scattering we observe location-specific crystal rotations due to the passage of ionic current. In conclusion, we posit that reducing energy dissipation due to rotation is essential for using plastic crystal electrolytes in practical applications.

Yap, Kyra M. K. [University of California, Berkele↗
DOE OSTI2026

Time-dependent scenario modeling for the ST-E1 fusion power plant

ST-E1 is a low aspect ratio fusion power plant being designed by Tokamak Energy targeting 1.5 GW of fusion power. Characterization of the ST-E1 flat-top scenario is described elsewhere McNamara et al (2026 Nucl. Fusion 66 086008); here we focus on addressing the question of how to ramp-up the ST-E1 plasma from an initial state following breakdown and flux-surface formation to the target flat-top state. Being low-aspect ratio, the available solenoid flux of ST-E1 is limited. Therefore, particular consideration is placed on developing ramp-up scenarios that predominantly use inductive flux provided by external vertical field coils. Through time-dependent modeling with METIS, we show that this is possible when the ramp-up is performed at relatively high plasma density: although auxiliary current drive efficiency is reduced, this is significantly outweighed by (1) higher electron-ion collisional equilibration, (2) higher fusion power once ions become sufficiently hot, (3) higher poloidal beta for increased vertical-field flux, and (4) potentially favorable exhaust compatibilities. Ultimately, we show the target ST-E1 flat-top performance can be reached after a ramp-up period lasting 150 s using less than 40 Vs of solenoid flux (with vertical field providing ∼ 90 Vs of flux). The sensitivity to model assumptions are presented, with the general observation that deleterious effects can be mitigated through minor alterations of the auxiliary power temporal waveform and/or total auxiliary power level. The impact of a solenoid and the auxiliary power mix (electron cyclotron heating only versus electron and ion cyclotron heating) on the ST-E1 ramp-up success are also discussed in appendices. On this latter topic, we show that the effect of direct-ion heating during ramp-up is obscured by the uncertainty in the pedestal dynamics, identifying a clear line of future work required to make a definite decision on the ST-E1 auxiliary power mix.

current ramp-up↗
DOE OSTI2025

Strong anharmonicity dictates ultralow thermal conductivities of type-I clathrates

Type-I clathrate solids have attracted significant interest due to their ultralow thermal conductivities and sub- sequent promise for thermoelectric applications, yet the mechanisms underlying these properties are not well understood. Here, we extend the framework of vibrational dynamical mean-field theory (VDMFT) to calculate temperature-dependent thermal transport properties of solids using a many-body Green’s function approach. When applied to a coarse-grained description of 𝑋 8 Ga 16 Ge 30 , where 𝑋= Ba, Sr, we find that nonresonant scattering between cage acoustic modes and rattling modes leads to a reduction of acoustic phonon lifetimes and thus thermal conductivities. Moreover, we find that the moderate temperature dependence of conductivities above 300 K, which is consistent with experimental measurements, cannot be reproduced by textbook perturbation theory calculations, which predict a 𝑇 −1 dependence. Therefore, we suggest that nonperturbative anharmonic effects, including four- and higher-phonon scattering processes, are responsible for the ultralow thermal conductivities of type-I clathrates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Plasma flow generation and particle acceleration from expanding magnetic bubbles

Impulsive plasma dynamics in the laboratory are often driven by rising electric currents, yet their quantitative plasma response has not been well established. By means of fully kinetic particle-in-cell simulations and laser-driven capacitor-coil experiments, we show that a rising current expels plasma, forming an expanding magnetic bubble and accelerating particles. The expansion front velocity scales with the Alfvén speed determined by the magnetic field at its inner edge and the plasma density at its outer edge. This mechanism establishes impulsive current drive as a fundamental way that generates plasma flows and accelerates particles in laboratory plasmas, with potential relevance to astrophysics.

Zhang, Yang [Princeton University, NJ (United Stat↗
DOE OSTI2026

PowerModelsGAT-AI: Physics-Informed Graph Attention for Multi-System Power Flow With Continual Learning

Solving the alternating current power flow equations in real time is essential for secure grid operation, yet classical Newton–Raphson solvers can be slow under stressed conditions. Existing graph neural networks for power flow are typically trained on a single system and often degrade on different systems. We present PowerModelsGAT-AI, a physics-informed graph attention network that predicts bus voltages and generator injections. The model uses bus-type-aware masking to handle different bus types and balances multiple loss terms, including a power-mismatch penalty, using learned weights. We evaluate the model on 14 benchmark systems (4 to 6,470 buses) and train a unified model on 13 of these under contingency conditions with up to two branch outages, achieving an average normalized mean absolute error of 0.89% for voltage magnitudes and R 2 >0.99 for voltage angles. We also show continual learning: when adapting a base model to a new 1,354-bus system, standard fine-tuning causes severe forgetting with error increases exceeding 1000% on base systems, while our experience replay and elastic weight consolidation strategy keeps error increases below 2% and in some cases improves base-system performance. Interpretability analysis shows that learned attention weights correlate with physical branch parameters (susceptance: r=0.38 ; thermal limits: r=0.22 ), and feature importance analysis supports that the model captures established power flow relationships.

24 POWER TRANSMISSION AND DISTRIBUTION↗