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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites

The 'Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties' project looks to apply machine learning (ML) methods to Microearthquake (MEQ) data for imaging geothermal reservoir properties and forecasting seismic events, in order to advance geothermal exploration and safe geothermal energy production. As part of the project, this submission provides data arrays for 149 microearthquakes between the year 2012 and 2013 at the Newberry EGS Site for use with the Deep Learning Algorithm that has been developed. The data provided includes raw waveform data, location data, normalized waveform data, and processed waveform data. Penn State Geothermal Team has shared the following files from the project: - 149 microearthquakes (MEQs) between 2012 and 2013 at Newberry EGS sites, 'Normalized Waveform Inputs.npz' are normalized waveforms. - labels of 149 MEQs: Processed Waveform Inputs.npz - location labels of 149 MEQs: Location Data.npz Note: .npz is the python file format by NumPy that provides storage of array data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

The impact of anisotropic redshift distributions on angular clustering

A leading way to constrain physical theories from cosmological observations is to test their predictions for the angular clustering statistics of matter tracers, a technique that is set to become ever more central with the next generation of large imaging surveys. Interpretation of this clustering requires knowledge of the projection kernel, or the redshift distribution of the sources, and the typical assumption is an isotropic redshift distribution for the objects. However, variations in the kernel are expected across the survey footprint due to photometric variations and residual observational systematic effects. Here, we develop the formalism for anisotropic projection and present several limiting cases that elucidate the key aspects. We quantify the impact of anisotropies in the redshift distribution on a general class of angular two-point statistics. In particular, we identify a mode-coupling effect that can add power to auto-correlations, including galaxy clustering and cosmic shear, and remove it from certain cross-correlations. If the projection anisotropy is primarily at large scales, the mode-coupling depends upon its variance as a function of redshift; furthermore, it is often of similar shape to the signal. In contrast, the cross-correlation of a field whose selection function is anisotropic with another one featuring no such variations — such as CMB lensing — is immune to these effects. We discuss explicitly several special cases of the general formalism including galaxy clustering, galaxy-galaxy lensing, cosmic shear and cross-correlations with CMB lensing, and publicly release a code to compute the biases.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Commissioning the H i Observing Mode of the Beam Former for the Cryogenically Cooled Focal L-band Array for the GBT (FLAG)

We present the results of commissioning observations for a new digital beam-forming back end for the Focal plane L-band Array for the Robert C. Byrd Green Bank Telescope (FLAG), a cryogenically cooled Phased Array Feed (PAF) with the lowest measured T {sub sys}/η of any PAF outfitted on a radio telescope to date. We describe the custom software used to apply beam-forming weights to the raw element covariances to create research-quality spectral-line images for the new fine-channel mode, study the stability of the beam weights over time, characterize FLAG’s sensitivity over a frequency range of 150 MHz, and compare the measured noise properties and observed distribution of neutral hydrogen emission from several extragalactic and Galactic sources with data obtained with the current single-pixel L-band receiver. These commissioning runs establish FLAG as the preeminent PAF receiver currently available for spectral-line observations on the world’s major radio telescopes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Robust estimation of charge carrier diffusivity using transient photoluminescence microscopy

Transient microscopy has emerged as a powerful tool for imaging the diffusion of excitons and free charge carriers in optoelectronic materials. In many excitonic materials, extraction of diffusion coefficients can be simplified because of the linear relationship between signal intensity and local excited state population. However, in materials where transport is dominated by free charge carriers, extracting diffusivities accurately from multidimensional data is complicated by the nonlinear dependence of the measured signal on the local charge carrier density. To obtain accurate estimates of charge carrier diffusivity from transient microscopy data, statistically robust fitting algorithms coupled to efficient 3D numerical solvers that faithfully relate local carrier dynamics to raw experimental measurables are sometimes needed. Here, we provide a detailed numerical framework for modeling the spatiotemporal dynamics of free charge carriers in bulk semiconductors with significant solving speed reduction and for simulating the corresponding transient photoluminescence microscopy data. To demonstrate the utility of this approach, we apply a fitting algorithm using a Markov chain Monte Carlo sampler to experimental data on bulk CdS and methylammonium lead bromide (MAPbBr 3 ) crystals. Parameter analyses reveal that transient photoluminescence microscopy can be used to obtain robust estimates of charge carrier diffusivities in optoelectronic materials of interest, but that other experimental approaches should be used for obtaining carrier recombination constants. Additionally, simplifications can be made to the fitting model depending on the experimental conditions and material systems studied. Our open-source simulation code and fitting algorithm are made freely available to the scientific community.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Nonnuclear Experimental Capabilities to Support Design, Development, and Demonstration of Microreactors

This work provides a summary of selected experimental capabilities being developed to support nonnuclear testing and demonstration of technology in support of microreactors under the U.S. Department of Energy’s (DOE’s) Microreactor Program. Major capabilities include the Single Primary Heat Extraction and Removal Emulator (SPHERE) and the Microreactor Agile Non-nuclear Experimental Test Bed (MAGNET). The SPHERE facility allows for controlled testing of the steady-state and transient heat rejection capabilities of a single heat pipe using electrical heaters that simulate nuclear heating. The facility is capable of monitoring axial temperature profiles along the heat pipe and surrounding test articles during startup, steady-state operation, and transients. Instrumentation includes noncontact infrared thermal imaging, surface thermocouples, spatially distributed fiber optic temperature and strain sensors, electrical power meters, and a water-cooled, gas-gap calorimeter for quantifying heat rejection from the heat pipe. The facility can be operated under both vacuum and inert-gas conditions. The MAGNET facility is a large-scale, 250-kW electrically heated microreactor test bed to enable nonnuclear experimental evaluation of a variety of microreactor concepts. It can be supplied to electrically heat a scaled section of a microreactor and further test the capabilities of heat rejection systems. The initial MAGNET experiments will support technology maturation and reduce uncertainty and risk associated with the design, operation, and deployment of monolithic heat pipe–based reactors. However, this test bed can broadly be applied to multiple microreactor concepts to evaluate a wide range of thermal-hydraulic and structural phenomena such as interface coupling with power conversion units and other collocated systems. MAGNET can evaluate integral thermomechanical effects during electrical heating of an array of heat pipes in a larger test article. Examples of initial testing will include thermal stresses in the monolith and the impact of debonding of a heat pipe from the core block and how that failure could impact surrounding heat pipes, i.e., understanding the potential for cascading failure. This work also discusses some modeling capabilities that can support experiment design, analysis, and interpretation, including the heat pipe code Sockeye and a comparison of thermal-structural simulations performed using ABAQUS and STAR-CCM+.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Machine Learning for Predictive Performance Analysis in Charged Particle Beam Tools

Imaging methods driven by probes, electrons, and ions have played a dominant role in modern science and engineering. Opportunities for machine vision and AI that focus on consumer problems like driving and feature recognition, are now presenting themselves for automating aspects of the scientific processes. This proposal aims to enable and drive discovery in ultra-low energy implantation by taking advantage of faster processing, flexible control and detection methods, and architecture-agnostic workflows that will result in higher efficiency and shorter scientific development cycles. Custom microscope control, collection and analysis hardware will provide a framework for conducting novel in situ experiments revealing unprecedented insight into surface dynamics at the nanoscale. Ion implantation is a key capability for the semiconductor industry. As devices shrink, novel materials enter the manufacturing line, and quantum technologies transition to being more mainstream. Traditional implantation methods fall short in terms of energy, ion species, and positional precision. Here we demonstrate 1 keV focused ion beam Au implantation into Si and validate the results via atom probe tomography. We show the Au implant depth at 1 keV is 0.8 nm and that identical results for low energy ion implants can be achieved by either lowering the column voltage, or decelerating ions using bias – while maintaining a sub-micron beam focus. We compare our experimental results to static calculations using SRIM and dynamic calculations using binary collision approximation codes TRIDYN and IMSIL. A large discrepancy between the static and dynamic simulation is found that is due to lattice enrichment with high stopping power Au and surface sputtering. Additionally, we demonstrate how model details are particularly important to the simulation of these low-energy heavy-ion implantations. Finally, we discuss how our results pave a way to much lower implantation energies, while maintaining high spatial resolution.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Analysis of a Prototype Multi-Detector Fast-Neutron Radiography Panel

A multi-detector fast neutron radiography panel was built using the previous work on scalable neutron radiography using the IDEAS ROSSPAD readout module. A new aluminum housing was built to accommodate a large number of detectors tiled together. Additional changes to startup and processing code were made to operate the detector as one cohesive unit. Spatial resolution of the full panel using Cs-137 gammas was reported to be 0.42 line pairs per centimeter at 90% MTF and 2.09 line pairs per centimeter at 10% MTF. Here, three neutron radiographs generated using a Cf-252 fission neutron source were used to determine the spatial resolution of the panel for neutrons. The experiments had 90% MTF values of 0.24, 0.3, and 0.27 line pairs per centimeter and 10% MTF values of 1.30, 1.46, and 1.40 line pairs per centimeter. An example neutron radiograph was also used to prove that the radiography panel can perform true neutron radiography.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

MOA-2009-BLG-319Lb: A Sub-Saturn Planet inside the Predicted Mass Desert

We present an adaptive optics (AO) analysis of images from the Keck II telescope NIRC2 instrument of the planetary microlensing event MOA-2009-BLG-319. The ∼10 yr baseline between the event and the Keck observations allows the planetary host star to be detected at a separation of 66.5 ± 1.7 mas from the source star, consistent with the light-curve model prediction. The combination of the host star brightness and light-curve parameters yields host star and planet masses of M {sub host} = 0.524 ± 0.048M {sub ⊙} and m {sub p} = 67.3 ± 6.2M {sub ⊕} at a distance of D {sub L} = 7.1 ± 0.7 kpc. The star−planet projected separation is 2.03 ± 0.21 au. The planet-to-star mass ratio of this system, q = (3.857 ± 0.029) × 10{sup −4}, places it in the predicted “planet desert” at 10{sup −4} < q < 4 × 10{sup −4} according to the runaway gas accretion scenario of the core accretion theory. Seven of the 30 planets in the Suzuki et al. sample fall in this mass ratio range, and this is the third with a measured host mass. All three of these host stars have masses of 0.5 ≤ M {sub host}/M {sub ⊙} ≤ 0.7, which implies that this predicted mass ratio gap is filled with planets that have host stars within a factor of two of 1M {sub ⊙}. This suggests that runaway gas accretion does not play a major role in determining giant planet masses for stars somewhat less massive than the Sun. Our analysis has been accomplished with a modified DAOPHOT code that has been designed to measure the brightness and positions of closely blended stars. This will aid in the development of the primary method that the Nancy Grace Roman Space Telescope mission will use to determine the masses of microlens planets and their hosts.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Dynamics of implosion, bounce, and re-implosion of a shell directly driven by a time-shaped laser pulse

Gas-filled plastic spherical shells have been imploded using the 60-beam OMEGA laser. The same laser pulse was used as in a previous experiment, which demonstrated the dynamic formation of a shell by irradiating a homogeneous plastic foam sphere. In the present experiment, the shell initially implodes, bounces, and expands; a new shell then forms and undergoes a second implosion. Analysis of x-ray self-emission-streaked images and sequences of 2D x-ray self-emission framing camera images show shell evolution that is in reasonable agreement with 1D simulations. Shell integrity and symmetry are well maintained until the formation of the new shell. The final implosion, instead, is affected by substantial asymmetries.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Radiation GRMHD simulations of M87: funnel properties and prospects for gap acceleration

Here, we use the public code EBHLIGHT to carry out 3D radiative general relativistic magnetohydrodynamics (GRMHD) simulations of accretion on to the supermassive black hole in M87. The simulations self-consistently evolve a frequency-dependent Monte Carlo description of the radiation field produced by the accretion flow. We explore two limits of accumulated magnetic flux at the black hole (SANE and MAD), each coupled to several subgrid prescriptions for electron heating that are motivated by models of turbulence and magnetic reconnection. We present convergence studies for the radiation field and study its properties. We find that the near-horizon photon energy density is an order of magnitude higher than is predicted by simple isotropic estimates from the observed luminosity. The radially dependent photon momentum distribution is anisotropic and can be modeled by a set of point-sources near the equatorial plane. We draw properties of the radiation and magnetic field from the simulation and feed them into an analytic model of gap acceleration to estimate the very high energy (VHE) γ-ray luminosity from the magnetized jet funnel, assuming that a gap is able to form. We find luminosities of $\rm \sim 10^{41} \, erg \, s^{-1}$ for MAD models and $\rm \sim 2\times 10^{40} \, erg \, s^{-1}$ for SANE models, which are comparable to measurements of M87’s VHE flares. The time-dependence seen in our calculations is insufficient to explain the flaring behaviour. Our results provide a step towards bridging theoretical models of near-horizon properties seen in black hole images with the VHE activity of M87.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Scale-up Unlearnable Examples Learning with High-performance Computing

Recent advancements in AI models, like ChatGPT, are structured to retain user interactions, which could inadvertently include sensitive healthcare data. In the healthcare field, particularly when radiologists use AI-driven diagnostic tools hosted on online platforms, there is a risk that medical imaging data may be repurposed for future AI training without explicit consent, spotlighting critical privacy and intellectual property concerns around healthcare data usage. Addressing these privacy challenges, a novel approach known as Unlearnable Examples (UEs) has been introduced, aiming to make data unlearnable to deep learning models. A prominent method within this area, called Unlearnable Clustering (UC), has shown improved UE performance with larger batch sizes but was previously limited by computational resources (e.g., a single workstation). To push the boundaries of UE performance with theoretically unlimited resources, we scaled up UC learning across various datasets using Distributed Data Parallel (DDP) training on the Summit supercomputer. Our goal was to examine UE efficacy at high-performance computing (HPC) levels to prevent unauthorized learning and enhance data security, particularly exploring the impact of batch size on UE’s unlearnability. Utilizing the robust computational capabilities of the Summit, extensive experiments were conducted on diverse datasets such as Pets, MedMNist, Flowers, and Flowers102. Our findings reveal that both overly large and overly small batch sizes can lead to performance instability and affect accuracy. However, the relationship between batch size and unlearnability varied across datasets, highlighting the necessity for tailored batch size strategies to achieve optimal data protection. The use of Summit’s high-performance GPUs, along with the efficiency of the DDP framework, facilitated rapid updates of model parameters and consistent training across nodes. Our results underscore the critical role of selecting appropriate batch sizes based on the specific characteristics of each dataset to prevent learning and ensure data security in deep learning applications. The source code is publicly available at https: // github. com/ hrlblab/ UE_ HPC .

Zhu, Yanfan [Vanderbilt University, Nashville, TN,↗
DOE OSTI2025

Data from a throughfall exclusion experiment: Fine root dynamics, morphology, chemistry, and AMF colonization across four lowland Panamanian forests

Fine roots regulate forest nutrient, carbon, and water cycling, yet their variation within and among tropical forests remains under-characterized. We quantified root productivity, disappearance, and stocks to 1 m using minirhizotron imaging, and we measured morphology, elemental composition [root carbon (C), root nitrogen (N), root phosphorus (P)], and arbuscular mycorrhizal fungi (AMF) colonization to 20 cm using ingrowth cores and sequential coring. Sampling took place in four distinct lowland Panamanian forests (32 plots; 8 per forest) from 2018 through 2022 under control and throughfall-exclusion (drought) treatments in the Panama Rainforest Changes with Experimental Drying (PARCHED) experiment.The dataset is presented as an Excel workbook with six tabs. The first tab is the data dictionary. Tab S1 contains ingrowth-core production and mortality, morphology and soil moisture. Tab S2 contains sequential-coring standing stocks with associated morphology and soil moisture. Tab S3 contains minirhizotron row data records to 1 m depth, including per-frame root length and diameter, normalized length metrics, and session timing. Tab S4 contains AMF colonization. Tab S5 contains fine-root chemistry at 0–10 cm, reporting %P, %C, %N, and C:N for samples collected via ingrowth cores and sequential-coring standing stocks. CSV mirrors for each tab are provided, and a KML file supplies coordinates for all 32 plots.Key variables span live and dead fine-root biomass (and coarse fractions where applicable), specific root length (SRL) and area (SRA), diameter, root tissue density (RTD), soil moisture, AMF colonization, root %N, %C, %P, and C:N, along with minirhizotron root length and diameter. Depth, season, treatment, and plot/site identifiers are included to support cross-tab integration and analysis from 0–100 cm (minirhizotron) and 0–20 cm (cores).Units are reported in-column and missing values are coded as NA. No special software is required to open or use the files (Excel, CSV, and KML compatible).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Transfer Learning using Denoising Auto-Encoders for Cellular-Level Annotation of Tumor in Pathology Slides

Adversarial examples can produce altered classifications using only seemingly innocuous, imperceptible perturbations to the original image. The imperceptibility of adversarial perturbations suggests that the corresponding classifiers use decision criteria different than those of a human. In a medical setting, inexplicable decision criteria confound a pathologist’s willingness to trust machine-generated annotations. Here, we analyze denoising tumor detection models to see if they are robust to imperceptible adversarial perturbations. Moreover, to be more fully trusted by pathologists, we require tumor detectors that generate interpretable annotations which segment pathology slides into tumorous and normal regions at the cellular level. We therefore compare transfer learning based on two different autoencoder architectures, one derived from a deep denoising bottleneck autoencoder and one from an over-complete sparse autoencoder. Both autoencoders were first trained in an unsupervised manner on a set of pathology slides drawn from the Camelyon16 dataset. The latent representations produced by each autoencoder were then passed to separate neural networks that were trained in a supervised manner on binary tumor-normal masks generated by pathologists at cellular resolution. Both tumor detectors supported better than 90% AUC PR as measured by the area under the precision/recall curve on a held-out pathology slide. To assess the underlying decision criteria used by both tumor detectors, we constructed imperceptible adversarial examples which reduced the AUC PR of both models to less than 70%. Random noise of the same amplitude had almost no effect on the AUC PR of either model. Additionally, each tumor detector was resistant to adversarial “transfer” attacks targeting the other. The adversarial perturbations showed strong characteristic differences: the deep denoising models perturbations were a very diffuse, seemingly unrecognizable pattern while the sparse coding models perturbations showed traces of tissue cells.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

An Architectural Survey of the REECo Maintenance Compound, Area 6, Nevada National Security Site, Nye County, Nevada

The U.S. Department of Energy (DOE), National Nuclear Security Administration Nevada Field Office (NNSA/NFO) plans to install a 26.45-mile-long 138-kilovolt (kV) transmission line on the Nevada National Security Site (NNSS) in Nye County, Nevada. This new transmission line will upgrade the electrical transmission system and provide reliable power between Mercury Switching Station in Area 23 and the U1a Facility in Area 1. The proposed upgrade to the power transmission system constitutes an undertaking subject to review under Section 106 of the National Historic Preservation Act (NHPA) (54 United States Code [USC] § 306101) and its implementing regulations, 36 Code of Federal Regulations (CFR) Part 800. A portion of this new transmission line will cross the western perimeter of the Reynolds Electrical and Engineering Company (REECo) Maintenance Compound located in Area 6 of the NNSS. This compound with its trade shops, storage sheds, and yards served as a general construction facility that supported nuclear testing activities on the NNSS from 1967 to 1992. Although three architectural resources in the compound have previously been recorded and evaluated for the National Register of Historic Places (NRHP), other resources within it have not, nor has the compound been recorded and evaluated as a potential district. The State Historic Preservation Officer (SHPO) concurred with NNSA/NFO’s determination that the unevaluated REECo Maintenance Compound will be adversely affected by the proposed undertaking (Reed 2021). The NNSA/NFO, in consultation with the SHPO, then developed the Memorandum of Agreement DE-GM58-22NA25553 between the U.S. Department of Energy and the Nevada State Historic Preservation Officer Regarding Installation of a 138-kilovolt Transmission Line from the Mercury Switching Station to the U1a Facility and the Removal of the Historic 138-kilovolt Transmission Line from the Mercury Switching Station to the U1a Facility in Areas 1, 3, 5, 6, and 23 of the Nevada National Security Site, Nye County, hereafter referred to as the MOA. This architectural survey report was prepared in accordance with MOA Stipulation III.B. It describes the origins, history, layout, and functions of the REECo Maintenance Compound during the Cold War and identifies contributing and noncontributing resources. The report defines a district boundary based on archival research and the field survey and concludes with an appendix that presents digital color images of the compound with descriptions and a map key showing image viewpoints and directions. Although none of the architectural resources are recommended individually eligible, the district evaluation concludes the REECo Maintenance Compound is eligible as a historic district for listing in the NRHP under the Secretary of the Interior’s Significance Criteria A and C at the local level. The historic district retains all seven aspects of integrity and conveys its significance under the abovementioned criteria.

138 kV↗
DOE OSTI2025

Extreme Longitudinal Compression of Optimized Beams for MEV Ultrafast Electron Diffraction (Final Technical Report)

We worked out the design of a high repetition rate MeV energy ultrafast electron diffraction instrument based on the existing Cornell photoinjector, which can readily be applied to the presented findings. This example is a blueprint of other similarly arranged UED setups. Using particle tracking simulations in conjunction with multiobjective genetic algorithm optimization, we explored the smallest bunch lengths, emittance, and probe spot sizes achievable. As two limits, we defined stroboscopic conditions (with single electrons per pulse) and operation with 10 5 electrons per bunch which may be suitable for single-shot diffraction images. In the stroboscopic case, the flexibility provided by the many cavity bunching and acceleration allows for longitudinal phase space linearization without a higher harmonic field, providing sub-fs bunch lengths at the sample. Given low emittance photoemission conditions, these small bunch lengths can be maintained with probe transverse sizes at the single micron (1 μm) scale and below. In the case of 10 5 electrons per pulse, we simulated state-of-the-art 5D brightness conditions: rms bunch lengths of 10 fs with 3-nm normalized emittances, while permitting repetition rates as high as 1.3 GHz. We showed that in conjunction with collimating apertures, a novel focusing scheme achieves very high-quality emittance compensation for the central core of the beam composing 40% of particles, for a resulting beam size of 5 μm (rms). Finally, to aid in the design of new SRF-based ultrafast electron diffraction machines, we simulated the trade-off between the number of cavities used and achievable bunch length and emittance. In the longitudinal dimension, we made use of the fact that MeV UED requires much lower energy than the 15-MeV maxi mum energy of Cornell’s CBETA injector, and we may therefore use several of the SRF cavities for bunch length compression. In practice, we used a genetic optimization algorithm to choose the phases and amplitudes of the cavities appropriately for optimal bunching. In the zero space charge case, we found that bunching and acceleration are distributed across the six cavities in a way that produces a linearizing effect. And we showed that the ultimate bunch length can be limited by time-of-flight differences arising from transverse size and transverse momentum spread. The space charge code developed and used for this development is now permanent part of the Bmad accelerator simulation code and has already contributed to other developments, e.g., for the EIC electron cooler design.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Impact of toroidal rotation on the resistive ballooning modes in ASDEX Upgrade tokamak

Abstract In this work, we investigate the behavior of instabilities appearing between type-I edge localized modes (ELMs), with increasing neutral beam injection (NBI) power concomitant increase in toroidal rotation, and compare it to the modeling result of the linear magneto-hydrodynamic (MHD) code CASTOR3D. An injection of one NBI beam, increasing toroidal rotation, results in the mode slowing down from 12 kHz to 7 kHz, and its associated radial displacement decreases from 5 mm to 3.5 mm. In addition, modes shift radially outwards towards higher q , decreasing their poloidal mode numbers. The mode velocity is measured to be close to the E × B velocity with significant uncertainties. Through a set of CASTOR3D simulations with varying profiles, resistivity has been identified as the primary contributor to the growth rates. Only a small stabilizing effect due to toroidal rotation has been observed. While experimental results show a decrease of mode frequency with rotation, the opposite trend is observed in modeling. Reasons for discrepancies between modeling and experiment are discussed. Nevertheless, a main contributor to the mode frequency has been identified to be rotation velocity. CASTOR3D classifies modes as resistive ballooning modes as they do not appear unstable in ideal MHD.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Studies of localized mix and asymmetry caused by the stalk in inertial fusion experiments at OMEGA

In an inertially confined fusion experiment, a capsule of fusion fuel is compressed to high temperatures and densities using lasers, creating a hot plasma in which thermonuclear fusion reactions take place. To achieve efficient conversion of laser energy to fuel heating, the implosion must be as spherically symmetric as possible. One common source of asymmetry that degrades performance is the stalk and glue spot that hold the capsule in place before the experiment. The exact nature and magnitude of this perturbation had not been previously quantified. In this work, inertially confined fusion experiments were executed with an additional stalk on the capsule to measure and quantify the effect of the stalk and glue spot on the symmetry and performance. It is found that the stalk drives low-mode asymmetry by shadowing the laser drive on the part of the shell near the glue spot, enhancing the hot-spot velocity toward the stalk by 40–80 km/s and elongating the hot-spot along the stalk axis. It is also found that localized mix due to the additional stalk has a negligible effect on implosion performance. Two-dimensional radiation-hydrodynamic simulations using the xRAGE code reproduce the direction of the experimentally observed change to hot-spot velocity and implosion performance metrics, but not the observed elongation, suggesting that this feature results from an interaction between stalk perturbations and other preexisting asymmetry seeds, such as laser drive asymmetry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Development of an ab initio learned model of electron deposition range in deuterium-tritium plasmas through time-dependent density functional theory calculations and machine learning

Accurate hydrodynamic modeling for laser-direct-drive (LDD) inertial-confinement-fusion (ICF) relies on precise calculations of the electron thermal conduction in all target materials. The nonlocal stopping range of electrons in ICF plasmas directly influences thermal conduction; yet, no first principles model exists for the electron mean free path in the conduction-zone regime. This work utilized time-dependent stochastic density-functional theory (TD-sDFT) to calculate the electron stopping power in deuterium-tritium (DT) plasmas at (ρ, T) conditions relevant to the conduction zone and the compressed shell in ICF. Using a combination of our TD-sDFT data and already established analytical models, we developed and trained an artificial neural network to create a global model for the nonlocal electron deposition range, λ E . We compared our machine-learning (ML) based model for λ E to the currently-used modified-Lee-More model in LDD radiation-hydrodynamic codes, such as lilac, and saw an overall decrease in the deposition range. To understand the effects of λ E on LDD ICF implosion dynamics, we implemented the ML-based model into lilac; specifically, we looked at designs consistent with a current experiment on the OMEGA laser and for a newly designed LDD-ICF target for the future OMEGA-Next facility. In both cases, we saw an overall drop in predicted ablation pressure, peak areal density, and neutron yield due to the reduced thermal conduction (smaller λ E ) in DT plasmas. Comparisons with the experiment on OMEGA are also made.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗