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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Anisotropic neutron response of trans-stilbene and impact on a handheld dual particle imager

Here this manuscript details measurements of the anisotropic light-output response of trans-stilbene, henceforth referred to as stilbene, along the three primary crystal planes and analyzes how this anisotropic response impacts neutron image reconstruction in a handheld dual particle imager (H2DPI). A prototype H2DPI composed of stilbene pillars (6 x 6 x 50 mm 3 ) coupled to silicon photomultipliers was built and is capable of imaging both fast neutrons and gamma rays from kilogram quantities of special nuclear material. Stilbene was chosen as the scintillating medium for the imager because of its pulse shape discrimination capability, time resolution and relatively high light output. A drawback to using stilbene, however, is the anisotropic response. A recoiling proton from a neutron elastic-scattering event in stilbene will yield different amounts of scintillation light depending on the direction of the recoiling proton with respect to the crystal lattice. This manuscript analyzes how this anisotropic response impacts neutron image reconstruction in 4π. The light output in the three primary crystal planes of stilbene were independently measured 6–7 times for proton recoil energies ranging from 0.5–5.0 MeV using a quasi-monoenergetic time-of-flight neutron source. The measured light-output data in the three crystal planes were fit with a semi-empirical function based on Birks’ formula. These fits were used to reconstruct the location of a 252 Cf source in front of the imager (0°, -0.8°), to the left of the imager (-90°, -0.8°) and directly above the imager (0°, 90°) to determine if it is necessary to apply the directionally-dependent response of stilbene to accurately reconstruct source locations. A bootstrapping technique was applied to the measured data sets to produce 1000 images composed of 2000 cone projections for each source location and each measured light-output curve. List-mode maximum likelihood expectation maximization was applied to each image; the highest pixel location and the full width at half maximum (FWHM) were then recorded. The average and standard deviations of these parameters were taken for each set of images. The maximum difference in the average azimuthal pixel location when varying between the appropriate and incorrect light-output curves for the source locations at (0°, -0.8°) and (-90°, -0.8°) were respectively found to be 1.85 ± 1.39° and 2.03 ± 1.78°. All other pixel locations and FWHMs were within a single standard deviation of uncertainty. Taken together, the anisotropic response of stilbene has negligible impact on the neutron image reconstruction capability of the H2DPI.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Physical mechanisms for the transition from type-III to large ELMs induced by impurity injection on EAST

Transition from type-III to large-amplitude ELMs induced by neon injection has been observed in the EAST tokamak at overlapping q 95 space between large and small ELMs. With neon injection, pedestal density gradient shows a remarkable increase accompanied by some decrease of pedestal electron temperature, and consequently the pressure gradient increases moderately and edge bootstrap current has minimal change. Further experiment demonstrates that the occurrence of large ELMs after neon injection is highly correlated with the change in edge density. Linear peeling-ballooning stability analysis indicates that the large ELM case is more unstable than the type-III ELM case during the ELM transition. A scan of pedestal density gradient in linear stability analysis shows that the direct destabilizing effect of steep pedestal density gradient on peeling-ballooning instabilities via two-fluid effects could also facilitate the transition to large ELMs. In conclusion, these results could provide more insight into the role of pedestal density gradient on pedestal stability and ELM behavior.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Quantum state preparation with resolution refinement

We introduce a method called resolution refinement that allows one to bootstrap eigenstate preparation on a quantum computer. We first prepare an eigenstate of a low-resolution Hamiltonian using any method of choice. The eigenstate is then lifted to higher resolution and adiabatically evolved to produce the corresponding eigenstate of a higher-fidelity Hamiltonian. We give examples of resolution refinement applied to both single-particle basis states as well as a spatial lattice grid. For basis refinement, we compute few-body ground states of the Busch model for interacting particles in a harmonic trap in one dimension. For lattice refinement, we compute Hartree-Fock nuclear states for a central Woods-Saxon potential in three dimensions, and we compute bound states and continuum states in a multi-species Hubbard model of fermions in one dimension. In all cases, the method is efficient and requires an adiabatic evolution time that scales with the inverse of the energy gap times the square root of the system size. We show that this very favorable scaling arises from the fact that resolution refinement does not make large changes to the structure or energies of the low-energy eigenstates.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Simple, general, realistic, robust, analytic tokamak equilibria. Part 2. Pedestals and flow

Part 1 described a wide range of analytic tokamak equilibria modelling smooth limiter surfaces, double- and single-null divertor surfaces, arbitrary aspect ratio, elongation, triangularity and beta. Part 2 generalizes the analysis to further include edge pedestals and toroidal flow. Specifically, edge pedestals are allowed in the pressure, pressure gradient and toroidal current density. Also, an edge-localized contribution to the bootstrap current is treated. In terms of flow, analytic solutions are obtained for two cases: a $\gamma = 2$ adiabatic and a $\gamma = \infty $ incompressible energy conservation relation.

Physics↗
DOE OSTI2024

Advanced surrogate model for electron-scale turbulence in tokamak pedestals

We derive an advanced surrogate model for predicting turbulent transport at the edge of tokamaks driven by electron temperature gradient (ETG) modes. Our derivation is based on a recently developed sensitivity-driven sparse grid interpolation approach for uncertainty quantification and sensitivity analysis at scale, which informs the set of parameters that define the surrogate model as a scaling law. Our model reveals that ETG-driven electron heat flux is influenced by the safety factor q, electron beta β e and normalized electron Debye length λ D , in addition to well-established parameters such as the electron temperature and density gradients. To assess the trustworthiness of our model's predictions beyond training, we compute prediction intervals using bootstrapping. The surrogate model's predictive power is tested across a wide range of parameter values, including within-distribution testing parameters (to verify our model) as well as out-of-bounds and out-of-distribution testing (to validate the proposed model). Overall, validation efforts show that our model competes well with, or can even outperform, existing scaling laws in predicting ETG-driven transport.

fusion plasma↗
DOE OSTI2021

Exploring Causal Physical Mechanisms via Non-Gaussian Linear Models and Deep Kernel Learning: Applications for Ferroelectric Domain Structures

Rapid emergence of multimodal imaging in scanning probe, electron, and optical microscopies has brought forth the challenge of understanding the information contained in these complex data sets, targeting the intrinsic correlations between different channels, and further exploring the underpinning causal physical mechanisms. Here, we develop such an analysis framework for Piezoresponse Force Microscopy. We argue that under certain conditions, we can bootstrap experimental observations with the prior knowledge of materials structure to get information on certain nonobserved properties, and demonstrate linear causal analysis for PFM observables. We further demonstrate that the strength of individual causal links between complex descriptors can be ascertained using the deep kernel learning (DKL) model. In this DKL analysis, we use the prior information on domain structure within the image to predict the physical properties. This analysis demonstrates the correlative relationships between morphology, piezoresponse, elastic property, etc., at nanoscale. The prediction of morphology and other physical parameters illustrates a mutual interaction between surface condition and physical properties in ferroelectric materials. Overall, this analysis is universal and can be extended to explore the correlative relationships of other multichannel data sets, and allow for high-fidelity reconstruction of underpinning functionalities and physical mechanisms.

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

Megadrought: A Series of Unfortunate La Niña Events?

Megadroughts are multidecadal periods of aridity more persistent than most droughts during the instrumental period. Paleoclimate evidence suggests that megadroughts occur in many parts of the world, including North America, Central America, western Europe, eastern Asia, and northern Africa. It remains unclear to what extent such megadroughts require external forcing or whether they can arise from internal climate variability alone. A novel statistical–dynamical approach is used to evaluate the possibility that such events arise solely as a function of interannual tropical sea surface temperature (SST) variations. A statistical emulator of tropical SST variations is constructed by using an empirical moving-blocks bootstrap approach that randomly samples multiyear sequences of the observational SST record. This approach preserves the power spectrum, seasonal cycle, and spatial pattern of El Niño-Southern Oscillation (ENSO) but removes longer timescale fluctuations embedded in the observational record. These resampled SST anomalies are then used to force an atmospheric model (the Community Atmosphere Model Version 5). As megadroughts emerge in this run, they should, therefore, be solely a consequence of La Niña sequences combined with internal atmospheric variability and persistence driven by soil moisture storage and other land-surface processes. We indeed find that megadroughts in this simulation have an amplitude-duration rate that is generally indistinguishable from the rate documented in paleoclimate records of the western United States. Our findings support the idea that megadroughts may occur randomly when the unforced climate system evolves freely over a sufficiently long period of time, implying that an unforced unusual but statistically plausible series of La Niña events may be sufficient to generate megadrought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Quasi‐Steady State Supersaturation: Do High Values Derived From ESCAPE Represent Real High Supersaturations and the Potential for Condensational Invigoration?

Deep convective clouds were intensively sampled during the Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE) with coordinated flights of the NRC Convair-580 and SPEC Learjet. A total of 219 updraft core segments were sampled over coastal Texas and Louisiana under diverse meteorological conditions. Median updraft properties included widths of ∼1 km, velocities of 4.8 m s −1 , droplet number concentrations of ∼400 cm −3 , and liquid water contents of 0.9 g m −3 . The limitations of using the quasi-steady state approximation to derive supersaturations were explored. Supersaturation (S QSS ) estimated from in situ observations under a quasi-steady state assumption averaged 0.4% but occasionally exceeded 2%, with values >1% (high supersaturations) identified as statistical outliers. Two case studies illustrated the conditions linked to high supersaturations. In a storm over the Gulf, median core S QSS reached 2.46% in the developing stage compared to 2.17% in the mature stage under similar thermodynamic conditions. In a storm over coastal Louisiana, S QSS peaked near 11% within a 13.7-m s −1 updraft, accompanied by predominantly supercooled liquid droplets at −13°C and exceptionally low diameter concentrations of 0.29 mm cm −3 . Bootstrap analysis of all sampled cores showed that high supersaturations are most probable in cold and mixed-phase regimes with moderate to strong updrafts and are strongly influenced by vertical velocity and droplet number concentrations. In conclusion, while extreme supersaturations (∼10%) were rare, their occurrence underscores the need for targeted multiplatform observations to resolve their spatiotemporal variability and assess their potential role in deep convective invigoration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Observation of a new type of self-generated current in magnetized plasmas

A tokamak, a torus-shaped nuclear fusion device, needs an electric current in the plasma to produce magnetic field in the poloidal direction for confining fusion plasmas. Plasma current is conventionally generated by electromagnetic induction. However, for a steady-state fusion reactor, minimizing the inductive current is essential to extend the tokamak operating duration. Several non-inductive current drive schemes have been developed for steady-state operations such as radio-frequency waves and neutral beams. However, commercial reactors require minimal use of these external sources to maximize the fusion gain, Q, the ratio of the fusion power to the external power. Apart from these external current drives, a self-generated current, so-called bootstrap current, was predicted theoretically and demonstrated experimentally. Here, we reveal another self-generated current that can exist in a tokamak and this has not yet been discussed by present theories. We report conclusive experimental evidence of this self-generated current observed in the KSTAR tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Atomistic learning in the electronically grand-canonical ensemble

Abstract A strategy is presented for the machine-learning emulation of electronic structure calculations carried out in the electronically grand-canonical ensemble. The approach relies upon a dual-learning scheme, where both the system charge and the system energy are predicted for each image. The scheme is shown to be capable of emulating basic electrochemical reactions at a range of potentials, and coupling it with a bootstrap-ensemble approach gives reasonable estimates of the prediction uncertainty. The method is also demonstrated to accelerate saddle-point searches, and to extrapolate to systems with one to five water layers. We anticipate that this method will allow for larger length- and time-scale simulations necessary for electrochemical simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Developing a complete AI-accelerated workflow for superconductor discovery

The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting the materials space that can be explored. Here, we introduce a Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET), a machine-learning model trained to predict the Eliashberg spectral function and superconducting critical temperature with a mean-absolute-error of 0.87 K relative to DFT-based Allen-Dynes calculations. Intriguingly, BEE-NET achieves a true-negative-rate of 99.4%, enabling highly efficient screening for the rare property of superconductivity. Integrated into a multi-stage, AI-accelerated discovery pipeline that incorporates elemental-substitution strategies and machine-learned interatomic potentials, our workflow reduced over 1.3 million candidate structures to 741 dynamically and thermodynamically stable compounds with DFT-confirmed T c > 5 K. We report the successful synthesis and experimental confirmation of superconductivity in two of these previously unreported compounds. This study establishes a data-driven framework that integrates machine learning, quantum calculations, and experiments to systematically accelerate superconductor discovery.

Gibson, Jason B. [Quantum Formatics, Cambridge, MA↗
DOE OSTI2020

Quantile-specific heritability of sibling leptin concentrations and its implications for gene-environment interactions

“Quantile-dependent expressivity” occurs when the effect size of a genetic variant depends upon whether the phenotype (e.g., leptin) is high or low relative to its distribution. Leptin concentrations are strongly related to adiposity, whose heritability is quantile dependent. Whether inheritance of leptin concentrations is quantile dependent, and whether this explains the greater heritability in women than men in accordance with their greater adiposity, and explains other gene-environment interactions, remains to be determined. Therefore, leptin and leptin receptor concentrations from 3068 siblings in 1133 sibships from the Framingham Heart Study Third Generation Cohort were analyzed. Free leptin index (FLI) was calculated as the ratio of leptin to soluble leptin receptor concentrations. Full-sib (β FS ) regression slopes were robustly estimated by quantile regression with nonparametric significance assigned from 1000 bootstrap samples. The analyses showed β FS increased significantly with increasing percentiles of the offspring’s age- and sex-adjusted leptin distribution (P linear = 0.0001), which was accelerated at the higher concentrations (P quadratic = 0.0003). β FS at the 90th percentile (0.418 ± 0.066) was 4.7-fold greater than at the 10th percentile (0.089 ± 0.032, P difference = 3.6 × 10 –6 ). Consistent with quantile-dependent expressivity, the β FS was greater in female sibs, which was attributable to their higher leptin concentrations. Reported gene-environment interactions involving adiposity and LEP , LEPR , MnSOD , PPARγ , PPARγ2 , and IRS-1 polymorphisms were consistent with quantile-dependent expressivity of leptin concentrations. β FS for leptin receptor concentrations and free leptin index also increased significantly with increasing percentiles of their distributions (P linear = 0.04 and P linear = 8.5 × 10 –6 , respectively). In conclusion, inherited genetic and shared environmental effects on leptin concentrations were quantile dependent, which likely explains male–female differences in heritability and some gene-environment interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Entropy removal of medical diagnostics

Shannon entropy is a core concept in machine learning and information theory, particularly in decision tree modeling. To date, no studies have extensively and quantitatively applied Shannon entropy in a systematic way to quantify the entropy of clinical situations using diagnostic variables (true and false positives and negatives, respectively). Decision tree representations of medical decision-making tools can be generated using diagnostic variables found in literature and entropy removal can be calculated for these tools. This concept of clinical entropy removal has significant potential for further use to bring forth healthcare innovation, such as quantifying the impact of clinical guidelines and value of care and applications to Emergency Medicine scenarios where diagnostic accuracy in a limited time window is paramount. This analysis was done for 623 diagnostic tools and provided unique insights into their utility. For studies that provided detailed data on medical decision-making algorithms, bootstrapped datasets were generated from source data to perform comprehensive machine learning analysis on these algorithms and their constituent steps, which revealed a novel and thorough evaluation of medical diagnostic algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: “definite AD” with diagnostic codes and dementia medication (n = 614) and “probable AD” with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Hidden orders and phase transitions for the fully packed quantum loop model on the triangular lattice

Abstract Quantum loop and dimer models are prototypical correlated systems with local constraints, which are not only intimately connected to lattice gauge theories and topological orders but are also widely applicable to the broad research areas of quantum materials and quantum simulation. Employing our sweeping cluster quantum Monte Carlo algorithm, we reveal the complete phase diagram of the triangular-lattice fully packed quantum loop model. Apart from the known lattice nematic (LN) solid and the even$${{\mathbb{Z}}}_{2}$$ Z 2 quantum spin liquid (QSL) phases, we discover a hidden vison plaquette (VP) phase, which had been overlooked and misinterpreted as a QSL for more than a decade. Moreover, the VP-to-QSL continuous transition belongs to the (2 + 1)D cubic * universality class, which offers a lattice realization of the (fractionalized) cubic fixed point that had long been considered as irrelevant towards the O(3) symmetry until corrected recently by conformal bootstrap calculations. Our results are therefore of relevance to recent developments in both experiments and theory, and facilitate further investigations of hidden phases and transitions.

Physics↗
DOE OSTI2021

SANS quantification of bound water in water-soluble polymers across multiple concentration regimes

Contrast-variation small-angle neutron scattering (CV-SANS) is a widely used technique for quantifying hydration water in soft matter systems, but it is predominantly applied in the dilute regime or for systems with a well-defined structure factor. Here, CV-SANS was used to quantify the number of hydration water molecules associating with three water-soluble polymers with different critical solution temperatures and types of water–solute interactions in dilute, semidilute, and concentrated solution through the exploration of novel methods of data fitting and analysis. Multiple SANS fitting workflows with varying levels of model assumptions were evaluated and compared to give insight into SANS model selection. These fitting pathways ranged from general, model-free algorithms to more standard form and structure factor fitting. In addition, Monte Carlo bootstrapping was evaluated as a method to estimate parameter uncertainty through simulation of technical replicates. The most robust fitting workflow for dilute solutions was found to be form factor fitting without CV-SANS ( i.e. polymer in 100% D 2 O). For semidilute and concentrated solutions, while the model-free approach can be mathematically defined for CV-SANS data, the addition of a structure factor imposes physical constraints on the optimization problem, suggesting that the optimal fitting pathway should include appropriate form and structure factor models. The measured hydration numbers were consistent with the number of tightly bound water molecules associated with each monomer unit, and the concentration dependence of the hydration number was largely governed by the chemistry-specific interactions between water and polymer. Polymers with weaker water–polymer interactions ( i.e. those with fewer hydration water molecules) were found to have more bound water at higher concentrations than those with stronger water–polymer interactions due to the increase in the number of forced water–polymer contacts in the concentrated system. This SANS-based method to count hydration water molecules can be applied to polymers in any concentration regime, which will lead to improved understanding of water–polymer interactions and their impact on materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Evaluation of DIII-D plasmas for Future Measurements of the Driven Current Density in the DIII-D High Field Side Lower Hybrid Experiment

High field side (HFS) lower hybrid current drive (LHCD) is a promising method for efficiently driving off-axis current in steady state tokamak power plants. The first test of this technology is underway at the DIII-D tokamak. The initial physics goal is to measure the LH-driven current density profile in order to validate the ray-tracing/Fokker-Plank codes GENRAY/CQL3D and gain confidence in their ability to predict LH deposition in future DIII-D experiments and fusion power plants. DIII-D’s motional Stark effect (MSE) diagnostic will be used to constrain the magnetic equilibrium reconstructions. From these reconstructions, the ohmic, bootstrap, and driven non-inductive components of the current density profile can be extracted. Several recent DIII-D plasmas have been identified in which GENRAY/CQL3D predict measurable amounts of current with only 100 kW of coupled power.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗