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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Vogel-Fulcher-Tammann freezing of a thermally fluctuating artificial spin ice probed by x-ray photon correlation spectroscopy

We report on the crossover from the thermal to the athermal regime of an artificial spin ice formed from a square array of magnetic islands whose lateral size, 30 nm × 70 nm, is small enough that they are dynamic at room temperature.We used resonant magnetic soft x-ray photon correlation spectroscopy as a method to observe the time-time correlations of the fluctuating magnetic configurations of spin ice during cooling, which are found to slow abruptly as a freezing temperature of T 0 = 178 ± 5 K is approached. This slowing is well described by a Vogel-Fulcher-Tammann law, implying that the frozen state is glassy, with the freezing temperature being commensurate with the strength of magnetostatic interaction energies in the array. The activation temperature, T A = 40 ± 10 K, is much less than that expected from a Stoner-Wohlfarth coherent rotation model. Zerofield- cooled/field-cooled magnetometry reveals a freeing up of fluctuations of states within islands above this temperature, caused by variation in the local anisotropy axes at the oxidised edges. This Vogel-Fulcher-Tammann behavior implies that the system enters a glassy state upon freezing, which is unexpected for a system with a well-defined ground state.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Geometrically necessary dislocation fingerprints of dislocation loop absorption at grain boundaries

Here we present a numerical methodology to compute the Nye-tensor fingerprints of dislocation loop absorption at grain boundaries (GBs) for comparison with TEM observations of irradiated polycrystals. Our approach links atomistic simulations of self-interstitial atom (SIA) prismatic loops gliding toward and interacting with GBs in body-centered cubic iron with experimentally extracted geometrically necessary dislocation (GND) maps to facilitate the interpretation of damage processes. The Nye-tensor analysis is strongly mesh-size dependent—corresponding to resolution-dependent TEM observations. The method computes GND fingerprints from discretized dislocation line segments extracted from molecular dynamics simulations of dislocation loops being absorbed at a GB. Specifically, we perform MD simulation of prismatic loops of two diameters and monitor the three stages of the absorption process: loop glide, the partial, and full absorption of the loops at a [1 0 0] symmetric tilt GB. These methods provide a framework for future investigations of the nature of defect absorption by grain boundaries under irradiation conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Online Anomaly Detection Using Machine Learning and HPC for Power System Synchrophasor Measurements

This paper presents an online early anomaly detection framework for phasor measurement units (PMUs) to monitor the power system dynamics and help prevent blackouts using machine learning approaches. Dynamical machine learning solutions including state space model and Kalman filter are conducted in this study to learn the nonlinear and nonstationary PMU measurements and accurately predict system behaviors in real-time. The anomalies can be detected within seconds by comparing the predicted system behaviors with the real system observations. The method proposed in this framework trains PMU data with a given time window (e.g., 5 seconds) using a dynamic nonlinear model, and then predicts system behaviors during the following time window. High prediction accuracy is achieved by applying the dynamic nonlinear model to the real-world PMU measurements – the anomalies detected are successfully validated given the recorded real-world events. High-performance-computing (HPC) techniques are utilized to further reduce computational time to provide real time power system situational awareness.

Ren, Huiying↗
DOE OSTI2013

Electron depletion via cathode spot dispersion of dielectric powder into an overhead plasma

The effectiveness of cathode spot delivered dielectric particles for the purpose of plasma depletion is investigated. Here, cathode spot flows kinetically entrain and accelerate dielectric particles originally at rest into a background plasma. The time variation of the background plasma density is tracked using a cylindrical Langmuir probe biased approximately at electron saturation. As inferred from changes in the electron saturation current, depletion fractions of up to 95% are observed. This method could be exploited as a means of communications blackout mitigation for manned and unmanned reentering spacecraft as well as any high speed vehicle enveloped by a dense plasma layer.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Knowledge-based prediction of plan quality metrics in intracranial stereotactic radiosurgery

Purpose: The objective of this work was to develop a comprehensive knowledge-based methodology for predicting achievable dose–volume histograms (DVHs) and highly precise DVH-based quality metrics (QMs) in stereotactic radiosurgery/radiotherapy (SRS/SRT) plans. Accurate QM estimation can identify suboptimal treatment plans and provide target optimization objectives to standardize and improve treatment planning. Methods: Correlating observed dose as it relates to the geometric relationship of organs-at-risk (OARs) to planning target volumes (PTVs) yields mathematical models to predict achievable DVHs. In SRS, DVH-based QMs such as brain V{sub 10Gy} (volume receiving 10 Gy or more), gradient measure (GM), and conformity index (CI) are used to evaluate plan quality. This study encompasses 223 linear accelerator-based SRS/SRT treatment plans (SRS plans) using volumetric-modulated arc therapy (VMAT), representing 95% of the institution’s VMAT radiosurgery load from the past four and a half years. Unfiltered models that use all available plans for the model training were built for each category with a stratification scheme based on target and OAR characteristics determined emergently through initial modeling process. Model predictive accuracy is measured by the mean and standard deviation of the difference between clinical and predicted QMs, δQM = QM{sub clin} − QM{sub pred}, and a coefficient of determination, R{sup 2}. For categories with a large number of plans, refined models are constructed by automatic elimination of suspected suboptimal plans from the training set. Using the refined model as a presumed achievable standard, potentially suboptimal plans are identified. Predictions of QM improvement are validated via standardized replanning of 20 suspected suboptimal plans based on dosimetric predictions. The significance of the QM improvement is evaluated using the Wilcoxon signed rank test. Results: The most accurate predictions are obtained when plans are stratified based on proximity to OARs and their PTV volume sizes. Volumes are categorized into small (V{sub PTV} < 2 cm{sup 3}), medium (2 cm{sup 3} < V{sub PTV} < 25 cm{sup 3}), and large (25 cm{sup 3} < V{sub PTV}). The unfiltered models demonstrate the ability to predict GMs to ∼1 mm and fractional brain V{sub 10Gy} to ∼25% for plans with large V{sub PTV} and critical OAR involvements. Increased accuracy and precision of QM predictions are obtained when high quality plans are selected for the model training. For the small and medium V{sub PTV} plans without critical OAR involvement, predictive ability was evaluated using the refined model. For training plans, the model predicted GM to an accuracy of 0.2 ± 0.3 mm and fractional brain V{sub 10Gy} to 0.04 ± 0.12, suggesting highly accurate predictive ability. For excluded plans, the average δGM was 1.1 mm and fractional brain V{sub 10Gy} was 0.20. These δQM are significantly greater than those of the model training plans (p < 0.001). For CI, predictions are close to clinical values and no significant difference was observed between the training and excluded plans (p = 0.19). Twenty outliers with δGM > 1.35 mm were identified as potentially suboptimal, and replanning these cases using predicted target objectives demonstrates significant improvements on QMs: on average, 1.1 mm reduction in GM (p < 0.001) and 23% reduction in brain V{sub 10Gy} (p < 0.001). After replanning, the difference of δGM distribution between the 20 replans and the refined model training plans was marginal. Conclusions: The results demonstrate the ability to predict SRS QMs precisely and to identify suboptimal plans. Furthermore, the knowledge-based DVH predictions were directly used as target optimization objectives and allowed a standardized planning process that bettered the clinically approved plans. Full clinical application of this methodology can improve consistency of SRS plan quality in a wide range of PTV volume and proximity to OARs and facilitate automated treatment planning for this critical treatment site.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-654: Quantifying Plan Quality Can Effectively Distinguish Between Competing Equivocal IMRT Prostate Plans

Purpose: The purpose of this study was to create a prostate IMRT plan quality index (PQI) that may be used to quantitatively compare competing plans using a methodology that mimics physician preference. This methodology allows planners to choose between plans with equivocal characteristics, prior to physician scrutiny. Methods: An observer study was conducted to gather data from 3 radiation oncology physicians who ranked a set of 20 patients (each with 5 plans). The rankings were used to optimize a PQI that combined weighted portions of the rectum, bladder, and planning target volume DVHs, such that the relative PQI mimicked physician rankings as best as possible. Once optimized, a test study assessed the PQI by comparison to physician rankings in a new set of 25 patients (each with 4 plans). The physician group in the test study included 6 physicians, 5 of whom were not included in the modeling study. PQI scores were evaluated against the physicians’ rank list using Spearman rank correlation. Results: The optimized plan quality index combined the following DVH features: high dose regions above 40Gy/60Gy (rectum/bladder), organ weightings, and PTV shoulder coverage. Mean correlation of the PQI vs. physicians’ rankings in the modeling study was 0.507 (range: 0.345–0.706). By comparison, the mean correlation between physicians was 0.301 (range: 0.242–0.334). The mean correlation of the PQI vs. physician rankings in test study was 0.726 (range: 0.416–0.936), indicating robustness of the PQI by virtue of producing similar results to the modeling study. Intra-physician correlation was 0.564 (range: 0.398–0.689). Conclusion: The correlation coefficients of the PQI vs. physicians were similar to the correlation coefficients of the physicians with each other, implying that the PQI developed in this work shows promise in reflecting physician clinical preference when selecting between competing, dosimetrically equivocal plans.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Attosecond-pump attosecond-probe x-ray spectroscopy of liquid water

Attosecond-pump/attosecond-probe experiments have long been sought as the most straightforward method for observing electron dynamics in real time. Although there has been much success with overlapped near-infrared femtosecond and extreme ultraviolet attosecond pulses combined with theory, true attosecond-pump/attosecond-probe experiments have been limited. Here we used a synchronized attosecond x-ray pulse pair from an x-ray free-electron laser to study the electronic response to valence ionization in liquid water through all x-ray attosecond transient absorption spectroscopy (AX-ATAS). Our analysis showed that the AX-ATAS response is confined to the subfemtosecond timescale, eliminating any hydrogen atom motion and demonstrating experimentally that the 1b 1 splitting in the x-ray emission spectrum is related to dynamics and is not evidence of two structural motifs in ambient liquid water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Acoustic anomalies in SrTiO{sub 3}−BiFeO{sub 3} solid solutions

The results of acoustic investigations of solid solutions SrTiO{sub 3}−BiFeO{sub 3} in the temperature range from 100 to 650 K have been presented. The measurements of the velocity and attenuation of the longitudinal ultrasonic mode at a frequency of 10 MHz were carried out by the pulse-echo method. The observed anomalies in velocity and attenuation correlate with the maxima of the dielectric constant in the temperature range of the relaxor state. In addition, the attenuation peaks in the temperature range 400–600 K, which define Burns and T* temperatures, which are characteristic of relaxors, have been identified. The obtained results have allowed the clarifying of the phase diagram of the solid solution system SrTiO{sub 3}−BiFeO{sub 3}.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Development of an NMR Relaxometer for Determining Magnetization Dynamics of Water Protons in Living Tissues and Its Use for Evaluating Age-Related Changes

A desktop NMR relaxometer was created, which allows determining spin–spin relaxation times of water protons in living tissues. Spin relaxation times of protons of water reflect changes in the mean number of paramagnetic centers, since the presence of these centers increases the magnetization recovery rate of water protons in living tissues tens of times. In particular, this method allowed observing age-related changes (sarcopenia) related to substitution of connective tissue for muscle fibers. Relaxometers of this type are comfortable to use and are promising devices for analysis and diagnostics of changes and disorders of metabolic processes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Estimation of Melting-Layer Cooling Rate from Dual-Polarization Radar: Spectral Bin Model Simulations

Diabatic cooling from hydrometeor phase changes in the stratiform melting layer is of great interest to both operational forecasters and modelers for its societal and dynamical consequences. Attempts to estimate the melting-layer cooling rate typically rely on either the budgeting of hydrometeor content estimated from reflectivity Z or model-generated lookup tables scaled by the magnitude of Z in the bright band. Recent advances have been made in developing methods to observe the unique polarimetric characteristics of melting snow and the additional microphysical information they may contain. However, to date no work has looked at the thermodynamic information available from the polarimetric radar brightband signature. In this study, a one-dimensional spectral bin model of melting snow and a coupled polarimetric operator are used to study the relation between the polarimetric radar bright band and the melting-layer cooling rate. Simulations using a fixed particle size distribution (PSD) and variable environmental conditions show that the height and thickness of the bright band and the maximum brightband Z and specific differential phase shift [Formula: see text] are all sensitive to the ambient environment, while the differential reflectivity [Formula: see text] is relatively insensitive. Additional simulations of 2700 PSDs based on in situ observations above the melting layer indicate that the maximum Z, [Formula: see text], and [Formula: see text] within the melting layer are poorly correlated with the maximum cooling rate while [Formula: see text] is strongly correlated. Finally, model simulations suggest that, in addition to riming, concurrent changes in aggregation and precipitation intensity and the associated cooling may plausibly cause observed sagging brightband signatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Dispersed metal cluster catalysts by design. Synthesis, characterization, structure, and performance

To understand the class of metal cluster catalysts better and to lay a foundation for the prediction of properties leading to improved catalysts, we have synthesized metal catalysts with well-defined structures and varied the cluster structures and compositions systematically—including the ligands bonded to the metals. These ligands include supports and bulky organics that are being tuned to control both the electron transfer to or from the metal and the accessibility of reactants to influence catalytic properties. We have developed novel syntheses to prepare these well-defined catalysts with atomic-scale control the environment by choice and placement of ligands and applied state-of-the art spectroscopic, microscopic, and computational methods to determine their structures, reactivities, and catalytic properties. The ligands range from nearly flat MgO surfaces to enveloping zeolites to bulky calixarenes to provide controlled coverages of the metal clusters, while also enforcing unprecedented degrees of coordinative unsaturation at the metal site—thereby facilitating bonding and catalysis events at exposed metal atoms. With this wide range of ligand properties and our arsenal of characterization tools, we worked to achieve a deep, fundamental understanding of how to synthesize robust supported and ligand-modified metal clusters with controlled catalytic properties, thereby bridging the gap between active site structure and function in unsupported and supported metal catalysts. We used methods of organometallic and inorganic chemistry combined with surface chemistry for the precise synthesis of metal clusters and nanoparticles, characterizing them at various stages of preparation and under various conditions (including catalytic reaction conditions) and determining their structures and reactivities and how their catalytic properties depend on their compositions and structures. Key characterization methods included IR, NMR, and EXAFS spectroscopies to identify ligands on the metals and their reactions; EXAFS spectroscopy and high-resolution STEM to determine cluster framework structures and changes resulting from reactant treatment and locations of metal atoms on support surfaces; X-ray diffraction crystallography to determine full structures of cluster-ligand combinations in the absence of a support, and TEM with tomographic methods to observe individual metal atoms and determine three-dimensional structures of catalysts. Electronic structure calculations were used to verify and interpret spectra and extend the understanding of reactivity beyond what is measurable experimentally.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

A nuclear data approach for the Hubble constant measurements

An extraordinary number of Hubble constant measurements challenges physicists with selection of the best numerical value. The standard U.S. Nuclear Data Program (USNDP) codes and procedures have been applied to resolve this issue. The nuclear data approach has produced the most probable or recommended Hubble constant value of 67.00(770) (km/sec)/Mpc. This recommended value is based on the last 25 years of experimental research and includes contributions from different types of measurements. The present result implies (14.6±1.7) x 10 9 years as a rough estimate for the age of the Universe. The complete list of recommended results is given and possible implications are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Development and Implementation of Digital Image Correlation for Out-of-Cell Burst Testing

This report details the progress made towards utilizing in-situ methods to observe and quantify unirradiated Zircaloy-4 (Zr4) fuel cladding deformation during out-of-cell design basis accident conditions, specifically during loss-of-coolant accident (LOCA) burst testing in the Severe Accident Test Station at Oak Ridge National Laboratory. Digital image correlation (DIC) was implemented to calculate cladding strain during burst and infrared (IR) thermography was used to attempt a correlation of strain with temperature and to investigate thermal gradients. For both techniques, early experimentation revealed experimental modifications were necessary for implementation. For DIC, this manifested as shortened cladding lengths which could be centered in front of the viewing chamber bored through the IR furnace side, and for IR thermography this meant conducting burst tests in air without a reaction tube and the incorporation of a SiC shell to eliminate direct IR reflections from the tungsten lamps. In the following, the technical issues and subsequent origin of these modifications are discussed, laying the framework for a proof-of-concept DIC/IR characterization of a burst test in the final section.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Report Summarizing Progress in Digital Image Correlation Analysis of Burst Phenomenon

This report is an update on the progress made towards utilizing in-situ methods to observe and quantify Zry-4 deformation behavior during burst testing. A heavily modified burst test was developed that allows both digital image correlation and infrared thermography to be utilized during testing to quantify strain and axial thermal gradient data during burst. A variety of tests ranging from 6.2-15.9 MPa internal pressure and utilizing both constant and transient pressure conditions were conducted and presently reported on. The test modifications, which are detailed extensively in a previous report, are effectively found to have no discernible impact on burst temperatures when compared to historical Zircaloy burst data. To support future modeling studies, the IR thermography data was used to approximate the thermal gradient down the axial length at burst for all the claddings. Then, DIC is first correlated to thermocouple temperature for a 10.3 MPa Transient pressure test, and then DIC correlated to IR thermal gradient data down the axial length of a 8.2 MPa Transient pressure test at several points.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Genomes to Structure and Function Workshop Report 2022

The goal of the U.S. Department of Energy (DOE) Biological and Environmental Research (BER) Program is to achieve a predictive understanding of complex biological, earth, and environmental systems with the aim of advancing the nation’s energy and infrastructure security. (https://www.energy.gov/science/ ber/biological-and-environmental-research). To pursue this goal, collaborations among experts in diverse research areas that lead to multidisciplinary projects are indispensable. The roles of DOE’s User Facilities, which offer unique and powerful resources for such research projects, are evolving, and expectations for the facilities are increasing. To respond to Users’ needs, the Joint Genome Institute (JGI) and Environmental Molecular Sciences Laboratory (EMSL) initiated the Facilities Integrating Collaborations for User Science (FICUS) program in 2014. This collaboration has grown into a popular and successful program, advancing more than 100 multidisciplinary projects to date. Similarly, the new interFacility collaborations among the JGI, EMSL, and User resources for BER structural biology and imaging at the Basic Energy Science (BES) Program’s synchrotron and neutron facilities are becoming essential for cutting-edge transdisciplinary science. To further explore the need for the BER research community to combine genomic, functional, and structural approaches to advance their research, an organizing committee was formed to develop and jointly host a 3-part workshop. The committee’s members represented seven DOE National Laboratory User Facilities (Appendix 1 lists the members). The “Genomes to Structure and Function” virtual workshop (see Appendices 2–5) was composed of three sessions. The first session, titled “Molecular Structures” (October 27– 28, 2021), highlighted diverse integrative experimental and computational approaches correlating structural data with sequencing and functional information, as well as predicting protein structures to model complex biological systems. The second session, “Intracellular Organization, and Material Synthesis and Decomposition” (December 15–16, 2021), covered imaging methods for observing, quantifying, and manipulating biosystems. The third session, “Imaging the Rhizosphere and Cellular Organization” (January 26–27, 2022) emphasized advanced and non-invasive imaging techniques applied to plant root-microbe-soil interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Reducing the Risk of Airborne Contamination through Intelligent Container Surveillance

DOE Manual 441.1-1 provides a set of performance criteria that nuclear material storage containers must achieve to provide protection to the worker, public, and environment. At Los Alamos National Laboratory, the SAVY-4000 is the container of choice for nuclear material storage, meeting all Manual requirements. At the outset, polymer components of the SAVY 4000 were considered to be life-limiting components due to their performance degradation resulting from radiation exposure. However, recent annual surveillance results revealed another life-limiting phenomenon for the container: a deterioration of the package integrity through corrosion of the stainless steel components, primarily the body. Container surveillance remains the choice method of observing, measuring, and tracking container health in service.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Hydrodynamic Coupling to a Homogenized Radiation Transport Method based on Young Measures

Resolving radiation transport fields subject to opacity profiles with strong, oscillatory line structure while potentially falling under intermediate optical depth conditions presents a numerical challenge in radiation transport modeling. The Young measure-based homogenization technique formulated by Haut et al. (2017) was investigated as a candidate method for resolving radiation fields under such conditions more accurately. The method was compared against frequently-utilized mean opacity methods as the Rosseland and Planck formulations. In this work, all methods were tested through radiation slab calculations separately comprised of aluminum, copper, and krypton, each for different thermodynamic conditions. Following these offline radiation slab calculations, demonstrations shifted towards the SCEPTRE radiation transport code and, subsequently, the multiphysics ALEGRA code for approximately-coupled radiation-material simulations. Throughout all the simulations shown in this study, for a fixed computational cost, the homogenized method was observed to be more accurate than any of the solutions determined through traditional mean opacity approaches.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Addressing GPU memory limitations for Graph Neural Networks in High-Energy Physics applications

Introduction Reconstructing low-level particle tracks in neutrino physics can address some of the most fundamental questions about the universe. However, processing petabytes of raw data using deep learning techniques poses a challenging problem in the field of High Energy Physics (HEP). In the Exa.TrkX Project, an illustrative HEP application, preprocessed simulation data is fed into a state-of-art Graph Neural Network (GNN) model, accelerated by GPUs. However, limited GPU memory often leads to Out-of-Memory (OOM) exceptions during training, due to the large size of models and datasets. This problem is exacerbated when deploying models on High-Performance Computing (HPC) systems designed for large-scale applications. Methods We observe a high workload imbalance issue during GNN model training caused by the irregular sizes of input graph samples in HEP datasets, contributing to OOM exceptions. We aim to scale GNNs on HPC systems, by prioritizing workload balance in graph inputs while maintaining model accuracy. Our paper introduces diverse balancing strategies aimed at decreasing the maximum GPU memory footprint and avoiding the OOM exception, across various datasets. Results Our experiments showcase memory reduction of up to 32.14% compared to the baseline. We also demonstrate the proposed strategies can avoid OOM in application. Additionally, we create a distributed multi-GPU implementation using these samplers to demonstrate the scalability of these techniques on the HEP dataset. Discussion By assessing the performance of these strategies as data loading samplers across multiple datasets, we can gauge their effectiveness in both single-GPU and distributed environments. Our experiments, conducted on datasets of varying sizes and across multiple GPUs, broaden the applicability of our work to various GNN applications that handle input datasets with irregular graph sizes.

Lee, Claire Songhyun↗