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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 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Computational determination of a primary diffusion mode in γ U-10Mo under irradiation

Low enriched uranium (< 20 % 235 U)-molybdenum (U-Mo) monolithic fuel is the primary candidate for high performance research and test reactors, and is in the process of being qualified to replace highly enriched uranium (≥ 20 % 23 5U) fuel. As part of the qualification process, it is critical to understand and predict the behavior of fission gas bubbles under irradiation, which affects fuel swelling and fuel failure. Mechanistic fuel models are being developed that can both reproduce the existing experimental data for fuel swelling, and be further applied to irradiation conditions beyond the experimental scope. Diffusion of species under irradiation conditions is an important parameter in the mechanistic fuel models; however, no temperature-relevant experimental diffusion data exists. In the present work, radiation-enhanced diffusion coefficients of U, Mo, and Xe in λU-10wt.%Mo were calculated in the temperature range between 300 K and 1400 K via rate-theory models and molecular dynamics simulations with an embedded-atom method interatomic potential for the U-Mo-Xe system. Accordingly, total diffusion coefficients under relevant irradiation conditions are determined using previously obtained intrinsic thermal diffusion and radiation-driven diffusion coefficients, as well as the newly calculated radiation-enhanced diffusion coefficients presented herein. Radiation-enhanced diffusion of U and Mo was dominant in the intermediate temperature range, whereas radiation-enhanced diffusion of Xe did not significantly contribute to total diffusion of Xe at the relevant fission rate densities. Radiation-enhanced diffusion of Xe became faster than both intrinsic thermal diffusion and radiation-driven diffusion at a fission rate density of 5 x 10 22 fissions/m 3 /s, which is higher than the typical fission rate density range in research reactors. The temperature regime that radiation-enhanced diffusion of each element dominated was dependent on the fission rate density. Finally, the total diffusion coefficients of U, Mo, and Xe, updated in this work, will be utilized as parameters in the mechanistic fuel models to help predict the behavior of fission gas bubbles under irradiation more accurately.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1996

Determination of Highly Accurate Heats of Formation

Two approaches for directly computing a molecular heat of formation based on sophisticated ab initio electronic structure theory axe discussed and example calculations are presented for several molecules of interest in atmospheric chemistry including HNO and CICN. The accuracy of these approaches for a small subset of the molecules is demonstrated by comparison to very accurate experimental data. A third approach for evaluating accurate heats of formation consists of combining experimental and theoretical data. The potential for this method to provide reliable thermochemical data on many molecules using accurate experimental beats of formation for only a small number of species is demonstrated by giving accurate (Delta)H(sup 0, sub f) quantities for a host of fluorine, chlorine, and bromine oxide and nitrogen oxide compounds. Potential pitfalls in all three approaches are discussed.

Lee, Timothy J.↗
NASA NTRS1992

Experimental and calculated LET distributions in the Cosmos-2044 biosatellite orbit

During the flight of the Cosmos-2044 biosatellite, joint U.S.S.R.-U.S.A. investigations of different characteristics of cosmic radiation (CR) in the near-Earth environment were carried out. The U.S. dielectric track detectors CR-39 and Soviet BYa- and BR-type nuclear photo-emulsions were used as detectors. The present work shows some results of experimental measurements of linear energy transfer (LET) spectra of CR particles obtained with the use of these detectors, which were placed both inside and outside the satellite. The LET spectra measurement with plastic detectors is composed of two parts: the measurement of galactic cosmic rays (GCR) particles, and of short-range particles. The contributions of these components to the total LET distribution at various thicknesses of the shielding were analyzed and the results of these studies are presented. Calculated LET spectra in the Cosmos-2044 orbit were compared with experimental data. On the basis of experimental and calculated values of the LET spectra, absorbed and equivalent CR doses were calculated. In the shielding range of 1-1.5 g cm-2, outside the spacecraft, the photo-emulsions yielded 10.3 mrad d-1 and 27.5 mrem d-1 (LET > or = 2 MeV cm-1) while the CR-39 yielded averages of 1.43 mrad d-1 and 13.4 mrem d-1 (LET > or = 40 MeV cm-1). Inside the spacecraft (> or = 10 g cm-2) the photo-emulsions yielded 8.9 mrad d-1 and 14.5 mrem d-1.

NASA Program Radiation Health↗
DOE OSTI2020

Evaluation of the Prompt Fission Gamma Properties for Neutron Induced Fission of 235,238 U and 239 Pu

Herein we present details of the prompt fission gamma property evaluations included in the released ENDF/B-VIII.0 files. The average prompt gamma multiplicity, the average prompt gamma spectrum, the average total prompt gamma energy released and the prompt gamma multiplicity distributions, included in the ENDF/B-VIII.0 files, are presented for 235 U(n,f), 238 U(n,f), and 239 Pu(n,f) reactions, as a function of incident neutron energy up to 30 MeV. The evaluation is based on available experimental data and model calculations. Notable data include recent measurements by experimental groups at Los Alamos and Lawrence Livermore National Laboratories, at Geel and Budapest, at CEA labs, as well as seminal historical gamma production measurements by Drake at Los Alamos.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Fractal analysis on Ag 2 O thin film using a data-driven approach

The synthesis of fractal Ag oxide (Ag 2 O) on the surface of Ag thin film has been achieved at room temperature by using Synchrotron X-ray irradiation. We have performed an automated quantitative analysis of a batch of 1879 fractal Ag 2 O patterns in a scanning electron microscopy (SEM) image within a radius of 2000 mm outward from the center of the X-ray beam. The morphology is similar to that of the diffusion-limited cluster aggregation (DLCA) model. The fractal dimension (D) of Ag 2 O is between 1.7 and 1.5 from the center to the edge. The area distribution density of fractal Ag 2 O follows a quadratic function with radius R. It is found that the branches’ number of fractal Ag 2 O is a key factor affecting the fractal dimension. The more branches the fractal has, the greater the D is. This is the first time that Ag fractal has been investigated by combining automated data analysis methods with batch experimental data. This data-driven approach provides a new research perspective for rationally regulating materials’ fractal morphology and performance.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bridging the Gap between the X-ray Absorption Spectroscopy and the Computational Catalysis Communities in Heterogeneous Catalysis: A Perspective on the Current and Future Research Directions

X-ray absorption spectroscopy (XAS) [extended X-ray absorption fine structure (EXAFS) and X-ray absorption near-edge structure (XANES)] is a key technique within the heterogeneous catalysis community to probe the structure and properties of the active site(s) for a diverse range of catalytic materials. Furthermore, the interpretation of the raw experimental data to derive an atomistic picture of the catalyst requires modeling and analysis; the EXAFS data are compared to a model, and a goodness of fit parameter is used to judge the best fit. This EXAFS modeling can often be nontrivial and time-consuming; overcoming or improving these limitations remains a central challenge for the community. Considering these limitations, this Perspective highlights how recent developments in analysis software, increased availability of reliable computational models, and application of data science tools can be used to improve the speed, accuracy, and reliability of EXAFS interpretation. In particular, we emphasize the advantages of combining theory and EXAFS as a unified technique that should be treated as a standard (when applicable) to identify catalytic sites and not two separate complementary methods. Building on the recent trends in the computational catalysis community, we also present a community-driven approach to adopt FAIR Guiding Principles for the collection, analysis, dissemination, and storage of XAS data. Written with both the experimental and theory audience in mind, we provide a unified roadmap to foster collaborations between the two communities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A machine learning Automated Recommendation Tool for synthetic biology

Abstract Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, fatty acids, and tryptophan. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

New, improved analysis of correlation ECE data to accurately determine turbulent electron temperature spectra and magnitudes (invited)

Turbulent electron temperature fluctuation measurement using a correlation electron cyclotron emission (CECE) radiometer has become an important diagnostic for studying energy transport in fusion plasmas, and its use is widespread in tokamaks (DIII-D, ASDEX Upgrade, Alcator C-Mod, Tore Supra, EAST, TCV, HL-2A, etc.). The CECE diagnostic typically performs correlation analysis between two closely spaced (within the turbulent correlation length) ECE channels that are dominated by uncorrelated thermal noise emission. This allows electron temperature fluctuations embedded in the thermal noise to be revealed and fluctuation level and spectra determined. We have demonstrated a new, improved CECE coherency-based analysis for calculating the temperature fluctuation frequency spectrum and level, which has been verified both numerically through the simulation of synthetic ECE radiometer data and through analysis of experimental data from the CECE system on DIII-D. The new formulation places coherency-based analysis on a firm foundational footing and corrects some currently published methodologies. This new method accurately accounts for bias error in the coherence function and correctly calculates noise levels for a fixed data record length. It provides excellent accuracy in determining temperature fluctuation level (e.g., <10% error) even for a small realization number in the ensemble average. The method also has a smaller uncertainty (i.e., error bar) in the power spectrum when compared to the more standard cross-power method when evaluated at low coherency. Direct calculation of system noise level using correlation between randomized intermediate frequency signals is recommended.

Wang, G. (ORCID:0000000225739827)↗
DOE OSTI2023

Parallel transport modeling of linear divertor simulators with fundamental ion cyclotron heating *

Abstract The Material Plasma Exposure eXperiment (MPEX) is a steady state linear device with the goal to perform plasma material interaction studies at future fusion reactor relevant conditions. A prototype of MPEX referred as ‘Proto-MPEX’ is designed to carry out research and development related to source, heating and transport concepts on the planned full MPEX device. The auxiliary heating schemes in MPEX are based on cyclotron resonance heating with radio frequency (RF) waves. Ion cyclotron heating (ICH) and electron cyclotron heating in MPEX are used to independently heat the ions and electrons and provide fusion divertor conditions ranging from sheath-limited to fully detached divertor regimes at a material target. A hybrid particle-in-cell code- PICOS++ is developed and applied to understand the plasma parallel transport during ICH in MPEX/Proto-MPEX to the target. With this tool, evolution of the distribution function of MPEX/Proto-MPEX ions is modeled in the presence of (a) Coulomb collisions, (b) volumetric particle sources and (c) quasi-linear RF-based ICH. The code is benchmarked against experimental data from Proto-MPEX and simulation data from B2.5 EIRENE. The experimental observation of ‘density-drop’ near the target in Proto-MPEX and MPEX during ICH is demonstrated and explained via physics-based arguments using PICOS++ modeling. In fact, the density drops at the target during ICH in Proto-MPEX/MPEX to conserve the flux and to compensate for the increased flow during ICH. Furthermore, sensitivity scans of various plasma parameters with respect to ICH power are performed for MPEX to investigate its role on plasma transport and particle and energy fluxes at the target.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Optimal adjustment sets for causal query estimation in partially observed biomolecular networks

Abstract Causal query estimation in biomolecular networks commonly selects a ‘valid adjustment set’, i.e. a subset of network variables that eliminates the bias of the estimator. A same query may have multiple valid adjustment sets, each with a different variance. When networks are partially observed, current methods use graph-based criteria to find an adjustment set that minimizes asymptotic variance. Unfortunately, many models that share the same graph topology, and therefore same functional dependencies, may differ in the processes that generate the observational data. In these cases, the topology-based criteria fail to distinguish the variances of the adjustment sets. This deficiency can lead to sub-optimal adjustment sets, and to miss-characterization of the effect of the intervention. We propose an approach for deriving ‘optimal adjustment sets’ that takes into account the nature of the data, bias and finite-sample variance of the estimator, and cost. It empirically learns the data generating processes from historical experimental data, and characterizes the properties of the estimators by simulation. We demonstrate the utility of the proposed approach in four biomolecular Case studies with different topologies and different data generation processes. The implementation and reproducible Case studies are at https://github.com/srtaheri/OptimalAdjustmentSet.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Machine learning for seismic low-frequency extrapolation

The cycle-skipping problem that plagues full waveform inversion (FWI) can be at least partially mitigated if low frequencies (which encode the kinematics of wave propagation in seismic data) are recorded. However, seismic sources and receivers are band-limited, so seismic data does not generally include signals down to 0 Hz. To improve our ability to solve the seismic inverse problem, one can synthesize this missing low-frequency (LF) content from the recorded high-frequency (HF) data using machine learning (ML) models. Deep learning models such as convolutional neural networks (CNNs) demonstrate impressive ability to perform low frequency extrapolation. However, such models require powerful hardware (GPU machines) and careful training. We assess the extrapolation capabilities of three different ML models that do not require GPU machines, namely, random forest, Gaussian process regression and gradient boosting, on both synthetic and real data. Experimental results on two synthetic data sets (generated from a low velocity lens embedded in a homogeneous medium, and the Marmousi model) demonstrate that FWI applied to the extrapolated data consistently improves inversion accuracy relative to FWI applied to the original data sets that do not contain low frequencies. Application of low-frequency extrapolation to real data from the Northwest Shelf of Australia demonstrates that tree-based ML models such as gradient boosting can outperform CNNs in terms of both accuracy and computational cost on non-GPU architectures.

58 GEOSCIENCES↗
DOE OSTI2024

DLSIA: Deep Learning for Scientific Image Analysis

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Quantum phase transition in CeCoIn 5 : Experimental facts and theory

Abstract The condensed-matter community is involved in a hot debate on the nature of quantum critical points (QCP) governing the low-temperature properties of heavy fermion metals. The smeared jump-like behavior revealed both in the residual resistivity and the Hall resistivity R H , along with the violation of the time invariance symmetry and the charge invariance , include the violation of quasiparticle-hole symmetry, and provide vital clues on the origin of both the non-Fermi-liquid behavior and QCP. For the first time, based on a number of important experimental data, we show that these experimental observations point out unambiguously that QCP of is accompanied by the symmetry violation, and QCP itself is represented by the topological fermion-condensation quantum phase transition (FCQPT) connecting two Fermi surfaces of different topological charges.

Physics↗
DOE OSTI2025

WEC-SIM Support for an Innovative Zero Discharge Supercritical Water Based Wave Energy Desalination System (CRADA Final Report)

NREL will assist East Carolina University in its development of the foundational knowledge and proof of concept that are needed for future process scale up and commercialization of a sustainable wave-to-water (direct pressurization) desalination unit powered by a wave energy converter. NREL will provide guidance and support to East Carolina University on its: use of the NREL developed WEC-Sim based wave to water system, exploring the WEC-Sim/ASPEN Plus data exchange communication, and using experimental data to hopefully provide validation of analytical models.

16 TIDAL AND WAVE POWER↗
NASA NTRS1972

Effects of eccentricities and lateral pressure on the design of stiffened compression panels

An analysis for determining the effects of eccentricities and lateral pressure on the design of stiffened compression panels is presented. The four types of panel stiffeners considered are integral, zee, integral zee, and integral tee. Mass-strength curves, which give the mass of the panel necessary to carry a specified load, are given along with related design equations needed to calculate the cross-sectional dimensions of the minimum-mass-stiffened panel. The results of the study indicate that the proportions of the panels are geometrically similar to the proportions of panels designed for no eccentricity or lateral pressure, but no cross-sectional dimensions are greater, resulting in significantly increased mass. The analytical minimum-mass designs of zee-stiffened panels are compared with designs from experimentally derived charts. An assumed eccentricity of 0.001 times the length of the panel is used to correlate the analytical and experimental data. Good correlation between the experimentally derived and the analytical curves is obtained for the range of loading where materials yield governs the design. At lower loads the mass given by the analytical curve using this assumed eccentricity is greater than that given by the experimental results.

Giles, G. L.↗
NASA NTRS1973

Viscosity of liquid anorthite.

The viscosity of liquid anorthite has been determined over the temperature ranges between 1450 to 1620 C and 820 to 950 C. The high-temperature data agree well with previous experimental data and with predictions of the Bottinga and Weill model. The overall log (viscosity) versus 1/T relation exhibits pronounced and rather continuous curvature. The viscosity of anorthite is higher at any given temperature and more strongly temperature-dependent than that of the anorthite-rich lunar compositions 14259, 14310, and 15418. The room-temperature density of glassy anorthite (2.64 gm/cu cm) and the thermal expansion coefficients of glassy and liquid anorthite have also been determined. The volume expansion coefficient for the glass is about 0.000018 per deg C, and that for the liquid is about 0.000048 per deg C. These values are used to relate the high-temperature flow data to the predictions of free-volume theories.

Cukierman, M.↗
NASA NTRS1975

Stopping cross sections for 0.25-3.0-MeV He-4 ions in cadmium sulfide

Stopping cross sections of He-4 ions with energies between 0.25 and 3.0 MeV have been measured for cadmium sulfide with a probable error of plus or minus 7% to 8%. The experimental method utilized the Rutherford backscattering technique and measured the energy loss of elastically scattered He-4 ions from films of cadmium sulfide sputtered on carbon substrates. The experimental data are compared with recent experimental and theoretical results.

Miller, W. E.↗
NASA NTRS1987

Magnetospheric studies using the UKS data

The magnetic field data from the UKS spacecraft were analyzed to learn more about the solar wind interaction with the Earth's magnetosphere and about the magnetosphere itself. The data was reduced from raw experimenter data records to engineering units. The evolution of the waves in the foreshock, the varying structure of the bow shock along the boundary, simultaneous behavior of the magnetopause in the north and south hemisphere and MHD waves in the magnetosphere and magnetosheath were examined.

Russell, C. T.↗