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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 1,243 records · Page 69

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DOE OSTI2018

Viscoelasticity and plasticity mechanisms of human dentin

Theoretical models of viscoelastic behavior and plastic deformation mechanisms of human dentin are considered. Using the linear viscoelasticity theory in which creep and relaxation kernels have the form of fraction-exponential functions, numerical values of instantaneous and long-time Young’s moduli and other characteristics of dentin viscoelasticity under uniaxial compression are found. As dentin plastic deformation mechanisms, mutual collagen fiber sliding in the region of contact of their side surfaces, separation of these fibers from each other, and irreversible tension of some collagen fibers, are proposed. It is shown that the second mechanism activation requires a smaller stress than that for activating others. The models of plastic zones at the mode I crack tip, which correspond to these mechanisms, are studied. It is shown that the plastic zone size can increase from a few hundreds of nanometers to hundreds of micrometers with increasing applied stress.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2019

Porting a 3D seismic modeling code (SW4) to CORAL machines

Seismic waves fourth order (SW4) solves the seismic wave equations on Cartesian and curvilinear grids using large compute clusters with O (100,000) cores. In this article we discuss the porting of SW4 to run on the CORAL architecture using the RAJA performance portability abstraction layer. The performances of key kernels using RAJA and CUDA are compared to estimate the performance penalty of using the portability abstraction layer. Code changes required for efficiency on GPUs and minimizing time spent in Message Passing Interface (MPI) are discussed. This article describes a path for efficiently porting large code bases to GPU-based machines while avoiding the pitfalls of a new architecture in the early stages of its deployment. Current bottlenecks in the code are discussed along with possible architectural or software mitigations. SW4 runs 28× faster on one 4-GPU CORAL node than on a CTS-1 node (Dual Intel Xeon E5-2695 v4). SW4 is now in routine use on problems of unprecedented resolution (203 billion grid points) and scale on 1,200 nodes of Summit.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

TwoFold: Highly accurate structure and affinity prediction for protein-ligand complexes from sequences

We describe our development of ab initio protein-ligand binding pose prediction models based on transformers and binding affinity prediction models based on the neural tangent kernel (NTK). Folding both protein and ligand, the TwoFold models achieve efficient and quality predictions matching state-of-the-art implementations while additionally reconstructing protein structures. In conclusion, solving NTK models points to a new use case for highly optimized linear solver benchmarking codes on HPC.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Notos - a galaxy tool to analyze CpN observed expected ratios for inferring DNA methylation types

Background: DNA methylation patterns store epigenetic information in the vast majority of eukaryotic species. The relatively high costs and technical challenges associated with the detection of DNA methylation however have created a bias in the number of methylation studies towards model organisms. Consequently, it remains challenging to infer kingdom-wide general rules about the functions and evolutionary conservation of DNA methylation. Methylated cytosine is often found in specific CpN dinucleotides, and the frequency distributions of, for instance, CpG observed/expected (CpG o/e) ratios have been used to infer DNA methylation types based on higher mutability of methylated CpG. Results: Predominantly model-based approaches essentially founded on mixtures of Gaussian distributions are currently used to investigate questions related to the number and position of modes of CpG o/e ratios. These approaches require the selection of an appropriate criterion for determining the best model and will fail if empirical distributions are complex or even merely moderately skewed. We use a kernel density estimation (KDE) based technique for robust and precise characterization of complex CpN o/e distributions without a priori assumptions about the underlying distributions. Conclusions: We show that KDE delivers robust descriptions of CpN o/e distributions. For straightforward processing, we have developed a Galaxy tool, called Notos and available at the ToolShed, that calculates these ratios of input FASTA files and fits a density to their empirical distribution. Based on the estimated density the number and shape of modes of the distribution is determined, providing a rational for the prediction of the number and the types of different methylation classes. Notos is written in R and Perl.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Simulation of Mechanical Fractionation of Chopped Whole-Plant Corn (WPC) Using Discrete Element Method (DEM)

Fractionating whole-plant corn (WPC) in a single-pass harvesting system requires studies on the WPC-to-equipment interaction for improved property control, as well as mechanical and air-driven separation processes compared to the traditional multi-pass grain and stover harvesting system. The discrete element method (DEM) technique has the potential to simulate WPC mechanical fractionation and support simulation-based design of WPC separation processes. In this study, methods to develop DEM particle models of WPC (kernel, cob, stalk, and husk) and their material properties for simulating mass fractionation using the ASABE standard mechanical shaker were proposed. Measurement was done on the axial dimensions (major, intermediate, and minor) and mass of each WPC type (mean sample size is 56), sampled from single-pass harvesting. Applying gaussian multivariate regression and bootstrapping re-sampling techniques, a DEM particle approximate to each WPC was developed. Sensitivity analysis of the DEM Young‘s modulus, Poisson‘s ratio, and interaction parameters of coefficient of restitution, coefficient of rolling friction, and coefficient of static friction on mass fraction was performed after 156 ASABE sieve-shaking DEM simulation runs, generated using Latin Hypercube Design (LHD) design of experiment (DOE) from 19 DEM material parameters. DEM simulation using Hertz-Mindlin with flexible bond contact laws and DOE optimized material properties successfully reproduced the mass fractions retained in ASABE sieves at 9.8% mean relative error and a coefficient of determination of R2 = 0.87. Here, the DEM methodology developed for mechanical WPC mass fractionation could be deployed to perform virtual design of feedstock handling equipment and performance analysis of mechanical fraction systems.

09 BIOMASS FUELS↗

DOE OSTI2014

Anisotropic Elastic Resonance Scattering model for the Neutron Transport equation

The resonance scattering transfer cross-section has been reformulated to account for anisotropic scattering in the center-of-mass of the neutron-nucleus system. The main innovation over previous implementations is the relaxation of the ubiquitous assumption of isotropic scattering in the center-of-mass and the actual effective use of scattering angle distributions from evaluated nuclear data files in the computation of the angular moments of the resonant scattering kernels. The formulas for the high order anisotropic moments in the laboratory system are also derived. A multi-group numerical formulation is derived and implemented into a module incorporated within the NJOY nuclear data processing code. An ultra-fine energy mesh cross section library was generated using these new theoretical models and then was used for fuel assembly calculations with the PARAGON lattice physics code. The results obtained indicate a strong effect of this new model on reactivity, multi-group fluxes and isotopic inventory during depletion.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Remote Sensing and GIS data at 1km-grid over Chesapeake Bay used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”

The package contains the data layers used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”. The study aims to use multi-source remote sensing and GIS datasets to investigate the spatial heterogeneity and identify spatial zones with similar environmental characteristics and understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. We employed unsupervised hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, then determined the main driving factors using Random Forest regression and SHapley Additive exPlanations (SHAP). Spatial data layers include soil respiration, kernel Normalized Difference Vegetation Index (kNDVI) computed from Harmonized Landsat 8 and Sentinel-2 time series, climate variables from the Daymet dataset, land cover, biodiversity, topographical metrics, soil property, and tidal elevation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

AGR-5/6/7 Fuel Fabrication Feedstock Chemical Purity Specifications

This specification has been prepared to provide fuel fabricators with minimum chemical purity specifications intended to ensure that fuel components have consistent properties and that impurity levels in the as-fabricated fuel is sufficiently low to minimize in-pile TRISO particle failures and to enhance fission product retention. Process chemical purity specifications are given for the following processes: • Kernel forming and sintering • TRISO particle coating • Matrix preparation • TRISO overcoating • Compact carbonization and heat treatment

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

ECAR-3873 Acceptance of Nonconforming Fuel for the AGR-5/6/7 Irradiation Experiments

Low-enriched fuel compacts fabricated for the AGR-5/6/7 irradiation experiments did not conform to all fuel specifications. Reasons why the fuel is acceptable for use in the AGR-5/6/7 irradiation are discussed. Revision 1 alters the characterization data for the dispersed uranium and exposed kernel defect fractions. Additional characterization data was obtained for fuel compacts analyzed by Oak Ridge National Laboratory. This enabled statistical outliers to be rejected and to confirm that results obtained from BWX Technologies Nuclear Operations Group were statistically independent of the Oak Ridge results and should be excluded from the analyses. Revision 2 alters the characterization data for the TRISO lot, J52R-16-98005, used in the fabrication of the AGR-5/6/7 fuel. Data from the BWXT J52R-16-98005 Certification package is used to calculate the reported values.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Improved Data Interpretation through Identification of Time Series Periodicity Changes

Analysis and interpretation of time series data is easiest when the data values occur at uniform intervals in time, but actual data may have differing data sampling frequencies, such as monthly and daily readings. Applying data analysis techniques, such as smoothing, to such a data set may not give a representative result between time segments. The ability to automatically distinguish time segments of differing data frequency would provide a means for applying data analysis independently to each segment, though a suitable blending at segment boundaries would be required. A method for detecting frequency changes was developed and applied to Gaussian and median smoothing of hydraulic head data from groundwater wells at the U.S. Department of Energy Hanford Site in southeastern Washington state. The process identifies time segments of high-frequency (daily) or low-frequency (greater than daily) data using adjusted-bandwidth Gaussian kernel density estimation and a threshold value, which are further refined to address small blocks of low-frequency data within larger blocks of high-frequency data. User-selectable levels of smoothing are then applied independently to the time segments prior to combining the segment results for a single smoothed data set. This time segment identification approach provides effective low- and high-frequency data separation, which provides a method to apply data analysis independently to each time segment.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Methods in PES-Learn: Direct-Fit Machine Learning of Born–Oppenheimer Potential Energy Surfaces

The release of PES-L EARN version 1.0 as an open-source software package for the automatic construction of machine learning models of semi-global molecular potential energy surfaces (PESs) is presented. Improvements to PES-L EARN ’s interoperability are stressed with new Python API that simplifies workflows for PES construction via interaction with QCSchema input and output infrastructure. In addition, a new machine learning method is introduced to PES-L EARN : kernel ridge regression (KRR). The capabilities of KRR are emphasized with examination of select semi-global PESs. All machine learning methods available in PES-L EARN are benchmarked with benzene and ethanol datasets from the rMD17 database to illustrate PES-L EARN ’s performance ability. Fitting performance and timings are assessed for both systems. Finally, the ability to predict gradients with neural network models is presented and benchmarked with ethanol and benzene. PES-L EARN is an active project and welcomes community suggestions and contributions.

kernel ridge regression↗

DOE OSTI2016

SELF-GRAVITATIONAL FORCE CALCULATION OF INFINITESIMALLY THIN GASEOUS DISKS ON NESTED GRIDS

We extend the work of Yen et al. and develop second-order formulae to accommodate a nested grid discretization for the direct self-gravitational force calculation for infinitesimally thin gaseous disks. This approach uses a two-dimensional kernel that is derived for infinitesimally thin disks and is free of artificial boundary conditions. The self-gravitational force calculation is presented in generalized convolution forms for a nested grid configuration. A numerical technique derived from a fast Fourier transform is employed to reduce the computational complexity to be nearly linear. By comparing with analytic potential–density pairs associated with the generalized Maclaurin disks, the extended approach is verified to be of second-order accuracy when using numerical simulations. The proposed method is accurate, computationally fast, and has the potential to be applied to studies of planetary migration and the gaseous morphology of disk galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

The Nature of Turbulence in the LITTLE THINGS Dwarf Irregular Galaxies

We present probability density functions and higher order (skewness and kurtosis) analyses of the galaxy-wide and spatially resolved distributions of H i column density in the LITTLE THINGS sample of dwarf irregular galaxies. This analysis follows that of Burkhart et al. for the Small Magellanic Cloud (SMC). About 60% of our sample have galaxy-wide values of kurtosis that are similar to that found for the SMC, with a range up to much higher values, and kurtosis increases with integrated star formation rate. Kurtosis and skewness were calculated for radial annuli and for a grid of 32 pixel × 32 pixel kernels across each galaxy. For most galaxies, kurtosis correlates with skewness. For about half of the galaxies, there is a trend of increasing kurtosis with radius. The range of kurtosis and skewness values is modeled by small variations in the Mach number close to the sonic limit and by conversion of H i to molecules at high column density. The maximum H i column densities decrease with increasing radius in a way that suggests molecules are forming in the weak-field limit, where H{sub 2} formation balances photodissociation in optically thin gas at the edges of clouds.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

A Gaussian Process Regression Reveals No Evidence for Planets Orbiting Kapteyn’s Star

Radial–velocity (RV) planet searches are often polluted by signals caused by gas motion at the star’s surface. Stellar activity can mimic or mask changes in the RVs caused by orbiting planets, resulting in false positives or missed detections. Here we use Gaussian process regression to disentangle the contradictory reports of planets versus rotation artifacts from Kapteyn’s star. To model rotation, we use joint quasiperiodic kernels for the RV and Hα signals, requiring that their periods and correlation timescales be the same. We find that the rotation period of Kapteyn’s star is 125 days, while the characteristic active-region lifetime is 694 days. Adding a planet to the RV model produces a best-fit orbital period of 100 yr, or 10 times the observing time baseline, indicating that the observed RVs are best explained by star rotation only. We also find no significant periodic signals in residual RV data sets constructed by subtracting off realizations of the best-fit rotation model and conclude that both previously reported “planets” are artifacts of the star’s rotation and activity. Our results highlight the pitfalls of using sinusoids to model quasiperiodic rotation signals.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2014

Production of small uranium dioxide microspheres for cermet nuclear fuel using the internal gelation process

The U.S. National Aeronautics and Space Administration (NASA) is developing a uranium dioxide (UO2)/tungsten cermet fuel for potential use as the nuclear cryogenic propulsion stage (NCPS). The first generation NCPS is expected to be made from dense UO2 microspheres with diameters between 75 and 150 m. Previously, the internal gelation process and a hood-scale apparatus with a vibrating nozzle were used to form gel spheres, which became UO2 kernels with diameters between 350 and 850 m. For the NASA spheres, the vibrating nozzle was replaced with a custom designed, two-fluid nozzle to produce gel spheres in the desired smaller size range. This paper describes the operational methodology used to make 3 kg of uranium oxide microspheres.

CERMET fuel↗

DOE OSTI2015

Predictive based monitoring of nuclear plant component degradation using support vector regression

Nuclear power plants (NPPs) are large installations comprised of many active and passive assets. Degradation monitoring of all these assets is expensive (labor cost) and highly demanding task. In this paper a framework based on Support Vector Regression (SVR) for online surveillance of critical parameter degradation of NPP components is proposed. In this case, on time replacement or maintenance of components will prevent potential plant malfunctions, and reduce the overall operational cost. In the current work, we apply SVR equipped with a Gaussian kernel function to monitor components. Monitoring includes the one-step-ahead prediction of the component’s respective operational quantity using the SVR model, while the SVR model is trained using a set of previous recorded degradation histories of similar components. Predictive capability of the model is evaluated upon arrival of a sensor measurement, which is compared to the component failure threshold. A maintenance decision is based on a fuzzy inference system that utilizes three parameters: (i) prediction evaluation in the previous steps, (ii) predicted value of the current step, (iii) and difference of current predicted value with components failure thresholds. The proposed framework will be tested on turbine blade degradation data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2015

TMD factorization and evolution at large $b_T$

In using transverse-momentum-dependent (TMD) parton densities and fragmentation functions, important non-perturbative information is at large transverse position $b_T$. This concerns both the TMD functions and their evolution. Fits to high energy data tend to predict too rapid evolution when extrapolated to low energies where larger values of $b_T$ dominate. I summarize a new analysis of the issues. It results in a proposal for much weaker $b_T$ dependence at large $b_T$ for the evolution kernel, while preserving the accuracy of the existing fits. The results are particularly important for using transverse-spin-dependent functions like the Sivers function.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2017

AGR-5/6/7 Fuel Fabrication Feedstock Chemical Purity Specifications

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