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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 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Extracting the number of short-range correlated nucleon pairs from inclusive electron scattering data

The extraction of the relative abundances of short-range correlated (SRC) nucleon pairs from inclusive electron scattering is studied using the generalized contact formalism (GCF) with several nuclear interaction models. GCF calculations can reproduce the observed scaling of the cross-section ratios for nuclei relative to deuterium at high xB and large Q 2 , a 2 =(σ A /A)/(σ d /2). In the nonrelativistic instant-form formulation, the calculation is very sensitive to the model parameters and only reproduces the data using parameters that are inconsistent with ab initio many-body calculations. Using a light-cone GCF formulation significantly decreases this sensitivity and improves the agreement with ab initio calculations. The ratio of similar mass isotopes, such as 40 Ca and 48 Ca, should be sensitive to the nuclear asymmetry dependence of SRCs, but is found to also be sensitive to low-energy nuclear structure. Thus the empirical association of SRC pair abundances with the measured a2 values is only accurate to about 20%. Finally, improving this will require cross-section calculations that reproduce the data while properly accounting for both nuclear structure and relativistic effects.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Temperature-dependent structure of Tb-doped magnetite nanoparticles

High quality 5 nm cubic Tb-doped magnetite nanoparticles have been synthesized by a wet-chemical method to investigate tailoring of magnetic properties for imaging and biomedical applications. We show that the Tb is incorporated into the octahedral 3+ sites. High-angle annular dark-field microscopy shows that the dopant is well-distributed throughout the particle, and x-ray diffraction measurements show a small lattice parameter shift with the inclusion of a rare-earth dopant. Magnetization and x-ray magnetic circular dichroism data indicate that the Tb spins are unpolarized and weakly coupled to the iron spin lattice at room temperature, and begin to polarize and couple to the iron oxide lattice at temperatures below 50 K. Broadband ferromagnetic resonance measurements show no increase in magnetic damping at room temperature for Tb-doped nanoparticles relative to undoped nanoparticles, further confirming weak coupling between Fe and Tb spins at room temperature. The Gilbert damping constant, α, is remarkably low for the Tb-doped nanoparticles, with α = 0.024 ± 0.003. These nanoparticles, which have a large fixed moment, a large fluctuating moment and optically active rare-earth elements, are potential high-relaxivity T1 and T2 MRI agents with integrated optical signatures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Predicting solid state material platforms for quantum technologies

Semiconductor materials provide a compelling platform for quantum technologies (QT). However, identifying promising material hosts among the plethora of candidates is a major challenge. Therefore, we have developed a framework for the automated discovery of semiconductor platforms for QT using material informatics and machine learning methods. Different approaches were implemented to label data for training the supervised machine learning (ML) algorithms logistic regression, decision trees, random forests and gradient boosting. We find that an empirical approach relying exclusively on findings from the literature yields a clear separation between predicted suitable and unsuitable candidates. In contrast to expectations from the literature focusing on band gap and ionic character as important properties for QT compatibility, the ML methods highlight features related to symmetry and crystal structure, including bond length, orientation and radial distribution, as influential when predicting a material as suitable for QT.

36 MATERIALS SCIENCE↗
DOE OSTI2023

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. Furthermore, we observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Structure and thermodynamics of core-softened models for alcohols

The phase behavior and the fluid structure of coarse-grain models for alcohols are studied by means of reference interaction site model (RISM) theory and Monte Carlo simulations. Specifically, we model ethanol and 1-propanol as linear rigid chains constituted by three (trimers) and four (tetramers) partially fused spheres, respectively. Thermodynamic properties of these models are examined in the RISM context, by employing closed formulæ for the calculation of free energy and pressure. Gas-liquid coexistence curves for trimers and tetramers are reported and compared with already existing data for a dimer model of methanol. Critical temperatures slightly increase with the number of CH{sub 2} groups in the chain, while critical pressures and densities decrease. Such a behavior qualitatively reproduces the trend observed in experiments on methanol, ethanol, and 1-propanol and suggests that our coarse-grain models, despite their simplicity, can reproduce the essential features of the phase behavior of such alcohols. The fluid structure of these models is investigated by computing radial distribution function g{sub ij}(r) and static structure factor S{sub ij}(k); the latter shows the presence of a low−k peak at intermediate-high packing fractions and low temperatures, suggesting the presence of aggregates for both trimers and tetramers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Structure and dynamics of short-chain polymerized ionic liquids

Combining experimental results obtained with X-ray scattering and field-gradient nuclear magnetic resonance (NMR) and an assessment of new and previous dielectric and rheology data, our study focuses on the molecular weight (M w ) evolution of local structure and dynamics in a homologous series of covalently bonded ionic liquids. Performed on a family of electrolytes with a tailored degree of ionic decoupling, this study reveals the differences between monomeric and oligomeric melts with respect to their structural organization, mass and charge transport, and molecular diffusion. Our work demonstrates that for the monomeric compound, the broadband conductivity and mechanical spectra reflect the same underlying distribution of activation barriers and that the Random Barrier Model describes fairly well both the ionic and structural relaxation processes in these materials. Moreover, the oligomers with chains comprising ten segments only exhibit both structural and dynamical fingerprints of a genuine polymer. A comparison of conductivity levels estimated using the self-diffusion coefficients probed via NMR and those probed directly with dielectric spectroscopy reflects the emerging of ion correlations which are affecting the macroscopic charge transport in these materials in a chain-length dependent manner.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Investigating the differential emission measure and energetics of microflares with combined SDO/AIA and RHESSI observations

An important question in solar physics is whether solar microflares, the smallest currently observable flare events in X-rays, possess the same energetic properties as large flares. Recent surveys have suggested that microflares may be less efficient particle accelerators than large flares, and hence contribute less non-thermal energy, which may have implications for coronal heating mechanisms. We therefore explore the energetic properties of microflares by combining EUV and X-ray measurements. We present forward-fitting differential emission measure (DEM) analysis of 10 microflares. The fitting is constrained by combining, for the first time, high-temperature Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) observations and flux data from the Solar Dynamics Observatory (SDO) Atmospheric Imaging Assembly (AIA). Two fitting models are tested for the DEM; a Gaussian distribution and a uniform DEM profile. A Gaussian fit proved unable to explain the observations for any of the studied microflares. However, 8 of 10 events studied were reasonably fit by a uniform DEM profile. Hence microflare plasma can be considered to be significantly multi-thermal, and may not be significantly peaked or contain resolvable fine structure, within the uncertainties of the observational instruments. The thermal and non-thermal energy is estimated for each microflare, comparing the energy budget with an isothermal plasma assumption. From the multi-thermal fits the minimum non-thermal energy content was found to average approximately 30% of the estimated thermal energy. By comparison, under an isothermal model the non-thermal and thermal energy estimates were generally comparable. Hence, multi-thermal plasma is an important consideration for solar microflares that substantially alters their thermal and non-thermal energy content.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Observation of Skewed Electromagnetic Wakefields in an Asymmetric Structure Driven by Flat Electron Bunches

Relativistic charged -particle beams that generate intense longitudinal fields in accelerating structures also inherently couple to transverse modes. The effects of this coupling may lead to beam breakup instability and thus must be countered to preserve beam quality in applications such as linear colliders. Beams with highly asymmetric transverse sizes (flat beams) have been shown to suppress the initial instability in slab -symmetric structures. However, as the coupling to transverse modes remains, this solution serves only to delay instability. In order to understand the hazards of transverse coupling in such a case, we describe here an experiment characterizing the transverse effects on a flat beam, traversing near a planar dielectric lined structure. Further, the measurements reveal the emergence of a previously unobserved skew-quadrupolelike interaction when the beam is canted transversely, which is not present when the flat beam travels parallel to the dielectric surface. We deploy a multipole field fitting algorithm to reconstruct the projected transverse wakefields from the data. We generate the effective kick vector map using a simple two -particle theoretical model, with particle -in -cell simulations used to provide further insight for realistic particle distributions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

On Distributed Model-Free Reinforcement Learning Control with Stability Guarantee

Distributed learning can enable scalable and effective decision making in numerous complex cyber-physical systems such as smart transportation, robotics swarm, power systems, etc. However, the stability of the system is usually not guaranteed in most existing learning paradigms; and this limitation can hinder the wide deployment of machine learning in decision making of safety-critical systems. This paper presents a stability guaranteed distributed reinforcement learning (SGDRL) framework for interconnected linear subsystems, without knowing the subsystem models. While the learning process requires data from a peer-to-peer (p2p) communication architecture, the control implementation of each subsystem is only based on its local state. The stability of the interconnected subsystems will be ensured by a diagonally dominant eigenvalue condition, which will then be used in a model-free RL algorithm to learn the feedback control gains. The RL algorithm structure follows an off-policy iterative framework, with interleaved policy evaluation and policy update steps. We numerically validate our theoretical results by performing simulations on four interconnected sub-systems.

Mukherjee, Sayak↗
DOE OSTI2018

Low-Dimensional Components of Flows With Large Free/Moving-Surface Motion

Flow systems with highly nonlinear free/moving surface motion are common in engineering applications, such as wave impact and fluid-structure interaction (FSI) problems. In order to reveal the dynamics of such flows, as well as provide a reduced-order modeling (ROM) for large-scale applications, we propose a proper orthogonal decomposition (POD) technique that couples the velocity flow field and the level-set function field, as well as a proper normalization for the snapshots data so that the low-dimensional components of the flow can be retrieved with a priori knowledge of equal distribution of the total variance between velocity and level-set function data. Through numerical examples of a sloshing problem and a water entry problem, we show that the low-dimensional components obtained provide an efficient and accurate approximation of the flow field. Moreover, we show that the velocity contour and orbits projected on the space of the reduced basis greatly facilitate understanding of the intrinsic dynamics of the flow systems.

Engineering↗
DOE OSTI2015

Neutron study of phospholipids 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-ethanolamine spray coating on titanium implants

Permanent implants made from titanium are widely used and successfully implemented in medicine to address problems related to orthopedic and oral disorders. However, implants that interact in all cases optimally and durably with bone tissue have yet to be developed. In this work, the authors suggest a phospholipids 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-ethanolamine (POPE) lipid coating to partially mimic the biological cell membrane. To improve the homogeneity of the POPE distribution on the metal surface, the lipids are applied by spray coating. It is shown that the spray coating leads to two types of multilamellar POPE structures. Our experimental results demonstrate that these coatings are stable in a liquid environment in the range of physiological temperatures due to the unique interbilayer interaction of POPE lipids. Additionally, the interaction of the POPE multilayer structure with human serum albumin is considered. A simultaneous analysis of the specular and off-specular data provides structural information necessary to assess the quality of the coating for future applications.

36 MATERIALS SCIENCE↗
DOE OSTI2019

EGS Collab Experiment 1: Microseismic Monitoring

The U.S. Department of Energy's Enhanced Geothermal System (EGS) Collab project aims to improve our understanding of hydraulic stimulations in crystalline rock for enhanced geothermal energy production through execution of intensely monitored meso-scale experiments. The first experiment is being performed at the 4850 ft level of the Sanford Underground Research Facility (SURF), approximately 1.5 km below the surface at Lead, South Dakota. Here we report on microseismic monitoring of repeated stimulation experiments and subsequent flow tests between two boreholes in the Poorman Formation. Stimulations were performed at several locations in the designated injection borehole at flow rates from 0.1 to 5 L/min over temporal durations from minutes to hours. Microseismic monitoring was performed using a dense 3D sensor array including two cemented hydrophone strings with 12 sensors at 1.75 m spacing accompanied by 18 3-C accelerometers, deployed in 6 monitoring boreholes, completely surrounding the stimulation region. Continuous records were obtained over a two-month period using a novel dual recording system consisting of a conventional 96 channel exploration seismograph and a high-performance 64 channel digitizer sampling sensors at 4 and 100 kHz respectively. Using a standard STA/LTA triggering algorithm, we detected thousands of microseismic events with recorded energy in a frequency range generally above 3 kHz and up to 40 kHz. The locations of these events are consistent with creation of a hydraulic fracture and additional reactivation of pre-existing structures. Using manual pick refinement and double-difference relocation we are able to track the fracture growth to high precision. We estimate the times and locations of the fracture intersecting a monitoring and the production borehole using microseismic events. They are in excellent agreement with independent measurements using distributed temperature sensing, in-situ strain observations and measurements of conductivity changes. This submission includes a microearthquake catalog, raw event files, a subset of the continuous microseismic monitoring data collected during stimulations and flow test activity on 05/22/2018, 05/23/2018, 05/24/2018, 05/25/2018, 06/25/2018, 07/19/2018, 07/20/2018, 12/7/2018, 12/20/2018, and 12/21/2018 (in binary format), and a binary file interpreter to read the continuous microseismic monitoring data. A Stanford Geothermal Workshop paper is also included to describe microseismic monitoring activities at SURF during these periods.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Nucleon Tomography through Deeply Virtual Compton Scattering @ CLAS and CLAS12

Deeply Virtual Compton Scattering (DVCS), in which the electron scatters from a parton inside a hadron and a high energy photon is produced as a result, is considered the golden channel for accessing Generalised Parton Distributions (GPDs), in a form of hadron tomography. One can interpret GPDs as functions relating the transverse position of partons to their longitudinal momentum and as such they carry sensitivity to the spin structure of the hadron. Extraction of GPDs is highly non-trivial and requires measurements of a range of complementary observables across large regions of phase space. Recently, a series of such measurements at Jefferson Lab (JLab), USA, have provided a wealth of new DVCS data in the valence quark regime of the nucleon, while the experimental programme with the JLab upgraded 11 GeV beam will greatly extend the dataset in its kinematic reach. This enables a global analysis of the data, dramatically constraining the extraction of GPDs and deepening our understanding of the three-dimensional structure of the nucleon.

Sokhan, Daria↗
DOE OSTI2024

Development of Lightweight Structural Materials with Improved Properties for Fission Batteries

The notion of a “fission battery” conveys a vision focused on realizing very simple “plug-and-play” nuclear systems that can be integrated into a variety of applications requiring affordable, reliable energy in the form of electricity and/or heat and function without operations and maintenance staff. Fission batteries require lightweight structural materials to increase their mobility, and the lightweight materials must demonstrate structural resilience under various conditions. The objective of this work is to develop lightweight structural material featuring a good combination of mechanical properties using advanced modeling and simulation together with an advanced additive manufacturing technique such as laser powder bed fusion. The preliminary results show that different lattice structures and types can be successfully meshed using nTopology software, and the lattice structure data can be successfully transformed to Multiphysics Object-Oriented Simulation Environment (MOOSE) input. Finite Element Analysis (FEA) displays that, at macro/engineering scale simulation, the weight saving design has an obvious effect on tensile behavior such as effective elastic modulus and yield stress. The novel approaches of this work are (1) development of optimized lattice structures for improved mechanical properties using advanced simulation and modeling techniques; and (2) model predictions of the mechanical properties (e.g., strength and stress distribution) of macroscopic materials in order to preliminarily select a lattice structure for additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization

By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850–near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: – How does the Earth system respond to forcing? – What are the origins and consequences of systematic model biases? – How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

High spatial resolution temperature profile measurements of solid-oxide fuel cells

Temperature gradients resulting from local electrochemical reactions, current distribution and geometry of gas flow channels in solid oxide fuel cells (SOFCs) create thermal stresses, localized thermophysical property gradients and uneven property evolution, contributing to SOFC degradation. This paper presents a new method to perform temperature measurements (up to 800°C) at high spatial resolutions to monitor the operation of SOFCs. Using femtosecond laser irradiation, distributed fiber sensors were hardened for high temperature environment applications. Distributed fiber sensors were embedded in interconnected plates using an additive manufacturing method to perform temperature measurements with 4-mm spatial resolution during the operation of a planar fuel cell. The measurement revealed the impact of various H 2 fuel concentrations and current loads have on temperature profiles of the SOFC tested. Temperature variation on the anode side was found to be less than 5°C, and 3°C on the cathode side. The measurements were compared to results from a multiphysics fuel cell performance model simulating similar conditions. These simulations predicted similar temperature gradients, indicating the experimental data obtained is reasonable. The model also predicts that the effect of the embedded sensor has on the local temperature will be minimal and that the gradient of temperature in the gas channels will be captured despite the separation between the sensor and the gas flow. Finally, the high spatial resolution data harnessed by these distributed fiber sensors provides experimental support for model-based design and optimization to improve the operational efficiency and longevity of solid oxide fuel cells and fuel cell assemblies.

25 ENERGY STORAGE↗
DOE OSTI2019

Computing three-point correlation function randoms counts without the randoms catalogue

ABSTRACT As we move towards future galaxy surveys, the three-point statistics will be increasingly leveraged to enhance the constraining power of the data on cosmological parameters. An essential part of the three-point function estimation is performing triplet counts of synthetic data points in random catalogues. Since triplet counting algorithms scale at best as $\mathcal {O}(N^2\log N)$ with the number of particles and the random catalogues are typically at least 50 times denser than the data; this tends to be by far the most time-consuming part of the measurements. Here, we present a simple method of computing the necessary triplet counts involving uniform random distributions through simple one-dimensional integrals. The method speeds up the computation of the three-point function by orders of magnitude, eliminating the need for random catalogues, with the simultaneous pair and triplet counting of the data points alone being sufficient.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Combinatorial Evaluation of Physical Feature Engineering, Classical Machine Learning, and Deep Learning Models for Synchrophasor Data at Scale

A major objective of the project was to train and evaluate the effectiveness of multiple event and anomaly detection, identification and classification deep temporal learning models for processing of real-time phasor measurement unit (PMU) data streams. A vast dataset, consisting of two years of phasor measurements from all three U.S. Interconnections, was curated and released by the Department of Energy (DOE) through Pacific Northwest National Laboratory (PNNL). The dataset also included an event log that provided event times and types (e.g. generator trips, line trips, planned service events, transformer operations, etc.). Our analysis of this dataset addressed six (6) of the eleven (11) research priorities identified in Funding Opportunity Announcement (FOA) DE-FOA-0001861 “Big Data Analysis of Synchrophasor Data” (FOA 1861). Rather than being limited to pre-determined specific algorithms, this project relied on the uniquely structured, highly performant underlying time series database capabilities of the PredictiveGrid platform to assess the vast dataset utilizing a wide variety of algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗