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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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NASA NTRS2019

Covariance Realism Is Not Enough

A great deal of effort has been put into improving the practice of space situational awareness such that covariance data associated with predicted close approaches is more “realistic.” However, “realistic” usually has meant “larger” and this presents a problem. In many cases, there exist multiple sources for predictive ephemerides, which may be fused to produce predictive states with smaller associated covariances. Ancillary to the fusion computation is the capability to assess consistency of the estimates. If actionable covariance information becomes available, interval estimates for the miss distance provide a more informative alternative to collision probability for risk assessment.

Carpenter, J. Russell↗

DOE OSTI2018

Structural Studies of Fucosylated N-Glycans by Ion Mobility Mass Spectrometry and Collision-Induced Fragmentation of Negative Ions

There is considerable potential for the use of ion mobility mass spectrometry in structural glycobiology due in large part to the gas-phase separation attributes not typically observed by orthogonal methods. Here, we evaluate the capability of traveling wave ion mobility combined with negative ion collision-induced dissociation to provide structural information on N-linked glycans containing multiple fucose residues forming the Lewis{sup x} and Lewis{sup y} epitopes. These epitopes are involved in processes such as cell-cell recognition and are important as cancer biomarkers. Specific information that could be obtained from the intact N-glycans by negative ion CID included the general topology of the glycan such as the presence or absence of a bisecting GlcNAc residue and the branching pattern of the triantennary glycans. Information on the location of the fucose residues was also readily obtainable from ions specific to each antenna. Some isobaric fragment ions produced prior to ion mobility could subsequently be separated and, in some cases, provided additional valuable structural information that was missing from the CID spectra alone. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

Exploring the decision-space for renewable energy generation to enhance spatial efficiency

Decentralized power generation can play a significant role in contributing to renewable energy (RE) supply. Accordingly, regions can be important players in the transformation of the energy system. However, only scarce spatial capacities are available for a sustainable RE generation. Knowledge gaps exist concerning data and methods for integrating RE assessment and environmental planning methods. This paper presents a methodology for the integrated assessment of different RE potentials and their land requirements. The potential is contrasted with the actual availability of land for RE-generation considering environmental restrictions. An application in the Hanover region demonstrates that using energy capacity maps supports using the most efficient mixture in RE generation. Generally, a combination of wind and solar energies produces the highest energy yield per ha. Furthermore, relying primarily on generalized environmental restrictions for defining exclusion areas can be only a first step: On the one hand the assessment is probably underrating the potential for environmentally sound RE generation due to the undifferentiated exclusion of large areas of a certain protective status. On the other hand, some energy sources, in particular energy crops for bioenergy generation, are malpositioned due to missing spatially explicit information about ecosystem sensitivities and a lack of regulative possibilities. Further research is needed to explore synergistic combinations of energy potentials and their environmentally sound spatial allocation in more detail. - Highlights: • We combine models of RE potentials with technical and environmental restrictions. • We explore the resulting “decision-spaces” for regional RE development. • “Decision-spaces” are small and need to be developed carefully to avoid trade-offs. • Planning needs more differentiated analyses to test RE allocation-scenarios. • We recommend detailed analyses on (cumulative) impacts of (multifunctional) RE use.

14 SOLAR ENERGY↗

DOE OSTI2024

Underground hydrogen storage leakage detection and characterization based on machine learning of sparse seismic data

Underground hydrogen storage (UHS) is considered as a scalable approach for massive storage and seasonal extraction of hydrogen (H 2 ). Although conventional leakage detection and characterization methods based on time-lapse seismic imaging and inversion generally apply to H 2 leakage detection problem, a high-fidelity yet cost effective geophysics approach is still missing to reliably inform leakage location and properties based on very sparse data. In response, we develop a novel supervised machine learning method to detect and characterize H 2 leakage from UHS. The input to our neural network are sparse time-lapse seismic waveforms, while the output from the neural network includes the spatial location and physical properties of a H 2 leakage. Here, we generate high-quality time-lapse waveforms using the elastic-wave equations to train the neural network. We train and validate our machine learning model and find that it attains high accuracy in using extremely sparse time-lapse seismic data to detect and characterize H 2 leakage. Our investigation is the first systematic study that focuses on applying machine learning to subsurface H 2 leakage detection and characterization and could potentially serve as a cost-effective geophysical tool for underground hydrogen leakage detection and characterization with high fidelity.

08 HYDROGEN↗

DOE OSTI2017

A Simple Preparation Method for Full-Range Electron Tomography of Nanoparticles and Fine Powders

Abstract Electron tomography has become a valuable and widely used tool for studying the three-dimensional nanostructure of materials and biological specimens. However, the incomplete tilt range provided by conventional sample holders limits the fidelity and quantitative interpretability of tomographic images by leaving a “missing wedge” of unknown information in Fourier space. Imaging over a complete range of angles eliminates missing wedge artifacts and dramatically improves tomogram quality. Full-range tomography is usually accomplished using needle-shaped samples milled from bulk material with focused ion beams, but versatile specimen preparation methods for nanoparticles and other fine powders are lacking. In this work, we present a new preparation technique in which powder specimens are supported on carbon nanofibers that extend beyond the end of a tungsten needle. Using this approach, we produced tomograms of platinum fuel cell catalysts and gold-decorated strontium titanate photocatalyst specimens. Without the missing wedge, these tomograms are free from elongation artifacts, supporting straightforward automatic segmentation and quantitative analysis of key materials properties such as void size and connectivity, and surface area and curvature. This approach may be generalized to other samples that can be dispersed in liquids, such as biological structures, creating new opportunities for high-quality electron tomography across disciplines.

Materials Science↗

DOE OSTI2015

X-ray structure determination using low-resolution electron microscopy maps for molecular replacement

Structures of multi-subunit macromolecular machines are primarily determined by either electron microscopy (EM) or X-ray crystallography. In many cases, a structure for a complex can be obtained at low resolution (at a coarse level of detail) with EM and at higher resolution (with finer detail) by X-ray crystallography. The integration of these two structural techniques is becoming increasingly important for generating atomic models of macromolecular complexes. A low-resolution EM image can be a powerful tool for obtaining the "phase" information that is missing from an X-ray crystallography experiment, however integration of EM and X-ray diffraction data has been technically challenging. Here we show a step-by-step protocol that explains how low-resolution EM maps can be placed in the crystallographic unit cell by molecular replacement, and how initial phases computed from the placed EM density are extended to high resolution by averaging maps over non-crystallographic symmetry. As the resolution gap between EM and Xray crystallography continues to narrow, the use of EM maps to help with X-ray crystal structure determination, as described in this protocol, will become increasingly effective.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Structural basis for TNA synthesis by an engineered TNA polymerase

Darwinian evolution experiments carried out on xeno-nucleic acid (XNA) polymers require engineered polymerases that can faithfully and efficiently copy genetic information back and forth between DNA and XNA. However, current XNA polymerases function with inferior activity relative to their natural counterparts. Here, we report five X-ray crystal structures that illustrate the pathway by which α-(L)-threofuranosyl nucleic acid (TNA) triphosphates are selected and extended in a template-dependent manner using a laboratory-evolved polymerase known as Kod-RI. Structural comparison of the apo, binary, open and closed ternary, and translocated product detail an ensemble of interactions and conformational changes required to promote TNA synthesis. Close inspection of the active site in the closed ternary structure reveals a sub-optimal binding geometry that explains the slow rate of catalysis. This key piece of information, which is missing for all naturally occurring archaeal DNA polymerases, provides a framework for engineering new TNA polymerase variants.

Science & Technology - Other Topics↗

DOE OSTI2023

Implicit learning of convective organization explains precipitation stochasticity

Accurate prediction of precipitation intensity is crucial for both human and natural systems, especially in a warming climate more prone to extreme precipitation. Yet, climate models fail to accurately predict precipitation intensity, particularly extremes. One missing piece of information in traditional climate model parameterizations is subgrid-scale cloud structure and organization, which affects precipitation intensity and stochasticity at coarse resolution. Here, using global storm-resolving simulations and machine learning, we show that, by implicitly learning subgrid organization, we can accurately predict precipitation variability and stochasticity with a low-dimensional set of latent variables. Using a neural network to parameterize coarse-grained precipitation, we find that the overall behavior of precipitation is reasonably predictable using large-scale quantities only; however, the neural network cannot predict the variability of precipitation (R 2 ~ 0.45) and underestimates precipitation extremes. The performance is significantly improved when the network is informed by our organization metric, correctly predicting precipitation extremes and spatial variability (R 2 ~ 0.9). The organization metric is implicitly learned by training the algorithm on a high-resolution precipitable water field, encoding the degree of subgrid organization. The organization metric shows large hysteresis, emphasizing the role of memory created by subgrid-scale structures. We demonstrate that this organization metric can be predicted as a simple memory process from information available at the previous time steps. These findings stress the role of organization and memory in accurate prediction of precipitation intensity and extremes and the necessity of parameterizing subgrid-scale convective organization in climate models to better project future changes of water cycle and extremes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

THE KINEMATICS OF MULTIPLE-PEAKED Ly{alpha} EMISSION IN STAR-FORMING GALAXIES AT z {approx} 2-3

We present new results on the Ly{alpha} emission-line kinematics of 18 z {approx} 2-3 star-forming galaxies with multiple-peaked Ly{alpha} profiles. With our large spectroscopic database of UV-selected star-forming galaxies at these redshifts, we have determined that {approx}30% of such objects with detectable Ly{alpha} emission display multiple-peaked emission profiles. These profiles provide additional constraints on the escape of Ly{alpha} photons due to the rich velocity structure in the emergent line. Despite recent advances in modeling the escape of Ly{alpha} from star-forming galaxies at high redshifts, comparisons between models and data are often missing crucial observational information. Using Keck II NIRSPEC spectra of H{alpha} (z {approx} 2) and [O III]{lambda}5007 (z {approx} 3), we have measured accurate systemic redshifts, rest-frame optical nebular velocity dispersions, and emission-line fluxes for the objects in the sample. In addition, rest-frame UV luminosities and colors provide estimates of star formation rates and the degree of dust extinction. In concert with the profile sub-structure, these measurements provide critical constraints on the geometry and kinematics of interstellar gas in high-redshift galaxies. Accurate systemic redshifts allow us to translate the multiple-peaked Ly{alpha} profiles into velocity space, revealing that the majority (11/18) display double-peaked emission straddling the velocity-field zero point with stronger red-side emission. Interstellar absorption-line kinematics suggest the presence of large-scale outflows for the majority of objects in our sample, with an average measured interstellar absorption velocity offset of ({Delta}v{sub abs}) = -230 km s{sup -1}. A comparison of the interstellar absorption kinematics for objects with multiple- and single-peaked Ly{alpha} profiles indicate that the multiple-peaked objects are characterized by significantly narrower absorption line widths. We compare our data with the predictions of simple models for outflowing and infalling gas distributions around high-redshift galaxies. While popular 'shell' models provide a qualitative match with many of the observations of Ly{alpha} emission, we find that in detail there are important discrepancies between the models and data, as well as problems with applying the framework of an expanding thin shell of gas to explain high-redshift galaxy spectra. Our data highlight these inconsistencies, as well as illuminating critical elements for success in future models of outflow and infall in high-redshift galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2012

WAVELET ANALYSIS AS A TOOL TO LOCALIZE MAGNETIC AND CROSS-HELICITY EVENTS IN THE SOLAR WIND

In this paper, we adopt the use of the wavelet transform as a new tool to investigate the time behavior at different scales of reduced magnetic helicity, cross-helicity, and residual energy in space plasmas. The main goal is a better characterization of the fluctuations in which interplanetary flux ropes are embedded. This kind of information is still missing in the present literature, and our tool can represent the basis for a new treatment of in situ measurements of this kind of event. There is a debate about the origins of small-scale flux ropes. It has been suggested that they are formed through magnetic reconnection in the solar wind, such as across the heliospheric current sheet. On the other hand, it has also been suggested that they are formed in the corona, similar to magnetic clouds. Thus, it looks like that there are two populations, one originating in the solar wind via magnetic reconnection across the current sheet in the inner heliosphere and the other originating in the corona. Small-scale flux ropes might be the remnants of the streamer belt blobs formed from disconnection; however, a one-to-one observation of a blob and a small-scale flux rope in the solar wind has yet to be found. Within this panorama of possibilities, this new technique appears to be very promising in investigating the origins of these objects advected by the solar wind.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Multidimensional modeling of non-equilibrium plasma generated by a radio-frequency corona discharge

Low-temperature plasma (LTP) ignition concepts rely on the production of radical and charged species to speed up the onset of combustion in spark-ignition engines. These features are responsible for the superior performance of LTP igniters under extremely dilute combustion operation that is not achievable by conventional spark igniters. Additionally, LTP discharges extend the lifetime of the igniters, due to the avoidance of spark processes. For these reasons, the engine research community and the automotive industry have shown growing interest in this technology in the recent years. As of today, computational fluid-dynamics (CFD) codes typically used by the multi-dimensional engine modeling community do not have reliable models to describe LTP ignition processes. One key missing piece of information is the physical and chemical properties of the plasma and their effect on combustion ignition. Most non-equilibrium plasma simulations reported in literature are based on simplified, canonical geometries, with simple discharge excitation schemes. Here we conduct multi-dimensional modeling of the non-equilibrium plasma generated by an application-relevant radio-frequency (RF) corona discharge in air. Three test cases are simulated, characterized by different environmental pressure levels and peak electrode voltage values at room temperature. Streamer penetration, electron number density, atomic oxygen production, and bulk gas temperature distribution in the first 10 sinusoidal pulses are presented and discussed. This model can be used as a key tool for an in-depth understanding of RF-corona discharge for automotive applications and provides the basis for future implementations of dedicated LTP ignition models in CFD codes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Electronic states dressed by an out-of-plane supermodulation in the quasi-two-dimensional kagome superconductor CsV 3 Sb 5

CsV 3 Sb 5 has attracted much recent attention as the first quasi-two-dimensional (2D) kagome superconductor. While the kagome layers are 2D in nature, increasing evidence has pointed to the importance of out-of-plane correlation in this material. However, it remains unclear whether such correlation can change the fundamental electronic structure of the quasi-2D system. Here, we reveal this missing piece of information, using angle-resolved photoemission spectroscopy, complemented by scanning tunneling microscope measurements. The three-dimensional electronic structures in the high-temperature state are revealed, which agree well with density-functional theory calculations. Electron energy bands are observed in the low-temperature state that exhibit additional periodicities along the out-of-plane momentum. Furthermore, these results reveal a direct response to the out-of-plane electronic supermodulation in the single-particle spectral function of CsV 3 Sb 5 , thus establishing an electronic platform to examine emergent phenomena beyond 2D limit in kagome superconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Mapping the 3D position of battery cathode particles in Bragg coherent diffractive imaging

In Bragg coherent diffractive imaging, the precise location of the measured crystals in the interior of the sample is usually missing. Obtaining this information would help the study of the spatially dependent behavior of particles in the bulk of inhomogeneous samples, such as extra-thick battery cathodes. This work presents an approach to determine the 3D position of particles by precisely aligning them at the instrument axis of rotation. In the test experiment reported here, with a 60 µm-thick LiNi 0.5 Mn 1.5 O 4 battery cathode, the particles were located with a precision of 20 µm in the out-of-plane direction, and the in-plane coordinates were determined with a precision of 1 µm.

25 ENERGY STORAGE↗

DOE OSTI2013

Planning Document for an NBSR Conversion Safety Analysis Report

The NIST Center for Neutron Research (NCNR) is a reactor-laboratory complex providing the National Institute of Standards and Technology (NIST) and the nation with a world-class facility for the performance of neutron-based research. The heart of this facility is the National Bureau of Standards Reactor (NBSR). The NBSR is a heavy water moderated and cooled reactor operating at 20 MW. It is fueled with high-enriched uranium (HEU) fuel elements. A Global Threat Reduction Initiative (GTRI) program is underway to convert the reactor to low-enriched uranium (LEU) fuel. This program includes the qualification of the proposed fuel, uranium and molybdenum alloy foil clad in an aluminum alloy, and the development of the fabrication techniques. This report is a planning document for the conversion Safety Analysis Report (SAR) that would be submitted to, and approved by, the Nuclear Regulatory Commission (NRC) before the reactor could be converted. This report follows the recommended format and content from the NRC codified in NUREG-1537, “Guidelines for Preparing and Reviewing Applications for the Licensing of Non-power Reactors,” Chapter 18, “Highly Enriched to Low-Enriched Uranium Conversions.” The emphasis herein is on the SAR chapters that require significant changes as a result of conversion, primarily Chapter 4, Reactor Description, and Chapter 13, Safety Analysis. The document provides information on the proposed design for the LEU fuel elements and identifies what information is still missing. This document is intended to assist ongoing fuel development efforts, and to provide a platform for the development of the final conversion SAR. This report contributes directly to the reactor conversion pillar of the GTRI program, but also acts as a boundary condition for the fuel development and fuel fabrication pillars.

42 ENGINEERING↗

DOE OSTI2012

Facility Measurements

Measurements are the basis of accounting for inventories and transfers and provide assurance that no nuclear material is missing. They provide information on quantities, isotopic composition and weight percent determination of nuclear material in given locations which is essential to determining category levels and protection requirements. Los Alamos National Laboratory (LANL) uses confirmatory and verification measurements to meet DOE requirements when moving or processing material on External Receipts, External Shipments, Internal Transfers, Processing, and Physical Inventories. This presentation provides details on what type of measurement is required based on several factors. These factors include but are not limited to Category of the Material, application of Tamper Indicating Devices (TID), application of the measurement, and graded safeguards. These elements are part of the overall MC&A program required to effectively control and account for nuclear materials. LANL depends on multiple organizations to take the measurements and monitor the controls of instruments required to have an effective program. The MC&A group does oversight and control of all standards and instruments. The NDA group manages the day-to-day measurement control activities and does NDA measurements while the operations people use balances in their day-to-day processing. Confirmatory and Verification measurements are used throughout the nuclear material life cycle to verify inventories and detect theft or diversion of nuclear materials.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2016

The Neutrino: A Better Understanding Through Astrophysics: Final Report

The final report for the award "The Neutrino: A Better Understanding Through Astrophysics" is given. The goals of the work were the following: to construct new theoretical approaches to the problem of neutrino propagation in media including where neutrino-neutrino interactions are important; to pioneer the use of new approaches, including super-scattering operators, for the evolution of neutrino thermal and statistical ensembles; to implement these new approaches in computer codes to study neutrino evolution in supernovae and other hot, dense environments; to increase the realism of simulated signals of a Galactic supernovae neutrino burst in current and future neutrino detectors; to study the simulated signals to determine the ability to extract information on the missing neutrino mixing parameters and the dynamics of the supernova explosion; and to study sterile neutrinos and non-standard interactions of neutrinos in supernovae and their effect upon the signal. Accomplishments made in these areas are described.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Assessing Cognitive Impacts of Errors from Machine Learning and Deep Learning Models: Final Report

Due to their recent increases in performance, machine learning and deep learning models are being increasingly adopted across many domains for visual processing tasks. One such domain is international nuclear safeguards, which seeks to verify the peaceful use of commercial nuclear energy across the globe. Despite recent impressive performance results from machine learning and deep learning algorithms, there is always at least some small level of error. Given the significant consequences of international nuclear safeguards conclusions, we sought to characterize how incorrect responses from a machine or deep learning-assisted visual search task would cognitively impact users. We found that not only do some types of model errors have larger negative impacts on human performance than other errors, the scale of those impacts change depending on the accuracy of the model with which they are presented and they persist in scenarios of evenly distributed errors and single-error presentations. Further, we found that experiments conducted using a common visual search dataset from the psychology community has similar implications to a safeguards- relevant dataset of images containing hyperboloid cooling towers when the cooling tower images are presented to expert participants. While novice performance was considerably different (and worse) on the cooling tower task, we saw increased novice reliance on the most challenging cooling tower images compared to experts. These findings are relevant not just to the cognitive science community, but also for developers of machine and deep learning that will be implemented in multiple domains. For safeguards, this research provides key insights into how machine and deep learning projects should be implemented considering their special requirements that information not be missed.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Application of NEAMS Multiphysics Framework for Species Tracking in Molten Salt Reactors

This report from Idaho National Laboratory (INL) summarizes the key modeling and simulation activities conducted under the Department of Energy (DOE) Molten Salt Reactor (MSR) Campaign during the Fiscal Year 2023 (FY23). The focus of the work was to leverage state-of-the-art modeling capabilities from the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) codes to enable novel multiphysics and multiscale modeling and simulation of MSRs. Through collaboration with NEAMS code developers, advanced multiphysics analysis capabilities for MSR systems were demonstrated by coupling depletion, thermal-hydraulics, and thermochemistry into an innovative framework for chemical species transport in MSRs. As a result, the framework can track nuclides throughout their lifetimes in the core, from production (depletion) to advection throughout the salt volume (thermal-hydraulics) and off-gassing or precipitation outside of the salt (thermochemistry). This work supports the near-term deployment of MSRs by integrating the synergistic efforts between the DOE’s MSR Campaign and NEAMS program. The resulting framework will help better connect system design modelers with experimentalists to better understand and predict complex physical behaviors in MSRs. Researchers and MSR developers alike can now leverage these new modeling and simulation capabilities to perform novel analyses with applications including: • MSR dynamics during normal operational transients and accident scenarios • Off-gas system design and performance for fuel cycle and depletion analysis • Corrosion and active chemistry control for reactor component health and lifetime determination • Source term, decay heat and activity determination in accident scenarios • Special nuclear material accountancy and chemical forensic analysis for safeguards • Digital twin development of experiments and experimental reactor demonstrations • Measurement requirements for instrumentation and control design • Uncertainty and sensitivity analysis of missing data to inform future experimental data collection.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗