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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Dynamic recrystallization of a wrought magnesium alloy: Grain size and texture maps and their application for mechanical behavior predictions

Effect of dynamic recrystallization (DRX) on the grain refinement and texture modification was studied by conducting a series of hot compression on AZ31B Mg alloy. Processing-grain size-texture maps were established as a function of temperature, strain rate, strain, and the Zener-Hollomon parameter (Z). Moreover, influence of simultaneous changes in the grain size and texture on tensile yield strength and ductility of the hot-worked Mg alloy was studied using the grain size and texture maps established. The processing-grain size map showed that the DRX grain size decreased with the increase in Z, also revealing various characteristics ranging from a grain growth, grain refinement, to a bimodal distribution of ultrafine grains and partially recrystallized grains. The effect of twinning on the grain refinement was also evident at high Z conditions. The processing-texture map revealed that the initial fiber texture was altered significantly to a shear, off-normal, or extension-twin texture with the increase in Z. The Schmid-factor maps were calculated to identify a dominant deformation mode during a subsequent tensile deformation of the hot-worked samples. Finally, a corresponding Hall-Petch relationship for each dominant deformation mode was used to establish tensile yield-strength maps, which agree well with the measured data.

36 MATERIALS SCIENCE↗
DOE OSTI2023

malbacR: A Package for Standardized Implementation of Batch Correction Methods for Omics Data

Mass spectrometry is a powerful tool for identifying and analyzing small molecules, such as metabolites and lipids, in com-plex biological samples. Liquid chromatography and gas chromatography mass spectrometry studies quite commonly in-volve large numbers of samples, which can require significant time for sample preparation and analyses. To accommodate such studies, the samples are commonly split into batches. Inevitably, variations in sample handling, temperature fluctua-tion, imprecise timing, column degradation and other factors result in systematic errors or biases of the measured abundances between the batches. Numerous methods are available via R packages to assist with batch correction for small molecule om-ics data; however, since these methods were developed by different research teams, the algorithms are available in separate R packages, each with different data input and output formats. We introduce the malbacR package which consolidates eleven common batch effect correction methods for small molecule omics data into one place so users can easily implement and compare: pareto scaling, power scaling, range scaling, ComBat, EigenMS, NOMIS, RUV-random, QC-RLSC, WaveI-CA2.0, TIGER, and SERRF. The malbacR package standardizes data input and output formats across these batch correction methods. The package works in conjunction with the pmartR package, allowing users to seamlessly include batch effect cor-rection in a pmartR workflow without needing any additional data manipulation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Autonomous organic synthesis for redox flow batteries via flexible batch Bayesian optimization

Traditional trial-and-error methods for materials discovery are inefficient to meet the urgent demands posed by the rapid progression of climate change. This urgency has driven the increasing interest in integrating robotics and machine learning into materials research to accelerate experimental learning. However, idealized decision-making frameworks to achieve maximum sampling efficiency are not always compatible with high-throughput experimental workflows inside a laboratory. For multi-step chemical processes, differences in hardware capacities can complicate the digital framework by introducing constraints on the maximum number of samples in each step of the experiment, hence causing varying batch sizes in variable selection within the same batch. Therefore, designing flexible sampling algorithms is necessary to accommodate the multi-step synthesis with practical constraints unique to each high-throughput workflow. In this work, we designed and employed three strategies on a high-throughput robotic platform to optimize the sulfonation reaction of redox-active molecules used in flow batteries. Our strategies adapt to the multi-step experimental workflow, where their formulation and heating steps are separate, causing varying batch size requirements. By strategically sampling using clustering and mixed-variable batch Bayesian optimization, we were able to iteratively identify optimal conditions that maximize the yields. Our work presents a flexible approach that allows tailoring the machine learning decision-making to suit the practical constraints in individual high-throughput experimental platforms, followed by performing resource-efficient yield optimization using available open-source Python libraries.

Tamura, Clara [Univ. of Washington, Seattle, WA (U↗
DOE OSTI2020

Magnesium abundances in cool metal-polluted white dwarfs

ABSTRACT The accretion of rocky material is responsible for the presence of heavy elements in the atmospheres of a large fraction of white dwarf stars. Those objects represent a unique opportunity to infer the bulk composition of exoplanetesimals. This chemical characterization requires the use of detailed atmosphere models to determine the elemental abundances at the photospheres of white dwarfs. In this work, we use a state-of-the-art model atmosphere code to reanalyse the first large survey of metal-polluted white dwarfs for which abundances are found for multiple elements. We show that the improved constitutive physics of our models lead to systematically higher Mg abundances than previous analyses. We find an average $\log \, {\rm Mg/Ca}$ number abundance ratio of 1.5. This value is significantly above the reference abundance for chondrites, which is expected as current diffusion models predict that for the cool helium-atmosphere white dwarfs of our sample, Mg should remain in the atmosphere longer than Ca. This helps resolve a recently identified Mg depletion problem, where the planetesimals accreted by white dwarfs were reported to be Mg-deficient compared to the expected composition of their planetary systems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Four new deeply eclipsing white dwarfs in Zwicky Transient Facility

ABSTRACT We present the results of a search for deeply eclipsing white dwarfs in the Zwicky Transient Facility (ZTF) Data Release 4 (DR4). We identify nine deeply eclipsing white dwarf candidates, four of which we followed up with high-cadence photometry and spectroscopy. Three of these systems show total eclipses in the ZTF data and our follow-up Apache Point Observatory 3.5 m telescope observations. Even though the eclipse duration is consistent with sub-stellar companions, our analysis shows that all four systems contain a white dwarf with low-mass stellar companions of ∼0.1 M⊙. We provide mass and radius constraints for both stars in each system based on our photometric and spectroscopic fitting. Finally, we present a list of 41 additional eclipsing WD+M candidates identified in a preliminary search of ZTF DR7, including 12 previously studied systems. We identify two new candidate short-period, eclipsing, white dwarf–brown dwarf binaries within our sample of 41 WD+M candidates based on Pan-STARRS colours.

Kosakowski, A. (ORCID:0000000298781647)↗
DOE OSTI2020

First Results on Dark Matter Substructure from Astrometric Weak Lensing

Low-mass structures of dark matter (DM) are expected to be entirely devoid of light-emitting regions and baryons. Precisely because of this lack of baryonic feedback, small-scale substructures of the Milky Way are a relatively pristine testing ground for discovering aspects of DM microphysics and primordial fluctuations on subgalactic scales. In this work, we report results from the first search for Galactic DM subhalos with time-domain astrometric weak gravitational lensing. The analysis is based on a matched-filter template of local lensing corrections to the proper motion of stars in the Magellanic Clouds. We describe a data analysis pipeline detailing sample selection, background subtraction, and handling outliers and other systematics. For tentative candidate lenses, we identify a signature based on an anomalous parallax template that can unequivocally confirm the presence of a DM lens, opening up prospects for robust discovery potential with full time-series data. We present our constraints on substructure fraction f l ≲5 at 90% CL (and f l ≲2 at 50% CL) for compact lenses with radii r l <1pc, with best sensitivity reached for lens masses M l around 10 7 -10 8 M ⊙ . Parametric improvements are expected with future astrometric data sets; by end of mission, Gaia could reach f l ≲10 -3 for these massive point-like objects, and be sensitive to lighter and/or more extended subhalos for O(1) substructure fractions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Data-driven Minimum Entropy Control for Stochastic Nonlinear Systems using the Cumulant-Generating Function

Here, we present a novel minimum entropy control algorithm for a class of stochastic nonlinear systems subjected to non-Gaussian noises. The entropy control can be considered as an optimization problem for the system randomness attenuation, but the mean value has to be considered separately. To overcome this disadvantage, a new representation of the system stochastic properties was given using the cumulant-generating function based on the moment-generating function, in which the mean value and the entropy was reflected by the shape of the cumulant-generating function. Based on the samples of the system output and control input, a time-variant linear model was identified, and the minimum entropy optimization was transformed to system stabilization. Then, an optimal control strategy was developed to achieve the randomness attenuation, and the boundedness of the controlled system output was analyzed. The effectiveness of the presented control algorithm was demonstrated by a numerical example. In this paper, a data-driven minimum entropy design is presented without pre-knowledge of the system model; entropy optimization is achieved by the system stabilization approach in which the stochastic distribution control and minimum entropy are unified using the same identified structure; and a potential framework is obtained since all the existing system stabilization methods can be adopted to achieve the minimum entropy objective.

42 ENGINEERING↗
DOE OSTI2023

Phylogeography of the blacklegged tick ( Ixodes scapularis ) throughout the USA identifies candidate loci for differences in vectorial capacity

Abstract The blacklegged tick ( Ixodes scapularis ( Journal of the Academy of Natural Sciences of Philadelphia , 1821, 2 , 59)) is a vector of Borrelia burgdorferi sensu stricto ( s.s .) ( International Journal of Systematic Bacteriology , 1984, 34 , 496), the causative bacterial agent of Lyme disease, part of a slow‐moving epidemic of Lyme borreliosis spreading across the northern hemisphere. Well‐known geographical differences in the vectorial capacity of these ticks are associated with genetic variation. Despite the need for detailed genetic information in this disease system, previous phylogeographical studies of these ticks have been restricted to relatively few populations or few genetic loci. Here we present the most comprehensive phylogeographical study of genome‐wide markers in I. scapularis , conducted by using 3RAD (triple‐enzyme restriction‐site associated sequencing) and surveying 353 ticks from 33 counties throughout the species' range. We found limited genetic variation among populations from the Northeast and Upper Midwest, where Lyme disease is most common, and higher genetic variation among populations from the South. We identify five spatially associated genetic clusters of I. scapularis . In regions where Lyme disease is increasing in frequency, the I. scapularis populations genetically group with ticks from historically highly Lyme‐endemic regions. Finally, we identify 10 variable DNA sites that contribute the most to population differentiation. These variable sites cluster on one of the chromosome‐scale scaffolds for I. scapularis and are within identified genes. Our findings illuminate the need for additional research to identify loci causing variation in the vectorial capacity of I. scapularis and where additional tick sampling would be most valuable to further understand disease trends caused by pathogens transmitted by I. scapularis .

3RAD↗
DOE OSTI2024

Human limits in machine learning: prediction of potato yield and disease using soil microbiome data

Abstract Background The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide one of the first comprehensive investigations into the predictive potential of machine learning models for understanding the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant performance from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network. Results Prediction improves when we add environmental features, such as soil properties and microbial density, along with microbiome data. Different preprocessing strategies show that human decisions significantly impact predictive performance. We show that the naive total sum scaling normalization that is commonly used in microbiome research is one of the optimal strategies to maximize predictive power. Also, we find that accurately defined labels are more important than normalization, taxonomic level, or model characteristics. ML performance is limited when humans can’t classify samples accurately. Lastly, we provide domain scientists via a full model selection decision tree to identify the human choices that optimize model prediction power. Conclusions Our study highlights the importance of incorporating diverse environmental features and careful data preprocessing in enhancing the predictive power of machine learning models for soil and biological phenotype connections. This approach can significantly contribute to advancing agricultural practices and soil health management.

Aghdam, Rosa↗
DOE OSTI2026

2008 East Tennessee Household Travel Survey

The 2008 East Tennessee Household Travel Survey, conducted by NuStats, was a comprehensive study of travel behavior in Knox, Blount, Anderson, Jefferson, Loudon, Roane, Sevier, and Union counties. The purpose of the survey was to collect weekday travel characteristics of households in the eight-county region during a 24-hour timeframe. The data will be used by the Knoxville Regional Transportation Planning Organization and local agencies to update transportation and air quality models and to identify transportation needs in the region. The survey collected demographic and travel data from a sample of 1,400 households between February and May 2008. In total, 12,012 trips were reported by 3,301 persons.

1Hz data↗
DOE OSTI2020

FY20 Status Report on Creep Rupture Testing to Support the Development of Alloy 709 Code Case

This report provides an update on the status of the creep rupture testing on the first commercial heat of Alloy 709 in support of the Alloy 709 Code Case development. A creep test laboratory has been set up at ANL that comprises of 15 indirectly loaded test frames capable of testing ASTM-size specimens of the commercial heat of Alloy 709. The systems are equipped with high temperature furnaces capable of operation up to 1000°C in an air environment. During FY18, uniaxial ASTM-sized creep specimens were fabricated from 9 Alloy 709 plates that were processed by AOD, ESR, and ESR-homogenized routes and subsequently solution annealed at 1050, 1100, and 1150°C, and creep-rupture tests were performed at 330MPa and 600°C. The test results led to the down selection of the ESR-1100°C condition to support the code case. In FY19, 15 creep-rupture tests of the ESR-1100°C material, requiring the use of Type K thermocouples, were initiated to support the 100,000-hour Code Case. 8 tests were completed in FY19. In FY20, 6 tests were completed at the time of writing this report. In addition, 1 more test were loaded in FY20, using Type S thermocouples, and was completed. This report presents the data from the completed tests in FY20 and also the microstructural characterization of the fractured specimens. The creep data enabled the preliminary analysis of the creep behavior of this material. The characterization showed that in brittle fractures, the pores were narrow and thin, and elongated in the direction perpendicular to the loading direction, while in ductile fractures, the pores were much rounder. Dense precipitates were observed in all the samples, the size of which grew with test temperature. Some of the precipitates could be identified to be M23C6 based on their cuboid shape.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Intelligent Process Visualization through Nuclear Operation Process Modeling, Reasoning, and Object Detection from Field Videos (Final Report)

This report is a deliverable for the “Final Report” task of DOE NEET Project 19-16790, "Context-Aware Safety Information Display for Nuclear Field Workers." This project's overall goal is to test the hypothesis that integrating computer vision and process reasoning methods will enable proactive visualization of the safe operation and maintenance processes of Nuclear Power Plants (NPP) for field workers. Augmented Reality (AR) glasses adopting such proactive safety information visualization techniques can significantly increase personnel safety and reduce the NPP’s operating costs. The current practice of monitoring NPPs requires workers to switch between digital models, data, and physical workspaces in identifying relevant but potentially occluded objects and in assessing the risks of operation and maintenance processes. On the other hand, frequently changed field conditions require field workers to report to supervisors for real-time guidance. Such guidance is essential to ensure that changing conditions will not invalidate or endanger the work order and other ongoing processes that may jeopardize NPP operations. Additionally, incorrect recognition of equipment objects can result in communication errors and safety problems. AR techniques can assist engineers in viewing the physical workspaces with objects labeled with detailed operation procedures and safety reminders during field operations. The project team developed an “Intelligent Context-Aware Safety Information Display” (ICAD) for supporting Nuclear Power Plant (NPP) field workers in achieving safe and efficient execution of a series of operational tasks in uncertain and changing workspaces of an NPP. Before designing the ICAD-AR prototype, the project team synthesized NPP operational knowledge models through literature review studies, surveys, interviews with domain experts, and knowledge modeling. The project team conducted an extensive study of the operational procedures of various NPPs, and digital technologies that can support the safe and efficient execution of those procedures in different NPP operational contexts. This literature review helped the project team conduct surveys and interviews with nuclear engineers and field workers to identify three categories of information. The NPP knowledge modeling efforts reveal that the three categories of information identified have different levels of importance in a typical procedure of carrying out a series of tasks to achieve a specific NPP operation goal (e.g., shutdown, mode changes). These three categories of information include 1) Workspace dynamics – the changing spatial arrangements of workspaces, tools, protection equipment, and supporting materials, 2) Workflow prognostics – the dynamic dependencies between different parts of an NPP that functionally support and influence each other in terms of safety and efficiency, and 3) Hazards – objects and spaces that contain hazardous materials or physical conditions that can pose risks to workers or mechanical systems. The project team has profiled the importance levels of these categories of information into a knowledge model. This knowledge model specifies what types of information are more critical for a given task in a given workspace so that computers can automatically identify critical objects and sensors in a scene for delivering context-ware safety information to field workers through AR devices. Significant research development of this project results in technical research outcomes and a prototyping system that illustrates the technical feasibility of establishing an ICAD-AR system supporting the proactive safety information display for nuclear field workers. This final report summarizes the project team’s technological achievements in the past three years. Overall, the project team completed the development and integration of five techniques into a prototype ICAD Augmented Reality (ICAD-AR) system and demonstrated the developed system’s real-time execution in a mechanical room. The project team completed the analysis of using this prototype in other types of workspaces based on 3D image data and digital design models collected from two additional workspaces (a water treatment plant and a flow loop training facility). The integrated techniques include 1) Natural Language Processing (NLP) algorithms supporting the generation and updates of nuclear fieldwork process models based on text analysis of work packages and operation manuals; 2) sensor log analysis for predicting control actions in given sensor reading contexts; 3) computer vision algorithms for automatic localization and navigation of workers; 4) object detection algorithms for identifying task-related objects and correlated sensors for safety checking; 5) AR technique as a platform for supporting the integration. The testing results of these five techniques have shown that 1) the sensor log analysis model can predict the next control action with an accuracy of 0.883; 2) the trained natural language processing model can extract more than 80% of the critical information from paper-based procedures (PBPs); 3) the navigation algorithm with the integration of Visual Inertial Odometry (VIO) and Non-Recursive Bayesian Filter methods make operator’s trajectory estimation resilient to drift error; 4) the computer vision algorithm can detect task-specific and safety-critical objects with an average accuracy of 95.3%. The project team used work procedures collected from a flow loop training facility and two datasets collected from two mechanical rooms simulating the workspaces of NPPs to demonstrate the technical capabilities of the developed ICAD-AR prototype. The demonstration validated the technical feasibility of establishing the ICAD-AR system for nuclear field workers and identified the challenges in 1) automatic text analysis of work packages; 2) use of limited samples of sensor logs for predicting the proper timings of control actions; 3) reliably tracking workers and their task progress in mechanical rooms with many similar objects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Thermal Decomposition of Cyclic, Oxygenated Hydrocarbons

This final technical report describes experimental and theoretical studies of the pyrolysis of molecules relevant to the production of biofuels from biomass. The primary goal of the project was to study how chemical structure affects the thermal decomposition mechanism of a series of cyclic, oxygenated hydrocarbons. To accomplish this, the products of thermal decomposition must first be identified. A pulsed hyperthermal nozzle was used to induce gas-phase pyrolysis of the samples at temperatures up to 1600 K. Product detection was accomplished with a matrix-isolation FTIR spectrometer.

09 BIOMASS FUELS↗
DOE OSTI2024

DESI Massive Poststarburst Galaxies at z ~ 1.2 Have Compact Structures and Dense Cores

Poststarburst galaxies (PSBs) are young quiescent galaxies that have recently experienced a rapid decrease in star formation, allowing us to probe the fast-quenching period of galaxy evolution. In this work, we obtained Hubble Space Telescope (HST)/WFC3 F110W imaging to measure the sizes of 171 massive (log(M $\ast$ /M ⊙ ) ~ 11) spectroscopically identified PSBs at 1 < z 1.3 selected from the DESI Survey Validation luminous red galaxy sample. This statistical sample constitutes an order of magnitude increase from the ~20 PSBs with space-based imaging and deep spectroscopy. We perform structural fitting of the target galaxies with pysersic and compare them to quiescent and star-forming galaxies in the 3D-HST survey. We find that these PSBs are more compact than the general population of quiescent galaxies, lying systematically ~0.1 dex below the established size–mass relation. However, their central surface mass densities are similar to those of their quiescent counterparts (log(Σ 1kpc /(M ⊙ kpc -2 ))~10.1). These findings are easily reconciled by later ex situ growth via minor mergers or a slight progenitor bias. These PSBs are round in projection (b/a median ~ 0.8), suggesting that they are primarily spheroids, not disks, in 3D. We find no correlation between the time since quenching and light-weighted PSB sizes or central densities. This disfavors apparent structural growth due to the fading of centralized starbursts in this galaxy population. Instead, we posit that the fast quenching of massive galaxies at this epoch occurs preferentially in galaxies with preexisting compact structures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The Fermi-LAT Lightcurve Repository

The Fermi Large Area Telescope (LAT) lightcurve repository (LCR) is a publicly available, continually updated library of gamma-ray lightcurves of variable Fermi-LAT sources generated over multiple timescales. The Fermi-LAT LCR aims to provide publication-quality lightcurves binned on timescales of 3, 7, and 30 days for 1525 sources deemed variable in the source catalog of the first 10 yr of Fermi-LAT observations. The repository consists of lightcurves generated through full likelihood analyses that model the sources and the surrounding region, providing fluxes and photon indices for each time bin. The LCR is intended as a resource for the time-domain and multimessenger communities by allowing users to search LAT data quickly to identify correlated variability and flaring emission episodes from gamma-ray sources. We describe the sample selection and analysis employed by the LCR and provide an overview of the associated data access portal.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Modeling Building Use and Population Distribution Opportunity Using Open Geosocial Data in Urban Areas

Knowing where people are and knowing how the land in urban areas is being used is critical for assessing human mobility patterns, monitoring population health, planning for transportation services, and responding to natural disasters and emergency situations. While geophysical phenomena like land cover are more easily observed through remote sensing, social phenomena like how a city block or building is being used by the population is generally unobservable through standard remote sensing practices. Current methods and land use data by their nature are also difficult to scale to national or global levels. While land use data are often available in large cities, there are abundant data gaps globally. Furthermore, land use data can often appear relatively coarse in its spatial and semantic resolution (i.e. 1km grid cells rather than building level, and "commercial" rather than "coffee shop"). In particular, there is a need to harvest scalable data and develop methods to create global land use maps. To this end, in this work we combine building footprints and Points of Interest (POI) to estimate land use at the building level, including its mixed-use intensity. Next, we propose a method for defining semantic temporal zones referring to Morning, Day, Evening, and Night times unique to a region, based on the cumulative hourly change in the POIs' hours of operation. We then use these POIs' hours of operation to estimate non-residential population distribution opportunity and land use change over a 24-hour period. We validate our results through manual checks of randomly sampled individual buildings using multiple sources. Finally, we discuss the impact of our results on identifying population distribution opportunity and the reliability of estimating building use.

Sparks, Kevin↗
DOE OSTI2021

Investigation of Machinability of Additively Manufactured Ti-6Al-4V

Additively manufactured metals differ from their conventionally produced counterparts due to the inherent material inhomogeneity, porosity, and thermal stress induced by the process. These differences make the machining of additively manufactured metals more difficult and cause premature tool failure or unexpected surface finish at certain conditions. This study takes the first step to investigate and identify the causes of these issues, particularly for Ti-6Al-4V. Printed and wrought samples, as well as heat treatment effect, are compared in a dry cutting condition at a cutting speed of 90 m/min in terms of cutting power, vibration, temperature, and produced surface finish. The results show a lower cutting power and more vibration for as-printed Ti samples, indicating a less ductile microstructure and inclusion of pores. Heat treatment can eliminate these phenomena. There is no significant difference found in the produced surface finish at the current cutting condition.

Raval, Jay↗
DOE OSTI2022

Use of lipid particles in medical diagnostics

Disclosed herein are methods for identifying one or more diseased cells in a subject. Some embodiments include providing a biological sample derived from a subject, analyzing the biological sample by mass spectrometry, and determining the abundance of one or more lipids in the biological sample, wherein an altered abundance of the one or more lipids in the biological sample, as compared to a reference level, indicates a presence of one or more diseased cells in the subject from which the biological sample is derived.

Louie, Katherine B.↗