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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Discovering methylated DNA motifs in bacterial nanopore sequencing data with MIJAMP

Abstract Bacterial DNA methylation is involved in diverse cellular functions, including modulation of gene expression, DNA repair, and restriction–modification systems for defense against viruses and other foreign DNA. Restriction systems hinder efforts to engineer organisms to produce fuels and chemicals from waste and renewable feedstocks by degrading DNA during transformation. Methylome analysis allows identification of motifs within a bacterial chromosome that may be targeted by native restriction enzymes. Further expression of the corresponding methyltransferases in Escherichia coli allows plasmid DNA to be protected from restriction in the target organism, thereby drastically enhancing transformation efficiency. Nanopore sequencing can detect methylated bases, but software is needed to transform modified base coordinates into methylated motifs. Here, we develop MIJAMP (MIJAMP Is Just A MethylBED Parser), a software package that was developed to discover methylated motifs from the output of ONT’s Modkit or other data in the methylBED format. MIJAMP employs a human-driven refinement strategy that empirically validates all motifs against genome-wide methylation data, thus eliminating incorrect motifs. MIJAMP also reports methylation data on specific, user-defined motifs. Using MIJAMP, we determined the methylated motifs both in a control strain (wild-type E. coli) and in Synecococcus sp. strain PCC7002, laying the foundation for improved transformation in this organism. MIJAMP is available at https://code.ornl.gov/alexander-public/mijamp/. One Sentence Summary: Here we describe software written to discover DNA methylation motifs from nanopore sequencing data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Characterization of Delayed-Particle Emission Signatures for Pyroprocessing, Part 1: Fuel Assembly

An initial study was conducted with nondestructive assay analysis using the MCNP6 Monte Carlo radiation-transport code to calculate delayed-neutron and delayed- gamma emission signature monitors to characterize burnup and isotopic composition for pyroprocessing. The study consists of five parts. In Part 1, a generic Westinghouse fuel assembly model was modified to create models with 3% and 5% 235 U enrichments, 20, 30, 40, and 50 GWd/MTU burnups, and 3-, 5-, 10-, 20-, and 30-year cooling times. MCNP6 was used to create detail-rich delayed-particle source terms for fuel assembly. Delayed-gamma activity ratios were then developed for conventional 134 Cs/ 137 Cs, 134 Cs/ 154 Eu, and 154 Eu/ 137 Cs markers as a function of burnup and of actinide mass for elemental uranium, neptunium, plutonium, americium, and curium. In addition, data mining using photon tally data was employed to discover potential new delayed-gamma activity ratios. The data mining effort was streamlined by the creation and use of several utility codes to extract and process data. The study confirms behavior for burnup dependence of 134 Cs/ 137 Cs, 134 Cs/ 154 Eu, and 154 Eu/ 137 Cs markers. A process for the discovery of potential new delayed-gamma markers has been developed, and results are presented. The actinide and nonactinide inventories for the fuel assembly models are used as source materials for the electrorefining models. Parts 2–5 contain analysis of a pyrochemical separations model that includes an MCNP6 mockup of the INL Mark IV electrorefiner (see companion documents). Part 2 focuses on delayed-particle emissions from the electrorefiner with loaded fuel dissolution baskets before electrorefining. Part 3 investigates delayed-particle emissions from the electrorefiner following electrorefining. Part 4 presents delayed-particle emissions from other pyroprocessing units that contain material developed by the electrorefining process in Part 3. Part 5 parallels Part 4, except for a 10 wt% plutonium reduction.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Panorama 360 (Final Report)

This is the final technical report for the DOE-funded Panorama 360 project. Panorama 360 provided a resource for the collection, analysis, and sharing of performance data about end-to-end scientific workflows executing on DOE facilities. The work focused on workflows that include experimental data generation at DOE facilities. The main activities of Panorama 360 include the development of: 1. A distributed repository that stores different types of workflow execution data (e.g., point and time series performance traces at fine- and coarse-grained levels); 2. A set of open-source data capture, curation, and publishing tools fully integrated with a state-of-the-art workflow management system that automates data ingestion to the repository and enables users to discover, query, and process data from the repository; 3. A set of analysis algorithms and machine learning based tools to perform analysis and characterization of the gathered data, which can be used to detect anomalous performance or system faults; and 4. Best practices and recommendations for workflow evaluation, analysis, execution, and architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Panorama 360 (Final Report)

This final technical report from the lead institution, USC grant #DE-SC0012636, serves as the final technical report for collaborative institution UNC-CH grant #DE-SC0012390. The goal was to develop a repository and associated capabilities for data collection, ingestion, and analysis for a broad class of DOE applications that span experimental and simulation science workflows. In particular, this work focuses on workflows that include experimental data generation at DOE facilities. The main activities of Panorama 360 include the development of: (1) A distributed repository that stores different types of workflow execution data (e.g., point and time series performance traces at fine- and coarse-grained levels); (2) A set of open-source data capture, curation, and publishing tools fully integrated with a state-of-the-art workflow management system that automates data ingestion to the repository and enables users to discover, query, and process data from the repository; (3) A set of analysis algorithms and machine learning based tools to perform analysis and characterization of the gathered data, which can be used to detect anomalous performance or system faults; and (4) Best practices and recommendations for workflow evaluation, analysis, execution, and architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Fluorescence Color by Data-Driven Design of Genomic Silver Clusters

DNA nucleobase sequence controls the size of DNA-stabilized silver clusters, leading to their well-known yet little understood sequence-tuned colors. The enormous space of possible DNA sequences for templating clusters has challenged the understanding of how sequence selects cluster properties and has limited the design of applications that employ these clusters. Here, we investigate the genomic role of DNA sequence for fluorescent silver clusters using a data-driven approach. Employing rapid parallel silver cluster synthesis and fluorimetry, we determine the fluorescence spectra of silver cluster products stabilized by 1432 distinct DNA oligomers. By applying pattern recognition algorithms to this large experimental data set, we discover certain DNA base patterns, or “motifs,” that correlate to silver clusters with similar fluorescence spectra. These motifs are employed in machine learning classifiers to predictively design DNA template sequences for specific fluorescence color bands. Our method improves selectivity of templates by 330% for silver clusters with peak emission wavelengths beyond 660 nm. The discovered base motifs also provide physical insights into how DNA sequence controls silver cluster size and color. Finally, this predictive design approach for color of DNA-stabilized silver clusters exhibits the potential of machine learning and data mining to increase the precision and efficiency of nanomaterials design, even for a soft-matter-inorganic hybrid system characterized by an extremely large parameter space.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

THE PULSAR SEARCH COLLABORATORY: DISCOVERY AND TIMING OF FIVE NEW PULSARS

We present the discovery and timing solutions of five new pulsars by students involved in the Pulsar Search Collaboratory, a NSF-funded joint program between the National Radio Astronomy Observatory and West Virginia University designed to excite and engage high-school students in Science, Technology, Engineering, and Mathematics (STEM) and related fields. We encourage students to pursue STEM fields by apprenticing them within a professional scientific community doing cutting edge research, specifically by teaching them to search for pulsars. The students are analyzing 300 hr of drift-scan survey data taken with the Green Bank Telescope at 350 MHz. These data cover 2876 deg{sup 2} of the sky. Over the course of five years, more than 700 students have inspected diagnostic plots through a web-based graphical interface designed for this project. The five pulsars discovered in the data have spin periods ranging from 3.1 ms to 4.8 s. Among the new discoveries are PSR J1926-1314, a long period, nulling pulsar; PSR J1821+0155, an isolated, partially recycled 33 ms pulsar; and PSR J1400-1438, a millisecond pulsar in a 9.5 day orbit whose companion is likely a white dwarf star.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1981

Electromagnetic deep-probing (100-1000 KMS) of the Earth's interior from artificial satellites: Constraints on the regional emplacement of crustal resources

Efforts continue in the development of a computer program for looking at the coupling of finite-dimensional source fields with a laterally heterogeneous Earth. An algorithm is also being developed for calculating a time-varying reference field using ground-based magnetic observatory data. It was discovered that ground-based standard magnetic observation is not as so available for the time of the MAGSAT mission as might be expected. Attempts are being made to determine the exact times and observatories from which data are avaliable.

Hermance, J. F.↗
DOE OSTI2020

Planet Microbe: a platform for marine microbiology to discover and analyze interconnected ‘omics and environmental data

In recent years, large-scale oceanic sequencing efforts have provided a deeper understanding of marine microbial communities and their dynamics. These research endeavors require the acquisition of complex and varied datasets through large, interdisciplinary and collaborative efforts. However, no unifying framework currently exists for the marine science community to integrate sequencing data with physical, geological, and geochemical datasets. Planet Microbe is a web-based platform that enables data discovery from curated historical and on-going oceanographic sequencing efforts. In Planet Microbe, each ‘omics sample is linked with other biological and physiochemical measurements collected for the same water samples or during the same sample collection event, to provide a broader environmental context. This work highlights the need for curated aggregation efforts that can enable new insights into high-quality metagenomic datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Discovering Physically Meaningful Structures from Climate Extreme Data

The original proposal described an interdisciplinary team spanning UC San Diego (lead), Columbia University, and UC Irvine, with Columbia investigators including Pierre Gentine, Elias Bareinboim, and Marcus van Lier-Walqui. The proposal further specified a leadership structure in which Columbia co-investigators contributed across the three aims, with Co-PI Gentine serving as a point of contact with science teams and with responsibilities distributed across aims.

42 ENGINEERING↗
DOE OSTI2025

A Data Processing Pipeline To Extract A Knowledge Graph From Heterogeneous Data For Socio-technical Analysis Of Critical Infrastructure Influence

The code is written in Python and consists of the following pipeline that is implemented in Apache Airflow. This pipeline intends to understand the companies that are directly or indirectly involved with a type of critical infrastructure system at some point in that system's lifecycle. The pipeline takes a configuration file that specifies a list of initial companies to consider, a geographic region of interest, and a set of SEC form types as well as other data sources (e.g. CrunchBase) from which to extract entities and relations. There are four main components to this pipeline as currently implemented: Entity Extraction, Network Construction, Analysis, and Visualization. First, Entity Extraction, is implemented as the `topear-extract_organizations` Apache Airflow workflow. Given an initial query that specifies a geographic region of interest and a time interval, the software will extract CI facilities of interest and organizations that have a direct influence relationship to those facilities (e.g. ownership). During the course of the LDRD, we focused on Electric Vehicle charging stations and this information is available via the Department of Energy (DOE) database on fueling stations maintained by NREL. Within the context of the DOE CESER project, we have focused on Battery Energy Storage Systems (BESS). Second, the Network Extraction component will iteratively construct a social network graph given the set of organizations and people extracted in the previous step. Organizations (and eventually People if desired) are then fed as a query to the `topgear-construct_social_network` Apache Airflow workflow which given a set of initial companies and data sets (e.g. SEC EDGAR form types, OpenCorporates, Crunchbase). This Airflow workflow will iteratively query such data sources to discover relationships with new organizations and people. For example, this module can iteratively query SEC EDGAR for metadata that documents the number of each type of form for the given set of companies and their location. This forms metadata represents a catalog of data sources from SEC EDGAR for the extracted social network knowledge graph. The pipeline then downloads these forms from the website and saves them in a build directory for further processing. These documents are then parsed for entities and relations. Again, we note that in additional to SEC data sources, this step can also pull in information on organizations via API services such as CrunchBase and OpenCorporates or bulk data sources. At the end of this step, the resultant social network, the Critical Infrastructure network, and the edges that encode relationships between organizations and CI facilities, form the Adversarial Socio-Technical Network (ASTN) that informs the analysis. Third, the Analysis component processes these generated ASTN. Previously, that has included the ability to compare prevalence of different vendors for a given infrastructure component type across different regions as well as identify common public and private investors across those vendors. This was demonstrated for EV Charging Stations across several different metropolitan areas within an IEEE PES GridEdge publication. More recently, we have looked at ways to identify infrastructure owners and operators of BESS with the most nameplate capacity across different states as well as other indictors of risk resulting from changes in ownership over time. Finally, the Visualization component consists of an HTML/CSS/JS framework by which users can interact geospatial, operational, and organizational relationships across a given portfolio of Critical Infrastructure facilities. The objective is to provide a library of UI/UX modules that can be repurposed for stakeholder-specific dashboards. All of the modules are related via a common event model that enables UI actions in one view to percolate across the other views.

Weaver, Gabriel [Idaho National Laboratory (INL), ↗
DOE OSTI2018

Einstein@Home discovers a radio-quiet gamma-ray millisecond pulsar

Millisecond pulsars (MSPs) are old neutron stars that spin hundreds of times per second and appear to pulsate as their emission beams cross our line of sight. To date, radio pulsations have been detected from all rotation-powered MSPs. Here, in an attempt to discover radio-quiet gamma-ray MSPs, we used the aggregated power from the computers of tens of thousands of volunteers participating in the Einstein@Home distributed computing project to search for pulsations from unidentified gamma-ray sources in Fermi Large Area Telescope data. This survey discovered two isolated MSPs, one of which is the only known rotation-powered MSP to remain undetected in radio observations. These gamma-ray MSPs were discovered in completely blind searches without prior constraints from other observations, raising hopes for detecting MSPs from a predicted Galactic bulge population.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Observation and confirmation of nine strong-lensing systems in Dark Energy Survey Year 1 data

ABSTRACT We describe the observation and confirmation of nine new strong gravitational lenses discovered in Year 1 data from the Dark Energy Survey (DES). We created candidate lists based on (i) galaxy group and cluster samples, and (ii) photometrically selected galaxy samples. We selected 46 candidates through visual inspection and then used the Gemini Multi-Object Spectrograph(GMOS) at the Gemini South telescope to acquire a spectroscopic follow-up of 21 of these candidates. Through an analysis of these spectroscopic follow-up data, we confirmed nine new lensing systems and rejected two candidates, and the analysis was inconclusive on 10 candidates. For each of the confirmed systems, our report measured spectroscopic properties, estimated source image–lens separations, and estimated enclosed masses as well. The sources that we targeted have an i-band surface brightness range of $i_{\rm SB} \sim 22\!-\!24\, {\rm mag}\,{\rm arcsec}^{-2}$ and a spectroscopic redshift range of zspec ∼ 0.8−2.6. The lens galaxies have a photometric redshift range of zlens ∼ 0.3−0.7. The lensing systems range in source image–lens separation from 2 to 9 arcsec and in enclosed mass from 1012 to 1013 M⊙.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

MINING NUCLEAR TRANSIENT DATA THROUGH SYMBOLIC CONVERSION

Dynamic Probabilistic Risk Assessment (DPRA) methodologies generate enormous amounts of data for a very large number of simulations. The data contain temporal information of both the state variables of the simulator and the temporal status of specific systems/components. In order to measure system performances, limitations and resilience, such data need to be carefully analyzed with the objective of discovering the correlations between sequence/timing of events and system dynamics. A first approach toward discovering these correlations from data generated by DPRA methodologies has been performed by organizing scenarios into groups using classification or clustering based algorithms. The identification of the correlations between system dynamics and timing/sequencing of events is performed by observing the temporal distribution of these events in each group of scenarios. Instead of performing “a posteriori” analysis of these correlations, this paper shows how it is possible to identify the correlations implicitly by performing a symbolic conversion of both continuous (temporal profiles of simulator state variables) and discrete (status of systems and components) data. Symbolic conversion is performed for each simulation by properly quantizing both continuous and discrete data and then converting them as a series of symbols. After merging both series together, a temporal phrase is obtained. This phrase preserves duration, coincidence and sequence of both continuous and discrete data in a uniform and consistent manner. In this paper it is also shown that by using specific distance measures, it is still possible to post-process such symbolic data using clustering and classification techniques but in considerably less time since the memory needed to store the data is greatly reduced by the symbolic conversion.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

An Integrated and Collaborative Approach for NASA Earth Science Data

Earth science research requires coordination and collaboration across multiple disparate science domains. Data systems that support this research are often as disparate as the disciplines that they support. These distinctions can create barriers limiting access to measurements, which could otherwise enable cross-discipline Earth science. NASA's Earth Observing System Data and Information System (EOSDIS) is continuing to bridge the gap between discipline-centric data systems with a coherent and transparent system of systems that offers up to date and engaging science related content, creates an active and immersive science user experience, and encourages the use of EOSDIS earth data and services. The new Earthdata Coherent Web (ECW) project encourages cohesiveness by combining existing websites, data and services into a unified website with a common look and feel, common tools and common processes. It includes cross-linking and cross-referencing across the Earthdata site and NASA's Distributed Active Archive Centers (DAAC), and by leveraging existing EOSDIS Cyber-infrastructure and Web Service technologies to foster re-use and to reduce barriers to discovering Earth science data (http://earthdata.nasa.gov).

Murphy, K.↗
DOE OSTI2024

Active learning of ternary alloy structures and energies

Abstract Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed.

Chemistry↗
NASA NTRS2004

Assessing Videogrammetry for Static Aeroelastic Testing of a Wind-Tunnel Model

The Videogrammetric Model Deformation (VMD) technique, developed at NASA Langley Research Center, was recently used to measure displacements and local surface angle changes on a static aeroelastic wind-tunnel model. The results were assessed for consistency, accuracy and usefulness. Vertical displacement measurements and surface angular deflections (derived from vertical displacements) taken at no-wind/no-load conditions were analyzed. For accuracy assessment, angular measurements were compared to those from a highly accurate accelerometer. Shewhart's Variables Control Charts were used in the assessment of consistency and uncertainty. Some bad data points were discovered, and it is shown that the measurement results at certain targets were more consistent than at other targets. Physical explanations for this lack of consistency have not been determined. However, overall the measurements were sufficiently accurate to be very useful in monitoring wind-tunnel model aeroelastic deformation and determining flexible stability and control derivatives. After a structural model component failed during a highly loaded condition, analysis of VMD data clearly indicated progressive structural deterioration as the wind-tunnel condition where failure occurred was approached. As a result, subsequent testing successfully incorporated near- real-time monitoring of VMD data in order to ensure structural integrity. The potential for higher levels of consistency and accuracy through the use of statistical quality control practices are discussed and recommended for future applications.

Spain, Charles V.↗
NASA NTRS2011

Using Open and Interoperable Ways to Publish and Access LANCE AIRS Near-Real Time Data

The Atmospheric Infrared Sounder (AIRS) Near-Real Time (NRT) data from the Land Atmosphere Near real-time Capability for EOS (LANCE) element at the Goddard Earth Sciences Data and Information Services Center (GES DISC) provides information on the global and regional atmospheric state, with very low temporal latency, to support climate research and improve weather forecasting. An open and interoperable platform is useful to facilitate access to, and integration of, LANCE AIRS NRT data. As Web services technology has matured in recent years, a new scalable Service-Oriented Architecture (SOA) is emerging as the basic platform for distributed computing and large networks of interoperable applications. Following the provide-register-discover-consume SOA paradigm, this presentation discusses how to use open-source geospatial software components to build Web services for publishing and accessing AIRS NRT data, explore the metadata relevant to registering and discovering data and services in the catalogue systems, and implement a Web portal to facilitate users' consumption of the data and services.

Zhao, Peisheng↗