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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Structural isomers of the S 2 state in photosystem II: do they exist at room temperature and are they important for function?

In nature, an oxo‐bridged Mn 4 CaO 5 cluster embedded in photosystem II (PSII), a membrane‐bound multi‐subunit pigment protein complex, catalyzes the water oxidation reaction that is driven by light‐induced charge separations in the reaction center of PSII. The Mn 4 CaO 5 cluster accumulates four oxidizing equivalents to enable the four‐electron four‐proton catalysis of two water molecules to one dioxygen molecule and cycles through five intermediate S‐states, S 0 – S 4 in the Kok cycle. One important question related to the catalytic mechanism of the oxygen‐evolving complex (OEC) that remains is, whether structural isomers are present in some of the intermediate S‐states and if such equilibria are essential for the mechanism of the O‐O bond formation. Here we compare results from electron paramagnetic resonance (EPR) and X‐ray absorption spectroscopy (XAS) obtained at cryogenic temperatures for the S 2 state of PSII with structural data collected of the S 1 , S 2 and S 3 states by serial crystallography at neutral pH (∼6.5) using an X‐ray free electron laser at room temperature. While the cryogenic data show the presence of at least two structural forms of the S 2 state, the room temperature crystallography data can be well‐described by just one S 2 structure. We discuss the deviating results and outline experimental strategies for clarifying this mechanistically important question.

Chatterjee, Ruchira↗
DOE OSTI2019

Ambidextrous Helical Nanotubes from Self-Assembly of Designed Helical Hairpin Motifs

Tandem repeat proteins exhibit native designability and represent potentially useful scaffolds for the construction of synthetic biomimetic assemblies. We have designed two synthetic peptides, HEAT R1 and LRV M3Δ1, based on the consensus sequences of single repeats of thermophilic HEAT (PBS HEAT) and Leucine- Rich Variant (LRV) structural motifs, respectively. Self-assembly of the peptides afforded high aspect-ratio helical helical nanotubes. Cryo-electron microscopy with direct electron detection was employed to analyze the structures of the solvated filaments. Three dimensional reconstructions from the cryo-EM maps led to atomic models for the HEAT R1 and LRV M3Δ1 filaments at resolutions of 6.0 Å and 4.4 Å, respectively. Surprisingly, despite sequence similarity at the lateral packing interface, HEAT R1 and LRV M3Δ1 filaments adopt the opposite helical hand and differ significantly in helical geometry, while retaining a local conformation similar to previously characterized repeat proteins of the same class. The differences in the two filaments could be rationalized on the basis of differences in cohesive interactions at the lateral and axial interfaces. These structural data reinforce previous observations regarding the structural plasticity of helical protein assemblies and the need for high resolution structural analysis. Despite these observations, the native designability of tandem repeat proteins offers the opportunity to engineer novel helical nanotubes. Furthermore, the resultant nanotubes have independently addressable and chemically distinguishable interior and exterior surfaces that would facilitate applications in selective recognition, transport, and release.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Determination of β -decay feeding patterns of 88 Rb and 88 Kr using the Modular Total Absorption Spectrometer at ORNL HRIBF

We report precise determination of ground-state feeding in the β decay of fission products is an important but challenging component in modeling reactor antineutrino flux and reactor decay heat. The Modular Total Absorption Spectrometer (MTAS) is a versatile NaI(Tl) detector array that determines the true β-decay pattern free from the pandemonium effect, including precise ground-state feeding intensities. In this paper, we report MTAS results of the β feeding intensities of 88 Rb and 88 Kr, fission products with large cumulative yields in nuclear reactors. By comparing MTAS results with previous measurements, 88 Rb provides a validation of MTAS's ability to determine ground-state feedings in β decays, while the precision of 88 Kr ground-state feeding is improved when compared with the Evaluated Nuclear Structure Data File (ENSDF). The investigation of sources that contribute to β feeding branching uncertainties in MTAS experiments is discussed in detail. Lastly, the deconvolution of 88 Rb decay spectra suggests that MTAS can distinguish an allowed β spectral shape from a first forbidden unique β spectral shape.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Topic modeling to discern irregular order patterns in unlabeled electronic health records

This paper presents an application of topic modeling on event sequences of Electronic Health Record (EHR) orders. Metaphorically, we approach clinical order event sequences of unlabeled data as if they are documents where words are the events that occurred in the history of an order. We demonstrate the approach leveraging Consult orders data. The details of the data preprocessing and the data structures are described along with the data sources. Latent Dirichlet Allocation (LDA) is leveraged to fit against the limited datasets prepared. Another open source tool – LDAvis is used for exploratory analysis of the LDA results. The preliminary results revealed some order patterns that are qualitatively evaluated as potential irregular transitions. The goal of this analysis is to provide unsupervised learning application to domain experts in the absence of labeled data where they can investigate captured patterns and identify irregular transitions of orders. Ultimately, such efforts will guide formalization of hazard detection algorithms that monitor EHR data to identify health information technology related hazards.

Ozmen, Ozgur↗
DOE OSTI2024

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. Here, in this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.

artificial intelligence↗
DOE OSTI2021

Axom

Axom is an open-source library of "building block" software components that provide core infrastructure capabilities for HPC applications. Such capabilities include: input file parsing and verification for various file formats, format specification for material shape input to multi-material simulations and tools for placing those shapes on meshes, parallel distributed coordination of diagnostic messages, geometric primitives, spatial queries and spatial search acceleration data structures, in memory key-value data store for managing simulation data and parallel file I/O. Axom components are used widely across LLNL Advanced Simulation and Computing program applications. It is also part of the LLNL institutionally-supported RADIUSS project, which promotes and funds its adoption by projects across LLNL.

Zagaris, George↗
DOE OSTI2018

Data Evaluation of Actinide Cross Sections: 240 Am, 241 Am, and 242 Am

Evaluations for neutron-induced reactions on the isotopes 240 Am, 241 Am, and 242 Am were per formed by optimizing reaction-model calculations based on existing experimental data from both direct measurements and surrogate reactions. The evaluated data were based on results obtained from the LLNL reaction codes YAHFC and FRESCO using up-to-date nuclear models; including an improvement upon some previous treatments for inelastic processes. Nuclear reaction codes generated data for all relevant reaction channels, which were then translated into Generalized Nu clear Data Structure and LLNL’s Evaluated Neutron Data Libraries (ENDL). These data libraries were tested for potential errors and applied to verification and validation (V&V) tests suites where applicable for comparisons.

42 ENGINEERING↗
DOE OSTI2021

Real-time Data Analytics for Condition Monitoring of Complex Industrial Systems

Modern industrial systems are now fitted with several sensors for condition monitoring. This is advantageous because these sensors can provide mass amounts of data that have the potential for aiding in tasks such as fault detection, diagnosis, and prognostics. However, the information valuable for performing these tasks is often clouded in noise and must be mined from high-dimensional data structures. Therefore, this dissertation presents a data analytics framework for performing these condition monitoring tasks using high-dimensional data. Demonstrations of this framework are detailed for challenges related to power generation systems in automobiles, power plants, and aircraft engines. These implementations leverage data collected from state-of-the-art, industry class test-rigs. Results indicate the ability of this framework to develop effective methodologies for condition monitoring of complex systems.

Peters, Benjamin↗
DOE OSTI2018

Composition and Structure of (Zr{sub 0.37}Ti{sub 0.63}){sub 3}AlC{sub 2} MAX Phase Crystals Prepared by Self-Propagating High-Temperature Synthesis

A (Zr{sub 0.37}Ti{sub 0.63}){sub 3}AlC{sub 2} MAX phase has been prepared by self-propagating high-temperature synthesis. The composition of its crystals has been determined by comparing X-ray structure analysis results with structural data for (Zr{sub x}Ti{sub 1–x}){sub 3}AlC{sub 2} (0 < x < 1) crystals prepared by other methods. Comparison of X-ray structure analysis data with density functional theory calculation results indicates that the arrangement of the Zr and Ti atoms in the metal–carbon layer is disordered.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Unified architecture for data-driven metadata tagging of building automation systems

This article presents a Unified Architecture (UA) for automated point tagging of Building Automation System (BAS) data, based on a combination of data-driven approaches. Advanced energy analytics applications—including fault detection and diagnostics and supervisory control—have emerged as a significant opportunity for improving the performance of our built environment. Effective application of these analytics depends on harnessing structured data from the various building control and monitoring systems, but typical BAS implementations do not employ any standardized metadata schema. While standards such as Project Haystack and Brick Schema have been developed to address this issue, the process of structuring the data, i.e., tagging the points to apply a standard metadata schema, has, to date, been a manual process. This process is typically costly, labor-intensive, and error-prone. In this work we address this gap by proposing a UA that automates the process of point tagging by leveraging the data accessible through connection to the BAS, including time-series data and the raw point names. The UA intertwines supervised classification and unsupervised clustering techniques from machine learning and leverages both their deterministic and probabilistic outputs to inform the point tagging process. Furthermore, we extend the UA to embed additional input and output data-processing modules that are designed to address the challenges associated with the real-time deployment of this automation solution. We test the UA on two datasets for real-life buildings: (i) commercial retail buildings and (ii) office buildings from the National Renewable Energy Laboratory (NREL) campus. We report the proposed methodology correctly applied 85–90% and 70–75% of the tags in each of these test scenarios, respectively for two significantly different building types used for testing UA's fully-functional prototype. The proposed UA, therefore, offers promising approach for automatically tagging BAS data as it reaches close to 90% accuracy. Further building upon this framework to algorithmically identify the equipment type and their relationships is an apt future research direction to pursue.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Calculating the grain boundary inclination of voxelated grain structures using a smoothing algorithm

We have developed a flexible method for calculating the grain boundary (GB) inclinations of voxelated grain structure data using smoothing algorithms. We compared the performance of four algorithms: the linear interpolation, Allen–Cahn, level-set, and vertex algorithms. We assessed their accuracy using 2D and 3D cases with known inclinations. The vertex algorithm provided the best balance between accuracy and efficiency for 2D structures while the linear interpolation algorithm provided the best balance for 3D structures. We compared the GB inclinations calculated using our smoothing method on a 3D high energy X-ray diffraction microscopy (HEDM) dataset to those determined by meshing the GBs. The two approaches determined similar GB plane distributions, though they varied significantly at triple junctions. In conclusion, the smoothing method was demonstrated for two sources of 3D voxelated grain structures: HEDM data and results from Monte Carlo Potts grain growth simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Structural features of ISG borosilicate nuclear waste glasses revealed from high-energy X-ray diffraction and molecular dynamics simulations

Vitrification is a widely accepted method to immobilize nuclear waste. Detailed structural information is critical to understand the physical and chemical behaviors, including the long-term chemical durability of these glasses that are to be stored in geological storage sites. High-energy X-ray diffraction studies are used to obtain accurate structural information of the International Simple Glass (ISG), a six-component borosilicate model nuclear waste glass with a composition (mol%) of 60.2SiO 2 -16.0B 2 O 3 -12.6Na 2 O-3.8Al 2 O 3 -5.7CaO-1.7ZrO 2 . Classical molecular dynamics (MD) simulations with recently developed effective potentials are utilized to generate structural models of the ISG glasses to provide interpretation of the high-energy X-ray structural data and additional short and medium range structural details. The atomic structure model generated from MD simulations shows an excellent agreement with high-energy X-ray diffraction, measured for the first time for ISG, with an R x value of 5.5%. Systematic investigations of the medium-range structural features of ISG are performed, built upon recent study of the short-range structure. In particular, the glass former cation oxygen polyhedral analysis show that [SiO 4 ]-[SiO 4 ] and [BO 4 ]-[SiO 4 ] connections are the most abundant while [ZrO 6 ] octohedra having higher probability to connect to another [SiO 4 ], [BO 4 ] and [BO 3 ]. Moreover, we have studied the ISG surface structure using MD simulations. Noticeable differences in terms of both chemical compositions and structural features between the bulk and surface are observed. There exist increased small-sized rings, under coordinated Si species, high 3-coordinated boron content (86.5%) on the surface. Very importantly, sodium ions are found to be enriched on the glass surface, consistent with recent experimental results. Furthermore, both bulk and surface structural information are discussed in terms of the dissolution and corrosion mechanisms of ISG and related nuclear waste glasses.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Structure analysis of the telomere resolvase from the Lyme disease spirochete Borrelia garinii reveals functional divergence of its C-terminal domain

Borrelia spirochetes are the causative agents of Lyme disease and relapsing fever, two of the most common tick-borne illnesses. A characteristic feature of these spirochetes is their highly segmented genomes which consists of a linear chromosome and a mixture of up to approximately 24 linear and circular extrachromosomal plasmids. The complexity of this genomic arrangement requires multiple strategies for efficient replication and partitioning during cell division, including the generation of hairpin ends found on linear replicons mediated by the essential enzyme ResT, a telomere resolvase. Using an integrative structural biology approach employing advanced modelling, circular dichroism, X-ray crystallography and small-angle X-ray scattering, we have generated high resolution structural data on ResT from B. garinii. Our data provides the first high-resolution structures of ResT from Borrelia spirochetes and revealed active site positioning in the catalytic domain. We also demonstrate that the C-terminal domain of ResT is required for both transesterification steps of telomere resolution, and is a requirement for DNA binding, distinguishing ResT from other telomere resolvases from phage and bacteria. These results advance our understanding of the molecular function of this essential enzyme involved in genome maintenance in Borrelia pathogens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

PoPsCpp

A new format for storing particle information (i.e., mass, spin, parity) has been developed. The format is a subset of the Generalized Nuclear Data Structure (GNDS) developed for storing nuclear reaction data. A C++ library dubbed "Property of Particles" (PoPs) has been developed to read particle data in the GNDS format. The PoPs library provides functions for reading in a GNDS particle database as well as for accessing a particle and its properties.

Beck, Bret↗
DOE OSTI2024

Myna

The additive manufacturing (AM) community has been developing digital factory tools over the past decade to better leverage the multi-modal process data coming out of the advanced manufacturing process. As a result, numerous databases of additive manufacturing process data exist in the literature and in the archival storage of disparate research groups. While some efforts have been made to create a standard ontology for storing and sharing AM data, in practice a variety of data structures are used to store AM build data, even within a single institution. This causes many problems for maintainability and extensibility when attempting to integrate computational modeling tools with experimental data to either validate models or to provide further insight into results and trends. Myna is a Python-based framework that aims to decrease the effort needed to connect individual computational models to the variety of AM process data that exist in different research groups and institutions. This type of software is sometimes referred to as "middleware" or “glueware,” in that it connects disparate databases and applications into a single computational ecosystem. Instead of maintaining unique interfaces between each application and each database, developers can create a single interface from each application to Myna and thereby gain access to the implemented database connections. Similarly, developing a database connection in Myna provides access to the developed simulation applications. This framework greatly simplifies the maintainability of model applications that rely on experimental data. Using external simulation tools, users will also be able to run pre-configured workflows using the built-in workflow manager. Several examples of input files are provided with Myna for different workflows, including melt pool geometry predictions and detailed melt pool and solidification microstructure predictions.

Knapp, GerryL. [Oak Ridge National Laboratory (ORN↗
DOE OSTI2025

rcsb-api : Python Toolkit for Streamlining Access to RCSB Protein Data Bank APIs

The Protein Data Bank (PDB) was founded in 1971 as the first open-access digital data resource in biology to serve as the single global archive for three-dimensional (3D) macromolecular structure data. Current PDB holdings exceed 230,000 experimentally determined structures of proteins, nucleic acids, viruses, and macromolecular machines. The RCSB Protein Data Bank RCSB.org research-focused web portal facilitates search, analyses, and visualization of every PDB structure along with more than one million Computed Structure Models from AlphaFold DB and the ModelArchive. It is powered by a set of publicly available Application Programming Interfaces (APIs) that both support RCSB.org users and provide programmatic access to PDB data. Given the breadth and levels of granularity encompassed in this rich data collection, efficiently accessing the information programmatically may be challenging for new users. RCSB PDB has developed a Python software package, rcsb-api , that facilitates easy and efficient use of RCSB PDB APIs within a Python environment. This software tool is designed to streamline access to the extensive corpus of data housed within the PDB, enabling researchers to search, retrieve, and analyze 3D biostructure data seamlessly. Its use will accelerate research in structural biology, molecular biology and biochemistry, drug discovery, and bioinformatics by providing more efficient tools for data integration and analysis. The new toolkit is available on GitHub (github.com/rcsb/py-rcsb-api) and published to the public Python package repository (PyPI) to foster wider usage and support basic and applied research in fundamental biology, biomedicine, and the energy sciences.

FAIR principles↗
DOE OSTI2021

MOSIQS: Persistent Memory Object Storage With Metadata Indexing and Querying for Scientific Computing

Scientific applications often require high-bandwidth shared storage to perform joint simulations and collaborative data analytics. Shared memory pools provide a chance to satisfy such needs. Recently, a high-speed network such as Gen-Z utilizing persistent memory (PM) offers an opportunity to create a shared memory pool connected to compute nodes. However, there are several challenges to use scientific applications on the shared memory pool directly such as scalability, failure-atomicity, and lack of scientific metadata-based search and query. In this paper, we propose MOSIQS, a persistent memory object storage framework with metadata indexing and querying for scientific computing. We design MOSIQS based on the key idea that memory objects on PM pool can live beyond the application lifetime and can become the sharing currency for applications and scientists. MOSIQS provides an aggregate memory pool atop an array of persistent memory devices to store and access memory objects to accelerate scientific computing. MOSIQS uses a lightweight persistent memory key-value store to manage the metadata of memory objects, which enables memory object sharing. To facilitate metadata search and query over millions of memory objects resident on memory pool, we introduce Group Split and Merge (GSM), a novel persistent index data structure designed primarily for scientific datasets. GSM splits and merges dynamically to minimize the query search space and maintains low query processing time while overcoming the index storage overhead. MOSIQS is implemented on top of PMDK. We evaluate the proposed approach on many-core server with an array of real PM devices. Experimental results show that MOSIQS gains a 100% write performance improvement and executes multi-attribute queries efficiently with 2.7× less index storage overhead offering significant potential to speed up scientific computing applications.

97 MATHEMATICS AND COMPUTING↗