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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

An invertible seven-dimensional Dirichlet cell characterization of lattices

Characterization of crystallographic lattices is an important tool in structure solution, crystallographic database searches and clustering of diffraction images in serial crystallography. Characterization of lattices by Niggli-reduced cells (based on the three shortest non-coplanar lattice vectors) or by Delaunay-reduced cells (based on four non-coplanar vectors summing to zero and all meeting at obtuse or right angles) is commonly performed. The Niggli cell derives from Minkowski reduction. The Delaunay cell derives from Selling reduction. All are related to the Wigner–Seitz (or Dirichlet, or Voronoi) cell of the lattice, which consists of the points at least as close to a chosen lattice point as they are to any other lattice point. The three non-coplanar lattice vectors chosen are here called the Niggli-reduced cell edges. Starting from a Niggli-reduced cell, the Dirichlet cell is characterized by the planes determined by 13 lattice half-edges: the midpoints of the three Niggli cell edges, the six Niggli cell face-diagonals and the four body-diagonals, but seven of the lengths are sufficient: three edge lengths, the three shorter of each pair of face-diagonal lengths, and the shortest body-diagonal length. These seven are sufficient to recover the Niggli-reduced cell.

Chemistry↗
DOE OSTI2022

The Preprocessing of Galaxies in the Early Stages of Cluster Formation in Abell 1882 at z = 0.139

A rare opportunity to distinguish between internal and environmental effects on galaxy evolution is afforded by "SuperGroups," systems that are rich and massive, but include several comparably rich substructures, surrounded by filaments. We present here a multiwavelength photometric and spectroscopic study of the galaxy population in the SuperGroup Abell 1882 (A1882) at z = 0.139, combining new data from the MMT and Hectospec with archival results from the Galaxy And Mass Assembly survey, the Sloan Digital Sky Survey, the Nasa/IPAC Extragalactic Database, the Gemini Multi-Object Spectrograph, and the Galaxy Evolution Explorer. These provide spectroscopic classifications for 526 member galaxies, across wide ranges of local density and velocity dispersion. We identify three prominent filaments along which galaxies seem to be entering the SuperGroup (mostly in E–W directions). A1882 has a well-populated red sequence, containing most galaxies with stellar mass >10 10.5 M Sun , and a pronounced color–density relation even within its substructures. Thus, galaxy evolution responds to the external environment as strongly in these unrelaxed systems as we find in rich and relaxed clusters. From these data, local density remains the primary factor, with a secondary role for distance from the inferred center of the entire structure's potential well. The effects on star formation, as traced by optical and near-UV colors, depend on galaxy mass. We see changes in lower-mass galaxies (M < 10 10.5 M Sun ) at four times the virial radius of major substructures, while the more massive near-UV Green Valley galaxies show low levels of star formation within two virial radii. The suppression of star formation ("quenching") occurs in the infall regions of these structures even before the galaxies enter the denser group environment.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Conquering Data Chaos: Research Data Management with Kubernetes

Managing massive volumes of data and effectively making it accessible to researchers poses significant challenges and is a barrier to scientific discovery. In many cases, critical data is locked up in unwieldy file formats or one-off databases and is too large to effectively process on a single machine. This talk explores the role of Kubernetes, an open-source container orchestration platform, in addressing research data management challenges. I will discuss how we are using a set of publicly available open-source and home-grown tools in the National Renewable Energy Lab (NREL) Data, Analysis, and Visualization (DAV) group to help researchers overcome data-related bottlenecks. The talk will begin by providing an overview of the data challenges faced in research data management, including data storage, processing, and analysis. I will highlight Kubernetes' ability to handle large-scale data by leveraging containerization and distributed computing, including distributed storage. Kubernetes allows researchers to encapsulate data processing infrastructure and workflows into portable containers, enabling reproducibility and ease of deployment. Kubernetes can then schedule and manage the resource allocation of these containers to enable efficient utilization of limited computing resources, leading to more efficient data processing and analysis. I will discuss some limitations of traditional, siloed approaches to dealing with data and emphasize the need for solutions which foster collaboration. I will highlight how we are using Kubernetes at NREL to facilitate data sharing and cooperation among research teams. Kubernetes' flexible architecture enables the deployment of shared computing environments, such as Apache Superset, where researchers can seamlessly access and analyze shared datasets. Providing the ability to have one research team easily consume data generated by another, utilizing Kubernetes' as a central data platform, is one of the major wins we've encountered by adopting the platform. Finally, I will showcase real-world use cases from NREL where we have used Kubernetes to solve some persistent data challenges involving large volumes of sensor and monitoring data. I will discuss the challenges we encountered when creating our cluster and making it available as a production-ready resource. I will also discuss the specific suite of tools, including Postgres and Apache Druid for columnar and timeseries data, and Redpanda Kafka for streaming data we have deployed in our infrastructure, and the process that went into the selection of these tools.

collaborative environment↗
DOE OSTI2023

Deep learning of electrochemical CO 2 conversion literature reveals research trends and directions

Large-scale and openly available material science databases are mainly composed of computer simulation results rather than experimental data. Some examples include the Materials Project, Open Quantum Materials Database, and Open Catalyst 2022. Unfortunately, building large-scale experimental databases remains challenging due to the difficulties in consolidating locally distributed datasets. In this work, focusing on the catalysis literature of CO 2 reduction reactions (CO 2 RRs), we present a machine learning (ML)-based protocol for selecting highly relevant papers and extracting important experimental data. First, we report a document embedding method (Doc2Vec) for collecting papers of greatest relevance to the specific target domain, which yielded 3154 CO 2 RR-related papers from six publishers. Next, we developed named entity recognition (NER) models to extract twelve entities related to material names (catalyst, electrolyte, etc.) and catalytic performance (Faradaic efficiency, current density, etc.). Further, among several tested models, the MatBERT-based approach achieved the highest accuracy, with an average F1-score of 90.4% and an F1-score of 95.2% in a boundary relaxation evaluation scheme. The accurate and accelerated NER-based data extraction from a large volume of catalysis literature enables temporal trend analyses of the CO 2 RR catalysts, products, and performances, revealing the potentially effective material space in CO 2 RRs. While this work demonstrates the effectiveness of our ML-based text mining methods for specifically CO 2 RR literature, the methods and approach are applicable to and may be used to accelerate the development of other catalytic chemical reactions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

TreeSAPP: the Tree-based Sensitive and Accurate Phylogenetic Profiler

Microbial communities drive matter and energy transformations integral to global biogeochemical cycles, yet many taxonomic groups facilitating these processes remain poorly represented in biological sequence databases. Due to this missing information, taxonomic assignment of sequences from environmental genomes remains inaccurate. We present the Tree-based Sensitive and Accurate Phylogenetic Profiler (TreeSAPP) software for functionally and taxonomically classifying genes, reactions and pathways from genomes of cultivated and uncultivated microorganisms using reference packages representing coding sequences mediating multiple globally relevant biogeochemical cycles. TreeSAPP uses linear regression of evolutionary distance on taxonomic rank to improve classifications, assigning both closely related and divergent query sequences at the appropriate taxonomic rank. TreeSAPP is able to provide quantitative functional and taxonomic classifications for both assembled and unassembled sequences and files supporting interactive tree of life visualizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES): a method for populating knowledge bases using zero-shot learning

Abstract Motivation Creating knowledge bases and ontologies is a time consuming task that relies on manual curation. AI/NLP approaches can assist expert curators in populating these knowledge bases, but current approaches rely on extensive training data, and are not able to populate arbitrarily complex nested knowledge schemas. Results Here we present Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES), a Knowledge Extraction approach that relies on the ability of Large Language Models (LLMs) to perform zero-shot learning and general-purpose query answering from flexible prompts and return information conforming to a specified schema. Given a detailed, user-defined knowledge schema and an input text, SPIRES recursively performs prompt interrogation against an LLM to obtain a set of responses matching the provided schema. SPIRES uses existing ontologies and vocabularies to provide identifiers for matched elements. We present examples of applying SPIRES in different domains, including extraction of food recipes, multi-species cellular signaling pathways, disease treatments, multi-step drug mechanisms, and chemical to disease relationships. Current SPIRES accuracy is comparable to the mid-range of existing Relation Extraction methods, but greatly surpasses an LLM’s native capability of grounding entities with unique identifiers. SPIRES has the advantage of easy customization, flexibility, and, crucially, the ability to perform new tasks in the absence of any new training data. This method supports a general strategy of leveraging the language interpreting capabilities of LLMs to assemble knowledge bases, assisting manual knowledge curation and acquisition while supporting validation with publicly-available databases and ontologies external to the LLM. Availability and implementation SPIRES is available as part of the open source OntoGPT package: https://github.com/monarch-initiative/ontogpt.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger

This report summarizes some of the ongoing work related to the development of an expert-elicitation-digital-twin framework for real time damage state prediction in heat exchanger components of a nuclear reactor. The framework is targeted towards predicting damage associated with coupled low cycle fatigue (associated with regular heat-up, cool-down and power operation transients) and high cycle fatigue (associated with flow induced vibration transients). The overall framework will be based on a NoSQL based database, physics-infused-geometry-dependent virtual-sensor data, different AI/ML techniques-based data-driven-predictive-model applications (Apps) and real-time plant sensor measurements available through few existing sensors. Towards this overall goal, this report updates some of the ongoing work, such as on implementation of a NoSQL Database (such as MongoDB), FE based heat transfer analysis of a heat exchanger (e.g. of a PWR steam generator) for generating geometry-dependent virtual sensor data and evaluation of various AI/ML models such as based on multivariate linear regression, ensembled decision-tree based Random-Forest and Gradient-Boosting regression and high-dimensional-kernel-function-transformation based Support-Vector-Machine regression models. The AI/ML models were evaluated for predicting multi-time-series thermal states at thousands of 3D point-clouds

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Bibliometric review and recent advances in total scattering pair distribution function analysis: 21 years in retrospect

Global research activities have been driven by the quest to develop and characterize novel materials for technological advancements. The total scattering pair distribution function (TSPDF) is a powerful and versatile characterization technique for examining the structural details of diverse complex materials including liquid, amorphous, disordered crystalline, and nanostructured materials. Thus, it is critical to keep track of research progress, identify research gaps, and future research directions of the application of the TSPDF technique in materials development and discovery. In this work, a bibliometric analysis of literature regarding the TSPDF technique between 2000 and 2021 was conducted using datasets retrieved from the Web of Science database. The research trends based on publication outputs, research subject distribution, co-authorships among institutions, countries/regions, co-citation of referenced sources, and keyword co-occurrence are evaluated and discussed herein. The impact of the TSPDF technique is projected to increase due to its importance in probing emerging functional materials, and the advances in specialized facilities and instrumentation among the scientific communities engaged with it. Finally, current and emerging research hotspots related to TSPDF technique such as catalysis, computer modeling and simulation, pharmaceutics, machine learning, hydrogen storage, battery materials, and layered structured materials are also identified and discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Procedure for Creating, Reviewing, and Submitting Input Files into the Benchmark Library

Los Alamos National Laboratory (LANL) has a number of criticality benchmark input files, modeled for use with MCNP, developed and maintained by various organizations including the Nuclear Criticality Safety (NCS) Division, XCP-3, and XCP-5. The purpose of this procedure is to standardize input file creation, review, and maintenance. This procedure incorporates existing practices used by IRSN as documented in the original report and the translation from French in addition to practices used by the LANL NCS Division as documented in. The report relates to input files used for the validation of the continuous energy Monte Carlo code MCNP6 but is general enough to be extended to other codes. This report describes the procedure on how to prepare and use benchmarks (taken mainly from the ICSBEP Handbook) for the validation of calculation routes. In particular, this report describes the procedure to follow in the creation of the input files and their integration in the experimental validation databases.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

IM3 Open Source Data Center Atlas

IM3 Open Source Data Center Atlas Description This dataset contains locations of existing data center facilities in the United States. Data center locations were derived from OpenStreetMap (OSM), a crowd-sourced database. Data points from OSM are processed in various ways to determine additional variables provided in the data including: facility area (square feet), associated US county, and US state. This dataset can be used to identify areas of concentrated data center development and inform government and private sector planning strategies for future buildout of data centers and the infrastructure necessary to support it. Usage Notes Validation of OSM-derived data center locations is an ongoing development under the IM3 project, and the database will be updated as new information becomes available. In some instances, both the data center area (e.g., campus) and individual data center buildings are included as overlapping areas in the database. Both values are retained. Data center points, buildings, and campus areas are provided as separate layers in the downloadable data package. Note that data items are not necessarily complete across layers. That is, a specific data center may only be present as a single point geometry in the "point" layer while other data centers are represented in both the campus and building layers. In some cases, data center campuses and/or buildings straddle a county boundary line. Mappings to both counties are retained in the database as separate rows. These data rows will have the same data center id information, but each will have different county information. Crowd-sourced data, by nature, relies on individuals and communities to provide information. As a result, some data may be missing where it has not yet been reported. As we collect information on additional data center locations and as OSM receives additional contributions, the database will be updated to capture additional data points not yet shown. Technical Information Data is available for download under the following formats: GeoPackage (GPKG) CSV Geospatial data is provided in the WGS84 (EPSG:4326) coordinate reference system. The GeoPackage download contains the following layers. See usage notes for more information. "point" "building" "campus" The "point" layer includes all data from OSM that had POINT geometry type (i.e., individual coordinates). The "building" layer includes all OSM data that did not have POINT geometry and where the building tag in the OSM export was neither equal to "no" or null. Data that did not meet the "point" or "building" qualification was assumed to be a facility campus and included in the "campus" layer. The dataset contains the following parameters. Variables provided by OSM are labeled with (OSM-provided). id - unique identification number (OSM-provided with prefix of "node/", "relation/" and similar attributes removed) state - name of US state state_abb - two letter US state abbreviation state_id - state ID number county - name of US county county_id - county ID number ref - reference numbers or codes (OSM-provided) operator - the name of the company, corporation, or person in charge facility (OSM-provided) name - name of facility (OSM-provided) sqft - surface area of facility polygon, measured in square feet. Only available for "building" and "campus" layers lat - latitude of data centroid point lon - longitude of data centroid point type – represented spatial information. One of "point", "building", or "campus". geometry – POLYGON geometry of area footprint (in "campus" and "building" layers) or POINT geometry of locations (in "point" layer). This parameter is not included in the csv download. Attribution Data center locations were derived from OpenStreetMap, which is made available at openstreetmap.org under the Open Database License (ODbL). US state and county boundary information was collected from the US Census Bureau for the year 2024, which is made publicly available at https://www.census.gov/geographies/mapping-files.html Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License The IM3 Open Source Data Center Atlas is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall [Pacific Northwest National Labor↗
DOE OSTI2026

IM3 Open Source Data Center Atlas

IM3 Open Source Data Center Atlas Description This dataset contains locations of existing data center facilities in the United States. Data center locations were derived from OpenStreetMap (OSM), a crowd-sourced database. Data points from OSM are processed in various ways to determine additional variables provided in the data including: facility area (square feet), associated US county, and US state. This dataset can be used to identify areas of concentrated data center development and inform government and private sector planning strategies for future buildout of data centers and the infrastructure necessary to support it. Usage Notes Validation of OSM-derived data center locations is an ongoing development under the IM3 project, and the database will be updated as new information becomes available. In some instances, both the data center area (e.g., campus) and individual data center buildings are included as overlapping areas in the database. Both values are retained. Data center points, buildings, and campus areas are provided as separate layers in the downloadable data package. Note that data items are not necessarily complete across layers. That is, a specific data center may only be present as a single point geometry in the "point" layer while other data centers are represented in both the campus and building layers. In some cases, data center campuses and/or buildings straddle a county boundary line. Mappings to both counties are retained in the database as separate rows. These data rows will have the same data center id information, but each will have different county information. Crowd-sourced data, by nature, relies on individuals and communities to provide information. As a result, some data may be missing where it has not yet been reported. As we collect information on additional data center locations and as OSM receives additional contributions, the database will be updated to capture additional data points not yet shown. Data items will occasionally be removed from OSM if they are misidentified, if they no longer exist, if they are duplicates of another item, or similar. For that reason, updated versions of this database may not contain all data center locations included in previous versions. Technical Information Data is available for download under the following formats: GeoPackage (GPKG) CSV Geospatial data is provided in the WGS84 (EPSG:4326) coordinate reference system. The GeoPackage download contains the following layers. See usage notes for more information. "point" "building" "campus" The "point" layer includes all data from OSM that had POINT geometry type (i.e., individual coordinates). The "building" layer includes all OSM data that did not have POINT geometry and where the building tag in the OSM export was neither equal to "no" or null. Data that did not meet the "point" or "building" qualification was assumed to be a facility campus and included in the "campus" layer. The dataset contains the following parameters. Variables provided by OSM are labeled with (OSM-provided). id - unique identification number (OSM-provided with prefix of "node/", "relation/" and similar attributes removed) state - name of US state state_abb - two letter US state abbreviation state_id - state ID number county - name of US county county_id - county ID number ref - reference numbers or codes (OSM-provided) operator - the name of the company, corporation, or person in charge facility (OSM-provided) name - name of facility (OSM-provided) sqft - surface area of facility polygon, measured in square feet. Only available for "building" and "campus" layers lat - latitude of data centroid point lon - longitude of data centroid point type – represented spatial information. One of "point", "building", or "campus". geometry – POLYGON geometry of area footprint (in "campus" and "building" layers) or POINT geometry of locations (in "point" layer). This parameter is not included in the csv download. Attribution Data center locations were derived from OpenStreetMap, which is made available at openstreetmap.org under the Open Database License (ODbL). US state and county boundary information was collected from the US Census Bureau for the year 2024, which is made publicly available at https://www.census.gov/geographies/mapping-files.html Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License The IM3 Open Source Data Center Atlas is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall [Pacific Northwest National Labor↗
DOE OSTI2020

BioSTEAM-LCA: An Integrated Modeling Framework for Agile Life Cycle Assessment of Biorefineries under Uncertainty

Biorefineries will play a critical role in sustainable bioeconomies, but projections of their environmental impacts vary widely. A core challenge with life cycle assessments (LCAs) of biorefineries is that they are often disconnected from biorefinery design, simulation, and techno-economic analysis (TEA). This lack of integration is a barrier to early stage technology and process evaluations, reducing consistency and transparency across sustainability indicators while limiting our understanding of the relative importance of individual factors (e.g., design decisions, greenhouse gas emission accounting procedures), how these factors interact, and trade-offs or synergies with process economics. In this study, we propose a new agile LCA framework, BioSTEAMLCA, which layers onto BioSTEAM (Biorefinery Simulation and Techno-Economic Analysis Modules, which automates biorefinery design, simulation, and TEA) to characterize the environmental impacts of biorefineries across a landscape of designs, technology performance assumptions, and contexts. Inventory databases and impact assessment methods are integrated to enable flexible user defined LCA system models, and the implications of uncertainties throughout the production system are characterized via Monte Carlo simulation. To demonstrate the capabilities of BioSTEAMLCA, we present a case study for sugarcane ethanol production. Overall, BioSTEAM-LCA enables computationally efficient, agile gate-to-gate LCA to evaluate biorefinery processes, the production of candidate biofuels and bioproducts, and trade-offs among productivity, economics, and environmental impacts under uncertainty.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A novel measurement of marginal Alfvén eigenmode stability during high power auxiliary heating in JET

The interaction of Alfvén eigenmodes (AEs) and energetic particles is one of many important factors determining the success of future tokamaks. In JET, eight in-vessel antennas were installed to actively probe stable AEs with frequencies ranging 25–250 kHz and toroidal mode numbers |n| < 20. During the 2019–2020 deuterium campaign, almost 7500 resonances and their frequencies f 0 , net damping rates γ < 0, and toroidal mode numbers were measured in almost 800 plasma discharges. From a statistical analysis of this database, continuum and radiative damping are inferred to increase with edge safety factor, edge magnetic shear, and when including non-ideal effects. Both stable AE observations and their associated damping rates are found to decrease with |n|. Active antenna excitation is also found to be ineffective in H-mode as opposed to L-mode; this is likely due to the increased edge density gradient’s effect on accessibility and ELM-related noise’s impact on mode identification. A novel measurement is reported of a marginally stable, edge-localized ellipticity-induced AE probed by the antennas during high-power auxiliary heating (ion cyclotron resonance heating and neutral beam injection) up to 25 MW. NOVA-K kinetic-MHD simulations show good agreement with experimental measurements of f 0 , γ, and n, indicating the dominance of continuum and electron Landau damping in this case. Similar experimental and computational studies are planned for the recent hydrogen and ongoing tritium campaigns, in preparation for the upcoming DT campaign.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Metatranscriptomics sheds light on the links between the functional traits of fungal guilds and ecological processes in forest soil ecosystems

Soil fungi belonging to different functional guilds, such as saprotrophs, pathogens, and mycorrhizal symbionts, play key roles in forest ecosystems. To date, no study has compared the actual gene expression of these guilds in different forest soils. We used metatranscriptomics to study the competition for organic resources by these fungal groups in boreal, temperate, and Mediterranean forest soils. Using a dedicated mRNA annotation pipeline combined with the JGI MycoCosm database, we compared the transcripts of these three fungal guilds, targeting enzymes involved in C- and N mobilization from plant and microbial cell walls. Genes encoding enzymes involved in the degradation of plant cell walls were expressed at a higher level in saprotrophic fungi than in ectomycorrhizal and pathogenic fungi. However, ectomycorrhizal and saprotrophic fungi showed similarly high expression levels of genes encoding enzymes involved in fungal cell wall degradation. Transcripts for N-related transporters were more highly expressed in ectomycorrhizal fungi than in other groups. Here, we showed that ectomycorrhizal and saprotrophic fungi compete for N in soil organic matter, suggesting that their interactions could decelerate C cycling. Metatranscriptomics provides a unique tool to test controversial ecological hypotheses and to better understand the underlying ecological processes involved in soil functioning and carbon stabilization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A novel measurement of marginal Alfven Eigenmode stability during high power auxiliary heating in JET

The interaction of Alfven Eigenmodes (AEs) and energetic particles is one of many important factors determining the success of future tokamaks. In JET, eight in-vessel antennas were installed to actively probe stable AEs with frequencies ranging 25-250 kHz and toroidal mode numbers |n| < 20. During the 2019-2020 deuterium campaign, almost 7500 resonances and their frequencies f0, net damping rates \gamma < 0, and toroidal mode numbers were measured in almost 800 plasma discharges. From a statistical analysis of this database, continuum and radiative damping are inferred to increase with edge safety factor, edge magnetic shear, and when including non-ideal effects. Both stable AE observations and their associated damping rates are found to decrease with |n|. Active antenna excitation is also found to be ineffective in H-mode as opposed to L-mode; this is likely due to the increased edge density gradient's effect on accessibility and ELM-related noise's impact on mode identification. A novel measurement is reported of a marginally stable, edge-localized Ellipticity-induced AE probed by the antennas during high-power auxiliary heating (ICRH and NBI) up to 25 MW. NOVA-K kinetic-MHD simulations show good agreement with experimental measurements of f0, \gamma, and n, indicating the dominance of continuum and electron Landau damping in this case. Similar experimental and computational studies are planned for the recent hydrogen and ongoing tritium campaigns, in preparation for the upcoming DT campaign.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Extending the Brick schema to represent metadata of occupants

Here, energy-related behaviors of occupants constitute a key factor influencing building performance; accordingly, the measured occupant data can support the objective assessment of the indoor environment and energy performance of buildings, which can inform building design and operational decisions. Existing data schemas focus on metadata of sensors, meters, physical equipment, and IoT devices in buildings; however, they are limited in representing the metadata of occupant data, including occupants' presence in spaces, movement between spaces, interactions with building systems or IoT devices, and preference of indoor environmental needs. To address this gap, an extension to the widely adopted metadata schema, Brick, is proposed to represent the contextual, behavioral, and demographic information of occupants. The proposed extension includes four parts: (1) a new “Occupant” class to represent occupants' demography and energy related behavioral patterns, (2) new subclasses under the Equipment class to represent envelope system and personal thermal comfort devices, (3) new subclasses under the Point class to represent occupant sensing and status, and (4) new auxiliary properties for occupant interactable equipment to represent the level of controllability for each piece of equipment by occupants. The extension is implemented in the Brick schema and has been tested using multiple occupant datasets from the ASHRAE Global Occupant Database. The extension enables Brick schema to capture diverse types of occupant sensing data and their metadata for FAIR data research and applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

A Study of the Transition to Turbulence in a Bed of 67 Spherical Pebbles

Packed beds are commonly found in many engineering systems and have been widely studied for decades. A relatively new packed bed system is the Pebble Bed Reactor, a type of generation-IV nuclear reactor. Unlike many of the packed beds encountered in chemical and process engineering applications, Pebble Bed Reactors are larger and operate at significantly higher Reynolds numbers. As a result of these differences, there is a very limited amount of information on the detailed flow physics that exist in these complex geometries. This work seeks to contribute to a growing database of flow data for Pebble Bed Reactor systems by performing Direct Numerical Simulations of the flow in an experimental bed of 67 pebbles for a range of conditions. Simulations are performed at a Prandtl number of 0.66 and Reynolds numbers from 300–600. These Reynolds numbers are chosen to gain additional knowledge on the spatial development of turbulence in these systems. Analysis of the Turbulent Kinetic Energy, turbulence anisotropy, and Turbulent Heat Flux is performed. Results demonstrate significant development of the TKE across the tested range of Reynolds numbers. Examination of both the TKE and THF reveal that development first occurs near the center of the bed and propagates radially as the flow moves further into the bed. Notable regions of negative production of turbulent kinetic energy are observed in regions where flow accelerates around pebble contact points. Furthermore, these regions are found to coincide with regions of 1-component turbulence.Kindly check and confirm, all authors email id is correctly identified.These are correct

Direct numberical simulation↗
DOE OSTI2021

Computational and experimental search for potential polyanionic K-ion cathode materials

Discovering high-energy cathode materials is critical to construct K-ion batteries for practical applications. Owing to the great success of layered oxides in Li- and Na-ion systems, K layered cathodes have also been investigated in recent years. However, the much larger size of K + compared to Li or Na introduces strong K + –K + interaction within the layer, which results in a sloped voltage profile thereby limiting the specific capacity and operating voltage. In contrast, polyanionic materials with a three-dimensional K + arrangement can effectively mitigate K + –K + interaction. In this work, ten K polyanionic compounds with theoretical capacity >100 mA h g –1 are screened from the Inorganic Crystal Structure Database as potential cathode materials for K-ion batteries. Among the ten proposed compounds, K 2 MnP 2 O 7 , K 2 Mn 2 P 2 O 7 F 2 , K 2 Fe 2 P 2 O 7 F 2 , and K 6 V 2 (PO 4 ) 4 with average voltage <4.5 V are synthesized and evaluated electrochemically. While the re-insertion of K into these compounds is not fully reversible, it may be related to the very high migration barrier that we compute for K ions. In addition, we show the successful synthesis of a series of K 3 V 3–x Cr x (PO 4 ) 4 (x = 0, 1, 2, 3) compounds. Among these, K 3 V 2 Cr(PO 4 ) 4 exhibits the largest reversible capacity, as revealed by the in situ investigation. Lastly, we find that the redox couples in many of these compounds sit at remarkably high potential, even higher than in equivalent Li compounds, which brings both opportunities and challenges in the future research of K polyanion cathodes.

25 ENERGY STORAGE↗