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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Comment on "Radiative forcings for 28 potential Archean greenhouse gases" by Byrne and Goldblatt (2014)

In the recent article by Byrne and Goldblatt, "Radiative forcing for 28 potential Archean greenhouse gases", Clim. Past. 10, 1779–1801 (2014), the authors employ the HITRAN2012 spectroscopic database to evaluate the radiative forcing of 28 Archean gases. As part of the evaluation of the status of the spectroscopy of these gases in the selected spectral region (50–1800 cm -1 ), the cross sections generated from the HITRAN line-by-line parameters were compared with those of the PNNL database of experimental cross sections recorded at moderate resolution. The authors claimed that for NO 2 , HNO 3 , H 2 CO, H 2 O 2 , HCOOH, C 2 H 4 , CH 3 OH and CH 3 Br there exist large or sometimes severe disagreements between the databases. In this work we show that for only three of these eight gases a modest discrepancy does exist between the two databases and we explain the origin of the differences. For the other five gases, the disagreements are not nearly at the scale suggested by the authors, while we explain some of the differences that do exist. In summary, the agreement between the HITRAN and PNNL databases is very good, although not perfect. Typically differences do not exceed 10 %, provided that HITRAN data exist for the bands/wavelengths of interest. It appears that a molecule-dependent combination of errors has affected the conclusions of the authors. In at least one case it appears that they did not take the correct file from PNNL (N 2 O 4 (dimer)+ NO 2 was used in place of the monomer). Finally, cross sections of HO 2 from HITRAN (which do not have a PNNL counterpart) were not calculated correctly in BG, while in the case of HF misleading discussion was presented there based on the confusion by foreign or noise features in the experimental PNNL spectra.

54 ENVIRONMENTAL SCIENCES↗
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 OSTI2020

HarDWR - Cumulative Water Rights Curves

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Recalculated based on Harmonized Water Rights Records v2.0 sourced from WestDAAT - Added "Unspecified" was a water source category, and files associated with this category v1.01 - Updated the names of each file with an ID number less than 3 digits to include leading 0s v1.0 - Initial public release Description This product an updated version of the database used as input to the WBM model (Grogan et al. in review; Grogan et al. 2022), and is the result of the step creating cumulative water rights curves described in Lisk et al. (2024). This database contains 2,667 individual .csv files, three for each Water Management Area (WMA) in the 11-state region. File Naming WMA_[###]_[X]W.csv, where [###] is the unique identifier for each WMA, and [X] is either "S" for surface water rights, "G" for groundwater rights, or "U" for unspecified. Column headers in each file: Year: the priority date year CUML: the total cumulative water rights allocated up to this priority date year (ft3s-1) Irrigation: The percent of cumulative water rights allocated to the Irrigation category up to this priority date year (%) Domestic: The percent of cumulative water rights allocated to the Domestic category up to this priority date year (%) Livestock: The percent of cumulative water rights allocated to the Livestock category up to this priority date year (%) Fish: The percent of cumulative water rights allocated to the Fish category up to this priority date year (%) Industrial: The percent of cumulative water rights allocated to the Industrial category up to this priority date year (%) Environmental: The percent of cumulative water rights allocated to the Environmental category up to this priority date year (%) Other: The percent of cumulative water rights allocated to the Other category up to this priority date year (%) In addition to the database files, there is a companion .csv file, called stateWMAs_ID.csv. The main purpose of this file is to provide the means of translating between the [###] unique identifier and the various other id of the WMA the file is attached to. This translation file has five columns, which are: basinNum: The official state given alpha-numeric identifier of the WMA basinName: the state provided English name of the area, where applicable state: the state name uniID: a unique identifier we created by concatenating the state name, and underscore, and the state numerical ID ID: a unique numeric identifier we created as a requirement for the files to be used within WBM (Grogan et al. in review) For those whom may be interested in exploring our code more in depth, we are also making available an internal data file for convenience. The file is in .RData format and contains everything described above as well as some minor additional objects used within the code calculating the cumulative curves. For completeness, here is a detailed description of the various objects which can be found within the .RData file: states: A character vector containing the state names for those states in the study region. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the second index in this object, the data for California will also be in the second index for each accompanying list. wmasRightsPersGrd: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The data frames in this object are the same as the above described .csv files, specifically for groundwater. wmasRightsPersSur: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The data frames in this object are the same as the above described .csv files, specifically for surface water. wmasRightsPersUnsp: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The data frames in this object are the same as the above described .csv files, specifically for water from an unspecified source. wmasRightsTotsGrd: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The values in the data frames of this object would be the equivalent to multiplying the CUML column by the sector percentage columns to create the sector total ft3s-1, specifically for groundwater. This object is provided for convenience to check out original total values, if desired. wmasRightsTotsSur: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The values in the data frames of this object would be the equivalent to multiplying the CUML column by the sector percentage columns to create the sector total ft3s-1, specifically for surface water. This object is provided for convenience to check out original total values, if desired. wmasRightsTotsUnsp: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The values in the data frames of this object would be the equivalent to multiplying the CUML column by the sector percentage columns to create the sector total ft3s-1, specifically for water from an unspecified source. This object is provided for convenience to check out original total values, if desired. wmaIDByState: A list of data frames which contain the by state allocation by WMA matrix. There is a matrix for each state in a different data frame within the object.

Economics↗
DOE OSTI2017

Ioss IO Subsystem

The Ioss is a database-independent package for providing an object-oriented, abstract interface to IO capabilities for a finite element application; and concrete database interfaces which provided input and/or output to exodusII, xdmf, generated, and heartbeat database formats. The Ioss provides an object-oriented C++-based IO interface for a finite element application code. The application code can perform all IO operations through the Ioss interface which is typically at a higher abstraction level than the concrete database formats. The Ioss then performs the needed operations to translate the finite element data to the specific format required by the concrete database implementations. The Ioss currently supports interfaces to exodusII, xdmf, generated, and heartbeat formats, but additional formats can be easily added.

Sjaardema, Gregory↗
DOE OSTI2019

EGS Collab Earth Modeling: Integrated 3D Model of the Testbed

The EGS Collab project is conducting a series of stimulation and interwell flow tests in an intermediate scale (~10-20 m) testbed located on the 4850 level in the Sanford Underground Research Facility (SURF) in Lead, South Dakota. The testbed consists of eight ~200 ft (~60 m) HQ-diameter (9.6 cm) boreholes that are drilled into the Poorman Formation from the western rib of the West Access Drift. Of the eight boreholes, one borehole is used as an injection/stimulation well, another sub-parallel borehole located about 10 m away from the injection well is used as a production well, and rest of the other boreholes are used as monitoring wells. For 3D visualization of the testbed as well as to provide spatially accurate and consistent parameter data for geomechanical and geophysical process modeling tasks, we are using Leapfrog Software (Seequent Limited). For analysis of fractures and creation of conditioned stochastic discrete fracture network (CS-DFN) model of the testbed, we are using FracMan (Golder Associates Inc.). As of mid-May 2019, the EGS Collab Leapfrog database contains the general layout of the drifts/shafts at the 4850 and 4100 levels; generalized geologic framework model of a domain that spans both the 4850 and 4100 levels; the CS-DFN of the testbed; and layouts of the EGS Collab testbed boreholes, kISMET testbed boreholes, and other existing boreholes at the 4850 level. This information has been obtained from the SURF Vulcan database, detailed laser scan and point surveys of borehole collar locations, directional surveys of borehole trajectories, and mapping of features in the drifts, boreholes, and cores. Similarly, other types of data that have been incorporated into the Leapfrog database include static features of the testbed such as natural discontinuities (e.g., fractures, weeps), location of monitoring instruments, and stimulation notches. Baseline geophysical characterization results (e.g., seismic, ERT, temperature, etc.) are also imported into the Leapfrog database. The location and size of packers, various point-source signals detected during stimulation and flow tests (e.g., temperature anomalies, micro-earthquake events, etc.) are also compiled in the database. All feature locations are reported using the local Homestake Mine coordinate system. In this paper, we present some of the geostructural characteristics as well as the status of the testbed as represented by Leapfrog/FracMan database/visualizations.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

AGR-5/6/7 Irradiation Monitoring Web Site Design

The Nuclear Data Management and Analysis System (NDMAS) manages irradiation experiment data collected under the Advanced Gas Reactor (AGR) Fuel Development and Qualification program within the Advanced Reactor Technologies Technology Development Office. The AGR-5/6/7 irradiation experiment combines the fifth, sixth, and seventh planned irradiation campaigns of the U.S. Department of Energy AGR Fuel Development and Qualification Program. These combined irradiation tests will consist of fuel qualification tests (AGR-5/6) and a margin test (AGR-7) for the industrially produced uranium oxycarbide (UCO) tristructural isotropic (TRISO) coated particle fuel developed by the AGR program. The objective of the AGR-5/6/7 irradiation experiment is to establish the performance of UCO TRISO fuel fabricated using engineering-scale equipment in a production environment under and beyond normal operating conditions (PNL-5245). Experimental irradiation measurements for this experiment (i.e., thermocouple temperature, gas flow rate, fission product release rate, gross gamma count, and Advanced Test Reactor (ATR) operational parameters) are captured automatically to the NDMAS database. Data derived from numerical simulation or other mathematical analyses (i.e., fast fluence, burnup, temperature, and fission product release to birth ratio) are captured to the database when provided by the researchers. The NDMAS database is a relational database accessed via structured query language and managed with Microsoft SQL Server. While select users can directly access the database, that is not the most convenient way to interact with the data. The NDMAS team constructs numerous tables that combine related datasets, and delivers those data to researchers via graphs and spreadsheets on a Microsoft SharePoint-based website. For the AGR-5/6/7 experimental data, a new subsite has been developed, along with a set of web pages. These pages will be used by members of fuel irradiation team for monitoring irradiation conditions according to specification (SPC-1749), by the physics modelers to access necessary model inputs, and by the nuclear engineering community to view and access data such as fuel temperatures, fission product release from capsules, and ATR operational parameters. The deliverable represented in this document is a SharePoint website containing tables of the AGR-5/6/7 measured and calculated data, graphs displaying the data, and links to source documents and references.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Andra's Underground Research Laboratory as a Tool to Prepare Monitoring System of Cigeo Project - 18070

Andra, as French radioactive waste management agency, plans to build an underground disposal facility, named Cigeo, dedicated to high and intermediate level, long lived radioactive wastes. To ensure the safety during the disposal operations that includes the principle of reversibility, the design of the associated monitoring system will be based on specific research and development program and feedback obtained over more than 15 years on tests performed in representative conditions. Andra's Underground Research Laboratory (URL), located at 500 m deep, is excavated in a clay-rich formation. It is dedicated to (i) assess the thermo-hydro-mechanical, chemical and radiologic behaviors of the rock and supporting layers (concrete and steel), (ii) test envisaged monitoring system, (iii) provide feedback on operational organization and safety management and (iv) qualify operating methods (digging, concrete pouring, monitoring system design, etc.) to be implemented in the future disposal. Data collected from (i) characteristics of samples retrieved from boreholes, (ii) experts' observations, (iii) sensors installed in situ and (iv) manual measurements, are integrated in a database. This database is dedicated to samples management (more than 73 600 classified samples), data measurements collection, and conversion to physical values and available to plots or exports. Each day, approximately 1.6 million of values are integrated in the database. Finally, a geo-referenced database is linked to the main database in order to allow the spatial localization, both on the surface or in the underground galleries. All these tools give confidence in Andra's capacity to monitor Cigeo and be ready for the future industrial pilot phase. The monitoring system will also allow, step by step, optimizing the disposal design, performing regular safety control, helping in decision making for reversibility and improving long term safety. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Computer Codes V and V Tests with a Large-Scale Sodium Thermal-Hydraulic Test Facility (STELLA)

The Korea Atomic Energy Research Institute (KAERI) is carrying out the design of a 'Prototype Generation IV Sodium-cooled Fast Reactor (PGSFR)' in cooperation with Argonne National Laboratory (ANL) in the US. In regard to computer codes V and V, a large-scale sodium thermal-hydraulic test program is currently being progressed in parallel with the PGSFR design, which is called STELLA (Sodium Test Loop for Safety Simulation and Assessment). The final objective of this program is to collect a separate and integral effect test database to validate the safety analysis codes and other thermal hydraulic design codes. In the first phase of the STELLA program, the sodium component test loop (hereafter called STELLA-1) was constructed at the KAERI site, and it has been used for performance tests of key sodium heat exchangers in PGSFR. Since this sodium test loop is basically for separate effects tests to verify the performance of key sodium components, the test database obtained from STELLA-1 has been used for the V and V of safety codes as well as heat exchanger design codes. The second phase of the program aiming at an integral effect test is called STELLA-2, which will be used for synthetic review of the key safety issues of PGSFR. The basic and detailed design phases of the STELLA-2 test facility are underway in accordance with the specific design requirements reflecting the whole design features of PGSFR. The facility installation is scheduled to be completed by 2018. Then the main experiments including the start-up tests will begin and the first integral effect test database will be constructed by the end of 2019. Based on the STELLA-2 platform, a simulation of the PGSFR transient will be made to evaluate the plant dynamic behaviors and demonstrate the decay heat removal performance. Multi-dimensional effects coming from a large sodium pool system will be identified as well. The test database obtained from the STELLA-2 experiment will be used for the validation of the safety analysis codes and other thermal hydraulics codes, and this project finally contributes to the specific design phase of PGSFR. This paper presents both the results of heat transfer tests for the key sodium heat exchangers using STELLA-1 and the first results of heat exchanger design codes V and V. The recent progress of the engineering design for the STELLA-2 test section is introduced and its specific design parameters are also presented in this paper with detailed evaluation results of their scaling distortions. A large-scale sodium thermal-hydraulic test program (STELLA) was introduced with the specific activities for separate and integral effects tests. The test database obtained from STELLA-1 was successfully used to validate both the heat exchanger design and safety analysis codes. Specific design features of the integral effects test facility (STELLA- 2) with a scaling ratio of 1/5 in length and 1/125 in volume were also presented. Validation of the STELLA-2 facility design is underway using system codes and CFD tools. Dedicated water-mockup experiments to verify the scaling design of key subsystems are currently being conducted as well. (authors)

42 ENGINEERING↗
DOE OSTI2020

Automated Data Review of Analytical Laboratory Results at Los Alamos National Laboratory - 20299

Newport News Nuclear BWXT-Los Alamos, LLC (N3B) collects samples in support of the U.S. Department of Energy's (DOE) Office of Environmental Management (EM) Los Alamos Legacy Cleanup Contract (LLCC). N3B receives and reviews over 1.6 million sample data points annually in support of various ongoing environmental monitoring and remediation projects of the LLCC. N3B must demonstrate and document that reported external analytical laboratory data produced for the LLCC are of sufficient quality to fulfill their intended purpose and to support defensible decision making as described in EPA QA/G4 Guidance for the Data Quality Objectives Process 1994. In 2018, N3B assumed management of the LLCC along with the Environmental Information Management (EIM) database that contains all historical and current environmental data associated with the LLCC. The entire EIM database is shared between N3B, Triad National Security, LLC (Triad), and New Mexico Environment Department (NMED). These three parties jointly manage the database, its configuration, and changes / updates. All environmental data that are entered into EIM are updated and available, on a daily basis, in the linked public database Intellus New Mexico (Intellus). The quality and defensibility of the environmental data generated from sampling activities is a key component of an effective remediation process. Providing quality data is accomplished through a data assessment process that includes examination, verification, and validation. Examination is the assessment of completeness of the deliverables, identification of any reporting errors, and determining the usability of the data based on the laboratory's evaluation of its data as described in the case narrative received with the data. Verification consists of an evaluation of the Electronic Data Deliverables (EDD) data report to determine the extent to which the external analytical laboratories met method and contract-specific quality control and reporting requirements. Validation consists of determining the data quality and the extent to which the external analytical laboratories accurately and completely reported all sample and quality control results and satisfied all contract requirements. EIM contains an automatic Data Validation Module which performs automated data review (DVM ADR). DVM ADR is a tool to assist in the validation process. When DVM ADR is used in conjunction with manual examination of sample data packages, the combination of the two will meet and exceed the requirements of verification. N3B recognized an opportunity for process improvement, focusing on DVM ADR configuration and enhancements in EIM. Testing EIM's configuration provided proof of the DVM ADR's capabilities and flexibility to accurately perform routine data checks based on analytical methods and regulatory requirements. In addition, the DVM ADR module was improved through enhancements for all analytes, particularly upgrades for radiochemistry data. Extensive testing of the DVM ADR module occurred using EDDs from actual laboratory analyses on the EIM testing site. During this process, N3B manipulated EDD information to verify that the actual outcomes matched the expected outcomes. The results of this testing were shared with the database architects, and configuration improvements were identified to address these results. During this process, N3B identified that the radiochemical DVM ADR capabilities were underutilized, and so enhanced the DVM ADR functionality with respect to radioanalytical assessment. N3B environmental data uploads to Intellus on a daily basis from EIM, once the analytical data undergoes examination and verification. As such, it is important to have a high level of confidence in the quality and defensibility of the data. The process of manual examination, along with the DVM ADR, in conjunction with full validation of a percentage the data specified through the Data Quality Objectives greatly increases efficiency of data review and confidence level of the quality of the data, and gives the project managers, governmental offices, and the public expedited access to high-quality data. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Design and implementation of HuREX analysis supporting interface - 171

Human error is recognized as one of the main causes to affect to the socio-technical system including nuclear power plant. Therefore, reducing human error through human reliability analysis (HRA) is a critical issue. Moreover, sufficient and reliable human performance data collection is a prerequisite for ensuring the safety of nuclear power plants. Korea Atomic Energy Research Institute developed the HuREX (Human Reliability data Extraction) framework to collect and analyze the responses of human operators from simulator training records of nuclear power plant. Thus, the development and computer software interfaces of the database are essential to efficiently support human reliability analysis. In this study, OPERA database was developed in accordance with the following process. First, the essential data required for the HRA analysis (e.g., plant data, procedures, system and equipment info, operating crews, results of simulator experiments, and so forth) was stored in the database. In addition, standardized procedures and related items were stored in the database to support HRA quantification. A data structure was developed to store crew responses conducted by operators to cope with an emergency situation during simulator experiments. In addition, various interfaces that escalate efficiency of the HuREX applications were developed. The HuREX analysis support interface was developed with HuREX Video Analyzer for observation of simulator collection data (e.g., audio-visual records, plant parameters, operator's action logs) and HuREX Data Analyzer for analysis of operator's conducted task. By using the OPERA database and HuREX analysis support interface developed through this research, maximizing the efficiency of human reliability analysis based on simulator training data. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Applying AI/ML Techniques to U.S. Nuclear Operating Experience Program

Idaho National Laboratory (INL) has provided technical assistance to the U.S. Nuclear Regulatory Commission (NRC) in reliability and risk analysis including the operating experience (OpE) program since the 1980s. The U.S. nuclear OpE program provides input parameters to the NRC Standardized Plant Analysis Risk models and the industry probabilistic risk assessment (PRA) models. While earlier PRA focuses were on at-power, internal event analysis, the risks from external hazards and during low power shutdown (LPSD) operation could be significant and the needs to develop LPSD PRA and external hazards PRA are on the rise. One issue in developing LPSD PRA is the reasonable estimation of shutdown initiative event (SDIE) frequencies. INL has developed and is maintaining an SDIE database for the NRC. However, this database is based on the reviewing of Licensee Event Reports (LERs), which is believed to be only a subset of “actual” shutdown initiating events occurred in the industry. This paper investigates a new approach to identify and characterize shutdown initiating events from the Institute of Nuclear Power Operations (INPO) industry database using machine learning techniques. The main process in this approach is to find out the relationship between key words in event descriptions and the SDIE categories as in the NRC SDIE database. The relationship can then be applied to the INPO database and search for SDIEs.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Integral nuclear data validation using experimental spent nuclear fuel compositions

Measurements of the isotopic contents of spent nuclear fuel provide experimental data that are a prerequisite for validating computer codes and nuclear data for many spent fuel applications. Under the auspices of the Organisation for Economic Co-operation and Development (OECD) Nuclear Energy Agency (NEA) and guidance of the Expert Group on Assay Data of Spent Nuclear Fuel of the NEA Working Party on Nuclear Criticality Safety, a new database of expanded spent fuel isotopic compositions has been compiled. The database, Spent Fuel Compositions (SFCOMPO) 2.0, includes measured data for more than 750 fuel samples acquired from 44 different reactors and representing eight different reactor technologies. Measurements for more than 90 isotopes are included. This new database provides data essential for establishing the reliability of code systems for inventory predictions, but it also has broader potential application to nuclear data evaluation. Furthermore, the database, together with adjoint based sensitivity and uncertainty tools for transmutation systems developed to quantify the importance of nuclear data on nuclide concentrations, are described.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Analyzing critical material demand: A revised approach

Apparent consumption has been widely used as a metric to estimate material demand. However, with technology advancement and complexity of material use, this metric has become less useful in tracking material flows, estimating recycling feedstocks, and conducting life cycle analysis of critical materials. Here, we call for future research efforts to focus on building a multi-tiered consumption database for the global trade network of critical materials. This approach will help track how raw materials are processed into major components (e.g., motor assemblies) and eventually incorporated into complete pieces of equipment (e.g., wind turbines). Foreseeable challenges would involve: 1) difficulty in obtaining a comprehensive picture of trade partners due to business sensitive information, 2) complexity of materials going into components of a machine, and 3) maintaining such a database. We propose ways to address these challenges such as making use of digital design, learning from the experience of building other databases, and developing a strategy for financial sustainability. Additionally, we recommend that, with the advancement of information technology, small steps toward building such a database will contribute significantly to our understanding of material flows in our society and the associated human impacts on the environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

“MS-Ready” structures for non-targeted high-resolution mass spectrometry screening studies

Chemical database searching has become a fxture in many non-targeted identifcation workfows based on highresolution mass spectrometry (HRMS). However, the form of a chemical structure observed in HRMS does not always match the form stored in a database (e.g., the neutral form versus a salt; one component of a mixture rather than the mixture form used in a consumer product). Linking the form of a structure observed via HRMS to its related form(s) within a database will enable the return of all relevant variants of a structure, as well as the related metadata, in a single query. A Konstanz Information Miner (KNIME) workfow has been developed to produce structural representations observed using HRMS (“MS-Ready structures”) and links them to those stored in a database. These MS-Ready structures, and associated mappings to the full chemical representations, are surfaced via the US EPA’s Chemistry Dashboard (https://comptox.epa.gov/dashboard/). This article describes the workfow for the generation and linking of ~700,000 MS-Ready structures (derived from ~760,000 original structures) as well as download, search and export capabilities to serve structure identifcation using HRMS. The importance of this form of structural representation for HRMS is demonstrated with several examples, including integration with the in silico fragmentation software application MetFrag. The structures, search, download and export functionality are all available through the CompTox Chemistry Dashboard, while the MetFrag implementation can be viewed at https://msbi.ipb-halle.de/MetFragBeta/.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Engagement opportunities in OECD NEA benchmark development

A myriad of opportunities is available to collaborate via international benchmark exercises and experimental data preservation activities. Many such opportunities abound under the auspices of the Nuclear Science Committee of the Organisation for Economic Co-operation and Development Nuclear Energy Agency (NEA). Key projects and activities of relevance to the development of advanced reactors design include the International Criticality Safety Benchmark Evaluation Project (ICSBEP), the International Reactor Physics Experiment Evaluation Project (IRPhEP), the International Assay Data of Spent Nuclear Fuel Database (SFCOMPO), the Shielding Integral Benchmark and Archive Database (SINBAD), and The International Experimental Thermal HYdraulicS Database (TIETHYS), and various cooperative benchmark exercises. Interested participants are encouraged to contact the leadership and secretariat of the various Technical Working Groups and Working Parties to become more engaged. This paper provides a summary of the current benchmark exercises and experimental databases available for international participation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

FeGenie: a comprehensive tool for the identification of iron genes and iron gene neighborhoods in genomes and metagenome assemblies

Iron is a micronutrient for nearly all life on Earth. It can be used as an electron donor and electron acceptor by iron-oxidizing and iron-reducing microorganisms and is used in a variety of biological processes, including photosynthesis and respiration. While it is the fourth most abundant metal in the Earth’s crust, iron is often limiting for growth in oxic environments because it is readily oxidized and precipitated. Much of our understanding of how microorganisms compete for and utilize iron is based on laboratory experiments. However, the advent of next-generation sequencing and surge in publicly available sequence data has made it possible to probe the structure and function of microbial communities in the environment. To bridge the gap between our understanding of iron acquisition, iron redox cycling, iron storage, and magnetosome formation in model microorganisms and the plethora of sequence data available from environmental studies, we have created a comprehensive database of hidden Markov models (HMMs) based on genes related to iron acquisition, storage, and reduction/oxidation in Bacteria and Archaea. Along with this database, we present FeGenie, a bioinformatics tool that accepts genome and metagenome assemblies as input and uses our comprehensive HMM database to annotate provided datasets with respect to iron-related genes and gene neighborhood. An important contribution of this tool is the efficient identification of genes involved in iron oxidation and dissimilatory iron reduction, which have been largely overlooked by standard annotation pipelines. We validated FeGenie against a selected set of 28 isolate genomes and showcase its utility in exploring iron genes present in 27 metagenomes, 4 isolate genomes from human oral biofilms, and 17 genomes from candidate organisms, including members of the candidate phyla radiation. We show that FeGenie accurately identifies iron genes in isolates. Furthermore, analysis of metagenomes using FeGenie demonstrates that the iron gene repertoire and abundance of each environment is correlated with iron richness. While this tool will not replace the reliability of culture-dependent analyses of microbial physiology, it provides reliable predictions derived from the most up-to-date genetic markers. FeGenie’s database will be maintained and continually updated as new genes are discovered.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A fast comparative genome browser for diverse bacteria and archaea

Genome sequencing has revealed an incredible diversity of bacteria and archaea, but there are no fast and convenient tools for browsing across these genomes. It is cumbersome to view the prevalence of homologs for a protein of interest, or the gene neighborhoods of those homologs, across the diversity of the prokaryotes. We developed a web-based tool, fast . genomics , that uses two strategies to support fast browsing across the diversity of prokaryotes. First, the database of genomes is split up. The main database contains one representative from each of the 6,377 genera that have a high-quality genome, and additional databases for each taxonomic order contain up to 10 representatives of each species. Second, homologs of proteins of interest are identified quickly by using accelerated searches, usually in a few seconds. Once homologs are identified, fast . genomics can quickly show their prevalence across taxa, view their neighboring genes, or compare the prevalence of two different proteins. Fast . genomics is available at https://fast.genomics.lbl.gov .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Toward a Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry‐Informed Transfer Learning

Optimally designing applications of molten salts requires knowledge of their thermophysical properties over a wide range of temperatures and compositions. There exist significant gaps in existing databases and this data can be challenging to experimentally measure due to high temperatures, salt corrosivity, and salt hygroscopicity. Existing databases have been used to create Redlich–Kister (RK) models for mixture density showing improved accuracy with respect to ideal mixing assumptions, but these models require subcomponent data measurements for each new system, therefore lacking generality. In order to address generalizability and data sparsity, a transfer learning procedure is proposed to train deep neural networks (DNNs) using a combination of semi‐empirical relationships (RK), data from the thermophysical arm of the molten salt thermal properties database and universal ab initio properties of component mixtures taken from the joint automated repository for various integrated simulations (JARVIS) classical force‐field inspired descriptors database to predict density in molten salts. Herein, it is shown that DNNs predict molten salt density with an r 2 over 0.99 and a mean absolute percentage error under 1%, outperforming alternative methods.

inorganic materials↗