Results for “Repository” — Page 14

DOE OSTI2023

Connecting People to Data: Enabling Data Connected Communities through Enhancements to the Geothermal Data Repository: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) has implemented a series of new features designed to connect people to data. These features, which are based on feedback from the GDR user community and surveys of the greater geothermal research community, are designed to improve data quality and empower members of all communities to better engage with geothermal data resources by providing universal access to data and by improving the connections between data providers, subject matter experts, and the communities of people using GDR data. This paper will explore some of the recent enhancements made to the GDR to improve data discoverability, reduce submission time, and result in better quality data submissions. These improvements include the ability for users to save a list of their favorite datasets, search for insight into geothermal datasets or data availability, or sign up to receive notifications of future updates to specific datasets. These improvements aim to enhance the overall user experience of the GDR while further connecting communities to the data they need to inform decisions, advance geothermal research, and develop innovative solutions to local energy problems.

DOE↗

DOE OSTI2023

An Update on the Geothermal Data Repository's Data Standards and Pipelines: Geospatial Data and Distributed Acoustic Sensing Data: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has implemented data standards and automated data pipelines for the following data types: 1) drilling data, 2) geospatial datasets, and 3) DAS data. An additional data pipeline is proposed for stimulation data. These data standards and pipelines are intended to improve the real-world applicability of geothermal machine learning outputs through improving the quality of data. More specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, allowing more time to be spent on actual research. By automating this process, the burden of standardization is taken off of the user, overall increasing the availability of standardized data. This paper provides an update on the GDR's transition toward data standardization through automated data pipelines and calls for feedback from the community on how we can improve this process.

cloud-optimized↗

DOE OSTI2023

An Update on the Geothermal Data Repository's Data Standards and Pipelines: Geospatial Data and Distributed Acoustic Sensing Data

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has implemented data standards and automated data pipelines for the following data types: 1) drilling data, 2) geospatial datasets, and 3) DAS data. An additional data pipeline is proposed for stimulation data. These data standards and pipelines are intended to improve the real-world applicability of geothermal machine learning outputs through improving the quality of data. More specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, allowing more time to be spent on actual research. By automating this process, the burden of standardization is taken off of the user, overall increasing the availability of standardized data. This paper provides an update on the GDR's transition toward data standardization through automated data pipelines and calls for feedback from the community on how we can improve this process.

cloud-optimized↗

DOE OSTI2013

Sub-Seabed Repository for Nuclear Waste - a Strategic Alternative - 13102

It was recognized at the outset of nuclear power generation in the 1950's that the waste products would require isolation away from humans for periods in excess of 10,000 years. After years studying alternatives, the DOE recommended pursuing the development of a SNF/HLW disposal facility within Yucca Mountain in the desert of Nevada. That recommendation became law with passage of the NWPAA, effectively stopping development of other approaches to the waste problem. In the face of political resistance from the state of Nevada, the 2010 decision to withdraw the license application for the geologic repository at Yucca Mountain has delayed further the most mature option for safe, long-term disposal of SNF and HLW. It is time to revisit an alternative option, sub-seabed disposal within the US Exclusive Economic Zone (EEZ), which would permanently sequester waste out of the biosphere, and out of the reach of saboteurs or terrorists. A proposal is made for a full scale pilot project to demonstrate burying radioactive waste in stable, deep ocean sediments. While much of the scientific work on pelagic clays has been done to develop a sub-seabed waste sequestration capability, this proposal introduces technology from non-traditional sources such as riser-less ocean drilling and the Navy's Sound Surveillance System. The political decisions affecting the issue will come down to site selection and a thorough understanding of comparative risks. The sub-seabed sequestration of nuclear waste has the potential to provide a robust solution to a critical problem for this clean and reliable energy source. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

An Update on the Geothermal Data Repository's Data Standards and Pipelines: Geospatial Data and Distributed Acoustic Sensing Data

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has implemented or is currently implementing data standards and automated data pipelines for the following geothermal data types: 1) drilling data, 2) geospatial datasets, and 3) Distributed Acoustic Sensing (DAS) data. These data standards and pipelines are intended to improve the real-world applicability of geothermal machine learning outputs through improving the quality of data. More specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, allowing more time to be spent on actual research. By automating this process, the burden of standardization is taken off of the user, overall increasing the availability of standardized data. This paper provides an update on the GDR's transition toward data standardization through automated data pipelines and calls for feedback from the community on how the GDR team can improve this process.

cloud-optimized↗

DOE OSTI2023

Connecting People to Data: Enabling Data Connected Communities through Enhancements to the Geothermal Data Repository

The Department of Energy's (DOE) Geothermal Data Repository (GDR) has implemented a series of new features designed to connect people to data. These features, which are based on feedback from the GDR user community and surveys of the greater geothermal research community, are designed to improve data quality and empower members of all communities to better engage with geothermal data resources by providing universal access to data and by improving the connections between data providers, subject matter experts, and the communities of people using GDR data. This paper will explore some of the recent enhancements made to the GDR to improve data discoverability, reduce submission time, and result in better quality data submissions. These improvements include the ability for users to save a list of their favorite datasets, search for insight into geothermal datasets or data availability, or sign up to receive notifications of future updates to specific datasets. These improvements aim to enhance the overall user experience of the GDR while further connecting communities to the data they need to inform decisions, advance geothermal research, and develop innovative solutions to local energy problems.

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DOE OSTI2024

Empowering Geothermal Research: The Geothermal Data Repository's New AI Research Assistant: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has integrated a Large Language Model (LLM) with the metadata and supporting documents associated with GDR datasets to create an Artificially Intelligent (AI) research assistant. By leveraging work done to make GDR metadata machine-readable and an open-source LLM integration model called the Energy Language Model, developed by the National Renewable Energy Laboratory, AskGDR serves as a virtual research assistant to GDR users. It provides answers to a variety of user-provided questions using natural language processing and generative machine learning. Users can get answers to questions about specific datasets, including inquiries about the equipment, assumptions and methodologies used in the origination of the data; or more abstract questions, such as the applicability of data to specific research fields. AskGDR improves the discoverability of geothermal data by helping guide users to datasets beyond simple keyword searches. It enables users to find data based on properties of the data, discover information contained within supporting documents, and explore data from projects related to their research objectives.

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DOE OSTI2025

Empowering Geothermal Research: The Geothermal Data Repository's New AI Research Assistant

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has integrated a Large Language Model (LLM) with the metadata and supporting documents associated with GDR datasets to create an Artificially Intelligent (AI) research assistant. By leveraging work done to make GDR metadata machine-readable and an open-source LLM integration model called the Energy Language Model, developed by the National Renewable Energy Laboratory, AskGDR serves as a virtual research assistant to GDR users. It provides answers to a variety of user-provided questions using natural language processing and generative machine learning. Users can get answers to questions about specific datasets, including inquiries about the equipment, assumptions and methodologies used in the origination of the data; or more abstract questions, such as the applicability of data to specific research fields. AskGDR improves the discoverability of geothermal data by helping guide users to datasets beyond simple keyword searches. It enables users to find data based on properties of the data, discover information contained within supporting documents, and explore data from projects related to their research objectives. This paper will outline the development, integration, output, and efficacy of the AskGDR LLM, including adherence to scientific rigor through improvements designed to increase the accuracy of generated answers, avoid speculation, and provide proper references for all resources used.

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DOE OSTI2025

Lessons Learned from AskGDR: Usage and Impact Analysis of the Geothermal Data Repository's AI Research Assistant: Preprint

In October of 2024, the Department of Energy's (DOE) Geothermal Data Repository (GDR) team officially launched AskGDR, an AI research assistant resulting from the integration of a Large Language Model (LLM) with the metadata and supporting documents associated with GDR datasets. AskGDR allows GDR users to ask deeper questions about the origin of datasets, the methods used to collect them, and the findings they help support. Using Retrieval Augmented Generation (RAG), AskGDR can be used to summarize findings spread across dozens of papers and technical reports or to extract relevant information describing a single data field. However, generative AI is experimental. The National Renewable Energy Laboratory (NREL) has been collecting metrics on AskGDR and documenting lessons learned during its deployment. This paper will outline the efficacy and impact of AskGDR through analysis of its use, operating costs, number and types of questions asked, and the quality of answers provided.

15 GEOTHERMAL ENERGY↗

DOE OSTI2018

FishGen.net: An Online Genetic Repository for Salmon and Steelhead Genetic Baselines

Abstract FishGen is a final repository for Pacific salmon Oncorhynchus spp. and steelhead O. mykiss genetic data generated as part of the genetic stock identification and parentage-based tagging projects in the Columbia River basin and throughout the Pacific Coast of North America. Resource Data, Inc., developed this web-based, GIS-interfaced software, which is freely available to the public, with funding from the Pacific Coastal Salmon Recovery Fund and Bonneville Power Administration. FishGen currently houses genetic stock identification baselines for both Chinook Salmon O. tshawytscha and steelhead in the Columbia and Snake river basins, as well as hatchery, parentage-based, tagging baselines for both species in the Snake River basin. Because it has a user-friendly interface and protocol for submitting and storing standardized genetic and sample metadata, it is an excellent tool for supporting genetic research and monitoring projects throughout the region.

McCane, Jesse↗

DOE OSTI2017

Anthropogenic influences on groundwater in the vicinity of a long-lived radioactive waste repository: Anthropogenic influences on groundwater

The groundwater flow system in the Culebra Dolomite Member (Culebra) of the Permian Rustler Formation is a potential radionuclide release pathway from the Waste Isolation Pilot Plant (WIPP), the only deep geological repository for transuranic waste in the United States. We did not expect that early conceptual models of the Culebra, groundwater levels would fluctuate markedly, except in response to long-term climatic changes, with response times on the order of hundreds to thousands of years. Recent groundwater pressures measured in monitoring wells record more than 25 m of drawdown. The fluctuations are attributed to pumping activities at a privately-owned well that may be associated with the demand of the Permian Basin hydrocarbon industry for water. Furthermore, the unprecedented magnitude of drawdown provides an opportunity to quantitatively assess the influence of unplanned anthropogenic forcings near the WIPP. Spatially variable realizations of Culebra saturated hydraulic conductivity and storativity were used to develop groundwater flow models to estimate a pumping rate for the private well and investigate its effect on advective transport. Simulated drawdown shows reasonable agreement with observations (average Model Efficiency coefficient = 0.7). Steepened hydraulic gradients associated with the pumping reduce estimates of conservative particle travel times across the domain by one-half and shift the intersection of the average particle track with the compliance boundary by more than two kilometers. Finally, the value of the transient simulations conducted for this study lie in their ability to (i) improve understanding of the Culebra groundwater flow system and (ii) challenge the notion of time-invariant land use in the vicinity of the WIPP.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

The new Wallula CO{sub 2} project may revive the old Columbia River Basalt (western USA) nuclear-waste repository project

A novel CO{sub 2} sequestration project at Wallula, Washington, USA, makes ample use of the geoscientific data collection of the old nuclear waste repository project at the Hanford Site nearby. Both projects target the Columbia River Basalt (CRB). The new publicity for the old project comes at a time when the approach to high-level nuclear waste disposal has undergone fundamental changes. The emphasis now is on a technical barrier that is chemically compatible with the host rock. In the ideal case, the waste container is in thermodynamic equilibrium with the host-rock groundwater regime. The CRB groundwater has what it takes to represent the ideal case.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Evaluation of desiccation cracking characteristics of inorganic micro-fiber-reinforced engineered barrier material (IMEBM) for geological repository

Abstract Buffer material is crucial for the engineering barrier system to dispose of high-level radioactive waste in a geological repository. A reliable buffer material should be able to maintain good sealing characteristics and minimize desiccation cracking. In this study, the effectiveness of inorganic fiber-reinforced engineering barrier material in reducing desiccation cracks in bentonite was evaluated via desiccation tests, image analysis, and air permeability tests. The effects of fiber type (E-glass fiber and basalt fiber) and fiber content (0.0%, 0.5%, 1.0%, 1.5%, 3.0%, and 5.0% of dry weight of the bentonite) on the development of desiccation cracks in the fiber–bentonite mixtures with the same given initial moisture content were evaluated. The results indicated that the addition of fibers could significantly reduce the crack size and area in bentonite during the drying. Basalt fibers showed a slightly better reinforcement effect than E-glass fibers when the fiber content was lower than 3.0%. The addition of fibers prevented the development of penetrating cracks and significantly reduced the permeability of the bentonite after drying. The permeabilities of basalt fiber- and E-glass fiber-reinforced bentonite composites with 3% reinforcement were 5.81 × 10 –11 m 2 and 7.24 × 10 –11 m 2 , respectively, which were 64 and 51 times smaller than that of pure bentonite. X-ray–CT observation of the internal structure of the samples after drying showed that the addition of fibers significantly changed the crack morphology and potentially increased the tortuosity.

Feng, Yuan↗

DOE OSTI2022

Sensitivity analysis of generic deep geologic repository with focus on spatial heterogeneity induced by stochastic fracture network generation

Geologic Disposal Safety Assessment Framework is a state-of-the-art simulation software toolkit for probabilistic post-closure performance assessment of systems for deep geologic disposal of nuclear waste developed by the United States Department of Energy. This paper presents a generic reference case and shows how it is being used to develop and demonstrate performance assessment methods within the Geologic Disposal Safety Assessment Framework that mitigate some of the challenges posed by high uncertainty and limited computational resources. Variance-based global sensitivity analysis is applied to assess the effects of spatial heterogeneity using graph-based summary measures for scalar and time-varying quantities of interest. Behavior of the system with respect to spatial heterogeneity is further investigated using ratios of water fluxes. This analysis shows that spatial heterogeneity is a dominant uncertainty in predictions of repository performance which can be identified in global sensitivity analysis using proxy variables derived from graph descriptions of discrete fracture networks. New quantities of interest defined using water fluxes proved useful for better understanding overall system behavior.

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DOE OSTI2016

A record of uranium-series transport at Nopal I, Sierra Pena Blanca, Mexico: Implications for natural uranium deposits and radioactive waste repositories

Studies of uranium-series (U-series) disequilibria within and around ore deposits provide valuable information on the extent and timing of actinide mobility, via mineral-fluid interaction, over a range of spatial and temporal scales. Such information is useful in studies of analogs of high-level nuclear-waste repositories, as well as for mining and mineral extraction sites, locations of previous nuclear weapons testing, and legacy nuclear waste contamination. In this study we present isotope dilution mass spectrometry U-series measurements for fracture-fill materials (hematite, goethite, kaolinite, calcite, dolomite and quartz) from one such analog; the Nopal I uranium ore deposit situated at Peña Blanca in the Chihuahua region of northern Mexico. The ore deposit is located in fractured, unsaturated volcanic tuff and fracture-fill materials from surface fractures as well as fractures in a vertical drill core have been analyzed. High uranium concentrations in the fracture-fill materials (between 12 and 7700 ppm) indicate uranium mobility and transport from the deposit. Furthermore, uranium concentrations generally decrease with horizontal distance away from the deposit but in this deposit there is no trend with depth below the surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Estimation of stress and stress-induced permeability change in a geological nuclear waste repository in a thermo-hydrologically coupled simulation

Geologic disposal is a promising solution for a safe permanent isolation of accumulated high-level nuclear waste from nuclear powerplants. The behavior of host rock is highly coupled thermally, hydromechanically, and chemically. Numerical simulations of such coupled phenomena for the extremely long term (>100,000 years) and large length scale (>1 km) of geologic disposal remain to be computationally challenging. In this study, a methodology has been developed to approximate stress and stress-induced permeability change in host rock using only thermo-hydrological (TH) variables. A coupled thermo-hydromechanical (THM) simulation is carried out using TOUGH-FLAC simulator to model THM behaviors of a generic nuclear waste repository, in order to evaluate the performance of the developed methodology, which is implemented in a coupled TH simulation. Results show that stress and permeability change estimated by the developed methodology in the TH-coupled simulation match those calculated in the THM-coupled simulation over the simulated timeframe of over 10,000 years. Details about errors in stress and permeability estimates accrued by the developed methodology are discussed here. The developed methodology will help incorporate stress-induced permeability change into existing TH simulators for the long-term radionuclide transport in geologic disposal.

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DOE OSTI2014

CPTAC Assay Portal: a repository of targeted proteomic assays

To address these issues, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) of the National Cancer Institute (NCI) has launched an Assay Portal (http://assays.cancer.gov) to serve as a public repository of well-characterized quantitative, MS-based, targeted proteomic assays. The purpose of the CPTAC Assay Portal is to facilitate widespread adoption of targeted MS assays by disseminating SOPs, reagents, and assay characterization data for highly characterized assays. A primary aim of the NCI-supported portal is to bring together clinicians or biologists and analytical chemists to answer hypothesis-driven questions using targeted, MS-based assays. Assay content is easily accessed through queries and filters, enabling investigators to find assays to proteins relevant to their areas of interest. Detailed characterization data are available for each assay, enabling researchers to evaluate assay performance prior to launching the assay in their own laboratory.

Environmental Molecular Sciences Laboratory↗

DOE OSTI2020

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov .

36 MATERIALS SCIENCE↗