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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Past Approaches for Spent Nuclear Fuel, Transuranic, and High-Level Waste Disposal in the United States—Part 2: Siting Process, Staged Development, and Public Preferences

This report presents pertinent aspects of the ~50-year United States experience in siting a mined geologic disposal repository for spent nuclear fuel (SNF), transuranic (TRU) waste, and high-level radioactive waste (HLW) as related to site selection and the staged process for site investigations as specified in the Nuclear Waste Policy Act of 1982 and generic and site-specific regulations of the US Department of Energy (DOE), US Environmental Protection Agency (EPA), and US Nuclear Regulatory Commission (NRC). The roles of the Environmental Impact Statement and guidance in international consensus standards by the International Atomic Energy Agency are also mentioned. The focus is on siting and developing the Waste Isolation Pilot Plant, an operating repository for TRU waste from atomic energy defense activities, and the proposed Yucca Mountain repository for commercial SNF and HLW. In the social dimension, the role of institutional stakeholders is described. Past national surveys related to waste management options for storage and disposal provide insight on public preferences of other stakeholders. The descriptions are intended to help other countries more fully understand the stages adopted for siting and developing repositories in the United States.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2023

Towards Streamlining Auditing for Compliance With Requirements in Open-Source Software at NASA

Context: NASA requires all software to meet several requirements (NPR 7150.2) depending on software criticality. The instantiation of these requirements may vary per project; however, once decided upon, projects must undergo audits to evaluate compliance with these requirements. Aim: We propose that audit effort can be reduced when requirements are realized by leveraging commonly used open-source infrastructure for version control, issue tracking and continuous integration, and the generated records are analyzed using a repository mining software tool to quantify process compliance. Method: We perform a case study in the NASA-funded Copilot project, utilizing Kaiaulu, a repository mining software tool. We define four software compliance metrics based on the Copilot’s requirements, and analyze their impact on source code quality. Results: Our work demonstrates how it is possible to leverage existing open source tools and platforms to facilitate software certification and qualification, and to streamline the auditing process required even when stringent requirements must be enforced. Conclusion: Together, both project and tool can be utilized to visualize project compliance, and metrics can be defined to more easily identify process irregularities to minimize auditing efforts. Project Repository: github.com/Copilot-Language/copilot Tool Repository: github.com/sailuh/kaiaulu

code-quality↗
DOE OSTI2023

Water-Weakening and Time-Dependent Deformation of Organic-Rich Chalks

Abstract The Ghareb Formation is a shallowly buried porous chalk in southern Israel that is being considered as a host rock for a geologic nuclear waste repository. Setup and operation of a repository will induce significant mechanical, hydrological and chemical perturbations in the Ghareb. Developing a secure repository requires careful characterization of the rock behavior to different loads. To characterize hydromechanical behavior of the Ghareb, several short- and long-term deformation experiments were conducted. Hydrostatic loading tests were conducted both dry and water-saturated, using different setups to measure elastic properties, time-dependent behavior, and permeability. A set of triaxial tests were conducted to measure the elastic properties and rock strength under differential loading at dry and water-saturated conditions. The hydrostatic tests showed the Ghareb began to deform inelastically around 12–15 MPa, a relatively low effective pressure. Long-term permeability measurements demonstrated that permeability declined with increasing effective pressure and was permanently reduced by ~ 1 order of magnitude after unloading pressure. Triaxial tests showed that water saturation significantly degrades the rock properties of the Ghareb, indicating water-weakening is a significant risk during repository operation. Time-dependent deformation is observed during hold periods of both the hydrostatic and triaxial tests, with deformation being primarily visco-plastic. The rate of deformation and permeability loss is strongly controlled by the effective pressure as well. Additionally, during holds of both hydrostatic and triaxial tests, it is observed that when water-saturated, radial strain surpassed axial strain when above effective pressures of 13–20 MPa. Thus, deformation anisotropy may occur in situ during operations even if the stress conditions are hydrostatic when above this pressure range.

58 GEOSCIENCES↗
DOE OSTI2018

A mathematical discussion concerning the performance of multilayer perceptron-type artificial neural networks through use of orthogonal bipolar vectors

This work proposes the use of orthogonal bipolar vectors (OBV) as targets for multilayer perceptron (MLP) Artificial neural networks pertaining to the pattern recognition field. Implications involving this proposal are supported by a proper mathematical discussion. This study shows that the use of OBV as targets improves the class identification performance of MLP networks for pattern recognition with more than two classes when compared to the widely used conventional target bipolar vector (CBV). In addition to the overall performance improvement, it is possible to obtain high recognition rates with fewer training epochs, while keeping a feasible generalization with other data. This behavior is related to the Euclidean distance of the points in the output space, which is much higher in the case of OBVs. Therefore, with a greater distance between the points in the output space, the MLP network becomes less prone to associate trained outputs with incorrect targets. The mathematical discussion in this study is based on the backpropagation training algorithm. In addition to the mathematical discussion, results from experiments with real data are presented. These results follow those obtained by mathematical derivations. Real data sets used in the experiments are available from: (a) the Semeion Handwritten Digit of Machine Learning Repository, international repository; (b) Iris Image Database from the Chinese Academy of Sciences—CASIA; (c) Australian Sign Language, signs of Machine Learning Repository, international repository. Experiments were conducted to measure the mean Euclidean distance between the MLP network output subjected to a given input pattern and outputs related to other pattern classifications. Experimental results show that MLP networks trained with OBVs were able to ensure a Euclidean distance between the output and incorrect targets greater than MLP networks trained with CBVs (by up to 4 times for handwritten, by up to 12 times for iris images and by up to 10 times for Auslan signs language). As such, the adoption of OBVs as target vectors for MLPs reduces the computational effort for training the network.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Value of abstraction in performance assessment – When is a higher level of detail necessary?

In this study, different approaches in performance assessment (PA) of the long-term safety of a repository for radioactive waste were examined. This investigation was carried out as part of the DECOVALEX-2023 project, an international collaborative effort for research and model comparison. One specific task of the DECOVALEX-2023 project was the Salt Performance Assessment Modelling task (Salt PA), which aimed at comparing various models and methods employed in the performance assessment of deep geological repositories in salt. In the context of the Salt PA task, three distinct teams from SNL (United States), Quintessa Ltd (United Kingdom), and GRS (Germany) examined the consequences of employing different levels of abstractions when modelling the repository's geometry and implementing various features and processes, using the example of a simple hypothetical repository structure in domal salt. Each team applied their own tools: PFLOTRAN (SNL), QPAC (Quintessa) and LOPOS (GRS). These differ essentially regarding numerical concept and degree of detail in the representation of the underlying physical processes. The discussion focused on when simplifications can be appropriately applied and what consequences result from them. Furthermore, it was explored when and if a higher level of fidelity in geometry or physical processes is required.

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DOE OSTI2020

Epsilon Metal: A Waste Form for Noble Metals from Used Nuclear Fuel

Epsilon metal (e-metal) is the metallic phase that forms as inclusions at the grain boundaries in the UO2 fuel during reactor operation. This metal is composed of Pd, Mo, Rh, Ru, and Tc. These metallic inclusions are insoluble in strong acid and remnants of these metallic inclusions have been found in the UO2 matrix that remains from the natural reactors in Gabon that were active 1.8 billion years ago, therefore e-metal should be an excellent waste form for the immobilization of the long-lived isotopes 107Pd (6.5 ? 106 a) and 99Tc (2.13 ? 105 a), with 99Tc being the isotope of interest for repository performance. Therefore, the chemical durability of this potential waste form is assessed in this study. Typically, corrosion rates for metallic materials are measured electrochemically because they are quick, inexpensive, and can reveal the mechanism by which a metal corrodes, at least initially. However, in a repository the waste form would be subjected to slowly flowing water without an applied electrical potential over long time periods. Therefore, the corrosion rates of e-metal specimens were measured with both electrochemical tests and the single-pass flow-through test (SPFT). Potentiodynamic and potentiostatic polarization results suggest that a thin passive film exists on the alloy surface, which seems to be responsible for its high corrosion resistance. Additionally, X-Ray photoelectron spectroscopic results suggest that Pd oxides are significantly enriched in the passive film Results from the SPFT show that the dissolution rates were weakly dependent on pH. Only Mo and Re were found in solution and were used for the calculation of the dissolution rates. In general, the electrochemically determined corrosion rates agree reasonably well with the initial dissolution rate measured with the SPFT test, but they are about one or two orders of magnitude higher than the steady state rates. The causes for this discrepancy are discussed.

epsilon-metal, nuclear waste form, corrosion, sing↗
DOE OSTI2024

The effect of cerium, neodymium, and ytterbium doping on UO 2 dissolution

Here, the dissolution rate of spent nuclear fuel has been studied heavily to understand the impacts of a failed waste package scenario on potential radionuclide release from a geologic repository. Multiple countries have evaluated these scenarios in oxidizing and reducing environments respective to their relevant repository conditions. The repository environment (e.g., water chemistry, temperature, oxygen content) and the fuel itself (e.g., chemical content of the fuel) both heavily impact the dissolution rate of the spent fuel. This work examined the impact that rare earth element dopants (Ce, Nd, Yb) have on the fuel dissolution under repository relevant conditions with decreasing oxidizing conditions using a single pass flowthrough system. UO 2 samples were doped with Ce, Nd, or Yb with concentrations between 1 and 5 at%. The addition of dopants to the samples reduced the dissolution rates on most samples relative to pure UO 2 samples. Scoping experiments that occurred in a less oxidizing environment showed a reduction in dissolution rate compared to fully oxidizing conditions. The results within highlight the importance of dopant behavior in used fuel dissolution modeling.

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DOE OSTI2020

CamRegBase: a gene regulation database for the biofuel crop, Camelina sativa

Abstract Camelina is an annual oilseed plant from the Brassicaceae family that is gaining momentum as a biofuel winter cover crop. However, a significant limitation in further enhancing its utility as a producer of oils that can be used as biofuels, jet fuels or bio-based products is the absence of a repository for all the gene expression and regulatory information that is being rapidly generated by the community. Here, we provide CamRegBase (https://camregbase.org/) as a one-stop resource to access Camelina information on gene expression and co-expression, transcription factors, lipid associated genes and genome-wide orthologs in the close-relative reference plant Arabidopsis. We envision this as a resource of curated information for users, as well as a repository of new gene regulation information.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Developing a concept for a national used fuel interim storage facility in the United States

In the United States (U.S.) the nuclear waste issue has plagued the nuclear industry for decades. Originally, spent fuel was to be reprocessed but with the threat of nuclear proliferation, spent fuel reprocessing has been eliminated, at least for now. In 1983, the Nuclear Waste Policy Act of 1982 [1] was established, authorizing development of one or more spent fuel and high-level nuclear waste geological repositories and a consolidated national storage facility, called a 'Monitored Retrievable Storage' facility, that could store the spent nuclear fuel until it could be placed into the geological repository. Plans were under way to build a geological repository, Yucca Mountain, but with the decision by President Obama to terminate the development of Yucca Mountain, a consolidated national storage facility that can store spent fuel for an interim period until a new repository is established has become very important. Since reactor sites have not been able to wait for the government to come up with a storage or disposal location, spent fuel remains in wet or dry storage at each nuclear plant. The purpose of this paper is to present a concept developed to address the DOE's goals stated above. This concept was developed over the past few months by collaboration between the DOE and industry experts that have experience in designing spent nuclear fuel facilities. The paper examines the current spent fuel storage conditions at shutdown reactor sites, operating reactor sites, and the type of storage systems (transportable versus non-transportable, welded or bolted). The concept lays out the basis for a pilot storage facility to house spent fuel from shutdown reactor sites and then how the pilot facility can be enlarged to a larger full scale consolidated interim storage facility. (authors)

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DOE OSTI2018

Pajarito models

Pajarito models are developed to perform various types of model analyses related to the LANL-site groundwater contamination problems. Pajarito models include several models representing the LANL Chromium (Cr) and RDX sites. Pajarito models also include a model that is at the scale of the Pajarito Plateau. The models are designed to perform model prediction of groundwater flow and contaminant transport. The models are bundled in repositories. The model repositories include scripts, input control files, and drivers. A detailed description of the various files included in the repositories is provided in electronic form within the repositories.

Vesselinov, Velimir↗
DOE OSTI2026

Data for KETCHUP: Parameterizing of Large-Scale Kinetic Models Using Multiple Datasets with Different Reference States

Repository for Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP can use K-FIT [2] input files. Example K-FIT input files are located in the K-FIT repository at https://github.com/maranasgroup/K-FIT.

Metabolomics↗
DOE OSTI2020

Validation of MCNP Critical Benchmark Models of PU-MET-FAST-016

A new centralized repository of high-quality MCNP models of critical benchmark experiments is currently under development at Los Alamos National Laboratory (LANL). The repository is the combined effort of the Nuclear Criticality Safety, Nuclear Data, and Monte Carlo code development/application organizations at LANL. The initial set of benchmark models in the repository are derived from the Whisper Suite provided with MCNP6.2 and are evaluated against the benchmark experiments described in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook. The goal is to build a single LANL benchmark collection that is up to date with the latest ICSBEP revision, has a formal review and revision process, is contained in an open-source repository, and utilizes new Python tools for improved input and output file review. This paper describes the validation of the models associated with PU-MET-FAST-016, “Flooded 3X3X3 Arrays of 3-kg Plutonium (Pu) Metal Cylinders – Phase I”. In this critical experiment, twenty-seven 3-kg Pu metal cylinders with a height of 46.33 ± 0.15 mm and diameter of 65.25 ± 0.05 mm were arranged in a cubic array. The Pu cylinders were sealed in aluminum cans, and three cans were placed in each of nine perforated aluminum sleeves, as shown in Fig. 1. The nine sleeves were supported by an aluminum frame that allowed for control of the lateral spacing of the cylinders, both in the X and Y directions. The benchmark experiment measured thirteen configurations. Of these configurations, six were modeled using MCNP.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification" Willard et al. (2025).

This data release provides all data and code used in the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025)" to model stream temperature, evaluate, and assess results. The associated manuscript explores the effect of different ensemble construction techniques across different common machine learning (ML) architectures for predictions in unmonitored basins. Modeling was done using long short-term memory (LSTM), gated recurrent unit (GRU), temporal convolution network (TCN), and extreme gradient boosting (XGBoost) models, and stream site coverage spans 1362 locations across the conterminous United States. The ensemble construction techniques investigated include ensemble by random weight initialization, differing hyperparameters, different random subsets of training data, different subselections of input features, different architectures, and Monte Carlo Dropout. The data is organized into these items items:Code repository and data for the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025).Code: stream_temp_ml_regionalization.zip contains the code repositoryData to run the code:- data_dir.zip -- contains all files that should be moved to the "DATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- metadata_dir.zip -- contains all files that should be moved to the "METADATA_DIR" variable defined in the "set_env_vars.sh" script in the code repositoryData produced by the code and used in the paper:- outputs_dir.zip - contains model output and results (outputs_dir/results), model weights (outputs_dir/models), and all other outputs used for the paper including feature importances.To cite this code, please use the following BibTeX or MLA entries:bibtex:@misc{willard2025streamensembles,author = {Jared Willard and Charuleka Varadharajan},title = {Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification"},year = {2024},doi = {10.15485/2527393},publisher = {ESS-DIVE Repository},url = {https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2527393}}MLA: Willard, Jared, et al. Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification". 2025. ESS-DIVE Repository, doi:10.15485/2448016.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Machine learning model inputs, outputs, and scripts associated with “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions” (Malhotra et al., in prep). This effort was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the contiguous United States (CONUS). New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Associated sediment and water geochemistry and in situ sensor data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775. This data package is associated with two GitHub repositories found at https://github.com/parallelworks/dynamic-learning-rivers and https://github.com/WHONDRS-Hub/ICON-ModEx_Open_Manuscript. In addition to this readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This data package consists of two main folders (1) dynamic-learning-rivers and (2) ICON-ModEx_Open_Manuscript which contain snapshots of the associated GitHub repositories. The input data, output data, and machine learning models used to guide sampling locations are within dynamic-learning-rivers. The folder is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning (ML) models trained on the data in “input_data”; (3) “examples” contains files for direct experimentation with the machine learning model, including scripts for setting up “hindcast” run; (4) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; and (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please see the top-level README.md in the GitHub repository for more details on the automation. The scripts and data used to create figures in the manuscript are within ICON-ModEx_Open_Manuscript. The folder is organized into four folders which contain the scripts, data, and pdf for each figure. Within the “fig-model-score-evolution” folder, there is a folder called “intermediate_branch_data” which contains some intermediate files pulled from dynamic-learning-rivers and reorganized to easily integrate into the workflows. NOTE: THIS FOLDER INCLUDES THE FILES AT THE POINT OF PAPER SUBMISSION. IT WILL BE UPDATED ONCE THE PAPER IS ACCEPTED WITH ANY REVISIONS AND WILL INCLUDE A DD/FLMD AT THAT POINT. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Optimization of Water-Alternating-CO2 Injection Field Operations Using a Machine-Learning-Assisted Workflow

Summary This paper will present a robust workflow to address multiobjective optimization (MOO) of carbon dioxide (CO2)-enhanced oil recovery (EOR)-sequestration projects with a large number of operational control parameters. Farnsworth unit (FWU) field, a mature oil reservoir undergoing CO2 alternating water injection (CO2-WAG) EOR, will be used as a field case to validate the proposed optimization protocol. The expected outcome of this work would be a repository of Pareto-optimal solutions of multiple objective functions, including oil recovery, carbon storage volume, and project economics. FWU’s numerical model is used to demonstrate the proposed optimization workflow. Because using MOO requires computationally intensive procedures, machine-learning-based proxies are introduced to substitute for the high-fidelity model, thus reducing the total computation overhead. The vector machine regression combined with the Gaussian kernel (Gaussian-SVR) is used to construct proxies. An iterative self-adjusting process prepares the training knowledge base to develop robust proxies and minimizes computational time. The proxies’ hyperparameters will be optimally designed using Bayesian optimization to achieve better generalization performance. Trained proxies will be coupled with multiobjective particle swarm Optimization (MOPSO) protocol to construct the Pareto-front solution repository. The outcomes of this workflow will be a repository containing Pareto-optimal solutions of multiple objectives considered in the CO2-WAG project. The proposed optimization workflow will be compared with another established methodology using a multilayer neural network (MLNN) to validate its feasibility in handling MOO with a large number of parameters to control. Optimization parameters used include operational variables that might be used to control the CO2-WAG process, such as the duration of the water/gas injection period, producer bottomhole pressure (BHP) control, and water injection rate of each well included in the numerical model. It is proved that the workflow coupling Gaussian-SVR proxies and the iterative self-adjusting protocol is more computationally efficient. The MOO process is made more rapid by squeezing the size of the required training knowledge base while maintaining the high accuracy of the optimized results. The outcomes of the optimization study show promising results in successfully establishing the solution repository considering multiple objective functions. Results are also verified by validating the Pareto fronts with simulation results using obtained optimized control parameters. The outcome from this work could provide field operators an opportunity to design a CO2-WAG project using as many inputs as possible from the reservoir models. The proposed work introduces a novel concept that couples Gaussian-SVR proxies with a self-adjusting protocol to increase the computational efficiency of the proposed workflow and to guarantee the high accuracy of the obtained optimized results. More importantly, the workflow can optimize a large number of control parameters used in a complex CO2-WAG process, which greatly extends its utility in solving large-scale MOO problems in various projects with similar desired outcomes.

Energy & Fuels↗
DOE OSTI2012

Towards a defensible safety case for deep geologic disposal of DOE HLW and DOE SNF in bedded salt

The primary objective of this study is to investigate the feasibility and utility of developing a defensible safety case for disposal of United States Department of Energy (U.S. DOE) high-level waste (HLW) and DOE spent nuclear fuel (SNF) in a conceptual deep geologic repository that is assumed to be located in a bedded salt formation of the Delaware Basin. A safety case is a formal compilation of evidence, analyses, and arguments that substantiate and demonstrate the safety of a proposed or conceptual repository. A safety case also provides the necessary structure for organizing and synthesizing existing knowledge in order to help DOE prioritize its future research and development (R&D) activities. We conclude that a defensible initial safety case for potential licensing could be readily compiled by capitalizing on the extensive technical basis that exists from prior work on the Waste Isolation Pilot Plant (WIPP), work on other repository development programs, and the work published through international efforts in salt repository programs such as in Germany.

Mackinnon, Robert James↗
DOE OSTI2013

Considerations of human intrusion in U.S. programs for deep geologic disposal of radioactive waste

Regulations in the United States that govern the permanent disposal of spent nuclear fuel and high-level radioactive waste in deep geologic repositories require the explicit consideration of hypothetical future human intrusions that disrupt the waste. Specific regulatory requirements regarding the consideration of human intrusion differ in the two sets of regulations currently in effect in the United States; one defined by the Environmental Protection Agency's 40 Code of Federal Regulations part 197, applied only to the formerly proposed geologic repository at Yucca Mountain, Nevada, and the other defined by the Environmental Protection Agency s 40 Code of Federal Regulations part 191, applied to the Waste Isolation Pilot Plant in New Mexico and potentially applicable to any repository for spent nuclear fuel and high-level radioactive waste in the United States other than the proposed repository at Yucca Mountain. This report reviews the regulatory requirements relevant to human intrusion and the approaches taken by the Department of Energy to demonstrating compliance with those requirements.

Swift, Peter N.↗
DOE OSTI2013

Disposal Systems Evaluation Framework DSEF User Manual (V.3.0)

The Disposal Systems Evaluation Framework (DSEF) has been developed at Lawrence Livermore National Laboratory (LLNL) to formalize and facilitate the development and documentation of repository conceptual design options for a range of waste forms, geologic environments, repository design concepts, and repository operating modes. DSEF is one component of the Engineered Barrier System (EBS) family of work packages at several national laboratories led by Sandia National Laboratories (SNL), in support of the DOE Office of Nuclear Energy, specifically of the Used Fuel Disposition (UFD) Campaign within the Office of Used Nuclear Fuel Disposition Research and Development (NE-53). DSEF is a knowledge management system that allows the user to intelligently access and draw data from a case library of hundreds of completed thermal analyses (currently 721 cases in the library), and draw input from databases of material properties and repository development cost data (currently the data in these databases are drawn from more than 100 references). The core functionality of DSEF is provided by a Microsoft Office Excel 2010 workbook with macros and form controls that create a structured environment and walk the user through the steps of creating the input data required to perform disposal system evaluations internally and by interfacing with external programs. The user can choose to work with built-in data from the case library of previous analysis cases, from the material property databases, or can define their own input descriptions and data. DSEF uses the Excel worksheet protection (implemented on a sheet by sheet basis) to prevent inadvertent over-writing of formulas with data. When worksheet protection is enabled, the user can see all underlying formulas, and enter data in unprotected cells (the unprotected cells generally correspond to the yellow highlighted cells). Worksheet protection also prevents restructuring the files (adding or deleting rows or columns) and changing the formatting, as well as preventing access to the Visual Basic Editor to prevent modification of the macros. If the user needs to modify the worksheet structure, formulas, or macros, they can contact LLNL to get the password to unprotect the worksheets. However, care should be taken in making any modifications, since the macros and formulas are dependent in some cases on the current structure, range names, and macro subroutines DSEF is set up to retrieve the results of external thermal analysis program runs, summarize them graphically, and provide a template for adding the new results to the Case Library. The Case Library worksheet in DSEF is set up to create a Case Library data record for each new analysis case, at the top of the list of prior cases. Therefore, a separate DSEF file (with unique name) should be saved for each run that is to be archived. Users should submit a copy of their DSEF files to LLNL so that their results can be added to the overall Case Library and made available to other laboratories and stakeholders. LLNL will maintain the DSEF Case Library and make the most current files available on the SNL SharePoint site. The Revision History sheet documents the file evolution. One of the key objectives of DSEF is to allow the user to compare the results of thermal analyses to the thermal constraints of the design components. These comparisons can then be used to consider design, layout, and operating mode modifications to find combinations that can meet the thermal constraints for a given combination of waste form, geologic medium, surface storage time, and system concept of operation.

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