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Danielson, Thomas L.

Publications and source records attributed to Danielson, Thomas L..

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Performance Assessment for the E-Area Low-Level Radioactive Waste Disposal Facility at the Savannah River Site: Appendix B

The total relative uncertainty, U, reported for each isotope in each waste cut is given by (Eq. 2-2) in Section 2.3.5.3. Waste Cut 1 of Container SD00003950 has a total activity of 737.990 Ci distributed among the isotopes H-3 and Am-241. Table B-1 summarizes the calculation results for the best-effort analysis example presented in Section 2.3.5.9.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Performance Assessment for the E-Area Low-Level Radioactive Waste Disposal Facility at the Savannah River Site: Appendix D

This appendix to Chapter 5, Section 5.1 provides supplemental concentration profiles for radionuclide species in STs and ETs that contribute to at least 0.1% of the sum-of-fractions. All concentrations are reported as pCi L -1 per Ci parent buried. The following nomenclature is used for all radionuclides in all DUs: an uppercase letter suffix indicates a SWF (e.g., I-129G, C-14N, H-3F, etc.), while the absence of an uppercase letter denotes a generic waste form (e.g., I-129, C-14, H-3, etc.).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Performance Assessment for the E-Area Low-Level Radioactive Waste Disposal Facility at the Savannah River Site: Appendix E

Supplemental transport model results for the LAWV from Chapter 5, Section 5.2.2 are provided in Section E.1.1 to compare concentrations at the 100-meter POA for the nominal PA case, best estimate case, and various sensitivity cases (Figure E-1 through Figure E-12). Second, Figure E-13 through Figure E-30 in Section E.1.2 display concentrations at the 100-meter POA for decay-chain daughter and parent radionuclides. Third, Figure E-31 through Figure E-34 in Section E.1.3 show maximum concentration contours for I-129. Only contour plots for I-129 from the remaining sensitivity runs are shown because I-129 is identified as the only radionuclide that impacts disposal limits for the LAWV. All concentration units, whether noted or not in the y-axis labels, are pCi L-1 per Ci parent buried.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Performance Assessment for the E-Area Low-Level Radioactive Waste Disposal Facility at the Savannah River Site: Appendix F

As a supplement to Chapter 6, Section 6.1.1.3, Table F-1 through Table F-24 provide tabular results of the sensitivity analysis calculations for waste disposal timing (Sensitivity Case S6) in NR07E (Cases 3 and 4) and NR26E (Cases 1 through 4). The title of each table identifies the DU, sensitivity case, and performance measure. Section F.1.1 (Table F-1 through Table F-8) presents results for NR07E and Section F.1.2 (Table F-9 through Table F-24) presents results for NR26E. In each table, radionuclides are sorted from highest to lowest nominal concentration or dose factor. Nominal values are when waste disposal occurs at the start of operations; timeline values are when waste disposal occurs at the end of operations. The differences (Δ values) in the last column of each table equal the timeline concentration or dose factor minus the nominal concentration or dose factor, where DF and CF are shorthand designations for dose factor and concentration factor, respectively.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Performance Assessment for the E-Area Low-Level Radioactive Waste Disposal Facility at the Savannah River Site: Appendix G

This appendix contains supporting information and key data used during the IHI analysis, including the following: • A list of parent radionuclides requiring IHI inventory limits (Section G.1) • Tables of IHI acute and chronic dose factors, inventory limits, and concentration limits for all DUs (Section G.2) • IHI acute and chronic dose history time profiles for all DUs (Section G.3)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Performance Assessment for the E-Area Low-Level Radioactive Waste Disposal Facility at the Savannah River Site: Appendix H

This section provides supporting material for the development of DU-specific final inventory limits for the GW pathways for every generic waste form and SWF parent radionuclide requiring an inventory limit. The final inventory limits are based on nominal PA transport simulations using PORFLOW as reported in Chapter 5. The nominal PA settings represent the compliance case where some modeling parameter settings are defined based on conservative (pessimistically leaning) arguments. In the overall computational approach employed in this PA, a multitiered radionuclide screening process is adopted as discussed in Chapter 2, Section 2.3.7. In the multitiered process, the initial list of 1,252 parent radionuclides is shortened substantially using conservative, but simple, transport models, along with a reasonably low cutoff criterion of 0.1% SOF value. Multidimensional PORFLOW flow and transport modeling is employed for every parent radionuclide that failed the GW screening. The generic waste form limits represent Tier-3 analyses, while Tier-4 analyses are employed for SWF limits, where warranted.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Performance Assessment for the E-Area Low-Level Radioactive Waste Disposal Facility at the Savannah River Site: Appendix I

As stated in Section 9.1.2.2, a final inventory of parent radionuclides is projected for every DU at the time of facility operational closure in 2065. These final closure inventories are upper-bound estimates wherein each DU is assumed to reach its activity capacity. Composition vectors based on existing DU inventories are employed to estimate the final compositions of all existing and future DUs. Specifically, (1) the existing DU compositions are assumed to remain constant; (2) future DUs are assigned averaged existing compositions based on appropriately averaged DUs. This information is detailed in Appendix H, Section H.7. In addition, the variability in composition among existing DUs is used to generate log-normal distributions for uncertainty quantification, which is detailed in Section I.1.1.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

A Semi-Automated Approach for Curating a Glossary of Key Terms for Open-Source Data Queries

In FY20, the Savannah River National Laboratory (SRNL) was funded by the National Nuclear Security Administration’s Office of Defense Nuclear Non-Proliferation Research and Development (NA-22) to build a machine learning based modeling pipeline that could extract proliferation events of interest from open text-based data sources. As a test case, the research team targeted the identification/fusion of events and indicators that fissile core fabrication would be executed at the Savannah River Site prior to its official announcement in May of 2018. The demonstration prototype proved successful by applying natural language processing and graph theoretical techniques to identify contextual shifts in key words and phrases that acted as indicators that pit production would be carried out at the Savannah River Site up to two years prior to the official announcement.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Hierarchical Conceptual Model Event and Activity Domains for Forecasting State-Sponsored Civil Nuclear Power Activities

In FY20, the Savannah River National Laboratory and the Sanghani Center for Artificial Intelligence and Data Analytics (Virginia Tech) entered a collaboration funded by the Department of Energy’s Office of Defense Nuclear Nonproliferation Research and Development to develop a machine learning based modeling pipeline to extract proliferation events of interest from open data sources. The prototype modeling pipeline that was developed relies on the use of time dependent word embedding models to identify contextual shifts in key words and phrases that act as indicators of events of interest. The FY20- 21 efforts were focused on a narrow topical domain of forecasting “fissile core fabrication” at the Savannah River Site prior to its official announcement in 2018. In FY22, the research team was funded to continue development of the modeling pipeline by applying it to the problem of forecasting new, and/or significant changes to existing, civil nuclear power reactors around the world. In this effort, the development will focus on proving applicability to a broader topical domain and in data environments that may contain more sparse information, relative to the United States. In this report, the worldwide landscape of civil nuclear reactors is outlined, the timeline of interest is defined, and a hierarchical conceptual model is established to identify the activity domains of interest and the event domains of interest. This preliminary effort will guide the curation of a glossary of key terms for data acquisition, as well as the downstream modeling efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Machine Learning Modeling Pipeline for Extracting Nuclear Proliferation Events of Interest from Open Data Sources (U)

In FY2020, the Savannah River National Laboratory (SRNL) and the Sanghani Center for Artificial Intelligence and Data Analytics at Virginia Polytechnic Institute and State University entered a collaboration funded by Department of Energy’s (DOE) Office of Defense Nuclear Nonproliferation Research and Development. The project’s mission was to take the first steps toward developing a demonstration prototype system that uses multiple machine learning and data analytics methods on largescale open data sources to identify new, developing, and/or undeclared nuclear programs. Given the SRNL team’s on-site perspective of events culminating in the DOE’s decision to pursue the Savannah River Plutonium Processing Facility (SRPPF), the team targeted the identification of events and indicators in retrospective datasets that pointed to the activity of “fissile core fabrication at the Savannah River Site” prior to the official announcement in May of 2018. A preliminary modeling pipeline was developed in FY20 that showed the datasets contained adequate signal for continuation of efforts. In FY21, a modular demonstration prototype modeling pipeline has continued in development for two text-based data sources: a broad internet archive (Webhose Ltd.) and a decahose Twitter database (i.e., a global sampling of one in every ten Tweets). The techniques that have been developed rely on graph theory and anomaly detection to identify contextual shifts in key words and phrases at various points in time such that indicators of events of interest could be identified and subsequently, events could be extracted from the corpuses. The foundational concept behind the approaches is that contextual shifts in key words and phrases can act as indicators of events of interest. Both datasets have proven successful in extracting events of interest related to pit production at the Savannah River Site prior to the official announcement. In addition, the pipelines have generated a wide range of events broadly summarized as: the awarding of DOE contracts at major sites, DOE investments in various programs, accidents at DOE national laboratories, speculations about the fate of pit production in the DOE complex, domestic and international shipments and receipts of nuclear materials at DOE sites, termination of non-proliferation agreements with Russia, termination of MOX, new weapons development approvals/testing, nuclear posture reviews, major DOE cleanup/production milestones, political opinions, and nuclear watch groups’ opinions, among many others.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The Application of Machine Learning Techniques to Meteorological Forecasting

Fog and inland-penetrating sea-breezes occur often at SRS and have a strong impact on site operations. Site personnel therefore require accurate forecasts of these events, but both are difficult to forecast using traditional techniques. Our goal is to apply machine learning (ML) techniques to the problem of forecasting fog and the sea breeze at the Savannah River Site. We apply several such techniques - decision trees, regression, and a series of classification/regression techniques – and train them using the large datasets collected by our group at SRS and from external organizations that maintain databases of regional meteorological variables.

54 ENVIRONMENTAL SCIENCES↗