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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Demonstration of a modeling toolkit for the design of building electrical distribution systems

The deployment of building equipment (e.g., lighting, security) and miscellaneous electrical loads that fundamentally require DC power for operation is increasing. Powering these DC loads has traditionally required an AC/DC converter, but the installation of photovoltaic (PV) and battery energy storage systems that primarily produce DC power eliminates the need for AC/DC converters at each end-device. However, analyzing system energy efficiency and cost for different electrical distribution architectures can be challenging as software tools that support this are not readily available. This paper presents preliminary results from a laboratory verification of the Building Electrical Efficiency Analysis Model (BEEAM) toolkit that was developed to address this gap. Three different eight-luminaire lighting systems comprised of market-available products were designed and modeled: one that used traditional AC distribution, a second that used a hybrid AC -to- centralized DC electrical distribution architecture, and a third that used a hybrid AC -to- distributed DC architecture. Notably, AC/DC conversions are required in all three systems. BEEAM models for LED drivers as well as Power-over-Ethernet (PoE) switches were created using laboratory characterization data. The lighting systems were simulated in Modelica, and the results were compared with each other and physics-based expectations. Simulation results show that PoE system energy efficiency is highly dependent on both device specification (e.g., LED driver and PoE switch efficiency) and system architecture (e.g., PoE switch loading) choices, as expected. For the products and system architectures selected for this study, the two DC systems were found to be less efficient than the AC system over the course of typical operation. In future work, simulation results will be compared with laboratory measurements to validate the usefulness of this software for design, and lighting systems with integrated PV and battery energy storage will be simulated to quantify the energy performance improvements that result from the elimination of some AC/DC converters.

Waghale, Anay S.↗
DOE OSTI2020

Analytic Modeling of a Deep Shielding Problem

Previous generations of scientists would make tremendous efforts to simplify non-tractable problems and generate simpler models that preserved the fundamental physics. This process involved applying assumptions and simplifications to reduce the complexity of the problem until it reached a solvable form. Each assumption and simplification was chosen and applied with the intent to preserve the essential physics of the problem, since, if the core physics of the problem were eliminated, the simplified model served no purpose. Moreover, if done correctly, solutions to the reduced model would serve as useful approximations to the original problem. In a sense, solving the simple models laid the ground-work for and provided insight into the more complex problem. Today, however, the affordability of high performance computing has essentially replaced the process for analyzing complex problems. Rather than "building up" a problem by understanding smaller, simpler models, a user generally relies on powerful computational tools to directly arrive at solutions to complex problems. As computational resources grow, users continue trying to simulate new, more complex, or more detailed problems, resulting in continual stress on both the code and computational resources. When these resources are limited, the user will have to make concessions by simplifying the problem while trying to preserve important details. In the context of MCNP, simplifications typically come as reductions in geometry, or by using variance reduction techniques. Both approaches can influence the physics of the problem, leading to potentially inaccurate or non-physical results. Errors can also be introduced as a result of faulty input into a computational tool: something as simple as transposing numbers in a tally input can result in incorrect answers. In this paradigm, reduced complexity computational and analytical models still have an important purpose. The explicit form of an analytic solution is arguably the best way to understand the qualitative properties of simple models. In contrast to "building up" a complex problem through understanding simpler problems, results from detailed computational scenarios can be better explained by "building down" the complex model through simple models rooted in the fundamental or essential phenomenology. Simplified analytic and computational models can be used to 1) increase a user's confidence in the computational solution of a complex model, 2) confirm there are no user input errors, and 3) ensure essential assumptions of the simulation tool are preserved. This process of using analytic models to develop a more valuable analysis of simulation results is named the results analysis methodology. The utility of the results assessment methodology and a complimentary sensitivity analysis is exemplified through the analysis of the neutron flux in a dry used fuel storage cask. This application was chosen due to current scientific interest in used nuclear fuel storage.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Analytic Modeling of a Deep Shielding Problem

Previous generations of scientists would make tremendous efforts to simplify non-tractable problems and generate simpler models that preserved the fundamental physics. This process involved applying assumptions and simplifications to reduce the complexity of the problem until it reached a solvable form. Each assumption and simplification was chosen and applied with the intent to preserve the essential physics of the problem, since, if the core physics of the problem were eliminated, the simplified model served no purpose. Moreover, if done correctly, solutions to the reduced model would serve as useful approximations to the original problem. In a sense, solving the simple models laid the ground-work for and provided insight into the more complex problem. Today, however, the affordability of high performance computing has essentially replaced the process for analyzing complex problems. Rather than "building up" a problem by understanding smaller, simpler models, a user generally relies on powerful computational tools to directly arrive at solutions to complex problems. As computational resources grow, users continue trying to simulate new, more complex, or more detailed problems, resulting in continual stress on both the code and computational resources. When these resources are limited, the user will have to make concessions by simplifying the problem while trying to preserve important details. In the context of the Monte Carlo N-Particle radiation transport simulation tool, simplifications typically come as reductions in geometry, or by using variance reduction techniques. Both approaches can influence the physics of the problem, leading to potentially inaccurate or non-physical results. Errors can also be introduced as a result of faulty input into a computational tool: something as simple as transposing numbers in a tally input can result in incorrect answers. In this paradigm, reduced complexity computational and analytical models still have an important purpose. The explicit form of an analytic solution is arguably the best way to understand the qualitative properties of simple models. In contrast to "building up" a complex problem through understanding simpler problems, results from detailed computational scenarios can be better explained by "building down" the complex model through simple models rooted in the fundamental or essential phenomenology. Simplified analytic and computational models can be used to 1) increase a user's confidence in the computational solution of a complex model, 2) confirm there are no user input errors, and 3) ensure essential assumptions of the simulation tool are preserved. This process of using analytic models to develop a more valuable analysis of simulation results is named the results assessment methodology. The utility of the results assessment methodology and a complimentary sensitivity analysis is exemplified through the analysis of the neutron flux in a dry used fuel storage cask. This application was chosen due to current scientific interest in used nuclear fuel storage.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Analytic Modeling of a Deep Shielding Problem

Previous generations of scientists would make tremendous efforts to simplify non-tractable problems and generate simpler models that preserved the fundamental physics. This process involved applying assumptions and simplifications to reduce the complexity of the problem until it reached a solvable form. Each assumption and simplification was chosen and applied with the intent to preserve the essential physics of the problem, since, if the core physics of the problem were eliminated, the simplified model served no purpose. Moreover, if done correctly, solutions to the reduced model would serve as useful approximations to the original problem. In a sense, solving the simple models laid the ground-work for and provided insight into the more complex problem. Today, however, the affordability of high performance computing has essentially replaced the process for analyzing complex problems. Rather than "building up" a problem by understanding smaller, simpler models, a user generally relies on powerful computational tools to directly arrive at solutions to complex problems. As computational resources grow, users continue trying to simulate new, more complex, or more detailed problems, resulting in continual stress on both the code and computational resources. When these resources are limited, the user will have to make concessions by simplifying the problem while trying to preserve important details. In the context of the Monte Carlo N-Particle radiation transport simulation tool, simplifications typically come as reductions in geometry, or by using variance reduction techniques. Both approaches can influence the physics of the problem, leading to potentially inaccurate or non-physical results. Errors can also be introduced as a result of faulty input into a computational tool: something as simple as transposing numbers in a tally input can result in incorrect answers. In this paradigm, reduced complexity computational and analytical models still have an important purpose. The explicit form of an analytic solution is arguably the best way to understand the qualitative properties of simple models. In contrast to "building up" a complex problem through understanding simpler problems, results from detailed computational scenarios can be better explained by "building down" the complex model through simple models rooted in the fundamental or essential phenomenology. Simplified analytic and computational models can be used to 1) increase a user's confidence in the computational solution of a complex model, 2) con firm there are no user input errors, and 3) ensure essential assumptions of the simulation tool are preserved. This process of using analytic models to develop a more valuable analysis of simulation results is named the results assessment methodology. The utility of the results assessment methodology and a complimentary sensitivity analysis is exempli fied through the analysis of the neutron flux in a dry used fuel storage cask. This application was chosen due to current scientific interest in used nuclear fuel storage.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

GDSA Framework Development and Process Model Integration FY2021

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and highlevel nuclear waste (HLW). A high priority for SFWST disposal R&D is disposal system modeling (DOE 2012, Table 6; Sevougian et al. 2019). The SFWST Geologic Disposal Safety Assessment (GDSA) work package is charged with developing a disposal system modeling and analysis capability for evaluating generic disposal system performance for nuclear waste in geologic media.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Ellipsoidal Fitting Methodology for Defect Clusters in Gallium Arsenide

In assessing the initial spatial distribution of defects from neutron or heavy ion irradiation, it is useful to have a reliable, automated, and fast-running tool to evaluate characteristic metrics such as the number of sub-clusters or the overall cluster volume. The latter metric, for instance, can be utilized to estimate a reference neutron fluence level at which inter-cluster interaction effects begin to become significant. This paper details a methodology to fit an arbitrarily complex defect map with a set of ellipsoids (one per identified sub-cluster) in which the constituent defects of a sub-cluster are determined using fuzzy degree-of-membership analysis. Specifically, a parameterized model is developed for point defects in gallium arsenide. Cluster volume calculations based on the model are compared against convex hull and single- ellipsoid representations. Results show that the parameterized sub-cluster model begins to deviate from the two reference models at a recoil energy of about 100 keV in GaAs, with the convex hull and single-ellipsoid representations increasingly overestimating the volume thereafter.

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

GDSA Framework Development and Process Model Integration FY2023

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). A high priority for SFWST disposal R&D is disposal system modeling (Sassani et al. 2021). The SFWST Geologic Disposal Safety Assessment (GDSA) work package is charged with developing a disposal system modeling and analysis capability for evaluating generic disposal system performance for nuclear waste in geologic media.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Beneficial Use of Harvested Ponded Fly Ash and Landfilled FGD Materials for High-Volume Surface Mine Reclamation

The overall motivation of this project was to demonstrate at laboratory, bench-scale, and full-scale demonstration levels that (a) coal ash surface impoundments can go through closure by removal as per USEPA and state regulations so that the material can be used as is (other than draining free water using CCRs piles) in high-volume beneficial applications, (b) FGD material from closed out FGD facilities can be excavated and recompacted for coal mine reclamation, and (c) harvested CCRs can be beneficially utilized (providing a net environmental gain) in large-volumes for reclamation at abandoned coal mine sites across the US, especially in the Eastern and Midwest coal mining regions. The objectives of this project were to: 1) promote the safe and cost-effective closure by removal of coal ash impoundments, 2) harvest landfilled FGD, and 3) promote the high-volume beneficial use of these harvested CCRs in the reclamation of abandoned surface coal mine sites across the eastern and midwestern coal mining regions of the United States. The major tasks carried out for this project are summarized below: 1) Conesville Full-Scale Demonstration Project: About 2 million tons of harvested CCR materials from the closure by removal of an inactive fly ash pond and an adjacent old FGD landfill were used for the full-scale demonstration project to fully reclaim a nearby partially completed abandoned surface coal mine. Site monitoring for the project duration was carried out and results are discussed. 2) Laboratory Testing: Geotechnical and environmental testing of harvested ponded fly ash and landfilled FGD material at the former Conesville power plant were carried out. Completing the laboratory testing allowed for QA/QC for the full-scale site construction and informed the formulation of the risk analysis. 3) Risk Analysis: We developed a reliable computational model for fate and transport. We used these models and the rich set of monitored data for the Conesville site to analyze risks to human health and ecological risks associated with high-volume surface mine reclamation using harvested CCRs. 4) GIS Siting Study: A Geographic Information System (GIS) study was carried out for three states in the Eastern coal mining region and two states in the Midwest coal region. This effort provided site specific GIS information for five states and allowed us to establish protocols that other states can follow in implementing their own state specific GIS study.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

GDSA Framework Development and Process Model Integration FY2024

The Disposal Research & Development (Disposal R&D) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & High-Level Waste Disposition is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). A high priority for Disposal R&D is disposal system modeling (Sassani et al. 2023). The Geologic Disposal Safety Assessment (GDSA) work package is charged with developing a disposal system modeling and analysis capability for evaluating generic disposal system performance for nuclear waste in geologic media.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Forecasting the Impact of Dual-credit Policy (2021-2023) on China's Electric Vehicle Market

China is well known for its ambition on large-scale vehicle electrification which currently is mainly driven by fuel economy and electric vehicle policies. This study adopts the New Energy and Oil Consumption Credits (NEOCC) model, a vehicle policy analysis tool developed by the Oak Ridge National Laboratory, to systematically quantify the potential impacts of the “Passenger Cars Corporate Average Fuel Consumption and New Energy Vehicle Credit Regulation” which was released in June 2020 for the timeframe 2021-2023, so called dual-credit policy (2021-2023). It is found that, under the dual-credit policy (2021-2023), the sales of low fuel consumption conventional vehicles will increase faster than they were in 2018-2020, and the share of plug-in electric vehicles (PEVs) could reach 11.4% by 2023 if it keeps the expansion as it was in 2017. Besides, the BEVs with long electric range (such as 400 km) and the plugin hybrid SUVs would be the most popular PEV types.

Ou, Shawn↗
DOE OSTI2026

Validating a Dynamic PWR Safety and Security Model?

Nuclear power plants (NPPs) are assessed for safety and security using separate models that cannot capture how an attacker's decisions and a plant's response unfold together in real time, leaving regulators and operators without a complete picture of true plant vulnerability. Traditional probabilistic risk assessment (PRA) methods treat adversarial events as fixed initiators with predetermined outcomes, and are structurally incapable of representing the time-dependent interplay between physical security events, safety system response, and operator mitigative actions. At Idaho National Laboratory (INL), I contributed to the development and validation of Modeling and Analysis for Safety and Security using the Dynamic EMRALD Framework (MASS-DEF). Where static PRA relies on event-tree logic that cannot evolve mid-scenario, MASS-DEF couples a time-dependent dynamic PRA tool EMRALD (Event Modeling Risk Assessment using Linked Diagrams) with attack simulation software, allowing attacker behavior, plant system states, and operator actions to interact across time. My work focused on validating a general Pressurized Water Reactor (PWR) model. I traced model logic against PWR plant to identified errors in logic and confirm accuracy. I then built and tested attack scenarios against a general PWR model to verify that the model produced expected outcomes across all logical pathways. I also contributed a section to a related technical paper applying the same EMRALD platform to radiation dose modeling. Results show that MASS-DEF can quantitatively demonstrate that many plants exceed their regulatory security thresholds. This demonstrated margin provides a technically defensible basis for reducing the number of guards without compromising regulatory compliance. Physical security costs represent roughly 10% of annual operating budgets, making such reductions directly meaningful to INL's mission of sustaining existing commercial NPPs. This internship strengthened my understanding of nuclear systems, probabilistic modeling, and technical writing, and has solidified my pursuit of a career at a national laboratory.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗
DOE OSTI2022

Evaluation of artificial neural network performance for classification of potato plants infected with potato virus Y using spectral data on multiple varieties and genotypes

Potato virus Y (Potyviridae, PVY) is a plant virus that poses a significant threat to potato producers on a global basis. The pathogen has disrupted seed potato supplies and negatively impacted yield and quality of commercial potato crops. The potato industry currently manages PVY infection levels via insecticide applications, regional seed certification programs that rely on field scouting to visually assess individual plants for infection status, and destructive and costly tissue sampling coupled with laboratory assays. Despite these efforts, PVY continues to confound potato industry stakeholders resulting in economic harm. Remote sensing and machine learning provide for the development of new tools to more accurately detect and spatially quantify PVY-infected plants versus the current state of the art. However, there is a need to understand how the occurrence of many different potato varieties impact the dynamics of developing models to detect potato plants impacted with PVY and their potential effectiveness. This study evaluates classification modelling outcomes using spectral datasets collected in different temporal and spatial environments (greenhouse and a production field) on multiple potato varieties consisting of labelled instances of plants infected with PVY and those not infected with the virus. A modelling framework was developed to support iterative modelling runs using artificial neural network (ANN) architectures configured as binary classifiers to develop sample populations to support statistical analysis on model performance using specific spectral subsets. When using spectral data to detect PVY-infected plants, ANN models achieved the highest mean accuracy of 0.894 on a single variety. Conversely, the same ANN model architecture only achieved a mean accuracy of 0.575 on a spectral data set representing 29 potato breeding lines. Additionally, statistical analysis indicates spectral regions including the red edge, near infrared and shortwave infrared contain more important spectral features for the ANN classifier introduced in this research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Uncertainty and Sensitivity Analysis Methods and Applications in the GDSA Framework (FY2025)

The Spent Fuel and Waste Science and Technology Campaign (SFWST) of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE) is conducting research and development on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two priorities for SFWST are design concept development and disposal system modeling. These priorities are directly addressed in the Geologic Disposal Safety Assessment (GDSA) control account, which is charged with developing a geologic repository system modeling and analysis capability, and the associated software, GDSA Framework, for evaluating disposal system performance for nuclear waste in geologic media. This report describes specific activities in the Fiscal Year (FY) 2025 associated with the GDSA Uncertainty and Sensitivity Analysis Methods work package. This report fulfills the GDSA Uncertainty and Sensitivity Analysis Methods work package (SF-25SN01030407) level 3 milestone, Uncertainty and Sensitivity Analysis Methods and Applications in GDSA Framework (FY2025) (M3SF-25SN010304072). This work was closely coordinated with the other Sandia National Laboratory GDSA work packages: the GDSA Framework Development work package (SF-25SN01030408), the GDSA Repository Systems Analysis work package (SF-25SN01030409), and the GDSA PFLOTRAN Development work package (SF-25SN01030410). This report builds on developments reported in previous GDSA Framework milestones, particularly M3SF-24SN010304072.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Assessing the drivers of Isoprene SOA: laboratory studies, field observations and modeling (Final Technical Report)

This report describes the research results and products stemming from the funding award DE-SC0018221. The main goals of this project were to develop a quantitative understanding of the factors driving secondary organic aerosol (SOA) formation from biogenic volatile organic compounds, namely isoprene and monoterpenes, using a combination of laboratory measurements, field data analysis, and modeling. Specifically, we aimed to: 1) develop an observationally constrained volatility distribution of trace gases produced from the oxidation of biogenic hydrocarbons that can explain the formation of SOA from in situ observations made during DOE ASR/ARM field campaigns such as HI-SCALE and BAECC 2) use new and existing (e.g. DOE ASR funded SOAFFEE experiments) laboratory chamber experiments to develop detailed parameterizations of isoprene-derived epxoy diol (IEPOX) reactive uptake, including product branching, product volatility, and solubility constants, as well as SOA formation generally from the formation of highly oxygenated organic molecules (HOM) formed from isoprene and monoterpene photo-oxidation that can be directly incorporated into models. 3) collaborate with PNNL modeling teams to incorporate new parameterizations into detailed box models, such as MOSAIC, and regional or Earth System models such as WRF.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Application of the Recharge Estimation Tool (RET) to Prepare Spatially and Temporally Variable Recharge Boundary Conditions for Hanford Site Composite Analysis Vadose Zone Models

This environmental calculation file (ECF) describes the development of a tool for translating recharge estimates into readable input for STOMP© (Subsurface Transport Over Multiple Phases) vadose zone models primarily supporting the vadose zone (VZ) facets of the updated Hanford Site Composite Analysis (CA) and the Hanford Site Cumulative Impact Evaluation (CIE). The recharge estimates are spatiotemporally variable and are produced by the Recharge Evolution Tool (RET) described in Hanford Site-wide Natural Recharge Boundary Condition for Groundwater Models (ECF-HANFORD-15-0019). Outputs from the RET are given in Esri’s™ feature class format with yearly estimates and associated metadata encapsulated in file geodatabase objects. For STOMP models, the translated output is a text file in the format of an input boundary condition card, consistent with STOMP software requirements. The text file contains assimilated spatiotemporal recharge estimates produced by the RET. The tool discussed in this document will be referred to as the “RET2STOMP” tool.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Advances in Uncertainty and Sensitivity Analysis Methods and Applications in GDSA Framework.

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Fuel Cycle Technology (FCT) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two high priorities for SFWST disposal R&D are design concept development and disposal system modeling. These priorities are directly addressed in the SFWST ''Geologic Disposal Safety Assessment'' (GDSA) control account, which is charged with developing a geologic repository system modeling and analysis capability, and the associated software, ''GDSA Framework'', for evaluating disposal system performance for nuclear waste in geologic media. ''GDSA Framework'' is supported by SFWST Campaign and its predecessor the Used Fuel Disposition (UFD) campaign. This report fulfills the GDSA Uncertainty and Sensitivity Analysis Methods work package (SF-20SN01030403) level 3 milestone — ''Advances in Uncertainty and Sensitivity Analysis Methods and Applications in GDSA Framework'' (M3SF-20SN010304032). It presents high level objectives and strategy for development of uncertainty and sensitivity analysis tools, demonstrates uncertainty quantification (UQ) and sensitivity analysis (SA) tools in GDSA Framework in FY20, and describes additional UQ/SA tools whose future implementation would enhance the UQ/SA capability of ''GDSA Framework''. This work was closely coordinated with the other Sandia National Laboratory GDSA work packages: the GDSA Framework Development work package (SF- 2051\101030404), the GDSA Repository Systems Analysis work package (SF-2051\101030405), and the GDSA PFLOTRAN Development work package (SF-20SN01030406). This report builds on developments reported in previous ''GDSA Framework'' milestones, particularly M2SF- 19SNO1030403.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Introduction to Engage: NASA Training Session

Welcome to Engage! Engage is a capacity expansion modeling tool supported by the National Renewable Energy Laboratory and based on the Calliope open-source capacity expansion model developed by the ETH Zurich University, maintained at the TU Delft University. Engage is an accessible (free, open-access, web-hosted) and flexible web-based energy system planning application for rapid multiple-energy-form energy system scenario exploration. Its cloud-based, collaborator-sharable data model, intuitive interface and visualization capabilities facilitate collaboration and communication among teams, with experts, and among diverse stakeholder groups exploring energy system implications from district to national-scale models. This training session was presented to the National Aeronautics and Space Administration (NASA) to help them understand how capacity expansion modeling can help them develop single site/distribution analysis of energy to regional airports.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Uncertainty and Sensitivity Analysis Methods and Applications in the GDSA Framework (FY2021)

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Fuel Cycle Technology (FCT) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two high priorities for SFWST disposal R&D are design concept development and disposal system modeling. These priorities are directly addressed in the SFWST Geologic Disposal Safety Assessment (GDSA) control account, which is charged with developing a geologic repository system modeling and analysis capability, and the associated software, GDSA Framework, for evaluating disposal system performance for nuclear waste in geologic media. GDSA Framework is supported by SFWST Campaign and its predecessor the Used Fuel Disposition (UFD) campaign. This report fulfills the GDSA Uncertainty and Sensitivity Analysis Methods work package (SF-21SN01030404) level 3 milestone, Uncertainty and Sensitivity Analysis Methods and Applications in GDSA Framework (FY2021) (M3SF-21SN010304042). It presents high level objectives and strategy for development of uncertainty and sensitivity analysis tools, demonstrates uncertainty quantification (UQ) and sensitivity analysis (SA) tools in GDSA Framework in FY21, and describes additional UQ/SA tools whose future implementation would enhance the UQ/SA capability of GDSA Framework. This work was closely coordinated with the other Sandia National Laboratory GDSA work packages: the GDSA Framework Development work package (SF-21SN01030405), the GDSA Repository Systems Analysis work package (SF-21SN01030406), and the GDSA PFLOTRAN Development work package (SF-21SN01030407). This report builds on developments reported in previous GDSA Framework milestones, particularly M3SF 20SN010304032.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗