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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 OSTI2016

Interrelated modules in cyanobacterial photosynthesis: the carbon-concentrating mechanism, photorespiration, and light perception

In this opinion paper, we consider the cyanobacterial carbon-concentrating mechanism (CCM) and photorespiration in the context of the regulation of light harvesting, using a conceptual framework borrowed from engineering: modularity. Broadly speaking, biological ‘modules’ are semi-autonomous functional units such as protein domains, operons, metabolic pathways, and (sub)cellular compartments. They are increasingly recognized as units of both evolution and engineering. Modules may be connected by metabolites, such as NADPH, ATP, and 2PG. While the Calvin–Benson–Bassham Cycle and photorespiratory salvage pathways can be considered as metabolic modules, the carboxysome, the core of the cyanobacterial CCM, is both a structural and a metabolic module. In photosynthetic organisms, which use light cues to adapt to the external environment and which tune the photosystems to provide the ATP and reducing power for carbon fixation, light-regulated modules are critical. The primary enzyme of carbon fixation, RuBisCO, uses CO 2 as a substrate, which is accumulated via the CCM. However RuBisCO also has a secondary reaction in which it utilizes O 2 , a by-product of the photochemical modules, which leads to photorespiration. A complete understanding of the interplay among CCM and photorespiration is predicated on uncovering their connections to the light reactions and the regulatory factors and pathways that tune these modules to external cues. We probe this connection by investigating light inputs into the CCM and photorespiratory pathways in the chromatically acclimating cyanobacterium Fremyella diplosiphon .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Capability Building Progression of an Insider Threat Mitigation Program at an International Research Reactor

The nuclear industry recognizes the difficulties involved in developing effective managerial and leadership skills in a highly technical and proficient workforce such as that found in nuclear facilities. Implementing an insider threat mitigation program (ITMP) within the nuclear industry is a complex and ongoing process that demands a comprehensive understanding of human behavior, an organization’s security culture, and rigorous regulatory requirements yet also accounts for facility characteristics, physical security, material flow, and activities involving nuclear material. Given the high-consequence nature of research reactor operations, even minor lapses can lead to safety, security, and reputational risks. An effective ITMP requires a defense-in-depth approach that incorporates behavioral analysis, robust vetting procedures, continuous monitoring, and cross-disciplinary coordination. It must also promote a culture of vigilance and accountability at all levels up to and including executive leadership but be flexible enough to adapt to evolving global threats and technological advances. Insider threat mitigation is not a one-time effort but rather a sustained commitment to excellence in safety and security. Establishing a culture in which personnel proactively report incidents and issues that could affect nuclear safety and security is vital to maintaining a safe and secure operational environment. This document was developed to guide senior management and research reactor organizations in creating comprehensive programs to effectively manage and mitigate insider threat behaviors and actions. It focuses on the key pillars of an effective ITMP, including the national legal framework, security culture, preventive and protective measures, cyber security, and performance evaluation. By using a systematic approach during implementation, facilities can foster environments conducive to insider threat detection and support long-term program sustainability. The document also provides strategies for improving communication across all levels of an organization, helping to eliminate barriers that hinder the development of robust ITMPs and enhance overall security culture. In today’s organizations, the concept of leveraging safety and security culture lessons to facilitate knowledge transfer is rapidly evolving to expedite insider threat management and security culture improvements. This document outlines the rationale for evaluating an ITMP based on national customs, culture, and stakeholders. The elements are all germane to reliability and trustworthiness and relate to security concerns that states may encounter. The document focuses not only on individual perceptions regarding security issues and capability building but also on team building and how to resolve concerns. The implementers of a facility’s ITMP may zero in on indicators of insider threats within their enterprise. This material will benefit organizations when it is applied using a systematic and structured approach as demonstrated throughout the document.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2026

Optimal CO 2 storage management considering safety constraints in multi-stakeholder multi-site GCS projects: A Markov game perspective

Geological carbon storage (GCS) projects could involve a diverse array of stakeholders or players from public, private, and regulatory sectors, each with different objectives and responsibilities. Given the complexity, scale, and long-term nature of GCS operations, determining whether individual stakeholders can independently optimize their interests — or whether collaborative coalition agreements are needed — remains a central question for effective GCS project planning and management. To access large, high-quality storage resources, future GCS deployment may increasingly occur in geologically connected sites, where shared geological features such as pressure space and reservoir pore capacity can lead to competitive behavior among stakeholders. In this work, we propose a paradigm based on Markov games to quantitatively investigate how different coalition structures affect the goals of stakeholders. We frame this multi-stakeholder multi-site problem as a multi-agent reinforcement learning problem with safety constraints. Our approach enables agents to learn optimal strategies while complying with safety regulations. We present an example where multiple operators are injecting CO 2 into their respective project areas in a geologically connected basin. To address the high computational cost of repeated simulations of high fidelity models, a previously developed surrogate model based on the Embed-to-Control (E2C) framework is employed. Our results demonstrate the effectiveness of the proposed framework in addressing optimal management of CO 2 storage when multiple stakeholders with different objectives and goals are involved.

58 GEOSCIENCES↗
DOE OSTI2013

Natural Capital Management: An Evolutionary Paradigm for Sustainable Restoration Investment - 13455

Unlike other forms of capital assets (built infrastructure, labor, financial capital), the supply of usable or accessible air, land, and water elements (termed Natural Capital Assets or NCA) available to enterprise processes is structurally shrinking due to increased demand and regulatory restriction. This supply/demand imbalance is affecting all forms of public and private enterprise (including Federal Facilities) in the form of encroachment, production limits, cost increases, and reduced competitiveness. Department of Energy (DOE) sites are comprised of significant stocks of NCA that function as both conserved capital (providing ecosystem services and other reserve capacity), and as natural infrastructure (supporting major Federal enterprise programs). The current rubric of 'Environmental Stewardship' provides an unduly constrained management paradigm that is focused largely on compliance process metrics, and lacks a value platform for quantifying, documenting, and sustainably re-deploying re-capitalized natural asset capacity and capability. By adopting value-based system concepts similar to built infrastructure accounting and information management, 'stewarded' natural assets relegated to liability- or compliance-focused outcomes become 're-capitalized' operational assets able to support new or expanded mission. This growing need for new accounting and management paradigms to capture natural capital value is achieving global recognition, most recently by the United Nations, world leaders, and international corporations at the Rio+20 Summit in June of 2012. Natural Capital Asset Management (NCAM){sup TM} is such an accounting framework tool. Using a quantification-based design, NCAM{sup TM} provides inventory, capacity and value data to owners or managers of natural assets such as the DOE that parallel comparable information systems currently used for facility assets. Applied to Environmental Management (EM) and other DOE program activities, the natural asset capacity and value generated by EM projects and other investment and operational programming can be recorded and then allocated to mission and/or ecosystem needs as part of overall site, complex, and Federal decision-making. NCAM{sup TM} can also document post-restoration asset capability and value for use in weighing loss mitigation and ecosystem damage claims arising from past operational activities. A prototype NCAM{sup TM} evaluation developed at the Savannah River Site (SRS) demonstrates use of this framework as an advanced paradigm for NCA accounting and decision-making for the larger DOE complex and other enterprise using natural capital in operations. Applying a quantified value paradigm, the framework catalogues the results of activities that sustain, restore, and modernize natural assets for enterprise-wide value beyond that of compliance milestones. Capturing and assigning recapitalization value using NCAM{sup TM} concepts and tools improves effective reuse of taxpayer-sustained assets, records ecosystem service value, enables mission and enterprise optimization, and assures the sustainability of shared natural capital assets in regional pools vital to both complex sites and local and regional economies. (authors)

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

RISMC Industry Application no. 1 (ECCS/LOCA) Core characterization automation: Reference PWR designs for IA no. 1

The U.S. NRC is currently proposing rulemaking '10 CFR 50.46c' to revise the loss-of-coolant-accident (LOCA) and emergency core cooling system (ECCS) acceptance criteria to include the effects of higher burn-up on cladding performance. The key implications of this proposition are that both cladding and ECCS performance need to be considered in a coupled manner. This implies that LOCA methodologies will need to be updated for regulatory purposes. The Risk-Informed Safety Margin Characterization (RISMC) Pathway, as part of the DOE Light Water Reactor Sustainability (LWRS) research and development program initialed several programs at the Idaho National Laboratory (INL) to support the industry by offering potential solutions to this new LOCA/ECCS analysis. One of these programs is the Industry Application no. 1 (IA1). It's goal is to develop an integrated evaluation model called LOCA Toolkit for the US (LOTUS) to revisit how risks are evaluated in a LOCA under 10 CFR 50.46c. The focus of LOTUS is to establish the automation interfaces among the physics disciplines involved, namely the core design automation, the fuels/clad performance, the system analysis, the risk assessment and the core design optimization. The risk-informed margins management approach for IA1 will provide a means of quantifying the impact on the key LOCA analysis figures-of-merit like peak cladding temperature (PCT), equivalent cladding reacted (ECR), etc. of a change in LOCA analysis inputs. The information obtained can then be used for decision-making and margin management. The modular approach for LOTUS will enable plant owners/vendors to further customize the LOTUS framework for use within their established codes and methods. One of the LOTUS modules deals with the core design automation (CD-A). The purpose of this module is to supply the LOCA analysis with initial conditions, i.e. assembly/pin power histories, power shapes, etc. to be employed by the Fuels/Clad Performance (EP) and System Analysis (SA) modules. In the CD-A module, the plant owner/operator will characterize his core design with the tools he has. For the current LOTUS demonstration, the tools used are the already coupled codes PHISICS and RELAP5-3D2,3. Cross section generation will be done using the Studsvik lattice code HELIOS-2.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Evaluating the limitations of Bayesian metabolic control analysis

AbstractBayesian Metabolic Control Analysis (BMCA) has emerged as a promising framework for inferring metabolic control coefficients in data-limited scenarios by integrating Bayesian inference with linlog rate laws. However, its predictive accuracy and limitations remain underexplored. This study systematically evaluates BMCA’s ability to infer elasticity values, flux control coefficients (FCCs), and concentration control coefficients (CCCs) under varying data availability conditions using three synthetic metabolic network models. Our findings highlight the strengths and weaknesses of BMCA, guiding its application in metabolic engineering and emphasizing the need for methodological refinements.Author summaryUnderstanding how enzymes control metabolic pathways is crucial for optimizing biomanufacturing and synthetic biology applications. Bayesian Metabolic Control Analysis (BMCA) is a promising computational method that integrates Bayesian inference with metabolic control analysis to estimate key control parameters, even in cases with limited experimental data. However, the accuracy and limitations of BMCA remain unclear. In this study, we systematically evaluate BMCA using three synthetic metabolic networks to determine how different types of physiological data impact its predictive performance. We find that BMCA requires flux and enzyme concentration data for accurate predictions, while external metabolite concentrations contribute little. Additionally, BMCA fails to predict elasticity values beyond a magnitude of 1.5 and reliably infer allosteric regulation, even when strong regulatory interactions exist. In addition, BMCA does not accurately rank metabolic control points, which may limit its utility in identifying key enzymes in engineered pathways. Our work provides practical insights into when and how BMCA can be applied, guiding future research in metabolic modeling and control analysis.

Shin, Janis (ORCID:0000000216572455)↗
DOE OSTI2025

Bridging Equipment Reliability Data and Risk Informed Decisions in a Plant Operation Context

Industry equipment reliability and asset management programs are essential elements that help ensure the safe and economical operation of nuclear power plants. The effectiveness of these programs is addressed in several industry-developed and regulatory programs. The Risk-Informed Asset Management (RIAM) project is tasked to develop tools in support of the equipment reliability and asset management programs at nuclear power plants. These tools are designed to create a direct bridge between component health/lifecycle data and decision making (e.g., maintenance scheduling and project prioritization). The goal of this article is to provide a guide for specific use cases that the RIAM project is targeting. We have grouped uses cases into three main areas. The first area focuses on the analysis of equipment reliability data with a particular emphasis on condition-based data, such as test/surveillance reports and component monitoring data. The second area focuses on the integration of equipment reliability into system/plant reliability models to determine system/plant health and identify the components that are critical to maintain an operational system. Lastly, the third area manages plant resources, such as maintenance activities and replacement scheduling using optimization methods. Here the primary focus is on supporting typical system engineer decisions regarding maintenance activity scheduling and component aging management. This is performed in a risk-informed context where the term “risk” is broadly constructed to include both plant reliability and economics. This framework combines data analytics tools to analyze equipment reliability data with risk-informed methods designed to support system engineer decisions (e.g., maintenance and replacement schedules, optimal maintenance posture) in a customizable workflow.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2016

R&D Plan for RISMC Industry Application #1: ECCS/LOCA Cladding Acceptance Criteria

The Nuclear Regulatory Commission (NRC) is finalizing a rulemaking change that would revise the requirements in 10 CFR 50.46. In the proposed new rulemaking, designated as 10 CFR 50.46c, the NRC proposes a fuel performance-based equivalent cladding reacted (ECR) criterion as a function of cladding hydrogen content before the accident (pre-transient) in order to include the effects of higher burnup on cladding performance as well as to address other technical issues. A loss of operational margin may result due to the more restrictive cladding embrittlement criteria. Initial and future compliance with the rule may significantly increase vendor workload and licensee costs as a spectrum of fuel rod initial burnup states may need to be analyzed to demonstrate compliance. The Idaho National Laboratory (INL) has initiated a project, as part of the DOE Light Water Reactor Sustainability Program (LWRS), to develop analytical capabilities to support the industry in the transition to the new rule. This project is called the Industry Application 1 (IA1) within the Risk-Informed Safety Margin Characterization (RISMC) Pathway of LWRS. The general idea behind the initiative is the development of an Integrated Evaluation Model (IEM). The motivation is to develop a multiphysics framework to analyze how uncertainties are propagated across the stream of physical disciplines and data involved, as well as how risks are evaluated in a LOCA safety analysis as regulated under 10 CFR 50.46c. This IEM is called LOTUS which stands for LOCA Toolkit for US, and it represents the LWRS Program’s response to the proposed new rule making. The focus of this report is to complete an R&D plan to describe the demonstration of the LOCA/ECCS RISMC Industry Application # 1 using the advanced RISMC Toolkit and methodologies. This report includes the description and development plan for a RISMC LOCA tool that fully couples advanced MOOSE tools already in development in order to characterize and optimize plant safety and operational margins. Advanced MOOSE tools that are needed to complete this integrated evaluation model are: RAVEN, RELAP-7, BISON, and MAMMOTH.

10CFR50.46C↗
DOE OSTI2026

Evaluating the limitations of Bayesian metabolic control analysis

Bayesian Metabolic Control Analysis (BMCA) is a promising framework for inferring metabolic control coefficients in data-limited scenarios, combining Bayesian inference with linear-logarithmic (lin-log) rate laws. These metabolic control coefficients quantify how changes in enzyme activities affect steady-state fluxes and metabolite concentrations across a metabolic network. However, its predictive accuracy and limitations remain underexplored. This study systematically evaluates BMCA’s ability to infer elasticity values, flux control coefficients (FCC), and concentration control coefficients (CCC) under varying data availability conditions using three synthetic metabolic network models. We demonstrate that BMCA predictions are highly dependent on the inclusion of flux and enzyme concentration data, with the omission of these datasets leading to severe inaccuracies. In our synthetic, enzyme-perturbation datasets, external metabolite concentrations had minimal impact and, in some cases, their exclusion improved predictions; when external-nutrient perturbations were introduced and those concentrations were observed, gains were at most modest. Additionally, we find that posterior estimation with both ADVI and HMC can underestimate large-magnitude elasticities in our synthetic settings, with ADVI showing somewhat higher variance under strong up-regulation; thus, recovering |elasticity| ≳ 1.5 remains challenging regardless of the inference engine. ADVI also fails to accurately infer allosteric interactions, even when regulatory effects are strong. While BMCA maintains reasonable accuracy in partially recovering the rankings of the highest FCC values, its estimates of absolute values remain constrained by prior assumptions and data limitations. Our findings reveal the BMCA algorithm’s strengths and weaknesses, providing guidance on its application in metabolic engineering, and highlighting the need for methodological refinements to enhance its predictive capabilities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Climate Information Needs for Financial Decision Making

Climate Information Needs for Financial Decision Making (Final Report) This Department of Energy workshop award (grant #DE-SC0008480) provided primary support for the American Meteorological Society’s study on climate information needs for financial decision making. The goal of this study was to help advance societal decision making by examining the implications of climate variability and change on near-term financial investments. We explored four key topics: 1) the conditions and criteria that influence returns on investment of major financial decisions, 2) the climate sensitivity of financial decisions, 3) climate information needs of financial decision makers, and 4) potential new mechanisms to promote collaboration between scientists and financial decision makers. Better understanding of these four topics will help scientists provide the most useful information and enable financial decision makers to use scientific information most effectively. As a result, this study will enable leaders in business and government to make well-informed choices that help maximize long-term economic success and social wellbeing in the United States The outcomes of the study include a workshop, which brought together leaders from the scientific and financial decision making communities, a publication of the study report, and a public briefing of the results to the policy community. In addition, we will present the results to the scientific community at the AMS Annual Meeting in February, 2014. The study results were covered well by the media including Bloomberg News and E&E News. Upon request, we also briefed the Office of Science Technology Policy (OSTP) and the Council on Environmental Quality (CEQ) on the outcomes. We presented the results to the policy community through a public briefing in December on Capitol Hill. The full report is publicly available at www.ametsoc.org/cin. Summary of Key Findings The United States invests roughly $1.5 trillion U.S. dollars (USD) in capital assets each year across the public and private sectors (Orszag 2008; United States Census Bureau 2013). Extreme weather events create and exacerbate risks to these financial investments by contributing to: • Direct physical impacts on the investments themselves • Degradation of critical supporting infrastructure • Changes in the availability of key natural resources • Changes to workforce availability or capacity • Changes in the customer base • Supply chain disruptions • Legal liability • Shifts in the regulatory environment • Reductions in credit ratings Even small changes in weather can impact operations in critical economic sectors. As a result, maximizing returns on financial investments depends on accurately understanding and effectively accounting for these risks. Climate variability and change can either exacerbate existing risks or cause new sources of risk to emerge. Managing these risks most effectively will depend on scientific advances and increases in the capacity of financial decision makers to use the scientific knowledge that results. Barriers to using climate information must also be overcome. This study proposes three predefined levels of certainty for communicating about weather and climate risks: 1) possible (i.e., unknown likelihood or less than 50% chance of occurrence), 2) probable (greater than 50% chance of occurrence), and 3) effectively certain (at least 95% chance of occurrence). For example, it is effectively certain that a change in climate will alter weather patterns. It is probable that climate warming will cause increases in the intensity of some extreme events. It is possible that climate change will cause major and widespread disruptions to key planetary life-support services. Key recommendations of this study: 1) Identify climate-related risks and opportunities for financial decision making. 2) Create a framework to translate scientific information in clear and actionable terms for financial decision makers. 3) Analyze existing climate assessments and translate projected impacts into possible, probable, and effectively certain impacts. 4) Improve climate projections with respect to precipitation (timing, amount, and intensity), extreme events, and tails of probability distributions (i.e., low-probability but high-consequence events). 5) Increase spatial resolution of climate projections in order to provide climate information at the scale most relevant to financial investments. 6) Improve projections of the societal consequences of climate impacts through integrated assessments of physical, natural, and social sciences. 7) Create a user-friendly information repository and portal that provides easy access to information relevant to financial decision making. 8) Create and maintain opportunities to bring together financial decision makers, scientists, and service providers. Near-term financial decisions have long-term implications for the United States’ social and economic well-being that depend, in part, on climate variability and change. Investments will be most successful, and will advance the interests of society most effectively, if they are grounded in the best available knowledge & understanding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Hydrogen Component Leak Rate Quantification for System Risk and Reliability Assessment through QRA and PHM Frameworks: Preprint

The National Renewable Energy Laboratory's (NREL) Hydrogen Safety Research and Development (HSR&D) program in collaboration with the University of Maryland's Systems Risk and Reliability Analysis Laboratory (SyRRA) are working to improve reliability and reduce risk in hydrogen systems. This approach strives to use quantitative data on component leaks and failures, together with Prognosis and Health Management (PHM), and Quantitative Risk Assessment (QRA) to identify at-risk components, reduce component failures and downtime, and predict when components require maintenance. Hydrogen component failures increase facility maintenance cost, facility downtime, and reduce public acceptance of hydrogen technologies, ultimately increasing facility size and cost because of potentially overly conservative requirements. Leaks are a predominant failure mode for hydrogen components. However, uncertainties in the amount of hydrogen emitted from leaking components and the frequency of those failure events limit the understanding of the risks that they present under real-world operational conditions. NREL has deployed a test fixture, the Leak Rate Quantification Apparatus (LRQA), to quantify the mass flow rate of leaking gases from medium and high-pressure components that have failed while in service. Quantitative hydrogen leak rate data from this system could ultimately be used to better inform risk assessment and Regulation Codes and Standards (RCS). Parallel activity explores the use of PHM and QRA techniques to assess and reduce risk, thereby improving safety and reliability of hydrogen systems. The results of QRAs could further provide a systematic and science-based foundation for the design and implementation of RCS, as in the latest versions of the NFPA 2 code for gaseous hydrogen stations. Alternatively, data-driven techniques of PHM could provide new damage diagnosis and health-state prognosis tools. This research will help end users, station owners and operators, and regulatory bodies move towards risk-informed preventative maintenance versus emergency corrective maintenance, reducing cost and improving reliability. Predictive modelling of failures could improve safety and affect RCS requirements such as setback distances at liquid refuelling sites. The combination of leak rate quantification research, PHM, and QRA can lead to better informed models enabling data-based decision to be made for hydrogen system safety improvements.

codes and standards↗
DOE OSTI2023

Quantification of storage required for preserving frequency security in wind‐integrated systems

Abstract The penetration of wind power generation into the power grid has been accelerated in recent times due to the aggressive emission targets set by governments and other regulatory authorities. Although wind power has the advantage of being environment‐friendly, wind as a resource is intermittent in nature. In addition, wind power contributes little inertia to the system as most wind turbines are connected to the grid via power electronic converters. These negative aspects of wind power pose serious challenges to the frequency security of power systems as penetration increases. In this work, an approach is proposed where an energy storage system (ESS) is used to mitigate frequency security issues of wind‐integrated systems. ESSs are well equipped to supply virtual inertia to the grid due to their fast‐acting nature, thus replenishing some of the energy storage capability of displaced inertial generation. In this work, a probabilistic approach is proposed to estimate the amount of inertia required by a system to ensure frequency security. Reduction in total system inertia due to the displacement of conventional synchronous generation by wind power generation is considered in this approach, while also taking into account the loss of inertia due to forced outages of conventional units. Monte Carlo simulation is employed for implementing the probabilistic estimation of system inertia. An ESS is then sized appropriately, using the system swing equation, to compensate for the lost inertia. The uncertainty associated with wind energy is modeled into the framework using an autoregressive moving average technique. Effects of increasing the system peak load and changing the wind profile on the expected system inertia are studied to illustrate various factors that might affect system frequency security. The proposed method is validated using the IEEE 39‐bus test system.

17 WIND ENERGY↗
NASA NTRS2020

UAM Vision Concept of Operations (ConOps) UAM Maturity Level (UML) 4

This Vision ConOps is intended as a foundation to engage members of the UAM community and provide a consensus on the future vision of UAM operations. It provides a concept for more detailed discussion and a basis for the exploration of ideas using a common framework to inform the continued development and integration of UAM as part of the broader transportation system. Advanced Air Mobility (AAM) encompasses a range of innovative aviation technologies (small drones, electric aircraft, automated air traffic management, etc.) that are transforming aviation’s role in everyday life, including the movement of goods and people. Urban Air Mobility (UAM) represents one of the most exciting and complex AAM concepts with highly automated aircraft, providing commercial services to the public over densely populated cities. This concept has generated tremendous interest and industry investment. UAM envisages a future in which advanced technologies and new operational procedures enable practical, cost-effective air travel as an integral mode of transportation in metropolitan areas. It represents one of the most exciting and complex AAM concepts with highly automated aircraft providing commercial services to the public over densely populated cities. For this reason, the National Aeronautics and Space Administration (NASA) selected UAM as the initial goal of its AAM efforts and the focus of this Vision Concept of Operations (ConOps) document. UAM Community Vision ConOps: This Vision ConOps effort was led by experts from NASA’s Aeronautics Research Mission Directorate (ARMD) in collaboration with the Federal Aviation Administration (FAA) and Deloitte’s Ecosystem Advisory Group (a cohort of advisers with aviation, aerospace, and regulatory expertise). To develop this Vision ConOps, NASA, FAA, and Deloitte built upon the current body of aeronautical research and consulted with more than 100 stakeholder organizations. This UAM community includes entities ranging from legacy aviation leaders to innovators and new market entrants. Stakeholders consulted included the federal government, state and local government, aerospace original equipment manufacturers (OEMs), local transportation organizations, prospective UAM operators, academia, industry standards-setting bodies, airports, service suppliers, and others (as described in Appendix G). This input was captured through the following methods: • A series of more than two dozen interviews with industry experts, federal regulators, state and local governments, and industry trade groups provided insight into the challenges of UAM integration into the National Airspace System (NAS), as well as technology developments and a variety of perspectives as to how UAM systems will integrate. • A series of two-day community workshops enabling active, detailed engagement of nearly 100 industry, academic, federal, and state stakeholder individuals. These workshops, hosted by NASA and Deloitte, explored UAM concepts in detail, and stakeholders were invited to collaboratively analyze and propose solutions to some of the greatest conceptual challenges behind UAM at an intermediate state. • A review of more than 160 sources of UAM literature from across government, industry, and academia, which are listed in Appendix H. • The public sharing of workshop input and document drafts for review and input across the UAM community. Feedback in the form of more than 1,000 comments and inputs on the document was received from industry groups, individual companies, academia, and government (federal, state, and local), among others. Although effort was made to incorporate inputs from across the UAM stakeholder group, not all comments could ultimately be incorporated in this version. The team resolved conflicting comments or ideas while maintaining consistency with the known direction of regulators and ensuring the document was coherent and consistent. It is recognized that this is a rapidly evolving area and that concepts will likely change over time; as such, this Vision ConOps is a living document and is expected to evolve as concepts mature. The ConOps does, however, provide a vision of UAM concepts and solutions based on the broad insights from across the UAM stakeholder community at the time of its publication and is intended to serve as a UAM North Star for continued research and development of UAM. As a broad Vision ConOps, is not a detailed engineering document; rather, it focuses primarily on outlining a broad, high-level vision across all aspects of a UAM transportation system.

Urban Air Mobility↗
DOE OSTI2019

RISMC industry application #1 (ECCS/LOCA). Core characterization automation: Lattice codes interface for PHISICS/RELAP5-3D

The United States Nuclear Regulatory Commission (U.S. NRC) is currently proposing rule-making “10 CFR 50.46c” to revise the loss-of-coolant-accident (LOCA) and emergency core cooling system (ECCS) acceptance criteria to include the effects of higher burn-up on cladding performance. The key implications of this proposition are that the cladding performance cannot be evaluated anymore as separated from other disciplines, but needs to be evaluated in a coupled manner with other physics. The Risk-Informed Safety Margin Characterization (RISMC) Pathway initiated several programs at Idaho National Laboratory (INL) to support the industry in the transition to the new rule. One of these programs is the Industry Application #1 (IA1). IA1’s goal is to develop an Integrated Evaluation Model (IEM) called LOCA Toolkit for the US (LOTUS). This tool connects five major disciplines involved in LOCA analysis, among them is (automated) core design (CD-A). Here, this paper focuses solely on the design of LOTUS' CD-A module as well as a demonstration application of the CD-A module to a generic Pressurized Water Reactor (PWR). The demonstration of the other LOTUS disciplines together with a demonstration example of the final risk-informed safety margin analysis is published in a separate paper. LOTUS is meant to provide the methodology framework into which the user can plug his established tools and codes. With the CD-A, the plant owner/operator will characterize his core design with the tools he has. He will then integrate his tools/methods into the LOTUS framework for the downstream safety analysis. For the current CD-A demonstration, the tools used are the already coupled codes PHISICS and RELAP5-3D, while cross section generation is done using the Studsvik lattice code HELIOS-2.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Taxonomy for Common-Cause Failure Vulnerability and Mitigation

Applying current guidance and practices for common-cause failure (CCF) mitigation to digital instrumentation and control (I&C) systems has proven problematic, and the regulatory environment has been unpredictable. The potential for CCF vulnerability inhibits I&C modernization, thereby challenging the long-term sustainability of existing plants. For new plants and advanced reactor concepts, concern about CCF vulnerability in highly integrated digital I&C systems imposes a design burden that results in higher costs and increased complexity. The regulatory uncertainty in determining which mitigation strategies will be acceptable (e.g., what diversity is needed and how much is sufficient) drives designers to adopt complicated, costly solutions devised for existing plants. To address the conditions that constrain the transition to digital I&C technology by the US nuclear industry, crosscutting research is needed to resolve uncertainty, demonstrate necessary characteristics, and establish an objective basis for qualification of digital technology for nuclear power plant (NPP) I&C applications. To fulfill this research need, Oak Ridge National Laboratory is investigating mitigation of CCF vulnerability for nuclear-qualified applications. The outcome of this research is expected to contribute to a fundamentally sound, comprehensive basis to qualify digital technology for nuclear power applications. This report documents the development of a CCF taxonomy. The basis for the CCF taxonomy was generated by determining consistent terminology and establishing a classification approach. The terminology is based on definitions from standards, guides, and relevant nuclear power industry technical reports. The classification approach is derived from identified classification schemes focused on I&C systems and key characteristics, including failure modes. The CCF taxonomy provides the basis for a systematic organization of key systems aspects relevant to analyzing the potential for CCF vulnerability and the suitability of mitigation techniques. Development of an effective CCF taxonomy will help to provide a framework for establishing the objective analysis and assessment capabilities desired to facilitate rigorous identification of fault types and triggers that are the fundamental elements of CCF.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Adsorbents and adsorption models for capture of Kr and Xe gas mixtures in fixed-bed columns

Off-gases produced during the reprocessing of used nuclear fuel (UNF) include 129-I 2 , 3-HHO, 14-CO 2 , 85-Kr, and 135-Xe, which are volatilized out into the off-gas. In order to meet regulatory requirements for reprocessing plant emissions, these gases must be captured and removed from the o?-gas stream prior to o?-gas emission. Of particular interest are the noble gases, Kr and Xe, which can be fairly difficult to remove from the off-gas due to their low chemical reactivity. Thus, this work is focused on utilizing engineered adsorbents, AgZ-PAN and HZ-PAN, to capture Kr and Xe from a mixed-gas stream at relatively low temperatures (191–295 K) and various flow rates (50–2000 mL/min). Isothermal data for Kr and Xe on each adsorbent are analyzed to produce the Langmuir parameters needed to model the mixture adsorption capacities at relevant temperatures using the Extended Langmuir model. Those parameters are then incorporated into a fixed-bed adsorption model developed in this work using the Mulitphysics Object-Oriented Simulation Environment (MOOSE). That model is used to simulate breakthrough times for Kr and Xe in packed columns of AgZ-PAN and HZ-PAN, ranging in length from 6 to 20 in., at relevant temperatures and flow rates. Breakthrough times varied from nearly instantaneous for Kr in AgZ-PAN to 30 h for Xe in HZ-PAN. After the developed model was validated by comparisons with experimental break-through data, the model framework was used to simulate the performance of multiple fixed-bed columns connected in series.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Methodology Development for Cybersecurity Robustness and Vulnerability Assessment of University Research Reactors

In 2012, the U.S. Nuclear Regulatory Commission (NRC) formed a working group in collaboration with the Test, Research and Training Reactors (TRTR) group to review cyber security programs at US nuclear nonpower reactor (NPR) facilities. Following their review of cyber security at four different university NPR (i.e. university research reactor (URR)) facilities, the NRC staff made recommendations for improvements at URR including 1) augmenting the URR licensees’ understanding of the protective features provided with their physical security systems, 2) educating URR facility staff on cyber security issues, and 3) developing guidance to help the URR facilities maintain adequate cyber security going forward. This three-year project to develop and implement a cyber-security risk assessment methodology and defense-in-depth mitigation strategy for application at URR facilities provides solutions effectively addressing each of the recommendations given in the NRC review referenced above. In support of this project, cyber security documentation and guidance from the NRC, Department of Homeland Security (DHS) Industrial Control Systems (ICS) Cyber Emergency Response Team (CERT), National Institute of Standards and Technology (NIST), and the International Atomic Energy Agency (IAEA), were reviewed. Most of this documentation did not take the unique resources, configuration, and infrastructure associated with URR facilities into account. The NRC-TRTR effective practices document provides useful guidance for implementing cyber security protections at URR, but lacks a structured framework for systematically auditing facility digital control assets (DCA) and evaluating associated cyber threats, vulnerabilities, and risk. The outcomes from this project, therefore, seek to build on the general resources above by providing a straightforward DCA auditing and risk assessment methodology, as well as mitigation strategies, that may be implemented to address the risk of cyber-attack at URR facilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

MECHANISTIC MULTIPHYSICS MODELING OF CLADDING RUPTURE IN NUCLEAR FUEL RODS DURING LOSS-OF-COOLANT ACCIDENT CONDITIONS

The Loss of Coolant Accident (LOCA) is a design basis accident that is included as part of the safety analysis of nuclear power plants. As the nuclear industry desires to increase the cycle length and discharge burnup of existing nuclear power plants they must demonstrate safe operation during a LOCA on high burnup fuel. During a LOCA transient on high burnup fuel rods, the rods may undergo a process known as fuel fragmentation, relocation, and dispersal (FFRD). To permit dispersal, the cladding encapsulating the fuel must rupture with an opening size large enough to allow the fragmented fuel particles to release. Current licensing tools used by industry and the United States Nuclear Regulatory Commission are limited in geometric fidelity and materials that can be analyzed. These simulation tools generally employ a quasi-two-dimensional (1.5D or Layered1D) or 2D-RZ axisymmetric geometric representations exclusively. While a valid approach under some instances, there are times when important physics have an asymmetric behavior in the fuel rod. Examples include fuel fragmentation, thermal-hydraulic boundary conditions, and cladding rupture, all of which are important for LOCA analysis. As industry pursues burnup extensions it must be demonstrated that fuel dispersal can be mitigated or eliminated. To do this, an understanding of the rupture opening after cladding failure is required. This work presents the development of a model for predicting the size and location of the rupture opening in failed fuel rods during LOCA conditions using advanced modeling and simulation tools. In order to supply the rupture model with appropriate boundary conditions, improvements to fuel fragmentation, axial relocation and oxidation modeling were required. First, the eXtended Finite Element Method (XFEM) is used to mechanistically predict the number of fuel fragments that form due to material strength randomization, criteria for strength randomization, mesh density, power ramping rates and irradiation effects. These predictions with associated uncertainty were compared to empirical correlations developed for UO2 verifying that they can be used with increased confidence in subsequent axial relocation analyses. Secondly, a new first-of-its-kind Layered2D computational framework was developed that provides the ability to apply azimuthally varying boundary conditions while providing discrete layers to track fuel movement during the LOCA. An existing fuel axial relocation model developed for Layered1D was extended to work within the Layered2D framework. A large sensitivity study was performed on the initial version of the model to identify modeling parameters of particular importance, with the emissivity used for radiation after blowdown being the primary source of uncertainty. Then, a simplistic approach to incorporate mechanical degradation of the cladding due to oxidation was also developed to investigate the impact of reduced cladding thickness on predictions of the time to failure of cladding tubes. It was found that the cladding will typically rupture prior to a reduction in thickness that is sufficient to impact the rupture behavior. A model was then developed for predicting cladding rupture that transfers the cladding surface temperatures, rod internal and external pressures, fast neutron fluence, and fast neutron flux from a more detailed Layered1D, Layered2D, or 2D-RZ analysis to a 3D cladding only analysis. Comparisons of the rupture model to a few experiments indicate reasonable predictions. The rupture model was then applied to two accident tolerant fuel concepts (FeCrAl and Cr-coated Zircaloy) where it predicted that both ATF concepts would have smaller rupture openings and delayed rupture times than the standard Zircaloy-4 cladding material under identical loading conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗