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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 OSTI2021

Exploring Social Dynamics of Hard-Disk Drives Circularity with an Agent-Based Approach

By 2025, it is estimated that installed data storage in the U.S. will be 2.2 Zettabytes, generating about 50 million units of end-of-life hard-disk drives (HDDs) per year. The circular economy (CE) tackles waste issues by maximizing value retention in the economy, for instance, through reuse and recycling. However, the reuse of hard disk drives is hindered by the lack of trust organizations have toward other means of data removal than physically destroying HDDs. Here, an agent-based approach explores how organizations' decisions to adopt other data removal means affect HDDs' circularity. The model applies the theory of planned behavior to model the decisions of HDDs end-users. Results demonstrate that the attitude (which is affected by trust) of end-users toward data-wiping technologies acts as a barrier to reuse. Moreover, social pressure can play a significant role as organizations that adopt CE behaviors can set an example for others.

28 EE - Advanced Manufacturing Office (EE-5A)↗
DOE OSTI2023

plastic-abm [SWR 23-74]

The plastic agent-based model (ABM) built with the MESA python library simulates agents decisions to recycle plastic waste according to an extended version of the theory of planned behavior.

Walzberg, Julien↗
DOE OSTI2022

Agent-Based Modeling for the Circular Economy: Lessons Learned From Three Case Studies

The circular economy (CE) aims at decoupling human activities from resource use, creating wealth in the process. Recently, many scholars have questioned the link between increased circularity and sustainability, resulting in many methodological approaches being developed for that purpose. This presentation summarizes the insights gained from the application of agent-based modeling (ABM) to study the techno-economic and social conditions promoting circularity and sustainability of three technologies: photovoltaic (PV) modules, hard disk drives (HDDs), and wind blades. Four main categories of agents are defined in the ABM: asset owners, service providers (e.g., refurbishers), recyclers, and manufacturers. Two main CE strategies are represented: lifetime extension (through repair or reuse) and recycling. The developed models start by projecting installed capacities and end-of-life (EOL) quantities. Then the theory of planned behavior - a social psychology model explaining behavior adoption based on attitude, peer influence, and costs - is used to model the asset owners' EOL decision (i.e., landfill, recycle or extend the lifetime of the asset). Then, the quantities of assets flowing to the recycler and service provider agents and quantities of materials flowing to manufacturers are computed. Recyclers' economies of scale are dynamically modeled, and the value generated by the CE strategies for the recyclers, service providers, and manufacturers is computed within the model. When data are available, avoided greenhouse gas emissions resulting from the CE strategies adoption are calculated exogenously from the ABM simulations. Results show that with improved used PV modules warranties, the reuse CE strategy adoption increases from 1% to 23% between 2020 and 2050. Similarly, improved standards could enhance HDDs end-users trust in data-wiping - a prerequisite to reuse - leading to a 3-fold increase in the reuse rate and avoid about 5 million tons of CO2 eq by 2050. Regarding wind blades, 5-15 years lifetime extension could reduce EOL blade quantities by 13%. High costs and logistic issues prevent blades from being recycled in greater quantities. One insight from the case studies is the necessity to have mature secondary markets for reuse to be a viable option. Interestingly, PV reuse is limited by the willingness of PV owners to purchase used modules (on the demand side), while HDDs reuse is constrained by the lack of trust toward data-wiping (limiting the supply of used HDDs). The six limits of the CE concepts presented by Korhonen et al. (2018) are finally used to interpret the results. The HDD case study is an exemplary lock-in, where the first accepted practice (shredding) retains most of the market. The PV results illustrate the technical limitation to reuse, as the growing demand cannot be supplied entirely with used PV modules. The wind case study shows the relevance of clearly defining physical flows - what type of waste should wind blades be considered, how should they be transported and landfilled? - a crucial consideration that also applies to PV modules. Finally, the three case studies highlight the relevance of studying a technology's technical, economic, and market material efficiency potentials altogether and the potential benefit of coupling ABM to life cycle assessment.

agent-based modeling↗
DOE OSTI2022

Regional Representation of Wind Stakeholders' End-of-Life Behaviors and Their Impact on Wind Blade Circularity

Wind plant power has seen tremendous growth in the US and worldwide, representing the most significant renewable energy installed capacity besides hydropower. While wind power enables decarbonizing the electricity grid, the rising amount of end-of-life (EOL) wind blades - which are arduous to recycle - present a challenge for landfills if disposed of whole and a missed opportunity to recover valuable composite materials. The circular economy (CE) concept proposes strategies to rethink, reuse and recover products, components, and materials. However, transitioning to a CE implies changing how business models, supply chains, and behaviors deal with products and waste; changes arduously captured with traditional methods used to assess circularity such as life cycle assessment or material flow analysis (MFA). Here we present an agent-based model (ABM) that captures behavioral aspects impacting wind blade circularity in the US. The ABM also accounts for wind plant projects and landfills heterogeneity - a characteristic not easily included in top-down approaches such as MFA, input-output analysis, or system dynamics. Results show that recycling is divided as most recycling facilities are on the eastern side of the country, a challenge that could be alleviated by shredding blades before transportation. Recycling programs from the wind industry could also seed recycling behaviors within wind plant owners. Better yet, new blade designs could increase circularity if original equipment manufacturers accept the risks involved with the investments needed to adapt the production lines.

17 WIND ENERGY↗
DOE OSTI2023

Assessing the Performance of a Circular Economy for Wind Energy Technologies: A Summary of Three Analytical Tools

A circular economy emphasizes the efficient use of all resources and presents opportunities for addressing series of economic and environmental objectives at local, regional, and national levels. Despite anticipated overall benefits to society, the transition to a circular economy is likely to create regional differences in impacts. As a result, it is important to evaluate the performance and tradeoffs associated with circular economy transitions. This poster summaries three previously published analytical tools that were used to assess the performance of developing a circular economy for wind energy technologies: the Renewable Energy Materials Properties Database (REMPD), a circular economy agent-based model for wind blades (CE Wind ABM), and the Circular Economy Lifecycle Assessment and Visualization (CELAVI) framework. The REMPD is a comprehensive database of materials used in wind and solar plants, including material quantities and physical materials availability. The CE Wind ABM allows us to understand how wind stakeholders' end-of-life behaviors influence wind blade circularity and evaluate the impact of regional variables (e.g., logistics and transportation). And, the CELAVI framework is a modular framework that can be used to evaluate the impacts associated with circular economy transitions. These three analytical tools have been applied to evaluate circular economy transitions for wind energy technologies and they could be expanded to other technologies and products.

agent-based modeling↗
DOE OSTI2021

An investigation of hard-disk drive circularity accounting for socio-technical dynamics and data uncertainty

The installed data storage capacity in the U.S. will reach 2.2 Zettabytes by 2025, generating about 50 million units of end-of-life (EOL) hard-disk drives (HDDs) per year. Due to data security concerns, most EOL HDDs are currently shredded (even when still functioning), representing an economic loss. Moreover, raw material extraction linked to the increased demand for storage causes environmental impacts. Besides mitigating the threat posed by sudden restrictions of raw materials, the circular economy (CE) offers to maximize value retention in the economy and reduce the environmental impacts of human activities. Common CE strategies are reusing and recycling products. However, the reuse of hard disk drives is currently burdened by the lack of trust HDD end-users have toward other non-physical means of data removal than shredding. Here, an agent-based modeling (ABM) approach is proposed to explore how techno-economic and social factors affect end-users' decisions to adopt EOL management practices other than shredding. The proposed method also accounts for data uncertainty by applying a semi-quantitative approach. Results demonstrate how increased green procurement and more robust standards could spur end-users' trust toward data-wiping technologies. Even when accounting for uncertainty, HDDs' reuse brings better environmental and economic benefits than HDD shredding followed by material recovery. The semi-quantitative approach proposed in this study could be more universally applied in future ABM, especially given the often-stochastic nature of such models. The developed ABM is also the first to represent several HDD industry stakeholders and demonstrate how the HDD shredding lock-in situation could be resolved.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Dynamic Modeling, Trajectory Optimization, and Linear Control of Cable-Driven Parallel Robots for Automated Panelized Building Retrofits

The construction industry faces a growing need for automation to reduce costs, improve accuracy and productivity, and address labor shortages. One area that stands to benefit significantly from automation is panelized prefabricated building envelope retrofits, which can improve a building’s energy efficiency in heating and cooling interior spaces. In this paper, we propose using cable-driven parallel robots (CDPRs), which can effectively lift and handle large objects, to install these panels. However, implementing CDPRs presents significant challenges because of their nonlinear dynamics, complex trajectory planning, and precise control requirements. To tackle these challenges, this work focuses on a new application of established control and trajectory optimization theories in a CDPR simulation of a building envelope retrofit under real-world conditions. We first model the dynamics of CDPRs, highlighting the critical role of damping in system behavior. Building on this dynamic model, we formulate a trajectory optimization problem to generate feasible and efficient motion plans for the robot under operational and environmental constraints. Given the high precision required in the construction industry, accurately tracking the optimized trajectory is essential. However, challenges such as partial observability and external vibrations complicate this task. To address these issues, a Linear Quadratic Gaussian control framework is applied, enabling the robot to track the optimized trajectories with precision. Simulation results show that the proposed controller enables precise end effector positioning with errors under 4 mm, even in the presence of external wind disturbances. Through comprehensive simulations, our approach allows for an in-depth exploration of the system’s nonlinear dynamics, trajectory optimization, and control strategies under controlled yet highly realistic conditions. The results demonstrate the feasibility of CDPRs for automating panel installation and provide insights into their practical deployment.

CDPR↗
DOE OSTI2022

Cyber risk assessment and investment optimization using game theory and ML-based anomaly detection and mitigation for wide-area control in smart grids

The electric power grid is increasingly becoming susceptible to cyber attacks that exploit vulnerabilities in the smart grid control, information, and physical layers. Successful cyber attacks can have catastrophic impacts on the social and economic well-being of any nation all over the globe. It has, thus, become imperative to secure the smart grid against such adversarial actions to ensure stable, secure, and reliable operation of the grid. The existing research and industry practices prove to be inadequate in terms of providing pragmatic and effective defense methodologies and measures for long-term cybersecurity planning and real-time cybersecurity for grid operation. For example, existing works lack models that incorporate uncertain behavior of cyber-attackers and pragmatic defense measures for cyber risk assessment and cybersecurity investment optimization which often provide unreliable and strictly qualitative solutions to these problems. At the same time, with the growing number of cyber incidents in the grid, there still exists a need to develop attack-resilient algorithms for wide-area monitoring, protection, and control (WAMPAC) applications like the wide-area voltage control systems (WAVCS) for Flexible AC Transmissions Systems (FACTS) that lack in scalable and feasible solutions from the cybersecurity perspective. This dissertation proposes novel models and methodologies for: (1) Cybersecurity planning, and (2) Cybersecurity for system operation. The cybersecurity planning is achieved through cyber risk assessment and cybersecurity resource investment optimization for long-term cybersecurity of the grid using game theory and attack-defense trees. Cybersecurity for system operation consists of development of cyber anomaly detection and mitigation algorithms for flexible AC transmission system (FACTS) controller-based wide-area voltage control systems (WAVCS) using machine learning (ML), and software defined networking-based moving target defense network routing for achieving real-time cyber-physical security for grid operations. This is followed by hardware-in-the-loop (HIL) implementation and evaluation of these attack prevention, detection, and mitigation algorithms and methodologies showcasing their feasibility in a close to real-world environment. For cybersecurity planning, a novel approach involving a combination of game theory and attack defense trees (ADT) for optimal cybersecurity resource allocation in the smart grid is proposed. This methodology involves modeling of the cyber-physical smart grid substations as ADTs, defining attacker costs, defense costs, and attack probabilities for attack access points. Using game theoretical formulation, optimal defense strategies for the defender of the system to invest cybersecurity resources in the grid are obtained. Additionally, a game-theoretic framework is developed for quantitative cyber-physical risk assessment of the grid under a dynamically changing cyber threat space and uncertain behavior of cyber attackers which is further used to optimize investments in the smart grid's cybersecurity resources. The attacker, defender, and the smart grid system are modeled while incorporating attacker-stochasticity and federal guidelines for smart grid cybersecurity. This allows quantification of threat, vulnerabilities, and attack impact of the grid for quantitative risk assessment. The defender's budget to invest in the security resources in the grid is optimized based on the strategies leading to minimum system risk. The evaluation of the proposed solutions highlight the feasibility for practical implementation of these methodologies and algorithms in the smart grid, while taking the federal requirements and guidelines for smart grid security into consideration. For achieving cybersecurity for system operation, attack prevention, detection, and mitigation algorithms and methodologies are developed specifically for FACTS-based WAVCS. Anomaly detection and mitigation in the WAVCS are achieved using algorithms based on machine learning which involves offline training and testing of ML models with CPS datasets incorporating physics-based features that allow accurate distinction between system faults and cyber attacks. For attack prevention, a methodology based on software defined network (SDN)-based moving target defense (MTD) network routing is proposed that enables prevention of Denial of Service (DoS) type attacks on the smart grid communication system. Subsequently, these methodologies and algorithms are implemented and evaluated on an HIL testbed that allows for real-time attack prevention, detection, and mitigation of emulated cyber attacks on the WAVCS in a close to real-world environment. The results show highly accurate and efficient performance of the implemented algorithms and methodologies with the smart grid system operating within the NERC's system operation limits even in the presence of DoS and data integrity cyber attacks. This work opens up future research opportunities in other directions such as (1) Expanding cybersecurity planning methodologies to real-time cyber contingency analysis with different game formulations; and (2) Applying the cybersecurity for system operation algorithms to broader categories of wide-area control applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

The Present and Future of QCD: QCD Town Meeting White Paper – An Input to the 2023 NSAC Long Range Plan

It is currently understood that there are four fundamental forces in nature: gravitational, electromagnetic, weak and strong forces. The strong force governs the interactions between quarks and gluons, elementary particles whose interactions give rise to the vast majority of visible mass in the universe. The mathematical description of the strong force is provided by the non-Abelian gauge theory Quantum Chromodynamics (QCD). While QCD is an exquisite theory, constructing the nucleons and nuclei from quarks, and furthermore explaining the behavior of quarks and gluons at all energies, remain to be complex and challenging problems. Such challenges, along with the desire to understand all visible matter at the most fundamental level, position the study of QCD as a central thrust of research in nuclear science. Experimental insight into the strong force can be gained using large particle accelerator facilities, which are necessary to probe the very short distance scales over which quarks and gluons interact. The Long Range Plans (LRPs) exercise of 1989 and 1996 led directly to the construction of two world-class facilities: the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab (JLab) that is focused on studying how the structure of hadrons emerges from QCD (cold QCD research), and the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Lab (BNL) that aims at the discovery and study of a new state of matter, the quark-gluon plasma (QGP), at extremely high temperatures (hot QCD research). These past investments have produced major advances. Nucleons and nuclei are being studied with increasing precision with a unified description of the partonic structure utilizing multi-dimensional imaging. Significant progress has been made, paving the way towards a complete picture of how quarks and gluons give rise to the mass, spin, and momentum of the nucleon. In hot QCD, the QGP is created in the collisions of nuclei at RHIC and the Large Hadron Collider (LHC) and is observed to behave like a fluid with very low specific shear viscosity; the current goals are to understand how the fluid behavior emerges from QCD and to characterize the temperature (and chemical potential) dependence of the properties of the QGP. As this White Paper is written, current experimental programs at CEBAF, RHIC and the LHC continue to provide exciting near term opportunities to capitalize on the investments in experimental equipment and accelerator operations. Most importantly, the QCD community looks forward to the construction of the Electron Ion Collider (EIC) as a major new facility to push forward QCD research in the next decades, with significant focus on exploring the properties of gluons, the mediators of the strong force.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Using Change Models to Envision Better Applications of Animal Behavior Research in Conservation Management and Beyond

While most animal behavior researchers have mastered the process of knowledge creation, generating knowledge that can readily be applied requires a different set of skills. The process and timeframe of fundamental scientific knowledge production is often not relevant to those who might apply it, such as conservation or wildlife managers. Additionally, the complex challenges that policy makers, managers and practitioners face are often not adequately communicated to and among scientists. This mutual disconnect in discourse, relationships, common terms, and practices is especially apparent when animal behavior researchers seek to have applied impact. We argue that bridging the complex implementation gap in animal behavior requires a formalized vision for change. We turn to change model theory, a tool commonly used in other fields for identifying the links between actions and outcomes necessary for enacting large-scale change. We focus on the subfield of conservation behavior with a change model that outlines specific ways to improve collaboration and coordination between animal behavior science and conservation practice. We present this targeted change model, review each strategy the model outlines, and highlight pressing actions that people from various career stages and backgrounds can take. We encourage researchers to further the alignment of science with management needs by developing the proper communication mechanisms for improved cultural exchange and plan future change model efforts directly targeting managers. Beyond the conservation behavior change model we present, we also discuss the broad applicability of change models to enhance the application of academic research to other fields. Fundamental science researchers are increasingly required to show impact of their work on society; the change model process we describe here can enable further impact.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Impacts and emerging research opportunities in Vehicle-Grid Integration for transportation: A review

This review provides a comprehensive examination of Vehicle-Grid Integration (VGI) technologies and their impacts on transportation systems, with a particular emphasis on the transportation-energy nexus. It systematically explores how VGI affects key transportation applications such as charging infrastructure planning, electric vehicle (EV) routing, smart charging coordination, shared mobility, and dynamic pricing. By synthesizing recent literature from both transportation and energy systems perspectives, this study highlights how advanced methodologies, such as reinforcement learning, game theory, and optimization techniques, are used to model the complex interactions between EVs, mobility patterns, and distributed energy systems. Furthermore, the review also identifies critical challenges, including behavioral factors, data limitations, and system scalability. Drawing on these insights, the paper outlines emerging research opportunities to support the design of integrated, resilient, and user-centric VGI solutions that advance sustainable mobility and energy system efficiency.

Charging coordination↗
DOE OSTI2021

A trait-based framework for linking microbial communities with carbon transformations under precipitation change

Droughts are common throughout the world. As the climate changes, droughts may become more frequent and intense. Still, scientists are uncertain about how drought will affect the natural world, particularly the bacteria, fungi, and other microbes that live in soils. These tiny life forms are crucial because they control the Earth’s flows of carbon and essential nutrients. Researchers at the University of California, Irvine, and Lawrence Berkeley National Laboratory teamed up to study how the microbiome, or collection of bacteria and fungi in the soil, deals with drought. Since 2007, the researchers have used shelters with retractable roofs to prevent nearly half of normal rainfall from reaching grass and shrub ecosystems, and their soil microbiomes, in Southern California. The study team discovered that microbes have some clever tricks up their sleeve for surviving drought. When growing on dead grass as a food source, microbes ramp up production of specialized chemicals called osmolytes that keep their cells from drying out. But microbes growing on dead shrubs face another problem. Unlike grass, shrub leaves are a lousy food source. To digest and consume shrub leaves, microbes have to exude more enzymes, which act like biochemical chef knives that chop complex leaf molecules into bite-sized pieces. Carbon is the coin of the microbial realm, earned via enzyme action or slurping up dead plant juices. Microbes growing under normal conditions on tasty dead grass have it easy: they can spend most of their carbon currency on growth. With drought, life gets harder as microbes need to pay up for osmolytes and ramp down their growth. It gets worse with shrub leaves because microbes have to multi-task among growth, enzyme secretion, and osmolyte production. When drought hits, microbes on shrub leaves forgo the osmolytes, probably because losing the carbon revenue from enzyme investment would be a deal-breaker for survival. The next question tackled by the researchers asked how the genes controlling microbial lifestyles sort out across the tree of life. Most microbiologists thought these lifestyles would correspond to rather large branches on the tree. But the study team found that in fact, very closely related microbes differ in important and interesting ways. For example, bacteria that have nearly identical housekeeping genes respond distinctively to warming, rainfall, and plant chemistry. As a result, soil microbiomes are much richer in diversity than originally thought. And studying microbial diversity in much greater detail could open up many more possibilities for how microbial life deals with changing environmental conditions. Trying to understand microbial life without the fine details of genetic diversity is like trying to stream Netflix on a dial-up connection. By looking across the landscape, the researchers revealed that microbial diversity is absolutely critical for maintaining the planet’s flows of carbon and nutrients. The study team designed a new technology—microbial cages—for transplanting intact microbiomes. With the cages, the researchers could move microbiomes to novel environments and compare their ability to cycle carbon and nutrients. In some cases, performance tailed off when microbiomes found themselves in a new environment, but in other cases, performance rivaled or even exceeded that of the resident microbiome. And even the low performers eventually caught up to the native microbiomes if given sufficient time. These findings mean that microbiomes—at least in Southern California—may be resilient to climate shifts due to a high diversity of microbial lifestyles. Coping with heat and drought may literally be in their DNA. The last piece of the research puzzle, and a “Holy Grail” for microbial ecologists, is to forecast the behavior of diverse microbiomes. To meet this challenge, the study team developed new theory and computer models. For the first time, these models account for hundreds of different microbes and how their intricate lifestyles cope with drought. The models are starting to connect the tiniest microbes with the global cycles that sustain Earth’s farms, fields, and forests. With that connection, it will be easier for society to plan for a world with more droughts and other climate disruptions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Advanced Fuels Campaign Execution Plan

The Advanced Fuels Campaign (AFC) Execution Plan outlines the strategy, mission, scope, near-term and long-term goals, structure, and organization associated with nuclear fuels and materials research, development, and demonstration activities within the Department of Energy’s (DOE) Nuclear Fuel Cycle and Supply Chain (NFCSC) program. NFCSC has been given responsibility to identify and mature advanced fuel technologies for the DOE using a science-based approach, focused on developing a fundamental understanding of nuclear fuels and materials to drive development of integrated nuclear fuel and materials technology. This science-based approach combines theory, experiments, and multiscale modeling and simulation to achieve a predictive understanding of relevant behaviors ranging from fuel fabrication processes (and their resulting fuel microstructures) through fuel/cladding performance under irradiation (in contrast to more empirical, observation-based approaches frequently used in fuel performance modeling and fuel qualification). The traditional scope of AFC includes the evaluation and development of multiple fuel forms to support two fuel cycle options: once-through and full recycle. The word “fuel” is used generically to include conventional fuels, transmutation targets, and any associated cladding or duct materials. The once-through fuel cycle addresses advanced light water reactor fuels with enhanced performance, extended burnup, and reduced waste generation. In fiscal year (FY) 2012, AFC’s scope expanded to include research, development, and demonstration (RD&D) for light water reactor (LWR) fuels with enhanced accident tolerance. Fuel fabrication activities include the development of innovative methods to enhance process efficiencies, reduce waste, and improve control over as-fabricated fuel microstructural properties to achieve desired in-reactor performance. Using modern modeling and simulation approaches, the objective is to predict fresh fuel properties given the feedstock characteristics and fabrication process parameters. The performance-related activities include small-scale, in-reactor, and out-of-reactor phenomenological testing (distinct from, but synergistic with, integral prototypic testing) and extensive, quantitative characterization (focusing on characterization of fuel and cladding materials at the scale of microstructure) both before and after testing. Larger-scale, prototypic experiments are conducted in concert with phenomenological testing to drive a Fuel Development and Qualification program, incorporating a fundamental understanding of fuel behavior performance characteristics. Then, using the tools developed under the productive science-based approach, fuels will be optimized to meet specific performance requirements, thereby minimizing the need to repeatedly perform large-scale, integral experiments over a wide parametric range as a means of experimental exploration. Two significant initiatives are underway within AFC. First, a gap analysis completed in early FY 2019 identified critical irradiation testing needs that are lacking within the national light water reactor (LWR) fuels testbed since the shutdown of the Halden Reactor in 2018. The identified gaps are for instrumented, prototypic testing of LWR fuels, especially under boiling water reactor conditions, ramp conditions, and conditions leading to fuel failure; these needs exist for supporting current LWR fuels and their possible extension to higher burnups, but are especially urgent relative to near-term development and qualification of accident-tolerant fuels. Recommendations that resulted from the Halden Gap Analysis focused on enhancements at Advanced Test Reactor (ATR) and Transient Reactor Test Facility (TREAT) to fill gaps in testing capabilities relative to these needs. Second, a concerted effort to develop and demonstrate a systematic approach to accelerating the development, testing, and qualification of new fuel systems has been initiated. This is highlighted by a test strategy that combines the considerable advances in multiscale, mechanistic fuel modeling of recent years with a MiniFuel separate effects test program in the High Flux Isotope Reactor (HFIR) and a Fission Accelerated Steady-state Testing (FAST) semi-integral accelerated test program in ATR. This approach is being tested/demonstrated using the metallic fuel system, but if successful it is expected to be extensible to multiple fuel types and diverse applications. This document includes an overview of the NFCSC program, a definition of science-based development of nuclear fuels, near-term goals for Advanced LWR fuels (ALFs), and longer-term goals for Advanced Reactor Fuels (ARFs) RD&D. This includes the activities that will be conducted to achieve success toward the grand challenge, as well as the goals and milestones to be achieved over the next few decades of research and development. Long-term goals are based on the DOE Office of Nuclear Energ

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Advanced Fuels Campaign Execution Plan

The Advanced Fuels Campaign (AFC) Execution Plan outlines the strategy, mission, scope, near-term and long-term goals, structure, and organization associated with nuclear fuels and materials research, development, and demonstration activities within the Department of Energy’s (DOE) Nuclear Fuel Cycle and Supply Chain (NFCSC) program. NFCSC has been given responsibility to identify and mature advanced fuel technologies for the DOE using a science-based approach, focused on developing a fundamental understanding of nuclear fuels and materials to drive development of integrated nuclear fuel and materials technology. This science-based approach combines theory, experiments, and multiscale modeling and simulation to achieve a predictive understanding of relevant behaviors ranging from fuel fabrication processes (and their resulting fuel microstructures) through fuel/cladding performance under irradiation (in contrast to more empirical, observation-based approaches frequently used in fuel performance modeling and fuel qualification). The traditional scope of AFC includes the evaluation and development of multiple fuel forms to support two fuel cycle options: once-through and full recycle. The word “fuel” is used generically to include conventional fuels, transmutation targets, and any associated cladding or duct materials. The once-through fuel cycle addresses advanced light water reactor fuels with enhanced performance, extended burnup, and reduced waste generation. In fiscal year (FY) 2012, AFC’s scope expanded to include research, development, and demonstration (RD&D) for light water reactor (LWR) fuels with enhanced accident tolerance. Fuel fabrication activities include the development of innovative methods to enhance process efficiencies, reduce waste, and improve control over as-fabricated fuel microstructural properties to achieve desired in-reactor performance. Using modern modeling and simulation approaches, the objective is to predict fresh fuel properties given the feedstock characteristics and fabrication process parameters. The performance-related activities include small-scale, in-reactor, and out-of-reactor phenomenological testing (distinct from, but synergistic with, integral prototypic testing) and extensive, quantitative characterization (focusing on characterization of fuel and cladding materials at the scale of microstructure) both before and after testing. Larger-scale, prototypic experiments are conducted in concert with phenomenological testing to drive a Fuel Development and Qualification program, incorporating a fundamental understanding of fuel behavior performance characteristics. Then, using the tools developed under the productive science-based approach, fuels will be optimized to meet specific performance requirements, thereby minimizing the need to repeatedly perform large-scale, integral experiments over a wide parametric range as a means of experimental exploration. Two significant initiatives are underway within AFC. First, a gap analysis completed in early FY 2019 identified critical irradiation testing needs that are lacking within the national light water reactor (LWR) fuels testbed since the shutdown of the Halden Reactor in 2018. The identified gaps are for instrumented, prototypic testing of LWR fuels, especially under boiling water reactor conditions, ramp conditions, and conditions leading to fuel failure; these needs exist for supporting current LWR fuels and their possible extension to higher burnups, but are especially urgent relative to near-term development and qualification of accident-tolerant fuels. Recommendations that resulted from the Halden Gap Analysis focused on enhancements at Advanced Test Reactor (ATR) and Transient Reactor Test Facility (TREAT) to fill gaps in testing capabilities relative to these needs. Second, a concerted effort to develop and demonstrate a systematic approach to accelerating the development, testing, and qualification of new fuel systems has been initiated. This is highlighted by a test strategy that combines the considerable advances in multiscale, mechanistic fuel modeling of recent years with a MiniFuel separate effects test program in the High Flux Isotope Reactor (HFIR) and a Fission Accelerated Steady-state Testing (FAST) semi-integral accelerated test program in ATR. This approach is being tested/demonstrated using the metallic fuel system, but if successful it is expected to be extensible to multiple fuel types and diverse applications. This document includes an overview of the NFCSC program, a definition of science-based development of nuclear fuels, near-term goals for Advanced LWR fuels (ALFs), and longer-term goals for Advanced Reactor Fuels (ARFs) RD&D. This includes the activities that will be conducted to achieve success toward the grand challenge, as well as the goals and milestones to be achieved over the next few decades of research and development. Long-term goals are based on the DOE Office of Nuclear Energ

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Dynamical Aspects of the Quark Gluon Plasma

Shortly after the Big Bang the entire universe was filled with a nearly perfect fluid known as the Quark Gluon Plasma. Relativistic heavy ion collisions can now reproduce this fluid in the laboratory where the Quark Gluon Plasma exhibits a rapid but smooth cross-over phase transition into hadrons at vanishing net-baryon densities. Recent experiments plan to explore finite baryon densities where a critical point is expected. If found, this would mark the first discovery of a critical point in a relativistic system described by a fundamental theory of nature, which would have far-reaching consequences for high-energy nuclear physics and nuclear astrophysics (such as in neutron star mergers). Characteristic temperatures of equilibrium (e.g. the inflection point of the entropy density) and transport coefficients (e.g. minimum of the shear viscosity over entropy density) vary widely at a cross-over phase transition but converge at a critical point, and extracting the behavior of these characteristic temperatures is a major focal point of this research. Specifically, the interplay between strange and light hadrons is exploited to study the flavor hierarchy in the cross-over region. To investigate this, a viscous relativistic hydrodynamics framework with two conserved charges is being developed into a new open-source code along with initial conditions that contain baryon number and strangeness. Flow observables sensitive to the equation of state and transport coefficients are calculated across beam energies. New techniques are being developed to study this flavor hierarchy from first principles and to extract the characteristic temperatures from experimental data. Through this new dynamical framework, this project provides essential guidance to the Beam Energy Scan II runs at Relativistic Heavy-Ion Collider and the future Facility for Antiproton and Ion Research facility in the search for the Quantum Chromodynamic critical point and subsequent investigation of the baryon-rich Quark Gluon Plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Optimizing Information Automation Using a New Method Based on System-Theoretic Process Analysis: Tool Development and Method Evaluation

This report is an update to a prior report that describes progress and findings for a program of research supporting the design and optimization of information automation systems for nuclear power plants. Much of the domestic nuclear fleet is currently focused on modernizing technologies and processes, including transitioning toward digitalization in the control room and throughout the plant, along with a greater interest in the use of automation, artificial intelligence, robotics, and other emerging technologies. While there are significant opportunities to apply these technologies toward greater plant safety, efficiency, and overall cost-effectiveness, optimizing their design and avoiding potential safety and performance risks depends on ensuring that human performance-related organizational and technical design issues are identified and addressed early in the design process. This report describes modeling tools and techniques, based on sociotechnical systems theory, to support these design goals and their application in the current research effort. The report is primarily intended for senior nuclear energy stakeholders, including regulators, corporate management, and senior plant management. We have developed and employed a method to design an optimized information automation ecosystem (IAE) based on the systems-theoretic constructs underlying sociotechnical systems theory in general and the Systems-Theoretic Accident Modeling and Processes (STAMP) approach in particular. We argue that an IAE can be modeled as an interactive information control system whose behavior can be understood in terms of dynamic control, feedback, and communication relationships amongst the system’s technical and organizational components. We have employed two STAMP-based tools in this effort. The first is Causal Analysis based on STAMP (CAST), an accident and incident analysis technique that was used to examine a performance- and safety-related incident at an industry partner’s plant involving the unintentional activation of an emergency diesel generator. This analysis provided insight into the behavior of the plant’s current information control structure within the context of a specific, significant event. The second tool is Systems Theoretic Process Analysis (STPA) which is a proactive risk analysis tool used to examine existing and potential, planned sociotechnical systems. STPA was used to identify risk factors in the current design of a generic nuclear power plant (NPP) preventive maintenance system. Our analyses focused on identifying near-term system improvements and longer-term design requirements for an optimized IAE system. CAST analysis findings indicate an important underlying contributor to the incident under investigation, and a significant risk to information automation system performance, was perceived time and schedule pressure, which exposed weaknesses in interdepartmental coordination between and within responsible plant organizations and challenged the resilience of established plant processes, until a human caused the eventual event. These findings are discussed in terms of their risk to overall system performance and their implications for information automation system resilience and brittleness. STPA findings exposed several areas of concern in the design of current preventive maintenance systems. We also present two preliminary information automation models. The proactive issue resolution (PIR) model is a test case of an information automation concept with significant near-term potential for application and subsequent reduction in significant plant events. The IAE model is a more general representation of a broader, plantwide information automation system and represents an end-state vision for our work. From our results, we have generated an initial set of preliminary system-level requirements and safety constraints for these models. We have also focused on early development of easy to learn, easy to use “transportable” tools for sociotechnical systems analysis. We intend these to be used by NPP personnel as a means of gaining reliable and relatively quick insight into (1) sociotechnical systems factors impacting incidents and accidents, (2) potential sociotechnical risk factors in existing or planned system designs, and (3) potential weaknesses in a system’s safety and/or information control structure. We conclude the report with a set of summary recommendations, a discussion of planned and potential follow-on research and development, and a draft list of system-level requirements and safety constraints for optimized information automation systems.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Demand Response Under Stochastic, Price-Dependent User Behavior

This letter focuses on price-based demand response (DR) implemented through dynamic adjustments of electricity prices. It extends existing DR models to a stochastic framework in which customer response is represented by price-dependent random variables, leveraging models and tools from the theory of stochastic optimization with decision-dependent distributions. The inherent epistemic uncertainty in the customers' responses renders open-loop, model-based DR strategies impractical. We propose a stochastic, feedback-based pricing strategy to compensate for estimation errors and uncertainty in customer response, establish theoretical results demonstrating the stability and near-optimality of the proposed approach, and validate its effectiveness through numerical simulations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

An empirical analysis of supply offers in the ERCOT operating reserves markets

Here, this paper seeks to improve theoretical and empirical understanding of supplier dynamics in wholesale markets for operating reserves, which have been understudied compared to energy markets. We begin by identifying several economic factors that unit owners may consider when submitting offers into operating reserves auctions in two-stage, co-optimized markets common across much of North America. Next, we analyze historical offer data from the Electric Reliability Council of Texas (ERCOT) market to assess whether actual reserve market behavior aligns with expectations based on economic theory, as well as with commonly used assumptions in electricity market modeling efforts. We find that the aggregate supply of operating reserves in ERCOT varies meaningfully over time, becoming more expensive during summer afternoons, which is consistent with theoretical expectations but contradicts the typical modeling assumption of temporally invariant reserve offers. Analysis of offers made by individual units uncovers additional insights, such as the existence of large offer pattern differences by unit owner and the tendency of battery storage units to submit very low offer prices. We conclude by discussing how our findings can be integrated into electricity market modeling assumptions to improve alignment with observed operating reserve offer inputs and pricing outcomes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗