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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Prioritizing ICS Beachhead Systems for Cyber Vulnerability Testing

Cyber Testing for Resilient Industrial Control Systems™ (CyTRICS™) is the Department of Energy’s (DOE’s) program for cybersecurity vulnerability testing, digital subcomponent enumeration, and forensic assessment. CyTRICS leverages best-in-class test facilities and analytic capabilities at six DOE National Laboratories and strategic partnerships with key stakeholders including technology developers, manufacturers, asset owners and operators, and interagency partners. During the program’s development, CyTRICS established a unique methodology for prioritizing digital components within operational technology (OT) and industrial control systems (ICS) in the Energy Sector Industrial Base (ESIB) for cyber vulnerability testing. The CyTRICS Prioritization Process leverages multiple characteristics of systems, components, and their contextual deployment to calculate a quantification of individual digital components for CyTRICS testing. The initial version of the CyTRICS Prioritization Process was premised largely upon the impact which could result to an industrial control system if the digital component under testing was compromised, either through malicious means, faulty engineering, or other modes. The worldwide compromise of the SolarWinds Orion platform, first reported in December 2020, through malicious interference with the digital patching cycle was a watershed event in cyber supply chain security. The SolarWinds compromised demonstrated the strategic importance of certain types of ubiquitous software, and the ability to generate widespread cybersecurity effects. To address this challenge and as a part of the Department of Energy’s response to the SolarWinds compromise, DOE’s Office of Cybersecurity, Energy Security, and Emergency Response (CESER) directed the National Laboratories to evolve the CyTRICS Prioritization Process methodology to encompass additional factors related to the strategic importance of digital components. CESER directed CyTRICS researchers to identify, characterize, and append strategic factors to the CyTRICS Prioritization Process to provide additional weight to these characteristics. National Laboratory expert researchers identified functionality, distribution, and platform characteristics for digital components in ICS and OT that they assessed would be likely targeted in strategic initial-access cyber attack. CyTRICS has termed these factors “ICS Beachhead Systems,” leveraging a definition first advanced by Schneider Electric, which is intended as a blanket term to encompass digital components, products, and systems in OT. This paper describes the ICS Beachhead Systems identified and the rationale for inclusion. As a next step in the research and refinement process, the National Laboratories will validate this initial set of characteristics against digital components evaluated by the CyTRICS program and current implementation of the CyTRICS Prioritization Process. After validation, CyTRICS researchers will then develop a scoring methodology to generate a quantitative score to assess the degree to which a digital component is characterized as an ICS Beachhead System. Finally, the National Laboratories will append this scoring to the existing CyTRICS Prioritization Process algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

GIS-Based Graphical User Interface Tools for Analyzing Solar Thermal Desalination Systems & High-Potential Implementation

This project developed a user-friendly, open-source, software that enables a comparative evaluation of solar thermal desalination technology options and employs geospatial data layers to identify regions of high-potential for solar thermal desalination. This was accomplished by integrating solar models with desalination models and enhancing their utility by providing GIS-based data inputs. The developed Solar Energy Desalination Analysis Tool (SEDAT) enables techno-economical evaluation of desalination technologies and selection of regions with the highest potential for using solar energy to power desalination plants. It simplifies the planning, design, and valuation of solar thermal and solar hybrid desalination systems in the U.S. and worldwide. SEDAT uses Dash for integrating various layers of large volumes of GIS data with Python-based models of solar energy generation and desalination technologies. It derives time-series of energy generation and water production, with details of plant performance and suggestions for improving the solar-desalination coupling. It is a one of-a-kind tool of analysis representing a definitive advancement in the state-of-the-art. Of solar desalination modeling This report summarizes the various phases of the tool’s development, and presents examples of the results.

14 SOLAR ENERGY↗
DOE OSTI2022

Improved Throughput and Analysis of Scratch Test Results via Automation and Machine Learning

A data analysis automation interface that incorporates machine learning (ML) has been developed to improve productivity, efficiency, and consistency in identifying and defining critical load values (or other values associated with optically identifiable characteristics) of a coating when a scratch test is performed. In this specific program, the machine learning component of the program has been trained to identify the Critical Load 2 (L C2 ) value by analyzing images of the scratch tracks created in each test. An optical examination of the scratch by a human operator is currently used to determine where this value occurs. However, the vagueness of the standard has led to varying interpretations and nonuniform usage by different operators at different laboratories where the test is implemented, resulting in multiple definitions of the desired parameter. Using a standard set of training and validation images to create the dataset, the critical load can be identified consistently amongst different laboratories using the automation interface without requiring the training of human operators. When the model was used in conjunction with an instrument manufacturer's scratch test software, the model produced accurate and repeatable results and defined L C2 values in as little as half of the time compared to a human operator. When combined with a program that automates other aspects of the scratch testing process usually conducted by a human operator, scratch testing and analysis can occur with little to no intervention from a human beyond initial setup and frees them to complete other work in the lab.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Artificial Intelligence/Machine Learning Technologies for Advanced Reactors (Workshop Summary Report)

A workshop on artificial intelligence and machine learning (AI/ML) for advanced reactors (AR) was held October 5-6, 2021. The workshop was to be attended in-person at ANL but COVID restrictions forced the workshop to go virtual. The objectives of the workshop were to identify the most promising AI/ML opportunities for improving advanced reactor design, optimizing plant performance, and enhancing economic competitiveness and to develop an understanding of the scientific, engineering and licensing challenges facing their application. The workshop planning committee included GAIN, EPRI and NEI and members of three national laboratories (ANL, INL, and ORNL). The workshop was attended by more than 200 individuals representing academic and scientific institutions and the nuclear power industry. The definition put forth for an AI/ML system was one that perceives its environment and takes actions that maximize its chance of achieving its goals. In this report AI/ML refers to next generation algorithms that include deep learning, statistical analysis and data analytics and associated scientific computing and their potential application to the design, licensing, operation and maintenance of ARs. These methods typically incorporate models built from process data and may also include data generated by simulations that represent the behavior of a system. The workshop was organized in response to the growing interest in application of AI/ML for improving the economic competitiveness of nuclear energy. Increasingly more resources are being allocated to investigating the benefits of AI/ML methods. The DOE created the Artificial Intelligence & Technology Office to promote their development. And within the Office of Nuclear Energy, resources have been allocated to explore and understand the potential benefits of AI/ML. Additionally, the national laboratories are strategically positioned with DOE computing facilities such as Summit, Perlmutter, Aurora and Frontier that support large-scale simulations, hybrid HPC models with AI surrogates, and the exploration of new types of generative models emerging from multi-model data streams and sources. The workshop was organized with members of the AR community to understand the effort and to identify the level of interest and progress in this emerging technology. The workshop discussions focused on identifying opportunities for AI/ML across diverse areas of the nuclear industry and identifying current scientific and engineering challenges for advanced reactors that might be addressed through transformational uses of AI/ML. Discussion panels focused on four high-interest technical domains for advanced reactors: design, maintenance and operations, energy storage, and materials. The results of those discussions are summarized in this report. This includes opportunities that were identified for exploiting AI techniques and methods to improve the efficacy and efficiency of reactor analysis and to improve the operation and optimization of advanced reactors. Advanced reactor developers expressed an interest in learning more about AI/ML methods and their application. This included understanding whether ML methods can provide an advantage over existing nonlinear data regression methods for collapsing high-fidelity simulation results into faster running models. A consensus emerged that AR advances planned for the next decade will benefit from the use of AI/ML tools. The need exists to understand and model complex systems across length scales and modalities. AI/ML is a tool for discovery that can yield a set of engineering principles for use by nuclear engineers, licensing bodies, and operators to solve problems in plant design, safety analyses, autonomous operation, and predictive maintenance. While AI/ML represents a new set of tools, an awareness by the nuclear community of the full potential is still in the early stages so there is a need to increase awareness. It appears that the wide-spread adoption of AI/ML tools for ARs would be facilitated by future educational workshops that describe foundational methods and capabilities and describe successful applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Quantifying the Role of APB Tubes on the Work-hardening of Ordered Phases

For sixty years, it has been theorized that antiphase boundary (APB) tubes, which are defects produced by deformation, contribute to the high strength of ordered alloys or intermetallic compounds. The concept of an APB tube was originally proposed by Vidoz and Brown [A.E. Vidoz, L.M. Brown, "On work-hardening in ordered alloys", Philosophical Magazine, 7 (1962) 1167–1175] in 1962 to explain the ~50% increase in strength observed when an alloy, such as Ni 3 Fe, is chemically ordered compared to when it is disordered. The existence of APB tubes was only definitively confirmed by Chou and Hirsch in 1981 [C.T. Chou, P.B. Hirsch Antiphase domain boundary tubes in plastically deformed ordered Fe 30.5 at.% Al alloy, Philosophical Magazine, A. 44 (1981) 1415–1419] using a transmission electron microscope (TEM). Prior to the current work, there had been no studies trying to correlate the presence of APB tubes with their effect on the mechanical properties. The main finding of this project was that APB tubes do not contribute to the strength of the strongly-ordered intermetallic compounds FeAl and Ni 3 Al. This was shown by removing the APB tubes by heating the materials and showing that the mechanical properties were unaffected. In addition, in-situ deformation experiments were undertaken in a TEM on both disordered and ordered Ni 3 Fe, in which it was shown that defects (superdislocations) in the ordered alloy underwent many more interaction events than defect (dislocations) in the disordered alloy. This difference can explain the increase in the increase in strength observed when this material is ordered. The project supported a PhD student, Rachel Osmundsen, who successfully defended her thesis in April, 2022. It also supported the principal investigator, Ian Baker, two PhD exchange students and an undergraduate researcher. The work involved collaborations with the following people and use of the following facilities: Dr. Si Chen, Advanced Photon Source at Argonne National Laboratory, for synchrotron X-ray diffraction; and Dr. Katherine Shanks and Dr. Kelly Nygren, Cornell High Energy Synchrotron Source (CHESS), for synchrotron X-ray diffraction. The work led to 12 papers published in refereed journals with two papers currently submitted to journals. There were also 16 presentation made at conferences and universities on the work, ten of which were invited.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Micromorphic Length-Scale Coupling Framework for the Determination of Higher-Order Constitutive Models and the Multi-Scale Simulation of Heterogeneous Materials [Thesis]

Heterogeneous materials and materials with complex microstructures pose a unique challenge in the development of accurate models of their response to external stimuli. These difficulties principally arise due to the difficulty in characterizing and modeling the constituents and their interactions. It is usually possible, though non-trivial, to construct an explicit representation of the microstructure (a direct numeric simulation or DNS) but the method by which the complex modes of deformation and other processes can be homogenized to a reduced-order approximation is frequently unclear. Many homogenization approaches are ad-hoc and lack a strong justification beyond ease of computation. Furthermore, the homogenization approach can, in some cases, not utilize the full breadth of information available in the computation of the macro-scale stresses and deformation measures. It is also noteworthy that homogenization, by its very nature, will tend to obfuscate details of processes occurring at the lower length-scale. It is therefore of interest to include as much information as possible in the construction of the reduced-order model so as to be predictive in a variety of loading environments. We here present a length-scale bridging technique based upon the micromorphic continuum mechanics of Eringen which incorporates volume and surface area averages as a part of its construction. This approach enforces the balance equations at the micro-scale and then studies the effect of the spatially varying nature on the macro-scale. This leads, naturally, to further balance equations which are solved at the macro-scale. This work details a homogenization framework which arises naturally from the micromorphic construction of Eringen attempting to introduce no definitions beyond which are justifiable from micro-structural considerations. One of the results of this effort is the so-called “micromorphic filter” which has been developed and applied to several DNS to demonstrate its effectively. In order to determine the macroscopic degrees of freedom we utilize the special case of an overlap coupling technique where the macro-scale is fully constrained to the micro-scale. This enables us to study the resulting material properties as expected but also allows us to further study the boundary conditions on the additional degrees of freedom at the macro-scale.

42 ENGINEERING↗
DOE OSTI2021

Artificial Intelligence for Multiphysics Nuclear Design Optimization with Additive Manufacturing

The geometric flexibility of additively manufactured metals and ceramics generates a very large and open design space that requires advanced modeling and simulation tools for physics simulations and the rigorous definition of design problems. This effort deploys artificial intelligence (AI) and machine learning (ML) algorithms to understand the design space, evaluate potential designs, and more efficiently generate optimized results. The Transformational Challenge Reactor (TCR) program is leveraging advances in several scientific areas—including materials, manufacturing, sensors and control systems, data analytics, and high-fidelity modeling and simulation—to accelerate the design, manufacturing, qualification, and deployment of advanced nuclear energy systems. Through a manufacturing-informed design approach, the TCR program seeks to integrate digital data for rapid nuclear innovation; accelerate the adoption of advances in manufacturing, materials, and computational sciences for nuclear applications; and dramatically reduce deployment costs and timelines for new nuclear reactor technologies. This report documents efforts under the TCR program to leverage advanced modeling and simulation techniques driven by AI/ML algorithms on high-performance computing (HPC) systems to yield more optimized TCR core designs. A multiphysics ML surrogate model was developed to run on the HPC architectures. The surrogate model is trained on high-fidelity simulation data of coupled neutronics and thermofluidics and is used to quickly evaluate thousands of candidate core designs in parallel, which drives the evolution of the cooling channel shapes to minimize temperature peaking and material stress. Outcomes from these activities provide design information and feedback into the core design efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Impact Response of Control Atmosphere Plasma Spray Deposited Materials

Thermal spray processing of metals and respective blends is becoming increasingly attractive due to the unique properties such as increased yield strength, low ductility, and differences in tensile and compressive strengths that result from microstructural features due to the spray process compared to other additive manufacturing methods. Here we report the results of plate impact experiments applied to Controlled Atmosphere Plasma Spray deposits of tantalum (Ta), niobium (Nb), and a tantalum-niobium blend (TaNb). These methods allowed for definition of the Hugoniot for each material type and the assessment of the Hugoniot Elastic Limit (HEL). Spallation experiments were conducted, and soft recovery of each material type allowed for scanning electron microscopy to characterize the fracture mechanism during tensile loading.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Non-Powered Dam Retrofit Exemplary Design for Hydropower Applications

Non-powered dams (NPDs) represent complex systems, situated at the intersection of natural stream environments and the built environment. The presence and operations of NPDs affect stream constituents (e.g., fish, recreational craft, sediment, water) and serve one or more engineered purposes (e.g., recreation, flood control, water supply, irrigation, tailings and debris control, navigation). By definition, NPDs do not provide hydropower generation. However, every NPD contains some untapped hydropower resource potential, represented by the pre-existing hydraulic head created by the dam and presence of flowing water. Adding power generation to an NPD requires retrofitting the NPD, which could involve the addition of any new component or function beyond that currently installed. Because this research is funded by the US Department of Energy Water Power Technologies Office, adding hydropower to NPDs is among the primary objectives for research investment. However, NPD retrofitting extends beyond the purview of hydropower additions. This report provides background information regarding NPD development in the United States, including an overview of the US NPD population, development potential, and recent development. It also summarizes challenges and opportunities facing NPD retrofit development, highlights the importance of maintaining or improving stream and dam functionality, notes key NPD characteristics, describes NPD retrofit methods, and identifies innovation areas for spurring future NPD retrofit development. Section 4 describes retrofit exemplary design principles and concepts that could apply to a wide range of NPD projects. These principles and concepts inform an NPD Retrofit Exemplary Design Specification (REDS), as included in Appendix A. The NPD REDS is heavily influenced by prior work under the standard modular hydropower technology acceleration research initiative, especially the work documented in the Exemplary Design Envelope Specification for Standard Modular Hydropower Technology (Witt et al., 2017). Whereas this prior work focuses on a particular technology class that applies most intentionally for new stream-reach development (i.e., greenfield sites), the NPD REDS contained herein applies more broadly to the NPD resource class and is technology-agnostic (i.e., the specific technologies intended to be described are not explicitly prescribed). This report serves as a part of early-phase research on NPD retrofit development, aimed toward spurring additional, follow-on efforts to specifically address development challenges, capitalize on development opportunities, and inspire innovation. As documented in the landmark “Hydropower Vision” report (DOE 2016), “transformative technical innovations able to meet the co-objectives of environmental sustainability and low-carbon energy will be critical to enabling additional hydropower growth.” This report serves as an important step in steering transformation of US non-powered dam infrastructure.

13 HYDRO ENERGY↗
DOE OSTI2022

MACCS (MELCOR Accident Consequence Code System) User Guide Version 4.0, Revision 1

The MELCOR Accident Consequence Code System (MACCS) is used by Nuclear Regulatory Commission (NRC) and various national and international organizations for probabilistic consequence analysis of nuclear power accidents. This User Guide is intended to assist analysts in understanding the MACCS/WinMACCS model and to provide information regarding the code. This user guide version describes MACCS Version 4.0. Features that have been added to MACCS in subsequent versions are described in separate documentation. This User Guide provides a brief description of the model history, explains how to set up and execute a problem, and informs the user of the definition of various input parameters and any constraints placed on those parameters. This report is part of a series of reports documenting MACCS. Other reports include the MACCS Theory Manual, MACCS Verification Report, Technical Bases for Consequence Analyses Using MACCS, as well as documentation for preprocessor codes including SecPop, MelMACCS, and COMIDA2.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Advanced Solar and Load Forecasting Incorporating HD Sky Imaging (Phase III)

Due to rapidly changing sky conditions, the available solar irradiance for energy generation is subject to wide swings in amplitude. As the market penetration of solar energy continues to increase rapidly, these variations in solar power generation are beginning to have an impact on grid stability and increased wear on power switches. Solar power forecasting plays a critical role in operations for Independent System Operators (ISOs) and utilities. Accurate forecasts help maintain grid reliability, optimize generation from renewables, and reduce operating costs. Of particular interest to the ISOs and utilities are sudden changes in solar irradiance, termed “ramp events,” due to the movement of clouds. One significant impact of ramp events on the grid is additional ancillary service requirements necessary to manage such variability. Ramp events can also cause voltage fluctuations in the distribution grids and trigger actions of automated line equipment (e.g., tap changers), leading to additional maintenance costs. In high penetration solar regions, forecasts must be made for both transmission-connected and distribution-connected resources – either behind the meter or in front of it. Particularly for distributed solar energy resources, forecasting can be a challenge due to the lack of visibility of the energy resource. Brookhaven National Laboratory (BNL) has been working towards nowcasting Global Horizontal Irradiance (GHI) using low-cost technologies for several years now. The Solar NowCasting technology being developed by BNL is a 0 – 30 min solar “nowcasting” technology applicable to a scale covering both large generating facilities and residential, distributed solar installations that relies on a network of ground-based high-definition (HD) cameras and surface pyranometers. GHI forecasts are being produced for both regions using 8 ground-based HD cameras and at least 2 surface pyranometers. When stitched together, the camera images can be used to forecast the impact of clouds on available solar irradiance over a domain of ~50 km 2 . Phase I of the project was the engineering scale up conceptual design stage. Phase II was the initial field test and demonstration, in which the technology was scaled up by a factor of 20 times and successfully demonstrated in eastern Long Island. In Phase III, an additional forecasting network was added in upstate NY and both networks are currently being operated until at least a full year of data is gathered by the Albany network to allow for the collection of sufficient data to evaluate performance against the persistence and smart persistence models using the Solar Arbiter.

14 SOLAR ENERGY↗
DOE OSTI2022

SIERRA/Aero User Manual - Version 5.8

SIERRA/Aero is a compressible fluid dynamics program intended to solve a wide variety compressible fluid flows including transonic and hypersonic problems. This document describes the commands for assembling a fluid model for analysis with this module, henceforth referred to simply as Aero for brevity. Aero is an application developed using the SIERRA Toolkit (STK). The intent of STK is to provide a set of tools for handling common tasks that programmers encounter when developing a code for numerical simulation. For example, components of STK provide field allocation and management, and parallel input/output of field and mesh data. These services also allow the development of coupled mechanics analysis software for a massively parallel computing environment. In the definitions of the commands that follow, the term Real_Max denotes the largest floating point value that can be represented on a given computer. Int_Max is the largest such integer value.

42 ENGINEERING↗
DOE OSTI2022

LANL Regular Employee Population and Demographics, FY15-21

This document summarizes an effort to compile and analyze the LANL regular employee population’s demographic attributes and trends between FY15-FY21 using MicroStrategy, a web-based data analytics and visualization tool. LANL’s HR Division collected and prepared data for the Weapons Program’s annual reporting activities for the 2017 through 2023 NNSA Stockpile Stewardship and Management Plan (SSMP). Los Alamos workforce data represent the fiscal year-end snapshot of the permanent employee population who are categorized by the Common Occupational Classification System (COCS). This work enables further analyses, including trends for all workforce attributes presented in the SSMPs as well as a definition of a crosswalk between COCS categorization and the internal laboratory job classification hierarchy. Data for other sites and the federal workforce, as shown in the SSMPs, are included and corresponding figures can be visualized within the MicroStrategy tool. A portable, Excel-based version of the tool was developed and shared with the multi-site workforce working group. This summary highlights selected aspects of the LANL regular workforce. The data analyses and visualizations communicate insights within three major themes regarding permanent LANL employees: 1) demographic transformation, 2) attrition, and 3) skills. Regarding demographic transformation, and consistent with the general trend across the enterprise, early career regular employees are the fastest growing group, with growth ranging between 12-34% year-to-year over the seven-year period analyzed. The data also reflect effects of internal and external factors on attrition; the large spike in separations in FY18 aligns with the contract transition from LANS to Triad and the dip in attrition between FY20-FY21 is likely a result of the global pandemic. The population of regular employees in general management, engineer, and operator COCS categories have the fastest growth rates amongst all the occupational categories. The MicroStrategy and Excel tools provide additional details and trends.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Gamma Irradiation of NaCl-UCl 3 Salt for the Molten Chloride Fast Reactor

This report documents the results of the gamma ray irradiation of NaCl-UCl 3 eutectic salt in the Advanced Test Reactor (ATR) spent fuel pool gamma tube and subsequent analysis of capsules utilizing the Gas Assay Sample and Recharge (GASR) system. NaCl-UCl 3 salt capsules at four different temperatures, 75C, 150C, 300C and 600C, were irradiated shortly after the core internal changeout (CIC) of the ATR, beginning April 28 th of 2021, and ending on August 18 th of 2021. Re-positioning of the fuel elements in the spent fuel pool to move more fresh fuel elements around the gamma tube occurred on May 11 th of 2021, resulting in an approximate 20% increase in the dose rate and consequently the total absorbed dose. In total, the salt-filled capsules underwent 2638 hours of gamma irradiation from the adjacent freshly discharged ATR spent fuel. Capsule internal pressure measurements were taken via the Gas Assay Sample and Recharge (GASR) system in the INL Hot Fuels Examination Facility (HFEF) following completion of the irradiation. Based on the GASR analysis results, the primary conclusion from this experiment to-date is that radiolytic generation of chlorine gas is insignificant for the dose rates and total absorbed dose that these samples experienced. To elucidate more detailed effects of radiolysis on NaCl-UCl 3 eutectic salts and reach a more definitive conclusion, further advanced analyses such as electron microscopy, simultaneous thermal analysis, and electron paramagnetic resonance are recommended to analyze the capsule wall and salt samples.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Adapting to a Changing World: Managing the Promise and Peril of Emerging Technologies and the Nuclear Nexus

This paper is focused on emerging technologies in three areas: outer space, cyberspace, and artificial intelligence. Technologies in each of these areas share a number of characteristics that make them a challenge for legally-binding approaches to arms control. For example, they each have significant definitional challenges. They are also dual use and within the reach of non-state actors. Verifying compliance with any legally-binding agreements in these areas poses significant challenges, at least at this time. Moreover, the rapid pace of technological change also contributes to the difficulty of crafting legally-binding arms control measures. The United States has generally championed a more normative approach to the governance of emerging technologies—the idea being that international agreement on norms serves as the basis for collective action to respond to activities that deviate from those norms.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100) (Six-Month Progress Update) [Slides]

Puerto Rico has committed to meeting its electricity needs with 100% renewable energy by 2050, along with realizing interim goals of 40% by 2025, 60% by 2040, the phase-out of coal-fired generation by 2028, and a 30% improvement in energy efficiency by 2040, as established in Puerto Rico Energy Public Policy Act (Act 17). Since hurricanes Irma and Maria in September 2017, DOE and its national laboratories have provided Puerto Rico energy system stakeholders with tools, training, and modeling support to enable planning and operation of the electric power grid with more resilience against further disruptions. On February 2, 2022, DOE, FEMA, and six national laboratories launched the two-year Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100) to conduct comprehensive analysis of stakeholder-driven pathways to Puerto Rico's energy future. The robust and objective energy analysis entails five activities, with an emphasis on power system reliability, resilience, and generation planning. This presentation was delivered in a public webinar on July 21, 2022, providing a high level summary of the progress in the first six months of the study, including presentation of four initial scenario definitions based on extensive stakeholder input.

14 SOLAR ENERGY↗
DOE OSTI2022

Pursuing the Ultimate Power of Xenon Dark Matter Detectors (Annual Progress Report)

Liquid xenon-based experiments have been leading direct searches for dark matter – a cornerstone of modern cosmology and particle physics. Despite rapid improvement of experimental sensitivities in the past two decades, no definitive dark matter interactions have been observed. This project aims to expand the physics reach of existing and future xenon dark matter experiments, especially for low-mass dark matter interactions that would fall below the energy thresholds of current detectors. The main approach is to thoroughly characterize and to suppress the low-energy electron background observed in dual-phase xenon Time Projection Chambers (TPCs), which has so far prevented these detectors from achieving lower energy thresholds. Per recent discussion with and approval from the program manager, we have added a new task of experimentally measuring the Migdal effect to this project. The Migdal effect predicts that ultra-low energy dark matter interactions may produce detectable electron recoil signals in liquid xenon at the keV level in addition to much lower energy nuclear recoils. If this effect is experimentally measured, it will drastically improve xenon detectors’ sensitivity to subGeV dark matter interactions. This new task shares strong synergy with the original project goal of pursuing the ultimate power of xenon dark matter experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

An Assessment of Current Understanding of the Greenhouse Gas Impacts from H 2 Emissions

With the anticipated growth in hydrogen generation and use as part of a broad shift in energy use away from fossil fuels, concerns have been raised regarding the impact of increased H 2 emissions on global warming. Atmospheric scientists have long recognized that H 2 emissions into the atmosphere do have an indirect impact on global warming, largely because a portion of emitted H 2 is consumed by the hydroxyl radical (OH), which is the primary reactant that removes the potent greenhouse gas methane from the atmosphere. Therefore, increases in H 2 emissions will result in decreases in the average OH concentrations in the atmosphere and an increase in the atmospheric lifetime of methane. Various assessments of the impact of H 2 emissions on global warming have been performed over the past couple of decades. These assessments have yielded significant variability and recognized uncertainty in the magnitude of the warming effect of a given quantity of emitted H 2 , and an even greater uncertainty in the magnitude of H 2 leakage and releases that can be anticipated with an expanded H 2 infrastructure. Consequently, definitive estimates of the magnitude of the warming effect of additional emitted H 2 are lacking. However, given the current understanding of the warming potential of emitted H 2 and given reasonable expectations of the emission rate of an expanded H 2 infrastructure, it is anticipated that warming effects from emitted H 2 will offset no more than 5% of the reduction in warming associated with avoided CO 2 emissions from using clean H 2 . Further, it is highly unlikely that the warming effects from emitted H 2 will offset more than 10% of the benefit from avoided CO 2 emissions, at least as considered over a typical 100-year accounting period. Because of the short atmospheric lifetimes of H 2 and methane, however, the warming effect of emitted H 2 is enhanced over the first few years following increases in H 2 emission.

54 ENVIRONMENTAL SCIENCES↗