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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware

Our group pioneers the use of Quantum Machine Learning (QML) on High Energy Physics analysis at LHC. We have successfully employed several QML classification algorithms in the ttH (Higgs production in association with a top quark pair) and Higgs to two muons (Higgs coupling to second generation fermions), two recent LHC flagship physics analysis, on gate-model quantum computer simulators and hardware. The simulation studies have been performed with the IBM Quantum Framework, Google Tensorflow Quantum Framework, and Amazon Braket Framework, and we have achieved good classification performance that is similar to the performances of the classical machine learning methods currently used in LHC physics analyses, classical SVM, classical BDT, and classical deep neural network for example. We have also performed our studies using IBM superconducting quantum computer hardware and the performance is promising and is approaching the performance from IBM quantum simulators. Moreover, we extend our studies to other QML areas such as quantum anomaly detection and quantum generative adversarial, and some preliminary results have been obtained. Also, we have overcome the challenges of intensive computing resources in the cases of large qubits (25 qubits or more) and large numbers of events using NVIDIA cuQuantum with NERSC Perlmutter HPC. Our studies give an example that Quantum Machine Learning performs as well as its classical counterpart for realistic High Energy Physics analysis datasets. Furthermore, our result on noisy quantum hardware provides important validation for the result on noiseless quantum simulators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Monte Carlo Statistical Convergence

This document reviews Monte Carlo Statistical Convergence over the last 60 years, based on the real World state of computers over this time. Stress is placed on providing examples of what was and was not practically possible over this period of time. I present a few examples to illustrate convergence based not only on sample size, but also how cross sections can be modelled.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

A Case Study of Tunable White LED Lighting with Networked Lighting Controls (Emory University Cognitive Empowerment Program)

Emory University and Georgia Institute of Technology (Georgia Tech) partnered to create the Charlie and Harriet Schaffer Cognitive Empowerment Program (CEP) facility in northeast Atlanta. Together with the funders they are building a program to help individuals experiencing mild cognitive impairment (MCI) to maintain their physical and cognitive health, and independence as long as possible. In addition to applying effective strategies and therapies, the two research groups are investigating the responses of the MCI members to treatments that involve acoustical conditions, exercise and movement, and lighting changes that may support retention or relearning of skills. Care partners and family members receive support and instruction to improve home and work life, promoting joy, purpose, and wellness in the family groups. The lighting system uses tunable-white LEDs, employing luminaires with both warm and cool-color emitters that can be dimmed separately to produce any white correlated color temperature (CCT) between 2700 K and 6500 K. All luminaires were dimmable to achieve subdued or lively surroundings for different treatments, time-of-day, and mood. A central networked digital control system was employed to allow tuning of multiple spaces together (for example, bright, cool morning light could be programmed for extra stimulation, or lighting in all spaces at the end of the day could be reduced in both light output and CCT to promote relaxation and not interfere with the melatonin cycle of occupants). Almost all spaces were equipped with individual room control of dimming and color temperature with touch screens to allow users to tune the lighting as desired, but each room’s controls could also be specially programmed through the server in case the research staff were investigating lighting settings on learning, for example. The server incorporates a timeclock, and it is able to send signals to switch off all lighting after occupancy hours, or enable occupancy sensors to control the lighting. The bulk of the construction work was completed in January 2020. It was clear from an initial walk-through that although the lighting system produced the expected high light level with low-glare qualities of light, that there were issues and inconsistencies to be resolved. These were noted in an initial punchlist visit and expected to be resolved when the tech representatives from the agency visited with the electrical contractor in the following few weeks. What followed was 2.5 years of identifying unexpected lighting performance in terms of light output, color, scheduling, and occupancy. This report documents the issues encountered in the effort to get the lighting and controls systems to operate as intended. It concludes with guidance for design professionals and manufacturers to help avoid problematic complexity in future projects.

42 ENGINEERING↗
DOE OSTI2022

Air Quality Analysis and Tools For Policy

South Asia is home to some of the most significant air quality challenges in the world. More than 99% of the region’s population is exposed to unhealthy fine particulate matter (PM2.5) concentrations, as defined by the World Health Organization. Recent policy initiatives in South Asia show that air quality policy is a priority, given the health impacts of air pollution. At the same time, capacity and data to assess and address air pollution remains limited in many areas. The main objective of this report is to outline a set of publicly-available tools that can be used to inform policies to improve air quality in South Asia. The report primarily focuses on tools such as emission inventories and integrated modeling systems with country-level resolution and illustrates their air quality assessment capabilities through examples and brief technical summaries. The report focuses on: (a) the Community Emissions Data System (CEDS) that provides historical emissions of key pollutants for all countries in South Asia, and (b) profiles an analysis of Indian air pollution and policy scenarios, using several integrated modeling tools. Both of these examples draw on recent research and data that outlines the scale of the challenge as well as the policy options for reducing air pollution in the region. The report concludes with key recommendations for regional stakeholders working towards mitigating air pollution in South Asia.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Physics Informed Neural Networks as Computational Physics Emulators

This report is a brief overview and evaluation of Physics Informed Neural Networks (PINNs). Karniadakis and co-workers, e.g., Karniadakis et al. (2021) assert that the PINNs approach integrates seamlessly both data and mathematical physics models, even in partially understood, uncertain and high-dimensional contexts. They further claim that PINNs are effective and efficient for ill-posed and inverse problems, and when combined with domain decomposition, are scalable to large problems and a tool to discover hidden physics. While they demonstrate the capabilities in specific academic instances, their overarching claims about PINNs seem to be an overstatement, at least at the current time. We have briefly considered a few of the limitations of PINNs in this investigation. It is not clear to us if the PINNs approach can ever be competitive with approaches that use specialized algorithms to achieve high-accuracy solutions of governing equations and other techniques that can combine observational data with such solutions. As an example of the latter, consistent with the principles of Bayesian inference, data assimilation, or more generally data-model fusion, is a process that fuses observational data typically with a computational model that respects certain constraints such as conservation laws. For example, improvements in observational network combined with data assimilation have been key in improving weather predictions over the past four decades Kalnay (2003).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

On the flow-stress model of Preston, Tonks, and Wallace - Observations, parameter constraints, and implementations (Rev. 1)

A detailed discussion about the PTW flow-stress model has been presented, including theoretical considerations, model calibrations to experimental data, and model implementations in LANL ASC hydro-codes. The main findings are: In order to reproduce the intended behavior of the PTW model at high strain rates, the following conditions on the model parameters need to be valid: y 1 ≥ s 0 and y 2 ≥ β ; When the conditions above are not satisfied, PTW and PTW_Mod1 have slightly different behavior in the phonon-drag regime, and should be treated as different models. PTW exhibits a discontinuity above the critical strain rate. PTW_Mod1 does not show such a discontinuity but implies a different strain-rate dependence at high strain rates. Examples of this behavior include older calibrations for stainless steel and depleted uranium; In the thermal-activation regime, some flexibility was introduced in the original PTW model to allow for different rate-dependent behaviors at intermediate strain rates. Example: copper. However, many material calibrations do not require this extra flexibility, and the complexity and ambiguity of the intended behavior can be partially blamed for the inconsistent model implementations across ASC codes; The limit for the material constant p going to zero is well defined and leads to a simplified flow-stress equation, discontinuity free; Modern calibrations of the PTW parameters are done using Impala. Constraints on the PTW parameters are enforced; Implementation of the PTW model in LANL ASC hydro-codes suffered of several shortcom ings. At the time of writing, FLAG and xRAGE contain consistent and correct implemen tations of the PTW and PTW_Mod1 models. Corresponding changes to Pagosa have been suggested.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Developing an Equity Framework for State Regulatory Decision-Making

The report presents a framework for states that seek to incorporate equity into regulatory decision-making. Berkeley Lab contextualizes approaches and metrics from various states into example processes to exemplify how this may be done, including topics such as the development of equity goals and definitions, intervenor funding, community engagement, performance-based ratemaking, and utility resource planning. The report offers five takeaways and considerations, supported by these examples: 1. Equity comprises multiple tenets and stages, all of which must be considered in parallel. 2. At the start of designing new equity-related processes, it is critical to establish clear and actionable goals, definitions, roles, and responsibilities to ensure progress. 3. Once goals are established, it is critical to align tools and metrics that bridge the gap between what an intervention may do and how it may impact communities and households. 4. Processes should be stakeholder driven. It is important to not only increase education and outreach, but to actively seek out and incorporate feedback from inclusive public processes and build in accountability mechanisms. Processes should be iterative. Feedback loops between evaluations and program design provide the flexibility to better align existing interventions with community priorities and to incorporate equity into future decision-making.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Hybrid Power Plants: Status of Operating and Proposed Plants, 2023 Edition [Slides]

Improving battery technology and the growth of variable renewable generation are driving a surge of interest in “hybrid” power plants that combine, for example, wind or solar generating capacity with co-located batteries. While most of the current interest involves pairing photovoltaic (PV) plants with batteries, other types of hybrid or co-located plants with wide-ranging configurations have been part of the U.S. electricity mix for decades. This annually updated briefing tracks and maps existing hybrid or co-located plants across the United States while also synthesizing data from power purchase agreements (PPAs) and generation interconnection queues to shed light on near- and long-term development pipelines. The scope includes “co-located hybrids” that pair two or more resources (e.g., multiple types of generation and/or generation with storage) that are operated largely independently behind a single point of interconnection, and “full hybrids” that also feature coordinated operations of the co-located resources. The focus is on plants with one megawatt (MW) or more of capacity; smaller (often behind-the-meter) projects are also increasingly common, but are not included in this data synthesis. Key findings from the latest briefing include: -At the end of 2022, there were 374 hybrid plants (>1 MW) operating across the United States (+25% compared to the end of 2021), totaling nearly 41 GW of generating capacity (+15%) and 5.4 GW/15.2 GWh of energy storage (+69%/+88%). PV+storage plants are by far the most common, dominating in terms of plant number (213), storage capacity (4.0 GW/12.5 GWh), storage:generator capacity ratio (49%), and storage duration (3.1 hours). But there are nearly twenty other hybrid plant configurations as well, including several different fossil hybrid categories (each dominated by the fossil component) as well as wind+storage, wind+PV, wind+PV+storage, geothermal+PV, and others. -Last year was another strong year for PV+storage hybrids in particular: 59 of the 62 hybrids added in 2022 were PV+storage. As of the end of 2022, there was roughly as much storage capacity operating within PV+storage hybrid plants as in standalone storage plants (~4 GW each). In storage energy terms, however, PV+storage edged out standalone storage by ~2 GWh (12.5 GWh vs. 10.4 GWh, respectively). -Interconnection queue data show continued strong developer interest in hybridization. At the close of 2022, there were 51% more hybrid plants—representing 59% more generating capacity—in interconnection queues across the United States than there were at the end of 2021. Solar dominates these proposed plants as well: at the close of 2022, there were 457 GW of solar capacity proposed as a hybrid (representing ~48% of all solar capacity in the queues), most typically pairing PV with battery storage. At the same time, there were 24 GW of wind capacity proposed as a hybrid (representing ~8% of all wind capacity in the queues), again most-often pairing wind with storage. Meanwhile, more than half of all storage in the queues is estimated to be part of a hybrid plant. While many of the plants proposed in the queues will not ultimately reach commercial operations, the depth of interest in hybrid plants—especially PV+storage—is notable, particularly in certain regions. For example, in CAISO, 97% of all solar capacity and 45% of all wind capacity in the queues is proposed as a hybrid. -The report also surveys power purchase agreement (PPA) price data from a sample of operating and proposed PV+storage plants. Though PV+storage PPA prices have fallen over time, “levelized storage adders” have recently increased somewhat to ~$\$ $7000/MW-month, ~$\$ $60/MWh-stored (assuming one full cycle per day), or ~$\$ $15/MWh-PV. Some of the recent price increase could simply reflect a trend towards higher battery:PV capacity ratios over time, which increases costs, all else being equal. The well-publicized impact of inflationary and supply chain pressures on battery prices is no doubt a contributor as well.

25 ENERGY STORAGE↗
DOE OSTI2023

The Influence of Soil Properties on Sea-Breeze Circulations in the Southeast U.S.

Sea-breeze circulations (SBCs) are common weather phenomena at and near coastal regions. They form because of a thermal gradient between the land surface at the coast and the sea surface. In a mid-day regime, a “thermal low” generated at the warm coast will lead to rising air motion, creating a wind shift coming from the sea near the surface displacing the coastal air. A “return flow” moving back towards the sea is generated by upper-level divergence because of the rising motion from the thermal low. SBCs propagate and serve as a method of urban pollutant dispersion in the Los Angeles region of California and are constrained to the coast due to the topography of surrounding mountains serving as a boundary for further inland propagation. Within the northeast U.S. SBCs are seen in the warm season but tend to remain coastally bound due to Coriolis distortion over long distances. Within the southeast U.S. (SEUS), the paradigmatic example of SBCs occurs over the Florida peninsula, where thunderstorms form on a nearly daily occurrence due to the convergence of SBCs from the east and west sides of the peninsula. However, there are further examples of sea-breezes in the SEUS that warrant study. Within the region bordering the SEUS and the Mid-Atlantic, just east of the southern Appalachian Mountains, warm-season SBCs form at the coast of Georgia and the Carolinas. Relatively flat topography ~150-200km inland allows for mostly unimpeded inland SBC propagation. Through visual analysis, Viner et al. catalogued several SBCs that propagated as far inland as the Central Savannah River Area surrounding Augusta, Georgia. Wermter et al. found that while the land-sea thermal gradient at the coast can influence coastal SBC genesis, the inland-coastal thermal gradient over the land is the primary influencer on the speed and depth of inland propagation of SBCs in this region. Additionally, soil moisture itself is a known correlative factor to sea-breeze formation, as it influences the soil temperature and the thermal gradient needed for SBC genesis and inland penetration. Physick determined that higher latent heat fluxes associated with wetter soil dampen the land-sea thermal gradient and suppress the formation of a SBC. Physick determined that higher latent heat fluxes associated with wetter soil dampen the land-sea thermal gradient and suppress the formation of a SBC. Conversely, drier soil enhances the thermal gradient and promotes SBC formation. However, while there is an inverse relationship between soil moisture and SBCs, higher soil moisture can actually promote more convective rainfall following a SBC if it does not significantly impact the thermal gradient. While the relationship between soil moisture and SBC formation has been conceptually explored and modeled numerically, there is a research gap in observed connections. The Soil Moisture Active Passive (SMAP) satellite mission has been operational since 2015 and has been used to create high-resolution re-analytical Level 4 (L4) datasets of soil moisture and soil temperature at different soil depths: the surface (0-5cm) and rootzone (0-1m). The surface soil temperature effectively acts as the “skin temperature” of the surface at these levels, and a spatial map of the land-sea as well as the coastal-inland thermal gradients can be represented. SMAP data are also assimilated in some atmospheric models such at the High Resolution Rapid Refresh (HRRR) mesoscale model. We propose leveraging the use of SMAP products to fill in spatial gaps left by weather and mesonet stations within the SEUS region, as well as assessing the effectiveness of utilizing SMAP products towards SBC forecasting in both deterministic and machine learning (ML) models.

58 GEOSCIENCES↗
DOE OSTI2023

Technology Strategy Assessment: Findings from Storage Innovations 2030 Bidirectional Hydrogen Storage

Hydrogen is the most common element in the universe, comprising nearly 75% of all normal matter, and it has been used by scientists for centuries, but it was not fully recognized as an element until 1766, when it was isolated by Henry Cavendish. Early work focused on the generation of hydrogen through the oxidation of metals in water, which released hydrogen gas. Hydrogen’s lighter-than-air and flammable properties were immediately used in engines, zeppelins, and as feedstock for a wide variety of chemical reactions. Several approaches were developed for the production of hydrogen with the most common being associated with the production and conversion of hydrocarbon-based fuels. Coal gasification, steam methane reforming, and other reformation processes provide the majority of current hydrogen production due to the relatively low cost of hydrogen produced through these processes. More than 95% of hydrogen production is used for industrial processes rather than energy storage. To facilitate affordable decarbonization of these industrial processes and to advance the use of hydrogen as a fuel in transportation, DOE launched the Hydrogen Shot as part of the Energy Earthshots Initiative. The goal of the Hydrogen Shot is to reduce the cost of clean hydrogen by 80% to $1/kg of clean hydrogen production within one decade (known as the “1 1 1” goal). This is distinct from the Long-Duration Storage Shot, which is the primary focus of this report; however, it is intrinsically linked to bidirectional hydrogen storage. Several important chemical synthesis processes are dependent upon hydrogen, and the production and use of hydrogen is generally driven by its connection to one of these markets. For example, ammonia is one of the most highly produced chemicals in the world and it depends chiefly on hydrogen. Ammonia is primarily used for agricultural fertilizer and is considered to be largely responsible for a doubling of agricultural production per unit of land over the last century. Another one of hydrogen’s primary uses is as a catalyst in petroleum refining during the desulfurization process. Beyond chemical production, hydrogen is used as a reductant in the production of steel and has been demonstrated as a substitute for metallurgical coal in the production of raw iron. It is even used in the hydrogenation reaction for food products to create more shelf-stable semi-solid fats. However, while hydrogen is produced on the order of 100 million metric tons/year globally to feed these industries, more than 95% of hydrogen is produced from hydrocarbons that emit CO2 during the process. Conversely, electrolysis is a process by which electricity is used to separate hydrogen and oxygen in water molecules, usually across a membrane. Hydrogen production via electrolysis lowers the carbon intensity of produced hydrogen when coupled with low-carbon electricity. Currently, global electrolysis capacity is on the order of 1 GW, which equates to about 500 metric tons/day of hydrogen production. To support large-scale industrial decarbonization, capacity will likely need to increase by two to three orders of magnitude. Electrolysis technology is broadly separated into groups that are defined by the electrolyte used, with further subdivision based on the operating characteristics. The majority of commercial electrolyzer systems are based around three main technology groups: liquid alkaline, proton exchange membrane, and solid oxide. Liquid Alkaline (LA) electrolysis is the oldest, most mature, least expensive, and most common commercial technology, with 400 plants in operation by 1902. Its hydrogen output is low relative to the size of the system due to a low current density. LA electrolysis utilizes a liquid potassium hydroxide solution as the electrolyte. Proton exchange membrane (PEM) electrolysis (also known as polymer electrolyte membrane electrolysis), described in 1960, relies on an acid-impregnated polymer membrane as the electrolyte and typically offers three to six times higher hydrogen production per unit cell area than LA electrolysis. Solid oxide electrolysis, or high-temperature electrolysis, utilizes a ceramic cell as the electrolyte and operates on steam rather than liquid water, enabling electrical efficiencies of more than 90%, which is up from 60% with PEM. Two pre-commercial electrolyzer technologies to note are alkaline exchange membrane (AEM) and proton-conducting solid oxide electrolysis cell (SOEC). AEM potentially has the advantages of both LA and PEM technologies in that it is able to use low-cost materials like LA but with the ability to operate at higher output pressures with a smaller footprint like PEM. Proton-conducting SOEC is similar to commercial SOEC, which uses an oxide-conducting ceramic; however, it uses a proton-conducting ceramic that has the potential to operate at lower temperatures and has lower capital costs. Each of these technologies is experiencing a rapid improvement in performance and a reduction in installed cost, and each appears to be well suited to specific applications. Besides differences in the type of electrolyzer used, the main difference in the architecture of bidirectional hydrogen systems is how the hydrogen is stored. Currently, the most cost-effective way to store large amounts of hydrogen gas is underground, such as in large salt caverns that have been hollowed out. These salt caverns are geographically concentrated in small portions of the United States and are not generally near large metropolitan areas; however, other subsurface architectures are being investigated to expand this reach. A more widely deployable option is aboveground pressurized tanks. These systems are about 10 times as expensive because of the materials and safety margins required to hold hydrogen at high pressures. A third option is using materials-based storage, such as liquid organic hydrogen carriers. By reversibly attaching the produced hydrogen to other molecules, it can be stored at near atmospheric pressure and room temperature. This has the potential to reduce the material cost of storage but may result in a reduction in the efficiency of the process because there are both hydrogen uptake and release processes. While materials-based storage has not been used extensively for large-scale hydrogen storage in the past, there is currently significant activity regarding developing materials and processes for use in large-scale hydrogen storage applications. Electrolysis-produced hydrogen offers an unusual opportunity for energy storage applications. Unlike more conventional energy storage approaches, such as batteries, which operate entirely within electrical markets, hydrogen is a valuable product beyond the electric market and can be directed to the most lucrative use. Hydrogen also can be directly converted back to electricity using either a fuel cell or turbine, or it can be sold to other markets, such as chemical synthesis, steel production, or even export. In this way, excess electricity can be upgraded to the most valuable product. Finally, its use can be actively managed between multiple off-takers; for example, local hydrogen storage can provide a specific amount of stored electricity and any excess can be exported to ammonia production. This flexibility is amplified by the fact that hydrogen storage has fully decoupled power and energy components, which allows for affordable scaling options. Together, this allows a substantial amount of creativity to enable the economic utilization of variable power resources while supporting decarbonization of the industry.

08 HYDROGEN↗
DOE OSTI2023

Centering Energy and Environmental Justice in the Buildings Energy Sector

We face incredible challenges for decarbonizing our economy and raising the standard of living for all members of our society at the same time. Historical energy efficiency efforts have been effective in making small steps, but they fall far short of the massive changes we need to make, and they completely miss helping communities of disadvantage (e.g. low-income, African American, Hispanic American, Native American and tribal nations, etc). Business as usual efforts do not take the time to connect with and understand the challenges of these historical underinvested communities and therefore have not been effective at helping these communities. The Biden Harris Administration has set ambitious goals for decarbonization of our economy that include a requirement that 40% of efforts support energy and environmental justice communities. If we are to meet our decarbonization goals, it is imperative that we change our approach to research, development, and deployment of new technologies. The Department of Energy has set energy justice as a priority and is working with the national laboratories to change our approaches. This paper starts with definitions of what we mean by energy and environmental justice and how they apply to building technologies and deployment efforts. We provide several examples of how historical efforts have succeeded and how they have failed to account for challenges of communities of disadvantage. We identify market and technology barriers to decarbonization and energy efficiency for specific technologies and how these barriers are exacerbated for disadvantaged communities. From these examples, we propose a new framework for integrating energy and environmental justice into all aspects of technology development, deployment, and policy efforts within the building energy sector.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

ESnet Requirements Review Program Through the IRI Lens: A Meta-Analysis of Workflow Patterns Across DOE Office of Science Programs (Final Report)

The Department of Energy (DOE) ensures America’s security and prosperity by addressing its energy, environmental, and nuclear challenges through transformative science and technology solutions. The DOE’s Office of Science (SC) delivers groundbreaking scientific discoveries and major scientific tools that transform our understanding of nature and advance the energy, economic, and national security of the United States. The SC’s programs advance DOE mission science across a wide range of disciplines and have developed the research infrastructure needed to remain at the forefront of scientific discovery. The DOE SC’s world-class research infrastructure — exemplified by the 28 SC scientific user facilities — provides the research community with premier observational, experimental, computational, and network capabilities. Each user facility is designed to provide unique capabilities to advance core DOE mission science for its sponsor SC program and to stimulate a rich discovery and innovation ecosystem. Research communities gather and flourish around each user facility, bringing together diverse perspectives. A hallmark of many facilities is the large population of students, postdoctoral researchers, and early-career scientists who contribute as full-fledged users. These facility staff and users collaborate over years to devise new approaches to utilizing the user facility’s core capabilities. The history of the SC user facilities has many examples of wildly inventive researchers challenging operational orthodoxy to pioneer new vistas of discovery; for example, the use of the synchrotron X-ray light sources for study of proteins and other large biological molecules. This continual reinvention of the practice of science — as users and staff forge novel approaches expressed in research workflows — unlocks new discoveries and propels scientific progress. Within this research ecosystem, the high-performance computing (HPC) and networking user facilities stewarded by SC’s Advanced Scientific Computing Research (ASCR) program play a dynamic cross-cutting role, enabling complex workflows demanding high performance data, networking, and computing solutions. The DOE SC’s three HPC user facilities and the Energy Sciences Network (ESnet) high-performance research network serve all of the SC’s programs as well as the global research community. Argonne Leadership Computing Facility (ALCF), the National Energy Research Scientific Computing Center (NERSC), and Oak Ridge Leadership Computing Facility (OLCF) conceive, build, and provide access to a range of supercomputing, advanced computing, and large-scale data-infrastructure platforms, while ESnet interconnects DOE SC research infrastructure and enables seamless exchange of scientific data. All four facilities operate testbeds to expand the frontiers of computing and networking research. Together, the ASCR facilities enterprise seeks to understand and meet the needs and requirements across SC and DOE domain science programs and priority efforts, highlighted by the formal requirements reviews (RRs) methodology. In recent years, the research communities around the SC user facilities have begun experimenting with and demanding solutions integrated with HPC and data infrastructure. This rise of integrated-science approaches is documented in many community and high-level government reports. At the dawn of the era of exascale science and the acceleration of artificial intelligence (AI) innovation, there is a broad need for integrated computational, data, and networking solutions. In response to these drivers, DOE has developed a vision for an Integrated Research Infrastructure (IRI): To empower researchers to meld DOE’s world-class research tools, infrastructure, and user facilities seamlessly and securely in novel ways to radically accelerate discovery and innovation.

42 ENGINEERING↗
DOE OSTI2023

Examination of Exogenous and Endogenous Irradiation Effects on PuO2 and its Precursors via EPR Spectroscopy

The spectroscopic examination of actinide compounds are of con-siderable interest for elucidating the role of 5f electrons in chemical bonding. For example, this could be directly detected via EPR spectroscopy through the hyperfine interaction of an unpaired Pu electron with a given ligand. However, typically fast spin-lattice re-laxation times [1] require the use of low temperatures, multiplying the difficulty of working with radio-toxic Pu compounds. Instead, we looked for radicals at room temperature produced from endoge-nous α-decay or exogenous γ-irradiation. PuO2 and its precursor PuIII2(C2O4)3∙nH2O were used as examples because of their ubiquity in the literature.

Kinyon, Jared S.↗
DOE OSTI2023

Detectors and diagnostics for multiprobe radiography using high-energy short-pulse lasers

Multiprobe radiography requires the following diagnostic capabilities at minimum: radiographic source diagnostics, spectroscopy, imaging, and tomography diagnostics. Depending on the repetition rate of the radiation sources, the diagnostics may be a single-shot diagnostic, such as photographic (radiochromic and X-ray films for high energy photons) films, or a multiple-shot diagnostic for repetitive source operations, such as a high-speed framing camera. Due to the drastically different detector physics of electrons, photons at different energies (keV to 10s of MeV in the radiography context), heavy ions such as protons, alphas, and neutrons, see, for example, Ref. [1, 2] for an overview, different types or configurations of detectors and constructions may be required for multiprobe radiography applications. Some of the common detector metricsinclude, independent of the particle/photon species, sensitivities or efficiency, energy resolution, spatial or temporal resolutions and others. Some unique detection scenarios arise in a multiprobe radiography setting, for example, crosstalk mitigation and particle identification (ID) when X-ray and higher energy photons, relativistic electrons, neutrons, other hadrons, and heavy particles coexist at the detector location.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Unfolding Cross Sections: A Brief Introduction

Unfolding Cross Sections has been around forever, so there is nothing new in this report. I only authored this report because I fear that the current generation of ENDF data creators and users may not be familiar with this concept. This will be a brief introduction to unfolding, so I will only present one example, namely, ENDF/B-VIII U235. Let me stress that in selecting this example it was only because of the obvious importance of U235. In this selection I am not making any judgement as to the quality of this evaluation. Indeed, as I will keep reminding readers throughout this report, in my opinion the evaluators did an excellent job, and this evaluation meets the needs of many users. But it does violate ENDF-102 [1] that states MT=5 can be used for complex reactions at high energy. This evaluation uses MT=5 all the way down to the traditional ENDF lower energy limit at 1.0e-5 eV. There are no complex reactions at 1.0e-5 eV and this is certainly not high energy. To illustrate unfolding I will start from what I call their current U235 BEST physics evaluation and present what I call my BEST engineering evaluation, that better meets my needs – and more importantly the needs of the codes I use to process and use ENDF data. You, the reader, are free to select either – or neither – for your personal use. ENDF has been such a GREAT success because of the close bond between data producers and data users, who have supplied user feedback to improve the next generation of ENDF data. Here I am merely one of those users, and this is my feedback.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Advanced Turbulence Models for Large-Scale Atmospheric Boundary Layer Flows

We present high-fidelity large-eddy-simulation (LES) modeling approaches for the turbulent atmospheric boundary layer (ABL) flows. Wind energy is a prime example of an application driven by ABL. Generation of electrical energy from farms of wind turbines at night in the stable ABL is a particularly interesting situation. In this report, we consider the well-known GEWEX (Global Energy and Water Cycle Experiment) Atmospheric Boundary Layer Study (GABLS) stably stratified benchmark LES case. We use a high-order spectral element code Nek5000/RS, which is supported under the DOE's Exascale Computing Project (ECP) Center for Efficient Exascale Discretizations (CEED) project, targeting application simulations on various acceleration-device based exascale computing platforms. In our earlier ANL report, we demonstrated our newly developed subgrid-scale (SGS) models based on high-pass filter (HPF), mean-field eddy viscosity (MFEV), and Smagorinsky (SMG) with no-slip and traction boundary conditions, provided with low-order statistics, convergence and turbulent structure analysis. In this report, we extend the range of our SGS modeling approaches in the context of the mean-field eddy viscosity (MFEV), to include the solution of an SGS turbulent kinetic energy equation (TKE). We demonstrate the model fidelity of Nek5000/RS in comparison to that of AMR-Wind, a block-structured second-order finite-volume code with adaptive-mesh-refinement capabilities, with which we studied scaling performance for both codes in comparison on DOE's leadership computing platforms.

17 WIND ENERGY↗
DOE OSTI2021

Considerations regarding the Use of Computer Vision Machine Learning in Safety-Related or Risk-Significant Applications in Nuclear Power Plants

With the advancements made to date in the field of artificial intelligence (AI), significant potential exists to utilize AI capabilities for nuclear power plant (NPP) applications. AI can replicate human decision making and it is usually faster and more accurate than humans. For implementations that impact critical NPP applications (e.g., safety-related or non-safety systems that potentially affect overall plant risk), a deeper safety analysis of the AI methods is necessary. AI applied to NPP operations could resemble the use of digital I&C (DI&C) because such applications involve digital computer hardware and custom-designed software that input plant data, execute complex software algorithms, and output the results to a system or licensed human operator to potentially provoke an action. For AI methods to be compliant with current safety requirements for DI&C, AI compatibility must be evaluated, and AI-related gaps may exist that prevent the prompt deployment of AI in NPPs. This effort aims to evaluate how example AI technologies align with the DI&C safety framework, and discusses how they could be analyzed, modeled, tested, and validated in a manner similar to typical DI&C technologies. Because AI is a broad field that encompasses areas such as machine learning (ML), natural language processing, and computer vision, this research focused on a subset of methods categorized as the computer vision ML (CVML) methods. This report explores two CVML use cases, gauge reading and fire watch, considered relevant to the DI&C standards, as they could play a safety-critical role. For the gauge reading use case, a CVML-enabled technology that can read gauges at oblique angles is utilized. For the fire watch use case, a CVML-enabled technology is utilized that migrates fire watch from a manual (human) approach to automated fire detection. These use cases are mainly intended to give context to the CVML system discussion. This effort assumes the worst-case scenario, with the CVML system being used to replace a safety-related or risk-significant system, thus requiring evaluation. Evaluating CVML against most of the relevant safety requirements for DI&C yielded several CVML-specific considerations due to the uniqueness of its characteristics in comparison with typical DI&C systems. For example, CVML models often employ commonly used (open-source) datasets, and it is not always possible to determine the level of overlap among open-source datasets. Therefore, the independence of the developed CVML models when demonstrating diversity is questionable, therefore creating vulnerability to common cause failure (CCF). The design verification process is also impacted since the data overlap could result in overestimation of the software validation and verification (V&V) performance results. Section 2 of this report evaluates a list of the identified CVML-specific characteristics and discusses the resulting considerations and potential solutions in the context of each referenced requirement. A summation is provided in Section 3. This report is not to be used as a guideline. It was developed to identify and consider issues in the implementation of ML technologies used to augment activities that may have a bearing on plant operation. The report draws parallels to the use of DI&C technologies, for which many standards are available to guide their use in nuclear plant operation. It considers the technologies and some of the potential implications of their use in safety-related applications but is not intended to address regulatory or licensing related issues.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Advanced Fuel Cycle Cost Basis Report: Supporting Document 5 Considerations on Learning

Learning refers here to the increased experience associated to each new build of a given type of fuel cycle facility, which results generally in design improvements, increased construction and operational efficiencies and reduced mistakes, and therefore in reduced costs. These are important considerations, for example, when modeling transitions from first-of-a-kind (FOAK) to nth-of-a-kind (NOAK) nuclear facilities and deciding the magnitude of the cost reduction (if any) to be applied to each successive build. Different assumptions on learning can significantly affect the results of the economic analyses during transitions. Other competing electricity-generation technologies, such as wind and solar power for example, have demonstrated substantial cost reductions through learning during the last few decades.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗