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At least 991 records · Page 55

Clean Energy Cybersecurity Accelerator Cohort 1: Authentication and Authorization

In the 2023 National Cybersecurity Strategy, the Biden-Harris Administration defines the need for a "defensible, resilient digital ecosystem where it is costlier to attack systems than defend them." The strategy cites the Clean Energy Cybersecurity Accelerator (CECA) as an exemplary effort to bolster the security and resilience of clean energy generation. These efforts help "secure the clean energy grid of the future and [generate] security best practices that extend to other critical infrastructure sectors" and promise broad and far-reaching impacts to bridge the capabilities of private industry and the needs of energy production. Cohort 1 of CECA launched in the fall of 2022 with a focus on solutions that provide strong authentication and authorization for industrial control systems to mitigate attacks on the energy grid. Authentication and authorization verify that the identity (authentication) and permissions (authorization) of a user or device are aligned with their assigned roles. Weaknesses in either can have serious repercussions. To assess the strength of Cohort 1's solutions, CECA devised threat scenarios grounded in historical precedents: the CECA team reviewed exploits from real-world case studies of state-sponsored actors to match the assessment's attack paths and targets. Cohort 1 results provided the energy industry, product vendors, and related agencies valuable insights into the efficacy and applicability of solutions in common system configurations under realistic threat scenarios. The results of the assessment highlight points for interrogation and improvement in subsequent technology iterations. CECA's evaluations are part of an ongoing conversation and collaboration to bolster U.S. cyber resilience against adversaries today and in the future.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Recent progress towards incorporating radiochemical analysis in LANL ASC codes

This document summarizes the recent progress towards incorporating charged-particle radiochemical (RadChem) analysis in different LANL codes within the Advanced Scientific Computing (ASC) Program. The first section is focused on xRAGE and the implementation of the necessary physics packages to build an in-line RadChem capability. The second section provides a summary of the current RadChem postprocessing capabilities.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

State Machine Operation of Complex Systems

SQMS, affiliated with Fermilab, is one of five research centers funded by the DOE dedicated to the development and deployment of advanced quantum computers and sensors. The objective of our research was to devise an experimental design for quantum transduction, with the aim of facilitating the development of a distributed quantum network to connect quantum computers over large distances.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Internship Presentation

SQMS, affiliated with Fermilab, is one of five research centers funded by the DOE dedicated to the development and deployment of advanced quantum computers and sensors. The objective of our research was to devise an experimental design for quantum transduction, with the aim of facilitating the development of a distributed quantum network to connect quantum computers over large distances.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Enterprise Risks for Scientific Software in the Post Exascale Era

The Department of Energy’s Office of Science’s Advanced Scientific Computing Research (ASCR) Office hosts three high performance computing Facilities and one high performance networking Facility. ASCR’s Facilities Division and these four Facilities established a joint working group to address the most severe threats to the software capabilities that can prevent the Facilities from supporting a diverse array of mission critical scientific research carried out by thousands of researchers from national labs, academia, and industry. This report outlines the risk categories, threat matrix calculations, and number of risks in each of the categories along with the threat levels. Two categories—programming environment/tools and system software—account for 13 out of the 20 risks encountered in the risk register. A significant risk to ASCR’s software ecosystem also arises from the possible loss of personnel and expertise past the end of the exascale computing project. The working group recommends that the software enterprise risks are reevaluated regularly, and are considered in future decision making regarding system acquisition, knowledge sharing, partnership development, and strategic planning

97 MATHEMATICS AND COMPUTING↗

An Assessment of Machine Learning Applied to Ultrasonic Nondestructive Evaluation

In the United States, the nuclear industry performs inservice inspection (ISI) through nondestructive examination (NDE) methods in accordance with guidelines specified in the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (BPVC), Section XI, Rules for Inservice Inspection of Nuclear Power Plant Components. Ultrasonic nondestructive testing and evaluation (NDT&E) is one of the more commonly used techniques for inspecting Class 1 structural components in nuclear power systems. As the number of qualified NDE inspectors declines, the nuclear industry is looking to take advantage of advances in automation to enhance inspection capabilities. Advances in computational power, cloud-based computing, and machine learning algorithms make automated data analysis possible. Machine learning (ML) has shown huge potential in automated data analyses for ultrasonic NDE in the context of weld inspections.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Perspectives on AI Architectures and Co-design for Earth System Predictability

Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper.

58 GEOSCIENCES↗

Nuclear Physics Network Requirements Review (Final Report)

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. ESnet interconnects DOE national laboratories, user facilities, and major experiments so that scientists can use remote instruments and computing resources as well as share data with collaborators, transfer large datasets, and access distributed data repositories. ESnet is specifically built to provide Between July 2023 and October 2023, ESnet and the Nuclear Physics program (NP) of the DOE SC organized an ESnet requirements review of NP-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the NP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.

97 MATHEMATICS AND COMPUTING↗

High Energy Physics Network Requirements Review: Two-Year Update

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. ESnet interconnects DOE national laboratories, user facilities, and major experiments so that scientists can use remote instruments and computing resources as well as share data with collaborators, transfer large datasets, and access distributed data repositories. ESnet is specifically built to provide a range of network services tailored to meet the unique requirements of the DOE’s data-intensive science. In July 2023, the Energy Sciences Network (ESnet) and the High Energy Physics program (HEP) of the DOE SC organized an interim ESnet requirements review of HEP-supported activities, to follow up on the work started during the 2020 HEP Network Requirements Review. Preparation for these events included checking back with the key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare updates to their previously submitted case study documents, so that ESnet could update the understanding of any changes to the current, near-term, and long-term status, expectations, and processes that will support the science activities of the program.

97 MATHEMATICS AND COMPUTING↗

Wind Turbine Design Guideline DG03: Yaw and Pitch Bearings

This design guideline describes the design criteria, calculation methods, and applicable standards recommended for use in performance and life analyses of ball and roller (rolling) bearings for yaw and pitch motion support in wind turbine applications. The formulae presented here for rolling bearing analytical methods and bearing-life ratings are consistent with methods currently used by wind turbine designers and rolling bearing manufacturers. The original yaw and pitch bearing design guideline was first drafted in 1999 by industry members and finally published in 2009 by the National Renewable Energy Laboratory. It was conceived as the third in a series of design guidelines, but it was the only one actually published by NREL and since has been known by the name "DG03." Other design guidelines were started, but instead of being published by NREL, they eventually became some of the International Electrotechnical Commission wind turbine standards in use today. This updated design guideline includes advances in research and field experiences made between 2009 and 2023. A better understanding of the operating conditions and damage mechanisms, advances in computational design, and publicly available test results facilitate more reliable yaw and pitch bearing designs.

17 WIND ENERGY↗

Improving qubit coherence time through detailed materials analysis

This study investigates the defects, imperfections, and variations in superconducting Nb thin films deposited on sapphire substrates. Two samples, W114 and W118, were fabricated using DC Magnetron Sputtering and Electron Cyclotron Resonance (ECR), respectively. Each sample was analyzed at five distinct spots to compare elemental homogeneity and thickness variations. Scanning Electron Microscopy (SEM) was employed to examine surface morphology, while Energy Dispersive X-ray Spectroscopy (EDS) and X-ray Photoelectron Spectroscopy (XPS) provided elemental and chemical state analyses. SEM revealed distinct structures in W114 and a uniform surface in W118. EDS showed higher aluminum and oxygen signals in W118, indicating a thicker film. XPS detected a higher Nb₂O₅ signal in W118, attributed to a thicker oxygen layer. These findings highlight the critical role of precise material characterization in advancing quantum computing technologies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Options for Meeting Data Center Demand in Virginia: Focus on GETs and Reconductoring [Slides]

Data center demand growth in Virginia will need to met with many options - GETs and/or reconductoring have the potential to be part of the solution suite in the short-term and medium-term. Almost all GETs and reconductoring technologies have more favorable economics than transmission wires investments considering relatively low levels of existing wide-scale deployment. Deployment of GETs and/or reconductoring is not a one size fits all - requires detailed analysis. The complementary nature of GETs and/or reconductoring impacts have the potential to be synergistic (more congestion relief, more transfer capability, more cost savings - when implemented together).

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Graph-Based Modeling for the Detection and Tracking of Sarin-Surrogate-Induced Neurotoxicity Using a Human-Relevant, In-Vitro Brain Model

Organophosphorus (OP) nerve agents are a chemical threat to the United States, to the civilian population (e.g., pesticides) and historically weaponized (e.g., sarin) as chemical warfare agents. The unprecedented, accelerated process from “bench-to-bedside” during the SARSCov2 pandemic has made it clear that technology and tools need to be readily available for immediate response. Advances in human organ tissue mimetic systems are a promising technology to evaluate the human-relevant response in vitro for basic and applied research and drug screening. In particular, current brain microphysiological systems (MPS) have the capability to monitor and detect changes in engineered human neural circuit activity. However, current data analytics approaches for these systems lack the granularity to functionally detect and distinguish the different mechanisms that occur in the brain following neurotoxicity, injury, and disease. The goal of this project was to advance the computational analytical capabilities of the brain MPS to detect functional changes in neural circuit structure at different stages of Sarin surrogate-induced neurotoxicity. We developed graph-based models to (1) identify the composition of the neural circuit structure; (2) detect and monitor how this structure changes following sarin-induced neurotoxicity; and (3) evaluate the analytical pipeline using known/promising oxime reactivators. Through experiments on the bMPS where in vitro neuronal cultures were exposed to a sarin surrogate, we demonstrated the capabilities of our computational pipeline to identify different responses in the functional networks of brain cells exposed to low and high concentrations of the nerve agent. We identified a biphasic response of human neural network activity following exposure to a sarin-surrogate that had not been reported in the literature before. The graph-based models and software developed in this project can be used for future studies that leverage the brain MPS technology, such as treatment efficacy assessment.

59 BASIC BIOLOGICAL SCIENCES↗

SIREN: Scaling Ion-Traps by REquiring iNnovative Heterogenous Integration

The SIREN (Scaling Ion Traps by Requiring iNnovative heterogenous integration) project explores the feasibility of heterogeneous integration (HI) as a transformative approach to scaling ion traps, a critical technology for advancing quantum computers and atomic clocks. Traditional ion trap architectures face significant challenges in scalability due to limitations in optical access, fabrication techniques, and material constraints. SIREN addresses these challenges by leveraging HI, which combines different materials and fabrication processes to create more complex and efficient ion trap structures. HI integrated structures can be manufactured without compromising the process to maintain compatibility to ion traps. This project focuses on integrating a separately fabricated waveguide with a fully functional ion trap. The respective alignment between the pieces needs to be accurate to less than 2 µm to ensure that the light from the waveguide can overlap with the trapping region. The fine alignment must also be maintained through an ultra-high vacuum bake, a critical step in preparing an ion trap experiment. The project's outcomes suggest that heterogeneous integration is a promising pathway for overcoming current scalability barriers, paving the way for the next generation of quantum technologies. SIREN's findings contribute significantly to the field, offering a scalable solution that could accelerate the development of practical quantum computers and highly accurate atomic clocks.

42 ENGINEERING↗

Physics-Reinforced Machine Learning Algorithms for Multiscale Closure Model Discovery

The central objective of this project was to address the challenge of modeling and simulating complex multiscale turbulence phenomena by leveraging physics-guided machine learning (PGML) and hybrid modeling approaches. By integrating physics-based methods with data-driven models, the research focused on achieving robust and scalable solutions for geophysical turbulence, enhancing numerical weather prediction and climate research tools. The project resulted in significant advancements in computational modeling paradigms, predictive tools for reduced-order modeling, and innovative algorithms for fluid dynamics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Interparticle Characterization of Mechanical Biomass Particle-Particle and Particle-Wall Interactions

The biomass materials industry faces significant challenges in managing material variability and its impact on storage and handling systems. Physical properties such as moisture content, particle size, and density fluctuate considerably, leading to operational issues like bridging and ratholing that disrupt material flow. These variations create a complex cascade effect throughout the process chain, affecting transportation, storage, and conversion processes. The economic consequences of this variability manifest in increased operational costs, maintenance requirements, and system downtime. Environmental factors further complicate the situation, as weather conditions and seasonal availability influence material properties and system performance. Engineers employ specialized equipment design, material characterization protocols, and pre-processing steps like size reduction and homogenization to address these challenges. A critical knowledge gap exists between continuous-level constitutive models and particle-scale behavior. This project developed a novel device to quantify interparticle mechanics between biomass particles, measuring friction and adhesion forces between particles and wall materials. The research focused on corn stover and southern pine forest residue, creating a comprehensive database of particle interactions. This breakthrough enables direct application in particle-based computational modeling, advancing the field's understanding of biomass handling characteristics and supporting the development of more reliable and efficient storage and handling systems. The project's outcomes contribute significantly to understanding biomass's mechanical and flow characteristics, particularly how variability at the particle level affects larger-scale handling operations. This knowledge is crucial for engineering feedstock supply systems that consistently meet quality and cost specifications for various conversion processes. The innovative experimental setup developed through this research represents a significant advancement in biomass characterization methodology. Providing precise measurements of particle-level interactions establishes a foundation for more accurate predictive modeling of bulk material behavior. This enhanced understanding of fundamental particle mechanics enables engineers to anticipate better and address handling challenges before they manifest in full-scale operations. This research opens new avenues for optimizing biomass handling systems through data-driven design approaches. The comprehensive database of particle interactions serves as a valuable resource for future research and development efforts, potentially leading to more efficient and cost-effective biomass processing solutions. This advancement in particle-level mechanics could revolutionize how biomass handling systems are designed and operated, contributing to more sustainable and reliable renewable energy production.

09 BIOMASS FUELS↗

Building a Long-Lived 3D Multimode Quantum Processing Unit with TESLA Cavities

Superconducting radio-frequency (SRF) cavities, such as TESLA-shaped cavities, are excellent candidates for long-lived quantum memories. Integrating these cavities with nonlinear elements, such as transmons, enables universal quantum control; however, this integration can often introduce unwanted losses. In this talk, we present the successful integration of a long-lived 2-cell TESLA cavity with an ancillary qubit, forming a quantum processing unit (QPU) that allows for robust control while maintaining coherence. We demonstrate universal control of the QPU through various schemes, including ancilla-cavity sideband and echoed conditional displacement (ECD) methods, enabling fault-tolerant high-fidelity preparation of large Fock states and fast entanglement generation. These results illustrate the versatility and efficiency of our 3D quantum processing unit, paving the way for advanced quantum computing applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Quantum Sensing Experiments at SQMS

The Superconducting Quantum Materials and Systems (SQMS) Center, led by Fermi National Accelerator Laboratory, is one of five U.S. Department of Energy-funded research centers dedicated to advancing quantum computing and quantum sensing. SQMS leverages its expertise in superconducting radio-frequency (SRF) cavities and transmon qubits to develop sensitive quantum sensors to probe fundamental physics. This presentation will highlight the progress and innovations in SQMS’s quantum sensing thrust, with a particular focus on searches for particles beyond the Standard Model, dark matter candidates, and gravitational waves. Recent developments include the search for dark photon dark matter using transmon qubits and widely tunable superconducting cavities (4 GHz–7 GHz), a quantum-enhanced axion dark matter search employing an itinerant single microwave photon counter, and efforts to detect low-mass axions and high-frequency gravitational waves using multimode superconducting cavities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗