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At least 469 records · Page 26

Validation Data for Benchmarking Wire Arc Additive Manufacturing Process Simulations

Residual stresses cause geometric distortion and affect mechanical performance of additively manufactured structures, yet they are notoriously difficult to assess and predict. Distortion (warpage) can drive parts outside dimensional tolerance limits, leading to part rejection or rework. For parts that meet tolerance, locked-in residual stress fields can affect structural integrity during operation, particularly subcritical cracking by fatigue, creep, or corrosion. This work develops benchmark data for a common additive manufacturing process (Wire Arc Additive Manufacturing) that can be applied for calibration and validation of physical process models that predict residual stress fields. The work includes design of two different samples of differing geometry, detailed manufacturing records for a set of physical samples, and an extensive set of residual stress measurement data developed using two diverse techniques (the contour method and neutron diffraction). An initial application of the work is also reported, where a modeling challenge was issued to secure residual stress model predictions from two independent laboratories that were blind to residual stress measurement data. These initial blind residual stress predictions show significant discrepancies relative to the measurement data, illustrating the potential value of the underlying validation data. An open repository for this work, including the sample designs, manufacturing process records, and the residual stress data, is also provided for future application in non-blind validation efforts.

36 MATERIALS SCIENCE↗

Laser ablation spectrometry for studies of uranium plasmas, reactor monitoring, and spent fuel safety

Nuclear security is one of the defining challenges of our time. Nuclear threats range from deliberate dispersal of radioactive material to contaminate the vital infrastructure to diversion and smuggling of special nuclear material for clandestine nuclear programs and nuclear terrorism, respectively. There is an associated need to develop and sustain the nuclear forensics capabilities, which requires the understanding of complex processes that occur in plasmas of nuclear materials. The area of nuclear safety has seen a resurgence of public interest, and there is a concomitant need to safely store used nuclear fuel and detect structural material failure in nuclear power systems, especially in innovative reactor designs envisioned for future adoption. Laser-produced plasmas are complicated extreme environments that can generate intense and rich, highly specific signatures of nuclear and radiological materials, which can then be explored in a wide range of applications. They include interdiction and rapid detection of nuclear materials, including their isotopic composition, detection over long distances, laboratory simulation of weapons effects, monitoring the condition of structural materials in dry cask storage containers, and novel instrumentation for nuclear power systems. We present a compilation of recent representative examples of the application of laser spectroscopy, and laser-induced breakdown spectroscopy in particular, to nuclear safety and security problems. A case is made that spectroscopic techniques based on laser-produced plasmas offer complementary, and sometimes unique, capabilities that motivate the continued exploration of their efficient production and understanding of the signatures they produce

(020.3260) Isotope shifts, (140.3440) Laser-induce↗

Emerging Technologies for Privacy Preservation in Energy Systems

This study explores the intersection of digitalization and privacy within the energy sector, focusing on the emerging challenges and opportunities presented by integrating Distributed Energy Resources (DERs) and advanced metering infrastructure. The need for robust digital privacy measures has become crucial as the energy industry evolves towards a more decentralized, digitalized, and decarbonized future. This study delves into four cutting-edge privacy-preserving technologies—Homomorphic Encryption (HE), Secure Multiparty Computation (SMPC), Differential Privacy (DP), and Federated Learning (FL)—each offering unique solutions to safeguard consumer data by increasing digital connectivity and data exchange. Through a detailed examination of these methods, the study explains how each technology operates, its applications within the energy sector, and the specific privacy challenges it addresses. Homomorphic Encryption allows for secure computations on encrypted data, enabling data analysis without compromising privacy. Secure Multiparty Computation enables collaborative data analysis across different entities while protecting the confidentiality of the inputs. Differential Privacy introduces randomness into the assembled data set, preventing the identification of individual records in statistical databases. Lastly, Federated Learning offers a paradigm shift in data analysis, where machine learning models are trained at the edge, minimizing the centralization of sensitive data. The research underscores the significance of implementing these privacy-enhancing technologies to comply with strict data protection regulations, foster consumer trust, and enhance the security of the energy infrastructure. By providing a comprehensive overview of these methodologies and their practical implications for the energy sector, this study aims to contribute to the ongoing discourse on digital privacy, offering insights into how the energy industry can navigate the complexities of data privacy in the digital age.

Cali, Umit↗

DER Cybersecurity Detection and Response Suite

SAND2024-08475O The Distributed Energy Resource (DER) Cybersecurity Detection and Response Suite is a solution for distributed energy resource (DER) systems. The DER Security Orchestration, Automation, and Response (SOAR) solution that uses alerts from signature- and behavior-based Intrusion Detection Systems are intended to be deployed as bump-in-the-wire (BITW) devices in front of DER equipment. The fielded application would use multiple intrusion detection systems that report data to SOAR to respond to cyberattacks. The suite consists of two software components: • The proactive intrusion detection and mitigation system (PIDMS) secures grid-edge photovoltaic smart inverters and other equipment in distributed energy resource systems. It is a distributed BITW solution; cyber and physical data are automatically processed using network inspection tools and custom machine learning algorithms to detect abnormal events and correlate cyber-physical events. • The Security Orchestration, Automation, and Response for Distributed Energy Resources (SOAR4DER) application ingests data from several intrusion detection systems to quickly block attacks and revert DER systems to good states. Using a collection of intrusion detection system technologies on a BITW device, it incorporates physical and cyber data to detect abnormal and potential malicious behaviors. Multiple SOAR playbooks then use the intrusion detection system data streams to automatically defend the system. SOAR4DER system testing showed detection and response times under 30 seconds for all adversary reconnaissance, denial-of-service attacks, malicious Modbus commands, brute-force logins, and machine-in-the-middle attacks. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Johnson, Jay↗

A Review of Machine Learning Applications in Power System Resilience

The integration of power electronics enabled devices and the high penetration of renewable energy drastically increase the complexity of power system operation and control. Power systems are still vulnerable to large-scale blackouts caused by extreme natural events or man-made attacks. With the recent development in artificial intelligence technique, machine learning has shown a processing ability in computational, perceptual and cognitive intelligence. It is an urgent challenge to integrate the advanced machine learning technology and large amount of real-time data from wide area measurement systems and intelligent electronic devices, in order to effectively enhance power system resilience and ensure the reliable and secure operation of power systems. Therefore, this paper aims to systematically review the existing application of machine learning methods on power system resilience enhancement, to expand the interest of researchers and scholars in this topic, and to jointly promote the application of artificial intelligence in the field of power systems.

Deep Learning↗

Remote inspection of adversary-controlled environments

Remotely monitoring the location and enduring presence of valuable items in adversary-controlled environments presents significant challenges. In this article, we demonstrate a monitoring approach that leverages the gigahertz radio-wave scattering and absorption of a room and its contents, including a set of mirrors with random orientations placed inside, to remotely verify the absence of any disturbance over time. Our technique extends to large physical systems the application of physical unclonable functions for integrity protection. Its main applications are scenarios where parties are mutually distrustful and have privacy and security constraints. Examples range from the verification of nuclear arms-control treaties to the securing of currency, artwork, or data centers.

47 OTHER INSTRUMENTATION↗

The 2023 terahertz science and technology roadmap

Abstract Terahertz (THz) radiation encompasses a wide spectral range within the electromagnetic spectrum that extends from microwaves to the far infrared (100 GHz–∼30 THz). Within its frequency boundaries exist a broad variety of scientific disciplines that have presented, and continue to present, technical challenges to researchers. During the past 50 years, for instance, the demands of the scientific community have substantially evolved and with a need for advanced instrumentation to support radio astronomy, Earth observation, weather forecasting, security imaging, telecommunications, non-destructive device testing and much more. Furthermore, applications have required an emergence of technology from the laboratory environment to production-scale supply and in-the-field deployments ranging from harsh ground-based locations to deep space. In addressing these requirements, the research and development community has advanced related technology and bridged the transition between electronics and photonics that high frequency operation demands. The multidisciplinary nature of THz work was our stimulus for creating the 2017 THz Science and Technology Roadmap (Dhillon et al 2017 J. Phys. D: Appl. Phys. 50 043001). As one might envisage, though, there remains much to explore both scientifically and technically and the field has continued to develop and expand rapidly. It is timely, therefore, to revise our previous roadmap and in this 2023 version we both provide an update on key developments in established technical areas that have important scientific and public benefit, and highlight new and emerging areas that show particular promise. The developments that we describe thus span from fundamental scientific research, such as THz astronomy and the emergent area of THz quantum optics, to highly applied and commercially and societally impactful subjects that include 6G THz communications, medical imaging, and climate monitoring and prediction. Our Roadmap vision draws upon the expertise and perspective of multiple international specialists that together provide an overview of past developments and the likely challenges facing the field of THz science and technology in future decades. The document is written in a form that is accessible to policy makers who wish to gain an overview of the current state of the THz art, and for the non-specialist and curious who wish to understand available technology and challenges. A such, our experts deliver a ‘snapshot’ introduction to the current status of the field and provide suggestions for exciting future technical development directions. Ultimately, we intend the Roadmap to portray the advantages and benefits of the THz domain and to stimulate further exploration of the field in support of scientific research and commercial realisation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Application of quantitative risk assessment to address stakeholder questions in geologic carbon storage

Ambitious international greenhouse gas emissions reduction targets demand a rapid transformation to a low-carbon economy. This transformation includes the accelerated adoption of carbon dioxide (CO2) capture and storage (CCS) technology. However, as with any large-scale engineering enterprise, the widespread commercial-scale deployment of geologic carbon storage (GCS) raises important questions about technology and cost-effectiveness, safety, environmental risk, and long-term liability. Effectively assessing and managing risks and liability associated with GCS projects is a key technical need throughout the project life cycle-from site selection and permitting to monitoring design, operational risk management, and post-operational site closure. This presentation highlights recent advancements in tools for quantitative risk assessment, being developed by the National Risk Assessment Partnership (NRAP). NRAP is a multi-year, multinational laboratory research collaboration sponsored by the U.S. Department of Energy's Office of Fossil Energy and Carbon Management. Our focus will be on these tools' applications in addressing critical stakeholder questions related to supporting permitting to ensure secure and environmentally protective storage; designing effective and efficient monitoring plans; evaluating the effectiveness of remedial actions and risk management alternatives; and informing liability assessment and investment decisions. This paper will detail the key functionality of NRAP’s Open-Source Integrated Assessment Model (NRAP-Open-IAM), a computational framework for assessing leakage risk and containment assurance. This model features streamlined workflows for calculating leakage risk profiles, delineating risk-based area of review, and assessing contingency plans and post-injection site care requirements. ORION is an open-source, observation-based ensemble forecasting toolkit to help operators assess the seismic hazard at a carbon storage site. The State of Stress Analysis Tool (SOSAT), designed to assess subsurface stress conditions and evaluate geomechanical risk resulting from CO2 injection in an area of interest will also be presented. We will also introduce a prototype model to evaluate storage project costs and liability associated with risk management. The Technoeconomic and Liability Evaluation for Storage (TALES) model uses results from forecasts of leakage and induced seismicity risk to estimate the lifecycle cost of managing risk. Finally, a preliminary example of how the NRAP Risk-based Adaptive Monitoring Plan (RAMP) tool can be used to design efficient and effective site monitoring plans and estimate the detectability of fluid leakage will be provided. The relevance of these tools for addressing key stakeholder questions amidst uncertainty will be emphasized.

decision support↗

Further Support of the Development of Advanced Multilayer Optics Coatings for X-Ray Imaging

The Smithsonian Astrophysical Observatory (SAO) is working with Lawrence Livermore National Laboratory (LLNL) to develop Wolter-I optics for x-ray imaging applications at energies of 15-50 keV and above for National Nuclear Security Administration (NNSA) facilities. SAO work includes R&D on advanced multilayer coatings as well as fabrication and testing of Wolter multilayer coated optics. It also includes delivery of custom 3.05-meter focal length Wolter optics with response at energies of interest to Sandia National Laboratories and LLNL. The optics are fabricated using an electroform nickel replication technique which has been used to fabricate optics for X-ray astronomy satellites over the past two decades. This technique starts with a figured, polished mandrel from which the optics are replicated. To carry this technique over to fabricate optics for NNSA facilities, several additional barriers had to be overcome to deposit multilayer coatings on integral optics with radii as small as 25 mm. These breakthroughs allow fabrication of optics which meet the spatial and spectral specifications required by NNSA facilities. During this present contract period SAO delivered four custom optics to LLNL with spectral response at or near the Kα line of silver, 22.16 keV.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A Strategy for NACS investment in Machine Learning

The Nuclear and Chemical Sciences (NACS) Division furnishes the expertise in the scientific areas of chemical, nuclear and isotopic sciences that are foundational in the Laboratory’s national security missions. This expertise is maintained and advanced through identification, development and application of state-of-the-art theoretical, computational and experimental methods and tools. Recent developments in artificial intelligence and machine learning (AI/ML) techniques enabled by advances in computing capabilities and widespread availability of powerful software implementations have made use of these techniques ubiquitous across both science and industry. While the scope of AI/ML applications is incredibly large and evolves very rapidly, the topics most relevant to NACS missions fall into the general category of detecting, categorizing or identifying features in large, complex datasets using either supervised or unsupervised learning. This covers both basic scientific data analysis and the development of efficient surrogate models of real-life technological systems, experimental detectors, or theoretical models. To remain at the forefront of its core scientific disciplines, NACS must both cultivate ML expertise as well as continuously explore applying this expertise to new problems or utilizing new methods. This document identifies the key areas where this support is critical and provides a strategy for investing in them.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Synchrotron-based Characterization Techniques for Radiation Detection Materials and Devices: An Overview

High-performance room-temperature radiation detectors (high energy resolution for spectrometers, high spatial resolution for imaging devices, and low defect-density for high flux applications) are needed for photon energies (>20 keV) that are not well suited for silicon detectors. Applications for such radiation detectors include nonproliferation, synchrotron, medical, astrophysics, and homeland security. Material- and device- characterization to understand and solve the limiting factors of radiation detection materials and devices is a core element of a radiation detector development R&D program. This presentation will give an overview on the two main synchrotron-based characterization techniques that have been employed by the authors in the last ~20 years: (1) White Beam X-ray Diffraction Topography and (2) Micron-scale detector mapping. A perfect (one domain) crystal (radiation detection material) is a requirement to achieve a highperformance radiation detector. White Beam X-ray Diffraction Topography (WBXDT) allows the rapid screening of the crystallinity of the detector material. With WBXDT we can quickly screen CZT and other crystals to make sure they have only one domain, and to see the presence of extended defects and strain fields.

Camarda, Giuseppe S.↗

Enhancing Cloud Cybersecurity: Prescriptive Controls for Operational Technology

This whitepaper provides strategic insights and recommendations into security cloud-based solutions for electric utilities, encompassing operational technology (OT), virtual power plants (VPP), distributed energy resources (DERs), applications, networks, and data storage as they transition to and leverage cloud infrastructure through managed service providers (MSPs) and cloud service providers (CSPs). Principles derived from established frameworks serve as a foundation for best practices across cybersecurity projects and remove the constraints of settling on a single framework. For organizations that prefer not to integrate a specific framework altogether, elements of the proposed approach could be adopted or tailored to best fit defined requirements and expected functionalities. The Cirrus assessment, a utility cloud feasibility tool, and the roadmap it provides serve as a precursor to this paper, which seeks to be a valuable resource for defining next steps following cloud technology integration feasibility appraisal. With its comprehensive approach to adoption, the Cirrus framework offers strategic guidance on responsibly preparing for or deploying a utility cloud solution. The previously published whitepaper, “Use Case-Informed Framework for Utility Cloud Migration,” details the guiding strategy, research, and deployment of cloud solutions within electric and interconnected grid systems. Before implementing the controls suggested in this document, it is recommended that stakeholders complete Cirrus's cloud integration assessment and pair the results with their unique cybersecurity controls to form a comprehensive cloud-based utility cybersecurity plan. The Cirrus outcome will consider a series of future architectures for the grid before and after the energy transition and evaluate the arguments for and against cloud applications for each electric and interconnected grid layer. This document is a companion to the original whitepaper, "Use Case-Informed Framework for Utility Cloud Migration" to further identify and recommend security controls based on Cirrus’s cloud integration assessment output. The following whitepaper outlines the cybersecurity controls that secure cloud-service models pertinent to the electric sector using the predefined categories identify, protect, detect, and respond and recover. The objective is to outline prescriptive security controls based on the type of architecture and data stored in the cloud. The focus includes dissecting the shared responsibility model and elucidating what on-premises Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) entail. A pivotal consideration in this context is allocating responsibility for foundational cybersecurity aspects—having used Cirrus for the cloud integration assessment. The ensuing controls detailed herein also represent a checklist of controls necessary for a secure cloud transition, equipping utilities with the knowledge to navigate this digital transformation with confidence and strategic foresight in a safe and responsible manner.

42 ENGINEERING↗

CROWBAR: Natively Fuzzing Trusted Applications Using ARM CoreSight

Abstract Trusted execution environments (TEE) are deployed on many platforms to provide both confidentiality and integrity, and their extensive use offers a secure environment for privacy-sensitive operations. Despite TEE prevalence in the smartphone and tablet market, vulnerability research into TEE security is relatively rare. This is, in part, due to the strong isolation guarantees provided by its implementation. In this paper, we propose a hardware assisted fuzzing framework, CROWBAR, that bypasses TEE isolation to natively evaluate trusted applications (TAs) on mobile devices by leveraging ARM CoreSight components. CROWBAR performs feedback-driven fuzzing on commercial, closed source TAs while running in a TEE protected environment. We implement CROWBAR on 2 prototype commercial-off-the-shelf (COTS) smartphones and one development board, finding 3 unique crashes in 5 closed source TAs that are previously unreported in the TrustZone fuzzing literature.

Shan, Haoqi↗

Module-OT: A Hardware Security Module for Operational Technology

Increased penetration levels of renewable energy and other types of distributed energy resources (DERs) on the modern electric grid-combined with technological advancements for electric system monitoring and control-introduce new cyberattack vectors and increase the cyberattack surface of energy systems. According to the IEEE Std. 1547-2018, DERs must use Modbus, Distributed Network Protocol 3 (DNP3), or Smart Energy Profile 2.0 (SEP2) as their communication protocol. Previous research identified several vulnerabilities and security breaches in each one of these communication protocols; despite this, existing standards for DERs do not recommend cybersecurity measures. In order to reduce vulnerabilities in power distribution systems, this paper presents a novel open-source hardware security module that improves both information and operational security to better protect data and communications on the distribution grid. The security hardware is called “module for operational technology,” or simply Module-OT, and it has been validated and tested in an emulated distribution system application. Module-OT is integrated within a communication system in the transport layer of the Open Systems Interconnection (OSI) model. It improves system security through encryption, authentication, authorization, certificate management, and user access control. The main advancement of Module-OT is the addition of hardware cryptographic acceleration that improves the overall communication performance in terms of end-to-end latency.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Frost prediction using machine learning and deep neural network models

This study describes accurate, computationally efficient models that can be implemented for practical use in predicting frost events for point-scale agricultural applications. Frost damage in agriculture is a costly burden to farmers and global food security alike. Timely prediction of frost events is important to reduce the cost of agricultural frost damage and traditional numerical weather forecasts are often inaccurate at the field-scale in complex terrain. In this paper, we developed machine learning (ML) algorithms for the prediction of such frost events near Alcalde, NM at the point-scale. ML algorithms investigated include deep neural network, convolution neural networks, and random forest models at lead-times of 6–48 h. Our results show promising accuracy (6-h prediction RMSE = 1.53–1.72°C) for use in frost and minimum temperature prediction applications. Seasonal differences in model predictions resulted in a slight negative bias during Spring and Summer months and a positive bias in Fall and Winter months. Additionally, we tested the model transferability by continuing training and testing using data from sensors at a nearby farm. We calculated the feature importance of the random forest models and were able to determine which parameters provided the models with the most useful information for predictions. We determined that soil temperature is a key parameter in longer term predictions (>24 h), while other temperature related parameters provide the majority of information for shorter term predictions. The model error compared favorable to previous ML based frost studies and outperformed the physically based High Resolution Rapid Refresh forecasting system making our ML-models attractive for deployment toward real-time monitoring of frost events and damage at commercial farming operations.

97 MATHEMATICS AND COMPUTING↗

Security-Constrained Unit Commitment for Electricity Market: Modeling, Solution Methods, and Future Challenges

This paper summarizes the technical activities of the IEEE Task Force on Solving Large Scale Optimization Problems in Electricity Market and Power System Applications. This Task Force was established by the IEEE Technology and Innovation Subcommittee to first review the state-of-the-art of the security-constrained unit commitment (SCUC) business model, its mathematical formulation, and solution techniques in solving electricity market clearing problems. The Task Force then investigated the emerging challenges of future market clearing problems and presented efforts in building benchmark mathematical and business models.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Nondestructive Evaluation of Concrete within Nuclear Applications

Structural defects that develop over the lifetime of nuclear power plants (NPPs) could threaten the safety and security of the concrete infrastructure. Developing a reliable nondestructive evaluation (NDE) method to evaluate the damage in NPPs and their associated structures will greatly improve the long-term operation and safety of NPPs. The purpose of the US Department of Energy Office of Nuclear Energy’s Light Water Reactor Sustainability program is to develop technologies and other solutions that can improve the reliability, sustain the safety, and extend the operating lifetimes of NPPs beyond 60 years. This report describes the progress made toward growing NDE capabilities for NPPs. It also documents expanded collaborations with the Electric Power Research Institute (EPRI) and future opportunities to collaborate with industry partners (i.e., vendors and utilities) and university partners. These collaborations will continue to stand as a framework to provide value to stakeholders by reducing uncertainties and potential delayed maintenance costs in NPPs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

On the Application of Cyber-Informed Engineering (CIE)

The 2023 National Cybersecurity Strategy has recommended a transition to secure-by-design methodologies in critical infrastructure. This paper presents the adoption of the National Cyber-Informed Engineering (CIE) Strategy as initiated by the U.S. DOE’s CESER office, advocating for the integration of cybersecurity at the earliest stages of system design. The strategy targets design engineers responsible for energy infrastructure to embed CIE principles within the engineering lifecycle, thus enhancing cyber resilience. This paper discusses the expansion of secure-by-design concepts to cyber-physical systems, moving beyond traditional IT security to include engineering considerations that can mitigate cyber risks through design choices. The paper introduces Digital Risk Management, balancing traditional cybersecurity with CIE to reduce both likelihood and impact of cyber threats. A set of CIE starter questions derived from 12 core principles is detailed, aiding engineers to consider cybersecurity in their designs and highlights the importance of CIE in anticipating and reducing the impacts of cyber attacks, suggesting that such integration is essential for national security and infrastructure resilience.

42 ENGINEERING↗