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At least 757 records · Page 42

A New Era of Discovery: The 2023 Long-Range Plan for Nuclear Science (V.1.2)

Nuclear science is the investigation of how protons and neutrons are formed from elementary particles and how the forces between those particles produce both nuclei and the vast variety of nuclear phenomena that occur in the universe. It has evolved into a broad field that addresses profound scientific questions: Where does the mass of visible matter come from? How do stars ignite, live, and die? How do nuclei illuminate the search for new laws of nature? This science points the way to using nuclei to build new technologies that benefit society. The 2015 Nobel Prize in physics was shared by nuclear physicists Art McDonald and Takaaki Kajita for the discovery of neutrino oscillations, which confirmed that neutrinos have mass. Our progress on big questions like this one since 2015 has been remarkable owing to new experimental tools, theoretical breakthroughs, powerful computational techniques, and the talented people who make these innovations possible. Focusing on these new tools, the Facility for Rare Isotope Beams (FRIB) at Michigan State University is already producing exciting results on decays of never-before-produced isotopes a year after it was completed on time and on budget. The energy upgrade of the Continuous Electron Beam Accelerator Facility (CEBAF) at the Thomas Jefferson National Accelerator Facility (Jefferson Lab) was also completed on schedule and on budget—new data from this facility are revealing the spectrum, structure, and dynamics of protons, neutrons, nuclei, and mesons. On the theory front, we can now calculate the distribution of quarks inside the proton from first principles. The implementation of artificial intelligence (AI) and machine learning (ML) techniques has led to improved data analysis and increased efficiency in running experiments and theoretical calculations. The impact of nuclear science goes beyond expanding the frontiers of knowledge about matter in the universe. We simultaneously develop a STEM work force that advances the security, technology, health, and wealth of our nation. Some connections are obvious. Expert scientists trained to work with radioactive nuclei are in demand in nuclear security arenas and are highly sought after by various government agencies and private industries. Graduate students and postdoctoral fellows (postdocs) obtain extensive computational, modeling, and data science skills that are similarly in high demand. Less obvious but equally important is the connection between these trained scientists and success in other professions, including medicine, energy, and entrepreneurial pursuits. The workforce that enables discovery in nuclear science also makes breakthroughs in technologies with tremendous impact on the nation’s economic advancement.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Project Boeing SGS (Final Report)

Boeing and its partner, PJM Interconnection, teamed to bring advanced “defense-grade” technologies for cyber security to the US regional power grid through demonstration in PJM’s energy management environment. Under this cooperative project with the Department of Energy, Boeing and PJM have developed and demonstrated a host of technologies specifically tailored to the needs of PJM and the electric sector as a whole. The team has demonstrated to the energy industry a combination of processes, techniques and technologies that have been successfully implemented in the commercial, defense, and intelligence communities to identify, mitigate and continuously monitor the cyber security of critical systems. Guided by the results of a Cyber Security Risk-Based Assessment completed in Phase I, the Boeing-PJM team has completed multiple iterations through the Phase II Development and Phase III Deployment phases. Multiple cyber security solutions have been completed across a variety of controls including: Application Security, Enhanced Malware Detection, Security Incident and Event Management (SIEM) Optimization, Continuous Vulnerability Monitoring, SCADA Monitoring/Intrusion Detection, Operational Resiliency, Cyber Range simulations and hands on cyber security personnel training. All of the developed and demonstrated solutions are suitable for replication across the electric sector and/or the energy sector as a whole. Benefits identified include; Improved malware and intrusion detection capability on critical SCADA networks including behavioral-based alerts resulting in improved zero-day threat protection; Improved Security Incident and Event Management system resulting in better threat visibility, thus increasing the likelihood of detecting a serious event; Improved malware detection and zero-day threat response capability; Improved ability to systematically evaluate and secure in house and vendor sourced software applications; Improved ability to continuously monitor and maintain secure configuration of network devices resulting in reduced vulnerabilities for potential exploitation; Improved overall cyber security situational awareness through the integration of multiple discrete security technologies into a single cyber security reporting console; Improved ability to maintain the resiliency of critical systems in the face of a targeted cyber attack of other significant event; Improved ability to model complex networks for penetration testing and advanced training of cyber security personnel

24 POWER TRANSMISSION AND DISTRIBUTION↗

Notes on a New Coherence Estimator

This document discusses some interesting features of the new coherence estimator in [1] . The estimator is d erived from a slightly different viewpoint. We discuss a few properties of the estimator, including presenting the probability density function of the denominator of the new estimator , which is a new feature of this estimator . Finally, we present an appr oximate equation for analysis of the sensitivity of the estimator to the knowledge of the noise value. ACKNOWLEDGEMENTS The preparation of this report is the result of an unfunded research and development activity. Sandia National Laboratories is a multi - program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE - AC04 - 94AL85000.

97 MATHEMATICS AND COMPUTING↗

Technical Analysis of SSP-21 Protocol

As part of the California Energy Systems for the Twenty-First Century (CES-21) program, in December 2016 San Diego Gas and Electric (SDG&E) contracted with Lawrence Livermore National Laboratory (LLNL) to perform an independent verification and validation (IV&V) of a white paper describing their Secure SCADA Protocol for the Twenty-First Century (SSP-21) in order to analyze the effectiveness and propriety of cryptographic protocol use within the SSP-21 specification. SSP-21 is designed to use cryptographic protocols to provide (optional) encryption, authentication, and nonrepudiation, among other capabilities. The cryptographic protocols to be used reflect current industry standards; future versions of SSP-21 will use other advanced technologies to provide a subset of security services.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Multicontinuum Flow Models for Assessing Two-Phase Flow in Containment Science

We present a new pre-processor tool written in Python that creates multicontinuum meshes for PFLOTRAN to simulate two-phase flow and transport in both the fracture and matrix continua. We discuss the multicontinuum modeling approach to simulate potentially mobile water and gas in the fractured volcanic tuffs at Aqueduct Mesa, at the Nevada National Security Site.

58 GEOSCIENCES↗

Combining Spike Time Dependent Plasticity (STDP) and Backpropagation (BP) for Robust and Data Efficient Spiking Neural Networks (SNN)

National security applications require artificial neural networks (ANNs) that consume less power, are fast and dynamic online learners, are fault tolerant, and can learn from unlabeled and imbalanced data. We explore whether two fundamentally different, traditional learning algorithms from artificial intelligence and the biological brain can be merged. We tackle this problem from two directions. First, we start from a theoretical point of view and show that the spike time dependent plasticity (STDP) learning curve observed in biological networks can be derived using the mathematical framework of backpropagation through time. Second, we show that transmission delays, as observed in biological networks, improve the ability of spiking networks to perform classification when trained using a backpropagation of error (BP) method. These results provide evidence that STDP could be compatible with a BP learning rule. Combining these learning algorithms will likely lead to networks more capable of meeting our national security missions.

97 MATHEMATICS AND COMPUTING↗

Designing Secure and Resilient Cyber-Physical Systems Using Formal Models

This work-in-progress paper proposes a design methodology that addresses the complexity and heterogeneity of cyber-physical systems (CPS) while simultaneously proving resilient control logic and security properties. The design methodology involves a formal methods-based approach by translating the complex control logic and security properties of a water flow CPS into timed automata. Timed automata are a formal model that describes system behaviors and properties using mathematics-based logic languages with precision. Due to the semantics that are used in developing the formal models, verification techniques, such as theorem proving and model checking, are used to mathematically prove the specifications and security properties of the CPS. This work-in-progress paper aims to highlight the need for formalizing plant models by creating a timed automata of the physical portions of the water flow CPS. Extending the time automata with control logic, network security, and privacy control processes is investigated. The final model will be formally verified to prove the design specifications of the water flow CPS to ensure efficacy and security.

42 ENGINEERING↗

US Department of Energy, Office of Science, High-Performance Computing Facility: 2023 Operational Assessment Oak Ridge Leadership Computing Facility

The Oak Ridge Leadership Computing Facility (OLCF) was established to accelerate scientific discovery by providing world-leading computational performance and advanced data infrastructure. As a US Department of Energy (DOE) Office of Science user facility, the OLCF has managed the successful deployment and operation of a succession of leadership-class resources dedicated to open science. In addition to these resources, the OLCF staff continually strive to develop innovative processes and technologies, improve security, and empower users through effective allocation management and comprehensive user support and training. These efforts support the advancement of science by the OLCF users and benefit high-performance computing (HPC) facilities around the world. In calendar year (CY) 2023, the OLCF supported 1,676 users and 598 projects and exceeded all targets for user satisfaction. The facility received an average satisfaction score of 4.52 out of 5 on the annual user survey, and 94% of respondents reported a high satisfaction rate with the OLCF overall. Of the 3,619 user tickets submitted in CY 2023, OLCF staff resolved 97% within 3 business days. The facility opened Frontier to full scientific operations this year. Two projects conducted on Frontier received the Association for Computing Machinery (ACM) Gordon Bell Prize and the Gordon Bell Special Prize for Climate Modeling, and a third earned a nomination as a Gordon Bell Prize finalist. The facility’s previous flagship machine, Summit, gained new life and was extended through 2024 in part to help provide resources to the Integrated Research Infrastructure (IRI) projects and the National Artificial Intelligence Research Resource (NAIRR) pilot program. The facility instantiated an Advanced Computing Ecosystem testbed in part to support IRI workflows. OLCF made interactivity easier and more accessible to users than ever through tools like Jupyter notebooks and workflows.

97 MATHEMATICS AND COMPUTING↗

Proof-of-work for securing IoT and autonomous systems

A first device in a network issues a proof-of-work challenge to a second device in the network. The second device computes a cryptographic function result to satisfy a criterion indicated in the challenge. The second device returns a challenge response comprising inputs to the cryptographic function that the second device used to compute the cryptographic function result satisfying the criterion. The first device, upon receiving the challenge response, verifies that the inputs to the cryptographic function yield a result satisfying the criterion. Responsive to verifying the challenge response, the first device updates a blockchain based upon the challenge response.

Chan, Calvin↗

Experimental Guesswork with Quantum Side Information Using Twisted Light

Guesswork is an information–theoretic quantity which can be seen as an alternate security criterion to entropy. Recent work has established the theoretical framework for guesswork in the presence of quantum side information, which we extend both theoretically and experimentally. We consider guesswork when the side information consists of the BB84 states and their higher-dimensional generalizations. With this side information, we compute the guesswork for two different scenarios for each dimension. We then performed a proof-of-principle experiment using Laguerre–Gauss modes to experimentally compute the guesswork for higher-dimensional generalizations of the BB84 states. We find that our experimental results agree closely with our theoretical predictions. This work shows that guesswork can be a viable security criterion in cryptographic tasks and is experimentally accessible in a number of optical setups.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Maize tissue culture, transformation, and genome editing

The importance of maize (Zea mays L.) to global agriculture, world economy, and food security is widely known and increasing. Current maize breeding programs are deeply integrated with recent and rapid technological advances in genome sequencing, computational biology, and new genotyping and phenotyping technologies. Transformation and genome editing capabilities are a central hub to an array of advanced molecular and breeding approaches to crop improvement. Tissue culture and somatic embryogenesis play essential and central roles in maize transformation biology. Synergistic applications of maize transformation, advanced genomics, and genome editing provide a potent interdependent triad for functional genomics research and advanced molecular breeding. Implementation of advanced capabilities to transform maize and conduct genome editing will profoundly influence the dynamics of global agriculture ushering in a new era of varietal development and molecular breeding. With over 60.9 Mha planted in 2019 alone, biotech maize accounts for 31% of the world’s maize production. Up to 10% higher yields are achieved using new varieties generated using genetic modification technologies compared to similar conventional varieties. By extension, the impact new varietal releases developed through genome editing will likely be more significant. Further, advances in transformation and genome editing technologies will facilitate an even wider applicability for the development of new varieties with increasingly complex traits. The introduction of biochemical pathways and the use of synthetic biology have become increasingly more attainable. The future is of genotype independent maize transformation and genome editing, as the working platform will impact world agriculture, global food security, and plant science well into the future.

59 BASIC BIOLOGICAL SCIENCES↗

Power System Transient Stability Analysis through a Homotopy Analysis Method

As an important function of energy management systems (EMSs), online contingency analysis plays an important role in providing power system security warnings of instability. At present, N-1 contingency analysis still relies on time-consuming numerical integration. To save computational cost, the paper proposes a quasi-analytical method to evaluate transient stability through time domain periodic solutions’ frequency sensitivities against initial values. First, dynamic systems described in classical models are modified into damping free systems whose solutions are either periodic or expanded (non-convergent). Second, because the sensitivities experience sharp changes when periodic solutions vanish and turn into expanded solutions, transient stability is assessed using the sensitivity. Third, homotopy analysis is introduced to extract frequency information and evaluate the sensitivities only from initial values so that time consuming numerical integration is avoided. Finally, a simple case is presented to demonstrate application of the proposed method, and simulation results show that the proposed method is promising.

Wang, Shaobu↗

Resilient Design of Continuous-time Distributed Optimization Algorithm in the Presence of Cyber-attacks

This paper presents a continuous-time resilient distributed optimization algorithm based on competitive interaction design method on connected graphs in the presence of adversaries. Here, the competitive interaction method allows us to design a network that protects the multi-agent systems from adversaries without requiring high network connectivity. In addition, the proposed algorithm does not require the global information about the number of adversaries. First, we show that the proposed distributed algorithm solves the resilient distributed optimization problem with no attack on the communication links. Second, we show that the proposed continuous-time distributed optimization algorithm on connected graphs converges to the small neighborhood of the optimal solution in the presence of cyber-attacks onto the communication channel. Simulations are presented to illustrate our theoretical results.

97 MATHEMATICS AND COMPUTING↗

Interactive Supercomputing With Jupyter

Rich user interfaces like Jupyter have the potential to make interacting with a supercomputer easier and more productive, consequently attracting new kinds of users and helping to expand the application of supercomputing to new science domains. For the scientist-user, the ideal rich user interface delivers a familiar, responsive, introspective, modular, and customizable platform upon which to build, run, capture, document, re-run, and share analysis workflows. From the provider or system administrator perspective, such a platform would also be easy to configure, deploy securely, update, customize, and support. Jupyter checks most if not all of these boxes. But from the perspective of leadership computing organizations that provide supercomputing power to users, such a platform should also make the unique features of a supercomputer center more accessible to users and more composable with high performance computing (HPC) workflows. Project Jupyter’s core design philosophy of extensibility, abstraction, and agnostic deployment, has allowed HPC centers like NERSC to bring in advanced supercomputing capabilities that can extend the interactive notebook environment. This has enabled a rich scientific discovery platform, particularly for experimental facility data analysis and machine learning problems.

97 MATHEMATICS AND COMPUTING↗

Cyber-Physical Attacks With Control Objectives

This study studies attackers with control objectives against cyber-physical systems (CPSs). The goal of the attacker is to counteract the CPS's controller and move the system to a target state while evading detection. We formulate a cost function that reflects the attacker's goals, and, using dynamic programming, we show that the optimal attack strategy reduces to a linear feedback of the attacker's state estimate. By changing the parameters of the cost function, we show how an attacker can design optimal attacks to balance the control objective and the detection avoidance objective. In conclusion, we provide a numerical illustration based on a remotely controlled helicopter under attack.

97 MATHEMATICS AND COMPUTING↗

Identifying Vulnerabilities and Hardening Attack Graphs for Networked Systems

We investigate efficient security control methods for protecting against vulnerabilities in networked systems. A large number of interdependent vulnerabilities typically exist in the computing nodes of a cyber-system; as vulnerabilities get exploited, starting from low level ones, they open up the doors to more critical vulnerabilities. These cannot be understood just by a topological analysis of the network, and we use the attack graph abstraction of Dewri et al. to study these problems. In contrast to earlier approaches based on heuristics and evolutionary algorithms, we study rigorous methods for quantifying the inherent vulnerability and hardening cost for the system. We develop algorithms with provable approximation guarantees, and evaluate them for real and synthetic attack graphs.

attack graphs↗

Bi-Level Adaptive Storage Expansion Strategy for Microgrids Using Deep Reinforcement Learning

Battery energy storage (BES) is a versatile resource for the secure and economic operation of microgrids (MGs). Prevailing stochastic optimization-based approaches for BES expansion planning for MGs are computationally complicated. This work proposes a data-driven bi-level multi-period BES expansion planning framework to determine the siting, sizing, and timing of BES installations. The proposed planning framework unifies deep reinforcement learning (DRL) and linear programming, thereby decoupling the determinations for the integer and continuous decision variables in two time scales, respectively. In the upper level, a rainbow DRL agent with quantile regression is trained to provide dynamic planning policies to accommodate stochastic renewable energy resources (RESs), load, and battery price changes efficiently. Further, the lower level computes the optimal operation of MGs with frequency constraints to hedge the islanding contingency. The two levels communicate with one another by exchanging storage configuration and operating expenses in order to accomplish the shared goal of minimizing investment and operation costs. Comparative case studies on an MG are carried out to demonstrate the superiority of the proposed DRL-based solution to the mixed-integer linear programming counterpart on efficiency, scalability, and adaptability.

24 POWER TRANSMISSION AND DISTRIBUTION↗