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At least 433 records · Page 24

Advanced Simulation and Computing Business Plan

To maintain a credible nuclear weapons program, the National Nuclear Security Administration’s (NNSA’s) Office of Defense Programs (DP) needs to make certain that the capabilities, tools, and expert staff are in place and are able to deliver validated assessments. This requires a complete and robust simulation environment backed by an experimental program to test ASC Program models. This ASC Business Plan document encapsulates a complex set of elements, each of which is essential to the success of the simulation component of the Nuclear Security Enterprise. The ASC Business Plan addresses the hiring, mentoring, and retaining of programmatic technical staff responsible for building the simulation tools of the nuclear security complex. The ASC Business Plan describes how the ASC Program engages with industry partners—partners upon whom the ASC Program relies on for today’s and tomorrow’s high performance architectures. Each piece in this chain is essential to assure policymakers, who must make decisions based on the results of simulations, that they are receiving all the actionable information they need.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Hydro-mechanical behavior of heated bentonite buffer for geologic disposal of high-level radioactive waste: A bench-scale X-ray computed tomography investigation

The behavior of heated bentonite buffer is critical for the security and long-term performance of a geological repository for high-level radioactive waste (HLW). While laboratory column experiments have been conducted to investigate compacted bentonite and coupled THMC (thermal-hydro-mechanical and chemical) processes for a moderate temperature range of up to 100 °C, data for a higher temperature range are limited. Understanding bentonite behavior and coupled THMC processes under higher temperatures (e.g., up to 200 °C) could allow for a more economic repository design and would expand the data and knowledge base for more reliable modeling. In this study, a bench-scale experiment was conducted in a compacted bentonite column experiencing both heating up to 200 °C in the center and hydration from a sand-clay boundary surrounding the column. During the experiment run for 1.5 years, frequent X-ray computed tomography (CT) scanning of bentonite provided insights into the spatiotemporal evolution of (1) hydration/dehydration, (2) clay swelling/shrinkage, (3) displacement, and (4) mineral precipitation. After the experiment, a comprehensive post-dismantling characterization of bentonite samples was conducted. Results showed that the bentonite hydration was axi-symmetrical despite the initial heterogeneity due to packing, confirming the ability of bentonite to seal fast flow/transport paths. Further, compared to a non-heated control experiment, the heated column showed greater CT density variations along the radial distance, indicating that homogenization of bentonite might be more difficult if a temperature gradient is maintained in the repository. Precipitation of an anhydrite layer occurred in the inner hot zone, pointing to potential concerns about salt precipitation causing canister corrosion. Ultimately, the experiments provided a high-resolution window into the strongly dynamic and coupled behavior of bentonite exposed to heating, hydration and swelling, which will be valuable for improving modeling of coupled processes, especially for the early state of a HLW repository.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Optoelectronic polymer memristors with dynamic control for power-efficient in-sensor edge computing

Abstract As the demand for edge platforms in artificial intelligence increases, including mobile devices and security applications, the surge in data influx into edge devices often triggers interference and suboptimal decision-making. There is a pressing need for solutions emphasizing low power consumption and cost-effectiveness. In-sensor computing systems employing memristors face challenges in optimizing energy efficiency and streamlining manufacturing due to the necessity for multiple physical processing components. Here, we introduce low-power organic optoelectronic memristors with synergistic optical and mV-level electrical tunable operation for a dynamic “control-on-demand” architecture. Integrating signal sensing, featuring, and processing within the same memristors enables the realization of each in-sensor analogue reservoir computing module, and minimizes circuit integration complexity. The system achieves 97.15% fingerprint recognition accuracy while maintaining a minimal reservoir size and ultra-low energy consumption. Furthermore, we leverage wafer-scale solution techniques and flexible substrates for optimal memristor fabrication. By centralizing core functionalities on the same in-sensor platform, we propose a resilient and adaptable framework for energy-efficient and economical edge computing.

Optics↗

The future of scientific workflows

Today’s computational, experimental, and observational sciences rely on computations that involve many related tasks. The success of a scientific mission often hinges on the computer automation of these workflows. In April 2015, the US Department of Energy (DOE) invited a diverse group of domain and computer scientists from national laboratories supported by the Office of Science, the National Nuclear Security Administration, from industry, and from academia to review the workflow requirements of DOE’s science and national security missions, to assess the current state of the art in science workflows, to understand the impact of emerging extreme-scale computing systems on those workflows, and to develop requirements for automated workflow management in future and existing environments. Here, this paper is a summary of the opinions of over 50 leading researchers attending this workshop. We highlight use cases, computing systems, workflow needs and conclude by summarizing the remaining challenges this community sees that inhibit large-scale scientific workflows from becoming a mainstream tool for extreme-scale science.

97 MATHEMATICS AND COMPUTING↗

An Intelligent Distributed Ledger Construction Algorithm for IoT

Blockchain is the next generation of secure data management that creates near-immutable decentralized storage. Secure cryptography created a niche for blockchain to provide alternatives to well-known security compromises. However, design bottlenecks with traditional blockchain data structures scale poorly with increased network usage and are extremely computation-intensive. This made the technology difficult to combine with limited devices, like those in Internet of Things networks. In protocols like IOTA, replacement of blockchain's linked-list queue processing with a lightweight dynamic ledger showed remarkable throughput performance increase. However, current stochastic algorithms for ledger construction suffer distinct trade-offs between efficiency and security. This work proposed a machine-learning approach with a multi-arm bandit that resolved these issues and was designed for auditing on limited devices. This algorithm was tested in a reinforcement-learning environment simulating the IOTA ledger's construction with a decision tree. This study showed through regret analysis and experimentation that this approach was secure against impulse manipulation attacks while remaining energy-efficient. Although the IOTA protocol was a pioneer for lightweight distributed ledgers, it is expected that future blockchain protocols will adopt techniques similar to those presented in this work.

multi-arm bandit↗

Impact of Reordering on the LU Factorization Performance of Bordered Block-Diagonal Sparse Matrix

Power engineers rely on computer-based simulation tools to assess grid performance and ensure security. At the core of these tools are solvers for sparse linear equations. When transformed into a bordered block-diagonal (BBD) structure, part of the sparse linear equation solving can be parallelized. This work focuses on using the Schur-complement-based method for LU factorization on BBD matrices, specifically, Jacobian matrices from large-scale systems. Our findings show that the natural ordering method outperforms the default ordering method in computational performance for each block of the BBD matrix. This observation is validated using synthetic 25k-bus and 70k-bus cases, showing a speedup of up to 38% when using natural ordering without permutation. Additionally, the impact of the number of partitions is studied, and the result shows that computational performance improves with more, smaller partitions in the BBD matrices.

BBD matrix↗

CIEPAT for Photovoltaic System Resilience

The Cyber-Informed Engineering Photovoltaic Analysis Tool (CIEPAT) was developed in collaboration with the U.S. Department of Energy's Office of Cybersecurity, Energy Security, and Emergency Response (CESER). This tool is an energy source subcomponent integrated into the CIEMAT ecosystem and is developed to enhance the security and resilience of Photovoltaic installations by incorporating Cyber-Informed Engineering (CIE) principles into the deployment of PV systems.

14 SOLAR ENERGY↗

Scalable, Physical Effects Measurable Microgrid for Cyber Resilience Analysis (SPEMMCRA)

The ability to advance state of the art automated protections for industrial control systems (ICS) has as a precursor in the ability to understand the tradeoff space. That is, to enable a cyber feedback loop in a control system environment you must first consider both the security mitigation available, the benefits and the impacts to the control system functionality when the mitigation is used. More damaging impacts could be precipitated that the mitigation was intended to rectify. This paper details networked ICS that controls a simulation of the frequency response represented with the swing equation. The microgrid loads and base generation can be balanced through the control of an emulated battery and power inverter. The simulated plant, which is implemented in Raspberry Pi computers, provides an inexpensive platform to realize the physical effects of cyber attacks to show the tradeoffs of available mitigatoins. This network design can include a commercial ICS controller to introduce real world implementation of feedback controls, and provides a scalable, physical effects measurable Microgrid for cyber resilience analysis (SPEMMCRA).

42 ENGINEERING↗

Secure ICCP Final Report

Inter-Control Center Communications Protocol (ICCP), defined by the IEC 60870-6 TASE.2 standard, was developed to enable data exchange over wide area networks between electric system entities, including utility control centers, Independent System Operators (ISOs), Regional Transmission Operators (RTOs) and Independent Power Producers (IPP) also known as Non-Utility Generators (NUG). ICCP is an unprotected protocol, and as a result is vulnerable to such actions as integrity violation, interception or alteration, spoofing, and eavesdropping. Because of these vulnerabilities with unprotected ICCP communication, security enhancements, referred to as Secure ICCP, have been added and are included in the ICCP products that utilities have received since 2003 when the standard was defined. This has resulted in an ICCP product whose communication can be encrypted and authenticated to address these vulnerabilities.

97 MATHEMATICS AND COMPUTING↗

Quantum optical microcombs

We present that a key challenge for quantum science and technology is to realize large-scale, precisely controllable, practical systems for non-classical secured communications, metrology and, ultimately, meaningful quantum simulation and computation. Optical frequency combs represent a powerful approach towards this goal, as they provide a very high number of temporal and frequency modes that can result in large-scale quantum systems. The generation and control of quantum optical frequency combs will enable a unique, practical and scalable framework for quantum signal and information processing. Here, we review recent progress on the realization of energy–time entangled optical frequency combs and discuss how photonic integration and the use of fibre-optic telecommunications components can enable quantum state control with new functionalities, yielding unprecedented capability.

42 ENGINEERING↗

Distribution System Behind-the-Meter DERs: Estimation, Uncertainty Quantification, and Control

This paper summarizes the three-year technical activities of the IEEE Task Force (TF) on behind-the-meter (BTM) distributed energy resources (DERs): estimation, uncertainty quantification, and control. The potential grid services from BTM DERs are discussed in detail. The paper also reviews the state-of-the-art for BTM DERs visibility, uncertainty quantification, and, optimization and control. Furthermore, different aspects of the market structures associated with BTM DERs are covered, including emerging market and business models. Finally, needs and recommendations are provided for additional areas such as system protection, computing capabilities, algorithm development, market structure design, cyberinfrastructure and security, and hardware and software developments.

behind-the-meter↗

Nebraska Integrated Carbon Capture and Storage Pre-Feasibility Study

In collaboration with the Nebraska Public Power District (NPPD), the Energy & Environmental Research Center (EERC) has conducted a pre-feasibility study for a commercial-scale carbon dioxide (CO 2 ) geologic storage complex in western Nebraska integrated with potential CO 2 capture at Gerald Gentleman Station (GGS). GGS is the largest coal-fired electricity-generating station in Nebraska, emitting 8.5 million metric tons (Mt) of CO 2 annually, and is located near the town of Sutherland. This pre-feasibility (Phase 1) project has been executed as part of the U.S. Department of Energy (DOE) CarbonSAFE program, a multiphase initiative to support the deployment of large-scale carbon capture and storage (CCS) projects. Each CarbonSAFE project is required to demonstrate the potential to capture and store at least 50 million tonnes (Mt) of CO 2 over a 25-year operational period. The EERC and NPPD established a coordination team to identify challenges to a potential Nebraska CCS project, comprising local stakeholder organizations, which met twice in Lincoln and via several Webinars, providing feedback and guidance throughout the pre-feasibility study. The EERC also secured the technical support of Schlumberger Carbon Services and Computer Modelling Group Ltd. (CMG). The project comprised four technical themes, all using published information sources. Regional and stakeholder analysis, including identification of sensitive environmental areas, potential resource conflicts, and strategies for public outreach. A review of geographic and socioeconomic characteristics, in combination with geologic characterization in the subbasinal analysis described below, identified an area to the southwest and within a 75-mile radius of GGS as the most prospective for development of a storage site. This area largely avoided lands with protected status such as wetlands. A public outreach plan has also been developed for implementation in any further phases of CCS assessment in western Nebraska, for example a CarbonSAFE Phase 2 feasibility study. Scenario analysis, addressing economic and regulatory factors. GGS is the only single major source of CO 2 emissions capable of satisfying the CarbonSAFE 50-Mt scale requirement within the study region. Chemical absorption using amines was identified as the most viable technology for postcombustion CO 2 capture at GGS. The total cost of a CCS project at GGS was estimated to be between $67/tonne CO 2 for capture and auxiliary boiler to minimize parasitic load and $70/tonne CO 2 avoided cost, using the Carnegie-Mellon University Integrated Environmental Control Model (IECM). The total avoided cost included the capture facility and parasitic load, a flue gas desulfurization plant required for the use of amine solvent technology, transport via pipeline, and dedicated storage infrastructure. Nebraska has no legislation in place to address typical CCS-specific issues, for example pore space ownership for storage. Long-term liability, therefore, falls under the U.S. Environmental Protection Agency Underground Injection Control (UIC) Class 6 program regulations. Subbasinal analysis, addressing the potential for a dedicated subsurface “container” to store the required 50-Mt quantity of CO 2 . Modeling and simulation studies identified an area to the southwest of GGS with the potential for storage of 50 Mt CO 2 in the Cloverly Formation, comprising sandstones with interbedded and intermingled shales. The area of review (AOR) that would be required for monitoring under a Class VI operating permit was estimated to be as high as 400–700 square miles, due to uncertain pressure effects. The viability of this storage option is subject to significant uncertainty due to the relatively limited amount of existing characterization data available to the pre-feasibility study; for example, dynamic simulation indicated that the proposed storage rate might require as little as two or as many as 14 injection wells. A key uncertainty is the relative proportion and distribution of sandstone and shale within the Cloverly Formation. A preliminary, semiquantitative risk assessment also suggested uncertainty over storage capacity and injectivity constitute the most significant project risks at this pre-feasibility stage. No assessed risks were considered to rule out the possibility of a project moving to deployment. National Risk Assessment Partnership (NRAP) validation, using software tools developed by the National Energy Technology Laboratory (NETL) to assess risks associated with the potential 50-Mt CO 2 storage complex. NRAP tools were used to assess hypothetical leakage scenarios. Results broadly supported the conclusion of the semiquantitative risk analysis – for example, even worst-case analysis of theoretical leakage scenarios found limited migration rates and impacts. In summary, the work undertaken in this Phase 1 pre-feasibility study has shown that western Nebraska has potential to host a commercial-scale CCS project, including a dedicated storage “container” for 50 Mt of CO 2 . However, the following key challenges would need to be overcome: The business case for deploying CCS projects is uncertain; recently announced federal tax credits and sales of CO 2 for enhanced oil recovery may not cover the full costs of a CCS project at GGS, as estimated by this pre-feasibility study. The potential 50-Mt CO 2 dedicated storage container defined in this pre-feasibility study should be regarded as having a relatively low level of readiness to support a CCS project. Public outreach would be a vital element in western Nebraska, where sensitivities around such environmental issues as water resource protection and pipeline construction would need to be carefully addressed.

54 ENVIRONMENTAL SCIENCES↗

BigData Express: Toward Predictable, Schedulable, and High-Performance Data Transfer [PowerPoint]

Topics include: • BigData Express: a schedulable, predictable, and high-performance data transfer service – A peer-to-peer, scalable, and extensible data transfer model – A visually appealing, easy-to-use web portal – A high-performance data transfer engine – A time-constraint-based scheduler – On-demand provisioning of end-to-end network paths with guaranteed QoS – Robust and flexible error handling – CILogon-based security

97 MATHEMATICS AND COMPUTING↗

Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks

The Deep Operator Network (DeepONet) framework is a different class of neural network architecture that one trains to learn nonlinear operators, i.e., mappings between infinite-dimensional spaces. Traditionally, DeepONets are trained using a centralized strategy that requires transferring the training data to a centralized location. Such a strategy, however, limits our ability to secure data privacy or use high-performance distributed/parallel computing platforms. To alleviate such limitations, in this paper, we study the federated training of DeepONets for the first time. That is, we develop a framework, which we refer to as Fed-DeepONet, that allows multiple clients to train DeepONets collaboratively under the coordination of a centralized server. To achieve Fed-DeepONets, we propose an efficient stochastic gradient-based algorithm that enables the distributed optimization of the DeepONet parameters by averaging first-order estimates of the DeepONet loss gradient. Then, to accelerate the training convergence of Fed-DeepONets, we propose a moment-enhanced (i.e., adaptive) stochastic gradient-based strategy. Finally, we verify the performance of Fed-DeepONet by learning, for different configurations of the number of clients and fractions of available clients, (i) the solution operator of a gravity pendulum and (ii) the dynamic response of a parametric library of pendulums.

Moya, Christian↗

Cybersecurity Standards, Certification, and Best Practices for DERs

Distributed energy resources (DERs) are becoming increasingly important to the electric grid, including solar energy systems. However, DERs also introduce new cybersecurity risks, including those posed by cloud computing. Standards harmonization is essential for ensuring that DERs are secure and can be safely integrated into the grid. This panel will discuss cyber standards harmonization for solar security. The panel will feature experts from the S2G Program, National Labs and Industry who will discuss the following topics: the cybersecurity risks and future benefits posed by ubiquitous solar energy systems, the development and implementation of cloud-based security solutions for DERs, including solar energy systems, the challenges and opportunities for harmonizing DER cybersecurity standards, and Cyber Informed Engineering and the solar security implementations The panel will also discuss the following specific initiatives: the S2G Program's DER Cybersecurity Framework, UL's DER Cybersecurity Certification Program, and IEEE 1547 Updates. The panel will conclude with a discussion of the future of standards harmonization for DER cybersecurity.

14 SOLAR ENERGY↗

Harnessing the Power of AI: Status and Expansion of Current Domestic Transport Security Through Flexible Embedded Hardware

As applications of Artificial Intelligence (AI) continue to expand, there are increasing opportunities to leverage applied AI methodologies with mobile transportation focused embedded systems. Current applications of AI in transportation focus on a variety of areas, including fuel efficiency, safety, security, and other broad fields of optimization or detection. To leverage these AI workflows and methodologies in the field, teams must utilize complex embedded systems capable of implementing these AI-enabled algorithms in real-time. In this paper, we will investigate how these algorithms can be integrated into existing technologies leveraging vehicle data - such as the Controller Area Network Transport Security Tracking and Reporting Unit (C-STAR). The C-STAR technology is an embedded platform with onboard computation capable of running next generation algorithms in vehicle systems AI, such as preventative maintenance, driver authentication, and transport security. As deployed in the field, the C-STAR has a limited AI functionality –this paper will directly discuss how a device like C-STAR can be utilized and the advantages of integrating these new technologies. We will open with relevant background information and transportation projects that leverage AI, focusing specifically on those around transport security such as vehicle identification, anomaly detection, and deterrence. We will then extend this into potential opportunities and scaling for AI methodologies using platforms like the C-STAR. Finally, we will speak directly to the challenges of deploying AI-powered workflows, such as computing power needs, bandwidth, hallucinations, and other regulatory considerations.

Cook, Adian [ORNL] (ORCID:0000000160825395)↗

Intelligent Partitioning based Fully Parallel AC Security-Constrained Optimal Power Flow

Today’s power grid is becoming more diverse and integrated with high-level distributed energy resources and smart control technologies that is creating a new set of grid management challenges in terms of large-scale, nonlinear, and non-convex problem modeling, complex and time-consuming computation, as well as difficult uncertainty handling. This project focused on solving a challenging multi-period security-constrained generation scheduling problem, which is of great importance for maximizing the social welfare of real-time dispatch, day-ahead market, as well as weekly planning of power systems. Our developed software explored parallel optimization algorithms for complex and realistic power system models, and develop fast, efficient, and robust grid optimization solutions on the high-performance computing platform that will enable increased grid economics, flexibility, resilience, as well as energy security in the United States.

24 POWER TRANSMISSION AND DISTRIBUTION↗