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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Computational Framework for Control Co-Design of Resilient Cyber–Physical Systems With Applications to Microgrids

Critical infrastructure networks, such as power and transportation networks, can be modelled as cyber-physical systems. As the complexity of such systems grow, there is need for developing metrics and design tools that will co-optimize the physical components of the system and the control policies to guarantee resilience against cyber and natural threats. To that end, we develop a simulation-based control co-design computational framework that will concurrently determine the system and control parameters of a cyber-physical system to meet pre-specified resilience, operational and economic objectives. Here, the capabilities of the developed co-design engine is demonstrated by designing the physical components and control parameters of a microgrid system that will meet its resiliency objectives when subjected to various cyber and physical threats.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Guest Editorial: Special section on Resilient Control of Cyber-Physical Power and Energy Systems

Our power and energy systems are becoming more and more integrated and interconnected. The increasing integration of edge devices and dependence on cyber infrastructure provides both the potential for benefits and risks. The integration enables more dynamic and flexible control paradigms while at the same time increasing the cyberattack surface and uncertainty of behavior. Control methodology in this new world must be designed for resilience and must have the ability to withstand, react, and respond to both physical faults and cyber-induced threats. Finally, understanding system resilience under adverse conditions requires studying control performance and how cyber infrastructure can integrate with and support the overall resilience of the system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems

Transient stability assessment (TSA) is a cornerstone for resilient operations of todays interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA issue. Here, we devise a quantum TSA (QTSA) method to enable scalable and efficient data-driven transient stability prediction for bulk power systems, which is the first attempt to tackle the TSA issue with quantum computing. Our contributions are three-fold: 1) A high expressibility, low-depth (HELD) quantum circuit is designed for accurate and noise-resilient TSA; 2) A quantum natural gradient descent algorithm is developed for efficient HELD circuit training; 3) A systematical analysis on QTSAs performance under various quantum factors is per-formed. QTSA underpins a foundation of quantum-enabled and data-driven power grid stability analytics. It renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of QTSA.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Probabilistic Resilience-Oriented Assessment Approach for Transmission Networks Under Wildfires

The rising threat of wildfires poses significant challenges to power transmission networks, particularly in areas prone to such disasters. Traditional approaches for wildfire risk assessment neglect some potential wildfire scenarios. Here, this paper introduces a probabilistic resilience-oriented assessment approach for power transmission networks to address this gap. Initially, a probabilistic wildfire model is developed to capture uncertainties in ignition, intensity, and fire spread. Next, a spatiotemporal fragility model is constructed to assess the impact of wildfires on transmission corridors, incorporating Thermal Aging (TA) and Dynamic Thermal Rate (DTR) change. Finally, a comprehensive resilience metric is defined to evaluate system performance, leveraging the fragility model to determine component and system-level resilience. The approach employs a combinatorial enumeration method to generate potential wildfire scenarios, enhanced by an impact-increment-based state enumeration (IISE) method for computational efficiency. The proposed method provides critical insights for identifying system vulnerabilities and developing robust strategies to protect transmission networks from wildfires. The efficacy of this approach is validated through extensive scenarios of the RTS-GMLC system across Southern California, Nevada and Arizona.

Vahedi, Soroush [Univ. of Connecticut, Storrs, CT ↗
DOE OSTI2018

Resilience of seed production to a severe El Niño-induced drought across functional groups and dispersal types

More frequent and severe El Niño Southern Oscillations (ENSO) are causing episodic periods of decreased rainfall. Although the effects of these ENSO-induced droughts on tree growth and mortality have been well studied, the impacts on other demographic rates such as reproduction are less well known. We use a four-year seed rain dataset encompassing the most severe ENSO-induced drought in more than 30 years to assess the resilience (i.e., resistance and recovery) of the seed composition and abundance of three forest types in a tropical dry forest. We found that forest types showed distinct differences in the timing, duration, and intensity of drought during the ENSO event, which likely mediated seed composition shifts and resilience. Drought-deciduous species were particularly sensitive to the drought with overall poor resilience of seed production, whereby seed abundance of this functional group failed to recover to predrought levels even two years after the drought. Liana and wind-dispersed species were able to maintain seed production both during and after drought, suggesting that ENSO events promote early successional species or species with a colonization strategy. Combined, these results suggest that ENSO-induced drought mediates the establishment of functional groups and dispersal types suited for early successional conditions with more open canopies and reduced competition among plants. Finally, the effects of the ENSO-induced drought on seed composition and abundance were still evident two years after the event suggesting the recovery of seed production requires multiple years that may lead to shifts in forest composition and structure in the long term, with potential consequences for higher trophic levels like frugivores.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Generative Design for Resilience of Interdependent Network Systems

Abstract Interconnected complex systems usually undergo disruptions due to internal uncertainties and external negative impacts such as those caused by harsh operating environments or regional natural disaster events. To maintain the operation of interconnected network systems under both internal and external challenges, design for resilience research has been conducted from both enhancing the reliability of the system through better designs and improving the failure recovery capabilities. As for enhancing the designs, challenges have arisen for designing a robust system due to the increasing scale of modern systems and the complicated underlying physical constraints. To tackle these challenges and design a resilient system efficiently, this study presents a generative design method that utilizes graph learning algorithms. The generative design framework contains a performance estimator and a candidate design generator. The generator can intelligently mine good properties from existing systems and output new designs that meet predefined performance criteria while the estimator can efficiently predict the performance of the generated design for a fast iterative learning process. Case studies results based on synthetic supply chain networks and power systems from the IEEE dataset have illustrated the applicability of the developed method for designing resilient interdependent network systems.

Engineering↗
DOE OSTI2016

Formal Approach For Resilient Reachability based on End-System Route Agility

The deterministic nature of existing routing protocols has resulted into an ossified Internet with static and predictable network routes. This gives persistent attackers (e.g. eavesdroppers and DDoS attackers) plenty of time to study the network and identify the vulnerable links (critical) to plan a devastating and stealthy attack. Recently, route mutation approaches have been proposed to address such issues. However, these approaches incur significantly high overhead and depend upon the availability of disjoint routes in the network, which inherently limit their use for mission critical services. To cope with these issues, we extend the current routing architecture to consider end-hosts as routing elements, and present a formal method based agile defense mechanism to increase resiliency of the existing cyber infrastructure. The major contributions of this paper include: (1) formalization of efficient and resilient End to End (E2E) reachability problem as a constraint satisfaction problem, which identifies the potential end-hosts to reach a destination while satisfying resilience and QoS constraints, (2) design and implementation of a novel decentralized End Point Route Mutation (EPRM) protocol, and (3) design and implementation of planning algorithm to minimize the overlap between multiple flows, for the sake of maximizing the agility in the system. Our implementation and evaluation validates the correctness, effectiveness and scalability of the proposed approach.

Rauf, Usman↗
DOE OSTI2021

Power Distribution Designing For Resilience Application

The number of power outages have been on the rise as extreme weather patterns have been occurring more frequently due to climate change. These outages have an economic impact in the billions of dollar. The modernization of the power grid and adoption of high penetration of distributed generation such as wind and solar have the potential to reduce the number and length of time of these power outages. However, planners and operators need a way to value the resilience the distributed assets provide to the grid. PowDDeR is a tool that allows them to evaluate the resilience each asset provides as well as how they provide resilience over the connected network.

Phillips, TylerB↗
DOE OSTI2026

Resilience Measurement Framework For Post-deployment Artificial Intelligence (ai) Integrated Systems

Resilience is largely defined as the ability to adapt or recover from adverse conditions, stresses, attacks, or compromises on systems that use or are enabled by digital resources. In Artificial Intelligence Management and Research for Advanced Networked Testbed Hub (AMARANTH), resilience is measured in the amount of time it took from the beginning of a testing period for the model to reach predictions outside of the original 95% confidence interval or using the Kullback-Leibler (KL) divergence theorem, the Population Stability Index (PSI), and traditional methods such as root mean squared error (RMSE) threshold. Artificial Intelligence (AI) model drift is of significant concern when deploying AI-integrated systems into critical and/or secure environments. Drift can impact resilience of the AI-integrated system post-deployment and requires consistent maintenance and upkeep to ensure the model is accurate and precise. To quantify model drift and predict the point when a model's drift becomes unacceptable, we describe using Kullback-Leibler (KL) divergence, Population Stability Index (PSI) and/or confidence interval width estimations to determine the point of failure and time to failure of a model post-deployment. Through simple code functions, the KL-divergence, PSI, confidence interval, and root mean squared (RMSE) point of failures can be used to derive when a model needs to be maintained as well as the impact of adversarial action through statistical means.

Yockey, Patience [Idaho National Laboratory (INL),↗
DOE OSTI2014

A study of the viability of exploiting memory content similarity to improve resilience to memory errors

Building the next-generation of extreme-scale distributed systems will require overcoming several challenges related to system resilience. As the number of processors in these systems grow, the failure rate increases proportionally. One of the most common sources of failure in large-scale systems is memory. In this paper, we propose a novel runtime for transparently exploiting memory content similarity to improve system resilience by reducing the rate at which memory errors lead to node failure. We evaluate the viability of this approach by examining memory snapshots collected from eight high-performance computing (HPC) applications and two important HPC operating systems. Based on the characteristics of the similarity uncovered, we conclude that our proposed approach shows promise for addressing system resilience in large-scale systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Advanced Computational Techniques for Improving Resilience of Critical Energy Infrastructure under Cyber-Physical Attacks

In this chapter, we present recent advances in improving the resilience of cyber-physical systems, especially with regards to energy systems. We provide discussions around various types of cyber-physical events that can cause disruptions and new advances in optimization, control, and reinforcement learning (RL) to deal with the challenges posed by such cyber-physical events. The presented methods range from distributed robust optimization, autonomous and coordinated control, reinforcement learning based resilient control and topology reconfiguration in Inter-System resilient control.

Nazir, Mohammad Nawaf [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2014

NCSE's 13th National Conference on Disasters and Environment: Science, Preparedness and Resilience, Post Conference Follow-up Activities and Dissemination

The National Council for Science and the Environment (NCSE) received $15,000 from the US Department of Energy to support post-conference activities of the 13th National Conference on the theme of Disasters and the Environment: Science, Preparedness and Resilience, held on January 15-17, 2013 at the Ronald Reagan Building and International Trade Center in Washington, DC. Over 1,000 participants from the scientific, emergency response, policy, conservation, and business communities, as well as federal and local government officials, and international entities attended the event. The conference developed actionable outcomes that constructively advance the science behind decision-making on environmental disasters, with an intended result of more prepared and resilient communities in light of a changing climate. Disasters and Environment topic was addressed through six organizing themes: Cascading Disasters; Intersection of the Built and Natural Environments; Disasters as Mechanisms of Ecosystem Change; Rethinking Recovery and Expanding the Vision of Mitigation; Human Behavior and its Consequences; and "No Regrets" Resilience. The program featured eight plenary sessions, 24 symposia and 23 breakout workshops and addressed pivotal issues surrounding disasters and environment including lifeline services, the energy, climate, hazard nexus, grid collapse, community vulnerability, and natural resource management. Sessions, symposia and workshops were conducted by over 200 distinguished thought leaders, scientists, government officials, policy experts and international speakers throughout the three day event. Following the conference, NCSE prepared a set of recommendations and results from the workshops and disseminated the results to universities, organizations and agencies, the business community. NCSE’s national dissemination involved organized several targeted trips and meetings to disseminate significant findings to key stakeholder groups.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

Rexsss Performance Analysis: Domain Decomposition Algorithm Implementations for Resilient Numerical Partial Differential Equation Solvers

The future of extreme-scale computing is expected to magnify the influence of soft faults as a source of inaccuracy or failure in solutions obtained from distributed parallel computations. The development of resilient computational tools represents an essential recourse for understanding the best methods for absorbing the impacts of soft faults without sacrificing solution accuracy. The Rexsss (Resilient Extreme Scale Scientific Simulations) project pursues the development of fault resilient algorithms for solving partial differential equations (PDEs) on distributed systems. Performance analyses of current algorithm implementations assist in the identification of runtime inefficiencies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Evaluating Application Resilience with XRay

The rising count and shrinking feature size of transistors within modern computers is making them increasingly vulnerable to various types of soft faults. This problem is especially acute in high-performance computing (HPC) systems used for scientific computing, because these systems include many thousands of compute cores and nodes, all of which may be utilized in a single large-scale run. The increasing vulnerability of HPC applications to errors induced by soft faults is motivating extensive work on techniques to make these applications more resilient to such faults, ranging from generic techniques such as replication or checkpoint/restart to algorithm-specific error detection and tolerance techniques. Effective use of such techniques requires a detailed understanding of how a given application is affected by soft faults to ensure that (i) efforts to improve application resilience are spent in the code regions most vulnerable to faults and (ii) the appropriate resilience technique is applied to each code region. This paper presents XRay, a tool to view the application vulnerability to soft errors, and illustrates how XRay can be used in the context of a representative application. In addition to providing actionable insights into application behavior XRay automatically selects the number of fault injection experiments required to provide an informative view of application behavior, ensuring that the information is statistically well-grounded without performing unnecessary experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Pacific Northwest National Laboratory’s Climate Resiliency Planning Process and Lessons Learned

In 2015, the Pacific Northwest National Laboratory (PNNL) developed its first Climate Resilience Plan for its Richland Campus. PNNL has performed Climate Resilience Planning for the Department of Defense, Nuclear Regulatory Commission, and Department of Energy (DOE) over the past 5 years. The assessment team included climate scientists, social scientists, engineers, and operations managers. A multi-disciplinary team was needed to understand the potential exposures to future changes at the site, the state of the science on future impacts, and the best process for “mainstreaming” new actions into existing activities. The team uncovered that the site’s greatest vulnerabilities, and therefore priorities for climate resilience planning, are high temperature due to degraded infrastructure, increased wildfire frequency, and intense precipitation impacts on stormwater conveyance systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Water Network Tool for Resilience Version 0.1

Water utilities are vulnerable to a wide variety of human-caused and natural disasters. These disruptive events can result in loss of water service, contaminated water, pipe breaks, and failed equipment. Furthermore, long term changes in water supply and customer demand can have a large impact on the operating conditions of the network. The ability to maintain drinking water service during and following these types of events is critical. Simulation and analysis tools can help water utilities explore how their network will respond to disruptive events and plan effective mitigation strategies. The U.S. Environmental Protection Agency and Sandia National Laboratories are developing new software tools to meet this need. The Water Network Tool for Resilience (WNTR, pronounced winter) is a Python package designed to help water utilities investigate resilience of water distribution systems over a wide range of hazardous scenarios and to evaluate resilience-enhancing actions. The following documentation includes installation instructions and examples, description of software features, and software license. It is assumed that the reader is familiar with the Python Programming Language.

42 ENGINEERING↗
DOE OSTI2018

Check the Stack: An Enabling Framework for Resilient Microgrids

Highly destructive natural disasters have illustrated the fragility of critical infrastructure including emergency services, healthcare, and water treatment during long-term electricity outages. One pathway to increase the resilience of critical infrastructure amidst disruptions to grid systems is developing microgrids, particularly those that incorporate solar photovoltaics (PV) and energy storage. This document defines a resilient microgrid as a set of interconnected load that includes critical infrastructure and distributed energy resources with the ability to island from the grid during an outage. Research is limited with respect to how much critical infrastructure could benefit from microgrids, how many states have pursued this prioritization approach, and what policies need to be in place to support more widespread deployment. This report explores each of these questions to offer the industry and policymakers a clearer understanding of the opportunity for resilient microgrids and how policy can be designed to support this market.

14 SOLAR ENERGY↗
DOE OSTI2019

Utility Investments in Resilience of Electricity Systems

Recent power outages caused by hurricanes, wildfires and other natural disasters, coupled with evolving cyber and physical threats, have increased interest in the resilience of electricity systems. The U.S. Department of Energy’s Grid Modernization Lab Consortium (GMLC) defines resilience as “the ability to prepare for and adapt to changing conditions and withstand and recover rapidly from disruptions, including the ability to withstand and recover from deliberate attacks, accidents, or naturally occurring threats or incidents.” In a new report for Berkeley Lab, organizations that represent state regulators, utilities and consumers discuss the level and scope of resilience needed, how to decide what investments are most impactful, and roles of local, state and federal officials.

24 POWER TRANSMISSION AND DISTRIBUTION↗