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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Comparative analysis of collaboration networks

In this paper we carry out a comparative analysis of the word network as the collaboration network based on the novel by M. Bulgakov 'Master and Margarita', the synonym network of the Russian language as well as the Russian movie actor network. We have constructed one-mode projections of these networks, defined degree distributions for them and have calculated main characteristics. In the paper a generation algorithm of collaboration networks has been offered which allows one to generate networks statistically equivalent to the studied ones. It lets us reveal a structural correlation between word network, synonym network and movie actor network. We show that the degree distributions of all analyzable networks are described by the distribution of q-type.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Demonstration of reconstruction-free static magnetic control of DIII-D plasma with deep reinforcement learning

This paper presents the development and experimental validation of a reinforcement learning (RL)-based magnetic controller on the DIII-D tokamak. The controller directly maps raw magnetic diagnostic signals to actuator commands, replacing the traditional isoflux control algorithm based on equilibrium reconstruction. Four RL controllers are trained using the Soft Actor–Critic algorithm with an asymmetric Actor–Critic architecture in the NSFsim simulator. All controllers are deployed in the DIII-D Plasma Control System and operated with a 4 kHz feedback loop. Two randomization strategies are evaluated during training: evolving kinetic profiles and fixed kinetic profiles within each episode. The latter approach is found to better capture experimental deviations in the current density profile and to provide overall improved control performance. Robust operation is demonstrated across heating power scans in both L- and H-mode plasmas, as well as during transient events such as L–H transitions and pellet injections. Control errors in plasma shape and radial position remained within 1.5–2.0 cm and 1 cm, respectively. A notable discrepancy was observed in the vertical X-point position, with errors of up to approximately 4 cm, attributed to the current density distribution mismatches between simulations and experiments.

DIII-D↗
DOE OSTI2015

Locality Aware Concurrent Start for Stencil Applications

Stencil computations are at the heart of many physical simulations used in scientific codes. Thus, there exists a plethora of optimization efforts for this family of computations. Among these techniques, tiling techniques that allow concurrent start have proven to be very efficient in providing better performance for these critical kernels. Nevertheless, with many core designs being the norm, these optimization techniques might not be able to fully exploit locality (both spatial and temporal) on multiple levels of the memory hierarchy without compromising parallelism. It is no longer true that the machine can be seen as a homogeneous collection of nodes with caches, main memory and an interconnect network. New architectural designs exhibit complex grouping of nodes, cores, threads, caches and memory connected by an ever evolving network-on-chip design. These new designs may benefit greatly from carefully crafted schedules and groupings that encourage parallel actors (i.e. threads, cores or nodes) to be aware of the computational history of other actors in close proximity. In this paper, we provide an efficient tiling technique that allows hierarchical concurrent start for memory hierarchy aware tile groups. Each execution schedule and tile shape exploit the available parallelism, load balance and locality present in the given applications. We demonstrate our technique on the Intel Xeon Phi architecture with selected and representative stencil kernels. We show improvement ranging from 5.58% to 31.17% over existing state-of-the-art techniques.

Locality Aware execution, jagged tiling↗
DOE OSTI2016

Integrated Agent-Based and Production Cost Modeling Framework for Renewable Energy Studies

The agent-based framework for renewable energy studies (ARES) is an integrated approach that adds an agent-based model of industry actors to PLEXOS and combines the strengths of the two to overcome their individual shortcomings. It can examine existing and novel wholesale electricity markets under high penetrations of renewables. ARES is demonstrated by studying how increasing levels of wind will impact the operations and the exercise of market power of generation companies that exploit an economic withholding strategy. The analysis is carried out on a test system that represents the Electric Reliability Council of Texas energy-only market in the year 2020. The results more realistically reproduce the operations of an energy market under different and increasing penetrations of wind, and ARES can be extended to address pressing issues in current and future wholesale electricity markets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Gregarious Data Re-structuring in a Many Core Architecture

this paper, we have developed a new methodology that takes in consideration the access patterns from a single parallel actor (e.g. a thread), as well as, the access patterns of “grouped” parallel actors that share a resource (e.g. a distributed Level 3 cache). We start with a hierarchical tile code for our target machine and apply a series of transformations at the tile level to improve data residence in a given memory hierarchy level. The contribution of this paper includes (a) collaborative data restructuring for group reuse and (b) low overhead transformation technique to improve access pattern and bring closely connected data elements together. Preliminary results in a many core architecture, Tilera TileGX, shows promising improvements over optimized OpenMP code (up to 31% increase in GFLOPS) and over our own previous work on fine grained runtimes (up to 16%) for selected kernels

data restructuring↗
DOE OSTI2023

Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning

This paper presents a novel federated reinforcement learning (Fed-RL) methodology to inject sufficient resiliency into the operations of the network of microgrids. We consider adversarial actions to the voltage and power control loop reference signals at the grid forming (GFM) inverters in the microgrids which are essential to integrate renewable resources. Therefore, we formulate a resilient reinforcement learning training setup that uses these adversarial injections to generate episodic trajectories and train the RL agents to alleviate their impact on performance. To circumvent the concerns about data-sharing and privacy for different owners of the microgrids in the networked setting, we bring in the aspects of the federated operation to propose novel Fed-RL algorithms. As the dynamics of each microgrid are coupled due to electrical interlinks, the conventional federated RL approaches using decoupled independent environments are not applicable, which leads us to propose a multi-agent vertically federated variation of actor-critic algorithms, namely federated soft actor-critic (FedSAC). We have performed numerical simulations on an IEEE 123-bus benchmark test feeder with three microgrids by creating a customized simulation setup by encapsulating the microgrid dynamic simulations in GridLAB-D/HELICS co-simulation platform with the OpenAI Gym environment and validated the proposed resilient and secured learning methodology.

Artificial Intelligence (AI), reinforcement learni↗
DOE OSTI2022

A Unified Testing Platform to Mature Blockchain Applications for Grid Emulation Environments

Blockchain technology is a relatively novel technology that can be used to develop more decentralized, autonomous and tamper-evident solutions. A feature that can aid Transactive Energy Systems to reach their goals by enabling individual actors to communicate and reach consensus with other participants in a more decentralized fashion. However, technical barriers to evaluate and adopt this type of technology within the electrical domain still exist. To facilitate this task, BLOSEM Unified Testing Platform (UTP), a DOE-sponsored, multi-lab effort intends to accelerate the development of solutions by offering a common set of reusable services that can be used to interconnect existent grid tools with blockchain services. UTP is intended to serve as development platform that can provide application engineers with the technical means to evaluate potential blockchain solutions, by enabling them to concentrate on the actual application functionalities while at the same time abstracting the connectivity and performance measurement tasks. The use of BLOSEM UTP is further demonstrated by implementing two potential use cases that are intended to validate both the feasibility of implementing these applications as blockchain-based solutions while also demonstrating the features provided by UTP.

blockchain co-simulation↗
DOE OSTI2025

Analyzing Infrastructure Interdependencies Using Network-Of-Networks Modeling

Infrastructure networks play a crucial role in our day-to-day lives, and modeling these infrastructure networks can help decisionmakers prepare for and respond to disruptions such as natural disasters or cyberattacks. Because these infrastructure networks depend on each other, it is not sufficient to model a single network in isolation. We build on previous single-network-modeling techniques to develop a methodology for modeling infrastructure interdependencies as a Network-of-Networks. Using distributionlevel data from a real U.S. city on the power grid, road geometry, and hospital locations, we show how to apply this methodology to modeling three of the U.S. Department of Homeland Security's Critical Infrastructure Sectors: Healthcare, Transportation, and Energy. We also analyze three primary metrics before and after a simulated disaster: 1) impact on hospital access; 2) road network impact with the change in betweenness centrality; 3) electric customer outage. We simulate three different disruptions: 1) road flooding from nearby rivers; 2) a malicious actor targeting the road networks; 3) a malicious actor targeting the electric grid. Finally, we discuss how our methodology can be applied to additional infrastructure networks and types of disruption, and how Artificial Intelligence (AI) techniques may be incorporated into this methodology for further research.

Scherrer, Evan [Drake University, Iowa]↗
DOE OSTI2011

Building Stronger State Partnerships With the US Dept. Of Energy (Energy Assurance)

From 2007 until 2011, the National Association of Regulatory Utility Commissioners (NARUC) engaged in a partnership with the National Energy Technology Lab (NETL) to improve State-Federal coordination on electricity policy and energy assurance issues. This project allowed State Public Utility Commissioners and their staffs to engage on the most cutting-edge level in the arenas of energy assurance and electricity policy. Four tasks were outlined in the Statement of Performance Objectives: Task 1 - Training for Commissions on Critical Infrastructure Topics; Task 2 - Analyze and Implement Recommendations on Energy Assurance Issues; Task 3 - Ongoing liaison activities & outreach to build stronger networks between federal agencies and state regulators; and Task 4 - Additional Activities. Although four tasks were prescribed, in practice these tasks were carried out under two major activity areas: the critical infrastructure and energy assurance partnership with the US Department of Energy's Infrastructure Security and Emergency Response office, and the National Council on Electricity Policy, a collaborative which since 1994 has brought together State and Federal policymakers to address the most pressing issues facing the grid from restructuring to smart grid implementation. On Critical Infrastructure protection, this cooperative agreement helped State officials yield several important advances. The lead role on NARUC's side was played by our Committee on Critical Infrastructure Protection. Key lessons learned in this arena include the following: (1) Tabletops and exercises work - They improve the capacity of policymakers and their industry counterparts to face the most challenging energy emergencies, and thereby equip these actors with the capacity to face everything up to that point as well. (2) Information sharing is critical - Connecting people who need information with people who have information is a key success factor. However, exposure of critical infrastructure information to bad actors also creates new vulnerabilities. (3) Tensions exist between the transparency-driven basis of regulatory activity and the information-protection requirements of asset protection. (4) Coordination between states is a key success factor - Because comparatively little federal authority exists over electricity and other energy infrastructure, the interstate nature of these energy grids defy centralized command and control governance. Patchwork responses are a risk when addressed at a state-by-state level. Coordination is the key to ensuring consistent response to shared threats. In Electricity Policy, the National Council on Electricity Policy continued to make important strides forward. Coordinated electricity policy among States remains the best surrogate for an absent national electricity policy. In every area from energy efficiency to clean coal, State policies are driving the country's electricity policy, and regional responses to climate change, infrastructure planning, market operation, and new technology deployment depend on a forum for bringing the States together.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Nested Narratives Final Report

In cybersecurity forensics and incident response, the story of what has happened is the most important artifact yet the one least supported by tools and techniques. Existing tools focus on gathering and manipulating low-level data to allow an analyst to investigate exactly what happened on a host system or a network. Higher-level analysis is usually left to whatever ad hoc tools and techniques an individual may have developed. We discuss visual representations of narrative in the context of cybersecurity incidents with an eye toward multi-scale illustration of actions and actors. We envision that this representation could smoothly encompass individual packets on a wire at the lowest level and nation-state-level actors at the highest. We present progress to date, discuss the impact of technical risk on this project and highlight opportunities for future work.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Cyber Threat and Vulnerability Analysis of the U.S. Electric Sector

With utilities in the U.S. and around the world increasingly moving toward smart grid technology and other upgrades with inherent cyber vulnerabilities, correlative threats from malicious cyber attacks on the North American electric grid continue to grow in frequency and sophistication. The potential for malicious actors to access and adversely affect physical electricity assets of U.S. electricity generation, transmission, or distribution systems via cyber means is a primary concern for utilities contributing to the bulk electric system. This paper seeks to illustrate the current cyber-physical landscape of the U.S. electric sector in the context of its vulnerabilities to cyber attacks, the likelihood of cyber attacks, and the impacts cyber events and threat actors can achieve on the power grid. In addition, this paper highlights utility perspectives, perceived challenges, and requests for assistance in addressing cyber threats to the electric sector. There have been no reported targeted cyber attacks carried out against utilities in the U.S. that have resulted in permanent or long term damage to power system operations thus far, yet electric utilities throughout the U.S. have seen a steady rise in cyber and physical security related events that continue to raise concern. Asset owners and operators understand that the effects of a coordinated cyber and physical attack on a utility’s operations would threaten electric system reliability–and potentially result in large scale power outages. Utilities are routinely faced with new challenges for dealing with these cyber threats to the grid and consequently maintain a set of best practices to keep systems secure and up to date. Among the greatest challenges is a lack of knowledge or strategy to mitigate new risks that emerge as a result of an exponential rise in complexity of modern control systems. This paper compiles an open-source analysis of cyber threats and risks to the electric grid, utility best practices for prevention and response to cyber threats, and utility suggestions about how the federal government can aid utilities in combating and mitigating risks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Radiological Dispersal Devices

The purpose of this paper is to review what a ‘dirty bomb” is and what the challenges are to actually developing and utilizing one. Additionally, it will review whether there are other radiological options for the non-state actor desiring to utilize them as a WMD. Integrated in this study and analysis is to determine if a non-state actor has the potential for better access to, and the ability to utilize, certain radiological WMDs.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2016

Radiological/Nuclear (RN) Terrorism: Global Assessment of Threat Intention Drivers.

There is a pressing need in international security policy to better understand the future landscape of radiological and nuclear (RN) dangers and how best to target resources to affect their reduction. The highly dynamic nature of the global geopolitical, social and technological environments means that ther e are many potential forces that might influence the magnitude of current RN dangers, especially those emanating from non-state actors like terrorists. These include: the spread of radical ideology, the growth of civilian nuclear energy in politically unstable regions of the world, the potential for increased nuclear proliferation in those regions, the continued dissemination of WMD information in open sources, and advances in technology that may facilitate VNSA violent non-state actor (VNSA) RN programs. Therefore, this study’s main purpose is to provide an overview of the high level future drivers of RN terrorism as they pertain to the motives and intentions1 of known and potential adversaries. A central approach of this study is to ask the question: Why have there not been more attempts to engage in RN terrorism and, particularly in the case of radiological terrorism, why have those attempts that have been made not been more successful? Once the probable factors that have curtailed the RN terrorist threat thus far have been identified, the study will consider whether there are extant and emerging drivers that might bring about changes in these mitigating factors over the next 10 years. Finally, the study attempts to assess the future dangers of RN terrorism based on these potential changes and what policy approaches can be implemented to reduce the threat, particularly the role of deterrence and dissuasion.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2018

A Scalable HPC Insider Threat Monitoring System

Cyber Equations, Inc. is reporting on the results of its DOE SBIR Phase I project for the development of a Scalable HPC Insider Threat Monitoring System. This final report to the Department of Energy for Task DE-FOA-0001618 covers the period from 02/21/2017 to 02/20/2018 and summarizes the larger research accomplishments, which can be divided in theoretical and laboratory experiments. One of the many challenges for the U.S. Department of Energy (DOE) is the support of the high-performance computing (HPC) centers while providing quality information services, strategic vision, leadership, technical expertise, and capabilities necessary to enable the DOE to deliver effective, efficient, economical, and secure HPC services. In order to realize a secure DOE laboratory enterprise, DOE facilities operations must counter a wide range of cyber insider threats, from unsophisticated adversaries up to advanced, nation state sponsored actors. The DOE laboratories also presents an enormous insider attack surface of an ever-growing number of computers, networks, and devices all running a myriad of software. In addition, the DOE laboratories are connected to a variety of external organizations which multiplies the possibility of insider threat. During this SBIR project Cyber Equations developed a strategic combination of supervised and unsupervised machine learning techniques to control scale and classify whether entities, such as hosts, actors, or documents, are showing suspicious insider behavior. We conducted experiments using real cyber data to show that changes in observable behavior (e.g., changes in outbound traffic patterns) are indicative of attacks executing (e.g., insiders exfiltrating data, bots calling home, or an attack spreading). The features and methods we use, have been used in the anomaly detection research and development community for a significant amount of time and robust implementations are openly available. K-Means clustering, Decision Trees, and Support Vector Machine based classifiers are readily available through the R language and open source H20 machine learning framework. The Cyber Equations approach is novel in its use of these classifiers combined with Hadoop based parallel processing technology. The five stages of processing used by CyberInsider improve scalability and reduce the high number of false positives normally associated with network anomaly detection. In summary, the Cyber Equations has successfully accomplished all of its Phase I tasks and demonstrated the feasibility of the Insider Threat Monitoring System.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Microgrid Cyber Security Reference Architecture (V2)

This document describes a microgrid cyber security reference architecture leveraging defense-in-depth techniques that are executed by first describing actor communication using data exchange attributes, then segmenting the microgrid control system network into enclaves, and finally grouping enclaves into functional domains. To illustrate the design approach, two notional microgrid control implementations are presented. Both include a discussion on types of communication occurring on that network, data exchange attributes for the actors, and examples of segmentation via enclaves and functional domains. The second example includes results from Red Team analysis and quantitative scoring according to a novel system that derives naturally from the implementation of the cyber security architecture.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Why does cyber deterrence fail and when might it succeed? A framework for cyber scenario analysis

Through cyberattacks on information technology and digital communications systems, antagonists have increasingly been able to alter the strategic balance in their favor without provoking serious consequences. Conflict within and through the cyber domain is inherently different from conflict in other domains that house our critical systems. These differences result in new challenges for defending and creating resilient systems, and for deterring those who would wish to disrupt or destroy them. The purpose of this paper is to further examine the question of whether or not deterrence can be an effective strategy in cyber conflict, given our broad and varied interests in cyberspace. We define deterrence broadly as the creation of conditions that dissuade antagonists from taking unwanted actions because they believe that they will incur unacceptably high costs and/or receive insufficient benefits from taking that action. Deterrence may or may not be the most credible or effective strategy for achieving our desired end states in cybersecurity. Regardless of the answer here, however, it is important to consider why deterrence strategies might succeed under certain conditions, and to understand why deterrence is not effective within the myriad contexts that it appears fail. Deterrence remains a key component of U.S. cyber strategy, but there is little detail on how to operationalize or implement this policy, how to bring a whole-of-government and whole-of- private-sector approach to cyber deterrence, which types of antagonists can or should be deterred, and in which contexts. Moreover, discussion about how nations can and should respond to significant cyber incidents largely centers around whether or not the incident constitutes a "use of force," which would justify certain types of responses according to international law. However, we believe the "use of force" threshold is inadequate to describe the myriad interests and objectives of actors in cyberspace, both attackers and defenders. In this paper, we propose an approach to further examine if deterrence is an effective strategy and under which conditions. Our approach includes systematic analysis of cyber incident scenarios using a framework to evaluate the effectiveness of various activities in influencing antagonist behavior. While we only examine a single scenario for this paper, we propose that additional work is needed to more fully understand how various alternative thresholds constrain or unleash options for actors to influence one another's behavior in the cyber domain.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2019

Additive Manufacturing and Nuclear Security: Calibrating Rewards and Risks

In a security environment marked by a high degree of technological dynamism, additive manufacturing (AM) stands out for its special significance. The AM field has emerged from infancy, with many new and potential applications in the commercial and military sectors. Its potential impact on nuclear security is a matter of rising debate, with some emphasizing the disruptive consequences of AM competition for international stability and others emphasizing the disruptive benefits for the United States. This essay addresses both aspects and draws on a technical analysis to inform the further development of U.S. policy. From a nuclear-security perspective, additive manufacturing offers both rewards and risks for the United States and its allies. The rewards are primarily (1) reduced scale of effort and cost in maintaining weapons-production infrastructure and (2) greatly increased responsiveness and agility in production infrastructure. Both these benefits are long-standing U.S. policy goals. The risks derive from the potential impact of AM on foreign nuclear weapons programs and are of three kinds. Those impacts will likely be a function of the degree of technical sophistication of the foreign actor. Non-state actors and entry-level proliferants pose modest AM risks relative to more advanced states, including both existing nuclear-weapon- states and those developed countries with a high degree of nuclear latency. A critical factor in predicting the impact of AM is current uncertainty as to the utility of AM techniques in the fabrication of special nuclear materials. To meet the emerging challenges of AM, U.S. policy makers face a number of decisions regarding which benefits to seek, how much risk to accept, and how to reduce that risk. The basic strategic approach of the United States to the AM problem is sound, but some fine-tuning is necessary and appropriate at this time. The good news is that we have time to act; the bad news is that the time to act decisively may be shorter than we would wish.

42 ENGINEERING↗
DOE OSTI2020

Knowledge Spillovers and Cost Reductions in Solar Soft Costs

Despite the commonly acknowledged importance of knowledge spillovers in reducing solar soft costs, we are only beginning to answer a fundamental question: who learns what (knowledge acquisition), from whom (knowledge production), and how (spillover mechanisms)? Until recently, this important topic has been largely unexplored in the case of solar soft costs. Thus, this project set out to identify how knowledge spillovers affect soft costs in the U.S. photovoltaic (PV) installation industry, specifically how important spillovers are, what types of knowledge are most likely to spillover, and how networks of actors affect spillovers. Our findings offer insights for designing solutions that address problems associated with knowledge spillovers and that leverage spillovers to reduce solar soft costs. Recognizing the ambiguity in the definition of soft costs, i.e., “non-hardware costs,” and variability in soft cost categories, we developed the Solar Soft Cost Ontology (SSCO) to systematically identify key concepts related to soft costs, network actors, learning processes, and the relationships between them. This ontology served as a foundational organizational structure for the methodology of the remaining tasks: case studies, surveys, pricing analysis, patent analysis, network analysis, and project integration across tasks. While there is substantial learning among installers that is reducing the soft costs for PV installations, most of that learning is retained by firms rather than spread across the industry. The positive relationship between experience accumulation and cost reductions is typically explained as learning by doing (LBD), but we find that LBD effects are mediated by other learning mechanisms, including learning by searching and learning by interacting. Knowledge spillovers have significant potential to reduce solar PV soft costs, but successful knowledge spillover pathways are complex and non-trivial. There are a wide variety of ways to construct an installation business, thus categories of firms that can effectively cross-learn directly are small and what knowledge is relevant to whom is challenging and costly for firms to assess. This fragmentation limits the critical mass needed for spillover related soft cost reductions. Knowledge does not flow directly between installers. Indirect knowledge transfer pathways are critical: distributors, software providers, collaboratives, and hiring. Furthermore, diverse, more integrated knowledge networks tend to promote successful learning by organizations and across the system as a whole. Accordingly, we find the need to supporting the whole ecosystem using an integrated policy and programmatic approach to support installers, distributors, complementary sector, and facilitators. Overall, a deliberate policy-mix design is needed to reduce the solar PV deployment barrier in terms of installation cost reductions, because deployment policies could potentially interact with policies that facilitate network-building and technological innovation. A combination of deployment policies, innovation-support policies, and network-facilitating policies could potentially lead to a more desired market outcome through achieving higher joint learning rates from firms’ cumulative experiences developed in a more integrated production and deployment ecosystem.

14 SOLAR ENERGY↗