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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Marshall Space Flight Center Propulsion Systems Department (PSD) Knowledge Management (KM) Initiative

NASA Marshall Space Flight Center's Propulsion Systems Department (PSD) is four months into a fifteen month Knowledge Management (KM) initiative to support enhanced engineering decision making and analyses, faster resolution of anomalies (near-term) and effective, efficient knowledge infused engineering processes, reduced knowledge attrition, and reduced anomaly occurrences (long-term). The near-term objective of this initiative is developing a KM Pilot project, within the context of a 3-5 year KM strategy, to introduce and evaluate the use of KM within PSD. An internal NASA/MSFC PSD KM team was established early in project formulation to maintain a practitioner, user-centric focus throughout the conceptual development, planning and deployment of KM technologies and capabilities within the PSD. The PSD internal team is supported by the University of Alabama's Aging Infrastructure Systems Center of Excellence (AISCE), lntergraph Corporation, and The Knowledge Institute. The principle product of the initial four month effort has been strategic planning of PSD KNI implementation by first determining the "as is" state of KM capabilities and developing, planning and documenting the roadmap to achieve the desired "to be" state. Activities undertaken to suppo~th e planning phase have included data gathering; cultural surveys, group work-sessions, interviews, documentation review, and independent research. Assessments and analyses have beon pedormed including industry benchmarking, related local and Agency initiatives, specific tools and techniques used and strategies for leveraging existing resources, people and technology to achieve common KM goals. Key findings captured in the PSD KM Strategic Plan include the system vision, purpose, stakeholders, prioritized strategic objectives mapped to the top ten practitioner needs and analysis of current resource usage. Opportunities identified from research, analyses, cultural1KM surveys and practitioner interviews include: executive and senior management sponsorship, KM awareness, promotion and training, cultural change management, process improvement, leveraging existing resources and new innovative technologies to align with other NASA KM initiatives (convergence: the big picture). To enable results based incremental implementation and future growth of the KM initiative, key performance measures have been identified including stakeholder value, system utility, learning and growth (knowledge capture, sharing, reduced anomaly recurrence), cultural change, process improvement and return-on-investment. The next steps for the initial implementation spiral (focused on SSME Turbomachinery) have been identified, largely based on the organization and compilation of summary level engineering process models, data capture matrices, functional models and conceptual-level svstems architecture. Key elements include detailed KM requirements definition, KM technology architecture assessment, - evaluation and selection, deployable KM Pilot design, development, implementation and evaluation, and justifying full implementation (estimated Return-on-Investment). Features identified for the notional system architecture include the knowledge presentation layer (and its components), knowledge network layer (and its components), knowledge storage layer (and its components), User Interface and capabilities. This paper provides a snapshot of the progress to date, the near term planning for deploying the KM pilot project and a forward look at results based growth of KM capabilities with-in the MSFC PSD.

Caraccioli, Paul↗
DOE OSTI2023

Envisioning the Future Renewable and Resilient Energy Grids—A Power Grid Revolution Enabled by Renewables, Energy Storage, and Energy Electronics

Today’s power grids are facing tremendous challenges because of the ever-increasing power demand, system complexity, infrastructure cost, knowledge base, and policy and regulatory issues to achieve supply–demand power balance and resiliency with respect to more frequent extreme weather events and cyberattacks. It is particularly challenging when the transition toward 100% intermittent renewable energy sources is considered. Many countries are calling for building up more transmission and distribution lines to increase power delivery capacities. This article is an attempt to answer two urgent questions: Is more transmission and distribution infrastructure really needed to meet the increasing power demand? What kind of future grid infrastructure should we envision and build? This article attempts to answer these questions and proposes the concept of community-centric asynchronous renewable and resilient energy grids. By clearly differentiating the concepts of grid resilience and reliability, the importance of building resilient power electronics’ devices and robust system-level control algorithms to achieve 100% renewable energy integrated resilient grids is presented. To identify the shortcomings and propose advancements, power electronics’ technologies are categorized using the proposed concepts of natural source frequencies (NSf), energy storage, direct energy conversion/control and fault protection (DeCaFp), and high-efficiency energy consumption and buffering (heECaB) technology. The ability of networked microgrids to greatly reduce power outages and power system restoration time is demonstrated by leveraging robust decentralized and centralized control algorithms, identified through a comprehensive literature review. Future research areas are proposed to further enhance grid stability, controllability, cybersecurity, and protection against faults in the presence of 100% renewable sources by leveraging the advanced capabilities of NSf, DeCaFp, and heECaB devices and system-level control algorithms.

14 SOLAR ENERGY↗
DOE OSTI2023

Decentralized Voltage Control with Peer-to-peer Energy Trading in a Distribution Network

Utilizing distributed renewable and energy storage resources via peer-to-peer (P2P) energy trading has long been touted as a solution to improve energy system’s resilience and sustainability. Consumers and prosumers (those who have energy generation resources), however, do not have expertise to engage in repeated P2P trading, and the zero-marginal costs of renewables present challenges in determining fair market prices. To address these issues, we propose a multi-agent reinforcement learning (MARL) framework to help automate consumers’ bidding and management of their solar PV and energy storage resources, under a specific P2P clearing mechanism that utilizes the so-called supply-demand ratio. In addition, we show how the MARL framework can integrate physical network constraints to realize decentralized voltage control, hence ensuring physical feasibility of the P2P energy trading and paving ways for real-world implementations.

Feng, Chen↗
NASA NTRS2002

Enabling Earth Science: The Facilities and People of the NCCS

The NCCS's mass data storage system allows scientists to store and manage the vast amounts of data generated by these computations, and its high-speed network connections allow the data to be accessed quickly from the NCCS archives. Some NCCS users perform studies that are directly related to their ability to run computationally expensive and data-intensive simulations. Because the number and type of questions scientists research often are limited by computing power, the NCCS continually pursues the latest technologies in computing, mass storage, and networking technologies. Just as important as the processors, tapes, and routers of the NCCS are the personnel who administer this hardware, create and manage accounts, maintain security, and assist the scientists, often working one on one with them.

Source record↗
DOE OSTI2023

AI-based Detection and Defense Against Cyberattacks in Distributed Energy Resources

This study will provide comprehensive artificial intelligence (AI)-based solution tools for network security, malware prevention, and sensor data anomaly detection for distributed energy resource (DER) research, development, and demonstration. DER technologies are energy systems (e.g., solar panels, wind turbines, and energy storage systems) that are often connected to the internet and thus vulnerable to cyberattacks. Cybersecurity should be of primary concern for DERs, which is why we propose an integrated multi-layer cyber-defense system for DERs. This system encompasses risk assessments, network security, malware prevention, and detection of anomalies in the sensor data. Implementation of a comprehensive risk assessment with an overview of the model architecture should be the primary step, and should include the potential impact of experiencing, at a given time, one or more cyberattacks on the system. The second step is to ensure that the network security includes firewalls, intrusion detection, and malware prevention. The third step is to provide solution tools that enable sensor data anomaly detection for DERs. By incorporating these considerations into DER research, development, and demonstration, organizations can help ensure the safety and security of their systems and protect against potential cyberattacks.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS1994

Performance evaluation of the Engineering Analysis and Data Systems (EADS) 2

The Engineering Analysis and Data System (EADS)II (1) was installed in March 1993 to provide high performance computing for science and engineering at Marshall Space Flight Center (MSFC). EADS II increased the computing capabilities over the existing EADS facility in the areas of throughput and mass storage. EADS II includes a Vector Processor Compute System (VPCS), a Virtual Memory Compute System (CFS), a Common Output System (COS), as well as Image Processing Station, Mini Super Computers, and Intelligent Workstations. These facilities are interconnected by a sophisticated network system. This work considers only the performance of the VPCS and the CFS. The VPCS is a Cray YMP. The CFS is implemented on an RS 6000 using the UniTree Mass Storage System. To better meet the science and engineering computing requirements, EADS II must be monitored, its performance analyzed, and appropriate modifications for performance improvement made. Implementing this approach requires tool(s) to assist in performance monitoring and analysis. In Spring 1994, PerfStat 2.0 was purchased to meet these needs for the VPCS and the CFS. PerfStat(2) is a set of tools that can be used to analyze both historical and real-time performance data. Its flexible design allows significant user customization. The user identifies what data is collected, how it is classified, and how it is displayed for evaluation. Both graphical and tabular displays are supported. The capability of the PerfStat tool was evaluated, appropriate modifications to EADS II to optimize throughput and enhance productivity were suggested and implemented, and the effects of these modifications on the systems performance were observed. In this paper, the PerfStat tool is described, then its use with EADS II is outlined briefly. Next, the evaluation of the VPCS, as well as the modifications made to the system are described. Finally, conclusions are drawn and recommendations for future worked are outlined.

Debrunner, Linda S.↗
DOE OSTI2024

Agent-based modeling for multimodal transportation of CO 2 for carbon capture, utilization, and storage: CCUS-agent

Here, to understand the system-level interactions between the entities in Carbon Capture, Utilization, and Storage (CCUS), an agent-based foundational modeling tool, CCUS-Agent, is developed for a large-scale study of transportation flows and infrastructure in the United States. Key features of the tool include (i) modular design, (ii) multiple transportation modes, (iii) capabilities for extension, and (iv) testing against various system components and networks of small and large sizes. Five matching algorithms for CO 2 supply agents (e.g., powerplants and industrial facilities) and demand agents (e.g., storage and utilization sites) are explored: Most Profitable First Year (MPFY), Most Profitable All Years (MPAY), Shortest Total Distance First Year (SDFY), Shortest Total Distance All Years (SDAY), and Shortest distance to long-haul transport All Years (ACAY). Before matching, the supply agent, demand agent, and route must be available, and the connection must be profitable. A profitable connection means the supply agent portion of revenue from the 45Q tax credit must cover the supply agent costs and all transportation costs, while the demand agent revenue portion must cover all demand agent costs. A case study employing over 5500 supply and demand agents and multimodal CCUS transportation infrastructure in the contiguous United States is conducted. The results suggest that it is possible to capture over 9 billion tonnes (GT) of CO 2 from 2025 to 2043, which will increase significantly to 22 GT if the capture costs are reduced by 40 %. The MPFY and SDFY algorithms capture more CO 2 earlier in the time horizon, while the MPAY and SDAY algorithms capture more later in the time horizon.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Effectiveness and predictability of in-network storage cache for Scientific Workflows

Large scientific collaborations often have multiple scientists accessing the same set of files while doing different analyses, which create repeated accesses to the large amounts of shared data located far away. These data accesses have long latency due to distance and occupy the limited bandwidth available over the wide-area network. To reduce the wide-area network traffic and the data access latency, regional data storage caches have been installed as a new networking service. To study the effectiveness of such a cache system in scientific applications, we examine the Southern California Petabyte Scale Cache for a high-energy physics experiment. By examining about 3TB of operational logs, we show that this cache removed 67.6% of file requests from the wide-area network and reduced the traffic volume on wide-area network by 12. 3TB (or 35.4%) an average day. The reduction in the traffic volume (35.4%) is less than the reduction in file counts (67.6%) because the larger files are less likely to be reused. Due to this difference in data access patterns, the cache system has implemented a policy to avoid evicting smaller files when processing larger files. We also build a machine learning model to study the predictability of the cache behavior. Tests show that this model is able to accurately predict the cache accesses, cache misses, and network throughput, making the model useful for future studies on resource provisioning and planning.

Sim, Caitlin↗
NASA NTRS1978

A computer program for analyzing unresolved Mossbauer hyperfine spectra

The program for analyzing unresolved Mossbauer hyperfine spectra was written in FORTRAN 4 language for the Control Data CYBER 170 series digital computer system with network operating system 1.1. With the present dimensions, the program requires approximately 36,000 octal locations of core storage. A typical case involving two innermost coordination shells in which the amplitudes and the peak positions of all three components were estimated in 25 iterations requires 30 seconds on CYBER 173. The program was applied to determine the effects of various near neighbor impurity shells on hyperfine fields in dilute FeAl alloys.

Schiess, J. R.↗
DOE OSTI2025

A Graph-Net with Node Embeddings to Detect False Data Injection Attacks in Photovoltaic Systems

Distributed energy resources (DER) contribute to the operational stability of the larger power grid both at utility-scale as well as commercial and residential scales in aggregated forms. These DER in-turn are susceptible to increasing cyber threats. An adversary can plug into the same local network that a field photovoltaic (PV) system uses to interconnect its data loggers and inverters and manipulate certain measurements collected from the network or trick existing irradiance and inverter readings through false data injection attacks (FDIA). Control routines that rely on these measurements can propagate the false data, impacting critical decisions that result in a suboptimal operation or even cause intentional harm leading to inverter-tripping or unscheduled loads that need to be shed. To detect FDIA in PV systems, the paper introduces an attention-based graph neural network with node embeddings and applied it to a simple prototypical DC-coupled microgrid with PV, energy storage, and load. The algorithm shows a detection accuracy of up to 98.95%. The proposed FDIA detection technique will provide micro-grid operators with an effective method to safeguard their systems, guaranteeing the secure and reliable operation.

Parvez, Imtiaz [Utah Valley University]↗
DOE OSTI2022

Joint Planning of Electricity Transmission and Hydrogen Transportation Networks

The abundance and uneven distribution of renewable energy might cause congestion and curtailment in electric power systems. Transmission expansions can potentially alleviate transmission congestion and reduce renewable energy curtailment. On the other hand, with the substantial cost reduction of electrolyzer technology and the continuing rise of hydrogen demand, converting surplus renewable energy to hydrogen provides synergistic benefits to power and hydrogen systems, but consequently requires joint planning of the two systems. To this end, we propose a joint planning approach for power transmission and hydrogen transportation networks to coordinately optimize the investment and operation of power and hydrogen infrastructure. In our proposed model, detailed truck routing, pipeline, and hydrogen storage are formulated to quantify the flexibility of hydrogen transportation system. A robust joint planning approach is also proposed to address various uncertainties from renewable energy, electric load, and hydrogen demand. Furthermore, our numerical simulations show that the proposed joint planning model can save the total system cost, reduce renewable energy curtailment, and increase the utilization level of transmission lines.

08 HYDROGEN↗
NASA NTRS2002

Accessing Wind Tunnels From NASA's Information Power Grid

The NASA Ames wind tunnel customers are one of the first users of the Information Power Grid (IPG) storage system at the NASA Advanced Supercomputing Division. We wanted to be able to store their data on the IPG so that it could be accessed remotely in a secure but timely fashion. In addition, incorporation into the IPG allows future use of grid computational resources, e.g., for post-processing of data, or to do side-by-side CFD validation. In this paper, we describe the integration of grid data access mechanisms with the existing DARWIN web-based system that is used to access wind tunnel test data. We also show that the combined system has reasonable performance: wind tunnel data may be retrieved at 50Mbits/s over a 100 base T network connected to the IPG storage server.

Becker, Jeff↗
DOE OSTI2025

Leveraging public AI tools to explore systems biology resources in mathematical modeling

Predictive mathematical modeling is an essential part of systems biology and is interconnected with information management. Systems biology information is often stored in specialized formats to facilitate data storage and analysis. These formats are not designed for easy human readability and thus require specialized software to visualize and interpret results. Therefore, comprehending modeling and underlying networks and pathways is contingent on mastering systems biology tools, which is particularly challenging for users with no or little background in data science or system biology. To address this challenge, we investigated the usage of public Artificial Intelligence (AI) tools in exploring systems biology resources in mathematical modeling. We tested public AI’s understanding of mathematics in models, related systems biology data, and the complexity of model structures. Our approach can enhance the accessibility of systems biology for non-system biologists and help them understand systems biology without a deep learning curve.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1996

Evolving Requirements for Magnetic Tape Data Storage Systems

Magnetic tape data storage systems have evolved in an environment where the major applications have been back-up/restore, disaster recovery, and long term archive. Coincident with the rapidly improving price-performance of disk storage systems, the prime requirements for tape storage systems have remained: (1) low cost per MB, (2) a data rate balanced to the remaining system components. Little emphasis was given to configuring the technology components to optimize retrieval of the stored data. Emerging new applications such as network attached high speed memory (HSM), and digital libraries, place additional emphasis and requirements on the retrieval of the stored data. It is therefore desirable to consider the system to be defined both by STorage And Retrieval System (STARS) requirements. It is possible to provide comparative performance analysis of different STARS by incorporating parameters related to (1) device characteristics, and (2) application characteristics in combination with queuing theory analysis. Results of these analyses are presented here in the form of response time as a function of system configuration for two different types of devices and for a variety of applications.

Gniewek, John J.↗
DOE OSTI2023

AOI-2, A Novel Access Control Blockchain Paradigm for Cybersecure Sensor Infrastructure in Fossil Power Generation Systems

Fossil power generation systems are increasingly vulnerable to attack from both cybercriminals as well as internal threats. These vulnerabilities demand that emerging technologies such as blockchains be utilized to secure the data involved in the information flows within the Supervisory Control and Data Acquisition (SCADA) systems of the fossil power generation plants. The publicly accessible blockchain protocols, although secure, are visible to everyone. Even private blockchains currently are unable to support different levels of access to different participants, which is a critical requirement for the existing SCADA systems running the power plants. In light of the above, novel blockchain protocols that are specifically adapted to fossil power generation environments need to be developed in order to achieve the goal of cybersecure sensor networks. In this work, we address this question by creating a novel blockchain technology, namely smart private ledger, for cybersecure communication within the fossil power generation systems. A lab-scale sensor network consisting of strain and temperature sensors is constructed to develop the ledger. The technology has hierarchical access control which is compatible with the existing SCADA systems in fossil power plants. The sensor data is used with cryptographic digital signatures and secret sharing protocols within the nodes of the blockchain technology. The research results will lead to cybersecurity for machine-to-machine interactions, infrastructure for secure data logging for sensors, decentralized data storage, and second-layer technologies for high volume machine-to-machine interactions in the power plants. The work aims to largely address the concerns for the security of distributed sensor networks in such systems that can be compromised by insider threats and by cybercriminals. The research has led to the training of the next generation of engineers and scientists in the important areas of sensor engineering and blockchain technology.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2018

Battery Energy Storage Scheduling for Optimal Load Variance Minimization

Generation portfolio can be significantly altered due to the deployment of distributed energy resources (DER) in distribution networks and the concept of microgrid. Generally,distribution networks can operate in a more resilient and economic fashion through proper coordination of DER. However, due to the partially uncontrollable and stochastic nature of some DER, the variance of net load of distribution systems increases, which raises the operational cost and complicates operation for transmission companies. This motivates peak shaving and valley filling using energy storage units deployed in distribution systems. This paper aims at theoretical formulation of optimal load variance minimization, where the infinity norm of net load is minimized. Then, the problem is reformulated equivalently as a linear program. A case study is performed with capacity limited battery energy storage model and the simplified power flow model of a radial distribution network. The influence of capacity limit and deployment location are studied.

Melin, Alexander↗
DOE OSTI2024

Cycle equivalence classes, orthogonal Weingarten calculus, and the mean field theory of memristive systems

Abstract It has been recently noted that for a class of dynamical systems with explicit conservation laws represented via projector operators, the dynamics can be understood in terms of lower dimensional equations. This is the case, for instance, of memristive circuits. Memristive systems are important classes of devices with wide-ranging applications in electronic circuits, artificial neural networks, and memory storage. We show that such mean-field theories can emerge from averages over the group of orthogonal matrices, interpreted as cycle-preserving transformations applied to the projector operator describing Kirchhoff’s laws. Our results provide insights into the fundamental principles underlying the behavior of resistive and memristive circuits and highlight the importance of conservation laws for their mean-field theories. In addition, we argue that our results shed light on the nature of the critical avalanches observed in quasi-two-dimensional nanowires as boundary phenomena.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

GridSTIX

SF-25-112 Grid-STIX is a comprehensive extension of the STIX (Structured Threat Information Expression) 2.1 ontology specifically designed for electrical grid cybersecurity applications. This ontology provides a standardized, machine-readable framework for modeling grid assets, operational technology devices, threats, vulnerabilities, supply chain risks, and security relationships in electrical power systems. ## Key Features - **Comprehensive Grid Coverage**: Physical assets, OT devices, grid components, sensors, and energy storage systems - **Zero Trust Architecture**: Policy decision points, enforcement points, trust brokers, and continuous monitoring - **AMI Infrastructure**: Advanced metering networks, head-end systems, mesh gateways, and MDM systems - **Advanced Security Modeling**: Attack patterns, vulnerabilities, mitigations, and supply chain risks - **Critical Grid Relationships**: Power flow, protection, control, and synchronization relationships - **Supply Chain Security**: Supplier modeling, country of origin tracking, and risk assessment - **Protocol Support**: DNP3, Modbus, IEC 61850, IEC 60870-5-104, OPC-UA, and IEEE standards - **Python Code Generation**: Automated STIX-compliant Python class generation from ontologies - **Interactive Visualization**: Enhanced HTML network graphs with grid-specific categorization - **STIX 2.1 Compliance**: Full compatibility with STIX threat intelligence ecosystem

Blakely, Benjamin [Argonne National Laboratory (AN↗