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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Double-Line-Frequency Ripple Model, Analysis & Impedance Design for Energy Stored Single-Phase Quasi-Z Source Photovoltaic System

The battery energy stored quasi-Z-source (BESqZS) based photovoltaic (PV) power generation system combines advantages of the qZS inverter and the battery energy storage system. But, the second harmonic (2ω) power ripple degrades the system’s performance and affects the system’s design. An accurate model to analyze the 2ω ripple is very important. The existing models did not consider the battery, or assumed a symmetric qZS network with L 1 =L 2 and C 1 =C 2 , which limits the design freedom and causes oversized impedance parameters. Our paper proposes a comprehensive model for the single-phase BES-qZS-PV inverter system, where the battery is considered and there is no restriction of L 1 =L 2 and C 1 =C 2 . Based on the built model, a BES-qZS impedance design method is proposed to mitigate the 2ω ripple with asymmetric qZS network. Simulation and experimental results verify the proposed 2ω ripple model and impedance design method.

25 ENERGY STORAGE↗
DOE OSTI2020

Detection of Synchrophasor False Data Injection Attack using Feature Interactive Network

The synchrophasor data recorded by Phasor Measurement Units (PMUs) plays an increasingly critical role in the regulation and situational awareness of power systems. However, the widely installed PMUs are vulnerable to multiple malicious attacks from cyber hackers during data transmission and storage. To address this problem, a Modified Ensemble Empirical Mode Decomposition (MEEMD) is proposed first to extract the intrinsic mode functions of each Synchrophasor Data Attacks (SDA). The frequency-based adaptive screening criterion embedded in MEEMD is used to eliminate the false intrinsic mode functions. Next, a Multivariate Convolutional Neural Network (MCNN) is proposed to identify multiple SDA by utilizing the extracted intrinsic mode functions and original SDA as input vectors. A fusion block as the main structure of MCNN is also leveraged to increase the diversity of features and compress the model parameters. Integrating MEEMD and MCNN, a framework with automatic feature extraction and multi-source information fusion capability, referred to as Feature Interactive Network (FIN), is proposed to detect multiple SDA. Based on the proposed FIN framework, six types of SDA are explored for the first time using actual synchrophasor data in FNET/Grideye that was collected from different locations in the U.S. Eastern Interconnection. Finally, a large quantity of experiments with different attack strengths are used to evaluate the adaptability and classification performance of the proposed FIN.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Demonstration of Decentralized Physics-Driven Learning

In typical artificial neural networks, neurons adjust according to global calculations of a central processor, but in the brain, neurons and synapses self-adjust based on local information. Contrastive learning algorithms have recently been proposed to train physical systems, such as fluidic, mechanical, or electrical networks, to perform machine-learning tasks from local evolution rules. However, to date, such systems have only been implemented in silico due to the engineering challenge of creating elements that autonomously evolve based on their own response to two sets of global boundary conditions. Here, we introduce and implement a physics-driven contrastive learning scheme for a network of variable resistors, using circuitry to locally compare the response of two identical networks subjected to the two different sets of boundary conditions. Using this method, our system effectively trains itself, optimizing its resistance values without the use of a central processor or external information storage. Once the system is trained for a specified allostery, regression, or classification task, the task is subsequently performed rapidly and automatically by the physical imperative to minimize power dissipation in response to the given voltage inputs. Here, we demonstrate that, unlike typical computers, such learning systems are robust to extreme damage (and thus manufacturing defects) due to their decentralized learning. Our twin-network approach is therefore readily scalable to extremely large or nonlinear networks, where its distributed nature will be an enormous advantage; a laboratory network of only 500 edges will already outpace its in silico counterpart.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2005

NASA Tech Briefs, August 2005

Topics include: Hidden Identification on Parts: Magnetic Machine-Readable Matrix Symbols; System for Processing Coded OFDM Under Doppler and Fading; Multipurpose Hyperspectral Imaging System; Magnetic-Flux-Compensated Voltage Divider; High-Performance Satellite/Terrestrial-Network Gateway; Internet-Based System for Voice Communication With the ISS; Stripline/Microstrip Transition in Multilayer Circuit Board; Dual-Band Feed for a Microwave Reflector Antenna; Quadratic Programming for Allocating Control Effort; Range Process Simulation Tool; Simulator of Space Communication Networks; Computing Q-D Relationships for Storage of Rocket Fuels; Contour Error Map Algorithm; Portfolio Analysis Tool; Glass Frit Filters for Collecting Metal Oxide Nanoparticles; Anhydrous Proton-Conducting Membranes for Fuel Cells; Portable Electron-Beam Free-Form Fabrication System; Miniature Laboratory for Detecting Sparse Biomolecules; Multicompartment Liquid-Cooling/Warming Protective Garments; Laser Metrology for an Optical-Path-Length Modulator; PCM Passive Cooling System Containing Active Subsystems; Automated Electrostatics Environmental Chamber; Estimating Aeroheating of a 3D Body Using a 2D Flow Solver; Artificial Immune System for Recognizing Patterns; Computing the Thermodynamic State of a Cryogenic Fluid; Safety and Mission Assurance Performance Metric; Magnetic Control of Concentration Gradient in Microgravity; Avionics for a Small Robotic Inspection Spacecraft; and Simulation of Dynamics of a Flexible Miniature Airplane.

Source record↗
DOE OSTI2019

Networked Control and Optimization for Widescale Integration of Power Electronic Devices in Residential Homes

Power electronics devices are emerging at residential houses, including electric vehicles, energy storage systems, solar photovoltaic systems, and variable-speed air conditioners. Little work has been done on scalable and end-to-end architectures for integrating and engaging large number of residential behind-the-meter assets to provide grid services. In this paper, a framework for integration of a demand management system, home energy management system, and residential smart inverters is presented. Full measured results of inverter response to control signals is also presented.

Starke, Michael↗
DOE OSTI2019

SDN-NGenIA, a Software Defined Next Generation Integrated Architecture for HEP and Data Intensive Science

This project aims to enable the LHC and other leading programs in high energy physics and other global science domains funded by the DOE to operate with a new level of efficiency and control, through the development of a Next Generation Integrated Architecture (NGenIA) based on intelligent software defined network (SDN)-driven network systems coupled to high throughput applications. While the initial focus is on the challenging LHC use case, the systems and products being developed are general, and apply to many fields of data intensive science ranging from astrophysical sky surveys to bioinformatics and earth observation, as well as other organizations facing the challenges of extracting knowledge from distributed multi-petabyte data stores. A central concept in this development program is a new paradigm ‘consistent network operations’ among widely distributed computing and storage facilities, where stable high throughput flows, at set rates, cross load-balanced network paths, up to flexible high water marks that are adjusted in real time to accommodate other network traffic. The large smooth flows are launched and managed by SDN services that act in concert with the experiments’ site-resident data distribution and management systems, to meet the expanding needs of the science programs. The technical goals include the construction of autonomous, intelligent site-resident services that interact dynamically with network-resident services, and with the science programs’ principal data distribution and management tools, to request or command network resources in support of high throughput petascale to exascale workflow Specific work items include: 1. Developing compact Data Transfer Nodes (DTNs) with auto-tuning functions that support data transfer rates in the 100 Gbps to the 1 Tbps range when used with high throughput data transfer applications 2. Deep site orchestration among virtualized clusters, storage subsystems and subnets to successfully coschedule CPU, storage and network resources 3. Science-program designed site architectures, operational modes, and policy and resource usage priorities, adjudicated across multiple network domains and virtual organizations, using the orchestration functions and methods 4. Seamlessly extending end-to-end operation across both extra-site and intra-site boundaries through the use of Open vSwitch (OVS), FireQoS + next generation Science DMZs 5. Novel methods of system integration that enable granular control of extreme scale long distance transfers through flow matching of scattered source-destination address pairs to multi-domain dynamic circuits 6. Funneling massive sets of streams to DTNs at the site edge hosting petascale buffer pools configured for flows of 100 Gbps and up, exploiting state of the art data transfers 7. Adaptive scheduling based on pervasive end-to-end monitoring, including DTN or compute-node resident agents providing comprehensive end-system profiling 8. Developing unsupervised and supervised machine learning and modeling methods to optimize the workflow involving terabyte to multi-petabyte datasets.

47 OTHER INSTRUMENTATION↗
NASA NTRS2012

Lunar Reconnaissance Orbiter (LRO) Command and Data Handling Flight Electronics Subsystem

A document describes a high-performance, modular, and state-of-the-art Command and Data Handling (C&DH) system developed for use on the Lunar Reconnaissance Orbiter (LRO) mission. This system implements a complete hardware C&DH subsystem in a single chassis enclosure that includes a processor card, 48 Gbytes of solid-state recorder memory, data buses including MIL-STD-1553B, custom RS-422, SpaceWire, analog collection, switched power services, and interfaces to the Ka-Band and S-Band RF communications systems. The C&DH team capitalized on extensive experience with hardware and software with PCI bus design, SpaceWire networking, Actel FPGA design, digital flight design techniques, and the use of VxWorks for the real-time operating system. The resulting hardware architecture was implemented to meet the LRO mission requirements. The C&DH comprises an enclosure, a backplane, a low-voltage power converter, a single-board computer, a communications interface board, four data storage boards, a housekeeping and digital input/output board, and an analog data acquisition board. The interfaces between the C&DH and the instruments and avionics are connected through a SpaceWire network, a MIL-STD-1553 bus, and a combination of synchronous and asynchronous serial data transfers over RS-422 and LVDS (low-voltage differential-signaling) electrical interfaces. The C&DH acts as the spacecraft data system with an instrument data manager providing all software and internal bus scheduling, ingestion of science data, distribution of commands, and performing science operations in real time.

Nguyen, Quang↗
DOE OSTI2022

Assessing the energy equity benefits of energy storage solutions

Safety, reliability, efficiency, and affordability are no longer the sole tenets of electric grid planning. The evolving social and policy climate have placed new explicit requirements on the electric grid, including resilience, decarbonization, and energy equity and justice. Integrating energy equity strategies into modern grid design is intended to achieve a fair and just distribution of benefits within the energy system. This study aims to characterize the energy equity and community benefits of energy storage systems (ESS) under the following three use-case models: utility ESS that are operated within the distribution system; community-owned ESS; and behind-the-meter ESS that are customer-owned to serve the household. The goal of this energy equity analysis is to characterize the environmental, economic, and social benefits of ESS through applied metrics such as energy burden, energy poverty, energy vulnerability, job creation, and more. A 13.8 kV, 265 node representative feeder corresponding to the hot-humid climate of Louisiana, chosen for its high energy burden, frequent hurricanes and outage events, was used to perform a storage adequacy analysis for six different outage scenarios to determine energy storage access for each use-case. The results of this analysis are then used to characterize both the grid and equity benefits of storage solutions to inform a prioritization framework matching community needs with system preferences for utility planning processes, market regulations, and the wider network of energy system stakeholders.

Energy Equity, energy justice, equity, reliability↗
NASA NTRS1994

A distributed parallel storage architecture and its potential application within EOSDIS

We describe the architecture, implementation, use of a scalable, high performance, distributed-parallel data storage system developed in the ARPA funded MAGIC gigabit testbed. A collection of wide area distributed disk servers operate in parallel to provide logical block level access to large data sets. Operated primarily as a network-based cache, the architecture supports cooperation among independently owned resources to provide fast, large-scale, on-demand storage to support data handling, simulation, and computation.

Johnston, William E.↗
DOE OSTI2019

Distributed energy management for community microgrids considering network operational constraints and building thermal dynamics

A distributed energy management system for community microgrids is developed here. Unlike a centralized optimization-based energy management system, it schedules distributed energy resources and energy storage systems, as well as residential appliances, indirectly through iterative interaction between the microgrid central controller and home energy management systems, based on price signals. In each iteration, the microgrid central controller adjusts the scheduling of distributed energy resources and energy storage systems at the microgrid level. Meanwhile, the home energy management system of each house adjusts the scheduling of residential appliances. Then, the energy price at each bus is updated according to the unbalanced power between generation and demand. The optimization converges when the unbalanced power of all buses is close to zero, i.e., the microgrid central controller and home energy management systems reach an agreement on the energy price and generation/consumption. In particular, a detailed thermal dynamic model of the house is integrated into the HEMS scheduling for intelligent control of the heating, ventilation, and air-conditioning system by customers. Lastly, the distribution network is also considered to make the energy price signal locational. Results of case studies validate the efficacy of the proposed distributed energy management system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

Analysis of Heat Transfer from Local Heating and Cooling Sources at Cryogenic Temperatures

Understanding the dispersion of heat around a cryogenic fluid tank, specifically the interaction between the cryogenic fluid and the tank wall is critical in the analysis of long duration cryogen storage in microgravity. The heat transfer interaction between a cryogenic storage tank and heat sources from external spacecraft structures is also one of the many factors that determine how much heat enters a tank. Recent flight experiments with two-phase fluids have indicated that local concentrations of heat input (also known as “hot spots”) can cause unwanted affects including local boiling. Computational fluid dynamic (CFD) models can provide a detailed assessment of the heat transfer occurring across a cryogenic storage system. However, CFD modeling takes time to construct and run. A simpler approach that can act as initial guidance for later CFD modeling analyzes external “hot spots” as point or finite heat sources. A radial, finite element network or a local direct solution can effectively estimate the heat spread across a cryogenic storage tank by calculating the temperature and heat load as a function of distance from the heat source. This calculation accounts for the convective heat transfer between the cryogenic fluid and storage tank surface. Similar approaches can be used to determine the effectiveness of cooling from a cryocooler as a finite, local heat sink. This approach allows for quick approximations of the thermal map across a cryogenic tank as well as sensitivity analysis under a wide range of design parameters including gravitational fields as implied through natural convection coefficients.

Cryogenic Fluid Management↗
NASA NTRS2012

Engineering a Multimission Approach to Navigation Ground Data System Operations

The Mission Design and Navigation (MDNAV) Section at the Jet Propulsion Laboratory (JPL) supports many deep space and earth orbiting missions from formulation to end of mission operations. The requirements of these missions are met with a multimission approach to MDNAV ground data system (GDS) infrastructure capable of being shared and allocated in a seamless and consistent manner across missions. The MDNAV computing infrastructure consists of compute clusters, network attached storage, mission support area facilities, and desktop hardware. The multimission architecture allows these assets, and even personnel, to be leveraged effectively across the project lifecycle and across multiple missions simultaneously. It provides a more robust and capable infrastructure to each mission than might be possible if each constructed its own. It also enables a consistent interface and environment within which teams can conduct all mission analysis and navigation functions including: trajectory design; ephemeris generation; orbit determination; maneuver design; and entry, descent, and landing analysis. The savings of these efficiencies more than offset the costs of increased complexity and other challenges that had to be addressed: configuration management, scheduling conflicts, and competition for resources. This paper examines the benefits of the multimission MDNAV ground data system infrastructure, focusing on the hardware and software architecture. The result is an efficient, robust, scalable MDNAV ground data system capable of supporting more than a dozen active missions at once.

Mission Design and Navigation (MDNAV)↗
DOE OSTI2022

GAAF: Searching Activation Functions for Binary Neural Networks Through Genetic Algorithm

Binary neural networks (BNNs) show promising utilization in cost and power-restricted domains such as edge devices and mobile systems. This is due to its significantly less computation and storage demand, but at the cost of degraded performance. To close the accuracy gap, in this paper we propose to add a complementary activation function (AF) ahead of the sign based binarization, and rely on the genetic algorithm (GA) to automatically search for the ideal AFs. These AFs can help extract extra information from the input data in the forward pass, while allowing improved gradient approximation in the backward pass. Fifteen novel AFs are identified through our GA-based search, while most of them show improved performance (up to 2.54% on ImageNet) when testing on different datasets and network models. Interestingly, periodic functions are identified as a key component for most of the discovered AFs, which rarely exist in human designed AFs. Our method offers a novel approach for designing general and application-specific BNN architecture.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2003

Review of the High Performance Antiproton Trap (HiPAT) Experiment

Many space propulsion concepts exist that use matter-antimatter reactions. Current antiproton production rates are enough to conduct proof-of-principle evaluation of these concepts. One enabling technology for such experiments is portable storage of low energy antiprotons, to transport antiprotons to experimental facilities. To address this need, HiPAT is being developed, with a design goal of containing 10(exp 12) particles for up to 18 days. HiPAT is a Penning-Malmberg trap with a 4 Tesla superconductor, 20kV electrodes, radio frequency (RF) network, and 10(exp -13) Torr vacuum. 'Normal' matter is being used to evaluate the system. An electron beam ionizes background gas in situ, and particle beams are captured dynamically. The experiment examines ion storage lifetimes, RF plasma diagnostics, charge exchange with background gases, and dynamic ion beam capture.

Martin, James J.↗
DOE OSTI2018

Distribution Locational Marginal Pricing (DLMP) for Congestion Management and Voltage Support

In this paper, a day-ahead market-clearing model for smart distribution systems is proposed. Various types of distributed energy resources (DERs), such as distributed energy storage, distributed generators, microgrids, and load aggregators, can bid into the day-ahead distribution-level electricity market. Considering system Volt/VAR control, network reconfiguration, and interactions with the wholesale market, an optimization model is built to clear the day-ahead market, through which the distribution locational marginal pricing (DLMPs) for both active power and reactive power are determined. Through derivations of the Lagrangian function and sensitivity factors, DLMPs are decomposed to five components (i.e., marginal costs for active power, reactive power, congestion, voltage support, and loss), which provide price signals to motivate DERs to contribute to congestion management and voltage support. Finally, case studies demonstrate the effectiveness of the proposed method.

congestion management↗
DOE OSTI2023

IceNet for FireBox - A Berkeley Warehouse-Scale Computer

Berkeley’s FireBox is a next-generation warehouse-scale computer (WSC) that utilizes the energy-efficiency and bandwidth density of integrated silicon-photonic interconnects to enable a new high-bandwidth and low-latency network fabric connecting thousands of compute nodes to petabytes of DRAM and Flash storage. The high bandwidth, low latency and high connectivity of FireBox’s WSC network fabric (IceNet) will enable dramatic improvements in the overall system energy efficiency enabling fine-grain power control on system resources (processors, links and memory/storage components). IceNet is a special 3-stage photonic Clos network architected to achieve ultra-low-latency connectivity between processor nodes and memory, drastically cutting down on the energy wasted in resource idling (processors and memory stalled due to pending network requests). This is achieved by integration of the first and last switch stages into processor/memory hub clients and by heavy over-provisioning of the high-radix middle switches (FlareSwitches). A key hardware component developed in this program is an active laser power management photonic integrated circuits called LightSpark. It interacts with the FlareSwitch and provides laser power to a subset of occupied switch ports, increasing the utilization of laser light in the photonic network by an order of magnitude. In addition to guiding the laser power where it is needed, the laser-power management module enables both wavelength and laser redundancy, significantly increasing the robustness of the system. The goal of the IceNet fabric is to enable communication between 1000s of processor nodes and PBs of memory/storage with <100ns latency, <10pJ/b wall-plug energy cost at multiple Pb/s of available connectivity bandwidth. These metrics represent two-orders of magnitude improvement with respect to the status of current data-center technology.

42 ENGINEERING↗
NASA NTRS1981

Energy storage systems for MPD thrusters

Because of its high thrust density, the magnetoplasmadynamic (MPD) thruster is a promising candidate for many advanced space missions. Its high power requirements lead to operation in a pulsed mode using an intermediate energy storage device. The characteristics of a system consisting of a solar array, energy storage capacitor, and MPD thruster are studied for array powers in the range 25-375 kw. Assuming simple analytic models for the circuit components, the circuit charge and discharge equations are solved numerically, resulting in the system efficiency and capacitance. The system efficiency is inversely proportional to array power and decreases with circuit resistance. Alternative methods of energy storage such as a pulse forming network and a homopolar generator, are presented, and an overall comparison between all of the methods is given.

Gabriel, S. B.↗
DOE OSTI2025

Securing the Modern Grid: Federal Investments, Digitization, and Supply Chain Strategy

Across the United States (U.S.) grid expansion and modernization is underway, paving the way for accelerated load growth and intelligent resource management. Digitization of the grid is supported by several state and federal programs, providing support for utilities installing advanced metering infrastructure (AMI), AI-powered analytics systems, battery energy storage systems (BESS), and distributed energy resource management systems (DERMS) to transform the grid from a one-way power delivery system into an intelligent, responsive network that will enable faster load growth and power expansion of data centers for advanced artificial intelligence (AI) applications. The digital transformation of America's grid presents opportunity for increased efficiency and resiliency but also introduces new digital risks that require careful management. Digital equipment often contains several vulnerabilities such as unencrypted communication protocols, and persistent remote access capabilities that could be exploited to manipulate device settings, coordinate service disruptions, or inject false data into grid operations. These digital risks become particularly important as the grid must rapidly scale to support AI-driven data centers, which the administration has identified as essential for maintaining U.S. technological leadership and economic competitiveness. These vulnerabilities are compounded by supply chain realities: Chinese manufacturers currently produce 70-90% of essential grid components including inverters, batteries, and control systems, with the U.S. lacking domestic manufacturing capacity for critical assets like extra-high voltage transformers. Recent federal legislation has established Foreign Entity of Concern (FEOC) restrictions to address these risks, requiring projects to achieve escalating thresholds of non-FEOC content to receive tax credits while utilities work to expand sourcing channels for their supply chains and strengthen security measures. These restrictions arrive precisely when utilities face unprecedented electricity demand growth driven by the rapid growth in data centers, creating a considerable challenge: rapidly expanding infrastructure while navigating complex compliance requirements while lacking viable alternatives for many critical components. Idaho National Laboratory (INL) and its partners have developed practical approaches to help utilities navigate these intersecting challenges as they leverage federal investment to strengthen and grow the grid. These solutions include Cyber-Informed Engineering (CIE) principles that build resilience directly into systems, the Cirrus tool for secure cloud migration, and enhanced procurement guidance that embeds security requirements throughout equipment lifecycles. Federal initiatives, such as the Technical Assistance for Digital Assurance (TADA) project, provide direct support to utilities implementing these approaches while facilitating knowledge sharing across the industry. While these tools and frameworks cannot eliminate all risks inherent in foreign supply chain dependencies, they offer pragmatic pathways for strengthening security posture without sacrificing the deployment momentum essential to meeting surging electricity demand. Ultimately, securing America's digital energy infrastructure demands dedicated coordination across multiple fronts: building domestic supply chains, implementing robust digital assurance practices, and maintaining the aggressive modernization timeline necessary for reliability, resilience, and energy independence.

24 POWER TRANSMISSION AND DISTRIBUTION↗