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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 523 records · Page 29

Using G4FETs as a Data Router for In-Plane Crossing of Signal Paths

Theoretical analysis and some experiments have demonstrated that siliconon- insulator (SOI) 4-gate transistors the type known as G(exp 4)FETs could be efficiently used for in-plane crossing of signal paths. Much of the effort of designing very-large-scale integrated (VLSI) circuits is focused on area-efficient routing of signals. The main source of difficulty in VLSI signal routing is the requirement to prevent crossing, in the same plane, of wires that are meant to be kept electrically insulated from each other. Consequently, it often becomes necessary to design and build VLSI circuits in multiple layers with vias (connections between conductors in different layers at selected locations). Suitable devices that would prevent, or at least sufficiently suppress, undesired electrical coupling (cross-talk) between wires crossing in the same plane would enable compact, simpler implementation complex interconnection networks with in-plane crossings that, heretofore, have not been possible in VLSI circuitry. The use of G4FETs as in-plane signal-crossing devices or routers, in combination with the use of G(exp 4)FETs as universal programmable logic gates, would create opportunities for reducing complexity in VLSI design.

Fijany, Amir↗

The Ames Power Monitoring System

The Ames Power Monitoring System (APMS) is a centralized system of power meters, computer hardware, and specialpurpose software that collects and stores electrical power data by various facilities at Ames Research Center (ARC). This system is needed because of the large and varying nature of the overall ARC power demand, which has been observed to range from 20 to 200 MW. Large portions of peak demand can be attributed to only three wind tunnels (60, 180, and 100 MW, respectively). The APMS helps ARC avoid or minimize costly demand charges by enabling wind-tunnel operators, test engineers, and the power manager to monitor total demand for center in real time. These persons receive the information they need to manage and schedule energy-intensive research in advance and to adjust loads in real time to ensure that the overall maximum allowable demand is not exceeded. The APMS (see figure) includes a server computer running the Windows NT operating system and can, in principle, include an unlimited number of power meters and client computers. As configured at the time of reporting the information for this article, the APMS includes more than 40 power meters monitoring all the major research facilities, plus 15 Windows-based client personal computers that display real-time and historical data to users via graphical user interfaces (GUIs). The power meters and client computers communicate with the server using Transmission Control Protocol/Internet Protocol (TCP/IP) on Ethernet networks, variously, through dedicated fiber-optic cables or through the pre-existing ARC local-area network (ARCLAN). The APMS has enabled ARC to achieve significant savings ($1.2 million in 2001) in the cost of power and electric energy by helping personnel to maintain total demand below monthly allowable levels, to manage the overall power factor to avoid low power factor penalties, and to use historical system data to identify opportunities for additional energy savings. The APMS also provides power engineers and electricians with the information they need to plan modifications in advance and perform day-to-day maintenance of the ARC electric-power distribution system.

Osetinsky, Leonid↗

Hierarchical Resilience Planning for Networked Microgrids: A Case Study of Puerto Rico

Microgrids can be designed to enhance the energy resilience of communities and critical infrastructures, such as hospitals, data centers, and communication networks, which are vulnerable to frequent weather-related disruption. Coordinating multiple microgrids in a network can leverage the geographical diversity of load and generation resources while enabling resilient and cost-effective planning of the distribution system. Designing a networked microgrid is complex, involving intricate technical assessment, cost-benefit analysis, site-specific requirements, and the evaluation of existing resources. Therefore, this paper proposes a hierarchical resilience planning framework and performs an extensive techno-economic analysis for the design of a networked microgrid. Hierarchical resilience planning involves technology sizing at an individual community level to meet the critical load and satisfy resilience criteria, and resource optimization at networked microgrid level to provide a higher level of resilience and energy adequacy. A real-world case of Puerto Rico's cooperative microgrid “Microrred de la Montaña” is investigated considering localized electricity tariffs, site-specific demand profiles, solar generation, and existing hydro resources. Multiple optimization scenarios are developed based on the resiliency requirement to estimate the capacity of solar photovoltaic and battery energy storage (BES) to be installed at each substation. The results provide the optimal sizing for individual community and networked microgrid to withstand 1day and 3-day outages along with the criteria for critical load.

13 - HYDRO ENERGY↗

Scaling Electric Machines to a Megawatt and Material Options

Megawatt (MW) electric aircraft propulsion (EAP) is seen as a significant contributor toward achieving the goals set forth by the Sustainable Flight National Partnership. A large part of enabling MW EAP is developing specific-power-dense electric machines. As specific-power-dense electric machines are scaled up from kW to MW power levels, the thermal stresses on the machines increase in both magnitude and performance-affecting characteristics. This is particularly true for the stators of these machines. Analysis via thermal resistance network modeling and multiscale modeling reveals that increasing amounts of heat will be trapped in the stator windings as the power levels increase. The challenges this presents can be addressed through material advancements whereby materials gain multifunctionality. Specifically, the electrical insulation and potting materials, along with the electrical conductor, that compose the stator slot must work together (gain multifunctionality) to relieve the increased thermal stress. Materials research at the NASA Glenn Research Center points to some useful solutions in this trade space.

Electric Machine↗

Scaling Electric Machines to a Megawatt and Material Options

Megawatt (MW) electric aircraft propulsion (EAP) is seen as a significant contributor toward achieving the goals set forth by the Sustainable Flight National Partnership. A large part of enabling MW EAP is developing specific-power-dense electric machines. As specific-power-dense electric machines are scaled up from kW to MW power levels, the thermal stresses on the machines increase in both magnitude and performance-affecting characteristics. This is particularly true for the stators of these machines. Analysis via thermal resistance network modeling and multiscale modeling reveals that increasing amounts of heat will be trapped in the stator windings as the power levels increase. The challenges this presents can be addressed through material advancements whereby materials gain multifunctionality. Specifically, the electrical insulation and potting materials, along with the electrical conductor, that compose the stator slot must work together (gain multifunctionality) to relieve the increased thermal stress. Materials research at the NASA Glenn Research Center points to some useful solutions in this trade space.

Electric Machine↗

A Survey of Open-Source Tools for Transmission and Distribution Systems Research

This work presents a review of open-source electric power transmission and distribution systems analysis tools suitable for use by industry professionals and academic researchers. Due to the high complexity of the electric grid, there exist numerous tools and extensive research pertaining to nearly every aspect of the design, operation, and control of transmission and distribution networks. In addition to the commercial tools, a wide range of free, open-source tools, models, and data usable by the scientific community for related research have been developed by different organizations, including both international and US universities and national laboratories. However, due to the absence of a catalog of available tools, models and data, researchers often lack a knowledge of existing capabilities and may develop duplicative software and tools. Increasing awareness of these available resources seeks to accelerate their broader use, leading to more efficient and standardized grid analysis. This review paper (which is part of a larger survey effort that studied over 400 tools in the transmission, distribution, buildings, and electric vehicles space) outlines selected open-source resources that have been developed in power transmission and distribution systems research. It is anticipated that this work can serve as a guide for industry and academic researchers alike, ensuring that research efforts are well-channeled.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Oceanic Storm Characteristics off the Kennedy Space Center Coast

Natural cloud-to-ground lightning may behave differently depending on the characteristics of the attachment mediums, including the peak current (inferred from radiation fields) and the number of ground strike locations per flash. Existing literature has raised questions over the years on these characteristics of lightning over oceans, and the behaviors are not yet well understood. To investigate this we will obtain identical electric field observations over adjacent land and ocean regions during both clear air and thunderstorm periods. Oceanic observations will be obtained using a 3-meter NOAA buoy that has been instrumented with a Campbell Scientific electric field mill and New Mexico Techs slow antenna, to measure the electric fields aloft. We are currently obtaining measurements from this system on-shore at the Florida coast, to calibrate and better understand the behavior of the system in elevated-field environments. Sometime during winter 2013, this system will be moored 20NM off the coast of the Kennedy Space Center. Measurements from this system will be compared to the existing on-shore electric field mill suite of 31 sensors and a coastal slow antenna. Supporting observations will be provided by New Mexico Techs Lightning Mapping Array, the Eastern Range Cloud to Ground Lightning Surveillance System, and the National Lightning Detection Network. An existing network of high-speed cameras will be used to capture cloud-to-ground lightning strikes over the terrain regions to identify a valid data set for analysis. This on-going project will demonstrate the value of off-shore electric field measurements for safety-related decision making at KSC, and may improve our understanding of relative lightning risk to objects on the ground vs. ocean. This presentation will provide an overview of this new instrumentation, and a summary of our progress to date.

Ocean↗

Scalable low-loss cryogenic packaging of quantum memories in CMOS-foundry processed photonic chips

Optically linked solid-state quantum memories such as color centers in diamond are a promising platform for distributed quantum information processing and networking. Photonic integrated circuits (PICs) have emerged as a crucial enabling technology for these systems, integrating quantum memories with efficient electrical and optical interfaces in a compact and scalable platform. Packaging these hybrid chips into deployable modules while maintaining low optical loss and resiliency to temperature cycling is a central challenge to their practical use. We demonstrate a packaging method for PICs using surface grating couplers and angle-polished fiber arrays that is robust to temperature cycling, offers scalable channel count, applies to a wide variety of PIC platforms and wavelengths, and offers pathways to automated high-throughput packaging. Using this method, we show optically and electrically packaged quantum memory modules integrating all required qubit controls on chip, operating at millikelvin temperatures with <3 dB losses achievable from fiber to quantum memory for the TE 0 mode at a wavelength of 737 nm.

Bernson, Robert [Tyndall National Institute, Cork ↗

Development of mid-infrared solid state lasers for spaceborne lidar

The laser performance of a Ho(3+):Tm(3+):Cr(3+):YAG crystal was measured under flashlamp pumping at various operating temperatures. The normal mode laser thresholds of a Ho(3+)(0.45 at. percent):Tm(3+)(2.5 at. percent):Cr(3+)(1.5 at. percent ):YAG crystal were found to range from 26 to 50 J between 120 and 200 K with slope efficiencies up to 0.36 percent with a 60 percent reflective output mirror. From the Q-switched operations a slope efficiency corresponding to 90 percent of the normal mode operation was observed. Laser wavelengths were measured for various operating conditions and fluorescence spectra were obtained at various temperatures in order to help understand the dynamic energy processes among the Ho(3+), Tm(3+), and Cr(3+) ions. A pulse forming network for a flashlamp pumped Cr:GSAG laser, which is to be used as a high power laser diode simulator in rare earth laser pumping, was completed and tested. The network provided critically damped, 1 ms FWHM, square pulses with a rise time of about 160 micrometers at an input electrical energy of 300 J.

Whitney, Donald A.↗

A convertor and user interface to import CAD files into worldtoolkit virtual reality systems

Virtual Reality (VR) is a rapidly developing human-to-computer interface technology. VR can be considered as a three-dimensional computer-generated Virtual World (VW) which can sense particular aspects of a user's behavior, allow the user to manipulate the objects interactively, and render the VW at real-time accordingly. The user is totally immersed in the virtual world and feel the sense of transforming into that VW. NASA/MSFC Computer Application Virtual Environments (CAVE) has been developing the space-related VR applications since 1990. The VR systems in CAVE lab are based on VPL RB2 system which consists of a VPL RB2 control tower, an LX eyephone, an Isotrak polhemus sensor, two Fastrak polhemus sensors, a folk of Bird sensor, and two VPL DG2 DataGloves. A dynamics animator called Body Electric from VPL is used as the control system to interface with all the input/output devices and to provide the network communications as well as VR programming environment. The RB2 Swivel 3D is used as the modelling program to construct the VW's. A severe limitation of the VPL VR system is the use of RB2 Swivel 3D, which restricts the files to a maximum of 1020 objects and doesn't have the advanced graphics texture mapping. The other limitation is that the VPL VR system is a turn-key system which does not provide the flexibility for user to add new sensors and C language interface. Recently, NASA/MSFC CAVE lab provides VR systems built on Sense8 WorldToolKit (WTK) which is a C library for creating VR development environments. WTK provides device drivers for most of the sensors and eyephones available on the VR market. WTK accepts several CAD file formats, such as Sense8 Neutral File Format, AutoCAD DXF and 3D Studio file format, Wave Front OBJ file format, VideoScape GEO file format, Intergraph EMS stereolithographics and CATIA Stereolithographics STL file formats. WTK functions are object-oriented in their naming convention, are grouped into classes, and provide easy C language interface. Using a CAD or modelling program to build a VW for WTK VR applications, we typically construct the stationary universe with all the geometric objects except the dynamic objects, and create each dynamic object in an individual file.

Wang, Peter Hor-Ching↗

Howard University Energy Expert Systems Institute Summer Program (EESI)

Howard University, under the auspices of the Center for Energy Systems and Controls runs the Energy Expert Systems Institute (EESI) summer outreach program for high school/pre-college minority students. The main objectives are to introduce precollege minority students to research in the power industry using modern state-of-the-art technology such as Expert Systems, Fuzzy Logic and Artificial Neural Networks; to involve minority students in space power management, systems and failure diagnosis; to generate interest in career options in electrical engineering; and to experience problem-solving in a teamwork environment consisting of faculty, senior research associates and graduate students. For five weeks the students are exposed not only to the exciting experience of college life, but also to the inspiring field of engineering, especially electrical engineering. The program consists of lectures in the fundamentals of engineering, mathematics, communication skills and computer skills. The projects are divided into mini and major. Topics for the 1995 mini projects were Expert Systems for the Electric Bus and Breast Cancer Detection. Topics on the major projects include Hybrid Electric Vehicle, Solar Dynamics and Distribution Automation. On the final day, designated as 'EESI Day' the students did oral presentations of their projects and prizes were awarded to the best group. The program began in the summer of 1993. The reaction from the students has been very positive. The program also arranges field trips to special places of interest such as the NASA Goddard Space Center.

Momoh, James A.↗

Communications Network

The Multi-Compatible Network Interface Unit (MCNIU) is intended to connect the space station's communications and tracking, guidance and navigation, life support, electric power, payload data, hand controls, display consoles and other systems, and also communicate with diverse processors. Honeywell is now marketing MCNIU commercially. It has applicability in certain military operations or civil control centers. It has nongovernment utility among large companies, universities and research organizations that transfer large amounts of data among workstations and computers. *This product is no longer commercially available.

Source record↗

NASA Tech Briefs, April 2008

Topics covered include: Gas Sensors Based on Coated and Doped Carbon Nanotubes; Tactile Robotic Topographical Mapping Without Force or Contact Sensors; Thin-Film Magnetic-Field-Response Fluid-Level Sensor for Non-Viscous Fluids; Progress in Development of Improved Ion-Channel Biosensors; Simulating Operation of a Complex Sensor Network; Using Transponders on the Moon to Increase Accuracy of GPS; Controller for Driving a Piezoelectric Actuator at Resonance; Coaxial Electric Heaters; Dual-Input AND Gate From Single-Channel Thin-Film FET; High-Density, High-Bandwidth, Multilevel Holographic Memory; Fabrication of Gate-Electrode Integrated Carbon-Nanotube Bundle Field Emitters; Hydroxide-Assisted Bonding of Ultra-Low-Expansion Glass; Photochemically Synthesized Polyimides; Optimized Carbonate and Ester-Based Li-Ion Electrolytes; Compact 6-DOF Stage for Optical Adjustments; Ultrasonic/Sonic Impacting Penetrators; Miniature, Lightweight, One-Time-Opening Valve; Supplier Management System; Improved CLARAty Functional-Layer/Decision-Layer Interface; JAVA Stereo Display Toolkit; Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool; PyPele Rewritten To Use MPI; Data Assimilation Cycling for Weather Analysis; Hydrocyclone/Filter for Concentrating Biomarkers from Soil; Activating STAT3 Alpha for Promoting Healing of Neurons; and Probing a Spray Using Frequency-Analyzed Light Scattering.

Source record↗

Multi-Variate LSTM Prediction of Alaska Magnetometer Chain Utilizing a Coupled Model Approach

During periods of rapidly changing geomagnetic conditions electric fields form within the Earth’s surface and induce currents known as geomagnetically induced currents(GICs), which interact with unprotected electrical systems our society relies on. In this study, we train multi-variate Long-Short Term Memory neural networks to predict magnitude of north-south component of the geomagnetic field (|BN|) at multiple ground magnetometer stations across Alaska provided by the SuperMAG database with a future goal of predicting geomagnetic field disturbances. Each neural network is driven by solar wind and interplanetary magnetic field inputs from the NASA OMNI database spanning from 2000–2015 and is fine tuned for each station to maximize the effectiveness in predicting |BN|. The neural networks are then compared against multivariate linear regression models driven with the same inputs at each station using Heidke skill scores with thresholds at the 50, 75, 85, and 99 percentiles for |BN|. The neural network models show significant increases over the linear regression models for |BN| thresholds. We also calculate the Heidke skill scores for d|BN|/dt by deriving d|BN|/dt from |BN| predictions. However, neural network models do not show clear outperformance compared to the linear regression models. To retain the sign information and thus predict BN instead of |BN|, a secondary so-called polarity model is utilized. The polarity model is run in tandem with the neural networks predicting geomagnetic field in a coupled model approach and results in a high correlation between predicted and observed values for all stations. We find this model a promising starting point for a machine learned geomagnetic field model to be expanded upon through increased output time history and fast turnaround times.

Matthew Blandin↗

Hybrid Modeling for Complex Systems Health Management

The research work presents application of hybrid physics-informed machine learning to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived and empirical equations, integrated with connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage. The powertrain model consists of Li-ion batteries, electronic speed controller with pulse-width modulation, and brush-less DC motor with connected propeller. Results obtained from combination of laboratory and simulation tests are discussed in this work.

PINNS↗

Physics Informed Neural Nets for Systems Health Management

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Development in data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. The research work presents application of physics-informed neural nets application to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived and empirical equations, integrated with connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage.

Physics Informed↗

Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study

Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions.

Ricks, Brian W.↗

Temporally-consistent koopman autoencoders for forecasting dynamical systems

Absence of sufficiently high-quality data often poses a key challenge in data-driven modeling of high-dimensional spatio-temporal dynamical systems. Koopman Autoencoders (KAEs) harness the expressivity of deep neural networks (DNNs), the dimension reduction capabilities of autoencoders, and the spectral properties of the Koopman operator to learn a reduced-order feature space with simpler, linear dynamics. However, the effectiveness of KAEs is hindered by limited and noisy training datasets, leading to poor generalizability. To address this, we introduce the Temporally-Consistent Koopman Autoencoder (tcKAE), designed to generate accurate long-term predictions even with limited and noisy training data. This is achieved through a consistency regularization term that enforces prediction coherence across different time steps, thus enhancing the robustness and generalizability of tcKAE over existing models. We provide analytical justification for this approach based on Koopman spectral theory and empirically demonstrate tcKAE’s superior performance over state-of-the-art KAE models across a variety of test cases, including simple pendulum oscillations, kinetic plasma, and fluid flow data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗