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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 91 records · Page 5

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↗

Network Extender for MIL-STD-1553 Bus

An extender system for MIL-STD-1553 buses transparently couples bus components at multiple developer sites. The bus network extender is a relatively inexpensive system that minimizes the time and cost of integration of avionic systems by providing a convenient mechanism for early testing without the need to transport the usual test equipment and personnel to an integration facility. This bus network extender can thus alleviate overloading of the test facility while enabling the detection of interface problems that can occur during the integration of avionic systems. With this bus extender in place, developers can correct and adjust their own hardware and software before products leave a development site. Currently resident at Johnson Space Center, the bus network extender is used to test the functionality of equipment that, although remotely located, is connected through a MILSTD- 1553 bus. Inasmuch as the standard bus protocol for avionic equipment is that of MIL-STD-1553, companies that supply MIL-STD-1553-compliant equipment to government or industry and that need long-distance communication support might benefit from this network bus extender

Marcus, Julius↗

Orthogonal feeding techniques for tapered slot antennas

For array of "brick" configuration there are electrical and mechanical advantages to feed the antenna with a feed on a substrate perpendicular to the antenna substrate. Different techniques have been proposed for exciting patch antennas using such a feed structure.Rncently, an aperture-coupled dielectric resonator antenna using a perpendicular feed substrate has been demonstrated to have very good power coupling efficiency. For a two-dimensional rectangular array with tapered slot antenna elements, a power combining network on perpendicular substrate is generally required to couple power to or from the array. In this paper, we will describe two aperture-coupled techniques for coupling microwave power from a linearly tapered slot antenna (LTSA) to a microstrip feed on a perpendicular substrate. In addition, we will present measured results for return losses and radiation patterns.

Lee, Richard Q.↗

Power converter

A dc-to-dc converter employs four transistor switches in a bridge to chop dc power from a source, and a voltage multiplying diode rectifying ladder network to rectify and filter the chopped dc power for delivery to a load. The bridge switches are cross coupled in order for diagonally opposite pairs to turn on and off together using RC networks for the cross coupling to achieve the mode of operation of a free running multivibrator, and the diode rectifying ladder is configured to operate in a push-pull mode driven from opposite sides of the multivibrator outputs of the ridge switches. The four transistor switches provide a square-wave output voltage which as a peak-to-peak amplitude that is twice the input dc voltage, and is thus useful as a dc-to-ac inverter.

Black, J. M.↗

Engine With Regression and Neural Network Approximators Designed

At the NASA Glenn Research Center, the NASA engine performance program (NEPP, ref. 1) and the design optimization testbed COMETBOARDS (ref. 2) with regression and neural network analysis-approximators have been coupled to obtain a preliminary engine design methodology. The solution to a high-bypass-ratio subsonic waverotor-topped turbofan engine, which is shown in the preceding figure, was obtained by the simulation depicted in the following figure. This engine is made of 16 components mounted on two shafts with 21 flow stations. The engine is designed for a flight envelope with 47 operating points. The design optimization utilized both neural network and regression approximations, along with the cascade strategy (ref. 3). The cascade used three algorithms in sequence: the method of feasible directions, the sequence of unconstrained minimizations technique, and sequential quadratic programming. The normalized optimum thrusts obtained by the three methods are shown in the following figure: the cascade algorithm with regression approximation is represented by a triangle, a circle is shown for the neural network solution, and a solid line indicates original NEPP results. The solutions obtained from both approximate methods lie within one standard deviation of the benchmark solution for each operating point. The simulation improved the maximum thrust by 5 percent. The performance of the linear regression and neural network methods as alternate engine analyzers was found to be satisfactory for the analysis and operation optimization of air-breathing propulsion engines (ref. 4).

Patnaik, Surya N.↗

Equivalent Skin Analysis of Wing Structures Using Neural Networks

An efficient method of modeling trapezoidal built-up wing structures is developed by coupling. in an indirect way, an Equivalent Plate Analysis (EPA) with Neural Networks (NN). Being assumed to behave like a Mindlin-plate, the wing is solved using the Ritz method with Legendre polynomials employed as the trial functions. This analysis method can be made more efficient by avoiding most of the computational effort spent on calculating contributions to the stiffness and mass matrices from each spar and rib. This is accomplished by replacing the wing inner-structure with an "equivalent" material that combines to the skin and whose properties are simulated by neural networks. The constitutive matrix, which relates the stress vector to the strain vector, and the density of the equivalent material are obtained by enforcing mass and stiffness matrix equities with rec,ard to the EPA in a least-square sense. Neural networks for the material properties are trained in terms of the design variables of the wing structure. Examples show that the present method, which can be called an Equivalent Skin Analysis (ESA) of the wing structure, is more efficient than the EPA and still fairly good results can be obtained. The present ESA is very promising to be used at the early stages of wing structure design.

Liu, Youhua↗

Three phase full wave dc motor decoder

A three phase decoder for dc motors is disclosed which employs an extremely simple six transistor circuit to derive six properly phased output signals for fullwave operation of dc motors. Six decoding transistors are coupled at their base-emitter junctions across a resistor network arranged in a delta configuration. Each point of the delta configuration is coupled to one of three position sensors which sense the rotational position of the motor. A second embodiment of the invention is disclosed in which photo-optical isolators are used in place of the decoding transistors.

Studer, P. A.↗

Artificial Neural Network with Hardware Training and Hardware Refresh

A neural network circuit is provided having a plurality of circuits capable of charge storage. Also provided is a plurality of circuits each coupled to at least one of the plurality of charge storage circuits and constructed to generate an output in accordance with a neuron transfer function. Each of a plurality of circuits is coupled to one of the plurality of neuron transfer function circuits and constructed to generate a derivative of the output. A weight update circuit updates the charge storage circuits based upon output from the plurality of transfer function circuits and output from the plurality of derivative circuits. In preferred embodiments, separate training and validation networks share the same set of charge storage circuits and may operate concurrently. The validation network has a separate transfer function circuits each being coupled to the charge storage circuits so as to replicate the training network s coupling of the plurality of charge storage to the plurality of transfer function circuits. The plurality of transfer function circuits may be constructed each having a transconductance amplifier providing differential currents combined to provide an output in accordance with a transfer function. The derivative circuits may have a circuit constructed to generate a biased differential currents combined so as to provide the derivative of the transfer function.

Tuan A Duong↗

Neural network based decomposition in optimal structural synthesis

The present paper describes potential applications of neural networks in the multilevel decomposition based optimal design of structural systems. The generic structural optimization problem of interest, if handled as a single problem, results in a large dimensionality problem. Decomposition strategies allow for this problem to be represented by a set of smaller, decoupled problems, for which solutions may either be obtained with greater ease or may be obtained in parallel. Neural network models derived through supervised training, are used in two distinct modes in this work. The first uses neural networks to make available efficient analysis models for use in repetitive function evaluations as required by the optimization algorithm. In the second mode, neural networks are used to represent the coupling that exists between the decomposed subproblems. The approach is illustrated by application to the multilevel decomposition-based synthesis of representative truss and frame structures.

Hajela, P.↗

Nutation damping in a dual-spin spacecraft

A dual-spin spacecraft having a momentum wheel spinning relative to a platform to provide bias momentum utilizes cross products of inertia (POI) existing in the platform between the spinning axis and the transverse axes to achieve nutation damping. When the platform is rotating or is displaced from a reference the cross products of inertia are also rotating or are displaced causing thereby degradation of the optimum nutation damping time constant. Two or more phase shifting networks are provided to be sequentially coupled into a control loop to shift nutation signals to effect optimum nutation damping at selected positions of the rotating or displaced platform without substantial nutation damping time constant degradation.

Phillips, Kevin J.↗

Command, Control and Communications Capabilities Enabling 21st Century Missions, a Historical Perspective

Command, Control and Communications (CCC) has evolved through the years from simple switches, dials, analogue hardwire networks and lights to a modern computer based digital network. However there are two closely coupled pillars upon which a CCC system is built. The first, is that technology drives the pace of advancement. The second is that a culture that fosters resistance to change can limit technological advancements in the CCC system. While technology has advanced at a tremendous rate throughout the years, the change in culture has moved slowly. This paper will attempt to show through a historical perspective where specific design decisions for early CCC systems have erroneously evolved into general requirements being imposed on later systems. Finally this paper will provide a glimpse into the future directions envisioned for CCC capabilities that will enable 21st century missions.

Waterman, Robert D.↗

NASA Tech Briefs, January 2003

Topics covered include: Optoelectronic Tool Adds Scale Marks to Photographic Images; Compact Interconnection Networks Based on Quantum Dots; Laterally Coupled Quantum-Dot Distributed-Feedback Lasers; Bit-Serial Adder Based on Quantum Dots; Stabilized Fiber-Optic Distribution of Reference Frequency; Delay/Doppler-Mapping GPS-Reflection Remote-Sensing System; Ladar System Identifies Obstacles Partly Hidden by Grass; Survivable Failure Data Recorders for Spacecraft; Fiber-Optic Ammonia Sensors; Silicon Membrane Mirrors with Electrostatic Shape Actuators; Nanoscale Hot-Wire Probes for Boundary-Layer Flows; Theodolite with CCD Camera for Safe Measurement of Laser-Beam Pointing; Efficient Coupling of Lasers to Telescopes with Obscuration; Aligning Three Off-Axis Mirrors with Help of a DOE; Calibrating Laser Gas Measurements by Use of Natural CO2; Laser Ranging Simulation Program; Micro-Ball-Lens Optical Switch Driven by SMA Actuator; Evaluation of Charge Storage and Decay in Spacecraft Insulators; Alkaline Capacitors Based on Nitride Nanoparticles; Low-EC-Content Electrolytes for Low-Temperature Li-Ion Cells; Software for a GPS-Reflection Remote-Sensing System; Software for Building Models of 3D Objects via the Internet; "Virtual Cockpit Window" for a Windowless Aerospacecraft; CLARAty Functional-Layer Software; Java Library for Input and Output of Image Data and Metadata; Software for Estimating Costs of Testing Rocket Engines; Energy-Absorbing, Lightweight Wheels; Viscoelastic Vibration Dampers for Turbomachine Blades; Soft Landing of Spacecraft on Energy-Absorbing Self-Deployable Cushions; Pneumatically Actuated Miniature Peristaltic Vacuum Pumps; Miniature Gas-Turbine Power Generator; Pressure-Sensor Assembly Technique; Wafer-Level Membrane-Transfer Process for Fabricating MEMS; A Reactive-Ion Etch for Patterning Piezoelectric Thin Film; Wavelet-Based Real-Time Diagnosis of Complex Systems; Quantum Search in Hilbert Space; Analytic Method for Computing Instrument Pointing Jitter; and Semiselective Optoelectronic Sensors for Monitoring Microbes.

Source record↗

Overview of the Design, Fabrication and Performance Requirements of Micro-Spec, an Integrated Submillimeter Spectrometer

Micro-Spec is a compact submillimeter (350-700 GHz) spectrometer which uses low loss superconducting niobium microstrip transmission lines and a single-crystal silicon dielectric to integrate all of the components of a grating-analog spectrometer onto a single chip. Here we present details of the fabrication and design of a prototype Micro-Spec spectrometer with resolution, R64, where we use a high-yield single-flip wafer bonding process to realize instrument components on a 0.45 m single-crystal silicon dielectric. We discuss some of the electromagnetic design concerns (such as loss, stray-light, cross-talk, and fabrication tolerances) for each of the spectrometer components and their integration into the instrument as a whole. These components include a slot antenna with a silicon lens for optical coupling, a phase delay transmission line network, parallel plate waveguide interference region, and aluminum microstrip transmission line kinetic inductance detectors with extremely low cross-talk and immunity to stray light. We have demonstrated this prototype spectrometer with design resolution of R64. Given the optical performance of this prototype, we will also discuss the extension of this design to higher resolutions suitable for balloon-flight.

spectrometer↗

Signal dispersion within a hippocampal neural network

A model network is described, representing two neural populations coupled so that one population is inhibited by activity it excites in the other. Parameters and operations within the model represent EPSPs, IPSPs, neural thresholds, conduction delays, background activity and spatial and temporal dispersion of signals passing from one population to the other. Simulations of single-shock and pulse-train driving of the network are presented for various parameter values. Neuronal events from 100 to 300 msec following stimulation are given special consideration in model calculations.

Horowitz, J. M.↗

Predicting Cost/Performance Trade-Offs for Whitney: A Commodity Computing Cluster

Recent advances in low-end processor and network technology have made it possible to build a "supercomputer" out of commodity components. We develop simple models of the NAS Parallel Benchmarks version 2 (NPB 2) to explore the cost/performance trade-offs involved in building a balanced parallel computer supporting a scientific workload. We develop closed form expressions detailing the number and size of messages sent by each benchmark. Coupling these with measured single processor performance, network latency, and network bandwidth, our models predict benchmark performance to within 30%. A comparison based on total system cost reveals that current commodity technology (200 MHz Pentium Pros with 100baseT Ethernet) is well balanced for the NPBs up to a total system cost of around $1,000,000.

Becker, Jeffrey C.↗

Application of adjoint operators to neural learning

A technique for the efficient analytical computation of such parameters of the neural architecture as synaptic weights and neural gain is presented as a single solution of a set of adjoint equations. The learning model discussed concentrates on the adiabatic approximation only. A problem of interest is represented by a system of N coupled equations, and then adjoint operators are introduced. A neural network is formalized as an adaptive dynamical system whose temporal evolution is governed by a set of coupled nonlinear differential equations. An approach based on the minimization of a constrained neuromorphic energylike function is applied, and the complete learning dynamics are obtained as a result of the calculations.

Barhen, J.↗

Neurocomputing strategies in decomposition based structural design

The present paper explores the applicability of neurocomputing strategies in decomposition based structural optimization problems. It is shown that the modeling capability of a backpropagation neural network can be used to detect weak couplings in a system, and to effectively decompose it into smaller, more tractable, subsystems. When such partitioning of a design space is possible, parallel optimization can be performed in each subsystem, with a penalty term added to its objective function to account for constraint violations in all other subsystems. Dependencies among subsystems are represented in terms of global design variables, and a neural network is used to map the relations between these variables and all subsystem constraints. A vector quantization technique, referred to as a z-Network, can effectively be used for this purpose. The approach is illustrated with applications to minimum weight sizing of truss structures with multiple design constraints.

Szewczyk, Z.↗

Establishment of a Spaceport Network Architecture

Since the beginning of the space age, the main actors in space exploration have been governmental agencies, enabling a privileged access to space, but with very restricted and rare missions. The last decade has seen the rise of space tourism, and the founding of ambitious private space mining companies, showing the beginnings of a new exploration era, that is based on a more generalized and regular access to space and which is not limited to the Earth's vicinity. However, the cost of launching sufficient mass into orbit to sustain these inspiring challenges is prohibitive, and the necessary infrastructures to support these missions is still lacking. To provide easy and affordable access into orbital and deep space destinations, there is the need to create a network of spaceports via specific waypoint locations coupled with the use of natural resources, or In Situ Resource Utilization (ISRU), to provide a more economical solution. As part of the International Space University Space Studies Program 2012, the international and intercultural team of Operations and Service Infrastructure for Space (OASIS) proposes an interdisciplinary answer to the problem of economical space access and transportation. This paper presents a summary of a detailed report [1] of the different phases of a project for developing a network of spaceports throughout the Solar System in a timeframe of 50 years. The requirements, functions, critical technologies and mission architecture of this network of spaceports are outlined in a roadmap of the important steps and phases. The economic and financial aspects are emphasized in order to allow a sustainable development of the network in a public-private partnership via the formation of an International Spaceport Authority (ISPA). The approach includes engineering, scientific, financial, legal, policy, and societal aspects. Team OASIS intends to provide guidelines to make the development of space transportation via a spaceports logistics network feasible, and believes that this pioneering effort will revolutionize space exploration, science and commerce, ultimately contributing to permanently expand humanity into space.

Larson, Wiley J.↗