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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Interim Service ISDN Satellite (ISIS) simulator development for advanced satellite designs and experiments

The simulation development associated with the network models of both the Interim Service Integrated Services Digital Network (ISDN) Satellite (ISIS) and the Full Service ISDN Satellite (FSIS) architectures is documented. The ISIS Network Model design represents satellite systems like the Advanced Communications Technology Satellite (ACTS) orbiting switch. The FSIS architecture, the ultimate aim of this element of the Satellite Communications Applications Research (SCAR) Program, moves all control and switching functions on-board the next generation ISDN communications satellite. The technical and operational parameters for the advanced ISDN communications satellite design will be obtained from the simulation of ISIS and FSIS engineering software models for their major subsystems. Discrete event simulation experiments will be performed with these models using various traffic scenarios, design parameters, and operational procedures. The data from these simulations will be used to determine the engineering parameters for the advanced ISDN communications satellite.

Pepin, Gerard R.↗
NASA NTRS1993

A small satellite design for deep space network testing and training

With the continuing exploration of the Solar System and the reemphasis on Earth focused missions, the need for faster data transmission rates has grown. Ka-band could allow a higher data delivery rate over the current X-band, however the adverse effects of the Earth's atmosphere on Ka are as yet unknown. The Deep Space Network and Jet Propulsion Lab have proposed to launch a small satellite that would simultaneously transmit X and Ka signals to test the viability of switching to Ka-band. The Mockingbird Design Team at the University of Texas at Austin applied small satellite design principles to achieve this objective. The Mockingbird design, named BATSAT, incorporates simple, low-cost systems designed for university production and testing. The BATSAT satellite is a 0.64 m diameter, spherical panel led satellite, mounted with solar cells and omni-directional antennae. The antennae configuration negates the need for active attitude control or spin stabilization. The space-frame truss structure was designed for 11 g launch loads while allowing for easy construction and solar-panel mounting. The communication system transmits at 1 mW by carrying the required Ka and X-band transmitters, as well as an S band transmitter used for DSN training. The power system provides the 8.6 W maximum power requirements via silicon solar arrays and nickel-cadmium batteries. The BATSAT satellite will be lofted into an 1163 km, 70 deg orbit by the Pegasus launch system. This orbit fulfills DSN dish slew rate requirements while keeping the satellite out of the heaviest regions of the Van Allen radiation belts. Each of the three DSN stations capable of receiving Ka-band (Goldstone, Canberra, and Madrid) will have an average of 85 minutes of view-time per day over the satellites ten year design life. Mockingbird Designs hopes that its small satellite design will not only be applicable to this specific mission scenario, but that it could easily be modified for instrument capability for university, government, and/or commercial research.

Mcwilliams, Dennis↗
NASA NTRS2020

Genetic Algorithm for Optimization of Neural Networks for Bayesian Inference of Model Uncertainty

The objective of this work was to develop a genetic optimization algorithm that can design a neural network capable of producing uncertainty estimates along with predictions. This algorithm is necessary because the inclusion of uncertainty modeling in a neural network greatly complicates the network’s design space, making the development of a converging model extremely difficult and time consuming. The genetic algorithm presented in this work uses a number of value ranges for various configurable neural network parameters to create a randomly generated population of network architectures. The initially generated population is then evolved over the course of several generations, with the best performing models breeding to produce novel network configurations. Mutations are randomly applied to the network designs to facilitate the development of adaptations beneficial to the task being performed. An experiment was conducted to validate the proposed algorithm, in which the genetic optimizer was tasked with producing a neural network capable of predicting the sound pressure level (SPL) resulting from jet-surface interaction (JSI) noise. The data used for this task was generated at the NASA Glenn Research Center in the Aero-Acoustic Propulsion Laboratory. Starting with an initial population size of 35 randomly generated networks, and evolved over the course of 10 generations, the genetic algorithm produced a design able to predict SPL as a result of JSI noise within 0.272 dB, on average.

Genetic algorithm↗
NASA NTRS2001

The Use of Eccentric and Circular Orbits in the Design of a Mars Network Constellation

This study examined different constellation configurations to determine their suitability for the Mars Network. Some variations on the baseline case of four circular orbits were initially studied. Eccentric orbits were then used to determine their effects on several figures of merit that were selected as representative of the design goals. It was eventually found that the use of eccentric orbits in combination with circular orbits can improve aspects of some navigation and communication figures of merit. The ability to use orbits at different inclinations helps smooth coverage over the middle and upper latitudes for these figures of merit. This configuration has more variability than one consisting of circular orbits, so the occurrence of unfavorable arrangements also results in degradation of some figures of merit. Some cases were used which improved different figures of merit, so a solution could be chosen depending on specified requirements.

Anderson, Rodney↗
NASA NTRS1996

Design and implementation of a Windows NT network to support CNC activities

The Manufacturing, Materials, & Processes Technology Division is undergoing dramatic changes to bring it's manufacturing practices current with today's technological revolution. The Division is developing Computer Automated Design and Computer Automated Manufacturing (CAD/CAM) abilities. The development of resource tracking is underway in the form of an accounting software package called Infisy. These two efforts will bring the division into the 1980's in relationship to manufacturing processes. Computer Integrated Manufacturing (CIM) is the final phase of change to be implemented. This document is a qualitative study and application of a CIM application capable of finishing the changes necessary to bring the manufacturing practices into the 1990's. The documentation provided in this qualitative research effort includes discovery of the current status of manufacturing in the Manufacturing, Materials, & Processes Technology Division including the software, hardware, network and mode of operation. The proposed direction of research included a network design, computers to be used, software to be used, machine to computer connections, estimate a timeline for implementation, and a cost estimate. Recommendation for the division's improvement include action to be taken, software to utilize, and computer configurations.

Shearrow, C. A.↗
DOE OSTI2024

Scale-bridging Optimization Framework for Desalination Integrated Produced Water Networks

Produced water (PW) management poses a major challenge to U.S. oil and gas development. Due to freshwater scarcity and PW injection curtailments, desalination may become a necessity in some parts of the country. Integrating rigorous desalination process models within a produced water network for co-optimization of desalination design and network operation can be challenging to scale-up due to computational complexity. In this work we use the trust region filter framework to decompose the integrated optimization problem into an optimization master problem consisting of the network model and simplified surrogates for the desalination units and an optimization sub problem for the rigorous desalination unit model. The approach is demonstrated on a multiperiod produced water network from the PARETO library with thermal desalination units.

Naik, Sakshi↗
DOE OSTI2021

Physics-informed machine learning

Despite great progress in simulating multiphysics problems using the numerical discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate noisy data into existing algorithms, mesh generation remains complex, and high-dimensional problems governed by parameterized PDEs cannot be tackled. Moreover, solving inverse problems with hidden physics is often prohibitively expensive and requires different formulations and elaborate computer codes. Machine learning has emerged as a promising alternative, but training deep neural networks requires big data, not always available for scientific problems. Instead, such networks can be trained from additional information obtained by enforcing the physical laws (for example, at random points in the continuous space-time domain). Such physics-informed learning integrates (noisy) data and mathematical models, and implements them through neural networks or other kernel-based regression networks. Moreover, it may be possible to design specialized network architectures that automatically satisfy some of the physical invariants for better accuracy, faster training and improved generalization. Furthermore, we review some of the prevailing trends in embedding physics into machine learning, present some of the current capabilities and limitations and discuss diverse applications of physics-informed learning both for forward and inverse problems, including discovering hidden physics and tackling high-dimensional problems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1999

Constellation Design of the Mars Network

Mars has become the focus of an unprecedented series of missions spanning many years, involving numerous nations and evolving from robotics to humans.

Constellation Design Communications Navigation↗
NASA NTRS2002

Evolutionary Design of a Robotic Material Defect Detection System

During the post-flight inspection of SSME engines, several inaccessible regions must be disassembled to inspect for defects such as cracks, scratches, gouges, etc. An improvement to the inspection process would be the design and development of very small robots capable of penetrating these inaccessible regions and detecting the defects. The goal of this research was to utilize an evolutionary design approach for the robotic detection of these types of defects. A simulation and visualization tool was developed prior to receiving the hardware as a development test bed. A small, commercial off-the-shelf (COTS) robot was selected from several candidates as the proof of concept robot. The basic approach to detect the defects was to utilize Cadmium Sulfide (CdS) sensors to detect changes in contrast of an illuminated surface. A neural network, optimally designed utilizing a genetic algorithm, was employed to detect the presence of the defects (cracks). By utilization of the COTS robot and US sensors, the research successfully demonstrated that an evolutionarily designed neural network can detect the presence of surface defects.

Ballard, Gary↗
NASA NTRS1995

Teledesic Global Wireless Broadband Network: Space Infrastructure Architecture, Design Features and Technologies

The Teledesic satellites are a new class of small satellites which demonstrate the important commercial benefits of using technologies developed for other purposes by U.S. National Laboratories. The Teledesic satellite architecture, subsystem design features, and new technologies are described. The new Teledesic satellite manufacturing, integration, and test approaches which use modern high volume production techniques and result in surprisingly low space segment costs are discussed. The constellation control and management features and attendant software architecture features are addressed. After briefly discussing the economic and technological impact on the USA commercial space industries of the space communications revolution and such large constellation projects, the paper concludes with observations on the trend toward future system architectures using networked groups of much smaller satellites.

Stuart, James R.↗
NASA NTRS2003

Neural Network and Regression Methods Demonstrated in the Design Optimization of a Subsonic Aircraft

The neural network and regression methods of NASA Glenn Research Center s COMETBOARDS design optimization testbed were used to generate approximate analysis and design models for a subsonic aircraft operating at Mach 0.85 cruise speed. The analytical model is defined by nine design variables: wing aspect ratio, engine thrust, wing area, sweep angle, chord-thickness ratio, turbine temperature, pressure ratio, bypass ratio, fan pressure; and eight response parameters: weight, landing velocity, takeoff and landing field lengths, approach thrust, overall efficiency, and compressor pressure and temperature. The variables were adjusted to optimally balance the engines to the airframe. The solution strategy included a sensitivity model and the soft analysis model. Researchers generated the sensitivity model by training the approximators to predict an optimum design. The trained neural network predicted all response variables, within 5-percent error. This was reduced to 1 percent by the regression method. The soft analysis model was developed to replace aircraft analysis as the reanalyzer in design optimization. Soft models have been generated for a neural network method, a regression method, and a hybrid method obtained by combining the approximators. The performance of the models is graphed for aircraft weight versus thrust as well as for wing area and turbine temperature. The regression method followed the analytical solution with little error. The neural network exhibited 5-percent maximum error over all parameters. Performance of the hybrid method was intermediate in comparison to the individual approximators. Error in the response variable is smaller than that shown in the figure because of a distortion scale factor. The overall performance of the approximators was considered to be satisfactory because aircraft analysis with NASA Langley Research Center s FLOPS (Flight Optimization System) code is a synthesis of diverse disciplines: weight estimation, aerodynamic analysis, engine cycle analysis, propulsion data interpolation, mission performance, airfield length for landing and takeoff, noise footprint, and others.

Hopkins, Dale A.↗
NASA NTRS1991

An architecture for designing fuzzy logic controllers using neural networks

Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.

Berenji, Hamid R.↗
DOE OSTI2021

Impact of Blockchain Delay on Grid-Tied Solar Inverter Performance

This paper investigates the impact of the delay resulting from a blockchain, a promising security measure, for a hierarchical control system of inverters connected to the grid. The blockchain communication network is designed at the secondary control layer for resilience against cyberattacks. To represent the latency in the communication channel, a model is developed based on the complexity of the blockchain framework. Taking this model into account, this work evaluates the plant’s performance subject to communication delays, introduced by the blockchain, among the hierarchical control agents. In addition, this article considers an optimal model-based control strategy that performs the system’s internal control loop. The work shows that the blockchain’s delay size influences the convergence of the power supplied by the inverter to the reference at the point of common coupling. In the results section, real-time simulations on OPAL-RT are performed to test the resilience of two parallel inverters with increasing blockchain complexity.

41 EE - Solar Energy Technologies Office (EE-4S)↗
NASA NTRS1989

DSN radio science system design and testing for Voyager-Neptune encounter

The Deep Space Network (DSN) Radio Science System presently implemented within the Deep Space Network was designed to meet stringent requirements imposed by the demands of the Voyager-Neptune encounter and future missions. One of the initial parameters related to frequency stability is discussed. The requirement, specification, design, and methodology for measuring this parameter are described. A description of special instrumentation that was developed for the test measurements and initial test data resulting from the system tests performed at Canberra, Australia and Usuda, Japan are given.

Ham, N. C.↗
NASA NTRS1992

Neural networks in structural analysis and design - An overview

The present paper provides an overview of the state-of-the-art in the application of neural networks in problems of structural analysis and design, including a survey of published applications in structural engineering. Such applications have included, among others, the use of neural networks in modeling nonlinear analysis of structures, as a rapid reanalysis capability in optimal design, and in developing problem parameter sensitivity of optimal solutions for use in multilevel decomposition based design. While most of the applications reported in the literature have been restricted to the use of the multilayer perceptron architecture and minor variations thereof, other network architectures have also been successfully explored, including the ART network, the counterpropagation network and the Hopfield-Tank model.

Hajela, P.↗