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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Deep Space Network Antenna Logic Controller

The Antenna Logic Controller (ALC) software controls and monitors the motion control equipment of the 4,000-metric-ton structure of the Deep Space Network 70-meter antenna. This program coordinates the control of 42 hydraulic pumps, while monitoring several interlocks for personnel and equipment safety. Remote operation of the ALC runs via the Antenna Monitor & Control (AMC) computer, which orchestrates the tracking functions of the entire antenna. This software provides a graphical user interface for local control, monitoring, and identification of faults as well as, at a high level, providing for the digital control of the axis brakes so that the servo of the AMC may control the motion of the antenna. Specific functions of the ALC also include routines for startup in cold weather, controlled shutdown for both normal and fault situations, and pump switching on failure. The increased monitoring, the ability to trend key performance characteristics, the improved fault detection and recovery, the centralization of all control at a single panel, and the simplification of the user interface have all reduced the required workforce to run 70-meter antennas. The ALC also increases the antenna availability by reducing the time required to start up the antenna, to diagnose faults, and by providing additional insight into the performance of key parameters that aid in preventive maintenance to avoid key element failure. The ALC User Display (AUD) is a graphical user interface with hierarchical display structure, which provides high-level status information to the operation of the ALC, as well as detailed information for virtually all aspects of the ALC via drill-down displays. The operational status of an item, be it a function or assembly, is shown in the higher-level display. By pressing the item on the display screen, a new screen opens to show more detail of the function/assembly. Navigation tools and the map button allow immediate access to all screens.

Ahlstrom, Harlow↗
NASA NTRS2012

Sampling Technique for Robust Odorant Detection Based on MIT RealNose Data

This technique enhances the detection capability of the autonomous Real-Nose system from MIT to detect odorants and their concentrations in noisy and transient environments. The lowcost, portable system with low power consumption will operate at high speed and is suited for unmanned and remotely operated long-life applications. A deterministic mathematical model was developed to detect odorants and calculate their concentration in noisy environments. Real data from MIT's NanoNose was examined, from which a signal conditioning technique was proposed to enable robust odorant detection for the RealNose system. Its sensitivity can reach to sub-part-per-billion (sub-ppb). A Space Invariant Independent Component Analysis (SPICA) algorithm was developed to deal with non-linear mixing that is an over-complete case, and it is used as a preprocessing step to recover the original odorant sources for detection. This approach, combined with the Cascade Error Projection (CEP) Neural Network algorithm, was used to perform odorant identification. Signal conditioning is used to identify potential processing windows to enable robust detection for autonomous systems. So far, the software has been developed and evaluated with current data sets provided by the MIT team. However, continuous data streams are made available where even the occurrence of a new odorant is unannounced and needs to be noticed by the system autonomously before its unambiguous detection. The challenge for the software is to be able to separate the potential valid signal from the odorant and from the noisy transition region when the odorant is just introduced.

Duong, Tuan A.↗
NASA NTRS2014

Application of Machine Learning Algorithms to the Study of Noise Artifacts in Gravitational-Wave Data

The sensitivity of searches for astrophysical transients in data from the Laser Interferometer Gravitationalwave Observatory (LIGO) is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high-enough rate such that accidental coincidence across multiple detectors is non-negligible. Furthermore, non-Gaussian noise artifacts typically dominate over the background contributed from stationary noise. These "glitches" can easily be confused for transient gravitational-wave signals, and their robust identification and removal will help any search for astrophysical gravitational-waves. We apply Machine Learning Algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. Terrestrial noise sources may manifest characteristic disturbances in these auxiliary channels, inducing non-trivial correlations with glitches in the gravitational-wave data. The number of auxiliary-channel parameters describing these disturbances may also be extremely large; high dimensionality is an area where MLAs are particularly well-suited. We demonstrate the feasibility and applicability of three very different MLAs: Artificial Neural Networks, Support Vector Machines, and Random Forests. These classifiers identify and remove a substantial fraction of the glitches present in two very different data sets: four weeks of LIGO's fourth science run and one week of LIGO's sixth science run. We observe that all three algorithms agree on which events are glitches to within 10% for the sixth science run data, and support this by showing that the different optimization criteria used by each classifier generate the same decision surface, based on a likelihood-ratio statistic. Furthermore, we find that all classifiers obtain similar limiting performance, suggesting that most of the useful information currently contained in the auxiliary channel parameters we extract is already being used. Future performance gains are thus likely to involve additional sources of information, rather than improvements in the MLAs themselves.

gravitational-wave data↗
NASA NTRS1988

Mission telemetry system monitor: A real-time knowledge-based system

The Galileo Mission Telemetry System (MTS) has a cluster of computer subsystems configured as a star network. The MTS handles the real-time processing of spacecraft telemetry and ground monitor data. Large volumes of status and fault messages are generated as a result of changes in the system environment. These messages are triggered by the conditions that exist on any one particular subsystem or device. The order of message generation is in time sequence and does not always correlate to the function sequence of active processes. A significant number of messages provide context with varying degrees of uncertainty. As such, highly skilled telemetry controllers are required to regularly go through high volumes of messages generated by the MTS to identify, diagnose, and isolate faults. A knowledge-based system prototype is being developed to monitor the Galileo Mission Telemetry System performance. The system design approach features temporal reasoning, uncertainty management, and intelligent graphic user interfaces.

Mouneimne, Samih A.↗
NASA NTRS1993

The Telecommunications and Data Acquisition Report

This quarterly publication provides archival reports on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition (TDA). In space communications, radio navigation, radio science, and ground-based radio and radar astronomy, it reports on activities of the Deep Space Network (DSN) in planning, supporting research and technology, implementation, and operations. Also included are standards activity at JPL for space data and information systems and reimbursable DSN work performed for other space agencies through NASA. The preceding work is all performed for NASA's Office of Space Communications (OSC). The TDA Office also performs work funded by another NASA program office through and with the cooperation of OSC. This is the Orbital Debris Radar Program with tile Office of Space Systems Development. The TDA Office is directly involved in several tasks that directly support the Office of Space Science (OSS), with OSC funding DSN operational support. In radio science, The TDA Progress Report describes the spacecraft radio science program conducted using tile DSN. For the High-Resolution Microwave Survey (HRMS), the report covers implementation and operations for searching the microwave spectrum. In solar system radar, it reports on the uses of the Goldstone Solar System Radar for scientific exploration of the planets, their rings and satellites, asteroids, and comets. In radio astronomy, the areas off support include spectroscopy, very long baseline interferometry, and astrometry.

Edward C Posner↗
NASA NTRS1995

Application of non-coherent Doppler data types for deep space navigation

Recent improvements in computational capability and Deep Space Network technology have renewed interest in examining the possibility of using one-way Doppler data alone to navigate interplanetary spacecraft. The one-way data can be formulated as the standard differenced-count Doppler or as phase measurements, and the data can be received at a single station or differenced if obtained simultaneously at two stations. A covariance analysis is performed which analyzes the accuracy obtainable by combinations of one-way Doppler data and compared with similar results using standard two-way Doppler and range. The sample interplanetary trajectory used was that of the Mars Pathfinder mission to Mars. It is shown that differenced one-way data is capable of determining the angular position of the spacecraft to fairly high accuracy, but has relatively poor sensitivity to the range. When combined with single station data, the position dispersions are roughly an order of magnitude larger in range and comparable in angular position as compared to dispersions obtained with standard data two-way types. It was also found that the phase formulation is less sensitive to data weight variations and data coverage than the differenced-count Doppler formulation.

Bhaskaran, Shyam↗
NASA NTRS1997

RIACS

Topics considered include: high-performance computing; cognitive and perceptual prostheses (computational aids designed to leverage human abilities); autonomous systems. Also included: development of a 3D unstructured grid code based on a finite volume formulation and applied to the Navier-stokes equations; Cartesian grid methods for complex geometry; multigrid methods for solving elliptic problems on unstructured grids; algebraic non-overlapping domain decomposition methods for compressible fluid flow problems on unstructured meshes; numerical methods for the compressible navier-stokes equations with application to aerodynamic flows; research in aerodynamic shape optimization; S-HARP: a parallel dynamic spectral partitioner; numerical schemes for the Hamilton-Jacobi and level set equations on triangulated domains; application of high-order shock capturing schemes to direct simulation of turbulence; multicast technology; network testbeds; supercomputer consolidation project.

Oliger, Joseph↗
NASA NTRS2014

High Spectral Resolution Lidar and MPLNET Micro Pulse Lidar Aerosol Optical Property Retrieval Intercomparison During the 2012 7-SEAS Field Campaign at Singapore

From August 2012 to February 2013 a High Resolution Spectral Lidar (HSRL; 532 nm) was deployed at that National University of Singapore near a NASA Micro Pulse Lidar NETwork (MPLNET; 527 nm) site. A primary objective of the MPLNET lidar project is the production and dissemination of reliable Level 1 measurements and Level 2 retrieval products. This paper characterizes and quantifies error in Level 2 aerosol optical property retrievals conducted through inversion techniques that derive backscattering and extinction coefficients from MPLNET elastic single-wavelength datasets. MPLNET Level 2 retrievals for aerosol optical depth and extinction/backscatter coefficient profiles are compared with corresponding HSRL datasets, for which the instrument collects direct measurements of each using a unique optical configuration that segregates aerosol and cloud backscattered signal from molecular signal. The intercomparison is performed, and error matrices reported, for lower (0-5km) and the upper (>5km) troposphere, respectively, to distinguish uncertainties observed within and above the MPLNET instrument optical overlap regime.

MPL↗
NASA NTRS1977

Low cost Ku-band earth terminals for voice/data/facsimile

A Ku-band satellite earth terminal capable of providing two way voice/facsimile teleconferencing, 128 Kbps data, telephone, and high-speed imagery services is proposed. Optimized terminal cost and configuration are presented as a function of FDMA and TDMA approaches to multiple access. The entire terminal from the antenna to microphones, speakers and facsimile equipment is considered. Component cost versus performance has been projected as a function of size of the procurement and predicted hardware innovations and production techniques through 1985. The lowest cost combinations of components has been determined in a computer optimization algorithm. The system requirements including terminal EIRP and G/T, satellite size, power per spacecraft transponder, satellite antenna characteristics, and link propagation outage were selected using a computerized system cost/performance optimization algorithm. System cost and terminal cost and performance requirements are presented as a function of the size of a nationwide U.S. network. Service costs are compared with typical conference travel costs to show the viability of the proposed terminal.

Kelley, R. L.↗
NASA NTRS1996

Highly Efficient Amplifier for Ka-Band Communications

An amplifier developed under a Small Business Innovation Research (SBIR) contract will have applications for both satellite and terrestrial communications. This power amplifier uses an innovative series bias arrangement of active devices to achieve over 40-percent efficiency at Ka-band frequencies with an output power of 0.66 W. The amplifier is fabricated on a 2.0- by 3.8-square millimeter chip through the use of Monolithic Microwave Integrated Circuit (MMIC) technology, and it uses state-of-the-art, Pseudomorphic High-Electron-Mobility Transistor (PHEMT) devices. Although the performance of the MMIC chip depends on these high-performance devices, the real innovations here are a unique series bias scheme, which results in a high-voltage chip supply, and careful design of the on-chip planar output stage combiner. This design concept has ramifications beyond the chip itself because it opens up the possibility of operation directly from a satellite power bus (usually 28 V) without a dc-dc converter. This will dramatically increase the overall system efficiency. Conventional microwave power amplifier designs utilize many devices all connected in parallel from the bias supply. This results in a low-bias voltage, typically 5 V, and a high bias current. With this configuration, substantial I(sup 2) R losses (current squared times resistance) may arise in the system bias-distribution network. By placing the devices in a series bias configuration, the total current is reduced, leading to reduced distribution losses. Careful design of the on-chip planar output stage power combiner is also important in minimizing losses. Using these concepts, a two-stage amplifier was designed for operation at 33 GHz and fabricated in a standard MMIC foundry process with 0.20-m PHEMT devices. Using a 20-V bias supply, the amplifier achieved efficiencies of over 40 percent with an output power of 0.66 W and a 16-dB gain over a 2-GHz bandwidth centered at 33 GHz. With a 28-V bias, a power level of 1.1 W was achieved with a 12-dB gain and a 36-percent efficiency. This represents the best reported combination of power and efficiency at this frequency. In addition to delivering excellent power and gain, this Ka-band MMIC power amplifier has an efficiency that is 10 percent greater than existing designs. The unique design offers an excellent match for spacecraft applications since the amplifier supply voltage is closely matched to the typical value of spacecraft bus voltage. These amplifiers may be used alone in applications of 1 W or less, or several may be combined or used in an array to produce moderate power, Ka-band transmitters with minimal power combining and less thermal stress owing to the combination of excellent efficiency and power output. The higher voltage operation of this design may also save mass and power because the dc-dc power converter is replaced with a simpler voltage regulator.

Source record↗
NASA NTRS2010

Recent Advancements in Lightning Jump Algorithm Work

In the past year, the primary objectives were to show the usefulness of total lightning as compared to traditional cloud-to-ground (CG) networks, test the lightning jump algorithm configurations in other regions of the country, increase the number of thunderstorms within our thunderstorm database, and to pinpoint environments that could prove difficult for any lightning jump configuration. A total of 561 thunderstorms have been examined in the past year (409 non-severe, 152 severe) from four regions of the country (North Alabama, Washington D.C., High Plains of CO/KS, and Oklahoma). Results continue to indicate that the 2 lightning jump algorithm configuration holds the most promise in terms of prospective operational lightning jump algorithms, with a probability of detection (POD) at 81%, a false alarm rate (FAR) of 45%, a critical success index (CSI) of 49% and a Heidke Skill Score (HSS) of 0.66. The second best performing algorithm configuration was the Threshold 4 algorithm, which had a POD of 72%, FAR of 51%, a CSI of 41% and an HSS of 0.58. Because a more complex algorithm configuration shows the most promise in terms of prospective operational lightning jump algorithms, accurate thunderstorm cell tracking work must be undertaken to track lightning trends on an individual thunderstorm basis over time. While these numbers for the 2 configuration are impressive, the algorithm does have its weaknesses. Specifically, low-topped and tropical cyclone thunderstorm environments are present issues for the 2 lightning jump algorithm, because of the suppressed vertical depth impact on overall flash counts (i.e., a relative dearth in lightning). For example, in a sample of 120 thunderstorms from northern Alabama that contained 72 missed events by the 2 algorithm 36% of the misses were associated with these two environments (17 storms).

Schultz, Christopher J.↗
NASA NTRS2013

Modeling and Evaluation of the Global Sea-Salt Aerosol Distribution: Sensitivity to Emission Schemes and Resolution Effects at Coastal/Orographic Sites

One of the major sources of uncertainty in model estimates of the global sea-salt aerosol distribution is the emission parameterization. We evaluate a new sea-salt aerosol life cycle module coupled to the online multi-scale chemical transport model NMMB/BSC-CTM. We compare 5 year global simulations using five state-of-the-art sea-salt open-ocean emission schemes with monthly averaged coarse aerosol optical depth (AOD) from selected AERONET sun photometers, surface concentration measurements from the University of Miami's Ocean Aerosol Network, and measurements from two NOAA/PMEL cruises (AEROINDOEX and ACE1). Model results are highly sensitive to the introduction of sea-surface-temperature (SST)-dependent emissions and to the accounting of spume particles production. Emission ranges from 3888 teragrams per year to 8114 teragrams per year, lifetime varies between 7.3 hours and 11.3 hours, and the average column mass load is between 5.0 teragrams and 7.2 teragrams. Coarse AOD is reproduced with an overall correlation of around 0.5 and with normalized biases ranging from +8.8 percent to +38.8 percent. Surface concentration is simulated with normalized biases ranging from minus 9.5 percent to plus 28 percent and the overall correlation is around 0.5. Our results indicate that SST-dependent emission schemes improve the overall model performance in reproducing surface concentrations. On the other hand, they lead to an overestimation of the coarse AOD at tropical latitudes, although it may be affected by uncertainties in the comparison due to the use of all-sky model AOD, the treatment of water uptake, deposition and optical properties in the model and/or an inaccurate size distribution at emission.

Bias↗
NASA NTRS1990

Parallelized reliability estimation of reconfigurable computer networks

A parallelized system, ASSURE, for computing the reliability of embedded avionics flight control systems which are able to reconfigure themselves in the event of failure is described. ASSURE accepts a grammar that describes a reliability semi-Markov state-space. From this it creates a parallel program that simultaneously generates and analyzes the state-space, placing upper and lower bounds on the probability of system failure. ASSURE is implemented on a 32-node Intel iPSC/860, and has achieved high processor efficiencies on real problems. Through a combination of improved algorithms, exploitation of parallelism, and use of an advanced microprocessor architecture, ASSURE has reduced the execution time on substantial problems by a factor of one thousand over previous workstation implementations. Furthermore, ASSURE's parallel execution rate on the iPSC/860 is an order of magnitude faster than its serial execution rate on a Cray-2 supercomputer. While dynamic load balancing is necessary for ASSURE's good performance, it is needed only infrequently; the particular method of load balancing used does not substantially affect performance.

Nicol, David M.↗
NASA NTRS1998

Tools for Atmospheric Radiative Transfer: Streamer and FluxNet

Two tools for the solution of radiative transfer problems are presented. Streamer is a highly flexible medium spectral resolution radiative transfer model based on the plane-parallel theory of radiative transfer. Capable of computing either fluxes or radiances, it is suitable for studying radiative processes at the surface or within the atmosphere and for the development of remote-sensing algorithms. FluxNet is a fast neural network-based implementation of Streamer for computing surface fluxes. It allows for a sophisticated treatment of radiative processes in the analysis of large data sets and potential integration into geophysical models where computational efficiency is an issue. Documentation and tools for the development of alternative versions of Fluxnet are available. Collectively, Streamer and FluxNet solve a wide variety of problems related to radiative transfer: Streamer provides the detail and sophistication needed to perform basic research on most aspects of complex radiative processes while the efficiency and simplicity of FluxNet make it ideal for operational use.

Key, Jeffrey R.↗
NASA NTRS2016

Enhanced Representation of Soil NO Emissions in the Community Multiscale Air Quality (CMAQ) Model Version 5.0.2

Modeling of soil nitric oxide (NO) emissions is highly uncertain and may misrepresent its spatial and temporal distribution. This study builds upon a recently introduced parameterization to improve the timing and spatial distribution of soil NO emission estimates in the Community Multiscale Air Quality (CMAQ) model. The parameterization considers soil parameters, meteorology, land use, and mineral nitrogen (N) availability to estimate NO emissions. We incorporate daily year-specific fertilizer data from the Environmental Policy Integrated Climate (EPIC) agricultural model to replace the annual generic data of the initial parameterization, and use a 12km resolution soil biome map over the continental USA. CMAQ modeling for July 2011 shows slight differences in model performance in simulating fine particulate matter and ozone from Interagency Monitoring of Protected Visual Environments (IMPROVE) and Clean Air Status and Trends Network (CASTNET) sites and NO2 columns from Ozone Monitoring Instrument (OMI) satellite retrievals. We also simulate how the change in soil NO emissions scheme affects the expected O3 response to projected emissions reductions.

remote sensing↗
NASA NTRSDate not supplied

QuantifyML: How good is my machine learning model?

This paper presents an approach, QuantifyML, which employs model counting to assess the learnability and robustness of machine learning models. Typically the efficacy of machine learning models is determined by computing their accuracy statistically on test data sets. However, this may be misleading, if the test data is not representative of the problem that is being studied. Further, two different models may have the same accuracy on a given data set, measured statistically, but may be very different in their behavior on unseen data. Also, models with high accuracy could have poor adversarial robustness. In QuantifyML, our goal is to precisely quantify the extent to which machine learning models have learned and generalized from the given data. In QuantifyML, a trained model is translated into a C program, which is fed to the CBMC model checking tool to produce a formula in Conjunctive Normal Form (CNF), which in turn is analyzed with state-of-the-art model counters to efficiently obtain precise counts w.r.t different outputs. QuantifyML enables i) evaluating the learnability of models by comparing the counts for the outputs to ground truth, expressed as logical predicates (if available), ii) comparing the performance of different models that may be built with different machine learning algorithms (e.g., decision-trees vs. neural networks), and iii) quantifying the robustness of trained models around given inputs. Our evaluation demonstrates these applications of QuantifyML on decision trees and neural networks trained to learn relational properties of graphs, for which we know the ground truth, and to perform image classification, for which we do not have the ground truth, but we can quantify local robustness.

Deep Neural Networks↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗
NASA NTRS1989

Morphological study of the innervation pattern of the rabbit sinoatrial node

The pattern of sinoatrial (SA) node innervations in rabbit was elucidated using a newly developed highly reproducible cholinesterase/silver impregnation staining procedure which made it possible to delineate large nerves, fine processes, and ganglion cells. The SA node and dominant pacemaker sites were identified by microelectrode recording. A generalized pattern of innnervation was recognized, which includes a large ganglionic complex inferior to the SA node; two or more moderately large nerves traversing the SA node parallel to the crista terminalis; nerves entering the intercaval region from the septum, the superior vena cava, and the inferior vena cava to impinge on the SA node; and a fine network of nerve processes, which was particularly dense in the SA node. From the location and distribution of the nerves and ganglionic branches, it can be inferred that the neural network in the intercaval region is capable of performing complex modulatory and integrative functions among the structures within this region.

Roberts, L. A.↗