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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 1,387 records · Page 77

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NASA NTRS2008

Adaptive Flight Control Research at NASA

A broad overview of current adaptive flight control research efforts at NASA is presented, as well as some more detailed discussion of selected specific approaches. The stated objective of the Integrated Resilient Aircraft Control Project, one of NASA s Aviation Safety programs, is to advance the state-of-the-art of adaptive controls as a design option to provide enhanced stability and maneuverability margins for safe landing in the presence of adverse conditions such as actuator or sensor failures. Under this project, a number of adaptive control approaches are being pursued, including neural networks and multiple models. Validation of all the adaptive control approaches will use not only traditional methods such as simulation, wind tunnel testing and manned flight tests, but will be augmented with recently developed capabilities in unmanned flight testing.

Motter, Mark A.↗

NASA NTRS2009

Delay/Disruption Tolerant Networks (DTN): Testing and Demonstration for Lunar Surface Applications

This slide presentation reviews the testing of the Delay/Disruption Tolerant Network (DTN) designed for use with Lunar Surface applications. This is being done through the DTN experimental Network (DEN), that permit access and testing by other NASA centers, DTN team members and protocol developers. The objective of this work is to demonstrate DTN for high return applications in lunar scenarios, provide DEN connectivity with analogs of Constellation elements, emulators, and other resources from DTN Team Members, serve as a wireless communications staging ground for remote analog excursions and enable testing of detailed communication scenarios and evaluation of network performance. Three scenarios for DTN on the Lunar surface are reviewed: Motion imagery, Voice and sensor telemetry, and Navigation telemetry.

Source record↗

NASA NTRS2011

The GOES-R Series Geostationary Lightning Mapper (GLM)

The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. Superior spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), which will have just completed Critical Design Review and move forward into the construction phase of instrument development. The GLM will operate continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development (an engineering development unit and 4 flight models), a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms, cal/val performance monitoring tools, and new applications. Proxy total lightning data from the NASA Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional ground-based lightning networks are being used to develop the pre-launch algorithms, test data sets, and applications, as well as improve our knowledge of thunderstorm initiation and evolution. In this presentation we review the planned implementation of the instrument and suite of operational algorithms

Goodman, Steven J.↗

NASA NTRS2011

High Impact Weather Forecasts and Warnings with the GOES-R Geostationary Lightning Mapper (GLM)

The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. A major advancement over the current GOES include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM). The GLM will operate continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development, a GOES-R Risk Reduction Science Team and Algorithm Working Group Lightning Applications Team have begun to develop cal/val performance monitoring tools and new applications using the GLM alone, in conjunction with other instruments, and merged or blended integrated observing system products combining satellite, radar, in-situ and numerical models. Proxy total lightning data from the NASA Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional ground-based lightning networks are being used to develop the pre-launch algorithms, test data sets, and applications, as well as improve our knowledge of thunderstorm initiation and evolution. In this presentation we review the planned implementation of the instrument and suite of operational algorithms.

Goodman, Steven↗

NASA NTRS2011

Nocturnal Aerosol Optical Depth Measurements with a Small-Aperture Automated Photometer Using the Moon as a Light Source

A method is described that enables the use of lunar irradiance to obtain nighttime aerosol optical depth (AOD) measurements using a small-aperture photometer. In this approach, the U.S. Geological Survey lunar calibration system was utilized to provide high-precision lunar exoatmospheric spectral irradiance predictions for a ground-based sensor location, and when combined with ground measurement viewing geometry, provided the column optical transmittance for retrievals of AOD. Automated multiwavelength lunar measurements were obtained using an unmodified Cimel-318 sunphotometer sensor to assess existing capabilities and enhancements needed for day/night operation in NASA s Aerosol Robotic Network (AERONET). Results show that even existing photometers can provide the ability for retrievals of aerosol optical depths at night near full moon. With an additional photodetector signal-to-noise improvement of 10-100, routine use over the bright half of the lunar phase and a much wider range of wavelengths and conditions can be achieved. Although the lunar cycle is expected to limit the frequency of observations to 30%-40% compared to solar measurements, nevertheless this is an attractive extension of AERONET capabilities.

Berkoff, Timothy A.↗

NASA NTRS2011

High Impact Weather Forecasts and Warnings with the GOES-R Geostationary Lightning Mapper (GLM)

Goodman, Steven J.↗

NASA NTRSDate not supplied

Simulations to Inform NASA's Future Lightning Missions

3-D monitoring of lightning with optical and VHF sensors in LEOCLIDE. CubeSpark is a constellation of small satellites acting as a 3D lightning mapping network in space. VHF radio measurements map lightning structure inside clouds. Bispectral, high-resolution optical measurements enhance detection of lightning in severe and anomalous thunderstorms and flashes that extend upward from cloud-top.

lightning↗

DOE OSTI2021

A Hardware Platform for Studying Naval Power Electronic Power Distribution Systems

Abstract – Future intelligent ship system designs will likely include electric propulsion, numerous highpower sensors, and directed energy weapons. Supply and control of these large nonlinear loads will require a networked, multi-converter power electronic power distribution system. This work presents a hardware platform to emulate a microgrid power system with multiple power converters and a power data communication network. The platform is reconfigurable and can include both AC and DC power distribution zones, representative of shipboard power systems. It also allows for the study of both power control actuation and power data communication delays. Since the platform is based on electric power hardware, spatial and temporal uncertainties are inherently embedded in the system. Specifically, this work examines the control actuation of multiple pulsed power loads in a single microgrid. Several pulse load levels and operating scenarios have been implemented and measured. A framework for control parameter quantification is presented, and various metrics are explored to capture pulse signal characteristics. The sensitivity of pulse load metric parameters is analyzed. Dynamic shipboard, mission-specific load profiles coupled with pulse loads can also be emulated in the hardware platform.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2018

New Neural Network Cloud Mask Algorithm Based on Radiative Transfer Simulations

Cloud detection and screening constitute critically important first steps required to derive many satellite data products. Traditional threshold-based cloud mask algorithms require a complicated design process and fine tuning for each sensor, and they have difficulties over areas partially covered with snow/ice. Exploiting advances in machine learning techniques and radiative transfer modeling of coupled environmental systems, we have developed a new, threshold-free cloud mask algorithm based on a neural network classifier driven by extensive radiative transfer simulations. Statistical validation results obtained by using collocated CALIOP and MODIS data show that its performance is consistent over different ecosystems and significantly better than the MODIS Cloud Mask (MOD35 C6) during the winter seasons over snow-covered areas in the mid-latitudes. Simulations using a reduced number of satellite channels also show satisfactory results, indicating its flexibility to be configured for different sensors. Comparedto threshold-based methods and previous machine-learning approaches, this new cloud mask (i) does not rely on thresholds, (ii) needs fewer satellite channels, (iii) has superior performance during winter seasons in mid-latitude areas, and (iv) can easily be applied to different sensors.

cloud mask algorithms↗

NASA NTRS1992

Analysis and modification of a single-mesh gear fatigue rig for use in diagnostic studies

A single-mesh gear fatigue rig was analyzed and modified for use in gear mesh diagnostic research. The fatigue rig allowed unwanted vibration to mask the test-gear vibration signal, making it difficult to perform diagnostic studies. Several possible sources and factors contributing to the unwanted components of the vibration signal were investigated. Sensor mounting location was found to have a major effect on the content of the vibration signal. In the presence of unwanted vibration sources, modal amplification made unwanted components strong. A sensor location was found that provided a flatter frequency response. This resulted in a more useful vibration signal. A major network was performed on the fatigue rig to reduce the influence of the most probable sources of the noise in the vibration signal. The slave gears were machined to reduce weight and increase tooth loading. The housing and the shafts were modified to reduce imbalance, looseness, and misalignment in the rotating components. These changes resulted in an improved vibration signal, with the test-gear mesh frequency now the dominant component in the signal. Also, with the unwanted sources eliminated, the sensor mounting location giving the most robust representation of the test-gear meshing energy was found to be at a point close to the test gears in the load zone of the bearings.

Zakrajsek, James J.↗

DOE OSTI2024

Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter

A novel method to reconstruct the energy of hadronic showersin the CMS High Granularity Calorimeter (HGCAL) is presented. TheHGCAL is a sampling calorimeter with very fine transverse andlongitudinal granularity. The active media are silicon sensors andscintillator tiles readout by SiPMs and the absorbers are acombination of lead and Cu/CuW in the electromagnetic section, andsteel in the hadronic section. The shower reconstruction method isbased on graph neural networks and it makes use of a dynamicreduction network architecture. It is shown that the algorithm isable to capture and mitigate the main effects that normally hinderthe reconstruction of hadronic showers using classicalreconstruction methods, by compensating for fluctuations in themultiplicity, energy, and spatial distributions of the shower'sconstituents. The performance of the algorithm is evaluated usingtest beam data collected in 2018 prototype of the CMS HGCALaccompanied by a section of the CALICE AHCAL prototype. Thecapability of the method to mitigate the impact of energy leakagefrom the calorimeter is also demonstrated.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1979

Evaluation of the spatial and temporal measurement requirements of remote sensors for monitoring regional air pollution episodes

The spatial and temporal measurement requirements of satellite sensors for monitoring regional air pollution episodes were evaluated. Use was made of two sets of data from the Sulfate Regional Experiment (SURE), which provided the first ground-based aerosol measurements from a regional-scale station network. The sulfate data were analyzed for two air pollution episode cases. The results of the analysis indicate that the key considerations required for episode mapping from satellite sensors are the following: (1) detection of sulfate levels exceeding 20 micron-g/cu m; (2) capability to view a broad area (of the order of 1500 km swath) because of regional extent of pollution episodes; (3) spatial resolution sufficient to detect variations in sulfate levels of greater than 10 micron-g/cu m over distances of the order of 50 to 75 km; (4) repeat coverage at least on a daily basis; and (5) satellite observations during the mid to late morning local time, when the sulfate levels have begun to increase after the early morning minimum levels, and convective-type cloud cover has not yet increased to the amount reached later in the afternoon. Analysis of the satellite imagery shows that convective clouds can obscure haze patterns. Additional parameters based on spectral analysis include wavelength and bandwidth requirements.

Burke, H. H. K.↗

NASA NTRS2013

NASA Orbital Debris Large-Object Baseline Population in ORDEM 3.0

The NASA Orbital Debris Program Office (ODPO) has created and validated high fidelity populations of the debris environment for the latest Orbital Debris Engineering Model (ORDEM 3.0). Though the model includes fluxes of objects 10 um and larger, this paper considers particle fluxes for 1 cm and larger debris objects from low Earth orbit (LEO) through Geosynchronous Transfer Orbit (GTO). These are validated by several reliable radar observations through the Space Surveillance Network (SSN), Haystack, and HAX radars. ORDEM 3.0 populations were designed for the purpose of assisting, debris researchers and sensor developers in planning and testing. This environment includes a background derived from the LEO-to-GEO ENvironment Debris evolutionary model (LEGEND) with a Bayesian rescaling as well as specific events such as the FY-1C anti-satellite test, the Iridium 33/Cosmos 2251 accidental collision, and the Soviet/Russian Radar Ocean Reconnaissance Satellite (RORSAT) sodium-potassium droplet releases. The environment described in this paper is the most realistic orbital debris population larger than 1 cm, to date. We describe derivations of the background population and added specific populations. We present sample validation charts of our 1 cm and larger LEO population against Space Surveillance Network (SSN), Haystack, and HAX radar measurements.

Krisco, Paula H.↗

DOE OSTI2018

Domain-Adapted Convolutional Networks for Satellite Image Classification: A Large-Scale Interactive Learning Workflow

Satellite imagery often exhibits large spatial extent areas that encompass object classes with considerable variability. This often limits large-scale model generalization with machine learning algorithms. Notably, acquisition conditions, including dates, sensor position, lighting condition, and sensor types, often translate into class distribution shifts introducing complex nonlinear factors and hamper the potential impact of machine learning classifiers. Here, this article investigates the challenge of exploiting satellite images using convolutional neural networks (CNN) for settlement classification where the class distribution shifts are significant. We present a large-scale human settlement mapping workflow based-off multiple modules to adapt a pretrained CNN to address the negative impact of distribution shift on classification performance. To extend a locally trained classifier onto large spatial extents areas we introduce several submodules: First, a human-in-the-loop element for relabeling of misclassified target domain samples to generate representative examples for model adaptation; second, an efficient hashing module to minimize redundancy and noisy samples from the mass-selected examples; and third, a novel relevance ranking module to minimize the dominance of source example on the target domain. The workflow presents a novel and practical approach to achieve large-scale domain adaptation with binary classifiers that are based-off CNN features. Experimental evaluations are conducted on areas of interest that encompass various image characteristics, including multisensors, multitemporal, and multiangular conditions. Domain adaptation is assessed on source–target pairs through the transfer loss and transfer ratio metrics to illustrate the utility of the workflow.

47 OTHER INSTRUMENTATION↗

NASA NTRS2006

Ikhana: A NASA UAS Supporting Long Duration Earth Science Missions

NASA's Ikhana unmanned aerial vehicle (UAV) is a General Atomics MQ-9 Predator-B modified to support the conduct of Earth science missions for the NASA Science Mission Directorate through partnerships, other government agencies and universities. Ikhana, a Native American word meaning 'intelligence', can carry over 2000 lbs of atmospheric and remote sensing instruments in the payload bay and external pods. The aircraft is capable of mission durations in excess of 24 hours at altitudes above 40,000 ft. Redundant flight control, avionics, power, and network systems increase the system reliability and allow easier access to public airspace. The aircraft is remotely piloted from a mobile ground control station (GCS) using both C-band line-of-sight and Ku-band over-the-horizon satellite datalinks. NASA's GCS has been modified to support on-site science monitoring, or the downlink data can be networked to remote sites. All ground support systems are designed to be deployable to support global Eart science investigations. On-board support capabilities include an instrumentation system and an Airborne Research Test System (ARTS). The ARTS can host research algorithms that will autonomously command and control on-board sensors, perform sensor health monitoring, conduct data analysis, and request changes to the flight plan to maximize data collection. The ARTS also has the ability to host algorithms that will autonomously control the aircraft trajectory based on sensor needs, (e.g. precision trajectory for repeat pass interferometry) or to optimize mission objectives (e.g. search for specific atmospheric conditions). Standard on-board networks will collect science data for recording and for inclusion in the aircraft's high bandwidth downlink. The Ikhana project will complete GCS development, science support systems integration, external pod integration and flight clearance, and operations crew training in early 2007. A large-area remote sensing mission is currently scheduled for the Summer 2007.

Cobleigh, Brent R.↗

DOE OSTI2024

Comprehensive AI-based System for Control, Sensor Estimation, and Fault Detection of Cascaded Multilevel Inverters

In this paper, an Artificial Intelligence-based (AI) system is proposed for an 11-level cascaded H-bridge multilevel inverter (MLI) with the aims of harmonic suppression and reliability enhancement. The system consists of three seamlessly integrated Neural Networks (NNs). First, a multilayer perceptron is used to generalize the optimal switching angles for selective harmonic elimination under non-equal DC voltages. Next, an autoencoder NN estimates the voltage sensor readings to address potential drifting. Finally, a perceptron NN detects inverter faults based solely on the output voltage of the MLI. Simulation scenarios were evaluated, and the results show that the proposed system provides a comprehensive solution for the robust operation of the MLI. The proposed solution is capable of minimizing the targeted harmonics orders with minimal impact on the fundamental voltage, even when the voltage sensor drifts. Furthermore, the inverter under fault conditions was successfully identified.

Rezende da Costa Reis Kimpara, Renata↗

NASA NTRSDate not supplied

SWIPE: Spectral Water Inversion Processor and Emulator

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will be discussing the progress made developing SWIPE: Spectral Water Inversion Processor and Emulator. SWIPE is a platform for advanced modeling of coastal and inland aquatic habitats. The goal is create a comprehensive and cohesive system to leverage recent advancements in computation and machine learning to develop a synthetic training ground for sensitivity studies and algorithm development. The four principal facets of SWIPE include: 1. Advanced two-layer coated sphere bio-optical modeling and GPU radiative transfer modeling, 2. Big Data involving massive synthetic spectral libraries of optical properties of various global aquatic particles, surface reflectance, and top-of-atmosphere reflectance, all at hyperspectral resolution leveraging high-end computing systems at NASA Ames Research Center, 3. Deep Learning for algorithm development for water quality inversion of concentrations of common biogeophysical variables as well as optics, full uncertainty characterization by water type, and forward emulation, and lastly, 4. Image Processing for application of developed retrieval algorithms for both hyperspectral and multispectral sensors with experimental corrections for global adjacency, noise, sunglint, and benthic reflectance. This presentation will demonstrate the Equivalent Algal Populations (EAP) two-layer coated sphere scattering model which has been used develop spectral libraries of hyperspectral inherent optical properties of roughly 80 species of phytoplankton, covering 15 different classes and nine taxonomic functional types. The EAP model was also used to derive spectral properties of 10 different non-algal particle functional types. Examples of how the SMART-G (Speed-up Monte-carlo Advanced Radiative Transfer using GPU) radiative transfer code is used to model optically complex aquatic signals will be presented and discussed in the context of creating a massive synthetic database which can leverage the full power of next generation machine learning techniques and high end computing for water quality inversion. We will discuss our active investigation in things like appropriate model architectures, dimensionality reduction techniques such as PCA and autoencoders, uncertainty quantification and abstaining, and which variables actually benefit most from hyperspectral information versus multispectral resolution. We are also curious about questions relating to cost/benefit analysis in terms of computation resources, neural network complexity, and data volumes. Answers to these questions will hopefully elaborate on cost efficiency for potential future sensor design considerations.

SWIPE↗

DOE OSTI2020

AmeriFlux US-xBA NEON Barrow Environmental Observatory (BARR)

This is the AmeriFlux version of the carbon flux data for the site US-xBA NEON Barrow Environmental Observatory (BARR). Site Description - Located three hundred miles north of the Arctic Circle, the Barrow field site landscape is representative of the polygon tundra and lake systems across the northern extent of the North Slope of Alaska. Barrow spans 50 square kilometers and includes a 26’ tall meteorological flux tower and nearby soil sensors. NEON scientists also collect field observations throughout the field site.

Network), NEON (National Ecological Observatory↗