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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Estimating Sea Surface Salinity and Wind Using Combined Passive and Active L-Band Microwave Observations

Several L-band microwave radiometer and radar missions have been, or will be, operating in space for land and ocean observations. These include the NASA Aquarius mission and the Soil Moisture Active Passive (SMAP) mission, both of which use combined passive/ active L-band instruments. Aquarius s passive/active L-band microwave sensor has been designed to map the salinity field at the surface of the ocean from space. SMAP s primary objectives are for soil moisture and freeze/thaw detection, but it will operate continuously over the ocean, and hence will have significant potential for ocean surface research. In this innovation, an algorithm has been developed to retrieve simultaneously ocean surface salinity and wind from combined passive/active L-band microwave observations of sea surfaces. The algorithm takes advantage of the differing response of brightness temperatures and radar backscatter to salinity, wind speed, and direction, thus minimizing the least squares error (LSE) measure, which signifies the difference between measurements and model functions of brightness temperatures and radar backscatter. The algorithm uses the conjugate gradient method to search for the local minima of the LSE. Three LSE measures with different measurement combinations have been tested. The first LSE measure uses passive microwave data only with retrieval errors reaching 1 to 2 psu (practical salinity units) for salinity, and 1 to 2 m/s for wind speed. The second LSE measure uses both passive and active microwave data for vertical and horizontal polarizations. The addition of active microwave data significantly improves the retrieval accuracy by about a factor of five. To mitigate the impact of Faraday rotation on satellite observations, the third LSE measure uses measurement combinations invariant under the Faraday rotation. For Aquarius, the expected RMS SSS (sea surface salinity) error will be less than about 0.2 psu for low winds, and increases to 0.3 psu at 25 m/s wind speed for warm waters (25 C). To achieve the required 0.2 psu accuracy, the impact of sea surface roughness (e.g. wind-generated ripples) on the observed brightness temperature has to be corrected to better than one tenth of a degree Kelvin. With this algorithm, the accuracy of retrieved wind speed will be high, varying from a few tenths to 0.6 m/s. The expected direction accuracy is also excellent (less than 10 ) for mid to high winds, but degrades for lower speeds (less than 7 m/s).

Yueh, Simon H.↗
NASA NTRS2014

Ground Data System Analysis Tools to Track Flight System State Parameters for the Mars Science Laboratory (MSL) and Beyond

Flight software parameters enable space mission operators fine-tuned control over flight system configurations, enabling rapid and dynamic changes to ongoing science activities in a much more flexible manner than can be accomplished with (otherwise broadly used) configuration file based approaches. The Mars Science Laboratory (MSL), Curiosity, makes extensive use of parameters to support complex, daily activities via commanded changes to said parameters in memory. However, as the loss of Mars Global Surveyor (MGS) in 2006 demonstrated, flight system management by parameters brings with it risks, including the possibility of losing track of the flight system configuration and the threat of invalid command executions. To mitigate this risk a growing number of missions have funded efforts to implement parameter tracking parameter state software tools and services including MSL and the Soil Moisture Active Passive (SMAP) mission. This paper will discuss the engineering challenges and resulting software architecture of MSL's onboard parameter state tracking software and discuss the road forward to make parameter management tools suitable for use on multiple missions.

Allard, Dan↗
NASA NTRSDate not supplied

Using AI/ML to Address Satellite Cloud Remote Sensing Challenges

Various AI/ML tools, employed within the Clouds and the Earth's Radiant Energy System (CERES) Satellite Cloud and Radiation Property retrieval System (SatCORPS) project, are being used to mitigate satellite radiance artifacts and thereby yield more accurate cloud and radiation data products. Neural network and K-nearest neighbor approaches have been developed that enable us to better address common passive satellite remote sensing challenges, such as corrupted imagery, day/night cloud property discontinuities, solar terminator artifacts, inadequate knowledge of the land surface emission temperature (i.e., skin temperature), and poor assumptions about vertical cloud structure, that have otherwise proven difficult to solve using more conventional methods. Fixing these problems promotes a more consistent Earth radiation budget record. These efforts demonstrate effective use of AI/ML architecture to exploit complex, multivariate predictor relationships and produce usable output at satellite spatial and temporal resolutions that would otherwise be ignored or have large biases.

Benjamin Scarino↗
NASA NTRS2012

Assessment of the Impacts of Radio Frequency Interference on SMAP Radar and Radiometer Measurements

The NASA Soil Moisture Active and Passive (SMAP) mission will measure soil moisture with a combination of Lband radar and radiometer measurements. We present an assessment of the expected impact of radio frequency interference (RFI) on SMAP performance, incorporating projections based on recent data collected by the Aquarius and SMOS missions. We discuss the impacts of RFI on the radar and radiometer separately given the differences in (1) RFI environment between the shared radar band and the protected radiometer band, (2) mitigation techniques available for the different measurements, and (3) existing data sources available that can inform predictions for SMAP.

radio frequency interference (RFI)↗
NASA NTRS1998

Contamination Observed on the Passive Optical Sample Assembly (POSA)-1 Experiment

The Passive Optical Sample Assembly-1 (POSA-1), part of the Mir Environmental Effects Payload (MEEP), was designed to study the combined effects of contamination, atomic oxygen, ultraviolet radiation, vacuum, thermal cycling, and other constituents of the space environment on spacecraft materials. The MEEP program is a Phase 1 International Space Station Risk Mitigation Experiment. The payload was attached by EVA to the exterior of the Mir docking module during the Space Shuttle mission STS-76. It was removed during STS-86 after exposure to the Mir environment for 18 months. POSA-1 consists of nearly 400 samples of thermal control paints, chemical conversion coatings, mirrors, optics, and other candidate materials for International Space Station Alpha (ISSA). POSA-1 also flew state-of-the-art materials, such as electrically conductive thermal control coatings developed under contract by IITRI and AZ Technology, and passive instruments for monitoring the atomic oxygen and ultraviolet radiation dose to the experiment. Contamination was detected on the POSA-1 experiment. On the side facing space (-Z(sub B) direction using the Mir coordinate system), visible contamination was observed. The contamination was uniform as would occur from a slow photodeposition process. A very definite film was deposited on the optical samples. The deposition appears to have directionality with definite shadowing effects. On the side facing the main Mr core (+ZB direction using the Mir coordinate system), no visible contamination was noted. Pre- and post-flight characterization of candidate spacecraft materials is discussed, particularly the effect of contamination on total hemispherical spectral reflectance (250 - 2500 nm), infrared emittance, the bi-directional reflectance distribution function (BRDF), and vacuum ultraviolet spectral reflectance. Extensive photography under normal and black light was performed. Results of electron spectroscopy for chemical analysis (ESCA) and Fourier Transform Infrared Analysis (FTIR) on selected samples are also discussed.

Zwiener, James M.↗
NASA NTRS2016

Assimilation of SMOS Brightness Temperatures or Soil Moisture Retrievals into a Land Surface Model

Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40 degree incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

SMOS↗
NASA NTRS2011

From Paper to Production to Test: An Update on NASA's J-2X Engine for Exploration

The NASA/industry team responsible for developing the J-2X upper stage engine for the Space Launch System (SLS) Program has made significant progress toward moving beyond the design phase and into production, assembly, and test of development hardware. The J-2X engine exemplifies the SLS Program goal of using proven technology and experience from more than 50 years of United States spaceflight experience combined with modern manufacturing processes and approaches. It will power the second stage of the fully evolved SLS Program launch vehicle that will enable a return to human exploration of space beyond low earth orbit. Pratt & Whitney Rocketdyne (PWR) is under contract to develop and produce the engine, leveraging its flight-proven LH2/LOX, gas generator cycle J-2 and RS-68 engine capabilities, recent experience with the X-33 aerospike XRS-2200 engine, and development knowledge of the J-2S tap-off cycle engine. The J- 2X employs a gas generator operating cycle designed to produce 294,000 pounds of vacuum thrust in primary operating mode with its full nozzle extension. With a truncated nozzle extension suitable to support engine clustering on the stage, the nominal vacuum thrust level in primary mode is 285,000 pounds. It also has a secondary mode, during which it operates at 80 percent thrust by altering its mixture ratio. The J-2X development philosophy is based on proven hardware, an aggressive development schedule, and early risk reduction. NASA Marshall Space Flight Center (MSFC) and PWR began development of the J-2X in June 2006. The government/industry team of more than 600 people within NASA and PWR successfully completed the Critical Design Review (CDR) in November 2008, following extensive risk mitigation testing. Assembly of the first development engine was completed in May 2011 and the first engine test was conducted at the NASA Stennis Space Center (SSC), test stand A2, on 14 July 2011. Testing of the first development engine will continue through the autumn of 2011, be paused for test stand modifications to the passive diffuser, and then restart in the spring of 2012. This testing will be followed by specialized powerpack testing intended to examine the design and operating margins of the engine turbomachinery. The development plan beyond this point leads through more system-level, engine testing of several samples, analytical model validation activities, functional and performance verification, and then ultimate certification to support human spaceflight. This paper will discuss the J-2X development background, provide top-level information on design and development planning, and will explore some of the development challenges and mitigation activities pursued to date.

Kynard, Michael↗
NASA NTRS2018

Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding

As spacecraft send back increasing amounts of telemetry data, improved anomaly detection systems are needed to lessen the monitoring burden placed on operations engineers and reduce operational risk. Current spacecraft monitoring systems only target a subset of anomaly types and often require costly expert knowledge to develop and maintain due to challenges involving scale and complexity. We demonstrate the effectiveness of Long Short-Term Memory (LSTMs) networks, a type of Recurrent Neural Network (RNN), in overcoming these issues using expert-labeled telemetry anomaly data from the Soil Moisture Active Passive (SMAP) satellite and the Mars Science Laboratory (MSL) rover, Curiosity. We also propose a complementary unsupervised and nonparametric anomaly thresholding approach developed during a pilot implementation of an anomaly detection system for SMAP, and offer false positive mitigation strategies along with other key improvements and lessons learned during development.

Soderstrom, Tom↗
NASA NTRSDate not supplied

Assessment of Cloud Fraction Derived from a New Geostationary Satellite Cloud Retrieval Algorithm for CERES and Progress Towards Cross-platform Continuity

For over two decades The Clouds and the Earth’s Radiant Energy System (CERES) project has produced long-term records of top-of-atmosphere (TOA) and surface irradiances for detecting changes in the Earth’s radiation budget and advancing understanding of how clouds contribute to those changes. Accurate characterization of the spatial and temporal distributions of clouds are a critical component for producing CERES datasets. The CERES Cloud Working Group (CWG) derives cloud properties from both geostationary (GEO) and low-Earth orbit (LEO) satellite sensors in order to provide complete global coverage at hourly temporal resolution. However, the use of multiple sensors to provide this spatiotemporal coverage throughout a long-term record presents some challenges, because the various sensors generally have different spectral band characteristics (e.g., spectral band width and response) and spatial resolution. These differences can result in spatial artifacts at the coverage boundary between two sensors or temporal artifacts when one sensor replaces another in the record. For the upcoming Edition 5 release of CERES products, the CWG is developing cloud retrieval algorithms which utilize only spectral bands common to most modern passive satellite radiometers and account for differences in spectral width and response. The goal with this approach is to provide global cloud properties for CERES with greater cross-platform consistency than the previous Edition 4 products and thus mitigate artifacts which are evident at the interface of two sensors. This study focuses on retrieval of total cloud fraction from various GEO sensors, and we use Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations to assess the accuracy of total cloud fraction derived from the various sensors. The Edition 5 cloud mask algorithm depends on estimates of cloud-free TOA spectral radiances to differentiate clear and cloudy conditions, and CALIOP is used to assess the accuracy of those estimates when cloud-free conditions are indeed observed.

CALIOP↗
NASA NTRS1982

Technology thrusts for passive remote sensing. Sensing Technology Panel report

Technology thrusts for broadband spectrometry and gas filter radiometry development are identified. The development of 'smart' processing and cryogenics/cooling systems for all types of broadband spectrometers is advised. The use of large fixed gratings and the development of 1000-element arrays is recommended. Improvements suggested for interferometers are: the mitigation of background fluctuations; the development of multiaperature, multiband interferometers; and provision of in-flight alignment verification. Specifically recommended for gas filter radiometers are the development of gas filter cell technology, of liner and high dynamic range detectors, and of high uniform optical elements.

Source record↗
NASA NTRS2017

Improved Calibration through SMAP RFI Change Detection

Anthropogenic Radio-Frequency Interference (RFI) drove both the SMAP (Soil Moisture Active Passive) microwave radiometer hardware and Level 1 science algorithm designs to use new technology and techniques for the first time on a spaceflight project. Care was taken to provide special features allowing the detection and removal of harmful interference in order to meet the error budget. Nonetheless, the project accepted a risk that RFI and its mitigation would exceed the 1.3-K error budget. Thus, RFI will likely remain a challenge afterwards due to its changing and uncertain nature. To address the challenge, we seek to answer the following questions: How does RFI evolve over the SMAP lifetime? What calibration error does the changing RFI environment cause? Can time series information be exploited to reduce these errors and improve calibration for all science products reliant upon SMAP radiometer data? In this talk, we address the first question.

Passive L-band radiometer↗
NASA NTRS2019

Thermal Development of a Commercial off the Shelf (COTS) Camera for Exploration Upper Stage (EUS)

NASA’s Flight Imagery Launch Monitoring Real-time System (FILMRS) cameras were originally developed for the Space Launch System (SLS) Core Stage. These Commercial Off the Shelf (COTS) cameras have been redesigned and reduced by an order of magnitude in size for the Exploration Upper Stage (EUS). The change in thermal environment has led to the application of various passive thermal control methods and the addition of a heater option. This paper will give a summary of the design and development test effort associated with adapting the COTS camera for the demands of the space environment and associated thermal mitigations applied as the project prepares to complete the design. The application of this camera for other space systems is discussed.

Hernandez, Deborah↗
NASA NTRS2026

PERSIANN-Unet: A Global Deep Learning Framework for Near-Real-Time Precipitation Estimation Using Infrared Data

Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster mitigation. Traditional tools such as rain gauges and radar networks, though effective, have limitations, including sparse coverage in remote areas and high operational costs. Satellite data, with its global coverage and high spatial and temporal resolutions, mitigates limitations in coverage. Satellite precipitation products like Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) utilize both geosynchronous thermal infrared (IR) and passive microwave (PMW) data in their operation. PMW sensors offer detailed atmospheric profiles but suffer from higher latency, whereas IR sensors provide lower latency but only capture cloud-top information. Despite this constraint, IR data remains attractive for low-latency precipitation estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces PERSIANN-Unet (PUnet or PERSIANN V3), a quasi-global algorithm covering 60°N–60°S that combines IR data, monthly climatology, and the UNet architecture to produce half-hourly precipitation estimates at 0.04° resolution. The product is evaluated against HE, IMERG, and PDIR-Now for 2022–2023. Results show that PUnet closely matches its training target, IMERG V07 Final, at the global scale, and performance is further evaluated against Stage IV as a reference over CONUS. Training PUnet on IMERG (2016–2021) leverages a high-quality, integrated PMW IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PUnet avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.

Phu Nguyen↗
NASA NTRS2016

Performance Analysis of a Hardware Implemented Complex Signal Kurtosis Radio-Frequency Interference Detector

Radio-frequency interference (RFI) is a known problem for passive remote sensing as evidenced in the L-band radiometers SMOS, Aquarius and more recently, SMAP. Various algorithms have been developed and implemented on SMAP to improve science measurements. This was achieved by the use of a digital microwave radiometer. RFI mitigation becomes more challenging for microwave radiometers operating at higher frequencies in shared allocations. At higher frequencies larger bandwidths are also desirable for lower measurement noise further adding to processing challenges. This work focuses on finding improved RFI mitigation techniques that will be effective at additional frequencies and at higher bandwidths. To aid the development and testing of applicable detection and mitigation techniques, a wide-band RFI algorithm testing environment has been developed using the Reconfigurable Open Architecture Computing Hardware System (ROACH) built by the Collaboration for Astronomy Signal Processing and Electronics Research (CASPER) Group. The testing environment also consists of various test equipment used to reproduce typical signals that a radiometer may see including those with and without RFI. The testing environment permits quick evaluations of RFI mitigation algorithms as well as show that they are implementable in hardware. The algorithm implemented is a complex signal kurtosis detector which was modeled and simulated. The complex signal kurtosis detector showed improved performance over the real kurtosis detector under certain conditions. The real kurtosis is implemented on SMAP at 24 MHz bandwidth. The complex signal kurtosis algorithm was then implemented in hardware at 200 MHz bandwidth using the ROACH. In this work, performance of the complex signal kurtosis and the real signal kurtosis are compared. Performance evaluations and comparisons in both simulation as well as experimental hardware implementations were done with the use of receiver operating characteristic (ROC) curves. The complex kurtosis algorithm has the potential to reduce data rate due to onboard processing in addition to improving RFI detection performance.

microwaves↗
NASA NTRS2016

Performance Analysis of a Hardware Implemented Complex Signal Kurtosis Radio-Frequency Interference Detector

Radio-frequency interference (RFI) is a known problem for passive remote sensing as evidenced in the L-band radiometers SMOS, Aquarius and more recently, SMAP. Various algorithms have been developed and implemented on SMAP to improve science measurements. This was achieved by the use of a digital microwave radiometer. RFI mitigation becomes more challenging for microwave radiometers operating at higher frequencies in shared allocations. At higher frequencies larger bandwidths are also desirable for lower measurement noise further adding to processing challenges. This work focuses on finding improved RFI mitigation techniques that will be effective at additional frequencies and at higher bandwidths. To aid the development and testing of applicable detection and mitigation techniques, a wide-band RFI algorithm testing environment has been developed using the Reconfigurable Open Architecture Computing Hardware System (ROACH) built by the Collaboration for Astronomy Signal Processing and Electronics Research (CASPER) Group. The testing environment also consists of various test equipment used to reproduce typical signals that a radiometer may see including those with and without RFI. The testing environment permits quick evaluations of RFI mitigation algorithms as well as show that they are implementable in hardware. The algorithm implemented is a complex signal kurtosis detector which was modeled and simulated. The complex signal kurtosis detector showed improved performance over the real kurtosis detector under certain conditions. The real kurtosis is implemented on SMAP at 24 MHz bandwidth. The complex signal kurtosis algorithm was then implemented in hardware at 200 MHz bandwidth using the ROACH. In this work, performance of the complex signal kurtosis and the real signal kurtosis are compared. Performance evaluations and comparisons in both simulation as well as experimental hardware implementations were done with the use of receiver operating characteristic (ROC) curves.

radiometers↗
NASA NTRS2023

Modeling and Mitigation of dv/dt and Transmission Line Effect for Motor Drive System in Electric Aircraft Propulsion

Due to constraints in space and structure as well as other practical limitations in aircraft system, the motor controller or electric motor are often installed far away from each other and interconnected via long cables. This amplifies the transmission line effect excited by the dv/dt PWM voltage and induces high voltage stress on the machine winding. This paper investigates the state-of-art modeling approaches for such systems, identifies several limitations in matching the tested voltage reflection waveform in terms of peak voltage, oscillation frequency and damping speed, and offers several key considerations not well covered in literature or in engineering practice. It then proposes a practical modeling methodology which, as validated by experimental results, truly captures these critical reflection voltage specs. To mitigate the high peak voltage and strong oscillation, dv/dt filter is applied and a parametric design optimization based on widely adopted LC-R topology is illustrated, which not only alleviates the voltage stress but also minimized the power loss of the passive damping resistor.

Inductor↗
NASA NTRS2019

Experimental Study on Mitigation of Lifetime-Limiting Dielectric Cracking in Extreme Temperature 4H-SiC JFET Integrated Circuits

While NASA Glenn Research Center's "Generation 10" 4H-SiC Junction Field Effect Transistor (JFET) integrated circuits (ICs) have uniquely demonstrated 500 °C electrical operation for durations of over a year, this experimental work has also revealed that physical cracking of chip dielectric passivation layers ultimately limits extreme-environment operating lifetime [1-3]. The prevention of such dielectric passivation cracks should therefore improve IC high temperature durability and yield, leading to more beneficial technology adoption into aerospace, automotive, and energy systems. This report describes Generation 10.2, 11.1, and 11.2 die tested under unbiased and unpackaged accelerated age testing at 500 °C, 600 °C, 720 °C, and 800 °C in air-atmosphere ovens for 100- and 200-hour duration. Additional samples were separately subjected to 10 thermal cycles between the same high temperatures (with 10-hour high-temperature soak each cycle) and 50 °C. It is shown that having two stoichiometric Si3N4 layers in the interconnect dielectric stack substantially decreases the amount of dielectric cracking observed following these oven tests.

Spry, D. J.↗