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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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NASA NTRS2012

High Operating Temperature Midwave Quantum Dot Barrier Infrared Detector (QD-BIRD)

The nBn or XBn barrier infrared detector has the advantage of reduced dark current resulting from suppressed Shockley-Read-Hall (SRH) recombination and surface leakage. High performance detectors and focal plane arrays (FPAs) based on InAsSb absorber lattice matched to GaSb substrate, with a matching AlAsSb unipolar electron barrier, have been demonstrated. The band gap of lattice-matched InAsSb yields a detector cutoff wavelength of approximately 4.2 ??m when operating at ~150K. We report results on extending the cutoff wavelength of midwave barrier infrared detectors by incorporating self-assembled InSb quantum dots into the active area of the detector. Using this approach, we were able to extend the detector cutoff wavelength to ~6 ?m, allowing the coverage of the full midwave infrared (MWIR) transmission window. The quantum dot barrier infrared detector (QD-BIRD) shows infrared response at temperatures up to 225 K.

quantum dot↗

NASA NTRS2010

HYTHIRM Radiance Modeling and Image Analyses in Support of STS-119, STS-125 and STS-128 Space Shuttle Hypersonic Re-entries

We provide the first geometrically accurate (i.e., 3-D) temperature maps of the entire windward surface of the Space Shuttle during hypersonic reentry. To accomplish this task we began with estimated surface temperatures derived from CFD models at integral high Mach numbers and used them, the Shuttle's surface properties and reasonable estimates of the sensor-to-target geometry to predict the emitted spectral radiance from the surface (in units of W sr-1 m-2 nm-1). These data were converted to sensor counts using properties of the sensor (e.g. aperture, spectral band, and various efficiencies), the expected background, and the atmosphere transmission to inform the optimal settings for the near-infrared and midwave IR cameras on the Cast Glance aircraft. Once these data were collected, calibrated, edited, registered and co-added we formed both 2-D maps of the scene in the above units and 3-D maps of the bottom surface in temperature that could be compared with not only the initial inputs but also thermocouple data from the Shuttle itself. The 3-D temperature mapping process was based on the initial radiance modeling process. Here temperatures were guessed for each node in a well-resolved 3-D framework, a radiance model was produced and compared to the processed imagery, and corrections to the temperature were estimated until the iterative process converged. This process did very well in characterizing the temperature structure of the large asymmetric boundary layer transition the covered much of the starboard bottom surface of STS-119 Discovery. Both internally estimated accuracies and differences with CFD models and thermocouple measurements are at most a few percent. The technique did less well characterizing the temperature structure of the turbulent wedge behind the trip due to limitations in understanding the true sensor resolution. (Note: Those less inclined to read the entire paper are encouraged to read an Executive Summary provided at the end.)

Gibson, David M.↗

NASA NTRS2018

NASA's GMAO Atmospheric Motion Vectors Simulator: Description and Application to the MISTiC Winds Concept

An atmospheric wind vectors (AMVs) simulator was developed by NASA's GMAO to simulate observations from future satellite constellation concepts. The synthetic AMVs can then be used in OSSEs to estimate and quantify the potential added value of new observations to the present Earth observing system and, ultimately, the expected impact on the current weather forecasting skill. The GMAO AMV simulator is a tunable and flexible computer code that is able to simulate AMVs expected to be derived from different instruments and satellite orbit configurations. As a case study and example of the usefulness of this tool, the GMAO AMV simulator was used to simulate AMVs envisioned to be provided by the MISTiC Winds, a NASA mission concept consisting of a constellation of satellites equipped with infrared spectral midwave spectrometers, expected to provide high spatial and temporal resolution temperature and humidity soundings of the troposphere that can be used to derive AMVs from the tracking of clouds and water vapor features. The GMAO AMV simulator identifies trackable clouds and water vapor features in the G5NR and employs a probabilistic function to draw a subset of the identified trackable features. Before the simulator is applied to the MISTiC Winds concept, the simulator was calibrated to yield realistic observations counts and spatial distributions and validated considering as a proxy instrument to the MISTiC Winds the Himawari-8 Advanced Imager (AHI). The simulated AHI AMVs showed a close match with the real AHI AMVs in terms of observation counts and spatial distributions, showing that the GMAO AMVs simulator synthesizes AMVs observations with enough quality and realism to produce a response from the DAS equivalent to the one produced with real observations. When applied to the MISTiC Winds scanning points, it can be expected that the MISTiC Winds will be able to collect approximately 60,000 wind observations every 6 hours, if considering a constellation composed of 12 satellites (4 orbital planes). In addition, one of the main expected impacts of the MISTiC Winds concept is the ability to derive water vapor feature tracking AMVs below 500-400 hPa, an unique feature among the water vapor AMVs derived from the current Earth observing system.

Carvalho, David↗

NASA NTRS2018

NASA's GMAO Atmospheric Motion Vectors Simulator: Description and Application to the MISTiC Winds Concept

Carvalho, David↗

DOE OSTI2021

NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms

The 2017-2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a "Designated Targeted Observable" (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380 - 2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3-5 µm; TIR: 8-12 µm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. Finally, this effort synthesizes the findings of more than 130 scientists.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2021

NASA's Surface Biology and Geology Designated Observable: A Perspective on Surface Imaging Algorithms

The 2017–2027 National Academies’ Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algo-rithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

Aquatic↗

NASA NTRS2001

Infrared Sensing Aeroheating Flight Experiment: STS-96 Flight Results

Major elements of an experiment called the Infrared Sensing Aeroheating Flight Experiment are discussed. The primary experiment goal is to provide reentry global temperature images from infrared measurements to define the characteristics of hypersonic boundary-layer transition during flight. Specifically, the experiment is to identify, monitor, and quantity hypersonic boundary layer windward surface transition of the X-33 vehicle during flight. In addition, the flight data will serve as a calibration and validation of current boundary layer transition prediction techniques, provide benchmark laminar, transitional, and fully turbulent global aeroheating data in order to validate existing wind tunnel and computational results, and to advance aeroheating technology. Shuttle Orbiter data from STS-96 used to validate the data acquisition and data reduction to global temperatures, in order to mitigate the experiment risks prior to the maiden flight of the X-33, is discussed. STS-96 reentry midwave (3-5 micron) infrared data were collected at the Ballistic Missile Defense Organization/Innovative Sciences and Technology Experimentation Facility site at NASA-Kennedy Space Center and subsequently mapped into global temperature contours using ground calibrations only. A series of image mapping techniques have been developed in order to compare each frame of infrared data with thermocouple data collected during the flight. Comparisons of the ground calibrated global temperature images with the corresponding thermocouple data are discussed. The differences are shown to be generally less than about 5%, which is comparable to the expected accuracy of both types of aeroheating measurements.

Blanchard, Robert C.↗

DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: Run in real-time on the edge (≥ 12 Hz), Classification: Achieve less than 5% false positive detection, Classification: Achieve ≥ 95% methane plume detection rate, Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time. In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗

DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy’s National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI’s Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: 1. Run in real-time on the edge (≥ 12 Hz) 2. Classification: Achieve less than 5% false positive detection 3. Classification: Achieve ≥ 95% methane plume detection rate 4. Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗

NASA NTRS1993

Infrared sensors and systems for enhanced vision/autonomous landing applications

There exists a large body of data spanning more than two decades, regarding the ability of infrared imagers to 'see' through fog, i.e., in Category III weather conditions. Much of this data is anecdotal, highly specialized, and/or proprietary. In order to determine the efficacy and cost effectiveness of these sensors under a variety of climatic/weather conditions, there is a need for systematic data spanning a significant range of slant-path scenarios. These data should include simultaneous video recordings at visible, midwave (3-5 microns), and longwave (8-12 microns) wavelengths, with airborne weather pods that include the capability of determining the fog droplet size distributions. Existing data tend to show that infrared is more effective than would be expected from analysis and modeling. It is particularly more effective for inland (radiation) fog as compared to coastal (advection) fog, although both of these archetypes are oversimplifications. In addition, as would be expected from droplet size vs wavelength considerations, longwave outperforms midwave, in many cases by very substantial margins. Longwave also benefits from the higher level of available thermal energy at ambient temperatures. The principal attraction of midwave sensors is that staring focal plane technology is available at attractive cost-performance levels. However, longwave technology such as that developed at FLIR Systems, Inc. (FSI), has achieved high performance in small, economical, reliable imagers utilizing serial-parallel scanning techniques. In addition, FSI has developed dual-waveband systems particularly suited for enhanced vision flight testing. These systems include a substantial, embedded processing capability which can perform video-rate image enhancement and multisensor fusion. This is achieved with proprietary algorithms and includes such operations as real-time histograms, convolutions, and fast Fourier transforms.

Kerr, J. Richard↗

NASA NTRS2017

Intelligent multi-spectral IR image segmentation

We present a neural network based multi-‐spectral image segmentation method. A neural network is trained on the selected features of both the objects and background in the longwave (LW) Infrared (IR) images. Multiple iterations of training are performed until the accuracy of the segmentation reaches satisfactory level. The segmentation boundary of the LW image is used to segment the midwave (MW) and shortwave (SW) IR images. A second neural network detects the local discontinuities and refines the accuracy of the local boundaries. The neural net based segmentation method is compared with Wavelet-‐threshold and Grab-‐Cut methods. Test results have shown increased accuracy and robustness of this segmentation scheme for multi-‐spectral IR images.

Torres, Gilbert↗

NASA NTRS2012

Measurement of Two-Phase Flow and Heat Transfer Parameters using Infrared Thermometry

A novel technique to measure heat transfer and liquid film thickness distributions over relatively large areas for two-phase flow and heat transfer phenomena using infrared (IR)thermometry is described. IR thermometry is an established technology that can be used to measure temperatures when optical access to the surface is available in the wavelengths of interest. In this work, a midwave IR camera (3.6-5.1 microns) is used to determine the temperature distribution within a multilayer consisting of a silicon substrate coated with a thin insulator. Since silicon is largely transparent to IR radiation, the temperature of the inner and outer walls of the multilayer can be measured by coating selected areas with a thin, IR opaque film. If the fluid used is also partially transparent to IR, the flow can be visualized and the liquid film thickness can be measured. The theoretical basis for the technique is given along with a description of the test apparatus and data reduction procedure. The technique is demonstrated by determining the heat transfer coefficient distributions produced by droplet evaporation and flow boiling heat transfer.

Kim, Tae-Hoon↗