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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 NTRSDate not supplied

Parameter, Post-Processing Sensitivities, and Qualification Approach of Laser Powder Bed Fusion Hydrogen Resistant Alloy NASA HR-1

Metal additive manufacturing (AM) processes are being used to enable economical manufacturing of legacy alloys as well as advancing new alloys. Laser powder bed fusion (L-PBF) is a metal AM process that has high maturity and being used to produce a variety of parts for space applications including complex propulsion components. The National Aeronautics and Space Administration (NASA) has identified the need to develop and advance new materials in unique space applications such as high-pressure hydrogen environments. NASA HR-1 is a high strength Fe-Ni based superalloy designed to resist high pressure hydrogen environment embrittlement (HEE), oxidation, and corrosion that has been successfully adapted to laser powder directed energy deposition (LP-DED). Insights gained from the NASA HR-1 development for LP-DED have guided the development process for L-PBF. However, adapting NASA HR-1 to L-PBF posed new challenges due to the distinct differences between the additive manufacturing processes. During parameter development, sensitivities were observed in post-processing that necessitated additional optimization of heat treatments. Additionally, the variations in thickness and how it influenced the microstructural response during heat treatment was characterized. Understanding these sensitivities is important to qualification of the material in a L-PBF machine. This ensures that the microstructures and properties of the material maintain consistency in production. This presentation will cover parameter development along with post-processing challenges and solutions will be discussed in addition to key material properties as it pertains to application performance and qualification per NASA-STD-6030. Improvements made by developing a derivative alloy, NASA HR-2, will be highlighted through preliminary small scale parameter development, material characterization, and initial property testing.

NASA HR-1↗
DOE OSTI2024

Artificial ground reflector size and position effects on energy yield and economics of single‐axis‐tracked bifacial photovoltaics

Abstract Artificial ground reflectors improve bifacial energy yield by increasing both front and rear‐incident irradiance. Studies have demonstrated an increase in energy yield due to the addition of artificial reflectors; however, they have not addressed the effect of varying reflector dimensions and placement on system performance and the impact of these parameters on the reflectors' financial viability. We studied the effect of high albedo (70% reflective) artificial reflectors on single‐axis‐tracked bifacial photovoltaic systems through ray‐trace modeling and field measurements. In the field, we tested a range of reflector configurations by varying reflector size and placement and demonstrated that reflectors increased daily energy yield up to 6.2% relative to natural albedo for PERC modules. To confirm the accuracy of our model, we compared modeled and measured power and found a root mean square error (RMSE) of 5.4% on an hourly basis. We modeled a typical meteorological year in Golden, Colorado, to demonstrate the effects of artificial reflectors under a wide range of operating conditions. Seventy percent reflective material can increase total incident irradiance by 1.9%–8.6% and total energy yield by 0.9%–4.5% annually after clipping is considered with a DC–AC ratio of 1.2. Clipping has a significant effect on reflector impact and must be included when assessing reflector viability because it reduces reflector energy gain. We calculated a maximum viable cost for these improvements of up to $2.50–4.60/m 2 , including both material and installation, in Golden. We expanded our analysis to cover a latitude range of 32–48°N and demonstrated that higher‐latitude installations with lower energy yield and higher diffuse irradiance content can support higher reflector costs. In both modeling and field tests, and for all locations, the ideal placement of the reflectors was found to be directly underneath the module due to the optimized rear irradiance increase.

14 SOLAR ENERGY↗
NASA NTRS1988

Fiber waveguide sensors for intelligent materials

This report, an addendum to the six month report submitted to NASA Langley Research Center in December 1987, covers research performed by the Fiber and Electro-Optics Research Center (FEORC) at Virginia Tech for the NASA Langley Research Center, Grant NAG1-780, for the period from December 1987 to June 1988. This final report discusses the research performed in the following four areas as described in the proposal: Fabrication of Sensor Fibers Optimized for Embedding in Advanced Composites; Fabrication of Sensor Fiber with In-Line Splices and Evaluation via OTR methods; Modal Domain Optical Fiber Sensor Analysis; and Acoustic Fiber Waveguide Implementation.

Flax, A. R.↗
NASA NTRS1990

Multilevel decomposition approach to the preliminary sizing of a transport aircraft wing

A multilevel/multidisciplinary optimization scheme for sizing an aircraft wing structure is described. A methodology using nonlinear programming in application to a very large engineering problem is presented. This capability is due to the decomposition approach. Over 1300 design variables are considered for this nonlinear optimization task. In addition, a mathematical link is established coupling the detail of structural sizing to the overall system performance objective, such as fuel consumption. The scheme is implemented as a three level system analyzing aircraft mission performance at the top level, the total aircraft structure as the middle level, and individual stiffened wing skin cover panels at the bottom level. Numerical show effectiveness of the method and its good convergence characteristics.

Wrenn, Gregory A.↗
NASA NTRS1990

Electronic Neural Networks

Viewgraphs on electronic neural networks for space station are presented. Topics covered include: electronic neural networks; electronic implementations; VLSI/thin film hybrid hardware for neurocomputing; computations with analog parallel processing; features of neuroprocessors; applications of neuroprocessors; neural network hardware for terrain trafficability determination; a dedicated processor for path planning; neural network system interface; neural network for robotic control; error backpropagation algorithm for learning; resource allocation matrix; global optimization neuroprocessor; and electrically programmable read only thin-film synaptic array.

Thakoor, Anil↗
NASA NTRS1998

Fabrication and Resistivity of IBr Intercalated Vapor-Grown Carbon Fiber Composites

Composites using vapor-crown carbon fibers (VGCF), the most conductive of the carbon fiber types, are attractive for applications where low density, high strength, and at least moderate conductivity are required, such as electromagnetic interference shielding covers for spacecraft. The conductivity can be enhanced another order of magnitude by intercalation of the VGCF. If a high Z intercalate is used, the protection of components from ionizing radiation can be enhanced also. Thus, the intercalation of VGCF with IBr is reported. Since composite testing is required to verify properties, the intercalation reaction optimization, stability of the intercalation compound, scale-up of the intercalation reaction, composite fabrication, and resistivity of the resulting composites is also reported. The optimum conditions for low resistivity and uniformity for the scaled up reaction (20-30 g of product) were 114 C for at least 72 hr, yielding a fiber with a resistivity of 8.7+/-2 micro-Omega-cm. The thermal stability of these fibers was poor, with degradation occurring at temperatures as low as 40 C in air, though they were insensitive to water vapor. Composite resistivity was 20-30 micro-Omega-cm, as measured by contactless conductivity measurements, about a factor of five higher than would be expected from a simple rule of mixtures. The addition of 1.0 percent Br2, intercalated microfibers increased the resistivity of the composites by more than 20 percent.

Gaier, James R.↗
NASA NTRS2003

The Extreme Ultraviolet Normal Incidence Spectrograph (EUNIS)

EUNIS (Extreme Ultraviolet Normal Incidence Spectrograph) is a high-efficiency extreme ultraviolet spectrometer that is expected to fly for the first time in 2004 as a sounding rocket payload. Using two independent optical systems, EUNIS will probe the structure and dynamics of the inner solar corona high spectral resolution in two wavelength regions: 17-21 nm with 3.5 pm resolution and 30-37 nm with 7 pm resolution. The long wavelength channel includes He II 30.4 nm and strong lines from Fe XI-XVI; the short wavelength channel includes strong lines of Fe IX-XIII. Angular resolution of 2 arcsec is maintained along a slit covering a full solar radius. EUNIS will have 100 times the throughput of the highly successful SERTS payloads that have preceded it. There are only two reflections in each optical channel, from the superpolished, off-axis paraboloidal primary and the toroidal grating. Each optical element is coated with a high-efficiency multilayer coating optimized for its spectral bandpass. The detector in each channel is a microchannel plate image intensifier fiber- coupled to three 1K x 1K active pixel sensors. EUNIS will obtain spectra with a cadence as short as 1 sec, allowing unprecedented studies of the physical properties of evolving and transient structures. Diagnostics of wave heating and reconnection wil be studied at heights above 2 solar radii, in the wind acceleration region. The broad spectral coverage and high spectral resolution will provide superior temperature and density diagnostics and will enable underflight calibration of several orbital instruments, including SOHO/CDS and EIT, TRACE, Solar-B/EIS, and STEREO/EUVI. EUNIS is supported by NASA through the Low Cost Access to Space Program in Solar and Heliospheric Physics.

Oegerle, William↗
NASA NTRS2016

Coupled Retrieval of Aerosol Properties and Surface Reflection Using the Airborne Multi-Angle SpectroPolarimetric Imager (AirMSPI)

The Airborne Multi-angle SpectroPolarimetric Imager (AirMSPI) has been flying aboard the NASA ER-2 high altitude aircraft since October 2010. In step-and-stare operation mode, AirMSPI acquires radiance and polarization data at 355, 380, 445, 470*, 555, 660*, 865*, and 935 nm (* denotes polarimetric bands). The imaged area covers about 10 km by 10 km and is observed from 9 view angles between ±67° off of nadir. We have developed an efficient and flexible code that uses the information content of AirMSPI data for a coupled retrieval of aerosol properties and surface reflection. The retrieval was built based on the multi-pixel optimization concept, with the use of a hybrid radiative transfer model that combines the Markov Chain and adding/doubling methods. The convergence and robustness of our algorithm is ensured by applying constraints on (a) the spectral variation of the Bidirectional Polarization Distribution Function (BPDF) and angular shape of the Bidirectional Reflectance Distribution Function (BRDF); (b) the spectral variation of aerosol optical properties; and (c) the spatial variation of aerosol parameters across neighboring image pixels. Our retrieval approach has been tested using over 20 AirMSPI datasets having low to moderately high aerosol loadings (~0.02<AOD550-nm<~0.45) and acquired during several field campaigns. Results are compared with AERONET aerosol reference data. We also explore the benefits of AirMSPI’s ultraviolet and polarimetric bands as well as the use of multiple view angles.

Xu, Feng↗
NASA NTRS2018

Water Across Synthetic Aperture Radar Data (WASARD): SAR Water Body Classification for the Open Data Cube

The detection of inland water bodies from Synthetic Aperture Radar (SAR) data provides a great advantage over water detection with optical data, since SAR imaging is not impeded by cloud cover. Traditional methods of detecting water from SAR data involves using thresholding methods that can be labor intensive and imprecise. This paper describes Water Across Synthetic Aperture Radar Data (WASARD): a method of water detection from SAR data which automates and simplifies the thresholding process using machine learning on training data created from Geoscience Australia’s WOFS algorithm. Of the machine learning models tested, the Linear Support Vector Machine was determined to be optimal, with the option of training using solely the VH polarization or a combination of the VH and VV polarizations. WASARD was able to identify water in the target area with a correlation of 97% with WOFS. Sentinel-1, Open Data Cube, Earth Observations, Machine Learning, Water Detection 1. INTRODUCTION Water classification is an important function of Earth imaging satellites, as accurate remote classification of land and water can assist in land use analysis, flood prediction, climate change research, as well as a variety of agricultural applications [2]. The ability to identify bodies of water remotely via satellite is immensely cheaper than contracting surveys of the areas in question, meaning that an application that can accurately use satellite data towards this function can make valuable information available to nations which would not be able to afford it otherwise. Highly reliable applications for the remote detection of water currently exist for use with optical satellite data such as that provided by LANDSAT. One such application, Geoscience Australia’s Water Observations from Space (WOFS) has already been ported for use with the Open Data Cube [6]. However, water detection using optical data from Landsat is constrained by its relatively long revisit cycle of 16 days [5], and water detection using any optical data is constrained in that it lacks the ability to make accurate classifications through cloud cover [2]. The alternative solution which solves these problems is water detection using SAR data, which images the Earth using cloud-penetrating microwaves. Because of its advantages over optical data, much research has been done into water detection using SAR data. Traditionally, this has been done using the thresholding method, which involves picking a polarization band and labeling all pixels for which this band’s value is below a certain threshold as containing water. The thresholding method works since water tends to return a much lower backscatter value to the satellite than land [1]. However, this method can be flawed since estimating the proper threshold is often imprecise, complicated, and labor intensive for the end user. Thresholding also tends to use data from only one SAR polarization, when a combination of polarizations can provide insight into whether water is present. [2] In order to alleviate these problems, this paper presents an application for the Open Data Cube to detect water from SAR data using support vector machine (SVM) classification. 2. PLATFORM WASARD is an application for the Open Data Cube, a mechanism which provides a simple yet efficient means of ingesting, storing, and retrieving remote sensing data. Data can be ingested and made analysis ready according to whatever specifications the researcher chooses, and easily resampled to artificially alter a scene’s resolution. Currently WASARD supports water detection on scenes from ESA’s Sentinel-1 and JAXA’s ALOS. When testing WASARD, Sentinel-1 was most commonly used due to its relatively high spatial resolution and its rapid 6 day revisit cycle [5]. With minor alterations to the application's code, however, it could support data from other satellites. 3. METHODOLOGY Using supervised classification, WASARD compares SAR data to a dataset pre-classified by WOFS in order to train an SVM classifier. This classifier is then used to detect water in other SAR scenes outside the training set. Accuracy was measured according to the following metrics:  Precision: a measure of what percentage of the points WASARD labels as water are truly water  Recall: a measure of what percentage of the total water cover WASARD was able to identify.  F1 Score: a harmonic average of the precision and recall scores Both precision and recall are calculated at the end of the training phase, when the trained classifier is compared to a testing dataset. Because the WOFS algorithm’s classifications are used as the truth values when training a WASARD classifier, when precision and recall are mentioned in this paper, they are always with respect to the values produced by WOFS on a similar scene of Landsat data, which themselves have a classification accuracy of 97% [6]. Visual representations of water identified by WASARD in this paper were produced using the function wasard_plot(), which is included in WASARD. 3.1 Algorithm Selection The machine learning model used by WASARD is the Linear Support Vector Machine (SVM). This model uses a supervised learning algorithm to develop a classifier, meaning it creates a vector which can be multiplied by the vector formed by the relevant data bands to determine whether a pixel in a SAR scene contains water. This classifier is trained by comparing data points from selected bands in a SAR scene to their respective labels, which in this case are “water” or “not water” as given by the WOFS algorithm. The SVM was selected over the Random Forest model, which outperformed the SVM in training speed, but had a greater classification time and lower accuracy, and the Multilayer Perceptron Artificial Neural Network, which had a slightly higher average accuracy than the SVM, but much greater training and classification times. Figure 1: Visual representation of the SVM Classifier. Each white point represents a pixel in a SAR scene. In Figure 1, the diagonal line separating pixels determined to be water from those determined not to be water represents the actual classification vector produced by the SVM. It is worth noting that once the model has been trained, classification of pixels is done in a similar manner as in the thresholding method. This is especially true if only one band was used to train the model. 3.1 Feature Selection Sentinel-1 collects data from two bands: the Vertical/Vertical polarization (VV) and the Vertical/Horizontal polarization (VH). When 100 SVM classifiers were created for each polarization individually, and for the combination of the two, the following results were achieved: Figure 2: Accuracy of classifiers trained using different polarization bands. Precision and Recall were measured with respect to the values produced by WOFS. Figure 2 demonstrates that using both the VV and VH bands trades slightly lower recall for significantly greater precision when compared with the VH band alone, and that using the VV band alone is inferior in both metrics. WASARD therefore defaults to using both the VV and VH bands, and includes the option to use solely the VH band. The VV polarization’s lower precision compared to the VH polarization is in contrast to results from previous research and may merit further analysis [4]. 3.2 Training a Classifier The steps in training a classifier with WASARD are 1. Selecting two scenes (one SAR, one optical) with the same spatial extents, and acquired close to each other in time, with a preference that the scenes are taken on the same day. 2. Using the WOFS algorithm to produce an array of the detected water in the scene of optical data, to be used as the labels during supervised learning 3. Data points from the selected bands from the SAR acquisition are bundled together into an array with the corresponding labels gathered from WOFS. A random sample with an equal number of points labeled “Water” and “Not Water” is selected to be partitioned into a training and a testing dataset 4. Using Scikit-Learn’s LinearSVC object, the training dataset is used to produce a classifier, which is then tested against the testing dataset to determine its precision and recall The result is a wasard_classifier object, which has the following attributes: 1. f1, recall, and precision: 3 metrics used to determine the classifier’s accuracy 2. Coefficient: Vector which the SVM uses to make its predictions. The classifier detects water when the dot product of the coefficient and the vector formed by the SAR bands is positive 3. Save(): allows a user to save a classifier to the disk in order to use it without retraining 4. wasard_classify(): Classifies an entire xarray of SAR data using the SVM classifier All of the above steps are performed automatically when the user creates a wasard_classifier object. 3.3 Classifying a Dataset Once the classifier has been created, it can be used to detect water in an xarray of SAR data using wasard_classify(). By taking the dot product of the classifier’s coefficients and the vector formed by the selected bands of SAR data, an array of predictions is constructed. A classifier can effectively be used on the same spatial extents as the ones where it was trained, or on any area with a similar landscape. While

Kreiser, Zachary↗
NASA NTRS1998

An Overview of Observations of Mars' North Polar Region From the Mars Global Surveyor Laser Altimeter

Since its arrival at Mars on September l5, 1997, the Mars Global Surveyor (MGS) has been in a near-polar elliptical orbit, with the orbital eccentricity decreasing during orbital periapse passes where the spacecraft aerobrakes through the martian atmosphere. The Mars Orbiter Laser Altimeter (MOLA), an instrument on the MGS, has the ability to range to the martian surface during nonaerobraking passes. MOLA can operate whenever the range from the spacecraft to the surface is less than 786 km, with the limit determined by the number of bits encoded for the range measurement During the capture orbit, aerobraking hiatus, and science phasing orbit (SPO) mission phases, MOLA acquired approximately 200 profiles across the northern hemisphere of Mars and provided more than 2,000,000 measurements of the radius of the planet. These observations cover the region from the north pole to about 10 degrees S latitude with a precision of a few tens of centimeters and an accuracy (at present) of about 30 in. Absolute accuracy of the elevations is limited by the knowledge of the MGS orbits; these should improve later in the mission due to a more optimal tracking geometry, an improved gravitational field, and the use of the high-gain antenna once the spacecraft achieves its approximately 400-km-altitude circular mapping orbit. MOLA measurements so far show a planet with a low, flat high-latitude region in the north and a higher, topographically rougher terrain nearer the equator. The north polar cap stands approximately 2-3 km above the surrounding terrain and displays deep chasms and complex structure. MOLA measurements of elevation, 1064-nm reflectivity, and backscattered pulse width indicate that the layered terrains are composed mainly of ice.

Smith, D. E.↗
NASA NTRSDate not supplied

Uncovering Hazards Using a Multi-Objective Optimization to Explore the Faulty State-Space

Considering resilience when designing complex engineered systems is crucial to ensure the system is safe under unexpected hazardous scenarios. Traditional risk-based approaches, such as Failure Modes and Effects Analysis (FMEA) are useful for designing the system to mitigate hazardous scenarios that can be identified by the designer, but often require experience or prior knowledge of system failures to generate. More recently, researchers have developed simulation tools that enable the designer to model large sets of hazardous scenarios (driven by both internal faults and external factors) through simulation. While these tools enable a wider scope of fault modes to be evaluated (e.g., by injecting combined set of fault modes or injecting modes at different times), the resulting assessments (like FMEA) still require knowledge of the specific modes to be evaluated. However, failure to analyze a wide variety of fault scenarios can lead to an incomplete picture of the system resilience, especially to "surprise events'' which may be difficult for the designer to identify and predict beforehand. To overcome this challenge, previous work developed a fault sampling approach for resilience simulations which would procedurally-generate a wide variety of potential faults by systematically perturbing the health states of the system. While the resulting fault modes generated covered a much larger space hazards than would be otherwise considered (and identified many unique failure trajectories which would not have otherwise been identified), it also significantly increased the computational cost of the analysis and resulted in the simulation and analysis of a large set of essentially duplicate scenarios. Additionally, as the number of dimensions in the faulty state-space increases, the full elaboration of possible modes becomes computationally infeasible, justifying the use of a more targeted search. To resolve this limitation, this work proposes the use of a multiobjective optimization algorithm to search the health state space for potential fault modes that are both (1) hazardous and (2) unique. To solve this type of problem, this work proposes the use of a cooperative co-evolutionary algorithm. To demonstrate this approach, it will be applied to a model of an autonomous rover which uses line markings to navigate, focusing on potential hazards in the drive system which could cause the rover to crash. To determine the merit of the approach, it will further be compared with the previously-presented range elaboration approach and a random mode generation approach on the basis of computational efficiency and found modes.

Resilience↗
NASA NTRS2024

Launch Complex 34, SWMU CC054 2023 DNAPL Source Zone Operations, Maintenance, and Monitoring, Site-Wide Long-Term Monitoring, and Hot Spot 6 Air Sparge System Annual Performance Monitoring and Phase Two Expansion Construction Completion Report Cape Canaveral Space Force Station, Florida

This Annual Performance Monitoring Report (PMR) for the Dense Non-Aqueous Phase Liquid (DNAPL) Source Zone (DSZ), Site-Wide Long-Term Monitoring (LTM), and Hot Spot 6 (HS 6) Air Sparge (AS) System presents the results of Year 14 operations and performance monitoring of the hydraulic containment (HC) Interim Measure (IM), details associated with construction and implementation of the HS 6 AS system expansion (Phase Two), and the results of operations and performance sampling of the HS 6 AS IM at Launch Complex 34 (LC34), located at Cape Canaveral Space Force Station (CCSFS), Florida. The timeframe for activities documented in this PMR extends from April 1, 2023 to March 31, 2024. LC34 has been designated Solid Waste Management Unit CC054 under the Kennedy Space Center (KSC) Resource Conservation and Recovery Act Corrective Action Program. The objective of the HC IM at LC34 is to contain the shallow and deep DSZ and surrounding dissolved-phase trichloroethene (TCE) high concentration plume via operation of a hydraulic containment system (HCS). The pre-IM design 300 micrograms per liter (μg/L) TCE groundwater contour was used to establish the deep zone capture area for deep recovery wells, and the shallow zone capture area was defined by the DSZ. The system began operating in 2010, and in 2015, the system was expanded to provide HC for areas within the 300 μg/L TCE groundwater isocontours of HS 3 and 4. In 2018 and 2019, an investigation was conducted to recharacterize the DSZ, which included investigating TCE mass in Layer 7. This data was subsequently used to optimize the pumping rates of the HCS and install additional recovery wells in Layer 7 to more adequately capture residual contaminant mass. The operational period for Year 14 of the HCS was from April 1, 2023 to March 31, 2024. Operational runtime for the system was 94 percent during Year 14, with downtime events attributed to planned maintenance, system repairs, and power outages. As of March 31, 2024, a total of 344,849,634 cumulative gallons of groundwater containing 94,656 pounds of chlorinated volatile organic compounds (CVOCs) have been removed by the HCS. During the reporting period covered under this report, the HCS recovered 31,176,393 gallons and approximately 6,319 pounds of CVOC mass. Total combined influent concentrations of TCE have decreased since startup from approximately 280,000 µg/L (January 2010) to 25,000 µg/L (March 2024). During the reporting period, all effluent concentrations from the HCS (aqueous and vapor) were below regulatory reporting limits, indicating the system continues to operate as intended. Performance monitoring was conducted in January 2024 within the DSZ to evaluate TCE contamination. Groundwater samples were collected via DPT at nine locations, consistent with previous events between 2017 and 2022. Full vertical profile sampling was completed at each DPT from 8 to 98 feet below land surface (bls), at 5-foot intervals. The DPT performance monitoring results are summarized in this PMR. The results revealed TCE remains at concentrations greater than 11,000 µg/L in the DSZ (1-percent solubility, indicative of DNAPL) at eight of the nine DPT locations and at depths ranging from 28 to 98 feet bls. An overall decreasing trend of TCE concentrations was observed in DPT samples during this reporting period, which is a reduction from the previous event (December 2022) and the peak event in December 2021, where TCE percentages appeared to increase in all depth zones because several recovery wells were turned off during the AS pilot study in the DSZ. The maximum TCE concentration in January 2024 was 1,600,000 µg/L in the 53 feet bls depth interval at DPT594 (previous maximum result in 2022 was 1,800,000 µg/L in the 48 feet bls depth interval at DPT599). This maximum concentration in the 53 feet bls depth interval is in the deep capture zone. During the January 2024 DPT event, the largest portion of TCE mass was observed in the 48 feet bls interval above/within Layer 4. This trend remains consistent with previous years and appears to indicate continued mass discharge from Layer 4 (fine-grained unit). In addition to DPT sampling, annual groundwater samples were collected from 11 deep monitoring wells in the DSZ area (Layers 7 and 8) in December 2023 to verify vertical and horizontal delineation. Three of the wells were also sampled biweekly to evaluate operations of recovery well RW21D (screened 86 to 106 feet bls), which was installed in January 2023. Of the Layer 7/8 monitoring sampled only annually, results were non-detect or less than groundwater cleanup target levels GCTLs in December 2023, with the exception of one well, IW45D2, which had a cis-1,2-dichloroethene (cDCE), detection greater than the GCTL. Of the three wells sampled biweekly during the operational period, the well located closest to Layer 7 recovery well RW21D (IW44D2, screened 105 to 115 feet bls) had concentrations of TCE, cDCE and vinyl chloride (VC) greater than GCTLs throughout the operational period, but displayed a decreasing trend since the peak concentrations in September 2023. The maximum TCE concentration during this operational period was 190,000 µg/L at IW44D2 in September 2023, but reduced to 700 µg/L in March 2024, indicating the HCS is still effectively removing mass from the source area. Expansion of the HCS and addition of new recovery wells is ongoing and will continue to be evaluated as the groundwater recovery scheme is optimized. Details of the expansion and optimization will be provided in a future PMR. The HS 6 AS IM was initiated in 2018 with 160 AS wells and expanded in 2019 with another 140 AS wells. An additional expansion of the HS 6 AS IM was completed during the reporting period covered under this report and details of the construction implementation and startup of the expansion are detailed in Section III of this report. The new expansion, referred to as Phase Two, was implemented between August 17, 2022 and August 28, 2023, and included the installation of 190 air sparge wells to treat an additional 11.2 acres. The original configuration (referred to as Phase One) operated until Phase Two came online, then all but 52 AS wells were turned off so the components could be moved and utilized in the Phase Two area. The 52 AS wells that remain on are in a barrier configuration preventing contaminated groundwater from impacting the treated area. The HS 6 AS system (both Phase One and Two) operated normally during the reporting period covered under this report. Semi-annual performance monitoring of the Phase One configuration was conducted in April and November 2023, consistent with previous years. For the Phase Two configuration, 21 new monitoring wells were installed and sampled quarterly, with a baseline event in July 2023, and quarterly events in November 2023 and February 2024 summarized in this report. Semi-annual monitoring results collected in April and October 2023 show concentrations of contaminants of concern (cDCE, trans-1,2-dichloroethene, and VC) have decreased to less than GCTLs in nearly all wells and not impacting the surface water drainage canal, indicating the HS 6 IM continues to meet objectives. The baseline and quarterly sampling for the Phase Two configuration indicate generally decreasing concentrations in wells within and around the perimeter of the treatment area. At least two more quarters of monitoring will be conducted and once those results are evaluated a reduced the sampling frequency may be considered. Overall, the tasks associated with Year 14 operation of the HC IM and operation of the HS 6 AS IM were performed in accordance with recommendations included in the previous 2022 LC34 (Year 13) PMR. Evaluation of results from the HC IM and HS 6 IM show that these systems are operating as designed and meeting performance objectives.

groundwater remediation↗
NASA NTRS1974

Eight cm technology thruster development

A structural integrated ion thruster with 8-cm beam diameter (SIT-8) was developed for attitude control and stationkeeping of synchronous satellites. As optimized, the system demonstrates a thrust T=1.14 mlb (not corrected for beam V sub B = 1200 V (I sub sp = 2200 sec) total propellant utilization efficiency nu sub u = 59.8% (is approximately 72% without auxiliary pulse-igniter electrode), and electrical efficiency n sub E 61.9%. The thruster incorporates a wire-mesh anode and tantalum cover surfaces to control discharge chamber flake formation and employs an auxiliary pulse-igniter electrode for hollow-cathode ignition. When the SIT-8 is integrated with the compatible SIT-5 propellant tankage, the system envelope is 35 cm long by 13 cm flange bolt circle with a mass of 9.8 kg including 6.8 kg of mercury propellant. Two thrust vectoring systems which generate beam deflections in two orthogonal directions were also developed under the program and tested with the 8-cm thruster. One system vectors the beam over + or - 10 degrees by gimbaling of the entire thruster (not including tankage), while the other system vectors the beam over + or - 7 degrees by translating the accel electrode relative to the screen electrode.

Hyman, J., Jr.↗
NASA NTRS1995

Ground-based measurements of water vapor in the middle atmosphere

We present measurements of the middle atmospheric water vapor mixing ratio profile obtained using the ground-based Naval Research Laboratory (NRL) water vapor millimeter-wave spectrometer (WVMS) instrument at the Jet Propulsion Laboratory (JPL) Table Mountain Observatory. The measurements cover a period of 262 days from January 23, 1992, to October 13, 1992. During this campaign it was possible to retrieve useful daily mixing ratio profiles for 186 days. We thus have a nearly continuous record of water vapor mixing ratios for altitudes from approximately equals 35 to 75 km. The retrievals are obtained using the optimal estimation method. Details of the error analysis are presented, and a technique is introduced that reduces baseline effects and helps to estimate the baseline error. The high-altitude (greater than or approx. equal to 65 km) data show a sharp rise prior to the expected maximum near the summer solstice and a gradual decline in the following months. The mixing ratios generally peak between 55 and 65 km, at which point the mixing ratios are 6 - 7 parts per million by volume. The highest peaks occur in January, May, and October.

Nedoluha, Gerald E.↗
NASA NTRS2009

Microstrip Antenna for Remote Sensing of Soil Moisture and Sea Surface Salinity

This compact, lightweight, dual-frequency antenna feed developed for future soil moisture and sea surface salinity (SSS) missions can benefit future soil and ocean studies by lowering mass, volume, and cost of the antenna system. It also allows for airborne soil moisture and salinity remote sensors operating on small aircraft. While microstrip antenna technology has been developed for radio communications, it has yet to be applied to combined radar and radiometer for Earth remote sensing. The antenna feed provides a key instrument element enabling high-resolution radiometric observations with large, deployable antennas. The design is based on the microstrip stacked-patch array (MSPA) used to feed a large, lightweight, deployable, rotating mesh antenna for spaceborne L-band (approximately equal to 1 GHz) passive and active sensing systems. The array consists of stacked patches to provide dual-frequency capability and suitable radiation patterns. The stacked-patch microstrip element was designed to cover the required L-band center frequencies at 1.26 GHz (lower patch) and 1.413 GHz (upper patch), with dual-linear polarization capabilities. The dimension of patches produces the required frequencies. To achieve excellent polarization isolation and control of antenna sidelobes for the MSPA, the orientation of each stacked-patch element within the array is optimized to reduce the cross-polarization. A specialized feed-distribution network was designed to achieve the required excitation amplitude and phase for each stacked-patch element.

Ramhat-Samii, Yahya↗
NASA NTRS2013

Advanced Exploration Systems Logistics Reduction and Repurposing Trash-to-Gas and Heat Melt Compactor KSC

Topics covered: 1. Project Structure 2. "Trash to Gas" 3. "Smashing Trash! The Heat Melt Compactor" 4. "Heat Melt Compaction as an Effective Treatment for Eliminating Microorganisms from Solid Waste" Thermal degradation of trash reduces volume while creating water, carbon dioxide and ash. CO2 can be fed to Sabatier reactor for CH4 production to fuel LOX/LCH4 ascent vehicle. Optimal performance: HFWS, full temperature ramp to 500-600 C. Tar challenges exist. Catalysis: Dolomag did eliminate allene byproducts from the product stream. 2nd Gen Reactor Studies. Targeting power, mass, time efficiency. Gas separation, Catalysis to reduce tar formation. Microgravity effects. Downselect in August will determine where we should spend time optimizing the technology.

Caraccio, Anne J.↗
NASA NTRS1988

A high-resolution gamma-ray and hard X-ray spectrometer for solar flare observations in Max 1991

A long duration balloon flight instrument for Max 1991 designed to study the acceleration of greater than 10 MeV ions and greater than 15 keV electrons in solar flares through high resolution spectroscopy of the gamma ray lines and hard X-ray and gamma ray continuum is described. The instrument, HIREGS, consists of an array of high-purity, n-type coaxial germanium detectors (HPGe) cooled to less than 90 K and surrounded by a bismuth germanate (BGO) anticoincidence shield. It will cover the energy range 15 keV to 20 MeV with keV spectral resolution, sufficient for accurate measurement of all parameters of the expected gamma ray lines with the exception of the neutron capture deuterium line. Electrical segmentation of the HPGe detector into a thin front segment and a thick rear segment, together with pulse-shape discrimination, provides optimal dynamic range and signal-to-background characteristics for flare measurements. Neutrons and gamma rays up to approximately 0.1 to 1 GeV can be detected and identified with the combination of the HPGe detectors and rear BGO shield. The HIREGS is planned for long duration balloon flights (LDBF) for solar flare studies during Max 1991. The two exploratory LDBFs carried out at mid-latitudes in 1987 to 1988 are described, and the LDBFs in Antarctica, which could in principle provide 24 hour/day solar coverage and very long flight durations (20 to 30 days) because of minimal ballast requirements are discussed.

Lin, R. P.↗
NASA NTRS2012

Utilizing NASA Earth Observing System (EOS) Data to Determine Ideal Planting Locations for Wetland Tree Species in St. Bernard Parish, Louisiana

St. Bernard Parish, in southeast Louisiana, is rapidly losing coastal forests and wetlands due to a combination of natural and anthropogenic disturbances (e.g. subsidence, saltwater intrusion, low sedimentation, nutrient deficiency, herbivory, canal dredging, levee construction, spread of invasive species, etc.). After Hurricane Katrina severely impacted the area in 2005, multiple Non-Governmental Organizations (NGOs) have worked not only on rebuilding destroyed dwellings, but on rebuilding the ecosystems that once protected the citizens of St. Bernard Parish. Volunteer groups, NGOs, and government entities often work separately and independently of each other and use different sets of information to choose the best planting sites for coastal forests. Using NASA EOS, NRCS soil surveys, and ancillary road and canal data in conjunction with ground truthing, the team created maps of optimal planting sites for several species of wetland trees to aid in unifying these organizations, who share a common goal, under one plan. The methodology for this project created a comprehensive Geographic Information System (GIS) to help identify suitable planting sites in St. Bernard Parish. This included supplementing existing elevation data using LIDAR data and classifying existing land cover in the study area from ASTER multispectral satellite data. Low altitude AVIRIS hyperspectral imagery was used to assess the health of vegetation over an area near the intersection of the Mississippi River Gulf Outlet Canal (MRGO) and Bayou la Loutre. Historic extent of coastal forests was mapped using aerial photos from USGS collected between 1952 and 1956. The final products demonstrated the utility of combining NASA EOS with other geospatial data in assessing, monitoring, and restoring of coastal ecosystems in Louisiana. This methodology also provides a useful template for other ecological forecasting and coastal restoration applications.

Reahard, Ross↗