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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Spherulitic (c-axis) Growth for Terrestrial (Mauna Kea, Hawaii) and Martian Hematite "blueberries"

Hematite concentrations observed by Thermal Emission Spectrometer (TES) onboard Mars Global Surveyor were considered a possible indicator for aqueous processes on Mars. Observations made by Opportunity show that the hematite at Meridiani Planum is present as spherules ( blueberries) and their fragments. The internal structure of the hematite spherules is not discernable at the resolution limit (approx.30 m/pixel) of Opportunity s Microscopic Imager (MI). A terrestrial analog for martian hematite spherules are spherules from hydrothermally altered and sulfate-rich tephra from the summit region of Mauna Kea volcano, Hawaii. The objective of this study is to determine the crystal growth fabric of the Mauna Kea hematite spherules using transmission electron microscopy (TEM) techniques and to relate that crystalline fabric to the observed TES signature of Meridiani Planum "blueberries." TEM analysis of Mauna Kea spherules exhibited a radial growth pattern consisting of "fibrous" hematite with the c-axis of hematite particles aligned along the elongation direction of the hematite fibers. The individual fibers appear to be made of coalesced nano-particles of hematite arranged with their c-axis oriented radially to form a spherical structure. Lattice fringes suggest long-range order across particles and along fibers. According to interpretations of thermal emission spectra for Meridian Planum hematite, the absence of a band at approx. 390/cm implies a geometry where c-face emission dominates. Because the c-face is perpendicular to the c-axis, this is precisely the geometry for the Mauna Kea spherules because the c-axis is aligned parallel to their radial growth direction. Therefore, we conclude as a working hypothesis that the martian spherules also have radial, c-axis growth pattern on a scale that is too small to be detected by the MER MI. Furthermore, by analogy with the Mauna Kea spherules, the martian blueberries could have formed during hydrothermal alteration of basaltic precursors under acid-sulfate conditions.

Golden, D. C.↗
NASA NTRS2024

A Specialized Ultrasound Protocol for NASA's Renal Stone Surveillance and Clinical Practice

INTRODUCTION Ample evidence exists on the effectiveness of ultrasound in the diagnosis of kidney stones. However, terrestrial practice is mostly concerned with diagnosis of clinically significant urolithiasis. Adapting this non-irradiating, low-cost imaging modality for the asymptomatic astronaut population offers high-sensitivity screening and limits their career radiation dose. METHODS NASA developed a novel ultrasound-based detection and surveillance protocol to identify, categorize, and monitor mineralized renal material (MRM) in astronauts. The term MRM was developed to denote abnormal, presumably calcified, focal material of unknown clinical significance in the kidneys. Sonographic criteria for MRMs include measurable echogenic foci, their detectability in multiple, designated imaging plains, posterior shadowing, and “twinkle” pattern visualized in Color Doppler and/or demonstrated in Spectral Doppler modes. The mere discovery of an MRM does not trigger further imaging or intervention, unless the affected crewmember is mission-assigned, or the MRM meets actionable criteria based on size, location, and sonographic characteristics. If the threshold is met, the crew surgeon is notified, and additional diagnostic imaging and urology consultation are considered. RESULTS In total, 73 individuals have been scanned using our specialized methods, 60 have had more than one study done. As of April 2023, 282 total studies have been performed. So far, over 30 pairs of pre-and postflight studies have been obtained, and the findings are presented elsewhere in this panel. DISCUSSION A surveillance program was initiated nine years ago to screen asymptomatic members of the astronaut corps for renal calcifications, from their initial hiring, through their mission timeframes, and throughout their career. The goal is to reduce the probability that an astronaut will fly with a renal stone that may become symptomatic. This also gives NASA the information it needs to improve screening criteria for future long-duration crewed missions.

H Lyons↗
NASA NTRS2023

Observed Changes in Hydroclimate Attributed to Human Forcing

Observational and modeling studies indicate significant changes in the global hydroclimate in the twentieth and early twenty-first centuries due to anthropogenic climate change. In this review, we analyze the recent literature on the observed changes in hydroclimate attributable to anthropogenic forcing, the physical and biological mechanisms underlying those changes, and the advantages and limitations of current detection and attribution methods. Changes in the magnitude and spatial patterns of precipitation minus evaporation ( P–E ) are consistent with increased water vapor content driven by higher temperatures. While thermodynamics explains most of the observed changes, the contribution of dynamics is not yet well constrained, especially at regional and local scales, due to limitations in observations and climate models. Anthropogenic climate change has also increased the severity and likelihood of contemporaneous droughts in southwestern North America, southwestern South America, the Mediterranean, and the Caribbean. An increased frequency of extreme precipitation events and shifts in phenology has also been attributed to anthropogenic climate change. While considerable uncertainties persist on the role of plant physiology in modulating hydroclimate and vice versa, emerging evidence indicates that increased canopy water demand and longer growing seasons negate the water-saving effects from increased water-use efficiency.

Drought↗
DOE OSTI2019

Subtask 1.4 - Techno-Economic Assessment of Regional Carbon Utilization Scenarios and Attendant Monitoring Technology

The U.S. Department of Energy (DOE) Carbon Storage Program seeks to develop technologies to significantly improve the effectiveness of carbon capture and storage (CCS), reduce the cost of implementation, and support the readiness of widespread commercial deployment by 2035. To accomplish this goal, technical and economic barriers must be addressed and the results used to inform regulators and industry on the safety, permanence, and viability of carbon storage. To help overcome these barriers, the Energy & Environmental Research Center (EERC) conducted three activities: 1) investigate the regional techno-economic life cycle of CO2 enhanced oil recovery (EOR) in North Dakota, 2) assist DOE in determining the prospective CO2 storage resource of residual oil zones (ROZs), and 3) develop a seismic technique for monitoring geologic storage of CO2. These three activities address two strategic goals enumerated in the DOE–EERC Cooperative Agreement, namely, 1) to advance foundational science and 2) to inform data-driven policies that enhance U.S. economic growth and job creation, energy security, and environmental quality. Activity 1 In North Dakota, there is synergistic incentive to develop and implement CO2 EOR technologies. Regional industry partners are actively seeking options that can cost-effectively improve the efficiency of power production, while also coproducing CO2 for use in EOR applications. A full-value-chain techno-economic assessment that links lignite resources, power generation, and EOR is needed to determine the implications for a state or region to continue to use these resources or look elsewhere for other energy sources and revenue streams. The integration of North Dakota’s lignite power industry with the state’s oil industry has the potential to revitalize legacy oil fields and produce up to 1 billion barrels of additional oil using CO2 EOR while providing a market for a CO2 commodity captured at coal-fired power plants. Revitalization of North Dakota’s legacy oil fields will bring new industry investment to the state, resulting in the growth of oil-related jobs and revenue through oil sales, severance tax, and associated oilfield development activities (drilling, pipelines, etc.). CO2 demand for EOR and incremental oil production were calculated for 201 conventional oil fields in North Dakota. Infrastructure development in the fields was estimated using 1) a traditional vertical well arrangement of CO2 injectors and oil producers and 2) an advanced case where horizontal wells were used exclusively. Pipeline, compression, and recycling infrastructure requirements were also estimated. When viewed from the CO2 capture and CO2 EOR sectors, the advanced case showed a positive investment scenario when including potential benefits of the 45Q tax credit policy. An economic evaluation of a full implementation of CO2 EOR showed an average $60 billion potential state economic impact estimated over the 37-year construction and production time frame could be realized with an assumed oil price of $65/bbl. Oil price ranges of $35–$90/bbl cause an estimated variation of ±$5 billion over the potential effort. The total net state government revenues were estimated over the 37-year construction and production time frame. With an assumed oil price of $65/bbl, total potential fiscal impact could average $7.4 billion. This result ranges about ±$2.5 billion for a potential EOR implementation effort for oil price ranges of $35–$90/bbl. This broad-brush assessment was focused on “magnitude-of-effect” perspectives of what CO2 capture and use for EOR could mean to the state. Whether CO2 EOR ever becomes widely adopted is not the issue at this stage of evaluation. The question being answered is, How valuable could CO2 capture and CO2 EOR be for the state and local jurisdictions in western North Dakota? The results of the current economic assessment remove any ambiguity to that question. Full implementation of CO2 EOR as modeled in this study provides substantial economic benefits to the state, even at low oil prices (i.e., $35/bbl). Job creation and government revenues could be substantial, depending on the scale of implementation. With a better understanding of what CO2 EOR might mean for the state’s economy, the focus can now shift to the policies and financial mechanisms needed to make private investment in CO2 capture and use as EOR competitive with other energy-related pursuits. Activity 2 ROZs contain remnants of oil not swept away by natural waterflooding. Although nearly all of the identified ROZs are found in West Texas, there is evidence that this category of oil reservoir can be found in other major oil-producing basins of the United States. Because the formation of these oil reservoirs is distinct from other conventional reservoirs, the method to estimate the prospective CO2 storage resource in ROZs may be equally distinct from the method used for conventional fields. As such, the EERC collaborated with DOE’s National Energy Technology Laboratory (NETL) to investigate the feasibility of CO2 storage in ROZs and determine what specific modification (if any) needs to be made to the current DOE volumetric method to estimate prospective CO2 storage resource in oil reservoirs. This activity built on a previous EERC investigation to identify ROZs in the Williston and Powder River Basins and upon collaborations between the EERC and NETL to develop storage resource estimation methods for different types of geologic CO2 storage. Review of the volumetric approach raised questions about the applicability of such an approach to hydrocarbon-bearing zones without the inclusion of potential hydrocarbon production. A production-based approach was presented by the EERC to inform the working group of additional methods of increasing the CO2 storage resource potential. Because the production-based method focuses more heavily on oil recovery than CO2 storage, it was not included in the working group manuscript. Activity 3 The EERC has used sparse and irregular seismic arrays to detect CO2 injected into a reservoir. The variability of seismic noise recorded by the sensors directly impacts the seismic interpretation. However, designing noise attenuation workflows effectively can be a more challenging task using sparse and irregular arrays for data acquisition. Based on the hypothesis that noncoherent noise is stationary during weekly data acquisition, a new machine-learning algorithm was developed to automatically estimate noise conditions using passive records, noise from active records, and ancillary information. The new noise attenuation processing workflow can improve the time-lapse detection of CO2 from sparse and irregular array acquisitions. Results show spatial and temporal noise patterns can be related to the location of the sensors and the weather conditions during the data acquisition and that our attenuation processing workflow can improve the time-lapse detection of CO2 from sparse and irregular array acquisitions. This effort was funded in part through the EERC–DOE Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Department of Commerce.

Peck, Wesly↗
DOE OSTI2020

An Applied Strategy for Using Empirical and Hybrid Models in Online Monitoring

The monitoring of plant equipment for failure prediction is one of the key contributors to operation and maintenance (O&M) costs for a nuclear power plant (NPP) because O&M monitoring depends on labor-intensive activities that are required to meet high equipment reliability standards. These activities rely primarily on humans for information gathering, condition diagnosis, and predictive analysis. Online monitoring aims to automate these activities by relying on sensors to replace human information gathering and machine learning to replace human analysis and decision making. To facilitate automated monitoring, a systematic strategy for anomaly detection is needed to optimally use the available sensor data, empirical models, and physics-supported models. This strategy is essential to provide credible reasoning on why and when an empirical (i.e., purely data-driven) versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. The extant methods usually adopt an ad hoc trial-and-error approach that, in addition to being time-consuming and costly, is also highly subjective; it is impacted by the background and the skill set of the personnel making the decisions. Thus, such an approach cannot guarantee an optimum outcome. This represents the motivation of the current research effort, which is focused on devising a scientifically supported strategy for the optimum selection of anomaly detection methods. This report presents a detailed assessment of the main anomaly detection techniques within the empirical or hybrid method streams. Empirical methods include pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The listed techniques within these two streams represent the vast majority of techniques performed for anomaly detection. Using the techniques as outcomes, a strategy was developed to enable a systematic decision-making process to lead to one of these techniques. The strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability and suitability for training and testing, cause-effect, entropy inference, and model fitting. Each of these decision points in the strategy is explained in detail in this report with examples, along with the scientific basis behind the decisions and outcomes in common and simplified terminology. The strategy is developed for use by any NPP staff with basic engineering or science knowledge. A user-friendly graphical state flow diagram was also developed as a visual presentation of the strategy. The strategy was tested and demonstrated through two pilot projects for the application of anomaly detection at an NPP. Each pilot had two use cases: an initial case in which certain decisions were made that resulted in one or more empirical techniques and a revised use case where one or more key decisions were modified resulting in using a set of hybrid methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Regional and Temporal Variability of Atmospheric River Seasonality: Influences of Detection Algorithms and Moisture Transport Dynamics

Abstract Understanding the regional and temporal variability of atmospheric river (AR) seasonality is crucial for preparedness and mitigation of extreme events. While ARs were thought to peak in winter, recent research shows they exhibit region‐specific seasonality and are heavily influenced by the chosen detection algorithm. This study examines the link between the year‐to‐year consistency of peak‐AR activity to the presence of a dominant seasonal pattern, considering both location and algorithm choice. Regions are categorized by their temporal characteristics: consistent patterns (e.g., East Asia), patterns with occasional outliers (e.g., British Columbia coast), and regions lacking a clear dominant peak season (e.g., South Atlantic, parts of Australia). Hence, not all regions display a consistent seasonal cycle of AR activity. This study quantifies the extent to which a region experiences a dominant peak season of AR activity (or lacks one) and offers insights to enhance decision‐making in water management, natural hazard preparedness, and forecasting. Furthermore, given our finding that detection algorithms influence the peak season of AR activity, we also examine two diagnostic variables representative of moisture transport to corroborate our results. Integrated vapor transport, which captures meridional and zonal moisture transport, and Moist Wave Activity, representing moisture intrusions from lower to higher latitudes, are examined. Our analysis indicates that inconsistencies in the seasonal cycle of AR activity are not solely due to discrepancies in detection algorithms but also arise from changes in moisture transport. Plain Language Summary Atmospheric rivers (ARs) are critical weather phenomena that can cause extreme events like heavy rainfall and flooding. Understanding when and where ARs are most likely to occur throughout the year is essential for preparing and responding to these events. Traditionally, ARs were thought to peak in winter, but recent studies show this varies by region. Our study helps address the challenge decision‐makers face in anticipating and preparing for AR events by providing insights into the consistency of peak seasonal patterns across different areas. Some regions, like East Asia, and the British Columbia coast, show a consistent peak season, while others, like the South Atlantic and parts of Australia, have significant year‐to‐year variations, making it hard to identify a dominant season. To better understand these changes over time, the study also examines how moisture moves in the atmosphere, using Integrated Vapor Transport (which looks at moisture movement in various directions) and Moist Wave Activity (which tracks moisture shifts from lower to higher latitudes). The findings suggest that inconsistencies in AR patterns are due not only to detection methods but also due to changes in moisture transport. Key Points The peak season of atmospheric river activity can change depending on the year in some areas Interannual variations in the peak season can make identifying a dominant season challenging for some regions Frequent shifts in peak season across years reflect inconsistencies tied to detection algorithms and to underlying dynamics

Kamnani, Diya↗
NASA NTRS2005

Detection Of Tornado Damage Tracks With EOS Data

The damage surveys conducted by the NWS in the aftermath of a reported tornadic event are used to document the location of the tornado ground damage track (path length and width) and an estimation of the tornado intensity. This study explored the possibility of using near real-time medium and high-resolution satellite imagery from the NASA EOS satellites to provide additional information for the surveys. MODIS and ASTER data were used to study the damage tracks from three tornadic storms; the La Plata, Maryland storm of 28 April 2002 and the Carter-Butler Counties and Madison County Missouri storms of 24 April 2002. These storms varied in intensity (from F0-F4) and occurred over regions with different land use. It was found that, depending on the nature of land use, tornado damage tracks from intense storms (F2 or greater) may be evident in both ASTER and MODIS satellite imagery. In areas of dense vegetation the scar patterns show up very clearly, while in areas of grassland and regions with few trees, scar patterns are not at all obvious in the satellite imagery. The detection of previously unidentified segments of a damage track caused by the 24 April 2004 Madison County, Missouri tornado demonstrates the utility of satellite imagery for damage surveys. However, the capability to detect tornado tracks in satellite imagery appears to be as much dependent on the nature of the underlying surface and land use as on the severity of the tornadic storm. The imaging sensors on the NPOESS operational satellites to be launched in 2006 will continue the unique observing capabilities of the EOS instruments.

Jedlovec, Gary J.↗
NASA NTRS1975

An X-ray shadowgraph to locate transient high-energy celestial sources

A new technique has been developed to locate strong, transient X-ray sources such as the recently discovered gamma ray bursts. The instrument, termed a shadowgraph, locates sources by detecting the X-ray shadow cast by a large occulting mask pattern on an imaging detector. Angular resolutions of from 2 to 10 arc minutes are obtainable while essentially full sky coverage is maintained. The optimum energy range of operation is between 20 keV and 100 keV. The high efficiency X-ray imaging detectors, which make it possible to locate bursts with intensities down to approximately 10 photons/sq cm sec, are capable of detecting single 20 keV photons with a spatial resolution of approximately 0.2 mm. The detectors consist of an X-ray to optical conversion phosphor, a multistage image intensifier, and a CCD image readout.

Fishman, G. J.↗
NASA NTRSDate not supplied

Multiclass Flight Anomaly Detection Using Sensor Fusion Based on Dempster-Shafer Theory

As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST toreduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over simple majority voting. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.

Risk detection↗
NASA NTRSDate not supplied

Multiclass Flight Anomaly Detection Using Sensor Fusion Based on Dempster-Shafer Theory

As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST to reduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over simple majority voting. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.

Risk detection↗
NASA NTRS2015

A Wavelet Analysis Approach for Categorizing Air Traffic Behavior

In this paper two frequency domain techniques are applied to air traffic analysis. The Continuous Wavelet Transform (CWT), like the Fourier Transform, is shown to identify changes in historical traffic patterns caused by Traffic Management Initiatives (TMIs) and weather with the added benefit of detecting when in time those changes take place. Next, with the expectation that it could detect anomalies in the network and indicate the extent to which they affect traffic flows, the Spectral Graph Wavelet Transform (SGWT) is applied to a center based graph model of air traffic. When applied to simulations based on historical flight plans, it identified the traffic flows between centers that have the greatest impact on either neighboring flows, or flows between centers many centers away. Like the CWT, however, it can be difficult to interpret SGWT results and relate them to simulations where major TMIs are implemented, and more research may be warranted in this area. These frequency analysis techniques can detect off-nominal air traffic behavior, but due to the nature of air traffic time series data, so far they prove difficult to apply in a way that provides significant insight or specific identification of traffic patterns.

wavelet analysis↗
NASA NTRS1971

Detection of southern corn leaf blight by remote sensing techniques.

Multispectral photographic and scanner data were collected over western Indiana in August and September 1970, to determine the detectability of southern corn leaf blight by remote sensing. Measurements were made at altitudes of 3000 to 7000 ft. Color, color IR, and multiband black and white photography were collected at altitudes from 3000 to 60,000 ft. Six levels of infection based on the amount of leaf damage were identified in the fields. Three levels of infection were detected with color IR photography by standard photo-interpretive techniques. Up to five levels of infection were distinguished by applying automatic pattern recognition techniques to the multispectral scanner data. The results illustrate the potential of remote sensing techniques in the detection of crop diseases.

Bauer, M. E.↗
DOE OSTI2024

Identification of Distorted Gamma-Ray Signature Patterns Using Digital Filtering and Auto-Associative Memory Implemented with a Hopfield Neural Network

The detection and identification of radioactive sources in search applications involve analyzing passive gamma-ray emissions from high-level radioactive materials. This process uses a mobile detector-spectrometer in a complex field test environment. Recently, the use of artificial intelligence for gamma-ray spectrum analysis has shown promising results. However, challenges persist in identifying isotopic signatures from spectral measurements that may be distorted due to source shielding, random variations in natural radioactive background, or insufficient measurement time to obtain clear spectral lines. Here, this paper presents a novel intelligent signature recognition method that combines digital filtering techniques with an artificial Hopfield Neural Network (HNN). The HNN leverages auto-associative memory to store training sample patterns and match them with incoming gamma spectra from distorted sources. It restores the testing sources’ measurements by finding the closest matching signature patterns in the spectral library. Before HNN recognition, the measured spectrum undergoes preprocessing with a digital image filter to reduce fluctuations. Performance of the proposed method is evaluated using a set of gamma-ray spectra measured with a sodium iodide detector. The data collected include measurements from six pure samples: 241 Am, 60 Co, 137 Cs, 192 Ir, 239 Pu, and 235 U, which are used for training and validation (i.e. six cases). Additionally, the data set contains 24 distorted synthesized sources with various fluctuating backgrounds. Test results demonstrate the potential of the proposed method to accurately recognize the correct isotope with high precision, achieving an accuracy rate exceeding 85%. Furthermore, the proposed method exhibits superior performance compared to the conventional multiple regression fitting and simple feedforward neural network methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1990

Identification and detection of anomalies through SSME data analysis

The goal of the ongoing research described in this paper is to analyze real-time ground test data in order to identify patterns associated with the anomalous engine behavior, and on the basis of this analysis to develop an expert system which detects anomalous engine behavior in the early stages of fault development. A prototype of the expert system has been developed and tested on the high frequency data of two SSME tests, namely Test #901-0516 and Test #904-044. The comparison of our results with the post-test analyses indicates that the expert system detected the presence of the anomalies in a significantly early stage of fault development.

Pereira, Lisa↗
NASA NTRS2015

Experimentally Shocked and Altered Basalt: Laboratory Analogs for Calibration of Mars Remote Sensing and In Situ Data

Calciumphosphate (likely chloroapatite) is formed in the alteration experiments and is more abundant in the altered and shocked sample probably due to increased surface area exposed to alteration fluids resulting from shock damage in the form of both brittle and structural deformation to the starting material (Figs 1 & 3). Apatite forms in basic conditions so the closed system alteration experiment must be buffered by the basalt starting material to create a fluid chemistry environment evolving from neutral at the start to alkaline after 21 days at 160 C. Plagioclase feldspar in the unshocked sample (Fig. 2) has undergone a solid-state transformation to maskelynite, a disordered phase that is not manifest in the XRD pattern of the shocked sample (Fig.4). Olivine and ulvospinel that are present in the starting material can be detected by XRD in the shocked and altered sample (Fig. 4). Tungsten from the sample holder used in the shock experiments dominates the XRD pattern of the shocked and altered sample (Fig. 4). Samples were weighed after the alteration experiments to determine mass loss and predict the amount of material available for the planned analyses from the shock experiments. Within the constraints of these experiments, mass loss is negligible. The samples will next be characterized by Moessbauer and Vis-Near IR spectroscopy, the results of which will be compared to the Mars Exploration Rovers and Mars Reconnaissance Orbiter data sets respectively.

Bell, M. S.↗
NASA NTRS1990

X ray sensitive area detection device

A radiation sensitive area detection device is disclosed which comprises a phosphor-containing film capable of receiving and storing an image formed by a pattern of incoming x rays, UV, or other radiation falling on the film. The device is capable of fluorescing in response to stimulation by a light source in a manner directly proportional to the stored radiation pattern. The device includes: (1) a light source capable of projecting light or other appropriate electromagnetic wave on the film so as to cause it to fluoresce; (2) a means to focus the fluoresced light coming from the phosphor-containing film after light stimulation; and (3) at least one charged coupled detector or other detecting element capable of receiving and digitizing the pattern of fluoresced light coming from the phosphor-containing film. The device will be able to generate superior x ray images of high resolution from a crystal or other sample and will be particularly advantageous in that instantaneous near-real-time images of rapidly deteriorating samples can be obtained. Furthermore, the device can be made compact and sturdy, thus capable of carrying out x ray or other radiation imaging under a variety of conditions, including those experienced in space.

Carter, Daniel C.↗
NASA NTRS1994

A feasibility study for long-path multiple detection using a neural network

Least-squares inverse filters have found widespread use in the deconvolution of seismograms and the removal of multiples. The use of least-squares prediction filters with prediction distances greater than unity leads to the method of predictive deconvolution which can be used for the removal of long path multiples. The predictive technique allows one to control the length of the desired output wavelet by control of the predictive distance, and hence to specify the desired degree of resolution. Events which are periodic within given repetition ranges can be attenuated selectively. The method is thus effective in the suppression of rather complex reverberation patterns. A back propagation(BP) neural network is constructed to perform the detection of first arrivals of the multiples and therefore aid in the more accurate determination of the predictive distance of the multiples. The neural detector is applied to synthetic reflection coefficients and synthetic seismic traces. The processing results show that the neural detector is accurate and should lead to an automated fast method for determining predictive distances across vast amounts of data such as seismic field records. The neural network system used in this study was the NASA Software Technology Branch's NETS system.

Feuerbacher, G. A.↗
NASA NTRS2012

Sensor for Boundary Shear Stress in Fluid Flow

The formation of scour patterns at bridge piers is driven by the forces at the boundary of the water flow. In most experimental scour studies, indirect processes have been applied to estimate the shear stress using measured velocity profiles. The estimations are based on theoretical models and associated assumptions. However, the turbulence flow fields and boundary layer in the pier-scour region are very complex and lead to low-fidelity results. In addition, available turbulence models cannot account accurately for the bed roughness effect. Direct measurement of the boundary shear stress, normal stress, and their fluctuations are attractive alternatives. However, most direct-measurement shear sensors are bulky in size or not compatible to fluid flow. A sensor has been developed that consists of a floating plate with folded beam support and an optical grid on the back, combined with a high-resolution optical position probe. The folded beam support makes the floating plate more flexible in the sensing direction within a small footprint, while maintaining high stiffness in the other directions. The floating plate converts the shear force to displacement, and the optical probe detects the plate s position with nanometer resolution by sensing the pattern of the diffraction field of the grid through a glass window. This configuration makes the sensor compatible with liquid flow applications.

Bao, Xiaoqi↗