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At least 307 records · Page 17

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NASA NTRS2003

Surface Time-Variable Gravity Signals and Possible Sources Including Core Mass Flow

Over two decades of geodetic satellite-laser-ranging (SLR) data show that the variation of the Earth's oblateness parameter J2 has a clear seasonal signal of amplitude of about 3e-10 and a secular decrease of about -2.8e-11/year, superimposed on some interesting interannual fluctuations. Physically, any change in mass distribution or/inside the Earth will be reflected in the time-variable gravity signal obtained outside the Earth, according to Newton s gravitational law. Therefore, such signal contains contributions from all geophysical sources that redistribute mass, on all temporal and spatial scales, including those from the core. Besides Earth rotation and geomagnetic field variations, the time-variable gravity also contains information linking Earth surface observations with internal core dynamical processes. The time scales of the gravity signal are critical in helping differentiate different contributions. The atmosphere and hydrosphere are responsible for the seasonal and much of the interannual and intraseasoanl fluctuations, while the secular trend is due mainly to the post-glacial rebound but possibly core mass flow. To estimate the latter effect, we use our MoSST (Modular, Scalable, Self-consistent, Three-dimensional) core dynamics model to forward simulate the core flow, and density variation due to the core convection. Our results suggest that, when upward continued to the surface, the J2 component of the core mass redistribution can reach an overall amplitude of e-11/year, approaching the SLR detectability and significant in geophysical terms. We also find a general westward drift of the mass flow, with a speed comparable to that of the geomagnetic westward drift.

Chao, Benjamin F.↗

NASA NTRS2001

Phase sensitive x-ray diffraction imaging of defects in biological macromolecular crystals

Conventional x-ray diffraction topography is currently used to map defects in the bulk of protein crystals, but the lack of sufficient contrast is frequently a limiting factor. We experimentally demonstrate that this barrier can be circumvented using a method that combines phase sensitive and diffraction imaging principles. Details of defects revealed in tetragonal lysozyme and cubic ferritin crystals are presented and discussed. The approach enabling the detection of the phase changes of diffracted x rays should prove to be useful in the study of defect structures in a broad range of biological macromolecular crystals.

Muramidase/chemistry↗

NASA NTRS2006

Enabling the Discovery of Recurring Anomalies in Aerospace System Problem Reports using High-Dimensional Clustering Techniques

This paper describes the results of a significant research and development effort conducted at NASA Ames Research Center to develop new text mining techniques to discover anomalies in free-text reports regarding system health and safety of two aerospace systems. We discuss two problems of significant importance in the aviation industry. The first problem is that of automatic anomaly discovery about an aerospace system through the analysis of tens of thousands of free-text problem reports that are written about the system. The second problem that we address is that of automatic discovery of recurring anomalies, i.e., anomalies that may be described m different ways by different authors, at varying times and under varying conditions, but that are truly about the same part of the system. The intent of recurring anomaly identification is to determine project or system weakness or high-risk issues. The discovery of recurring anomalies is a key goal in building safe, reliable, and cost-effective aerospace systems. We address the anomaly discovery problem on thousands of free-text reports using two strategies: (1) as an unsupervised learning problem where an algorithm takes free-text reports as input and automatically groups them into different bins, where each bin corresponds to a different unknown anomaly category; and (2) as a supervised learning problem where the algorithm classifies the free-text reports into one of a number of known anomaly categories. We then discuss the application of these methods to the problem of discovering recurring anomalies. In fact the special nature of recurring anomalies (very small cluster sizes) requires incorporating new methods and measures to enhance the original approach for anomaly detection. ?& pant 0-

Srivastava, Ashok, N.↗

NASA NTRS2012

Development of IR Contrast Data Analysis Application for Characterizing Delaminations in Graphite-Epoxy Structures

Objective: Develop infrared (IR) flash thermography application based on use of a calibration standard for inspecting graphite-epoxy laminated/honeycomb structures. Background: Graphite/Epoxy composites (laminated and honeycomb) are widely used on NASA programs. Composite materials are susceptible for impact damage that is not readily detected by visual inspection. IR inspection can provide required sensitivity to detect surface damage in composites during manufacturing and during service. IR contrast analysis can provide characterization of depth, size and gap thickness of impact damage. Benefits/Payoffs: The research provides an empirical method of calibrating the flash thermography response in nondestructive evaluation. A physical calibration standard with artificial flaws such as flat bottom holes with desired diameter and depth values in a desired material is used in calibration. The research devises several probability of detection (POD) analysis approaches to enable cost effective POD study to meet program requirements.

Havican, Marie↗

NASA NTRS2014

Bayesian Statistics and Uncertainty Quantification for Safety Boundary Analysis in Complex Systems

The analysis of a safety-critical system often requires detailed knowledge of safe regions and their highdimensional non-linear boundaries. We present a statistical approach to iteratively detect and characterize the boundaries, which are provided as parameterized shape candidates. Using methods from uncertainty quantification and active learning, we incrementally construct a statistical model from only few simulation runs and obtain statistically sound estimates of the shape parameters for safety boundaries.

Active Learning↗

NASA NTRS2016

Ultrasonic NDE Simulation for Composite Manufacturing Defects

The increased use of composites in aerospace components is expected to continue into the future. The large scale use of composites in aerospace necessitates the development of composite-appropriate nondestructive evaluation (NDE) methods to quantitatively characterize defects in as-manufactured parts and damage incurred during or post manufacturing. Ultrasonic techniques are one of the most common approaches for defect/damage detection in composite materials. One key technical challenge area included in NASA's Advanced Composite's Project is to develop optimized rapid inspection methods for composite materials. Common manufacturing defects in carbon fiber reinforced polymer (CFRP) composites include fiber waviness (in-plane and out-of-plane), porosity, and disbonds; among others. This paper is an overview of ongoing work to develop ultrasonic wavefield based methods for characterizing manufacturing waviness defects. The paper describes the development and implementation of a custom ultrasound simulation tool that is used to model ultrasonic wave interaction with in-plane fiber waviness (also known as marcelling). Wavefield data processing methods are applied to the simulation data to explore possible routes for quantitative defect characterization.

Leckey, Cara A. C.↗

NASA NTRS2019

All-Sky Medium-Energy Gamma-Ray Observatory (AMEGO)

The gamma-ray energy range from a few hundred keV (kiloelectronvolts) to a few hundred MeV (megaelectronvolts) has remained largely unexplored since the pioneering but limited observations by COMPTEL (The Imaging Compton Telescope) on the CGRO (Compton Gamma Ray Observatory) (1991-2000). Fundamental astrophysics questions can be addressed by a mission in the MeV range, from astrophysical jets and extreme physics of compact objects to a large population of unidentified objects. Such a mission will also provide critical inputs for multimessenger astrophysics by identifying and exploring the astrophysical objects that produce gravitational waves and neutrinos. To address these questions, we are developing AMEGO: All-sky Medium Energy Gamma-ray Observatory, as a NASA probe-class mission, to investigate the energy range from 200 keV to greater than10 GeV with good energy (ranging from less than 1 percent at the low end to approximately 10 percent at the high end) and angular resolution (from 2 to 6 degrees depending on energy) and with sensitivity a factor of 20-50 better than previous instruments. Measurements at these energies are challenging, mainly due to the fact that two photon interaction processes, Compton scattering and pair production, compete. These interaction processes require different approaches in both detection and data analysis, and consequently in the instrument concept. AMEGO will be capable of measuring both Compton-scattering events at lower energies and pair-production events at higher energies. AMEGO will also have sensitivity to linear polarization of detected radiation at a level of 20 percent minimum detectable polarization from a source 1 percent of the Crab intensity, observed for 106 seconds. AMEGO will be operating mainly in scanning (discovery) mode with a field-of-view of 2.5 sr (Special Relativity) (20 percent of the sky observation any time), with the capability to be pointed to particular regions of interest..

Moiseev, Alexander↗

NASA NTRSDate not supplied

Upgrades to the International Space Station Urine Processor Assembly

Since November 2008, the ISS Urine Processor Assembly (UPA) has successfully generated distillate from crew urine. Several rounds of modifications and upgrades have also been implemented to improve overall system performance throughout the years. The Distillation Assembly (DA) is the latest upgraded unit available, installed in September 2020. This unit has shown immediate performance improvements and redesign success. Most significant upgrades have been a redesigned drive train to eliminate belt slips, improved sealing to reduce leak paths and improve wear, and a redesigned liquid level sensor. The Purge Pump and Separator Assembly (PPSA) technology demonstration is the next available upgraded unit and has finished ground assembly and testing and is targeted for ISS delivery within the coming year. Most recent ground studies have identified areas to focus future UPA upgrade considerations with exploration missions in mind, specifically for better fault detection and isolations approaches as well as potential hardware redesigns to facilitate successful dormancy operations.

Urine Processor Assembly↗

NASA NTRS2021

Changes in Satellite Retrievals of Atmospheric Composition over Eastern China During the 2020 COVID-19 Lockdowns

We examined daily level-3 satellite retrievals of Atmospheric Infrared Sounder (AIRS) CO, Ozone Monitoring Instrument (OMI) SO2 and NO2, and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) over eastern China to understand how COVID-19 lockdowns affected atmospheric composition. Changes in 2020 were strongly dependent on the choice of background period since 2005 and whether trends in atmospheric composition were accounted for. Over central east China during the 23 January–8 April lockdown window, CO in 2020 was between 3 % and 12 % lower than average depending on the background period. The 2020 CO was not consistently less than expected from trends beginning between 2005 and 2016 and ending in 2019 but was 3 %–4 % lower than the background mean during the 2017–2019 period when CO changes had flattened. Similarly for AOD, 2020 was between 14 % and 30 % lower than averages beginning in 2005 and 14 %–17 % lower compared to different background means beginning in 2016. NO2 in 2020 was between 30 % and 43 % lower than the mean over different background periods and between 17 % and 33 % lower than what would be expected for trends beginning later than 2011. Relative to the 2016–2019 period when NO2 had flattened, 2020 was 30 %–33 % lower. Over southern China, 2020 NO2 was between 23 % and 27 % lower than different background means beginning in 2013, the beginning of a period of persistently lower NO2. CO over southern China was significantly higher in 2020 than what would be expected, which we suggest was partly because of an active fire season in neighboring countries. Over central east and southern China, 2020 SO2 was higher than expected, but this depended strongly on how daily regional values were calculated from individual retrievals and reflects background values approaching the retrieval detection limit. Future work over China, or other regions, needs to take into account the sensitivity of differences in 2020 to different background periods and trends in order to separate the effects of COVID-19 on air quality from previously occurring changes or from variability in other sources.

atmospheric composition↗

NASA NTRS2022

Northeast US Ecological Forecasting: Modeling Invasive Plant Habitat Suitability to Support Management Efforts in the American Northeast

Invasive plant species threaten environmental and economic interests when they spread into new areas, outcompete native species, and disrupt ecosystem services. If the spread is not controlled early, species can become well-established and increasingly difficult to manage. The National Park Service (NPS) Invasive Plant Management Teams (IPMTs) strive for an “early detection, rapid response” approach to reducing invasive species spread. Management teams can better prioritize their work with the help of species distribution models (SDMs), which map habitat suitability by combining species occurrences with environmental predictor variables. Scarce invaded range data for newly arrived invasive species presents a particular challenge for producing accurate models. To improve future modeling efforts, this project compared SDM methods using different spatial scales to model two plant species invasive to the Northeast US: the well-established Japanese stiltgrass (Microstegium vimineum) and newer invasive species wavyleaf basketgrass (Oplismenus undulatifolius). The team used NASA Earth observations and climate datasets to model occurrence data and predictor layers at a US-specific extent (90m2 spatial resolution) and global extent (1 km2 spatial resolution). Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) provided data for US Normalized Difference Moisture Indices (NDMI), while global NDMI and topographic predictor layers were derived from Shuttle Radar Topography Mission (SRTM) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting models indicated important predictor variables for each species and explored the benefits and tradeoffs of using global data to model habitat suitability for new-arrival invasive species.

Rebecca Ohman↗

NASA NTRS2023

A Machine Learning-Based Approach to Time-Series Wave Identification in the Solar Wind

The Wind spacecraft has yielded several decades of high-resolution magnetic field data, a large fraction of which displays small-scale structures. In particular, the solar wind is full of wavelike fluctuations that appear in both the field magnitude and its components. The nature of these fluctuations can be tied to the properties of other structures in the solar wind, such as shocks, that have implications for the time evolution of the solar wind. As such, having a large collection of wave events would facilitate further study of the effects that these fluctuations have on solar wind evolution. Given the large volume of magnetic field data available, machine learning is the most practical approach to classifying the myriad small-scale structures observed. To this end, a subset of Wind data is labeled and used as a training set for a multi-branch 1D convolutional neural network aimed at classifying circularly polarized wave modes. Using this algorithm, a preliminary statistical study of one year of data is performed, yielding about 300,000 wave intervals out of about 5,000,000 solar wind intervals. The wave intervals come about more often in the fast solar wind and at higher temperatures, and the number of waves per day is highly periodic. This machine learning-based approach to wave detection has the potential to be a powerful, inexpensive way to catalog waves throughout decades of spacecraft data.

Samuel Fordin↗

NASA NTRS2007

Assessment of Vegetation Stress Using Reflectance or Fluorescence Measurements

Current methods for large-scale vegetation monitoring rely on multispectral remote sensing, which has serious limitation for the detection of vegetation stress. To contribute to the establishment of a generalized spectral approach for vegetation stress detection, this study compares the ability of high-spectral resolution reflectance (R) and fluorescence (F) foliar measurements to detect vegetation changes associated with common environmental factors affecting plant growth and productivity. To obtain a spectral dataset from a broad range of species and stress conditions, plant material from three experiments was examined, including (i) corn, nitrogen (N) deficiency/excess; (ii) soybean, elevated carbon dioxide, and ozone levels; and (iii) red maple, augmented ultraviolet irradiation. Fluorescence and R spectra (400-800 nm) were measured on the same foliar samples in conjunction with photosynthetic pigments, carbon, and N content For separation of a wide range of treatment levels, hyperspectral (5-10 nm) R indices were superior compared with F or broadband R indices, with the derivative parameters optimal results. For the detection of changes in vegetation physiology, hyperspectral indices can provide a significant improvement over broadband indices. The relationship of treatment levels to R was linear, whereas that to F was curvilinear. Using reflectance measurements, it was not possible to identify the unstressed vegetation condition, which was accomplished in all three experiments using F indices. Large-scale monitoring of vegetation condition and the detection of vegetation stress could be improved by using hyperspectral R and F information, a possible strategy for future remote sensing missions.

Campbell, P. K. E.↗

NASA NTRS2001

Microbial detection with low molecular weight RNA

The need to monitor microorganisms in the environment has increased interest in assays based on hybridization probes that target nucleic acids (e.g., rRNA). We report the development of liquid-phase assays for specific bacterial 5S rRNA sequences or similarly sized artificial RNAs (aRNAs) using molecular beacon technology. These beacons fluoresce only in the presence of specific target sequences, rendering as much as a 27-fold fluorescence enhancement. The assays can be used with both crude cell lysates and purified total RNA preparations. Minimal sample preparation (e.g., heating to promote leakage from cells) is sufficient to detect many Gram-negative bacteria. Using this approach it was possible to detect an aRNA-labeled Escherichia coli strain in the presence of a large background of an otherwise identical E. coli strain. Finally, by using a longer wavelength carboxytetramethylrhodamine beacon it was possible to reduce the fraction of the signal due to cellular autofluorescence to below 0.5%.

NASA Discipline Life Sciences Technologies↗

NASA NTRS1986

Natural ELF noise evaluation for TSS emissions detection on the Earth's surface. The electric field component approach

The preliminary estimate of the local noise structure in the proximity of a receiver is essential to establishing the detectability of a given signal in presence of such noise. The possibility of detecting the Electric Field Component of the background noise by means of electric dipoles horizontally placed on the sea bed in shallow water is outlined, in order to find its spectral and statistical characteristics for the definition of the optimal receiving system.

Tacconi, G.↗

NASA NTRS2020

Improving Rain/No-Rain Detection Skill by Merging Precipitation Estimates from Different Sources

Rain/no-rain detection error is a key source of uncertainty in regional and global precipitation products that propagates into offline hydrological and land surface modeling simulations. Such detection error is difficult to evaluate and/or filter without access to high-quality reference precipitation datasets. For cases where such access is not available, this study proposes a novel approach for improved rain/no-rain detection. Based on categorical triple collocation (CTC) and a probabilistic framework, a weighted merging algorithm (CTC-M) is developed to combine noisy, but independent, precipitation products into an optimal binary rain/no-rain time series. Compared with commonly used approaches that directly apply the best parent product for rain/no-rain detection, the superiority of CTC-M is demonstrated analytically and numerically using spatially dense precipitation measurements over Europe. Our analysis also suggests that CTC-M is tolerant to a range of cross-correlated rain/no-rain detection errors and detection biases of the parent products. As a result, CTC-M will benefit global precipitation estimation by improving the representation of precipitation occurrence in gauge-based and multisource merged precipitation products.

Jianzhi Dong↗

NASA NTRS1994

Structural Health Monitoring of Large Structures

This paper describes a damage detection and health monitoring method that was developed for large space structures using on-orbit modal identification. After evaluating several existing model refinement and model reduction/expansion techniques, a new approach was developed to identify the location and extent of structural damage with a limited number of measurements. A general area of structural damage is first identified and, subsequently, a specific damaged structural component is located. This approach takes advantage of two different model refinement methods (optimal-update and design sensitivity) and two different model size matching methods (model reduction and eigenvector expansion). Performance of the proposed damage detection approach was demonstrated with test data from two different laboratory truss structures. This space technology can also be applied to structural inspection of aircraft, offshore platforms, oil tankers, ridges, and buildings. In addition, its applications to model refinement will improve the design of structural systems such as automobiles and electronic packaging.

Kim, Hyoung M.↗

NASA NTRS1987

Design and evaluation of executable assertions for concurrent error detection

System level concurrent detection of errors due to hardware faults can be accomplished by introducing software redundancy in the form of executable assertions into a program to monitor the correct operation of the system during its execution. This paper attempts to formalize the use of executable assertions for the purpose of concurrent error detection, and discusses a transformation approach to the design of assertion statements. The second part deals with the effectiveness of the error detection technique. A fault simulation method for the evaluation of the reliability of the assertion statements is described. This scheme was used to study the effectiveness of a nontrivial program in providing protection against faults in the underlying system. The experimental results shows that the use of executable assertions provides an effective approach to achieve concurrent error detection.

Hua, Kien A.↗

NASA NTRS1994

The Use of Spontaneous Raman Scattering for Hydrogen Leak Detection

A fiber optic probe has been built and demonstrated that utilizes back scattered spontaneous Raman spectroscopy to detect and identify gaseous species. The small probe, coupled to the laser and data acquisition equipment with optical fibers, has applications in gaseous leak detection and process monitoring. The probe design and data acquisition system are described. Raman scattering theory has been reviewed and the results of intensity calculations of hydrogen and nitrogen Raman scattering are given. Because the device is in its developmental stage, only preliminary experimental results are presented here. Intensity scans across the rotational-vibrational Raman lines of nitrogen and hydrogen are presented. Nitrogen at a partial pressure of 0.077 MPa was detected. Hydrogen at a partial pressure of 2 kPa approached the lower limit of detectability with the present apparatus. Potential instrument improvements that would allow more sensitive and rapid hydrogen detection are identified.

Degroot, Wim A.↗