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At least 397 records · Page 22

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NASA NTRS2011

Sustainability of the Catalytic Activity of a Silica-Titania Composite (STC) for Long-Term Indoor Air Quality Control

TiO2-assisted photocatalytic oxidation (PCO) is an emerging technology for indoor air quality control and is also being evaluated as an alternative trace contaminant control technology for crew habitats in space exploration. Though there exists a vast range of literature on the development of photocatalysts and associated reactor systems, including catalyst performance and performance-influencing factors, the critical question of whether photocatalysts can sustain their initial catalytic activity over an extended period of operation has not been adequately addressed. For a catalyst to effectively serve as an air quality control product, it must be rugged enough to withstand exposure to a multitude of low concentration volatile organic compounds (VOCs) over long periods of time with minimal loss of activity. The objective of this study was to determine the functional lifetime of a promising photocatalyst - the silica-titania composite (STC) from Sol Gel Solutions, LLC in a real-world scenario. A bench-scale STC-packed annular reactor under continuous irradiation by a UV-A fluorescent black-light blue lamp ((lambda)max = 365 nm) was exposed to laboratory air continuously at an apparent contact time of 0.27 sand challenged with a known concentration of ethanol periodically to assess any changes in catalytic activity. Laboratory air was also episodically spiked with halocarbons (e.g., octafluoropropane), organosulfur compounds (e.g., sulfur hexafluoride), and organosilicons (e.g., siloxanes) to simulate accidental releases or leaks of such VOCs. Total organic carbon (TOC) loading and contaminant profiles of the laboratory air were also monitored. Changes in STC photocatalytic performance were evaluated using the ethanol mineralization rate, mineralization efficiency, and oxidation intermediate (acetaldehyde) formation. Results provide insights to any potential catalyst poisoning by trace halocarbons and organosulfur compounds.

Coutts, Janelle L.↗

NASA NTRS2012

Photogrammetric Verification of Fiber Optic Shape Sensors on Flexible Aerospace Structures

Multi-core fiber (MCF) optic shape sensing offers the possibility of providing in-flight shape measurements of highly flexible aerospace structures and control surfaces for such purposes as gust load alleviation, flutter suppression, general flight control and structural health monitoring. Photogrammetric measurements of surface mounted MCF shape sensing cable can be used to quantify the MCF installation path and verify measurement methods.

Moore, Jason P.↗

NASA NTRS2017

Full-Coverage High-Resolution Daily PM(sub 2.5) Estimation using MAIAC AOD in the Yangtze River Delta of China

Satellite aerosol optical depth (AOD) has been used to assess population exposure to fine particulate matter (PM (sub 2.5)). The emerging high-resolution satellite aerosol product, Multi-Angle Implementation of Atmospheric Correction(MAIAC), provides a valuable opportunity to characterize local-scale PM(sub 2.5) at 1-km resolution. However, non-random missing AOD due to cloud snow cover or high surface reflectance makes this task challenging. Previous studies filled the data gap by spatially interpolating neighboring PM(sub 2.5) measurements or predictions. This strategy ignored the effect of cloud cover on aerosol loadings and has been shown to exhibit poor performance when monitoring stations are sparse or when there is seasonal large-scale missngness. Using the Yangtze River Delta of China as an example, we present a Multiple Imputation (MI) method that combines the MAIAC high-resolution satellite retrievals with chemical transport model (CTM) simulations to fill missing AOD. A two-stage statistical model driven by gap-filled AOD, meteorology and land use information was then fitted to estimate daily ground PM(sub 2.5) concentrations in 2013 and 2014 at 1 km resolution with complete coverage in space and time. The daily MI models have an average R(exp 2) of 0.77, with an inter-quartile range of 0.71 to 0.82 across days. The overall Ml model 10-fold cross-validation R(exp 2) (root mean square error) were 0.81 (25 gm(exp 3)) and 0.73 (18 gm(exp 3)) for year 2013 and 2014, respectively. Predictions with only observational AOD or only imputed AOD showed similar accuracy.Comparing with previous gap-filling methods, our MI method presented in this study performed bette rwith higher coverage, higher accuracy, and the ability to fill missing PM(sub 2.5) predictions without ground PM(sub 2.5) measurements. This method can provide reliable PM(sub 2.5)predictions with complete coverage that can reduce biasin exposure assessment in air pollution and health studies.

PM2.5↗

NASA NTRSDate not supplied

Characterization of Composite Damage Using Passive Thermography

Passive thermography is used to monitor small increases in temperature on a composite structure during loading. A rise in temperature can be attributed to damage formation due to matrix cracking and fiber breaks. An infrared camera allows for imaging the damage location, size, and shape. Recently, a technique has been developed to estimate the depth of the damage in a composite hat-stiffened panel during quasi-static loading. This technique is based on two heat generation components. The first component is an instantaneous thermoelastic response due to a strain release. The second component is mechanical heating, at the interface of failure, due to fracture damage. This second component produces a transient rise in temperature that is a function of the damage depth and thermal diffusivity. The first component defines the thermal start time for the transient response. A one-dimensional thermal model is used to determine the damage depth for a known thermal diffusivity. The results are compared to ultrasonic and X-ray CT data and show good agreement for damage depth.

Thermal nondestructive evaluation↗

NASA NTRSDate not supplied

Impact of Canadian Wildfires on Mid Atlantic’s Region Air Quality: An Analysis Using ASDC Data

Wildfires pose a growing concern in North America due to their harmful impacts on air quality and public health, with increased wildfire activity in recent years leading to widespread smoke plumes that can transcend borders. The exposure of New York City (NYC), the most populous city in North America, to Canadian wildfire smoke highlights the substantial implications for public health and urban environments. To better understand the impact of Canadian wildfires on air quality in NYC, satellite data from the NASA Atmospheric Science Data Center (ASDC) at Langley Research Center, along with ground-based measurements and atmospheric modeling results, are analyzed. We examine concentrations of atmospheric aerosols—particularly PM2.5 particulate matter originating from Canadian wildfires—their dispersion patterns, and the duration and intensity of smoke events impacting NYC. Data from multiple satellites, such as those from the Earth Polychromatic Imaging Camera (EPIC), are synergistically used to identify regions affected by wildfires and estimate aerosol loading. Ground-based measurements, including data from air quality monitoring stations, provide localized information for validation and calibration purposes. The findings of this study contribute to our understanding of the impact of Canadian wildfires on NYC's air quality and emphasize the importance of monitoring and prediction of transboundary smoke events using data synthesized from multiple sources, such as those provided by the ASDC. This information is crucial for policymakers, public health officials, and residents in affected areas to develop effective strategies for mitigating the health risks associated with wildfire smoke and improving air quality during wildfire seasons. The utilization of ASDC data in this research highlights the critical role of atmospheric remote sensing in addressing the challenges posed by wildfires and their consequences on regional scales.

Ingrid Garcia-Solera↗

NASA NTRSDate not supplied

Analyzing the Impact of Canadian Wildfires on Air Quality in the U.S. Mid-Atlantic: with Data and Tools from NASA’s Atmospheric Sciences Data Center

Ingrid Garcia-Solera↗

NASA NTRSDate not supplied

Method Development for Experimental Characterization of Dynamic Strength of Aluminum Structures

Space Flight Hardware (SFH) experiences intense vibratory loading during flight, which only lasts a few minutes. When determining the appropriate size of these components to withstand such loading without becoming damaged (plastically deforming or breaking), standard design practice is to assume that the peak dynamic loads are applied statically. In doing so, the resulting stress is compared against a material strength parameter obtained from a quasi-static experiment. Since the near-peak stresses are only experienced over a small fraction of time in reality, this approach leads to design conservatism that unnecessarily increases structural mass as well as the associated inefficiency and financial cost. In an effort to modernize engineering design standards to appropriately consider the higher practical strength of dynamically loaded structures, this overarching research project seeks to develop an experimental test procedure for quantifying the dynamic strength of metallic alloys as a function of excitation frequency. In the ideal case, the characterization test would include an in-situ method for monitoring the onset and progression of plastic deformation of the test specimen undergoing vibratory loading. These new tests are designed to be high intensity (forcing amplitude), short term (60 seconds at full amplitude), and cyclic in nature (sinusoidal excitation via attached stinger to an otherwise cantilevered beam). In addition, the initial metallic alloy under investigation is 6061 aluminum, due to its wide use and applicability for SFH. Thus far, the most promising monitoring method under development with live-capacity is to track hysteresis behavior of the beam from power dissipation trends, calculated via force (from transducer on excitation stinger) and velocity (measurements from Laser Doppler Vibrometer) data and work to distinguish between elastic and plastic features. As a key component of this ongoing method development, any pseudo-live indication of plasticity can be corroborated against the outcome of a pre-post assessment of damage via macroscopic evaluation of beam geometry (i.e. assessing any permanent change in the beam’s tip deflection). Support for experimental design decisions as well as dynamic strength data from tests with excitation frequencies of 10, 40, and 55 Hz will be discussed. This work contributes to the foundation for a new type of vibration-based characterization experiments and generates initial data on the functional strength of 6061 aluminum under the conditions considered. Keywords: vibrations, plastic deformation, aluminum

vibrations↗

DOE OSTI2020

Aerosol jet printed capacitive strain gauge for soft structural materials

Soft structural textiles, or softgoods, are used within the space industry for inflatable habitats, parachutes and decelerator systems. Evaluating the safety and structural integrity of these systems occurs through structural health monitoring systems (SHM), which integrate non-invasive/non-destructive testing methods to detect, diagnose, and locate damage. Strain/load monitoring of these systems is limited while utilizing traditional strain gauges as these gauges are typically stiff, operate at low temperatures, and fail when subjected to high strain that is a result of high loading classifying them as unsuitable for SHM of soft structural textiles. For this work, a capacitance based strain gauge (CSG) was fabricated via aerosol jet printing (AJP) using silver nanoparticle ink on a flexible polymer substrate. Printed strain gauges were then compared to a commercially available high elongation resistance-based strain gauge (HE-RSG) for their ability to monitor strained Kevlar straps having a 26.7?kN (6?klbf) load. Dynamic, static and cyclic loads were used to characterize both types of strain monitoring devices. Printed CSGs demonstrated superior performance for high elongation strain measurements when compared to commonly used HE-RSGs, and were observed to operate with a gauge factor of 5.2 when the electrode arrangement was perpendicular to the direction of strain.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico

Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1996

Detection of Delamination in Composite Beams Using Broadband Acoustic Emission Signatures

Delamination in composite structure may be caused by imperfections introduced during the manufacturing process or by impact loads by foreign objects during the operational life. There are some nondestructive evaluation methods to detect delamination in composite structures such as x-radiography, ultrasonic testing, and thermal/infrared inspection. These methods are expensive and hard to use for on line detection. Acoustic emission testing can monitor the material under test even under the presence of noise generated under load. It has been used extensively in proof-testing of fiberglass pressure vessels and beams. In the present work, experimental studies are conducted to investigate the use of broadband acoustic emission signatures to detect delaminations in composite beams. Glass/epoxy beam specimens with full width, prescribed delamination sizes of 2 inches and 4 inches are investigated. The prescribed delamination is produced by inserting Teflon film between laminae during the fabrication of composite laminate. The objectives of this research is to develop a method for predicting delamination size and location in laminated composite beams by combining smart materials concept and broadband AE analysis techniques. More specifically, a piezoceramic (PZT) patch is bonded on the surface of composite beams and used as a pulser. The piezoceramic patch simulates the AE wave source as a 3 cycles, 50KHz, burst sine wave. One broadband AE sensor is fixed near the PZT patch to measure the AE wave near the AE source. A second broadband AE sensor, which is used as a receiver, is scanned along the composite beams at 0.25 inch step to measure propagation of AE wave along the composite beams. The acquired AE waveform is digitized and processed. Signal strength, signal energy, cross-correlation of AE waveforms, and tracking of specific cycle of AE waveforms are used to detect delamination size and location.

Okafor, A. C.↗

NASA NTRS1989

Identifying atmospheric monitoring needs for Space Station Freedom

The atmospheric monitoring needs for Space Station Freedom were identified by examining the following from an industrial hygiene perspective: the experiences of past missions; ground based tests of proposed life support systems; the unique experimental and manufacturing facilities; the contaminant load model; metabolic production; and a fire. A target list of compounds to be monitored is presented and information is provided relative to the frequency of analysis, concentration ranges, and locations for monitoring probes.

Casserly, Dennis M.↗

NASA NTRS1993

Model-based reasoning for power system management using KATE and the SSM/PMAD

The overall goal of this research effort has been the development of a software system which automates tasks related to monitoring and controlling electrical power distribution in spacecraft electrical power systems. The resulting software system is called the Intelligent Power Controller (IPC). The specific tasks performed by the IPC include continuous monitoring of the flow of power from a source to a set of loads, fast detection of anomalous behavior indicating a fault to one of the components of the distribution systems, generation of diagnosis (explanation) of anomalous behavior, isolation of faulty object from remainder of system, and maintenance of flow of power to critical loads and systems (e.g. life-support) despite fault conditions being present (recovery). The IPC system has evolved out of KATE (Knowledge-based Autonomous Test Engineer), developed at NASA-KSC. KATE consists of a set of software tools for developing and applying structure and behavior models to monitoring, diagnostic, and control applications.

Morris, Robert A.↗

NASA NTRS1998

Experimental Study of Load Carrying Capacity of Point Contacts at Zero Entrainment Velocity

A capacitance technique was used to monitor the film thickness separating two steel balls while subjecting the ball-ball contact to highly stressed, zero entrainment velocity conditions. Tests were performed in a nitrogen atmosphere and utilized 52100 steel balls and a polyalphaolefin lubricant. Capacitance to film thickness accuracy was verified under pure rolling conditions using established EHL theory. Zero entrainment velocity tests were performed at sliding speeds from 6.0 to 10.0 m/s and for sustained amounts of time to 28.8 min. The protective lubricant film separating the specimens at zero entrainment velocity had a film thickness between 0.10 to 0.14 microns (4 to 6 micro in.), which corresponded to a k value of 4. The formation of an immobile surface film formed by lubricant entrapment is discussed as an explanation of the load carrying capacity at zero entrainment velocity conditions, relevant to the ball-ball contacts occurring in retainerless ball bearings.

Shogin, B. A.↗

NASA NTRS2001

Validation of Satellite Aerosol Retrievals from AERONET Ground-Based Measurements

Accurate and comprehensive assessment of the parameters that control key atmospheric and biospheric processes including assessment of anthropogenic effects on climate change is a fundamental measurement objective of NASA's EOS program (King and Greenstone, 1999). Satellite assessment programs and associated global climate models require validation and additional parameterization with frequent reliable ground-based observations. A critical and highly uncertain element of the measurement program is characterization of tropospheric aerosols requiring basic observations of aerosols optical and microphysical properties. Unfortunately as yet we do not know the aerosol burden man is contributing to the atmosphere and thus we will have no definitive measure of change for the future. This lack of aerosol assessment is the impetus for some of the EOS measurement activities (Kaufman et al., 1997; King et al., 1999) and the formation of the AERONET program (Holben et al., 1998). The goals of the AERONET program are to develop long term monitoring at globally distributed sites providing critical data for multiannual trend changes in aerosol loading and optical properties with the specific goal of providing a data base for validation of satellite derived aerosol optical properties. The AERONET program has evolved into an international federated network of approximately 100 ground-based remote sensing monitoring stations to characterize the optical and microphysical properties of aerosols.

Holben, Brent↗

DOE OSTI2026

UV–Vis–NIR Reflectance Spectroscopy and Chemometrics for Monitoring Pu Directly on an Ion Exchange Column

Here, we present a fiber-optic UV–vis–NIR reflectance spectroscopy method for direct, noninvasive monitoring of Pu(IV) in a glass ion exchange column during dynamic loading and elution in a glovebox. A movable probe enables spatially resolved spectral acquisition along the column axis, capturing distinct features associated with Pu(IV) nitrate complexes during loading and free ions during elution. Principal component analysis was applied to extract the dominant spectral variance and resolve relative concentration profiles without requiring precise knowledge of optical penetration depth or species identity. This in situ approach reveals spatial gradients and speciation dynamics in real time, which provides actionable insight into Pu(IV) ion migration, resin saturation, and breakthrough behavior under evolving flow conditions. The method offers a practical, fiber-compatible strategy to monitor glass column–based separations for Pu and other lanthanides or actinides and to characterize metal–resin interactions in flow-through systems.

actinide↗

NASA NTRS2011

Design, Development and Test Challenges: Separation Mechanisms for the Orion Pad Abort-1 Flight Test

On May 6, 2010, NASA launched the first successful integrated flight test, Pad Abort-1, of the Orion Project from the White Sands Missile Range in Las Cruces, New Mexico. This test demonstrated the ability to perform an emergency pad abort of a full-scale 4.8 m diameter, 8200 kg crew capsule. During development of the critical separation mechanisms for this flight test, various challenges were overcome related to environments definition, installation complications, separation joint retraction speed, thruster ordnance development issues, load path validation and significant design loads increases. The Launch Abort System retention and release (LAS R&R) mechanism consisted of 6 discrete structural connections between the LAS and the crew module (CM) simulator, each of which had a preloaded tension tie, Superbolt torque-nut and frangible nut. During the flight test, the frangible nuts were pyrotechnically split, permitting the CM to separate from the LAS. The LAS separation event was the driving case in the shock environment for many co-located hardware items. During development testing, it was necessary to measure the source shock during the separation event so the predicted shock environment could be validated and used for certification testing of multiple hardware items. The Lockheed Martin test team measured the source separation shock due to the LAS R&R function, which dramatically decreased the predicted environment by 90% at 100 Hz. During development testing a hydraulic tensioner was used to preload the joint; however, the joint relaxation with the tensioner proved unsatisfactory so the design was modified to include a Superbolt torque-nut. The observed preload creep during lab testing was 4% after 30 days, with 2.5% occurring in the first 24 hours. The conversion of strain energy (preload) to kinetic energy (retraction) was measured to be 50-75%. Design features and careful monitoring of multiple strain gauges on each tension tie allowed a pure tensile load to be applied after stacking at the launch pad. Following installation, preload in each joint was monitored for 24 hours. Due to unforeseen complications and the influence of temperature on the portable data acquisition system, the team encountered difficulty in tracking the joint relaxation. In some cases, bond-line failure of the strain gauges occurred.

Dinsel, Alison↗

NASA NTRS1974

Arctic and subarctic environmental analyses utilizing ERTS-1 imagery

The author has identified the following significant results. ERTS-1 imagery provides a means of distinguishing and monitoring estuarine surface water circulation patterns and changes in the relative sediment load of discharging rivers on a regional basis. Physical boundaries mapped from ERTS-1 imagery in combination with ground truth obtained from existing small scale maps and other sources resulted in improved and more detailed maps of permafrost terrain and vegetation for the same area. Snowpack cover within a research watershed has been analyzed and compared to ground data. Large river icings along the proposed Alaska pipeline route from Prudhoe Bay to the Brooks Range have been monitored. Sea ice deformation and drift northeast of Point Barrow, Alaska have been measured during a four day period in March and shore-fast ice accumulation and ablation along the west coast of Alaska have been mapped for the spring and early summer seasons.

Anderson, D. M.↗

DOE OSTI2023

Unsupervised acoustic detection of fatigue-induced damage modes from wind turbine blades

This paper proposes a new in-situ damage detection approach for wind turbine blades, which leverages blade-internal non-stationary acoustic pressure fluctuations caused by the mechanical loading as the main source of excitation. This acoustic excitation was leveraged for the detection of fatigue-related damage modes on a full-scale wind turbine blade undergoing edgewise fatigue testing. An unsupervised, data-driven structural health monitoring strategy was developed to learn the normal cavity-internal acoustic sequences generated by the blade’s load cycles and to detect damage-related anomalies in the context of those sequences. A linear cepstral-coefficient based feature set was used to characterize the cavity-internal acoustics and LSTM-autoencoders were trained to accurately reconstruct healthy-case sequences. The reconstruction error was then used to characterize anomalous acoustic patterns within the blade cavity. The technique was able to detect a damage event earlier than a strain-based system by 120,000 load cycles.

17 WIND ENERGY↗