Search NASA⌕ Search

SEARCH · Search NASA

Results for “quality monitoring”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Online and Offline Monitoring of the ICARUS Detector

In preparation for the launch of the ICARUS -T600 far detector in the Short Baseline Neutrino program, (SBN), it is important to monitor the performance of the detector and quality of the data in real-time. Particularly important to monitor are noise levels of the TPC readout electronics, and the purity of the liquid argon in the TPC, as these can affect the overall sensitivity of the detector to detecting and reconstructing neutrino interactions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

X-ray Emission Spectroscopy as an in Situ Diagnostic Tool for X-ray Crystallography of Metalloproteins Using an X-ray Free-Electron Laser

Serial femtosecond crystallography (SFX) using the ultrashort X-ray pulses from a X-ray free-electron laser (XFEL) provides a new way of collecting structural data at room temperature that allows for following the reaction in real time after initiation. XFEL experiments are conducted in a shot-by-shot mode as the sample is destroyed and replenished after each X-ray pulse, and therefore, monitoring and controlling the data quality by using in situ diagnostic tools is critical. To study metalloenzymes, we developed the use of simultaneous collection of X-ray diffraction of crystals along with X-ray emission spectroscopy (XES) data that is used as a diagnostic tool for crystallography, by monitoring the chemical state of the metal catalytic center. We have optimized data analysis methods and sample delivery techniques for fast and active feedback to ensure the quality of each batch of samples and the turnover of the catalytic reaction caused by reaction triggering methods. Here, we describe this active in situ feedback system using Photosystem II as an example that catalyzes the oxidation of H2O to O2at the Mn4CaO5active site. We used the first moments of the Mn Kβ1,3emission spectra, which are sensitive to the oxidation state of Mn, as the primary diagnostics. This approach is applicable to different metalloproteins to determine the integrity of samples and follow changes in the chemical states of the reaction that can be initiated by light or activated by substrates and offers a metric for determining the diffraction images that are used for the final data sets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2011

Development of Meandering Winding Magnetometer (MWM (Register Trademark)) Eddy Current Sensors for the Health Monitoring, Modeling and Damage Detection of High Temperature Composite Materials

The increased use of high-temperature composite materials in modern and next generation aircraft and spacecraft have led to the need for improved nondestructive evaluation and health monitoring techniques. Such technologies are desirable to improve quality control, damage detection, stress evaluation and temperature measurement capabilities. Novel eddy current sensors and sensor arrays, such as Meandering Winding Magnetometers (MWMs) have provided alternate or complimentary techniques to ultrasound and thermography for both nondestructive evaluation (NDE) and structural health monitoring (SHM). This includes imaging of composite material quality, damage detection and .the monitoring of fiber temperatures and multidirectional stresses. Historically, implementation of MWM technology for the inspection of the Space Shuttle Orbiter Reinforced Carbon-Carbon Composite (RCC) leading edge panels was developed by JENTEK Sensors and was subsequently transitioned by NASA as an operational pre and post flight in-situ inspection at the Kennedy Space Center. A manual scanner, which conformed'automatically to the curvature of the RCC panels was developed and used as a secondary technique if a defect was found during an infrared thermography screening, During a recent proof of concept study on composite overwrapped pressure vessels (COPV's), three different MWM sensors were tested at three orientations to demonstrate the ability of the technology to measure stresses at various fiber orientations and depths. These results showed excellent correlation with actual surface strain gage measurements. Recent advancements of this technology have been made applying MWM sensor technology for scanning COPVs for mechanical damage. This presentation will outline the recent advance~ in the MWM.technology and the development of MWM techniques for NDE and SHM of carbon wra~ped composite overwrapped pressure vessels (COPVs) including the measurement of internal stresses via a surface mounted sensor array. In addition, this paper will outline recent efforts to produce sensors capable of making real-time measurements at temperatures up to 850 C, and discuss previous results demonstrating capability to monitor carbon fiber temperature changes within a composite material.

Russell, Richard↗
NASA NTRS2019

GEOS-Carb III: Delivering Mature Carbon Flux and Concentration Datasets in Support of NASA's Carbon Monitoring System

The GEOS-Carb project seeks to make high quality, data-driven products that support NASA's Carbon Monitoring System Program. These include bottom-up land and ocean flux estimates along with fossil fuel emissions, all informed by satellite data and the MERRA-2 reanalysis. These flux data support atmospheric carbon products that include both simulations and data assimilation products. We show examples of this work and discuss applications to future mission planning.

Ott, L.↗
NASA NTRS2010

Use of MODIS Terra Imagery to Estimate Surface Water Quality Standards, Using Lake Thonotosassa, Florida, as a Case Study

Lake Thonotosassa is a highly eutrophied lake located in an area with rapidly growing population in the Tampa Bay watershed, Florida. The Florida Administrative Code has designated its use for "recreation, propagation and maintenance of a healthy, well-balanced population of fish and wildlife." Although this lake has been the subject of efforts to improve water quality since 1970, overall water quality has remained below the acceptable state standards, and has a high concentration of nutrients. This condition is of great concern to public health since it has favored episodic blooms of Cyanobacteria. Some Cyanobacterial species release toxins that can reach humans through drinking water, fish consumption, and direct contact with contaminated water. The lake has been historically popular for fishing and water sports, and its overflow water drains into the Hillsborough River, the main supply of municipal water for the City of Tampa, this explains why it has being constantly monitored in situ for water quality by the Environmental Protection Commission of Hillsborough County (EPC). Advances in remote sensing technology, however, open the possibility of facilitating similar types of monitoring in this and similar lakes, further contributing to the implementation of surveillance systems that would benefit not just public health, but also tourism and ecosystems. Although traditional application of this technology to water quality has been focused on much larger coastal water bodies like bays and estuaries, this study evaluates the feasibility of its application on a 46.6 km2 freshwater lake. Using surface reflectance products from Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra, this study evaluates associations between remotely sensed data and in situ data from the EPC. The parameters analyzed are the surface water quality standards used by the State of Florida and general indicators of trophic status.

Moreno, Max J.↗
DOE OSTI2023

Systems and methods for modeling water quality

A system, method, device and computer-readable medium for creating an ensemble model of water quality. The ensemble model is generated by determining a set of optimal component models for spectral regions of a body of water, and combining the optimal models. The optimal models can be based on remote sensing data, including satellite imagery. A K-fold partition approach or a global approach can be used to determine the optimal component models, and the optimal component models can be combined through spectral space partition rules to generate an ensemble model of water quality. The ensemble model not only has improved water quality prediction ability, but also has strong spatial and temporal extensibility. The spatial and temporal extensibility of the ensemble model is fundamentally important and desirable for long-term and large-scale remote sensing monitoring and assessment of water quality.

Liu, Hongxing↗
NASA NTRSDate not supplied

Nondestructive Evaluation and In-Situ Monitoring Efforts for Additive Manufacturing

Nondestructive evaluation (NDE) is required to determine the quality of additive manufactured (AM) parts due to the inherent variability of AM processes. In-situ monitoring technologies endeavor to characterize the process and part quality during production. Qualifying an in-situ monitoring technology requires a proven, causal correlation between indications in the monitoring data and flaws in the finished part.

James Mavo↗
NASA NTRSDate not supplied

Gulf of Mexico Health & Air Quality: Mapping Methane Emission Plumes Using Sunglint-configured Imagery for Monitoring Offshore Oil and Gas Activity

We had three project objectives that we set out to accomplish. The first objective is to demonstrate how remote sensing can complement the current bottom-up methane emission assessments. Currently, quantities of disposed gas vented and flared are self-reported by operators to BOEM and BSEE. Meanwhile, inspections are uncommon, and ground sensors are non-existent. Therefore, remote sensing may be the agencies' best bet in verifying these operator accounts. The second objective is to identify methane plumes in the Gulf of Mexico, Asia, and elsewhere using Sentinel-2, LandSat, and PRISMA images, and then present an estimate of their emission rate. While methane retrievals from oil and gas infrastructure on-shore has been relatively well-documented, doing so on the offshore oil and gas platforms is more difficult, less prevalent, and contingent on the presence of specific conditions. The final objective is to give our partners, BOEM and BSEE, a more robust understanding of methane emissions from oil and gas production in the Gulf of Mexico. This increased knowledge, paired with a demonstration of methane monitoring, is intended to help our partners think about how they can incorporate remote sensing into their monitoring, regulatory and enforcement framework.

Ben Dahan↗
DOE OSTI2022

Improving Coal-Fired Plant Performance Through Integrated Predictive and Condition-Based Monitoring Tools

The project demonstrated the ability to improve boiler performance and reliability through the integrated use of condition-based monitoring (CBM) and predictions of the impacts of coal quality on boiler operations at a full-scale coal-fired power plant. The advanced tool developed actively monitors and manages coal quality and overall boiler conditions that maximizes availability and maintains generating capacity while reducing cost. The tool is used to forecast and alert plant operators and engineers about poor boiler conditions which may occur as a result of incoming coal and/or current power plant operating conditions. The Combustion System Performance Indices (CSPI) and CoalTracker (CT) programs predicts fireside performance of the plant including slagging, fouling, erosion, slag flow, strength development (sintering/densification), and slag layer thickness based on the composition of delivered coal. CT program is tailored for each plant and is used to track the coal from the point of delivery to the burner. The most successful application of the CSPI-CT has been at plants where the coal composition is determined through the use of a full stream elemental analyzers based on prompt gamma neutron analysis (PGNAA). The CSPI-CT program information is used by coal procurement, coal mining and plant operations personnel to select and blend coal properties for the best plant performance.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS1994

The meteorological monitoring system for the Kennedy Space Center/Cape Canaveral Air Station

The Kennedy Space Center (KSC) and Cape Canaveral Air Station (CCAS) are involved in many weather-sensitive operations. Manned and unmanned vehicle launches, which occur several times each year, are obvious example of operations whose success and safety are dependent upon favorable meteorological conditions. Other operations involving NASA, Air Force, and contractor personnel, including daily operations to maintain facilities, refurbish launch structures, prepare vehicles for launch, and handle hazardous materials, are less publicized but are no less weather-sensitive. The Meteorological Monitoring System (MMS) is a computer network which acquires, processes, disseminates, and monitors near real-time and forecast meteorological information to assist operational personnel and weather forecasters with the task of minimizing the risk to personnel, materials, and the surrounding population. CLIPS has been integrated into the MMS to provide quality control analysis and data monitoring. This paper describes aspects of the MMS relevant to CLIPS including requirements, actual implementation details, and results of performance testing.

Dianic, Allan V.↗
NASA NTRS2010

Imaging Modalities Relevant to Intracranial Pressure Assessment in Astronauts: A Case-Based Discussion

Introduction: Intracranial pressure (ICP) elevation has been inferred or documented in a number of space crewmembers. Recent advances in noninvasive imaging technology offer new possibilities for ICP assessment. Most International Space Station (ISS) partner agencies have adopted a battery of occupational health monitoring tests including magnetic resonance imaging (MRI) pre- and postflight, and high-resolution sonography of the orbital structures in all mission phases including during flight. We hypothesize that joint consideration of data from the two techniques has the potential to improve quality and continuity of crewmember monitoring and care. Methods: Specially designed MRI and sonographic protocols were used to image eyes and optic nerves (ON) including the meningeal sheaths. Specific crewmembers multi-modality imaging data were analyzed to identify points of mutual validation as well as unique features of complementary nature. Results and Conclusion: Magnetic resonance imaging (MRI) and high-resolution sonography are both tomographic methods, however images obtained by the two modalities are based on different physical phenomena and use different acquisition principles. Consideration of the images acquired by these two modalities allows cross-validating findings related to the volume and fluid content of the ON subarachnoid space, shape of the globe, and other anatomical features of the orbit. Each of the imaging modalities also has unique advantages, making them complementary techniques.

Sargsyan, Ashot E.↗
NASA NTRS2007

Production and Distribution of NASA MODIS Remote Sensing Products

The two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on-board NASA's Earth Observing System (EOS) Terra and Aqua satellites make key measurements for understanding the Earth's terrestrial ecosystems. Global time-series of terrestrial geophysical parameters have been produced from MODIS/Terra for over 7 years and for MODIS/Aqua for more than 4 1/2 years. These well calibrated instruments, a team of scientists and a large data production, archive and distribution systems have allowed for the development of a new suite of high quality product variables at spatial resolutions as fine as 250m in support of global change research and natural resource applications. This talk describes the MODIS Science team's products, with a focus on the terrestrial (land) products, the data processing approach and the process for monitoring and improving the product quality. The original MODIS science team was formed in 1989. The team's primary role is the development and implementation of the geophysical algorithms. In addition, the team provided feedback on the design and pre-launch testing of the instrument and helped guide the development of the data processing system. The key challenges the science team dealt with before launch were the development of algorithms for a new instrument and provide guidance of the large and complex multi-discipline processing system. Land, Ocean and Atmosphere discipline teams drove the processing system requirements, particularly in the area of the processing loads and volumes needed to daily produce geophysical maps of the Earth at resolutions as fine as 250 m. The processing system had to handle a large number of data products, large data volumes and processing loads, and complex processing requirements. Prior to MODIS, daily global maps from heritage instruments, such as Advanced Very High Resolution Radiometer (AVHRR), were not produced at resolutions finer than 5 km. The processing solution evolved into a combination of processing the lower level (Level 1) products and the higher level discipline specific Land and Atmosphere products in the MODIS Science Investigator Lead Processing System (SIPS), the MODIS Adaptive Processing System (MODAPS), and archive and distribution of the Land products to the user community by two of NASA s EOS Distributed Active Archive Centers (DAACs). Recently, a part of MODAPS, the Level 1 and Atmosphere Archive and Distribution System (LAADS), took over the role of archiving and distributing the Level 1 and Atmosphere products to the user community.

Wolfe, Robert↗
NASA NTRS2014

Satellite Data of Atmospheric Pollution for U.S. Air Quality Applications: Examples of Applications, Summary of Data End-User Resources, Answers to FAQs, and Common Mistakes to Avoid

Satellite data of atmospheric pollutants are becoming more widely used in the decision-making and environmental management activities of public, private sector and non-profit organizations. They are employed for estimating emissions, tracking pollutant plumes, supporting air quality forecasting activities, providing evidence for "exceptional event" declarations, monitoring regional long-term trends, and evaluating air quality model output. However, many air quality managers are not taking full advantage of the data for these applications nor has the full potential of satellite data for air quality applications been realized. A key barrier is the inherent difficulties associated with accessing, processing, and properly interpreting observational data. A degree of technical skill is required on the part of the data end-user, which is often problematic for air quality agencies with limited resources. Therefore, we 1) review the primary uses of satellite data for air quality applications, 2) provide some background information on satellite capabilities for measuring pollutants, 3) discuss the many resources available to the end-user for accessing, processing, and visualizing the data, and 4) provide answers to common questions in plain language.

Duncan, Bryan↗
NASA NTRS2020

Towards a Soil Moisture Drought Monitoring System for South Korea

The Korea Land Data Assimilation System (KLDAS) has been established for agricultural drought (i.e. soil moisture deficit) monitoring in South Korea, running the Noah-MP land surface model within the NASA Land Information System (LIS) framework with the added value of local precipitation forcing dataset and soil texture maps. KLDAS soil moisture is benchmarked against three global products: the Global Land Data Assimilation System (GLDAS), the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), and the European Space Agency Climate Change Initiative (ESA CCI) satellite product. The evaluation is performed using in situ measurements for 2013–2015 and one month standardized precipitation index (SPI-1) for 1982–2016, focusing on four major river basins in South Korea. The KLDAS outperforms all benchmark products in capturing soil moisture states and variability at a basin scale. Compared to GLDAS and FLDAS products, the EAS CCI product is not feasible for long term agricultural monitoring due to lower data quality for early periods (1979–1991) of soil moisture estimates. KLDAS shows that the most recent 2015 drought event leads to highest drought areas in the Han and Geum River basins in the past 35 years. This work supports KLDAS as an effective agricultural drought monitoring system to provide continuous regional high-resolution soil moisture estimates in South Korea.

Agricultural drought↗
DOE OSTI2016

On-line, continuous monitoring in solar cell and fuel cell manufacturing using spectral reflectance imaging

A monitoring system 100 comprising a material transport system 104 providing for the transportation of a substantially planar material 102, 107 through the monitoring zone 103 of the monitoring system 100. The system 100 also includes a line camera 106 positioned to obtain multiple line images across a width of the material 102, 107 as it is transported through the monitoring zone 103. The system 100 further includes an illumination source 108 providing for the illumination of the material 102, 107 transported through the monitoring zone 103 such that light reflected in a direction normal to the substantially planar surface of the material 102, 107 is detected by the line camera 106. A data processing system 110 is also provided in digital communication with the line camera 106. The data processing system 110 is configured to receive data output from the line camera 106 and further configured to calculate and provide substantially contemporaneous information relating to a quality parameter of the material 102, 107. Also disclosed are methods of monitoring a quality parameter of a material.

14 SOLAR ENERGY↗
NASA NTRS2022

Gulf of Mexico Health & Air Quality Ii: Mapping Methane Emission Plumes Using Sunglint-Configured Imagery for Monitoring Offshore Oil and Gas Activity

Offshore oil and gas production in the United States is a major source of anthropogenic greenhouse gas emissions and accounts for nearly 30% of global oil and gas production. Methane venting and flaring are primary contributors to offshore emissions, and monitoring these activities is crucial for mitigating greenhouse gas emissions. Limited ground truthing and intermittent offshore satellite revisits make monitoring venting and flaring challenging. The Bureau of Ocean Energy Management (BOEM) and the Bureau of Safety and Environmental Enforcement (BSEE) oversee offshore oil and gas activity but rely primarily on operator-reported data. The non-profit organization SkyTruth monitors natural resources like methane and identifies sources of fugitive emissions. By combining BOEM and BSEE’s operational data along with observations from Sentinel-2 Multispectral Instrument (MSI), Landsat 8 Operational Land Imager (OLI) and Landsat 9 OLI-2, and PRecursore IperSpettrale della Missione Applicativa (PRISMA), the team further identified ultra-emitter point sources in the Gulf of Mexico using sunglint-configured imagery. We quantified these plume emission rates using the methodology from Varon et al. (2020). The team found three plumes in the Gulf of Mexico occurring between 2020 and 2022 using Sentinel-2 MSI and Landsat 9 OLI-2 imagery, in addition to the single plume identified by the Gulf of Mexico Health & Air Quality I team, and successfully quantified three plumes. Our statistical retrieval of three PRISMA images tasked over areas of interest yielded no methane plumes, despite a successful test of a known plume in Assam, India. These analyses serve as a proof of concept for the utility of remote sensing for methane emission monitoring offshore, which can complement regulator emission inventories and validate self-reported operator records.

sunglint↗
DOE OSTI2021

Streamflow simulation in data-scarce basins using Bayesian and physics-informed machine learning models

Hydrologic predictions at rural watersheds are important but also challenging due to data shortage. Long short-term memory (LSTM) networks are a promising machine learning approach and have demonstrated good performance in streamflow predictions. However, due to its data-hungry nature, most LSTM applications focus on well-monitored catchments with abundant and high-quality observations. In this work, we investigate predictive capabilities of LSTM in poorly monitored watersheds with short observation records. To address three main challenges of LSTM applications in data-scarce locations, i.e., overfitting, uncertainty quantification (UQ), and out-of-distribution prediction, we evaluate different regularization techniques to prevent overfitting, apply a Bayesian LSTM for UQ, and introduce a physics-informed hybrid LSTM to enhance out-of-distribution prediction. Through case studies in two diverse sets of catchments with and without snow influence, we demonstrate that 1) when hydrologic variability in the prediction period is similar to the calibration period, LSTM models can reasonably predict daily streamflow with Nash–Sutcliffe efficiency above 0.8, even with only 2 years of calibration data; 2) when the hydrologic variability in the prediction and calibration periods is dramatically different, LSTM alone does not predict well, but the hybrid model can improve the out-of-distribution prediction with acceptable generalization accuracy; 3) L2 norm penalty and dropout can mitigate overfitting, and Bayesian and hybrid LSTM have no overfitting; and 4) Bayesian LSTM provides useful uncertainty information to improve prediction understanding and credibility. In conclusion, these insights have vital implications for streamflow simulation in watersheds where data quality and availability are a critical issue.

54 ENVIRONMENTAL SCIENCES↗