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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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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Effects of Piezoelectric (PZT) Sensor Bonding and the Characteristics of the Host Structure on Impedance Based Structural Health Monitoring

This study was conducted to investigate the effects of certain factors on the impedance signal in structural health monitoring. These factors were: the quality of the bond between the sensor and the host structure, and the characteristics of the host structure, such as geometry, mass, and material properties. This work was carried out to answer a set of questions, related to these factors, that were developed by the project team. The project team was comprised of Dr. Doug Ramers and Dr. Abdul Jalloh of the Summer Faculty Fellowship Program, Mr. Arnaldo Colon- Perez, a student intern from the University of Puerto Rico of Turabo, and Mr. John Lassiter and Mr. Bob Engberg of the Structural and Dynamics Test Group at NASA Marshall Space Flight Center (MSFC). This study was based on a review of the literature on structural health monitoring to investigate the factors referred to above because there was not enough time to plan and conduct the appropriate tests at MSFC during the tenure of the Summer Faculty Fellowship Program project members. The surveyed literature documents works on structural health monitoring that were based on laboratory tests that were conducted using bolted trusses and other civil engineering type structures for the most part. These are not the typical types of structures used in designing and building NASA s space vehicles and systems. It was therefore recommended that tests be conducted using NASA type structures, such as pressure vessels, to validate the observations made in this report.

Jalloh, Abdul↗
DOE OSTI2012

A Comprehensive Comparison of IMRT and VMAT Plan Quality for Prostate Cancer Treatment

Purpose: We performed a comprehensive comparative study of the plan quality between volumetric-modulated arc therapy (VMAT) and intensity-modulated radiation therapy (IMRT) for the treatment of prostate cancer. Methods and Materials: Eleven patients with prostate cancer treated at our institution were randomly selected for this study. For each patient, a VMAT plan and a series of IMRT plans using an increasing number of beams (8, 12, 16, 20, and 24 beams) were examined. All plans were generated using our in-house-developed automatic inverse planning (AIP) algorithm. An existing eight-beam clinical IMRT plan, which was used to treat the patient, was used as the reference plan. For each patient, all AIP-generated plans were optimized to achieve the same level of planning target volume (PTV) coverage as the reference plan. Plan quality was evaluated by measuring mean dose to and dose-volume statistics of the organs at risk, especially the rectum, from each type of plan. Results: For the same PTV coverage, the AIP-generated VMAT plans had significantly better plan quality in terms of rectum sparing than the eight-beam clinical and AIP-generated IMRT plans (p < 0.0001). However, the differences between the IMRT and VMAT plans in all the dosimetric indices decreased as the number of beams used in IMRT increased. IMRT plan quality was similar or superior to that of VMAT when the number of beams in IMRT was increased to a certain number, which ranged from 12 to 24 for the set of patients studied. The superior VMAT plan quality resulted in approximately 30% more monitor units than the eight-beam IMRT plans, but the delivery time was still less than 3 min. Conclusions: Considering the superior plan quality as well as the delivery efficiency of VMAT compared with that of IMRT, VMAT may be the preferred modality for treating prostate cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2012

Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data

This paper describes an application of data mining technology called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM transforms the data into aircraft performance models, flight-to-flight trends, and individual flight anomalies by fitting a multi-level regression model to the data. The model represents aircraft flight performance and takes into account fixed effects: flight-to-flight and vehicle-to-vehicle variability. The regression parameters include aerodynamic coefficients and other aircraft performance parameters that are usually identified by aircraft manufacturers in flight tests. Using DFM, the multi-terabyte FOQA data set with half-million flights was processed in a few hours. The anomalies found include wrong values of competed variables, (e.g., aircraft weight), sensor failures and baises, failures, biases, and trends in flight actuators. These anomalies were missed by the existing airline monitoring of FOQA data exceedances.

Gorinevsky, Dimitry↗
DOE OSTI2016

SU-F-T-233: Evaluation of Treatment Delivery Parameters Using High Resolution ELEKTA Log Files

Purpose: As modern linacs have become more technologically advanced with the implementation of IGRT and IMRT with HDMLCs, a requirement for more elaborate tracking techniques to monitor components’ integrity is paramount. ElektaLog files are generated every 40 milliseconds, which can be analyzed to track subtle changes and provide another aspect of quality assurance. This allows for constant monitoring of fraction consistency in addition to machine reliability. With this in mind, it was the aim of the study to evaluate if ElektaLog files can be utilized for linac consistency QA. Methods: ElektaLogs were reviewed for 16 IMRT patient plans with >16 fractions. Logs were analyzed by creating fluence maps from recorded values of MLC locations, jaw locations, and dose per unit time. Fluence maps were then utilized to calculate a 2D gamma index with a 2%–2mm criteria for each fraction. ElektaLogs were also used to analyze positional errors for MLC leaves and jaws, which were used to compute an overall error for the MLC banks, Y-jaws, and X-jaws by taking the root-meansquare value of the individual recorded errors during treatment. Additionally, beam on time was calculated using the number of ElektaLog file entries within the file. Results: The average 2D gamma for all 16 patient plans was found to be 98.0±2.0%. Recorded gamma index values showed an acceptable correlation between fractions. Average RMS values for MLC leaves and the jaws resulted in a leaf variation of roughly 0.3±0.08 mm and jaw variation of about 0.15±0.04 mm, both of which fall within clinical tolerances. Conclusion: The use of ElektaLog files for day-to-day evaluation of linac integrity and patient QA can be utilized to allow for reliable analysis of system accuracy and performance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Data Validation Package - June 2015 Groundwater and Surface Water Sampling at the Green River, Utah, Disposal Site

Groundwater samples were collected during the 2015 sampling event from point-of-compliance (POC) wells 0171, 0173, 0176, 0179, 0181, and 0813 to monitor the disposition of contaminants in the middle sandstone unit of the Cedar Mountain Formation. Groundwater samples also were collected from alluvium monitoring wells 0188, 0189, 0192, 0194, and 0707, and basal sandstone monitoring wells 0182, 0184, 0185, and 0588 as a best management practice. Surface locations 0846 and 0847 were sampled to monitor for degradation of water quality in the backwater area of Brown’s Wash and in the Green River immediately downstream of Brown’s Wash. The Green River location 0801 is upstream from the site and is sampled to determine background-threshold values (BTVs). Sampling and analyses were conducted as specified in Sampling and Analysis Plan for U.S. Department of Energy Office of Legacy Management Sites (LMS/PRO/S04351, continually updated, http://energy.gov/lm/downloads/sampling-and- analysis-plan-us-department-energy-office-legacy-management-sites). Water levels were measured at each sampled well. The analytical data and associated qualifiers can be viewed in environmental database reports and are also available for viewing with dynamic mapping via the GEMS (Geospatial Environmental Mapping System) website at http://gems.lm.doe.gov/#. All six POC wells are completed in the middle sandstone unit of the Cedar Mountain Formation and are monitored to measure contaminant concentrations for comparison to proposed alternate concentration limits (ACLs), as provided in Table 1. Contaminant concentrations in the POC wells remain below their respective ACLs.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

The Salinity Pilot-Mission Exploitation Platform (Pi-MEP): A Hub for Validation and Exploitation of Satellite Sea Surface Salinity Data

The Pilot-Mission Exploitation Platform (Pi-MEP) for salinity is an ESA initiative originally meant to support and widen the uptake of Soil Moisture and Ocean Salinity (SMOS) mission data over the ocean. Starting in 2017, the project aims at setting up a computational web-based platform focusing on satellite sea surface salinity data, supporting studies on enhanced validation and scientific process over the ocean. It has been designed in close collaboration with a dedicated science advisory group in order to achieve three main objectives: gathering all the data required to exploit satellite sea surface salinity data, systematically producing a wide range of metrics for comparing and monitoring sea surface salinity products’ quality, and providing user-friendly tools to explore, visualize and exploit both the collected products and the results of the automated analyses. The Salinity Pi-MEP is becoming a reference hub for the validation of satellite sea surface salinity missions by providing valuable information on satellite products (SMOS, Aquarius, SMAP), an extensive in situ database (e.g., Argo, thermosalinographs, moorings, drifters) and additional thematic datasets (precipitation, evaporation, currents, sea level anomalies, sea surface temperature, etc.). Co-localized databases between satellite products and in situ datasets are systematically generated together with validation analysis reports for 30 predefined regions. The data and reports are made fully accessible through the web interface of the platform. The datasets, validation metrics and tools (automatic, user-driven) of the platform are described in detail in this paper. Several dedicated scientific case studies involving satellite SSS data are also systematically monitored by the platform, including major river plumes, mesoscale signatures in boundary currents, high latitudes, semi-enclosed seas, and the high-precipitation region of the eastern tropical Pacific. Since 2019, a partnership in the Salinity Pi-MEP project has been agreed between ESA and NASA to enlarge focus to encompass the entire set of satellite salinity sensors. The two agencies are now working together to widen the platform features on several technical aspects, such as triple-collocation software implementation, additional match-up collocation criteria and sustained exploitation of data from the SPURS campaigns

ocean↗
NASA NTRSDate not supplied

Hemispheric Airborne Measurements of Air Quality (HAMAQ)

Hemispheric Airborne Measurements of Air Quality (HAMAQ, pronounced “hammock”) was selected for funding under NASA’s Earth Venture Suborbital (EVS-4) solicitation. As originally proposed, HAMAQ would have flown under each of the geostationary satellites in the Atmospheric Composition Virtual Constellation (AC-VC). Selected under a reduced budget ($15M), HAMAQ has been descoped to include only sampling under TEMPO. The science objectives and approach are still relevant to the broader constellation, and partnerships are still in place to continue the pursuit of the larger vision of HAMAQ to also sample in Asia and Europe. HAMAQ plans include two deployments in 2028, including the Mexico City megalopolis and a second North American site yet to be selected. The effort will include two aircraft, NASA’s B777 for in situ sampling and G-III for remote sensing. These aircraft will be used to complete the integrated observing system, combining satellite observations, ground-based monitoring, research observations, and air quality modeling. With the long lead time for deployments in 2028, HAMAQ leadership are soliciting feedback on the project white paper available for download at https://science.larc.nasa.gov/HAMAQ.

Laura M Judd↗
NASA NTRSDate not supplied

Lessons Learned from the Airborne Particulate Monitor ISS Payload

Particulate monitoring on spacecraft has not been undertaken for air quality purposes for the first twenty years of human habitation on the International Space Station (ISS). The Airborne Particulate Monitor (APM) is a reference-quality instrument technology demonstration that characterized the airborne particles in the ISS cabin in real-time. Onboard aerosols have been measured with this higher fidelity instrument, so future miniaturized low-power aerosol instruments can be reliably compared in future ISS experiments. Several issues were encountered during the payload operations that are a result of the unique environment on ISS, which could not have been anticipated or eliminated by ground testing. First, the ISS had very small amounts of particulate matter in the particle measurement size range of the APM, which was unexpected. Second, despite the measured ‘clean’ environment, larger debris such as lint accumulated regularly on the cleanable inlet screen, which required regular inspection and crew time. The third issue is that particle emissions measured on ISS depend only on the activities in the immediate vicinity of the particle instrument and total particle concentrations cannot be generalized for the entire module. Finally, the sampling efficiency of APM on ISS is unknown because aisle-deployed instruments attached to wall panels of ISS are in the boundary layer of the large-scale ventilation flow of the modules. These issues are discussed and potential solutions for future particulate monitors are presented.

aerosol↗
DOE OSTI2026

Ecological Insights from Transferable Plant Biomass Mapping across the Arctic using High-resolution Structure-from-Motion and LiDAR Data

Warmer temperatures, permafrost thaw, and increased wildfire activity are driving rapid ecological change across the Arctic, significantly altering plant productivity and aboveground biomass (AGB). These rapid changes highlight the urgent need to improve monitoring of vegetation dynamics in the Earth’s northern ecosystems, where high spatiotemporal heterogeneity occurs at scales finer than those captured by traditional satellite observations. The growing use of Unoccupied Aerial Systems (UASs) presents an opportunity to overcome this limitation. Yet, the diversity of UAS platforms, sensors, and data collection and processing workflows presents challenges for developing standardized, generalizable approaches. To address this challenge, we compiled 672 AGB plots co-located with 183 UAS-based Structure-from-Motion (SfM) or Light Detection and Ranging (LiDAR) surveys collected across the Arctic. Here, we: (1) evaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB, (2) assessed scaling errors and their sources in two recent satellite-based AGB products derived from Landsat and MODIS, and (3) demonstrated the use of high-resolution AGB maps to quantify biomass variation across tundra plant functional types (PFTs) and to monitor post-fire recovery. Our results show that both SfM and LiDAR accurately captured AGB and its variability across tundra PFTs using a Random Forest (RF) model (overall RMSE: 0.336 kg/m2), with mapping performance varying slightly by region and data source. Using UAS-derived AGB maps as a benchmark, we identified systematic biases in satellite-derived AGB products, largely attributable to the magnitude of AGB and structural heterogeneity within coarse-resolution pixels. Applying our model to repeat UAS surveys following a tundra fire on Seward Peninsula, we observed rapid AGB recovery in non-shrub patches, with biomass recovering to pre-fire levels within 2 years. In contrast, shrub patches recovered more slowly, with AGB gains continuing over 2–4 years through both in-patch growth and lateral expansion (via dispersal) into remaining burned areas. Overall, these findings demonstrate the generalizability of UAS-based SfM and LiDAR data for estimating tundra AGB and highlight the potential of our approach to be broadly applied to generate high-quality AGB data for ecological monitoring and model benchmarking across the Arctic.

Yang, Daryl [ORNL] (ORCID:0000000317057823)↗
NASA NTRS2015

Development and Testing of a Post-Installable Deepwater Monitoring System Using Fiber-Optic Sensors

This paper addresses the design and development of a fiber-optic monitoring system that can be deployed on existing deepwater risers and flowlines; and provides a summary of test article fabrication and the subsequent laboratory testing performed at the National Aeronautics and Space Administration-Johnson Space Center (NASA-JSC). A major challenge of a post-installed instrumentation system is to ensure adequate coupling between the instruments and the riser or flowline of interest. This work investigates the sensor coupling for pipelines that are suspended in a water column (from topside platform to seabed) using a fiber-optic sensor clamp and subsea bonding adhesive. The study involved the design, fabrication, and test of several prototype clamps that contained fiber-optic sensors. A mold was produced by NASA using 3-D printing methods that allowed the casting of polyurethane clamp test articles to accommodate 4-inch and 8-inch diameter pipes. The prototype clamps were installed with a subsea adhesive in a "wet" environment and then tested in the NASA Structures Test Laboratory (STL). The tension, compression, and bending test data showed that the prototype sensor clamps achieved good structural coupling, and could provide high quality strain measurement for active monitoring.

Seaman, Calvin H.↗
NASA NTRS2016

Formaldehyde Column Density Measurements as a Suitable Pathway to Estimate Near-Surface Ozone Tendencies from Space

In support of future satellite missions that aim to address the current shortcomings in measuring air quality from space, NASA's Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) field campaign was designed to enable exploration of relationships between column measurements of trace species relevant to air quality at high spatial and temporal resolution. In the DISCOVER-AQ data set, a modest correlation (r2 = 0.45) between ozone (O3) and formaldehyde (CH2O) column densities was observed. Further analysis revealed regional variability in the O3-CH2O relationship, with Maryland having a strong relationship when data were viewed temporally and Houston having a strong relationship when data were viewed spatially. These differences in regional behavior are attributed to differences in volatile organic compound (VOC) emissions. In Maryland, biogenic VOCs were responsible for approx.28% of CH2O formation within the boundary layer column, causing CH2O to, in general, increase monotonically throughout the day. In Houston, persistent anthropogenic emissions dominated the local hydrocarbon environment, and no discernable diurnal trend in CH2O was observed. Box model simulations suggested that ambient CH2O mixing ratios have a weak diurnal trend (+/-20% throughout the day) due to photochemical effects, and that larger diurnal trends are associated with changes in hydrocarbon precursors. Finally, mathematical relationships were developed from first principles and were able to replicate the different behaviors seen in Maryland and Houston. While studies would be necessary to validate these results and determine the regional applicability of the O3-CH2O relationship, the results presented here provide compelling insight into the ability of future satellite missions to aid in monitoring near-surface air quality.

Surface Conditions from Column and Vertically Reso↗
DOE OSTI2012

Automated size-specific CT dose monitoring program: Assessing variability in CT dose

Purpose: The potential health risks associated with low levels of ionizing radiation have created a movement in the radiology community to optimize computed tomography (CT) imaging protocols to use the lowest radiation dose possible without compromising the diagnostic usefulness of the images. Despite efforts to use appropriate and consistent radiation doses, studies suggest that a great deal of variability in radiation dose exists both within and between institutions for CT imaging. In this context, the authors have developed an automated size-specific radiation dose monitoring program for CT and used this program to assess variability in size-adjusted effective dose from CT imaging. Methods: The authors radiation dose monitoring program operates on an independent health insurance portability and accountability act compliant dosimetry server. Digital imaging and communication in medicine routing software is used to isolate dose report screen captures and scout images for all incoming CT studies. Effective dose conversion factors (k-factors) are determined based on the protocol and optical character recognition is used to extract the CT dose index and dose-length product. The patient's thickness is obtained by applying an adaptive thresholding algorithm to the scout images and is used to calculate the size-adjusted effective dose (ED{sub adj}). The radiation dose monitoring program was used to collect data on 6351 CT studies from three scanner models (GE Lightspeed Pro 16, GE Lightspeed VCT, and GE Definition CT750 HD) and two institutions over a one-month period and to analyze the variability in ED{sub adj} between scanner models and across institutions. Results: No significant difference was found between computer measurements of patient thickness and observer measurements (p= 0.17), and the average difference between the two methods was less than 4%. Applying the size correction resulted in ED{sub adj} that differed by up to 44% from effective dose estimates that were not adjusted by patient size. Additionally, considerable differences were noted in ED{sub adj} distributions between scanners, with scanners employing iterative reconstruction exhibiting significantly lower ED{sub adj} (range: 9%-64%). Finally, a significant difference (up to 59%) in ED{sub adj} distributions was observed between institutions, indicating the potential for dose reduction. Conclusions: The authors developed a robust automated size-specific radiation dose monitoring program for CT. Using this program, significant differences in ED{sub adj} were observed between scanner models and across institutions. This new dose monitoring program offers a unique tool for improving quality assurance and standardization both within and across institutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Machine Learning Applications to Maintain the NuMI Neutrino Beam Quality at Fermilab

The NuMI target facility at Fermilab produces an intense muon neutrino beam for NOvA (NuMI Off-axis νe Appearance) long baseline neutrino experiment. Three arrays of muon monitors located in the downstream of the hadron absorber in the NuMI beamline provide the measurements of the primary beam and horn current quality. We have studied the response of muon monitors with the proton beam profile changes and focusing horn current variations. The responses of muon monitors are used to develop Machine Learning (ML) algorithms to monitor the beam quality. We present the development of the machine learning applications and the future plans. This effort is important for future applications such as beam quality assurance, anomaly detections, neutrino beam systematics studies and neutrino beam quality assurance. Our results demonstrate the advantages of developing useful ML applications that can be leveraged for future beamlines such as LBNF.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1977

Remote water-monitoring system

General-purpose, water-quality sampling process detects microorganisms and integrates sampling, sample preservation, and sample analysis. System overcomes difficulties in correlation of surface measurements with remote measurements and minimizes human error in handling of samples and instrumentation.

Grana, D. C.↗