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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Solar Influencer Next Door: Predicting Low-Income Solar Referrals and Leads

Increasing the adoption of solar among low-to-moderate income (LMI) households remains an important policy goal because of its promise to simultaneously reduce energy burden and support the just distribution of benefits of renewable energy. However, scaling LMI solar remains challenging due to affordability and access issues. Most existing LMI adoption has occurred under public-funded programs, highlighting the importance of increasing the cost-effectiveness of these programs at scale. We develop a new household-level data set on LMI solar lead acquisition, referrals, and adoption to understand the processes through which LMI solar uptake has occurred in California. Then, we develop models to predict two sub-mechanisms in the solar adoption process: whether an otherwise qualified lead becomes "lost" i.e. non-responsive to outreach and, for existing clients, whether they refer solar to others. For the program analyzed, participants received their solar system at no cost, which deemphasizes economic drivers of solar adoption and could differ from other program experiences. Both models substantially improved the accuracy of prediction relative to a baseline. Overall, we find that peer effects and solar economics are important to predicting referrals, and household demographic factors in lead loss prediction. Finally, we find that referrals are both the highest quality and largest source of LMI solar leads, providing a promising mechanism to expand LMI programs further.

customer acquisition costs↗
NASA NTRS2003

Decay Properties of K-Vacancy States in Fe X-Fe XVII

We report extensive calculations of the decay properties of fine-structure K-vacancy levels in Fe X-Fe XVII. A large set of level energies, wavelengths, radiative and Auger rates, and fluorescence yields has been computed using three different standard atomic codes, namely Cowan's HFR, AUTOSTRUCTURE and the Breit-Pauli R-matrix package. This multi-code approach is used to the study the effects of core relaxation, configuration interaction and the Breit interaction, and enables the estimate of statistical accuracy ratings. The Ksigma and KLL Auger widths have been found to be nearly independent of both the outer-electron configuration and electron occupancy keeping a constant ratio of 1.53 +/- 0.06. By comparing with previous theoretical and measured wavelengths, the accuracy of the present set is determined to be within 2 m Angstrom. Also, the good agreement found between the different radiative and Auger data sets that have been computed allow us to propose with confidence an accuracy rating of 20% for the line fluorescence yields greater than 0.01. Emission and absorption spectral features are predicted finding good correlation with measurements in both laboratory and astrophysical plasmas.

Mendoza, C.↗
DOE OSTI2025

Contaminant Investigation and Pre‐Processing Opportunities for Textile‐To‐Textile Recycling

Millions of metric tons of textiles are landfilled or incinerated each year in the United States, with less than 1% of textiles recycled into new clothing or fabrics. To counter this trend, a growing number of companies and researchers are exploring how a circular economy can be applied to support textile‐to‐textile recycling. A significant barrier they face comes down to quickly and efficiently extracting pure feedstock material from post‐consumer garments that feature a mix of natural and synthetic fibers. Textile recyclers prefer pure feedstocks, as working with mixed sources typically means lower throughput, higher risk of equipment failure, and diminished business margins. To facilitate a circular economy for textiles, methods, and technologies are needed that can efficiently separate out materials and contaminants from end‐of‐life textiles to increase the flow of pure feedstocks to recyclers. This paper summarizes findings from interviews with a cross section of textile recyclers and from a review of literature to define basic feedstock requirements. In addition to our qualitative research, we deconstruct a bale of post‐consumer textiles and analyze them using computer‐vision imaging, Fourier transform infrared spectroscopy (FTIR), and machine learning. The resulting data are used to set system‐level design inputs for an automated contaminant removal system to process post‐consumer clothing into appropriate feedstocks for recycling. To set the system's levels for automated real‐time near‐infrared analysis, we identify the minimum percentage of primary material that any single garment in a load of used clothing must contain for the average of the full output stream to meet the target purity levels of recyclers. Here, the envisioned automated system can also address undesirable trace materials that might contaminate the processed stream by using imaging cameras coupled with artificial intelligence to identify sections of clothing for de‐trimming. Proof‐of‐concept machine learning algorithms are evaluated to locate and identify trims or garment areas with hidden contaminant materials. Integrating these methods into automated textile cutting systems can provide a cost‐effective means for increasing feedstock purity from used clothing, which can advance circularity for textiles by helping recyclers to reach production volumes and quality targets that were not possible solely with manual dismantling operations.

Parsons, Ryan [Rochester Institute of Technology, ↗
NASA NTRS1999

Evaluation of the Self-Nulling Rotating Eddy Current Probe System

In order to detect multi-site fatigue cracks located under flush-head rivets, automated eddy current equipment is required. To assure a reliable system, the eddy current probe must be centered easily over the installed rivets. To meet these requirements, the NDE Group at NASA LaRC developed the Self-Nulling Rotating Eddy Current Probe System (SNRECPS) which will be referred to as RPS in this document. The system was evaluated at the FAA, NDI Validation Center, in Albuquerque, New Mexico. The system was capable of detecting a 0.032 inch long crack with a 90/95% PoD. Further evaluations were conducted at Boeing in Long Beach, California. These evaluations included fatigue cracks and notches in a range from 0.025 to 0.100 inch long under flush-head aluminum rivets, and titanium or steel flush-head fasteners. The results of these tests are reported herein. Subsequently, the system was loaned to the USAF Structures Laboratory for the purpose of detecting and measuring short cracks under flush-head rivets in a variety of fatigue test specimens. The inspection task was to detect and plot crack growth from numbered fasteners in lettered rows. In January, 1998, the system was taken to Northwest Airlines Maintenance Base, in Atlanta, to inspect a DC-9, for multi-site cracks in three circumferential splices. The aircraft had 83,000 cycles. The inspection was conducted at 30 kHz from longeron 5 left to longeron 5 right. The system was calibrated using a 0,030 EDM first layer notch. The instrument gain was set to 19 mV from the notch. The reject level was set at 10 mV and the unflawed fasteners yielded a signal amplitude of 2 to 3 mV. Only one fastener location, out of about 2,500 tested, yielded a signal of 58 mV. The rivet was removed and visually evaluated. It appeared to be a slight gouge in the counter-sink zone. No fatigue cracks were detected. The same fastener locations were also inspected using the Boeing MAUS system at 60 kHz. No cracks were detected. Thus far, the rotating probe eddy current system has been found to be very user friendly and capable of detecting first layer cracks on the order of 0.030 inch long or longer.

Hagemaier, Don↗
DOE OSTI2022

Intersections of Disadvantaged Communities and Renewable Energy Potential: Analyses to Inform Equitable Investment Prioritization

Renewable energy development can bolster local economies through job creation, local tax revenues, and reduced energy costs; however, communities most in need of economic development and employment opportunities often see lower levels of renewable energy deployment. We sought to identify areas where indicators of disadvantaged communities intersect with high generation potential from cost- effective renewable energy opportunities. This presentation highlights the geospatial intersection of the technical potential and levelized cost of energy for three renewable technologies (residential solar, utility solar, and land-based wind) and three sociodemographic metrics (energy burden, unemployment, and employment in mining, quarrying, and oil and gas extraction). This research and the associated county-level data set are intended to inform national- and state-level energy-related assistance programs, economic development efforts, and infrastructure programs seeking to prioritize investments in disadvantaged communities.

disadvantaged communities↗
NASA NTRS2018

Improving the SMAP Level-4 Soil Moisture Product

The NASA Soil Moisture Active Passive (SMAP) mission generates, among other data sets, the Level 4 Soil Moisture (L4 SM) product. The L4 SM algorithm is based on the assimilation of SMAP radiometer brightness temperature observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. The L4 SM data are published with a mean latency of approx. 2.5 days from the time of observation and provide global, three-hourly, 9 km resolution estimates of surface and root-zone soil moisture and related land surface states and fluxes. In 2018, the product was upgraded from Version 3 to Version 4. Underlying the new version is a revised modeling system that includes improved input parameter datasets for land cover, topography, and vegetation height that are based on recent, high quality space-borne remote sensing observations. Land cover inputs were updated to the GlobCover2009 product, which is based on satellite observations from the Medium Resolution Imaging Spectrometer. Topographic statistics now rely on observations from the Shuttle Radar Topography Mission. Finally, vegetation height inputs are derived from space-borne Lidar measurements. Additionally, SMAP Level-2 soil moisture retrievals and in situ soil moisture measurements were used to calibrate a particular Catchment model parameter that governs the recharge of soil moisture from the models root-zone excess reservoir into the surface excess reservoir. Specifically, the replenishment of soil moisture near the surface from below under non-equilibrium conditions was substantially reduced, which brings the models surface soil moisture more in line with the SMAP Level 2 and in situ soil moisture. Finally, the calibration of the assimilated SMAP brightness temperatures changed substantially from Version 3 to Version 4. Considerable effort went into the version upgrade, creating an expectation that the new version is improved over the old version. Indeed, some aspects of the new version are clearly better. However, other aspects are not, and on balance, the overall improvement is modest at best. In this presentation we summarize the skill of the new and old versions vs. independent in situ measurements and in terms of data assimilation diagnostics, including, for example, the statistics of the (soil moisture) analysis increments and the observation minus forecast (brightness temperatures) residuals. We share our experience with trying to improve to the L4 SM product and the lessons learned from the effort.

Reichle, Rolf↗
NASA NTRS2018

Recent Updates in the SMAP Level-4 Soil Moisture Algorithm

The NASA Soil Moisture Active Passive (SMAP) mission generates, among other data sets, the Level-4 Soil Moisture (L4_SM) product. The L4_SM data are published with a mean latency of ~2.5 days from the time of observation and provide global, three-hourly, 9-km resolution estimates of surface and root-zone soil moisture and related land surface states and fluxes. The L4_SM algorithm is based on the assimilation of SMAP radiometer brightness temperature observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter (EnKF). In 2018, the L4_SM algorithm was upgraded from Version 3 to Version 4. Underlying the new version is a revised modeling system that includes improved input parameter datasets for land cover, topography, and vegetation height that are based on recent, high-quality, space-borne remote sensing observations. Additionally, SMAP Level-2 soil moisture retrievals and in situ soil moisture measurements were used to calibrate a particular Catchment model parameter that governs the recharge of surface soil moisture from below under non-equilibrium conditions, which brings the model's surface soil moisture more in line with the SMAP Level-2 and in situ soil moisture. Moreover, the calibration of the assimilated SMAP brightness temperatures changed substantially from Version 3 to Version 4, and the "catchment deficit" model variable was removed from the EnKF state vector to avoid degrading the model's groundwater estimates.Considerable effort went into the version upgrade, creating an expectation that the new version is improved over the old version. Indeed, some aspects of the new version are clearly better. However, other aspects are not. In this presentation we summarize the skill of the new and old versions vs. independent in situ measurements and in terms of data assimilation diagnostics, including, for example, the statistics of the (soil moisture) analysis increments and the observation-minus-forecast (brightness temperatures) residuals. We share our experience with trying to improve to the L4_SM product and the lessons learned from the effort.

Reichle, Rolf H.↗
DOE OSTI2021

Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies

Tropical leaf phenology-particularly its variability at the tree-crown scale-dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red-green-blue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive leaf fraction at the tree-crown scale. To evaluate our model, we used a year of data from six phenocams in two contrasting forests in Panama. Here, we first built a comprehensive library of leaf and non-leaf pixels across various acquisition times, exposure conditions and specific phenocams. We then divided this library into training and testing components. We evaluated the model at three levels: 1) superpixel level with a testing set, 2) crown level by comparing the model-derived leaf fractions with those derived using image-specific supervised classification, and 3) temporally using all daily images to assess the diurnal stability of the model-derived leaf fraction. Finally, we compared the model-derived leaf fraction phenology with leaf phenology derived from GCC. Our results show that: 1) the SP-ResNet50 model accurately differentiates leaves from non-leaves (overall accuracy of 93%) and is robust across all three levels of evaluations; 2) the model accurately quantifies leaf fraction phenology across tree-crowns and forest ecosystems; and 3) the combined use of leaf fraction and GCC helps infer the timing of leaf emergence, maturation and senescence, critical information for modeling photosynthetic seasonality of tropical forests. Collectively, this study offers an improved means for automated tropical phenology monitoring using phenocams.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost

Machine-learned interatomic potentials (MLIPs) are revolutionizing computational materials science and chemistry by offering an efficient alternative to ab initio molecular dynamics (MD) simulations. However, fitting high-quality MLIPs remains a challenging, time-consuming, and computationally intensive task where numerous trade-offs have to be considered, e.g., How much and what kind of atomic configurations should be included in the training set? Which level of ab initio convergence should be used to generate the training set? Which loss function should be used for fitting the MLIP? Which machine learning architecture should be used to train the MLIP? The answers to these questions significantly impact both the computational cost of MLIP training and the accuracy and computational cost of subsequent MLIP MD simulations. In this study, we use a configurationally diverse beryllium dataset and quadratic spectral neighbor analysis potential. We demonstrate that joint optimization of energy versus force weights, training set selection strategies, and convergence settings of the ab initio reference simulations, as well as model complexity can lead to a significant reduction in the overall computational cost associated with training and evaluating MLIPs. This opens the door to computationally efficient generation of high-quality MLIPs for a range of applications which demand different accuracy versus training and evaluation cost trade-offs.

36 MATERIALS SCIENCE↗
NASA NTRS1983

Pupillary escape intensified by large pupillary size

Pupil responses to light are greatly influenced by initial pupil size. Small pupils, operating under photopic conditions, show tonic responses to step increases of light and high gains; thus the pupil is a good regulator of light. Large pupils, operating under mesopic or scotopic conditions show phasic responses, 'pupillary escape', and smaller gains: the pupil only transiently influences retinal flux. By using accommodation level to set the size of the pupil, the mechanism of the 'pupil size effect' is shown to be dependent on retinal light level only so far as retinal activity sets pupil size.

Stark, L.↗
NASA NTRS2014

Exploring Advanced Technology Gas Turbine Engine Design and Performance for the Large Civil Tiltrotor (LCTR)

A Large Civil Tiltrotor (LCTR) conceptual design was developed as part of the NASA Heavy Lift Rotorcraft Systems Investigation in order to establish a consistent basis for evaluating the benefits of advanced technology for large tiltrotors. The concept has since evolved into the second-generation LCTR2, designed to carry 90 passengers for 1,000 nautical miles at 300 knots, with vertical takeoff and landing capability. This paper explores gas turbine component performance and cycle parameters to quantify performance gains possible for additional improvements in component and material performance beyond those identified in previous LCTR2 propulsion studies and to identify additional research areas. The vehicle-level characteristics from this advanced technology generation 2 propulsion architecture will help set performance levels as additional propulsion and power systems are conceived to meet ever-increasing requirements for mobility and comfort, while reducing energy use, cost, noise and emissions. The Large Civil Tiltrotor vehicle and mission will be discussed as a starting point for this effort. A few, relevant engine and component technology studies, including previous LCTR2 engine study results will be summarized to help orient the reader on gas turbine engine architecture, performance and limitations. Study assumptions and methodology used to explore engine design and performance, as well as assess vehicle sizing and mission performance will then be discussed. Individual performance for present and advanced engines, as well as engine performance effects on overall vehicle size and mission fuel usage, will be given. All results will be summarized to facilitate understanding the importance and interaction of various component and system performance on overall vehicle characteristics.

vehicle sizing↗
NASA NTRS1973

A model based technique for the design of flight directors

A new technique for designing flight directors is discussed. This technique uses the optimal-control pilot/vehicle model to determine the appropriate control strategy. The dynamics of this control strategy are then incorporated into the director control laws, thereby enabling the pilot to operate at a significantly lower workload. A preliminary design of a control director for maintaining a STOL vehicle on the approach path in the presence of random air turbulence is evaluated. By selecting model parameters in terms of allowable path deviations and pilot workload levels, a set of director laws is achieved which allows improved system performance at reduced workload levels. The pilot acts essentially as a proportional controller with regard to the director signals, and control motions are compatible with those appropriate to status-only displays.

Levison, W. H.↗
NASA NTRS1977

Ergometer

The bicycle ergometer was designed for use in experiments M171 and M093 and for use in optional exercise by the crewmen. The ergometer allows a crewman to exercise in zero-gravity using either his hands or his feet. It provides a precisely calibrated and, if needed, a programmable standard of work rate for the crewman. The ergometer operates in any of three selectable modes. The set heart rate mode varies the ergometer work rate, as necessary, to achieve and maintain a preselected heart rate. The sequenced heart rate mode is similar to the set heart rate mode except that the heart rate is programmable through five preset levels with a period of time for each level. The set work rate mode delivers a preset work load to the subject.

Lem, J. D.↗
NASA NTRS1986

Judgments of eye level in light and in darkness

Subjects judged eye level in the light and in the dark by raising and lowering themselves in a dental chair until a stationary target appeared to be at the level of their eyes. This method reduced the possibility of subjects' using visible landmarks as reference points for setting eye level during lighted trials, which may have contributed to artificially low estimates of the variability of this judgment in previous studies. Chair settings were 2.5 deg higher in the dark than in the light, and variability was approximately 66 percent greater in the dark than in the light. These results are discussed in terms of possible interactions of two separate systems, one sensitive to the orientations of visible surfaces and the other sensitive to bodily and gravitational information.

Stoper, Arnold E.↗
NASA NTRS2020

High-Resolution Gridded Level 3 Aerosol Optical Depth Data from MODIS

The state-of-art satellite observations of atmospheric aerosols over the last two decades from NASA's MODIS instruments have been extensively utilized in climate change and air quality research and applications. The operational algorithms now produce level 2 aerosol data at varying spatial resolutions (1, 3, and 10 km) and level 3 data at 1 degree. The local and global applications have been benefited from the coarse resolution gridded data sets (i.e., level 3, 1 degree), as it is easier to use since data volume is low and, several online and offline tools are readily available to access and analyze the data with minimal computing resources. At the same time, researchers who require data at much finer spatial scales have to go through a challenging process of obtaining, processing, and analyzing larger volumes of data sets that require high-end computing resources and coding skills. Therefore, we have created a high spatial resolution (HRG, 0.1x0.1 degree) daily and monthly aerosol optical depth (AOD) product by combining two MODIS operational algorithms, namely Deep Blue (DB) and Dark Target (DT). The new HRG AODs meets the accuracy requirements of level 2 AOD data and provide either the same or more spatial coverage on daily and monthly scales. The data sets are provided in daily and monthly files through open Ftp server with python scripts to read and map the data. The reduced data volume with an easy to use format and tools to access the data will encourage more users to utilize the data for research and applications.

aerosol↗
DOE OSTI2023

Model-independent search for the presence of new physics in events including H → γγ with $\sqrt{s}$ = 13 TeV pp data recorded by the ATLAS detector at the LHC

A model-independent search for new physics leading to final states containing a Higgs boson, with a mass of 125.09 GeV, decaying to a pair of photons is performed with 139 fb -1 of $\sqrt{s}$ = 13 TeV $pp$ collision data recorded by the ATLAS detector at the Large Hadron Collider at CERN. This search examines 22 final states categorized by the objects that are produced in association with the Higgs boson. These objects include isolated electrons or muons, hadronically decaying $τ$-leptons, additional photons, missing transverse momentum, and hadronic jets, as well as jets that are tagged as containing a $b$-hadron. No significant excesses above Standard Model expectations are observed and limits on the production cross section at 95% confidence level are set. Detector efficiencies are reported for all 22 signal regions, which can be used to convert detector-level cross-section limits reported in this paper to particle-level cross-section constraints.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Melting rate correlation with batch properties and melter operating conditions during conversion of nuclear waste melter feeds to glasses

The rate of conversion of nuclear waste melter feed to glass is affected by the selection of melter feed materials and by melter design and operation. The melting rate correlation (MRC) is an equation that relates the glass production rate with two types of variables: (1) feed and melt properties: conversion heat, cold-cap bottom temperature, and glass melt viscosity; and (2) melter design and operation parameters: melter geometry, melter operating temperature, and gas bubbling rate. The MRC shows good agreement for an extended melting-rate data set of high-level waste (HLW) melter feeds and a data set generated for low-activity waste (LAW) melter feeds. Laboratory observation of heated melter feed samples is often used to assess the cold-cap bottom temperature of HLW melter feeds (moderately foaming feeds), but this technique appears inadequate for LAW melter feeds (vigorously foaming feeds). For LAW feeds, an adequate assessment of the cold-cap bottom temperature was achieved using evolved gas analysis, which allows identification of the collapse of primary foam for oxidized feeds. This assessment shows that the cold-cap bottom temperature for vigorously foaming LAW feeds is higher than that for moderately foaming HLW feeds. When the results of MRC are compared, LAW feeds are generally less sensitive to the bubbling rate and melt viscosity, and more sensitive to the cold-cap bottom temperature than HLW feeds. The MRC qualifies as a promising tool to support the selection of melter feed materials and melter operating conditions, which is determined from expensive independent scaled melter experiments, and sophisticated mathematical models.

Lee, Seung Min↗
NASA NTRS2019

Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements

The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun–sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality controlled utilizing mainly cloud-screening methodology, while additional AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality-assured status (Level 2.0). The large growth in the number of AERONET sites over the past 25 years resulted in significant burden to the manual quality control of millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near-real-time data as well as post-field-deployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter-specific temperature characterizations for all visible and near-infrared wavelengths, updated gaseous and water vapor absorption coefficients, and ancillary data sets. The Level 2.0 AOD quality-assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near-real-time estimated uncertainty is determined using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near-real-time uncertainty estimate for which average differences of AOD suggest a +0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly averages analyzed for the entire V3 and V2 databases produced average differences (V3–V2) of +0.002 with a ±0.02 SD (standard deviation), yet monthly averages calculated using time-matched observations in both databases were analyzed to compute an average difference of −0.002 with a ±0.004 SD. The high statistical agreement in multiyear monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases.

David M. Giles↗