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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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Changes in global vegetation activity and its driving factors during 1982–2013

Vegetation activity plays a crucial role in the global carbon cycle and climate. Many studies have examined recent changes in vegetation growth and the associated local climatic drivers. They revealed a global greening trend during the recent decades. However, few studies have analyzed how remote oceanic conditions affect land vegetation growth through atmospheric teleconnection, and the causes of the recent greening needs further investigation. Here in this study, we investigate the spatio-temporal variations (including trends) of vegetation activity using satellite data of growing-season normalized difference vegetation index (NDVI gs ), and examine their relationship to local and remote climate oscillations and external anthropogenic forcing by statistical means. As expected, there is an increasing trend in global-mean NDVI gs from 1982–2013, with significant greening over Europe and many other land areas. NDVI gs is temperature-limited at northern high-latitudes, but water-limited in arid and semi-arid regions, and radiation-limited in the Amazon and eastern and southern Asia. Globally, El Niño-Southern Oscillation (ENSO) is the leading climatic driver of interannual variability of NDVI gs , especially over southern and eastern Africa, eastern Australia, northeastern Asia, and northern South America. Consistent with previous modeling studies, a regression-based attribution analysis suggests that historical anthropogenic forcing (mainly increases in greenhouse gases) explains about two thirds of the NDVI gs trend from 1982 to 2013, with the rest coming mainly from the Atlantic Multi-decadal Oscillation (AMO). Contributions to the recent NDVI gs trend from ENSO and Pacific decadal variability and Artctic Oscillation appear to be small.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Autonomous Human Activity Classification From Wearable Multi-Modal Sensors

There has been significant amount of research work on human activity classification relying either on Inertial Measurement Unit (IMU) data or data from static cameras providing a third-person view. There has been relatively less work using wearable cameras, providing first-person or egocentric view, and even fewer approaches combining egocentric video with IMU data. Utilizing only IMU data limits the variety and complexity of the activities that can be detected. For instance, the sitting activity can be detected by IMU data, but it cannot be determined whether the subject has sat on a chair or a sofa, or where the subject is. To perform fine-grained activity classification, and to distinguish between activities that cannot be differentiated by only IMU data, we present an autonomous and robust method using data from both wearable cameras and IMUs. In contrast to convolutional neural network-based approaches, we propose to employ capsule networks to obtain features from egocentric video data. Moreover, Convolutional Long Short Term Memory framework is employed both on egocentric videos and IMU data to capture the temporal aspect of actions. We also propose a genetic algorithm-based approach to autonomously and systematically set various network parameters, rather than using manual settings. Experiments have been conducted to perform 9- and 26-label activity classification, and the proposed method, using autonomously set network parameters, has provided very promising results, achieving overall accuracies of 86.6% and 77.2%, respectively. The proposed approach, combining both modalities, also provides increased accuracy compared to using only egovision data and only IMU data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Modeling Current Transfer from PV Modules Based on Meteorological Data

Current transferred from the active cell circuit to ground in modules undergoing potential-induced degradation (PID) stress is analyzed with respect to meteorological data. Duration and coulombs transferred as a function of whether the module is wet (from dew or rain) or the extent of uncondensed surface humidity are quantified based on meteorological indicators. With this, functions predicting the mode and rate of coulomb transfer are developed for use in estimating the relative PID stress associated with temperature, moisture, and system voltage in any climate. Current transfer in a framed crystalline silicon module is relatively high when there is no condensed water on the module, whereas current transfer in a thin-film module held by edge clips is not, and displays a greater fraction of coulombs transferred when wet compared to the framed module in the natural environment.

14 SOLAR ENERGY↗
DOE OSTI2025

Biosynthesis of bioprivileged, linear molecules via novel carboligase reactions

Over the award period, we made progress on the three aims. We screened twenty-five carboligases for activity coupling twenty-one possible -keto acids (Aim 1). The carboligases were selected across a diverse set of protein sequences. Using Q-Exactive UHPLC-MS, we tested a total of 210 coupled products per enzyme and generated a dataset of 5250 enzyme-substrate activity relationships. We identified multiple enzymes that had activity for synthesizing suberic acid and heptanoic acid (Aim 2). We built a random forest model for predicting the activity of each enzyme toward substrates on which it was not tested using the data from Aim 1. Finally, we evaluated growth defects that occurred due to expression of different carboligases in E. coli (Aim 3). We were able to identify specific metabolites and putative pathways that, when supplemented in the media, recovered the growth defect associated with the presence of specific carboligases. We are in the process of publishing two manuscript describing the methods for high-throughput screening of enzyme promiscuity, using machine learning to predict activity on untested substrates, and enzyme activity data we collected. This project has produced enabling data for biosynthesis of a range of new-to-nature compounds to support biomanufacturing.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1988

The Telecommunications and Data Acquisition Report

In space communications, radio navigation, radio science, and ground based radio and radar astronomy, activities of the Deep Space Network and its associated Ground Communications Facility in planning, in supporting research and technology, in implementation, and in operations are reported. Also included is TDA funded activity at JPL on data and information systems and reimbursable DSN work performed for other space agencies through NASA.

Posner, E. C.↗
NASA NTRS1984

Solar-geophysical data number 479, July 1984. Part 1: (Prompt reports). Data for June 1984, May 1984 and later data

Solar and geophysical data for May and June 1984 are reported. Topics include: detailed index for 1983/1984; data for June 1984 (solar activity indices, solar flares, solar radio emission, mean solar magnetic field, boulder geomagnetic substorm log); data for May 1984 (solar active regions, sudden ionospheric disturbances, solar radio spectral observations, cosmic ray measurements by neutron monitor, geomagnetic indices, radio propagation indices); and late data (geomagnetic indices March and April 1984 sudden commencements/solar flare effects, cosmic ray measurements by neutron monitor, and solar active regions).

Coffey, H. E.↗
NASA NTRS2015

An Overview of ISS Human Research Data Sharing

This presentation is an attempt to clarify several aspects of the current procedures, tools, and challenges of human data sharing for ISS flight activities. There are several binary variables to consider with respect to human spaceflight data sharing: Medical vs. Research, Active Flight vs. Non-Flight, Tactical vs. Supplemental, Prospective vs. Retrospective. This presentation will address each of these variables and how they determine which processes and mechanisms are used both to document and facilitate human data sharing. Some of these variables will likely be so obvious that they induce eye rolls. Please bear with us. We're trying to make these slides fairly rudimentary for a wide, (eventually) international audience. Other distinctions are made if data originated from a NASA vs. IP crewmember. Those distinctions will be made apparent when needed.

Morshedi, Pasha↗
DOE OSTI2025

Gravitational waves from supercooled phase transitions in conformal Majoron models of neutrino mass

We study supercooled first-order phase transitions above the QCD scale in a wide class of conformal Majoron-like U(1)′ models that explain the totality of active neutrino oscillation data and produce a detectable stochastic gravitational wave background (SGWB) at LIGO, LISA and ET. We place constraints on the U(1)′ breaking scale and gauge coupling using current LIGO-Virgo-Kagra data. We find that strong supercooling can be ruled out in large regions of parameter space if a SGWB is not detected by these experiments. A null signal at LIGO and ET will disfavor a type-I seesaw scale above 10 14 GeV, while a positive signal is a signature of heavy right-handed neutrinos. On the other hand, LISA will be sensitive to seesaw scales as low as a TeV, and could detect a SGWB even if the right-handed neutrinos are decoupled.

early universe particle physics↗
DOE OSTI2022

Fibrotic activity quantified in serum by measurements of type III collagen pro-peptides can be used for prognosis across different solid tumor types

Due to activation of fibroblast into cancer-associated fibroblasts, there is often an increased deposition of extracellular matrix and fibrillar collagens, e.g. type III collagen, in the tumor microenvironment (TME) that leads to tumor fibrosis (desmoplasia). Tumor fibrosis is closely associated with treatment response and poor prognosis for patients with solid tumors. To assure that the best possible treatment option is provided for patients, there is medical need for identifying patients with high (or low) fibrotic activity in the TME. Measuring unique collagen fragments such as the pro-peptides released into the bloodstream during fibrillar collagen deposition in the TME can provide a non-invasive measure of the fibrotic activity. Based on data from 8 previously published cohorts, this review provides insight into the prognostic value of quantifying tumor fibrosis by measuring the pro-peptide of type III collagen in serum of a total of 1692 patients with different solid tumor types and discusses the importance of tumor fibrosis for understanding prognosis and for potentially guiding future drug development efforts that aim at overcoming the poor outcome associated with a fibrotic TME.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Geomechanical risk assessment for subsurface fluid disposal operations

Numerical models are commonly used to estimate the state of stress in the subsurface for various engineering applications. These estimates are subject to considerable uncertainty, and yet, the estimates are almost always deterministic, yielding no information about the certainty of the prediction. For some applications, unquantified uncertainties in stress are often acceptable, because the risks related to geomechanics may be of low relative importance compared to other risks (e.g. recoverable resource volume), for which uncertainties are often quantified. Furthermore, many geomechanics-related risks in the petroleum industry are relatively short lived (e.g. well bore stability), and decrease in importance with time. In contrast, for waste water injection or geologic carbon sequestration (GCS), geomechanics-related risks (e.g. seal integrity, induced seismicity) are on par with resource-related risks and are of long-term concern, with the risk generally increasing in importance for a significant period of time. For these reasons, the deterministic stress estimation and risk analysis approaches generally applied in the petroleum industry are insufficient for GCS applications. This paper describes a Bayesian approach to geomechanical uncertainty quantification and risk assessment. The method is demonstrated using data from an active enhanced oil recover/geologic carbon sequestration field as a case study

Burghardt, Jeffrey A.↗
DOE OSTI2025

Hydrometallurgical Recycling of Black Mass of Spent Lithium-Ion Batteries Using Methanesulfonic Acid: Leaching, Kinetic Studies, and Potential for Total Recovery of Valuable Components

Methanesulfonic acid (MSA) exhibits several advantageous properties rendering it a promising candidate for circular hydrometallurgical processes. These properties include a high acidity (pKa = - 1.9) comparable to that of classical mineral acids as well as biodegradability, high stability, and high solubility of metal-MSA complexes in aqueous solutions. In this study, MSA was employed as a lixiviant for the leaching of metals (lithium, nickel, cobalt, and manganese) from the black mass of spent lithium-ion batteries (LIBs). The effect of various parameters, including MSA concentration, H 2 O 2 concentration, temperature, and pulp density, was systematically investigated. Under the optimized conditions (1.5 M MSA, 0.2 M H 2 O 2 , 60 °C, and 50 g/L pulp density), quantitative leaching of lithium was achieved within 30 min, while for nickel and cobalt it was after 2 h, and 4 h for manganese leaching. The leaching kinetics of Li, Ni, Co, and Mn were studies using the shrinking particle models (SPM) and the Avrami model. The results indicated that the Avrami model provided the best fit to the kinetic data, with apparent activation energies of 46.81 kJ/mol for Li, 58.61 kJ/mol for Ni, 59.69 kJ/mol for Co, and 58.86 kJ/mol for Mn, within the temperature range of 25-70 °C (except for Li, which was analyzed in the range of 25-60 °C), consistent with chemical reaction control. Subsequently, residual contaminants in the leaching residue were eliminated through pyrolysis. The quantitative leaching of metals in MSA solution (a green lixiviant), combined with the pyrolytic treatment of leaching residues, represents a circular strategy for the total recovery of valuable components from the black mass of spent LIBs.

25 ENERGY STORAGE↗
DOE OSTI2015

Interactions of a potent cyclic peptide inhibitor with the light chain of botulinum neurotoxin A: insights from x-ray crystallography

The seven antigenically distinct serotypes (A to G) of botulinum neurotoxin (BoNT) are responsible for the deadly disease botulism. BoNT serotype A (BoNT/A) exerts its lethal action by cleaving the SNARE protein SNAP-25, leading to inhibition of neurotransmitter release, flaccid paralysis and autonomic dysfunction. BoNTs are dichain proteins: the heavy chain is responsible for neurospecific binding, internalization and translocation, and the light chain is responsible for substrate cleavage. Because of their extreme toxicity and prior history of weaponization, the BoNTs are considered to be potential bioterrorism agents. No post-symptomatic therapeutic interventions are available for BoNT intoxication other than critical care; therefore it is imperative to develop specific antidotes against this neurotoxin. To this end, a cyclic peptide inhibitor (CPI-1) was synthesized and found to inhibit BoNT/A light chain (Balc) with high affinity. When tested in a cell-free Förster resonance excitation transfer (FRET) assay, CPI-1 was found to have a K i of 13.9 nM using full-length Balc448 and 42.1 nM using a truncated crystallizable form of light chain (Balc424). Co-crystallization of CPI-1 with Balc424 revealed that in the Balc-CPI-1 complex, the inhibitor adopts a helical conformation, occupies a high percentage of the active site cavity and interacts in an amphipathic manner with critical active site residues. The data suggest that CPI-1 prevents SNAP-25 from accessing the Balc active site by blocking both the substrate binding path at the surface and the Zn 2+ binding region involved in catalysis. This is in contrast to linear peptide inhibitors described to date which block only the latter

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

New opportunities in produced water management: A market-based approach to produced water trading

Produced water (PW) is a byproduct of oil and gas (O&G) production. Obtained alongside the more valuable energy products, PW is usually characterized by high levels of salinity and often contains many contaminants (chemicals, soluble and insoluble oil, organics, etc.) making it unsuitable for release without substantial treatment. Couple this with the fact that PW is typically obtained at multiple times the rate of oil or gas, and the added transport, treatment, and disposal costs become a serious challenge for operators. These realities have led to ad-hoc practices including cooperation between industry competitors to recycle, share, or otherwise mitigate PW costs. The National Energy Technology Laboratory (NETL) in partnership with the Ground Water Protection Council (GWPC) is pursuing novel technology solutions to address PW issues that complement or improve ad-hoc practices adopted by operators. In this paper, we observe that well-established market management practices used in electrical power generation have natural analogues in the PW supply chain. These parallels open up a new line of research where we view PW management as a market equilibrium problem, and explore solutions that foster active and data-based collaboration among operators through market structures similar to power markets, with the ultimate objective of improving PW management costs and recycling rates. Here, we make a case for our observations, present a PW market clearing optimization model that shows how such a market system could operate in the O&G space, and provide an illustrative case study for demonstration.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective

Radiation oncology has always been deeply rooted in modeling, from the early days of isoeffect curves to the contemporary Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) initiative. In recent years, medical modeling for both prognostic and therapeutic purposes has exploded thanks to increasing availability of electronic data and genomics. One promising direction that medical modeling is moving toward is adopting the same machine learning methods used by companies such as Google and Facebook to combat disease. Broadly defined, machine learning is a branch of computer science that deals with making predictions from complex data through statistical models. These methods serve to uncover patterns in data and are actively used in areas such as speech recognition, handwriting recognition, face recognition, “spam” filtering (junk email), and targeted advertising. Although multiple radiation oncology research groups have shown the value of applied machine learning (ML), clinical adoption has been slow due to the high barrier to understanding these complex models by clinicians. Here, we present a review of the use of ML to predict radiation therapy outcomes from the clinician's point of view with the hope that it lowers the “barrier to entry” for those without formal training in ML. We begin by describing 7 principles that one should consider when evaluating (or creating) an ML model in radiation oncology. We next introduce 3 popular ML methods—logistic regression (LR), support vector machine (SVM), and artificial neural network (ANN)—and critique 3 seminal papers in the context of these principles. Although current studies are in exploratory stages, the overall methodology has progressively matured, and the field is ready for larger-scale further investigation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

EBSD and TEM Characterization of High Burn-up Mixed Oxide Fuel

Understanding and studying the irradiation behavior of high burn-up oxide fuel is critical to licensing of future fast breeder reactors. Advancements in experimental techniques and equipment are allowing for new insights into previously irradiated samples. In this work dual column focused ion beam (FIB)/scanning electron microscope (SEM) was utilized to prepared transmission electron microscope samples from mixed oxide fuel with a burn-up of 6.7% FIMA. Utilizing the FIB/SEM for preparation resulted in samples with a dose rate of <0.5 mRem/h compared to approximately 1.1 R/h for a traditionally prepared TEM sample. The TEM analysis showed that the sample taken from the cooler rim region of the fuel pellet had approximately 2.5x higher dislocation density than that of the sample taken from the mid-radius due to the lower irradiation temperature of the rim. The dual column FIB/SEM was additionally used to prepared and serially slice approximately 25 um cubes. High quality electron back scatter diffraction (EBSD) were collected from the face at each step, showing, for the first time, the ability to obtain EBSD data from high activity irradiated fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Recent status and improvement of reduced-activation ferritic-martensitic steels for high-temperature service

Reduced-activation ferritic-martensitic (RAFM) steels, candidate structural materials for fusion reactors, have achieved technological maturity after about three decades of research and development. The recent status of a few developmental aspects of current RAFM steels, such as aging resistance, plate thickness effects, fracture toughness, and fatigue, is updated in this paper, together with ongoing efforts to develop next-generation RAFM steels for superior high-temperature performance. Additionally, to thermomechanical treatments, including nonstandard heat treatment, alloy chemistry refinements and modifications have demonstrated some improvements in high-temperature performance. Castable nanostructured alloys (CNAs) were developed by significantly increasing the amount of nanoscale MX (M = V/Ta/Ti, X = C/N) precipitates and reducing coarse M 23 C 6 (M = Cr). Preliminary results showed promising improvement in creep resistance and Charpy impact toughness. We present and compare limited low-dose neutron irradiation results for one of the CNAs and China low activation martensitic with data for F82H and Eurofer97 irradiated up to ~70 displacements per atom at ~300–325 °C.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Reduced deuterium retention in simultaneously damaged and annealed tungsten

Deuterium (D) retention in polycrystalline tungsten (W) with copper (Cu) ion damage concurrently produced at elevated surface temperature is investigated in this paper. An in situ heated stage held W samples at a controlled temperature up to 1243 K, which were subjected to displacement damage produced by 3.4 MeV Cu ions. D retention is subsequently explored by exposure of the W samples held at 383 K to a D 2 plasma ion fluence of 10 24 D + /m 2 . Nuclear reaction analysis (NRA), utilizing the D( 3 He,p) 4 He nuclear reaction, is used to probe the D concentration in the near surface up to 6 μm. Thermal desorption spectroscopy (TDS) is used to measure outgassed HD and D 2 molecules to determine the bulk D concentration. Both NRA and TDS measure a significant reduction in D retention for samples damaged at elevated temperature. TDS quantitatively shows that the lowest energy trap remains largely unaffected while higher energy traps, induced by Cu ions, are annealed and approach intrinsic concentrations as the temperature during ion damage approaches 1243 K. Finally, analysis of TDS data yields an activation energy of (0.10 ± 0.02) eV for recovery of ion-damage induced traps at elevated temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2017

A novel muon detector for borehole density tomography

Muons can be used to image the density of materials through which they pass, including geological structures. Subsurface applications of the technology include tracking fluid migration during injection or production, with increasing concern regarding such timely issues as induced seismicity or chemical leakage into aquifers. Current density monitoring options include gravimetric data collection and active or passive seismic surveys. One alternative, or complement, to these methods is the development of a muon detector that is sufficiently compact and robust for deployment in a borehole. Such a muon detector can enable imaging of density structure to monitor small changes in density – a proxy for fluid migration – at depths up to 1500 m. Such a detector has been developed, and Monte Carlo modeling methods applied to simulate the anticipated detector response. Testing and measurements using a prototype detector in the laboratory and shallow underground laboratory demonstrated robust response. Lastly, a satisfactory comparison with a large drift tube-based muon detector is also presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗